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28 pages, 13098 KiB  
Systematic Review
Seroprevalence of Antibodies to Filoviruses with Outbreak Potential in Sub-Saharan Africa: A Systematic Review to Inform Vaccine Development and Deployment
by Christopher S. Semancik, Hilary S. Whitworth, Matt A. Price, Heejin Yun, Thomas S. Postler, Marija Zaric, Andrew Kilianski, Christopher L. Cooper, Monica Kuteesa, Sandhya Talasila, Nina Malkevich, Swati B. Gupta and Suzanna C. Francis
Vaccines 2024, 12(12), 1394; https://doi.org/10.3390/vaccines12121394 - 11 Dec 2024
Abstract
Background/Objectives: Orthoebolaviruses and orthomarburgviruses are filoviruses that can cause viral hemorrhagic fever and significant morbidity and mortality in humans. The evaluation and deployment of vaccines to prevent and control Ebola and Marburg outbreaks must be informed by an understanding of the transmission [...] Read more.
Background/Objectives: Orthoebolaviruses and orthomarburgviruses are filoviruses that can cause viral hemorrhagic fever and significant morbidity and mortality in humans. The evaluation and deployment of vaccines to prevent and control Ebola and Marburg outbreaks must be informed by an understanding of the transmission and natural history of the causative infections, but little is known about the burden of asymptomatic infection or undiagnosed disease. This systematic review of the published literature examined the seroprevalence of antibodies to orthoebolaviruses and orthomarburgviruses in sub-Saharan Africa. Methods: The review protocol was registered on PROSPERO (ID: CRD42023415358) and previously published. Eighty-seven articles describing 85 studies were included, of which seventy-six measured antibodies to orthoebolaviruses and forty-one measured antibodies to orthomarburgviruses. Results: The results highlight three central findings that may have implications for vaccine development and deployment. First, substantial antibody seropositivity to Ebola virus (EBOV) and Sudan virus (SUDV) was observed in populations from outbreak-affected areas (≤33% seroprevalence among general populations; ≤41% seroprevalence among healthcare workers and close contacts of disease cases). Second, antibody seropositivity to EBOV, SUDV, and Marburg virus (MARV) was observed among populations from areas without reported outbreaks, with seroprevalence ranging from <1 to 21%. Third, in Central and East Africa, MARV antibody seroprevalence was substantially lower than EBOV or SUDV antibody seroprevalence, even in outbreak-affected areas and in populations at a moderate or high risk of infection (with MARV seroprevalence mostly ranging from 0 to 3%). Conclusions: Whilst gaps remain in our understanding of the significance of antibody seropositivity in some settings and contexts, these findings may be important in considering target indications for novel filovirus vaccines, in defining study designs and strategies for demonstrating vaccine efficacy or effectiveness, and in planning and evaluating vaccine deployment strategies to prevent and control outbreaks. Full article
(This article belongs to the Section Vaccines against Tropical and other Infectious Diseases)
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Figure 1

Figure 1
<p>Systematic review flowchart. Abbreviations: n, number; SSA, sub-Saharan Africa. <sup>a</sup> Records describing studies that measured the prevalence of active infection (i.e., through viral detection) were eligible for inclusion in the systematic review. However, they were excluded from this paper, which presents the results on antibody seroprevalence (excluded as part of “ineligible study design”). “Other” refers to excluded systematic reviews, methods papers, or non-virus studies that came up during the search.</p>
Full article ">Figure 2
<p>EBOV outbreaks and antibody seroprevalence studies in sub-Saharan Africa from (<b>a</b>) 1975 to 1987, (<b>b</b>) 1988 to 2000, (<b>c</b>) 2001–2013, and (<b>d</b>) 2014–2024. The outbreaks are indicated with grey spots and annotated (number of cases, year(s)) with grey boxes. Antibody seroprevalence studies are indicated with yellow spots (or an X if no [i.e., 0%] antibody seropositivity was reported). Brief study details (the dates of sample collection, country/-ies where the study was conducted, type of assay used, and risk level of study population) are provided in white boxes. The red circles and highlighting identify studies that were conducted during or after an EBOV outbreak. Some studies (which are labeled with “<sup>a</sup>”) used assays that measured antibody responses to multiple orthoebolaviruses combined (i.e., they did not distinguish between the antibodies to the different viruses). Where studies were conducted in an area affected by an outbreak of EBOV, we assumed that the seropositivity identified primarily reflects responses to EBOV [<a href="#B36-vaccines-12-01394" class="html-bibr">36</a>,<a href="#B37-vaccines-12-01394" class="html-bibr">37</a>,<a href="#B38-vaccines-12-01394" class="html-bibr">38</a>,<a href="#B39-vaccines-12-01394" class="html-bibr">39</a>,<a href="#B40-vaccines-12-01394" class="html-bibr">40</a>,<a href="#B41-vaccines-12-01394" class="html-bibr">41</a>,<a href="#B42-vaccines-12-01394" class="html-bibr">42</a>,<a href="#B43-vaccines-12-01394" class="html-bibr">43</a>,<a href="#B44-vaccines-12-01394" class="html-bibr">44</a>,<a href="#B45-vaccines-12-01394" class="html-bibr">45</a>,<a href="#B46-vaccines-12-01394" class="html-bibr">46</a>,<a href="#B47-vaccines-12-01394" class="html-bibr">47</a>,<a href="#B48-vaccines-12-01394" class="html-bibr">48</a>,<a href="#B49-vaccines-12-01394" class="html-bibr">49</a>,<a href="#B50-vaccines-12-01394" class="html-bibr">50</a>,<a href="#B51-vaccines-12-01394" class="html-bibr">51</a>,<a href="#B52-vaccines-12-01394" class="html-bibr">52</a>,<a href="#B53-vaccines-12-01394" class="html-bibr">53</a>,<a href="#B54-vaccines-12-01394" class="html-bibr">54</a>,<a href="#B55-vaccines-12-01394" class="html-bibr">55</a>,<a href="#B56-vaccines-12-01394" class="html-bibr">56</a>,<a href="#B57-vaccines-12-01394" class="html-bibr">57</a>,<a href="#B58-vaccines-12-01394" class="html-bibr">58</a>,<a href="#B59-vaccines-12-01394" class="html-bibr">59</a>,<a href="#B60-vaccines-12-01394" class="html-bibr">60</a>,<a href="#B61-vaccines-12-01394" class="html-bibr">61</a>,<a href="#B62-vaccines-12-01394" class="html-bibr">62</a>,<a href="#B63-vaccines-12-01394" class="html-bibr">63</a>,<a href="#B64-vaccines-12-01394" class="html-bibr">64</a>,<a href="#B65-vaccines-12-01394" class="html-bibr">65</a>,<a href="#B66-vaccines-12-01394" class="html-bibr">66</a>,<a href="#B67-vaccines-12-01394" class="html-bibr">67</a>,<a href="#B68-vaccines-12-01394" class="html-bibr">68</a>,<a href="#B69-vaccines-12-01394" class="html-bibr">69</a>,<a href="#B70-vaccines-12-01394" class="html-bibr">70</a>,<a href="#B71-vaccines-12-01394" class="html-bibr">71</a>,<a href="#B72-vaccines-12-01394" class="html-bibr">72</a>,<a href="#B73-vaccines-12-01394" class="html-bibr">73</a>,<a href="#B74-vaccines-12-01394" class="html-bibr">74</a>,<a href="#B75-vaccines-12-01394" class="html-bibr">75</a>,<a href="#B76-vaccines-12-01394" class="html-bibr">76</a>,<a href="#B77-vaccines-12-01394" class="html-bibr">77</a>,<a href="#B78-vaccines-12-01394" class="html-bibr">78</a>,<a href="#B79-vaccines-12-01394" class="html-bibr">79</a>,<a href="#B80-vaccines-12-01394" class="html-bibr">80</a>,<a href="#B81-vaccines-12-01394" class="html-bibr">81</a>,<a href="#B82-vaccines-12-01394" class="html-bibr">82</a>,<a href="#B83-vaccines-12-01394" class="html-bibr">83</a>,<a href="#B84-vaccines-12-01394" class="html-bibr">84</a>,<a href="#B85-vaccines-12-01394" class="html-bibr">85</a>,<a href="#B86-vaccines-12-01394" class="html-bibr">86</a>,<a href="#B87-vaccines-12-01394" class="html-bibr">87</a>,<a href="#B88-vaccines-12-01394" class="html-bibr">88</a>,<a href="#B89-vaccines-12-01394" class="html-bibr">89</a>,<a href="#B90-vaccines-12-01394" class="html-bibr">90</a>,<a href="#B91-vaccines-12-01394" class="html-bibr">91</a>,<a href="#B92-vaccines-12-01394" class="html-bibr">92</a>,<a href="#B93-vaccines-12-01394" class="html-bibr">93</a>,<a href="#B94-vaccines-12-01394" class="html-bibr">94</a>,<a href="#B95-vaccines-12-01394" class="html-bibr">95</a>,<a href="#B96-vaccines-12-01394" class="html-bibr">96</a>,<a href="#B97-vaccines-12-01394" class="html-bibr">97</a>,<a href="#B98-vaccines-12-01394" class="html-bibr">98</a>,<a href="#B99-vaccines-12-01394" class="html-bibr">99</a>,<a href="#B100-vaccines-12-01394" class="html-bibr">100</a>,<a href="#B101-vaccines-12-01394" class="html-bibr">101</a>,<a href="#B102-vaccines-12-01394" class="html-bibr">102</a>,<a href="#B103-vaccines-12-01394" class="html-bibr">103</a>,<a href="#B104-vaccines-12-01394" class="html-bibr">104</a>,<a href="#B105-vaccines-12-01394" class="html-bibr">105</a>,<a href="#B106-vaccines-12-01394" class="html-bibr">106</a>,<a href="#B107-vaccines-12-01394" class="html-bibr">107</a>,<a href="#B108-vaccines-12-01394" class="html-bibr">108</a>].</p>
Full article ">Figure 2 Cont.
<p>EBOV outbreaks and antibody seroprevalence studies in sub-Saharan Africa from (<b>a</b>) 1975 to 1987, (<b>b</b>) 1988 to 2000, (<b>c</b>) 2001–2013, and (<b>d</b>) 2014–2024. The outbreaks are indicated with grey spots and annotated (number of cases, year(s)) with grey boxes. Antibody seroprevalence studies are indicated with yellow spots (or an X if no [i.e., 0%] antibody seropositivity was reported). Brief study details (the dates of sample collection, country/-ies where the study was conducted, type of assay used, and risk level of study population) are provided in white boxes. The red circles and highlighting identify studies that were conducted during or after an EBOV outbreak. Some studies (which are labeled with “<sup>a</sup>”) used assays that measured antibody responses to multiple orthoebolaviruses combined (i.e., they did not distinguish between the antibodies to the different viruses). Where studies were conducted in an area affected by an outbreak of EBOV, we assumed that the seropositivity identified primarily reflects responses to EBOV [<a href="#B36-vaccines-12-01394" class="html-bibr">36</a>,<a href="#B37-vaccines-12-01394" class="html-bibr">37</a>,<a href="#B38-vaccines-12-01394" class="html-bibr">38</a>,<a href="#B39-vaccines-12-01394" class="html-bibr">39</a>,<a href="#B40-vaccines-12-01394" class="html-bibr">40</a>,<a href="#B41-vaccines-12-01394" class="html-bibr">41</a>,<a href="#B42-vaccines-12-01394" class="html-bibr">42</a>,<a href="#B43-vaccines-12-01394" class="html-bibr">43</a>,<a href="#B44-vaccines-12-01394" class="html-bibr">44</a>,<a href="#B45-vaccines-12-01394" class="html-bibr">45</a>,<a href="#B46-vaccines-12-01394" class="html-bibr">46</a>,<a href="#B47-vaccines-12-01394" class="html-bibr">47</a>,<a href="#B48-vaccines-12-01394" class="html-bibr">48</a>,<a href="#B49-vaccines-12-01394" class="html-bibr">49</a>,<a href="#B50-vaccines-12-01394" class="html-bibr">50</a>,<a href="#B51-vaccines-12-01394" class="html-bibr">51</a>,<a href="#B52-vaccines-12-01394" class="html-bibr">52</a>,<a href="#B53-vaccines-12-01394" class="html-bibr">53</a>,<a href="#B54-vaccines-12-01394" class="html-bibr">54</a>,<a href="#B55-vaccines-12-01394" class="html-bibr">55</a>,<a href="#B56-vaccines-12-01394" class="html-bibr">56</a>,<a href="#B57-vaccines-12-01394" class="html-bibr">57</a>,<a href="#B58-vaccines-12-01394" class="html-bibr">58</a>,<a href="#B59-vaccines-12-01394" class="html-bibr">59</a>,<a href="#B60-vaccines-12-01394" class="html-bibr">60</a>,<a href="#B61-vaccines-12-01394" class="html-bibr">61</a>,<a href="#B62-vaccines-12-01394" class="html-bibr">62</a>,<a href="#B63-vaccines-12-01394" class="html-bibr">63</a>,<a href="#B64-vaccines-12-01394" class="html-bibr">64</a>,<a href="#B65-vaccines-12-01394" class="html-bibr">65</a>,<a href="#B66-vaccines-12-01394" class="html-bibr">66</a>,<a href="#B67-vaccines-12-01394" class="html-bibr">67</a>,<a href="#B68-vaccines-12-01394" class="html-bibr">68</a>,<a href="#B69-vaccines-12-01394" class="html-bibr">69</a>,<a href="#B70-vaccines-12-01394" class="html-bibr">70</a>,<a href="#B71-vaccines-12-01394" class="html-bibr">71</a>,<a href="#B72-vaccines-12-01394" class="html-bibr">72</a>,<a href="#B73-vaccines-12-01394" class="html-bibr">73</a>,<a href="#B74-vaccines-12-01394" class="html-bibr">74</a>,<a href="#B75-vaccines-12-01394" class="html-bibr">75</a>,<a href="#B76-vaccines-12-01394" class="html-bibr">76</a>,<a href="#B77-vaccines-12-01394" class="html-bibr">77</a>,<a href="#B78-vaccines-12-01394" class="html-bibr">78</a>,<a href="#B79-vaccines-12-01394" class="html-bibr">79</a>,<a href="#B80-vaccines-12-01394" class="html-bibr">80</a>,<a href="#B81-vaccines-12-01394" class="html-bibr">81</a>,<a href="#B82-vaccines-12-01394" class="html-bibr">82</a>,<a href="#B83-vaccines-12-01394" class="html-bibr">83</a>,<a href="#B84-vaccines-12-01394" class="html-bibr">84</a>,<a href="#B85-vaccines-12-01394" class="html-bibr">85</a>,<a href="#B86-vaccines-12-01394" class="html-bibr">86</a>,<a href="#B87-vaccines-12-01394" class="html-bibr">87</a>,<a href="#B88-vaccines-12-01394" class="html-bibr">88</a>,<a href="#B89-vaccines-12-01394" class="html-bibr">89</a>,<a href="#B90-vaccines-12-01394" class="html-bibr">90</a>,<a href="#B91-vaccines-12-01394" class="html-bibr">91</a>,<a href="#B92-vaccines-12-01394" class="html-bibr">92</a>,<a href="#B93-vaccines-12-01394" class="html-bibr">93</a>,<a href="#B94-vaccines-12-01394" class="html-bibr">94</a>,<a href="#B95-vaccines-12-01394" class="html-bibr">95</a>,<a href="#B96-vaccines-12-01394" class="html-bibr">96</a>,<a href="#B97-vaccines-12-01394" class="html-bibr">97</a>,<a href="#B98-vaccines-12-01394" class="html-bibr">98</a>,<a href="#B99-vaccines-12-01394" class="html-bibr">99</a>,<a href="#B100-vaccines-12-01394" class="html-bibr">100</a>,<a href="#B101-vaccines-12-01394" class="html-bibr">101</a>,<a href="#B102-vaccines-12-01394" class="html-bibr">102</a>,<a href="#B103-vaccines-12-01394" class="html-bibr">103</a>,<a href="#B104-vaccines-12-01394" class="html-bibr">104</a>,<a href="#B105-vaccines-12-01394" class="html-bibr">105</a>,<a href="#B106-vaccines-12-01394" class="html-bibr">106</a>,<a href="#B107-vaccines-12-01394" class="html-bibr">107</a>,<a href="#B108-vaccines-12-01394" class="html-bibr">108</a>].</p>
Full article ">Figure 2 Cont.
