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Search Results (2,183)

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16 pages, 718 KiB  
Article
The Prevalence of Childhood Asthma, Respiratory Symptoms and Associated Air Pollution Sources Among Adolescent Learners in Selected Schools in Vhembe District, South Africa
by Funzani Rathogwa-Takalani, Thabelo Rodney Mudau, Sean Patrick, Joyce Shirinde and Kuku Voyi
Int. J. Environ. Res. Public Health 2024, 21(11), 1536; https://doi.org/10.3390/ijerph21111536 - 20 Nov 2024
Viewed by 285
Abstract
This study investigated the prevalence of childhood asthma and respiratory symptoms with their associated air pollution sources among adolescents aged 13–14 years residing in a Malaria-endemic region. Methods: A cross-sectional survey was conducted with 2855 adolescents from fourteen (14) selected schools in communities [...] Read more.
This study investigated the prevalence of childhood asthma and respiratory symptoms with their associated air pollution sources among adolescents aged 13–14 years residing in a Malaria-endemic region. Methods: A cross-sectional survey was conducted with 2855 adolescents from fourteen (14) selected schools in communities exposed to high levels of air pollution from indoor residual spraying (IRS) that is used for malaria vector control in the Vhembe region. Data were collected using a self-administered standardized International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. Statistical software STATA version 17 was used to analyze the data. Binary logistic regression was used to determine the relationship between air pollution sources and childhood asthma/symptoms. Results: The prevalences of asthma, ‘wheeze ever’ and ‘wheeze in the past’ were 18.91%, 37.69% and 24.69%, respectively. The results from the adjusted binary logistic regression model indicated that exposure to tobacco smoke (OR = 1.84; 95% CI: 1.08–3.16), smoking a water pipe (OR = 1.65; 95% CI: 1.16–2.36) and the use of paraffin as fuel for heating (OR = 1.70; 95% CI: 0.97–2.88) and cooking (OR = 0.48; 95% CI: 0.29–1.00) were significant risk factors for asthma. Trucks passing through the streets, having a cat at home and using open fires were significantly associated with ‘wheeze in the past’. Finally, using gas for cooking (OR = 0.72; 95% CI: 0.53–0.99), open fires for heating (OR = 0.53; 95% CI: 0.35–0.80) and smoking a water pipe (OR = 2.47; 95% CI: 1.78–3.44) were associated with ‘wheeze ever’. Conclusions: School children living in these communities had an increased risk of developing asthma and presenting with wheezing due to exposure to environmental air pollution sources. Full article
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<p>Flow chart of procedures followed and the participation rate.</p>
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21 pages, 1870 KiB  
Article
Modeling the Impact of Human Awareness and Insecticide Use on Malaria Control: A Fractional-Order Approach
by Mlyashimbi Helikumi, Thobias Bisaga, Kimulu Ancent Makau and Adquate Mhlanga
Mathematics 2024, 12(22), 3607; https://doi.org/10.3390/math12223607 - 19 Nov 2024
Viewed by 279
Abstract
In this research work, we developed a fractional-order model for the transmission dynamics of malaria, incorporating two control strategies: health education campaigns and the use of insecticides. The theoretical analysis of the model is presented, including the computation of disease-free equilibrium and basic [...] Read more.
In this research work, we developed a fractional-order model for the transmission dynamics of malaria, incorporating two control strategies: health education campaigns and the use of insecticides. The theoretical analysis of the model is presented, including the computation of disease-free equilibrium and basic reproduction number. We analyzed the stability of the proposed model using a well-formulated Lyapunov function. Furthermore, model parameter estimation was carried out using real data from malaria cases reported in Zimbabwe. We found that the fractional-order model provided a better fit to the real data compared to the classical integer-order model. Sensitivity analysis of the basic reproduction number was performed using computed partial rank correlation coefficients to assess the effect of each parameter on malaria transmission. Additionally, we conducted numerical simulations to evaluate the impact of memory effects on the spread of malaria. The simulation results indicated that the order of derivatives significantly influences the dynamics of malaria transmission. Moreover, we simulated the model to assess the effectiveness of the proposed control strategies. Overall, the interventions were found to have the potential to significantly reduce the spread of malaria within the population. Full article
(This article belongs to the Special Issue Mathematical Modeling of Disease Dynamics)
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<p>Model flowchart illustrating the dynamics of malaria transmission.</p>
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<p>Number of reported disease cases over 12 years in Zimbabwe.</p>
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<p>Model fit versus reported malaria cases at <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Sensitivity analysis of <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math> to key model parameters.</p>
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<p>Plot of global sensitivity analysis of model (<a href="#FD3-mathematics-12