<p>EBOV outbreaks and antibody seroprevalence studies in sub-Saharan Africa from (<b>a</b>) 1975 to 1987, (<b>b</b>) 1988 to 2000, (<b>c</b>) 2001–2013, and (<b>d</b>) 2014–2024. The outbreaks are indicated with grey spots and annotated (number of cases, year(s)) with grey boxes. Antibody seroprevalence studies are indicated with yellow spots (or an X if no [i.e., 0%] antibody seropositivity was reported). Brief study details (the dates of sample collection, country/-ies where the study was conducted, type of assay used, and risk level of study population) are provided in white boxes. The red circles and highlighting identify studies that were conducted during or after an EBOV outbreak. Some studies (which are labeled with “<sup>a</sup>”) used assays that measured antibody responses to multiple orthoebolaviruses combined (i.e., they did not distinguish between the antibodies to the different viruses). Where studies were conducted in an area affected by an outbreak of EBOV, we assumed that the seropositivity identified primarily reflects responses to EBOV [<a href="#B36-vaccines-12-01394" class="html-bibr">36</a>,<a href="#B37-vaccines-12-01394" class="html-bibr">37</a>,<a href="#B38-vaccines-12-01394" class="html-bibr">38</a>,<a href="#B39-vaccines-12-01394" class="html-bibr">39</a>,<a href="#B40-vaccines-12-01394" class="html-bibr">40</a>,<a href="#B41-vaccines-12-01394" class="html-bibr">41</a>,<a href="#B42-vaccines-12-01394" class="html-bibr">42</a>,<a href="#B43-vaccines-12-01394" class="html-bibr">43</a>,<a href="#B44-vaccines-12-01394" class="html-bibr">44</a>,<a href="#B45-vaccines-12-01394" class="html-bibr">45</a>,<a href="#B46-vaccines-12-01394" class="html-bibr">46</a>,<a href="#B47-vaccines-12-01394" class="html-bibr">47</a>,<a href="#B48-vaccines-12-01394" class="html-bibr">48</a>,<a href="#B49-vaccines-12-01394" class="html-bibr">49</a>,<a href="#B50-vaccines-12-01394" class="html-bibr">50</a>,<a href="#B51-vaccines-12-01394" class="html-bibr">51</a>,<a href="#B52-vaccines-12-01394" class="html-bibr">52</a>,<a href="#B53-vaccines-12-01394" class="html-bibr">53</a>,<a href="#B54-vaccines-12-01394" class="html-bibr">54</a>,<a href="#B55-vaccines-12-01394" class="html-bibr">55</a>,<a href="#B56-vaccines-12-01394" class="html-bibr">56</a>,<a href="#B57-vaccines-12-01394" class="html-bibr">57</a>,<a href="#B58-vaccines-12-01394" class="html-bibr">58</a>,<a href="#B59-vaccines-12-01394" class="html-bibr">59</a>,<a href="#B60-vaccines-12-01394" class="html-bibr">60</a>,<a href="#B61-vaccines-12-01394" class="html-bibr">61</a>,<a href="#B62-vaccines-12-01394" class="html-bibr">62</a>,<a href="#B63-vaccines-12-01394" class="html-bibr">63</a>,<a href="#B64-vaccines-12-01394" class="html-bibr">64</a>,<a href="#B65-vaccines-12-01394" class="html-bibr">65</a>,<a href="#B66-vaccines-12-01394" class="html-bibr">66</a>,<a href="#B67-vaccines-12-01394" class="html-bibr">67</a>,<a href="#B68-vaccines-12-01394" class="html-bibr">68</a>,<a href="#B69-vaccines-12-01394" class="html-bibr">69</a>,<a href="#B70-vaccines-12-01394" class="html-bibr">70</a>,<a href="#B71-vaccines-12-01394" class="html-bibr">71</a>,<a href="#B72-vaccines-12-01394" class="html-bibr">72</a>,<a href="#B73-vaccines-12-01394" class="html-bibr">73</a>,<a href="#B74-vaccines-12-01394" class="html-bibr">74</a>,<a href="#B75-vaccines-12-01394" class="html-bibr">75</a>,<a href="#B76-vaccines-12-01394" class="html-bibr">76</a>,<a href="#B77-vaccines-12-01394" class="html-bibr">77</a>,<a href="#B78-vaccines-12-01394" class="html-bibr">78</a>,<a href="#B79-vaccines-12-01394" class="html-bibr">79</a>,<a href="#B80-vaccines-12-01394" class="html-bibr">80</a>,<a href="#B81-vaccines-12-01394" class="html-bibr">81</a>,<a href="#B82-vaccines-12-01394" class="html-bibr">82</a>,<a href="#B83-vaccines-12-01394" class="html-bibr">83</a>,<a href="#B84-vaccines-12-01394" class="html-bibr">84</a>,<a href="#B85-vaccines-12-01394" class="html-bibr">85</a>,<a href="#B86-vaccines-12-01394" class="html-bibr">86</a>,<a href="#B87-vaccines-12-01394" class="html-bibr">87</a>,<a href="#B88-vaccines-12-01394" class="html-bibr">88</a>,<a href="#B89-vaccines-12-01394" class="html-bibr">89</a>,<a href="#B90-vaccines-12-01394" class="html-bibr">90</a>,<a href="#B91-vaccines-12-01394" class="html-bibr">91</a>,<a href="#B92-vaccines-12-01394" class="html-bibr">92</a>,<a href="#B93-vaccines-12-01394" class="html-bibr">93</a>,<a href="#B94-vaccines-12-01394" class="html-bibr">94</a>,<a href="#B95-vaccines-12-01394" class="html-bibr">95</a>,<a href="#B96-vaccines-12-01394" class="html-bibr">96</a>,<a href="#B97-vaccines-12-01394" class="html-bibr">97</a>,<a href="#B98-vaccines-12-01394" class="html-bibr">98</a>,<a href="#B99-vaccines-12-01394" class="html-bibr">99</a>,<a href="#B100-vaccines-12-01394" class="html-bibr">100</a>,<a href="#B101-vaccines-12-01394" class="html-bibr">101</a>,<a href="#B102-vaccines-12-01394" class="html-bibr">102</a>,<a href="#B103-vaccines-12-01394" class="html-bibr">103</a>,<a href="#B104-vaccines-12-01394" class="html-bibr">104</a>,<a href="#B105-vaccines-12-01394" class="html-bibr">105</a>,<a href="#B106-vaccines-12-01394" class="html-bibr">106</a>,<a href="#B107-vaccines-12-01394" class="html-bibr">107</a>,<a href="#B108-vaccines-12-01394" class="html-bibr">108</a>].</p>
Full article ">Figure 3
<p>Forest plots showing EBOV antibody seroprevalence with 95% CIs from studies that evaluated population groups at a (<b>a</b>) low, (<b>b</b>) moderate, or (<b>c</b>) high risk of infection. Within each forest plot, the studies are stratified by African region and then ordered by study date (the year that the study was conducted, or the year of sample collection if earlier). The study details provided include the publication date, first author name, study location, and risk of bias rating. For each study, the plot shows the number of individuals who were seropositive (EBOV+) out of the total number sampled. Studies conducted within the context of an EBOV outbreak are shaded in red. Some studies used assays that measured antibody responses to multiple orthoebolaviruses combined (i.e., they did not distinguish between the antibodies to the different viruses) (see <a href="#vaccines-12-01394-f003" class="html-fig">Figure 3</a> and <a href="#app1-vaccines-12-01394" class="html-app">Tables S4 and S6</a>). Where studies were conducted in an area affected by an outbreak of EBOV, we assumed that the seropositivity identified primarily reflects responses to EBOV [<a href="#B36-vaccines-12-01394" class="html-bibr">36</a>,<a href="#B37-vaccines-12-01394" class="html-bibr">37</a>,<a href="#B38-vaccines-12-01394" class="html-bibr">38</a>,<a href="#B39-vaccines-12-01394" class="html-bibr">39</a>,<a href="#B40-vaccines-12-01394" class="html-bibr">40</a>,<a href="#B41-vaccines-12-01394" class="html-bibr">41</a>,<a href="#B42-vaccines-12-01394" class="html-bibr">42</a>,<a href="#B43-vaccines-12-01394" class="html-bibr">43</a>,<a href="#B44-vaccines-12-01394" class="html-bibr">44</a>,<a href="#B45-vaccines-12-01394" class="html-bibr">45</a>,<a href="#B46-vaccines-12-01394" class="html-bibr">46</a>,<a href="#B47-vaccines-12-01394" class="html-bibr">47</a>,<a href="#B48-vaccines-12-01394" class="html-bibr">48</a>,<a href="#B49-vaccines-12-01394" class="html-bibr">49</a>,<a href="#B50-vaccines-12-01394" class="html-bibr">50</a>,<a href="#B51-vaccines-12-01394" class="html-bibr">51</a>,<a href="#B52-vaccines-12-01394" class="html-bibr">52</a>,<a href="#B53-vaccines-12-01394" class="html-bibr">53</a>,<a href="#B54-vaccines-12-01394" class="html-bibr">54</a>,<a href="#B55-vaccines-12-01394" class="html-bibr">55</a>,<a href="#B56-vaccines-12-01394" class="html-bibr">56</a>,<a href="#B57-vaccines-12-01394" class="html-bibr">57</a>,<a href="#B58-vaccines-12-01394" class="html-bibr">58</a>,<a href="#B59-vaccines-12-01394" class="html-bibr">59</a>,<a href="#B60-vaccines-12-01394" class="html-bibr">60</a>,<a href="#B61-vaccines-12-01394" class="html-bibr">61</a>,<a href="#B62-vaccines-12-01394" class="html-bibr">62</a>,<a href="#B63-vaccines-12-01394" class="html-bibr">63</a>,<a href="#B64-vaccines-12-01394" class="html-bibr">64</a>,<a href="#B65-vaccines-12-01394" class="html-bibr">65</a>,<a href="#B66-vaccines-12-01394" class="html-bibr">66</a>,<a href="#B67-vaccines-12-01394" class="html-bibr">67</a>,<a href="#B68-vaccines-12-01394" class="html-bibr">68</a>,<a href="#B69-vaccines-12-01394" class="html-bibr">69</a>,<a href="#B70-vaccines-12-01394" class="html-bibr">70</a>,<a href="#B71-vaccines-12-01394" class="html-bibr">71</a>,<a href="#B72-vaccines-12-01394" class="html-bibr">72</a>,<a href="#B73-vaccines-12-01394" class="html-bibr">73</a>,<a href="#B74-vaccines-12-01394" class="html-bibr">74</a>,<a href="#B75-vaccines-12-01394" class="html-bibr">75</a>,<a href="#B76-vaccines-12-01394" class="html-bibr">76</a>,<a href="#B77-vaccines-12-01394" class="html-bibr">77</a>,<a href="#B78-vaccines-12-01394" class="html-bibr">78</a>,<a href="#B79-vaccines-12-01394" class="html-bibr">79</a>,<a href="#B80-vaccines-12-01394" class="html-bibr">80</a>,<a href="#B81-vaccines-12-01394" class="html-bibr">81</a>,<a href="#B82-vaccines-12-01394" class="html-bibr">82</a>,<a href="#B83-vaccines-12-01394" class="html-bibr">83</a>,<a href="#B84-vaccines-12-01394" class="html-bibr">84</a>,<a href="#B85-vaccines-12-01394" class="html-bibr">85</a>,<a href="#B86-vaccines-12-01394" class="html-bibr">86</a>,<a href="#B87-vaccines-12-01394" class="html-bibr">87</a>,<a href="#B88-vaccines-12-01394" class="html-bibr">88</a>,<a href="#B89-vaccines-12-01394" class="html-bibr">89</a>,<a href="#B90-vaccines-12-01394" class="html-bibr">90</a>,<a href="#B91-vaccines-12-01394" class="html-bibr">91</a>,<a href="#B92-vaccines-12-01394" class="html-bibr">92</a>,<a href="#B93-vaccines-12-01394" class="html-bibr">93</a>,<a href="#B94-vaccines-12-01394" class="html-bibr">94</a>,<a href="#B95-vaccines-12-01394" class="html-bibr">95</a>,<a href="#B96-vaccines-12-01394" class="html-bibr">96</a>,<a href="#B97-vaccines-12-01394" class="html-bibr">97</a>,<a href="#B98-vaccines-12-01394" class="html-bibr">98</a>,<a href="#B99-vaccines-12-01394" class="html-bibr">99</a>,<a href="#B100-vaccines-12-01394" class="html-bibr">100</a>,<a href="#B101-vaccines-12-01394" class="html-bibr">101</a>,<a href="#B102-vaccines-12-01394" class="html-bibr">102</a>,<a href="#B103-vaccines-12-01394" class="html-bibr">103</a>,<a href="#B104-vaccines-12-01394" class="html-bibr">104</a>,<a href="#B105-vaccines-12-01394" class="html-bibr">105</a>,<a href="#B106-vaccines-12-01394" class="html-bibr">106</a>,<a href="#B107-vaccines-12-01394" class="html-bibr">107</a>,<a href="#B108-vaccines-12-01394" class="html-bibr">108</a>].</p>
Full article ">Figure 3 Cont.
<p>Forest plots showing EBOV antibody seroprevalence with 95% CIs from studies that evaluated population groups at a (<b>a</b>) low, (<b>b</b>) moderate, or (<b>c</b>) high risk of infection. Within each forest plot, the studies are stratified by African region and then ordered by study date (the year that the study was conducted, or the year of sample collection if earlier). The study details provided include the publication date, first author name, study location, and risk of bias rating. For each study, the plot shows the number of individuals who were seropositive (EBOV+) out of the total number sampled. Studies conducted within the context of an EBOV outbreak are shaded in red. Some studies used assays that measured antibody responses to multiple orthoebolaviruses combined (i.e., they did not distinguish between the antibodies to the different viruses) (see <a href="#vaccines-12-01394-f003" class="html-fig">Figure 3</a> and <a href="#app1-vaccines-12-01394" class="html-app">Tables S4 and S6</a>). Where studies were conducted in an area affected by an outbreak of EBOV, we assumed that the seropositivity identified primarily reflects responses to EBOV [<a href="#B36-vaccines-12-01394" class="html-bibr">36</a>,<a href="#B37-vaccines-12-01394" class="html-bibr">37</a>,<a href="#B38-vaccines-12-01394" class="html-bibr">38</a>,<a href="#B39-vaccines-12-01394" class="html-bibr">39</a>,<a href="#B40-vaccines-12-01394" class="html-bibr">40</a>,<a href="#B41-vaccines-12-01394" class="html-bibr">41</a>,<a href="#B42-vaccines-12-01394" class="html-bibr">42</a>,<a href="#B43-vaccines-12-01394" class="html-bibr">43</a>,<a href="#B44-vaccines-12-01394" class="html-bibr">44</a>,<a href="#B45-vaccines-12-01394" class="html-bibr">45</a>,<a href="#B46-vaccines-12-01394" class="html-bibr">46</a>,<a href="#B47-vaccines-12-01394" class="html-bibr">47</a>,<a href="#B48-vaccines-12-01394" class="html-bibr">48</a>,<a href="#B49-vaccines-12-01394" class="html-bibr">49</a>,<a href="#B50-vaccines-12-01394" class="html-bibr">50</a>,<a href="#B51-vaccines-12-01394" class="html-bibr">51</a>,<a href="#B52-vaccines-12-01394" class="html-bibr">52</a>,<a href="#B53-vaccines-12-01394" class="html-bibr">53</a>,<a href="#B54-vaccines-12-01394" class="html-bibr">54</a>,<a href="#B55-vaccines-12-01394" class="html-bibr">55</a>,<a href="#B56-vaccines-12-01394" class="html-bibr">56</a>,<a href="#B57-vaccines-12-01394" class="html-bibr">57</a>,<a href="#B58-vaccines-12-01394" class="html-bibr">58</a>,<a href="#B59-vaccines-12-01394" class="html-bibr">59</a>,<a href="#B60-vaccines-12-01394" class="html-bibr">60</a>,<a href="#B61-vaccines-12-01394" class="html-bibr">61</a>,<a href="#B62-vaccines-12-01394" class="html-bibr">62</a>,<a href="#B63-vaccines-12-01394" class="html-bibr">63</a>,<a href="#B64-vaccines-12-01394" class="html-bibr">64</a>,<a href="#B65-vaccines-12-01394" class="html-bibr">65</a>,<a href="#B66-vaccines-12-01394" class="html-bibr">66</a>,<a href="#B67-vaccines-12-01394" class="html-bibr">67</a>,<a href="#B68-vaccines-12-01394" class="html-bibr">68</a>,<a href="#B69-vaccines-12-01394" class="html-bibr">69</a>,<a href="#B70-vaccines-12-01394" class="html-bibr">70</a>,<a href="#B71-vaccines-12-01394" class="html-bibr">71</a>,<a href="#B72-vaccines-12-01394" class="html-bibr">72</a>,<a href="#B73-vaccines-12-01394" class="html-bibr">73</a>,<a href="#B74-vaccines-12-01394" class="html-bibr">74</a>,<a href="#B75-vaccines-12-01394" class="html-bibr">75</a>,<a href="#B76-vaccines-12-01394" class="html-bibr">76</a>,<a href="#B77-vaccines-12-01394" class="html-bibr">77</a>,<a href="#B78-vaccines-12-01394" class="html-bibr">78</a>,<a href="#B79-vaccines-12-01394" class="html-bibr">79</a>,<a href="#B80-vaccines-12-01394" class="html-bibr">80</a>,<a href="#B81-vaccines-12-01394" class="html-bibr">81</a>,<a href="#B82-vaccines-12-01394" class="html-bibr">82</a>,<a href="#B83-vaccines-12-01394" class="html-bibr">83</a>,<a href="#B84-vaccines-12-01394" class="html-bibr">84</a>,<a href="#B85-vaccines-12-01394" class="html-bibr">85</a>,<a href="#B86-vaccines-12-01394" class="html-bibr">86</a>,<a href="#B87-vaccines-12-01394" class="html-bibr">87</a>,<a href="#B88-vaccines-12-01394" class="html-bibr">88</a>,<a href="#B89-vaccines-12-01394" class="html-bibr">89</a>,<a href="#B90-vaccines-12-01394" class="html-bibr">90</a>,<a href="#B91-vaccines-12-01394" class="html-bibr">91</a>,<a href="#B92-vaccines-12-01394" class="html-bibr">92</a>,<a href="#B93-vaccines-12-01394" class="html-bibr">93</a>,<a href="#B94-vaccines-12-01394" class="html-bibr">94</a>,<a href="#B95-vaccines-12-01394" class="html-bibr">95</a>,<a href="#B96-vaccines-12-01394" class="html-bibr">96</a>,<a href="#B97-vaccines-12-01394" class="html-bibr">97</a>,<a href="#B98-vaccines-12-01394" class="html-bibr">98</a>,<a href="#B99-vaccines-12-01394" class="html-bibr">99</a>,<a href="#B100-vaccines-12-01394" class="html-bibr">100</a>,<a href="#B101-vaccines-12-01394" class="html-bibr">101</a>,<a href="#B102-vaccines-12-01394" class="html-bibr">102</a>,<a href="#B103-vaccines-12-01394" class="html-bibr">103</a>,<a href="#B104-vaccines-12-01394" class="html-bibr">104</a>,<a href="#B105-vaccines-12-01394" class="html-bibr">105</a>,<a href="#B106-vaccines-12-01394" class="html-bibr">106</a>,<a href="#B107-vaccines-12-01394" class="html-bibr">107</a>,<a href="#B108-vaccines-12-01394" class="html-bibr">108</a>].</p>
Full article ">Figure 4
<p>SUDV, BDBV, and TAFV outbreaks and antibody seroprevalence studies in sub-Saharan Africa from 1975 to 2024. Outbreaks are indicated with grey spots and annotated (number of cases, causative virus(es), year(s)) with grey boxes. No human RESTV infections have been reported in sub-Saharan Africa to date. Antibody seroprevalence studies are indicated with yellow spots (or an X if no antibody seropositivity was reported). Brief study details (the dates of sample collection, country/-ies where the study was conducted, type of assay used, and risk level of study population) are provided in white boxes. The red circles and highlighting identify studies that were conducted during or after an outbreak. <sup>a</sup> Some studies used assays that measured antibody responses to multiple orthoebolaviruses combined (i.e., they did not distinguish between the antibodies to the different viruses). Where studies were conducted in an area affected by an outbreak of SUDV, we assumed that the seropositivity identified primarily reflects responses to SUDV [<a href="#B37-vaccines-12-01394" class="html-bibr">37</a>,<a href="#B41-vaccines-12-01394" class="html-bibr">41</a>,<a href="#B42-vaccines-12-01394" class="html-bibr">42</a>,<a href="#B43-vaccines-12-01394" class="html-bibr">43</a>,<a href="#B53-vaccines-12-01394" class="html-bibr">53</a>,<a href="#B60-vaccines-12-01394" class="html-bibr">60</a>,<a href="#B64-vaccines-12-01394" class="html-bibr">64</a>,<a href="#B72-vaccines-12-01394" class="html-bibr">72</a>,<a href="#B77-vaccines-12-01394" class="html-bibr">77</a>,<a href="#B82-vaccines-12-01394" class="html-bibr">82</a>,<a href="#B98-vaccines-12-01394" class="html-bibr">98</a>,<a href="#B102-vaccines-12-01394" class="html-bibr">102</a>,<a href="#B103-vaccines-12-01394" class="html-bibr">103</a>,<a href="#B104-vaccines-12-01394" class="html-bibr">104</a>,<a href="#B106-vaccines-12-01394" class="html-bibr">106</a>,<a href="#B108-vaccines-12-01394" class="html-bibr">108</a>,<a href="#B109-vaccines-12-01394" class="html-bibr">109</a>,<a href="#B110-vaccines-12-01394" class="html-bibr">110</a>].</p>
Full article ">Figure 5
<p>Forest plots showing SUDV antibody seroprevalence with 95% CIs from studies that evaluated population groups at a (<b>a</b>) low, (<b>b</b>) moderate, or (<b>c</b>) high risk of infection. Within each forest plot, the studies are stratified by African region and then ordered by study date (the year that the study was conducted, or the year of sample collection if earlier). The study details provided include the publication date, first author name, study location, and risk of bias rating. For each study, the plot shows the number of individuals who were seropositive (SUDV+) out of the total number sampled. Studies conducted within the context of a SUDV outbreak are shaded in red. Some studies used assays that measured antibody responses to multiple orthoebolaviruses combined (i.e., they did not distinguish between the antibodies to the different viruses) (see <a href="#vaccines-12-01394-f004" class="html-fig">Figure 4</a> and <a href="#app1-vaccines-12-01394" class="html-app">Tables S4 and S6</a>). Where studies were conducted in an area affected by an outbreak of SUDV, we assumed that the seropositivity identified primarily reflects responses to SUDV [<a href="#B37-vaccines-12-01394" class="html-bibr">37</a>,<a href="#B41-vaccines-12-01394" class="html-bibr">41</a>,<a href="#B42-vaccines-12-01394" class="html-bibr">42</a>,<a href="#B43-vaccines-12-01394" class="html-bibr">43</a>,<a href="#B53-vaccines-12-01394" class="html-bibr">53</a>,<a href="#B60-vaccines-12-01394" class="html-bibr">60</a>,<a href="#B64-vaccines-12-01394" class="html-bibr">64</a>,<a href="#B72-vaccines-12-01394" class="html-bibr">72</a>,<a href="#B77-vaccines-12-01394" class="html-bibr">77</a>,<a href="#B82-vaccines-12-01394" class="html-bibr">82</a>,<a href="#B98-vaccines-12-01394" class="html-bibr">98</a>,<a href="#B102-vaccines-12-01394" class="html-bibr">102</a>,<a href="#B103-vaccines-12-01394" class="html-bibr">103</a>,<a href="#B104-vaccines-12-01394" class="html-bibr">104</a>,<a href="#B106-vaccines-12-01394" class="html-bibr">106</a>,<a href="#B108-vaccines-12-01394" class="html-bibr">108</a>,<a href="#B109-vaccines-12-01394" class="html-bibr">109</a>,<a href="#B110-vaccines-12-01394" class="html-bibr">110</a>].</p>
Full article ">Figure 6
<p>MARV and RAVV outbreaks and antibody seroprevalence studies in sub-Saharan Africa from 1975 to 2024. Outbreaks are indicated with grey spots and annotated (number of cases, causative virus(es), year(s)) with grey boxes. Full details are not yet available for the 2024 Rwandan Marburg outbreak, which is ongoing with 49 confirmed cases and 12 fatalities at the time of writing. The viral sequencing results from the Rwandan outbreak are pending, so the specific causative virus is unknown. Antibody seroprevalence studies are indicated with yellow spots (or an X if no antibody seropositivity was reported). Brief study details (the dates of sample collection, country/-ies where the study was conducted, type of assay used, and risk level of study population) are provided in white boxes. The red circles and highlighting identify studies that were conducted during or after a MARV/RAVV outbreak. The orange circles and shading identify studies that were conducted in areas from where an index case of a MARV outbreak traveled. All studies evaluated MARV antibody seroprevalence [<a href="#B36-vaccines-12-01394" class="html-bibr">36</a>,<a href="#B37-vaccines-12-01394" class="html-bibr">37</a>,<a href="#B38-vaccines-12-01394" class="html-bibr">38</a>,<a href="#B39-vaccines-12-01394" class="html-bibr">39</a>,<a href="#B41-vaccines-12-01394" class="html-bibr">41</a>,<a href="#B42-vaccines-12-01394" class="html-bibr">42</a>,<a href="#B43-vaccines-12-01394" class="html-bibr">43</a>,<a href="#B44-vaccines-12-01394" class="html-bibr">44</a>,<a href="#B53-vaccines-12-01394" class="html-bibr">53</a>,<a href="#B59-vaccines-12-01394" class="html-bibr">59</a>,<a href="#B60-vaccines-12-01394" class="html-bibr">60</a>,<a href="#B61-vaccines-12-01394" class="html-bibr">61</a>,<a href="#B63-vaccines-12-01394" class="html-bibr">63</a>,<a href="#B64-vaccines-12-01394" class="html-bibr">64</a>,<a href="#B65-vaccines-12-01394" class="html-bibr">65</a>,<a href="#B72-vaccines-12-01394" class="html-bibr">72</a>,<a href="#B75-vaccines-12-01394" class="html-bibr">75</a>,<a href="#B76-vaccines-12-01394" class="html-bibr">76</a>,<a href="#B77-vaccines-12-01394" class="html-bibr">77</a>,<a href="#B81-vaccines-12-01394" class="html-bibr">81</a>,<a href="#B82-vaccines-12-01394" class="html-bibr">82</a>,<a href="#B83-vaccines-12-01394" class="html-bibr">83</a>,<a href="#B84-vaccines-12-01394" class="html-bibr">84</a>,<a href="#B98-vaccines-12-01394" class="html-bibr">98</a>,<a href="#B101-vaccines-12-01394" class="html-bibr">101</a>,<a href="#B102-vaccines-12-01394" class="html-bibr">102</a>,<a href="#B103-vaccines-12-01394" class="html-bibr">103</a>,<a href="#B104-vaccines-12-01394" class="html-bibr">104</a>,<a href="#B105-vaccines-12-01394" class="html-bibr">105</a>,<a href="#B106-vaccines-12-01394" class="html-bibr">106</a>,<a href="#B107-vaccines-12-01394" class="html-bibr">107</a>,<a href="#B108-vaccines-12-01394" class="html-bibr">108</a>,<a href="#B111-vaccines-12-01394" class="html-bibr">111</a>,<a href="#B112-vaccines-12-01394" class="html-bibr">112</a>,<a href="#B113-vaccines-12-01394" class="html-bibr">113</a>,<a href="#B114-vaccines-12-01394" class="html-bibr">114</a>,<a href="#B115-vaccines-12-01394" class="html-bibr">115</a>,<a href="#B116-vaccines-12-01394" class="html-bibr">116</a>,<a href="#B117-vaccines-12-01394" class="html-bibr">117</a>,<a href="#B118-vaccines-12-01394" class="html-bibr">118</a>,<a href="#B119-vaccines-12-01394" class="html-bibr">119</a>,<a href="#B120-vaccines-12-01394" class="html-bibr">120</a>,<a href="#B121-vaccines-12-01394" class="html-bibr">121</a>].</p>
Full article ">Figure 7
<p>Forest plots showing MARV antibody seroprevalence with 95% CIs from studies that evaluated population groups at a (<b>a</b>) low, (<b>b</b>) moderate, or (<b>c</b>) high risk of infection. Within each forest plot, the studies are stratified by African region and then ordered by the study date (the year that the study was conducted, or the year of sample collection if earlier). The study details provided include the publication date, first author name, study location, and risk of bias rating. For each study, the plot shows the number of individuals that were seropositive (MARV+) out of the total number sampled. Studies conducted within the context of a MARV outbreak are shaded in red. Three studies that were conducted in areas where the index case of a MARV outbreak traveled from are shaded in orange [<a href="#B36-vaccines-12-01394" class="html-bibr">36</a>,<a href="#B37-vaccines-12-01394" class="html-bibr">37</a>,<a href="#B38-vaccines-12-01394" class="html-bibr">38</a>,<a href="#B39-vaccines-12-01394" class="html-bibr">39</a>,<a href="#B41-vaccines-12-01394" class="html-bibr">41</a>,<a href="#B42-vaccines-12-01394" class="html-bibr">42</a>,<a href="#B43-vaccines-12-01394" class="html-bibr">43</a>,<a href="#B44-vaccines-12-01394" class="html-bibr">44</a>,<a href="#B53-vaccines-12-01394" class="html-bibr">53</a>,<a href="#B59-vaccines-12-01394" class="html-bibr">59</a>,<a href="#B60-vaccines-12-01394" class="html-bibr">60</a>,<a href="#B61-vaccines-12-01394" class="html-bibr">61</a>,<a href="#B63-vaccines-12-01394" class="html-bibr">63</a>,<a href="#B64-vaccines-12-01394" class="html-bibr">64</a>,<a href="#B65-vaccines-12-01394" class="html-bibr">65</a>,<a href="#B72-vaccines-12-01394" class="html-bibr">72</a>,<a href="#B75-vaccines-12-01394" class="html-bibr">75</a>,<a href="#B76-vaccines-12-01394" class="html-bibr">76</a>,<a href="#B77-vaccines-12-01394" class="html-bibr">77</a>,<a href="#B81-vaccines-12-01394" class="html-bibr">81</a>,<a href="#B82-vaccines-12-01394" class="html-bibr">82</a>,<a href="#B83-vaccines-12-01394" class="html-bibr">83</a>,<a href="#B84-vaccines-12-01394" class="html-bibr">84</a>,<a href="#B98-vaccines-12-01394" class="html-bibr">98</a>,<a href="#B101-vaccines-12-01394" class="html-bibr">101</a>,<a href="#B102-vaccines-12-01394" class="html-bibr">102</a>,<a href="#B103-vaccines-12-01394" class="html-bibr">103</a>,<a href="#B104-vaccines-12-01394" class="html-bibr">104</a>,<a href="#B105-vaccines-12-01394" class="html-bibr">105</a>,<a href="#B106-vaccines-12-01394" class="html-bibr">106</a>,<a href="#B107-vaccines-12-01394" class="html-bibr">107</a>,<a href="#B108-vaccines-12-01394" class="html-bibr">108</a>,<a href="#B111-vaccines-12-01394" class="html-bibr">111</a>,<a href="#B112-vaccines-12-01394" class="html-bibr">112</a>,<a href="#B113-vaccines-12-01394" class="html-bibr">113</a>,<a href="#B114-vaccines-12-01394" class="html-bibr">114</a>,<a href="#B115-vaccines-12-01394" class="html-bibr">115</a>,<a href="#B116-vaccines-12-01394" class="html-bibr">116</a>,<a href="#B117-vaccines-12-01394" class="html-bibr">117</a>,<a href="#B118-vaccines-12-01394" class="html-bibr">118</a>,<a href="#B119-vaccines-12-01394" class="html-bibr">119</a>,<a href="#B120-vaccines-12-01394" class="html-bibr">120</a>,<a href="#B121-vaccines-12-01394" class="html-bibr">121</a>].</p>
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<p>Forest plots showing MARV antibody seroprevalence with 95% CIs from studies that evaluated population groups at a (<b>a</b>) low, (<b>b</b>) moderate, or (<b>c</b>) high risk of infection. Within each forest plot, the studies are stratified by African region and then ordered by the study date (the year that the study was conducted, or the year of sample collection if earlier). The study details provided include the publication date, first author name, study location, and risk of bias rating. For each study, the plot shows the number of individuals that were seropositive (MARV+) out of the total number sampled. Studies conducted within the context of a MARV outbreak are shaded in red. Three studies that were conducted in areas where the index case of a MARV outbreak traveled from are shaded in orange [<a href="#B36-vaccines-12-01394" class="html-bibr">36</a>,<a href="#B37-vaccines-12-01394" class="html-bibr">37</a>,<a href="#B38-vaccines-12-01394" class="html-bibr">38</a>,<a href="#B39-vaccines-12-01394" class="html-bibr">39</a>,<a href="#B41-vaccines-12-01394" class="html-bibr">41</a>,<a href="#B42-vaccines-12-01394" class="html-bibr">42</a>,<a href="#B43-vaccines-12-01394" class="html-bibr">43</a>,<a href="#B44-vaccines-12-01394" class="html-bibr">44</a>,<a href="#B53-vaccines-12-01394" class="html-bibr">53</a>,<a href="#B59-vaccines-12-01394" class="html-bibr">59</a>,<a href="#B60-vaccines-12-01394" class="html-bibr">60</a>,<a href="#B61-vaccines-12-01394" class="html-bibr">61</a>,<a href="#B63-vaccines-12-01394" class="html-bibr">63</a>,<a href="#B64-vaccines-12-01394" class="html-bibr">64</a>,<a href="#B65-vaccines-12-01394" class="html-bibr">65</a>,<a href="#B72-vaccines-12-01394" class="html-bibr">72</a>,<a href="#B75-vaccines-12-01394" class="html-bibr">75</a>,<a href="#B76-vaccines-12-01394" class="html-bibr">76</a>,<a href="#B77-vaccines-12-01394" class="html-bibr">77</a>,<a href="#B81-vaccines-12-01394" class="html-bibr">81</a>,<a href="#B82-vaccines-12-01394" class="html-bibr">82</a>,<a href="#B83-vaccines-12-01394" class="html-bibr">83</a>,<a href="#B84-vaccines-12-01394" class="html-bibr">84</a>,<a href="#B98-vaccines-12-01394" class="html-bibr">98</a>,<a href="#B101-vaccines-12-01394" class="html-bibr">101</a>,<a href="#B102-vaccines-12-01394" class="html-bibr">102</a>,<a href="#B103-vaccines-12-01394" class="html-bibr">103</a>,<a href="#B104-vaccines-12-01394" class="html-bibr">104</a>,<a href="#B105-vaccines-12-01394" class="html-bibr">105</a>,<a href="#B106-vaccines-12-01394" class="html-bibr">106</a>,<a href="#B107-vaccines-12-01394" class="html-bibr">107</a>,<a href="#B108-vaccines-12-01394" class="html-bibr">108</a>,<a href="#B111-vaccines-12-01394" class="html-bibr">111</a>,<a href="#B112-vaccines-12-01394" class="html-bibr">112</a>,<a href="#B113-vaccines-12-01394" class="html-bibr">113</a>,<a href="#B114-vaccines-12-01394" class="html-bibr">114</a>,<a href="#B115-vaccines-12-01394" class="html-bibr">115</a>,<a href="#B116-vaccines-12-01394" class="html-bibr">116</a>,<a href="#B117-vaccines-12-01394" class="html-bibr">117</a>,<a href="#B118-vaccines-12-01394" class="html-bibr">118</a>,<a href="#B119-vaccines-12-01394" class="html-bibr">119</a>,<a href="#B120-vaccines-12-01394" class="html-bibr">120</a>,<a href="#B121-vaccines-12-01394" class="html-bibr">121</a>].</p>
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18 pages, 25764 KiB  
Article
Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, Kenya
by Mary C. Henry and John K. Maingi
Fire 2024, 7(12), 472; https://doi.org/10.3390/fire7120472 - 11 Dec 2024
Abstract
Chyulu Hills, Kenya, serves as one of the region’s water towers by supplying groundwater to surrounding streams and springs in southern Kenya. In a semiarid region, this water is crucial to the survival of local people, farms, and wildlife. The Chyulu Hills is [...] Read more.
Chyulu Hills, Kenya, serves as one of the region’s water towers by supplying groundwater to surrounding streams and springs in southern Kenya. In a semiarid region, this water is crucial to the survival of local people, farms, and wildlife. The Chyulu Hills is also very prone to fires, and large areas of the range burn each year during the dry season. Currently, there are no detailed fire records or burn scar maps to track the burn history. Mapping burn scars using remote sensing is a cost-effective approach to monitor fire activity over time. However, it is not clear whether spectral burn indices developed elsewhere can be directly applied here when Chyulu Hills contains mostly grassland and bushland vegetation. Additionally, burn scars are usually no longer detectable after an intervening rainy season. In this study, we calculated the Differenced Normalized Burn Ratio (dNBR) and two versions of the Relative Differenced Normalized Burn Ratio (RdNBR) using Landsat Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data to determine which index, threshold values, instrument, and Sentinel near-infrared (NIR) band work best to map burn scars in Chyulu Hills, Kenya. The results indicate that the Relative Differenced Normalized Burn Ratio from Landsat OLI had the highest accuracy for mapping burn scars while also minimizing false positives (commission error). While mapping burn scars, it became clear that adjusting the threshold value for an index resulted in tradeoffs between false positives and false negatives. While none were perfect, this is an important consideration going forward. Given the length of the Landsat archive, there is potential to expand this work to additional years. Full article
(This article belongs to the Special Issue Fire in Savanna Landscapes, Volume II)
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<p>Location of Chyulu Hills, Kenya, in East Africa. Protected areas are shown in hatch-filled areas with labels in legend. Study area falls within three counties, Kajiado, Makueni, and Taita Taveta, as shown in map. Elevation is also shown in map, with higher elevations in white. Major roads include Mombasa Road to east of study area.</p>
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<p>Flow chart showing methods used in this study. OLI = Operational Land Imager, OLI2 = Operational Land Imager 2; MSI = MultiSpectral Instrument; BOA = Bottom of Atmosphere Reflectance; NBR = Normalized Burn Ratio; dNBR = Differenced Normalized Burn Ratio; RdNBR = Relative Differenced Normalized Burn Ratio; RdNBR2 = Relative Differenced Normalized Burn Ratio alternate calculation. Boxes with bold outline indicate inputs to final analysis.</p>
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<p>Mapped burn scars for the 2021 fire season in Chyulu Hills, Kenya. Yellow shows areas mapped as burned using Landsat RdNBR with a threshold of 0.23. Clouds and cloud shadows are masked out and shown in black. Purple boundaries indicate protected areas in the Chyulu Hills.</p>
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<p>Mapped burn scars for the 2021 fire season in Chyulu Hills, Kenya. Yellow shows areas mapped as burned using Sentinel-2 RdNBR with a threshold of 0.22. Clouds and cloud shadows are masked out and shown in black. Purple boundaries indicate protected areas in the Chyulu Hills.</p>
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26 pages, 2700 KiB  
Review
Chronic Kidney Disease of Unknown Etiology: A Global Health Threat in Rural Agricultural Communities—Prevalence, Suspected Causes, Mechanisms, and Prevention Strategies
by Zineb Ben Khadda, Haitam Lahmamsi, Yahya El Karmoudi, Said Ezrari, Laila El Hanafi and Tarik Sqalli Houssaini
Pathophysiology 2024, 31(4), 761-786; https://doi.org/10.3390/pathophysiology31040052 - 9 Dec 2024
Viewed by 642
Abstract
Chronic Kidney Disease of Unknown Etiology (CKDu) is a worldwide hidden health threat that is associated with progressive loss of kidney functions without showing any initial symptoms until reaching end-stage renal failure, eventually leading to death. It is a growing health problem in [...] Read more.
Chronic Kidney Disease of Unknown Etiology (CKDu) is a worldwide hidden health threat that is associated with progressive loss of kidney functions without showing any initial symptoms until reaching end-stage renal failure, eventually leading to death. It is a growing health problem in Asia, Central America, Africa, and the Middle East, with identified hotspots. CKDu disease mainly affects young men in rural farming communities, while its etiology is not related to hypertension, kidney stones, diabetes, or other known causes. The main suspected causal factors are heat-stress, dehydration, exposure to agrochemicals, heavy metals and use of hard water, infections, mycotoxins, nephrotoxic agents, altitude, and genetic factors. This review gives an overview of CKDu and sheds light on its medical history, geographic distribution, and worldwide prevalence. It also summarizes the suspected causal factors, their proposed mechanisms of action, as well as the main methods used in the CKDu prior detection and surveillance. In addition, mitigation measures to reduce the burden of CKDu are also discussed. Further investigation utilizing more robust study designs would provide a better understanding of the risk factors linked to CKDu and their comparison between affected regions. Full article
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<p>Geographical distribution of CKDu. in the Mesoamerican region [<a href="#B11-pathophysiology-31-00052" class="html-bibr">11</a>,<a href="#B12-pathophysiology-31-00052" class="html-bibr">12</a>], in Srilanka [<a href="#B13-pathophysiology-31-00052" class="html-bibr">13</a>], in Morocco [<a href="#B14-pathophysiology-31-00052" class="html-bibr">14</a>], in Tunisia [<a href="#B15-pathophysiology-31-00052" class="html-bibr">15</a>], in Egypt [<a href="#B16-pathophysiology-31-00052" class="html-bibr">16</a>], in India [<a href="#B17-pathophysiology-31-00052" class="html-bibr">17</a>], and in Mexico [<a href="#B7-pathophysiology-31-00052" class="html-bibr">7</a>].</p>
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<p>Heat stress-induced renal injury. Heat stress-mediated hyperosmolarity activates vasopressin; released vasopressin increases vasoconstriction. The activation of the polyol pathway converts glucose to fructose and increases ROS levels, activating the inflammatory pathway. ER and mitochondrial stress also activate the cell death pathway. This figure was exported under a paid subscription with BioRender.</p>
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<p>Common pathways of toxins induced renal injury. Alteration in the mitochondrial electron transport chain and main oxidases such as NADPH oxidase led to excessive ROS production with toxins exposure. Increased ROS generation activates and induces some pathways, such as the caspase pathway, inhibition of lysosome enzymatic function, lipid peroxidation, cell death, and inflammation. This figure was exported under a paid subscription with BioRender.</p>
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<p>Kidney injury biomarkers.</p>
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16 pages, 5688 KiB  
Article
Combined Effects of Deficit Irrigation and Biostimulation on Water Productivity in Table Grapes
by Susana Zapata-García, Abdelmalek Temnani, Pablo Berríos, Laura Marín-Durán, Pedro J. Espinosa, Claudia Monllor and Alejandro Pérez-Pastor
Plants 2024, 13(23), 3424; https://doi.org/10.3390/plants13233424 - 6 Dec 2024
Viewed by 330
Abstract
Biostimulation and precision irrigation are strategies that increase the sustainability of agriculture, and both have been widely studied in table grapes, but their interaction is a new approach for viticulture. The objective of this field trial was to assess the physiological effects of [...] Read more.
Biostimulation and precision irrigation are strategies that increase the sustainability of agriculture, and both have been widely studied in table grapes, but their interaction is a new approach for viticulture. The objective of this field trial was to assess the physiological effects of water deficit on table grapes pretreated for two consecutive years with five different biostimulation programs. Therefore, during the first year, vines were preconditioned with biostimulants composed of microorganisms, seaweed, and plant extracts and compared to an untreated control. During the second year, the same biostimulation treatments were evaluated under two different irrigation schedules: (i) farmer irrigation (FI), according to a farmer’s criteria; and (ii) a deficit irrigation program, precision irrigation (PI), in which irrigation water was reduced from the post-veraison period to harvest, setting a threshold for allowable soil water depletion of 10% with respect to field capacity in order to minimize water leaching. The water inputs in the treatments under PI were reduced by 30% with respect to the FI treatment. While the deficit irrigation treatment clearly affected the plant water status indicators, biostimulation enhanced the root colonization by mycorrhizae and showed a trend of increased new root density. The combined effect of biostimulation and PI was shown to be an efficient strategy for optimizing the available resources, promoting the yield precocity. Full article
(This article belongs to the Special Issue Grapevine Response to Abiotic Stress)
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<p>Weekly evolution of the climatic parameters: Vapor pressure deficit (VDP), reference crop evapotranspiration (ET<sub>0</sub>), and rainfall (<b>A</b>,<b>B</b>). Daily growing degree days (GDDs) and accumulated GDDs from sprouting (−62 DAFBs) (<b>C</b>,<b>D</b>). Weekly and accumulated irrigation applied for each season 2021 (<b>E</b>) and 2022 (<b>F</b>). DAFBs: days after full bloom. 0 DAFBs corresponds to 14 May 2021 and 17 May 2022, respectively, for each season. The gray dashed lines indicate the harvest period, and the red dashed line in 2022 indicates the beginning of the irrigation reduction in PI with respect to farmer irrigation.</p>
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<p>Daily evolution of the minimum volumetric water content in the soil profile (0 to 60 cm depth), relativized to the field capacity for each depth during the experimental period. The red dashed line indicates the beginning of the irrigation reduction in PI. Means ± SE, <span class="html-italic">n</span> = 3. The gray squares indicate the days with significant differences between irrigation treatments (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>(<b>A</b>) Root density and (<b>B</b>) mycorrhization rate for biostimulated (T1–T4) and not biostimulated (T5) vines under the different irrigation programs (farmer irrigation (FI) or precision irrigation (PI)) in 2021 and 2022. Bars represent means ± SE (<span class="html-italic">n</span> = 4). Different letters indicate significant differences for the factors biostimulation, year, or irrigation in each parameter according to Duncan’s test (<span class="html-italic">p</span> &lt; 0.05). Average value for each factor is shown. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001; <span class="html-italic">ns</span>: not significant.</p>
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<p>Root starch (<b>A</b>) and soluble sugar (<b>B</b>) concentration (%, p/p) for biostimulated (T1–T4) and not biostimulated (T5) vines under the different irrigation programs (farmer irrigation (FI) or precision irrigation (PI)) in 2021 and 2022. Bars represent means ± SE (<span class="html-italic">n</span> = 4). Different letters indicate significant differences for the factors biostimulation, year, or irrigation in each parameter according to Duncan’s test (<span class="html-italic">p</span> &lt; 0.05). Average value for each factor is shown. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; <span class="html-italic">ns</span>: not significant.</p>
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<p>Irrigation water productivity (WP<sub>I</sub>) for biostimulation treatments under the different irrigation programs (farmer or precision irrigation) during the years 2021 and 2022. Bars represent means ± SE (<span class="html-italic">n</span> = 4). Average value for each factor (Y: year; B: biostimulation; I: irrigation) is shown. Different letters indicate significant differences according to Duncan’s test (<span class="html-italic">p</span> &lt; 0.05). **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001; <span class="html-italic">ns</span>: not significant.</p>
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34 pages, 10057 KiB  
Article
Optimized E-Mobility and Portable Storage Integration in an Isolated Rural Solar Microgrid in Uganda
by Josephine Nakato Kakande, Godiana Hagile Philipo and Stefan Krauter
Solar 2024, 4(4), 694-727; https://doi.org/10.3390/solar4040033 - 5 Dec 2024
Viewed by 458
Abstract
This work analyses load profiles for East African microgrids, and then investigates the integration of electric two-wheelers and portable storage into a solar PV with battery microgrid in Uganda, East Africa. By introducing e-mobility and portable storage, demand side management strategic load growth [...] Read more.
This work analyses load profiles for East African microgrids, and then investigates the integration of electric two-wheelers and portable storage into a solar PV with battery microgrid in Uganda, East Africa. By introducing e-mobility and portable storage, demand side management strategic load growth can thus be achieved and electricity access can be expanded. Battery degradation is also considered. The results showed a 98.5% reduction in PV energy curtailment and a 57% reduction in the levelized cost of energy (LCOE) from 0.808 USD/kWh to 0.350 USD/kWh when the electric two-wheeler and portable storage loads were introduced. Such reductions are important enablers of financial viability and sustainability of microgrids. It is possible to avoid emissions of up to 73.27 tons of CO2/year with the proposed e-bikes, and an average of 160 customers could be served annually as off-microgrid consumers without requiring an investment in additional distribution infrastructure. Annual revenue could be increased by 135% by incorporating the additional loads. Sensitivity analyses were conducted by varying component costs, the battery lifetime, the interest rate, and the priority weighting of the additional loads. The battery costs were found to be a major contributor to lifecycle costs (LCC) and also have a big impact on the LCOE. The interest rate significantly affects the LCC as well. Full article
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<p>General setup of the eight microgrids in Uganda.</p>
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<p>Average daily consumption of customers of eight microgrids in Uganda.</p>
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<p>Silale microgrid setup.</p>
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<p>(<b>a</b>) Silale measurement setup; (<b>b</b>) Mavowatt and Kipp and Zonen Meteon display for the SP Lite2 irradiance meter.</p>
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<p>Average load profile for the Silale microgrid from 15 to 29 December 2022.</p>
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<p>Senyondo microgrid equipment.</p>
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<p>Proposed microgrid loads including e-mobility and portable storage.</p>
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<p>The irradiance distribution per hour (curve indicates mean) for the Senyondo (rectangle lower border, inner line, and top border indicate the first quartile (Q1), median, and third quartile (Q3), respectively). The circles outside the rectangles represent outliers i.e. data points outside the range of 1.5 times the IQR (interquartile range) from Q1 and Q3.</p>
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<p>Monthly average irradiance and ambient temperature for Senyondo.</p>
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<p>Load profile for Senyondo microgrid over 18 months (the dashed line represents the average values).</p>
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<p>Average power drawn per weekday for Senyondo microgrid over a year.</p>
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<p>Semi-log plot of number of cycles to failure versus DOD at 25 °C for the Hoppecke VR-L battery.</p>
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<p>Flow chart of the dispatch and optimisation approach.</p>
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<p>Average hourly energy and SOC profiles for the base case for a year: (<b>a</b>) Scenario 1; (<b>b</b>) Scenario 2 with e-bikes and portable storage. Average hourly energy and SOC profiles for the base case for the first week: (<b>c</b>) Scenario 1; (<b>d</b>) Scenario 2 with e-bikes and portable storage. Average hourly energy and SOC values for day 2: (<b>e</b>) Scenario 1; (<b>f</b>) Scenario 2 with e-bikes and portable storage.</p>
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<p>Average hourly energy and SOC profiles with half the base case PV, battery, and converter capacities for a year: (<b>a</b>) Scenario 3; (<b>b</b>) Scenario 4 with e-bikes and portable storage. Average hourly energy and SOC profiles with half the base case PV, battery, and converter capacities for the first week: (<b>c</b>) Scenario 3; (<b>d</b>) Scenario 4 with e-bikes and portable storage. Average hourly energy and SOC values for day 2: (<b>e</b>) Scenario 3; (<b>f</b>) Scenario 4 with e-bikes and portable storage.</p>
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<p>Average hourly energy and SOC profiles for the base case for the day with the lowest irradiance: (<b>a</b>) Scenario 1; (<b>b</b>) Scenario 2 with e-bikes and portable storage. Average hourly energy and SOC profiles with half the base case PV, battery and converter capacities for the day with the lowest irradiance: (<b>c</b>) Scenario 3; (<b>d</b>) Scenario 4 with e-bikes and portable storage.</p>
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<p>Number of portable batteries charged daily and e-bikes that can be charged daily with enough energy for 50 km daily distance (<b>a</b>) Scenario 2; and (<b>b</b>) Scenario 4.</p>
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<p>Monthly number of portable batteries and e-bikes charged for 50 km distance for equal charging priorities.</p>
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24 pages, 14921 KiB  
Article
Estimating the Effects of Climate Fluctuations on Precipitation and Temperature in East Africa
by Edovia Dufatanye Umwali, Xi Chen, Brian Odhiambo Ayugi, Richard Mumo, Hassen Babaousmail, Dickson Mbigi and David Izere
Atmosphere 2024, 15(12), 1455; https://doi.org/10.3390/atmos15121455 - 5 Dec 2024
Viewed by 396
Abstract
This study evaluated the effectiveness of the NASA Earth Exchange Global Daily Downscaled models from CMIP6 experiments (hereafter; NEX-GDDP-CMIP6) in reproducing observed precipitation and temperature across East Africa (EA) from 1981 to 2014. Additionally, climate changes were estimated under various emission scenarios, namely [...] Read more.
This study evaluated the effectiveness of the NASA Earth Exchange Global Daily Downscaled models from CMIP6 experiments (hereafter; NEX-GDDP-CMIP6) in reproducing observed precipitation and temperature across East Africa (EA) from 1981 to 2014. Additionally, climate changes were estimated under various emission scenarios, namely low (SSP1-2.6), medium (SSP2-4.5), and high (SSP5-8.5) scenarios. Multiple robust statistics metrics, the Taylor diagram, and interannual variability skill (IVS) were employed to identify the best-performing models. Significant trends in future precipitation and temperature are evaluated using the Mann-Kendall and Sen’s slope estimator tests. The results highlighted IPSL-CM6A-LR, EC-Earth3, CanESM5, and INM-CM4-8 as the best-performing models for annual and March to May (MAM) precipitation and temperature respectively. By the end of this century, MAM precipitation and temperature are projected to increase by 40% and 4.5 °C, respectively, under SSP5-8.5. Conversely, a decrease in MAM precipitation and temperature of 5% and 0.8 °C was projected under SSP2-4.5 and SSP1-2.6, respectively. Long-term mean precipitation increased in all climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), with near-term MAM precipitation showing a 5% decrease in Rwanda, Burundi, Uganda, and some parts of Tanzania. Under the SSP5-8.5 scenario, temperature rise exceeded 2–6 °C in most regions across the area, with the fastest warming trend of over 6 °C observed in diverse areas. Thus, high greenhouse gas (GHG) emission scenarios can be very harmful to EA and further GHG control is needed. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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<p>(<b>a</b>) Location of EA within longitude 28°00′00″ to 42°00′00″ E and latitude 12°00′00″ S to 5°00′00″ N with topographical distribution. (<b>b</b>) The relative location of EA over Africa is depicted on the top right insert of the African map with red color. The bottom right insert depicts the legend and color bar for the EA map (left column).</p>
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<p>Long-term mean over EA during 1981–2014 (<b>a</b>) precipitation (units: mm) showing blue for CMIP6 models and green for MME and CRU observations and (<b>b</b>) temperature (units: °C) showing red for CMIP6 models and yellow for MME and CRU observations.</p>
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<p>Spatial distributions of (<b>a</b>,<b>b</b>) observed precipitation (units: mm), (<b>c</b>,<b>d</b>) MME simulated precipitation (units: mm), and (<b>e</b>,<b>f</b>) biases in MME simulation compared to the observation (simulation minus observation, units: mm) for the period 1981–2014. The panels progress from the left to the right, representing annual and MAM, respectively. Note that the color bar scales vary across the panels.</p>
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<p>Spatial distributions of (<b>a</b>,<b>b</b>) observed temperature (units: °C), (<b>c</b>,<b>d</b>) MME simulated temperature (units: °C), and (<b>e</b>,<b>f</b>) biases in MME simulation compared to the observation (simulation minus observation, units: mm) for the period 1981–2014. The panels progress from the left to the right, representing annual and MAM, respectively. Note that the color bar scales vary across the panels.</p>
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<p>Taylor diagrams comparing CMIP6 observations (1981–2014) for (<b>a</b>) annual precipitation; (<b>b</b>) annual temperature; (<b>c</b>) MAM precipitation; (<b>d</b>) MAM temperature. Red represents CMIP6 models, blue lines indicate the correlation coefficient, green lines show RMSD and black lines represent the standard deviation.</p>
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<p>Interannual variability skill score (IVS) of the CMIP6 models for both annual and MAM, (<b>a</b>,<b>c</b>) precipitation, and (<b>b</b>,<b>d</b>) temperature over EA. Blue represents CMIP6 models for precipitation, while red represents CMIP6 models for temperature.</p>
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<p>Time series of the annual mean precipitation and temperature from the best-performing models under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios over EA. The blue and red shadings are the corresponding model spread about the MME for the near (2031–2065) and far (2066–2100) terms.</p>
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<p>Time series of the MAM mean precipitation and temperature from the best-performing models under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios over EA. The blue and red shadings are the corresponding model spread about the MME for the near (2031–2065) and far (2066–2100) terms.</p>
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<p>Spatial distributions of changes in annual (<b>a</b>–<b>f</b>) and MAM (<b>g</b>–<b>l</b>) precipitation (unit: %) in the near-term (2031–2065) and far-term (2066–2100) relative to historical 1981–2014 over EA under SSP1-2.6, SSP2-4.5, and SSP5-8.5 respectively.</p>
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<p>Spatial distribution of changes in annual (<b>a</b>–<b>f</b>) and MAM (<b>g</b>–<b>l</b>) temperature (unit: °C) in the near-term (2031–2065) and far-term (2066–2100) relative to historical 1981–2014 over EA under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios respectively.</p>
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<p>Projected spatial trends of annual (<b>a</b>–<b>f</b>) and MAM (<b>g</b>–<b>l</b>) precipitation (unit: mm/year) relative to historical under SSP1-2.6, SSP2-4.5, and SSP5-8.5 respectively over EA. The black dots show changes that are statistically significant with a 95% confidence level.</p>
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<p>Projected spatial trends of annual (<b>a</b>–<b>f</b>) and MAM (<b>g</b>–<b>l</b>) temperature (unit: ℃) relative to historical under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, over EA. The black dots show changes that are statistically significant with a 95% confidence level.</p>
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22 pages, 984 KiB  
Article
Forage Seed Systems to Close the Ruminant Feed Deficit in Eastern Africa
by Stefan Burkart and Solomon Mwendia
Grasses 2024, 3(4), 333-354; https://doi.org/10.3390/grasses3040025 - 4 Dec 2024
Viewed by 560
Abstract
This study examines key challenges and opportunities for improving ruminant productivity in East Africa, with a focus on enhancing access to forage seeds critical for livestock systems in Ethiopia, Tanzania, Kenya, Uganda, Rwanda, and Burundi. Despite high potential for increased livestock production, the [...] Read more.
This study examines key challenges and opportunities for improving ruminant productivity in East Africa, with a focus on enhancing access to forage seeds critical for livestock systems in Ethiopia, Tanzania, Kenya, Uganda, Rwanda, and Burundi. Despite high potential for increased livestock production, the region faces a significant feed deficit—nearly 40% of annual feed demand remains unmet—due to the limited availability and affordability of forage seeds. The research identifies a critical gap in quality seed access, with many farmers relying on outdated materials. We propose the promotion of recently improved forage varieties and local seed production as a solution to reduce dependence on costly imports and enhance adoption. Our analysis suggests that bridging the forage deficit would require the cultivation of 2 million hectares and the involvement of 1.5 million farmers, highlighting the scale of intervention needed. Additionally, the regional forage seed market presents an economic opportunity, potentially valued at USD 877 million over the next decade, underlining the importance of government policies, the development of seed systems, and market incentives. The study concludes with recommendations for fostering seed production, improving seed distribution, and addressing socio-economic barriers to ensure widespread adoption and enhance livestock productivity in the region. Full article
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<p>Overview of the key steps for literature search applied in the study.</p>
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23 pages, 2248 KiB  
Systematic Review
A Global Regional Comparison of the Risk of Breast Cancer in Woman Using Oral Contraceptives—Systematic Review and Meta-Analysis
by Agnieszka Drab, Krystian Wdowiak, Wiesław Kanadys, Maria Malm, Joanna Dolar-Szczasny, Grzegorz Zieliński, Mariola Borowska and Urszula Religioni
Cancers 2024, 16(23), 4044; https://doi.org/10.3390/cancers16234044 - 2 Dec 2024
Viewed by 490
Abstract
Background: Incidence of breast cancer (BrCa) may be correlated with country development, with a rise in cases anticipated in regions of the world that are currently undergoing an economic transformation. Herein, differences with regard to the occurrence of breast cancer between individual [...] Read more.
Background: Incidence of breast cancer (BrCa) may be correlated with country development, with a rise in cases anticipated in regions of the world that are currently undergoing an economic transformation. Herein, differences with regard to the occurrence of breast cancer between individual countries may depend on the distribution of risk factors, the level of early detection, also ethnicity and race, as well as clinical characteristics. The aim of our study was to identify and then investigate observational studies in which the risk of breast cancer was associated with the use of oral hormonal contraceptives (OCs), with particular emphasis on geographic region, and to conduct a systematic review and meta-analysis of the obtained data. Methods: RR (relative risk) was calculated and displayed in forest plots for visual interpretation. Accordingly, 74 studies involving a total of 198,579 women were eligible for inclusion in the meta-analysis. This is the first meta-analysis to comprehensively summarize the evidence between OC use and BrCa risk in connection with geographical region. Results: The cumulative results of the meta-analysis for specific parts of the world are: Africa (RR = 1.16, p = 0.216) and the Americas (RR = 1.03, p = 0.597); Asia (RR = 1.29, p = 0.014); European countries (RR = 1.01, p = 0.904); and Middle East countries (RR = 1.29, p = 0.043). Subgroup analyses showed an increased risk of BrCa for the analyzed variables that depended upon the geographical region. Conclusions: Our meta-analysis suggests that OC use may be associated with a higher BrCa risk, although a statistically significant association was not found for all geographical regions of the world. Full article
(This article belongs to the Special Issue Feature Paper in Section 'Cancer Epidemiology and Prevention' in 2024)
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<p>Flow diagram of studies exploring the association between OC and BrCa risk in accordance with the PRISMA guidelines.</p>
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<p>Forest plots showing the summary RR and 95% CI studies conducted in African countries [<a href="#B37-cancers-16-04044" class="html-bibr">37</a>,<a href="#B38-cancers-16-04044" class="html-bibr">38</a>,<a href="#B39-cancers-16-04044" class="html-bibr">39</a>,<a href="#B40-cancers-16-04044" class="html-bibr">40</a>,<a href="#B41-cancers-16-04044" class="html-bibr">41</a>,<a href="#B42-cancers-16-04044" class="html-bibr">42</a>,<a href="#B43-cancers-16-04044" class="html-bibr">43</a>,<a href="#B44-cancers-16-04044" class="html-bibr">44</a>,<a href="#B45-cancers-16-04044" class="html-bibr">45</a>].</p>
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<p>Forest plots showing the summary RR and 95% CI studies conducted in American countries [<a href="#B46-cancers-16-04044" class="html-bibr">46</a>,<a href="#B47-cancers-16-04044" class="html-bibr">47</a>,<a href="#B48-cancers-16-04044" class="html-bibr">48</a>,<a href="#B49-cancers-16-04044" class="html-bibr">49</a>,<a href="#B50-cancers-16-04044" class="html-bibr">50</a>,<a href="#B51-cancers-16-04044" class="html-bibr">51</a>,<a href="#B52-cancers-16-04044" class="html-bibr">52</a>,<a href="#B53-cancers-16-04044" class="html-bibr">53</a>,<a href="#B54-cancers-16-04044" class="html-bibr">54</a>,<a href="#B55-cancers-16-04044" class="html-bibr">55</a>,<a href="#B56-cancers-16-04044" class="html-bibr">56</a>,<a href="#B57-cancers-16-04044" class="html-bibr">57</a>,<a href="#B58-cancers-16-04044" class="html-bibr">58</a>,<a href="#B59-cancers-16-04044" class="html-bibr">59</a>,<a href="#B60-cancers-16-04044" class="html-bibr">60</a>,<a href="#B61-cancers-16-04044" class="html-bibr">61</a>,<a href="#B62-cancers-16-04044" class="html-bibr">62</a>].</p>
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<p>Forest plots showing the summary RR and 95% CI studies conducted in Asian countries [<a href="#B63-cancers-16-04044" class="html-bibr">63</a>,<a href="#B64-cancers-16-04044" class="html-bibr">64</a>,<a href="#B65-cancers-16-04044" class="html-bibr">65</a>,<a href="#B66-cancers-16-04044" class="html-bibr">66</a>,<a href="#B67-cancers-16-04044" class="html-bibr">67</a>,<a href="#B68-cancers-16-04044" class="html-bibr">68</a>,<a href="#B69-cancers-16-04044" class="html-bibr">69</a>,<a href="#B70-cancers-16-04044" class="html-bibr">70</a>,<a href="#B71-cancers-16-04044" class="html-bibr">71</a>,<a href="#B72-cancers-16-04044" class="html-bibr">72</a>,<a href="#B73-cancers-16-04044" class="html-bibr">73</a>,<a href="#B74-cancers-16-04044" class="html-bibr">74</a>,<a href="#B75-cancers-16-04044" class="html-bibr">75</a>,<a href="#B76-cancers-16-04044" class="html-bibr">76</a>,<a href="#B77-cancers-16-04044" class="html-bibr">77</a>,<a href="#B78-cancers-16-04044" class="html-bibr">78</a>].</p>
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<p>Forest plots showing the summary RR and 95% CI studies conducted in European countries [<a href="#B79-cancers-16-04044" class="html-bibr">79</a>,<a href="#B80-cancers-16-04044" class="html-bibr">80</a>,<a href="#B81-cancers-16-04044" class="html-bibr">81</a>,<a href="#B82-cancers-16-04044" class="html-bibr">82</a>,<a href="#B83-cancers-16-04044" class="html-bibr">83</a>,<a href="#B84-cancers-16-04044" class="html-bibr">84</a>,<a href="#B85-cancers-16-04044" class="html-bibr">85</a>,<a href="#B86-cancers-16-04044" class="html-bibr">86</a>,<a href="#B87-cancers-16-04044" class="html-bibr">87</a>,<a href="#B88-cancers-16-04044" class="html-bibr">88</a>,<a href="#B89-cancers-16-04044" class="html-bibr">89</a>,<a href="#B90-cancers-16-04044" class="html-bibr">90</a>].</p>
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<p>Forest plots showing the summary RR and 95% CI studies conducted in Middle East countries [<a href="#B91-cancers-16-04044" class="html-bibr">91</a>,<a href="#B92-cancers-16-04044" class="html-bibr">92</a>,<a href="#B93-cancers-16-04044" class="html-bibr">93</a>,<a href="#B94-cancers-16-04044" class="html-bibr">94</a>,<a href="#B95-cancers-16-04044" class="html-bibr">95</a>,<a href="#B96-cancers-16-04044" class="html-bibr">96</a>,<a href="#B97-cancers-16-04044" class="html-bibr">97</a>,<a href="#B98-cancers-16-04044" class="html-bibr">98</a>,<a href="#B99-cancers-16-04044" class="html-bibr">99</a>,<a href="#B100-cancers-16-04044" class="html-bibr">100</a>,<a href="#B101-cancers-16-04044" class="html-bibr">101</a>,<a href="#B102-cancers-16-04044" class="html-bibr">102</a>,<a href="#B103-cancers-16-04044" class="html-bibr">103</a>,<a href="#B104-cancers-16-04044" class="html-bibr">104</a>,<a href="#B105-cancers-16-04044" class="html-bibr">105</a>,<a href="#B106-cancers-16-04044" class="html-bibr">106</a>,<a href="#B107-cancers-16-04044" class="html-bibr">107</a>,<a href="#B108-cancers-16-04044" class="html-bibr">108</a>,<a href="#B109-cancers-16-04044" class="html-bibr">109</a>,<a href="#B110-cancers-16-04044" class="html-bibr">110</a>].</p>
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16 pages, 4275 KiB  
Article
Improving Irrigation Water Use Efficiency and Maximizing Vegetable Yields with Drip Irrigation and Poly-Mulching: A Climate-Smart Approach
by Denis Bwire, Fumio Watanabe, Shinji Suzuki and Kana Suzuki
Water 2024, 16(23), 3458; https://doi.org/10.3390/w16233458 - 1 Dec 2024
Viewed by 765
Abstract
Water management is a significant aspect of sustainable vegetable farming, especially in water-scarce regions. This, in addition to weed infestations, limits vegetable yields, which negatively affect food security in developing regions, particularly East Africa, where livelihoods chiefly depend on rain-fed agriculture. Vegetable farming, [...] Read more.
Water management is a significant aspect of sustainable vegetable farming, especially in water-scarce regions. This, in addition to weed infestations, limits vegetable yields, which negatively affect food security in developing regions, particularly East Africa, where livelihoods chiefly depend on rain-fed agriculture. Vegetable farming, especially tomato cultivation, requires more water. By promoting mulching, a soil water conservation tool, we can control surface evaporation (E), which, together with irrigation, enhances effective water use and vegetable yields. The experiments for this study were conducted at the Tokyo University of Agriculture, Japan, to evaluate the influences of different irrigation conditions and poly-mulching on weed control, tomato yields, and water use efficiency. The study was conducted from May to September 2018 on a 30 m2 plot in an open-ended greenhouse using drip irrigation for tomato cultivation. Three predetermined irrigation conditions of 4, 3, and 2 mm/day were applied on black poly-mulched and bare ridges. Data on soil conditions—soil temperature, as well as meteorological variables, including solar radiation and temperature—were measured using thermocouple sensors and micro-hobo weather stations, respectively, during the tomato cultivation, while yield components—growth, yield, water productivity, and sugar content—were determined after harvest. The results of a two-way ANOVA show that irrigation conditions with poly-mulching reduced the weed biomass significantly, and improved yields and water use efficiency compared to the irrigation conditions on bare ridges. The application of 4, 3, and 2 mm/day irrigation with poly-mulching significantly reduced the weed biomass by 5% compared to the same irrigation conditions on bare ridges. Similarly, 4 and 3 mm/day irrigation conditions with poly-mulching significantly increased the tomato yield by 5% compared to 2 mm/day on bare ridges. The bigger roots were concentrated and widely distributed at the shallow soil depth (0–20 cm) of the ridges with high irrigation amounts, while the small and thin roots were in deeper soil layers (30–45 cm). This study provides scientific knowledge on the application of predetermined irrigation conditions that can be (i) integrated into irrigation scheduling and (ii) adopted for regions facing water scarcity and limited or no in situ meteorological data, to improve water use efficiency for vegetable cultivation. Full article
(This article belongs to the Special Issue Advances in Agricultural Irrigation Management and Technology)
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<p>Schematic experimental design and tomato cultivation with poly-mulch in an open-ended greenhouse. 1, NPC (low pressure) drip lines; <span class="html-fig-inline" id="water-16-03458-i001"><img alt="Water 16 03458 i001" src="/water/water-16-03458/article_deploy/html/images/water-16-03458-i001.png"/></span>, location of tensiometers and thermocouple sensors; 2, 3 and 4 are the main field, ridges and trenches, respectively; <span class="html-fig-inline" id="water-16-03458-i002"><img alt="Water 16 03458 i002" src="/water/water-16-03458/article_deploy/html/images/water-16-03458-i002.png"/></span> denotes the thermocouple data lodgers, while gold and dark colors represent bare and mulched ridges.</p>
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<p>Schematic illustration of the installed sensors (<b>a</b>) and photo of the thermal couples (<b>b</b>).</p>
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<p>Average diurnal temperature variation and relative humidity as measured by the thermo recorder-TR-72U for a given tomato cultivation period.</p>
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<p>The effect of black poly-mulch on soil temperature measured at a 10 cm soil depth.</p>
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<p>Effects of irrigation conditions and poly-mulching on tomato growth. M is poly-mulch and NM is bare ridges, and * indicates a significant difference at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Influence of irrigation conditions on (<b>a</b>) chlorophyll content and (<b>b</b>) tomato sugar content. Here, M is poly-mulch and NM is bare ridges, with significant difference at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Influence of irrigation conditions and poly-mulch on tomato yield components. M is poly-mulch, and NM is bare ridges, with yield in kg (<b>a</b>) and fruit number (<b>b</b>) at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Root distribution, where (<b>a</b>) mulch is poly-mulch and (<b>b</b>) no-mulch is bare soil ridges under different irrigation conditions.</p>
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<p>Root length density (RLD) under different irrigation conditions, (<b>a</b>) poly-mulched and (<b>b</b>) bare soil ridges.</p>
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<p>Comparison of irrigation conditions and tomato crop water needs (ET<sub>C</sub>) during tomato cultivation.</p>
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18 pages, 1851 KiB  
Article
Comparative Evolutionary Epidemiology of SARS-CoV-2 Delta and Omicron Variants in Kuwait
by Moh A. Alkhamis, Abrar Hussain and Fayez Al-Therban
Viruses 2024, 16(12), 1872; https://doi.org/10.3390/v16121872 - 30 Nov 2024
Viewed by 589
Abstract
Continuous surveillance is critical for early intervention against emerging novel SARS-CoV-2 variants. Therefore, we investigated and compared the variant-specific evolutionary epidemiology of all the Delta and Omicron sequences collected between 2021 and 2023 in Kuwait. We used Bayesian phylodynamic models to reconstruct, trace, [...] Read more.
Continuous surveillance is critical for early intervention against emerging novel SARS-CoV-2 variants. Therefore, we investigated and compared the variant-specific evolutionary epidemiology of all the Delta and Omicron sequences collected between 2021 and 2023 in Kuwait. We used Bayesian phylodynamic models to reconstruct, trace, and compare the two variants’ demographics, phylogeographic, and host characteristics in shaping their evolutionary epidemiology. The Omicron had a higher evolutionary rate than the Delta. Both variants underwent periods of sequential growth and decline in their effective population sizes, likely linked to intervention measures and environmental and host characteristics. We found that the Delta strains were frequently introduced into Kuwait from East Asian countries between late 2020 and early 2021, while those of the Omicron strains were most likely from Africa and North America between late 2021 and early 2022. For both variants, our analyses revealed significant transmission routes from patients aged between 20 and 50 years on one side and other age groups, refuting the notion that children are superspreaders for the disease. In contrast, we found that sex has no significant role in the evolutionary history of both variants. We uncovered deeper variant-specific epidemiological insights using phylodynamic models and highlighted the need to integrate such models into current and future genomic surveillance programs. Full article
(This article belongs to the Section Coronaviruses)
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<p>Reconstructed ML trees of Omicron and Delta variants combing local and global sequences datasets and temporal dynamics of SARS-CoV-2 observed cases in Kuwait between January 2021 and November 2022. (<b>A</b>) A flow chart of ML trees showing the stages of selecting the best representative collection of sequences and their related lineages for the Omicron and Delta variants isolated in Kuwait and worldwide. The first tree on the top left comprises NextStrain sequences and all Delta and Omicron viruses isolated in Kuwait between December 2019 and October 2022. The ML tree on the middle top is a subsample of NextStrain isolates combined with all Delta and Omicron viruses isolated in Kuwait, which was selected using genome-sampler (G-S) version 2.0. G-S was used again to select the final datasets for the Delta and Omicron variants with their global descendant lineages. Blue branches represent the delta isolates and red branches represent Omicron isolates. All ML trees were reconstructed using the GTR + F + R4 substitution model implemented in IQ-Tree version 2.0. The scale bar below each tree represents the substitution rate per site. Root-to-tip divergence (R2) was estimated using TempEst version 1.5.3. (<b>B</b>) Temporal distribution of weekly confirmed SARS-CoV-2 cases between January 2021 and November 2022. Blue bars represent the period when Delta was dominant, while red bars represent the period when Omicron was dominant in Kuwait. The epidemic curve is superimposed by the estimated curve of the time-dependent reproductive numbers (R<sub>td</sub>); the green-shaded areas indicate their 95% confidence interval.</p>
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<p>Maximum clade credibility (MCC) phylogeny of the Delta and Omicron variants in Kuwait (with related global isolates), their effective population sizes through time, and regional exchange routes between January 2021 and November 2022. (<b>A</b>) represents the MCC tree and Skygrid reconstructions for the Delta variant. (<b>C</b>) represents the MCC tree and Skygrid reconstructions for the Omicron variant. The colors of the branches represent the most probable location state of their descendent nodes and correspond to the legend on the upper left. Black dot sizes on the nodes are proportional to the posterior support. The red arrows on the MCC trees point to the earliest probable introduction of each variant to Kuwait from other regions with their inferred root state posterior probability. Green Skygrids plots on the right are reconstructions from the Kuwaiti clades encompassed by black circles. The posterior median estimate is indicated by the dark green line, while the light green shades indicate the 95% highest posterior density. The vertical dotted lines indicate the inferred time each variant transitioned from slow to fast population growth. (<b>B</b>,<b>D</b>) indicate significant dispersal routes (Bayes factor &gt; 3) of the Delta and Omicron variants, respectively, from and to Kuwait from other regions, and their colors correspond to the magnitude of their significance (legend on the left). (<b>E</b>,<b>F</b>) are bar charts summarizing the expected reverse and forward Markov jumps for the Delta and Omicron variants, respectively, between Kuwait and other regions.</p>
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<p>Maximum clade credibility (MCC) trees and host transmission routes over the phylogeny of the Delta and Omicron variants in Kuwait. The colors of the branches indicate the most probable host age (<b>A</b>,<b>B</b>) and sex (<b>C</b>,<b>D</b>) state of their descendent nodes and correspond to the legends on the left. (<b>A</b>,<b>C</b>) represent the delta variant. (<b>B</b>,<b>D</b>) represent the Omicron variant. Significantly supported transmission (Bayes factors &gt; 10) routes. The numbers adjacent to the arrows indicate the inferred values of the expected reverse and forward Markov jumps between age and sex groups.</p>
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14 pages, 272 KiB  
Article
Pentecostalisation, the American Christian Right, and Civil Religion in Ghana
by Jeffrey Haynes
Religions 2024, 15(12), 1448; https://doi.org/10.3390/rel15121448 - 28 Nov 2024
Viewed by 921
Abstract
Christianity’s political prominence in Ghana has attracted the attention of scholars interested in the wider issues of religion and politics in sub-Saharan Africa. Analyses of the political influence of Christianity in Ghana generally focus on domestic factors, without considering external actors’ involvement. This [...] Read more.
Christianity’s political prominence in Ghana has attracted the attention of scholars interested in the wider issues of religion and politics in sub-Saharan Africa. Analyses of the political influence of Christianity in Ghana generally focus on domestic factors, without considering external actors’ involvement. This article examines the political impact of the leading form of Christianity in Ghana, Pentecostalism, in relation to both domestic and external factors. The aims of the article are, first, to explain and account for Pentecostals’ political impact in Ghana. The second aim is to explain and account for the links between elements of the American Christian Right and prominent Pentecostals in Ghana. Both support normatively conservative, even regressive, policies which, the article argues, encourages the breakdown of Ghana’s civil religion. Ghana is the first west African nation to be subject to sustained attention from elements of the US Christian Right, following similar efforts in east Africa, particularly in relation to Kenya and Uganda. This novelty makes the American Christian Right’s focus on Ghana both noteworthy and an important topic of research in the context of the internationalisation of the former. The article is divided into four sections. The introductory section presents the main sections of the article, and provides a thorough account of the background of the study. The second section surveys what has been called the ‘pentecostalisation’ of Christianity in Ghana, which aligns with similar processes in other sub-Saharan African countries, including Nigeria. The third section examines the links between Ghana’s Pentecostals and elements of the American Christian Right and explains how this helps to fuel a breakdown Ghana’s longstanding allegiance to civil religion. The final section describes the main political result: the scapegoating and criminalisation of Ghana’s numerically small, beleaguered gay community. Full article
(This article belongs to the Special Issue Religious Nationalism in Global Perspective)
14 pages, 1628 KiB  
Review
Recent Advance on Biological Activity and Toxicity of Arecoline in Edible Areca (Betel) Nut: A Review
by Gang Huang, Deyong Zeng, Tisong Liang, Yaping Liu, Fang Cui, Haitian Zhao and Weihong Lu
Foods 2024, 13(23), 3825; https://doi.org/10.3390/foods13233825 - 27 Nov 2024
Viewed by 713
Abstract
Areca nut (Areca catechu L. AN), which is the dried, mature seed of the palm species Areca catechu L., is consumed by over 600 million individuals, predominantly in South Asia, East Africa, and certain regions of the tropical Pacific. The International Agency [...] Read more.
Areca nut (Areca catechu L. AN), which is the dried, mature seed of the palm species Areca catechu L., is consumed by over 600 million individuals, predominantly in South Asia, East Africa, and certain regions of the tropical Pacific. The International Agency for Research on Cancer (IARC) has classified it as a species carcinogenic to humans and designated it as a Group 1 human carcinogen. Arecoline, which has attracted attention for its therapeutic potential in the treatment of mental illness and the relief of gastrointestinal disorders, is the main active alkaloid in the areca nut. However, in 2020, the IARC said that arecoline might be a “probable human carcinogen”. Arecoline can cause various types of cellular damage, primarily leading to the destruction of cell morphology, reduced survival rates, abnormal physiological functions, and even cell apoptosis. The research on its toxic mechanisms includes several aspects, such as increased levels of reactive oxygen species, autophagy, epigenetic dysregulation, and immune dysfunction, but these research findings are scattered and lack systematic integration. This article summarizes the effect mechanisms of arecoline on the oral cavity, neurological and cardiovascular systems, and other organs, as well as embryogenesis, and provides detailed and valuable insights for the clinical practice and targeted therapy of arecoline. Full article
(This article belongs to the Section Food Toxicology)
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<p>The alkaloid structures in several common areca nuts [<a href="#B16-foods-13-03825" class="html-bibr">16</a>]. (1. arecoline; 2. arecaidine; 3. arecolidine; 4. norarecoline hydrochloride; 5. guvacine hydrochloride; 6. N-methyl-1,2,5,6-tetrahydrogen-pyridine-3-ethyl carboxylate; 7. isoguvacine; 8. homoarecolin; 9. methyl nicotinate; 10. ethyl nicotinate; 11. N-methylpiperidine-3-methylcarboxylate; 12. N-methylpiperidine-3-ethyl carboxylate; 13. trigonelline; 14. nicotine; 15. cotinine; 16. caffeine; 17. febrifugine; 18. vicine; 19. hordenine; 20. dophoridine).</p>
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<p>A potential mechanism for the development of arecoline-induced OSF and OSCC [<a href="#B45-foods-13-03825" class="html-bibr">45</a>].</p>
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<p>The effect mechanisms of arecoline on human organs [<a href="#B45-foods-13-03825" class="html-bibr">45</a>].</p>
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18 pages, 281 KiB  
Article
Does Quality Education and Governance Matter for Women’s Empowerment? The Role of Structural Factors and Governance in the MENA Region
by Hawazen Zam Almugren, A. C. Muhammadu Kijas, Masahina Sarabdeen and Jawaher Binsuwadan
Sustainability 2024, 16(23), 10277; https://doi.org/10.3390/su162310277 - 24 Nov 2024
Viewed by 567
Abstract
Women’s empowerment can be critical in achieving sustainable development goals. We analyse the influence of the quality of education, structural factors, and governance on women’s empowerment in Middle East and North Africa (MENA) countries using a generalised method of moments (GMM). Furthermore, this [...] Read more.
Women’s empowerment can be critical in achieving sustainable development goals. We analyse the influence of the quality of education, structural factors, and governance on women’s empowerment in Middle East and North Africa (MENA) countries using a generalised method of moments (GMM). Furthermore, this article examines the moderating effect of governance on the relationship between quality of education and women’s empowerment. The role of governance is measured along economic and political dimensions. Quality education is measured by enrolment in secondary education, women’s empowerment is measured by the ratio of women’s participation to men’s employment, and structural factors are measured by electricity accessibility and the fertility rate. These variables were selected from existing studies published by global entities. The findings revealed that women’s empowerment substantially influenced the quality of education in the MENA region. Further findings show that governance-induced changes substantially and positively influence inclusive education in all contexts. However, the results show negative and significant interaction coefficients between women’s empowerment and political and economic governance. This indicates that the interaction between women’s empowerment and governance has a complementary effect. Furthermore, our results should motivate regulators and governments to initiate more policies to improve the quality of education and women’s empowerment. This study provides policymakers with insights into the potential role of governance and structural factors in promoting women’s empowerment through quality education. Full article
(This article belongs to the Special Issue Sustainable Education for All: Latest Enhancements and Prospects)
15 pages, 5076 KiB  
Article
Somatic Recombination Between an Ancient and a Recent NOTCH2 Gene Variant Is Associated with the NOTCH2 Gain-of-Function Phenotype in Chronic Lymphocytic Leukemia
by Rainer Hubmann, Martin Hilgarth, Tamara Löwenstern, Andrea Lienhard, Filip Sima, Manuel Reisinger, Claudia Hobel-Kleisch, Edit Porpaczy, Torsten Haferlach, Gregor Hoermann, Franco Laccone, Christof Jungbauer, Peter Valent, Philipp B. Staber, Medhat Shehata and Ulrich Jäger
Int. J. Mol. Sci. 2024, 25(23), 12581; https://doi.org/10.3390/ijms252312581 - 22 Nov 2024
Viewed by 584
Abstract
Constitutively active NOTCH2 signaling is a hallmark in chronic lymphocytic leukemia (CLL). The precise underlying defect remains obscure. Here we show that the mRNA sequence coding for the NOTCH2 negative regulatory region (NRR) is consistently deleted in CLL cells. The most common NOTCH2ΔNRR-DEL2 [...] Read more.
Constitutively active NOTCH2 signaling is a hallmark in chronic lymphocytic leukemia (CLL). The precise underlying defect remains obscure. Here we show that the mRNA sequence coding for the NOTCH2 negative regulatory region (NRR) is consistently deleted in CLL cells. The most common NOTCH2ΔNRR-DEL2 deletion is associated with two intronic single nucleotide variations (SNVs) which either create (CTTAT, G>A for rs2453058) or destroy (CTCGT, A>G for rs5025718) a putative splicing branch point sequence (BPS). Phylogenetic analysis demonstrates that rs2453058 is part of an ancient NOTCH2 gene variant (*1A01) which is associated with type 2 diabetes mellitus (T2DM) and is two times more frequent in Europeans than in East Asians, resembling the differences in CLL incidence. In contrast, rs5025718 belongs to a recent NOTCH2 variant (*1a4) that dominates the world outside Africa. Nanopore sequencing indicates that somatic reciprocal crossing over between rs2453058 (*1A01) and rs5025718 (*1a4) leads to recombined NOTCH2 alleles with altered BPS patterns in NOTCH2*1A01/*1a4 CLL cases. This would explain the loss of the NRR domain by aberrant pre-mRNA splicing and consequently the NOTCH2 gain-of-function phenotype. Together, our findings suggest that somatic recombination of inherited NOTCH2 variants might be relevant to CLL etiology and may at least partly explain its geographical clustering. Full article
(This article belongs to the Special Issue Notch Signaling Pathways)
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<p><span class="html-italic">NOTCH2ΔNRR</span> mRNA deletions in CLL. (<b>a</b>) Representative RT-PCR and sanger sequencing of the most frequent <span class="html-italic">NOTCH2ΔNRR</span> mRNA deletions in CLL cells. The fused <span class="html-italic">NOTCH2</span> mRNA/protein sequences, the short direct repeat at the deletion breakpoints, and a pie chart showing the frequency of individual <span class="html-italic">NOTCH2∆NRR</span> mRNA deletions in 20 CLL cases are indicated. (<b>b</b>) Localization of <span class="html-italic">NOTCH2ΔNRR</span> deletions at the <span class="html-italic">NOTCH2</span> mRNA and corresponding protein level. The S1–3 cleavage sites, the deletion hotspots (HS), and the minimal deleted region (MDR: E24 and 25) are indicated. Abbreviations: UTR, untranslated region; EGFR, epidermal growth factor-like repeats; LNR, Lyn/Notch repeats; HDD, hetero dimerization domain; TM, transmembrane domain; AR, ankyrin repeats; PEST, pest domain.</p>
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<p>Minor allele frequency (MAF %) of <span class="html-italic">NOTCH2ΔNRR-DEL2</span>-associated SNVs in European CLL patients (Austria/Spain/Germany, n = 387) compared to different world regions (1000Genomes_30×; 54KJPN).</p>
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<p>Evolution of the human <span class="html-italic">NOTCH2</span> gene. (<b>a</b>) Genomic context of <span class="html-italic">NOTCH2ΔNRR-DEL2</span>-associated SNVs in European CLL patients (Austria/Spain/Germany, n = 387) compared to different world regions (1000Genomes_30×; 54KJPN). (<b>b</b>) <span class="html-italic">NOTCH2</span> variants and haplogroups (IG, intergenic), MAF % in Europe, and associated diseases (dbSNP). SNVs shared with great apes are indicated (IBD, Inflammatory bowel disease; LBR, ligand-binding region). (<b>c</b>) Global distribution of <span class="html-italic">NOTCH2</span> variants in 46 contemporary ethnicities (HGDP; O, Oceania) and archaic humans (AH) compared to Spanish CLL patients (ICGC). Ethnicities with <span class="html-italic">NOTCH2</span> pre-variants are indicated. (<b>d</b>) Ethnicities with their highest frequency, putative local recombination events, and the 60 K out of Africa bottleneck are indicated. (<b>c</b>,<b>d</b>).</p>
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<p>Global distribution of the CLL relevant <span class="html-italic">NOTCH2*1A01/*1a4</span> haplotype combination. Frequencies of <span class="html-italic">NOTCH2*1A01</span> and <span class="html-italic">*1a4</span> haplotypes (including all subvariants), and their combination in Africans (HGDP, n = 66), Europeans (HGDP, n = 96), European CLL patients (Austria/Spain/Germany, n = 387), and East Asians (HGDP, n = 92). The 2.2-fold higher frequency of <span class="html-italic">NOTCH2*1A01/*1a4</span> in Europeans compared to East Asians is indicated.</p>
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<p>Somatic recombination of <span class="html-italic">NOTCH2</span> in CLL. (<b>a</b>) Reciprocal crossing over between (a) <span class="html-italic">NOTCH2*1A01(b1)</span> and (b) <span class="html-italic">NOTCH2*1a4</span> led to recombined <span class="html-italic">NOTCH2</span> alleles (ba and ab) with mutated BPS pattern in CLL cells. (<b>b</b>) Nanopore single-read alignments (IGV) of six <span class="html-italic">NOTCH2*1A01(b1)/*1a4</span> CLL cases showing two representative reads of wild-type (a and b) and recombined (ba and ab) alleles in each case. VAFs of the affected SNVs, read depth (in brackets), the percentage of wild-type and recombined <span class="html-italic">NOTCH2</span> alleles, and exons are indicated.</p>
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<p><span class="html-italic">NOTCH2*1A01/*1a4</span>-RFLP. (<b>a</b>) Expected DNA fragments of HpyCH4IV/PsiI digested rs2453058/rs2793830-PCR products in cases homozygous for <span class="html-italic">NOTCH2*1a4</span> and heterozygous for <span class="html-italic">NOTCH2*1A01/1a4</span>. (<b>b</b>) Proof of concept RFLP showing the expected DNA fragments in 4 HD and 9 CLL samples.</p>
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12 pages, 939 KiB  
Article
Molecular Diversity and Distribution of Whiteflies (Bemisia tabaci) in Cassava Fields Across South West and North Central, Nigeria
by Oghenevwairhe P. Efekemo, Olabode A. Onile-ere, Isaac O. Abegunde, Folashade T. Otitolaye, Justin S. Pita, Titus Alicai and Angela O. Eni
Insects 2024, 15(11), 906; https://doi.org/10.3390/insects15110906 - 20 Nov 2024
Viewed by 607
Abstract
Whitefly Bemisia tabaci (Gennadium, Hemiptera) causes severe damage to cassava plants through excessive feeding on leaves and transmitting viruses, such as African cassava mosaic virus (ACMV), East African cassava mosaic virus (EACMV), and ipomoviruses that cause cassava brown streak disease. Currently, little is [...] Read more.
Whitefly Bemisia tabaci (Gennadium, Hemiptera) causes severe damage to cassava plants through excessive feeding on leaves and transmitting viruses, such as African cassava mosaic virus (ACMV), East African cassava mosaic virus (EACMV), and ipomoviruses that cause cassava brown streak disease. Currently, little is known about the molecular diversity and distribution of whitefly species in the major cassava-growing zones of Nigeria. This study aimed to address the knowledge gap by assessing the genetic diversity, distribution, and associated cassava mosaic begomoviruses (CMBs) in whiteflies across South West and North Central, Nigeria. Whitefly samples were systematically collected from cassava plants during georeferenced epidemiological surveys in 2017, 2020, and 2022. The samples were genotyped using the mitochondrial cytochrome oxidase I (mtCOI) marker, and CMBs were detected by PCR with virus-specific primers. Phylogenetic analyses revealed four distinct genetic groups of B. tabaci: Sub-Saharan Africa 1 (SSA1; 84.8%), SSA2 (1.4%), SSA3 (13.1%), and Mediterranean (MED) (0.7%). The SSA1 group was the predominant and most widely distributed genotype across the surveyed zones, with three subgroups identified: SSA1-SG1, SSA1-SG3, and SSA1-SG5. The second most frequently identified genotype, SSA3, was restricted to the North Central zone, along with the SSA2 group, which was only identified in two North Central states (Niger and Plateau). African cassava mosaic virus (ACMV) was detected in SSA1-SG1, SSA1-SG5, and SSA3, whereas EACMV was found in only the SSA1-SG3. The findings of this study will aid in developing better whitefly management strategies to reduce the impact of CMD on cassava production in Nigeria. Full article
(This article belongs to the Section Insect Pest and Vector Management)
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<p>Phylogenetic tree of haplotypes obtained in this study alongside representative whitefly biotypes from the NCBI GenBank and <span class="html-italic">Bemsia afer</span> as outgroup (a list of sequences belonging to each haplotype can be found in <a href="#app1-insects-15-00906" class="html-app">Supplementary Materials</a>).</p>
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<p>Distribution of whitefly (<span class="html-italic">B. tabaci</span>) genotypes in cassava farms in South West and North Central, Nigeria. SSA—Sub-Saharan African. SG1—SSA1 subgroups 1; SG3—SSA1 subgroups 3; SG5—SSA1 subgroups 5.</p>
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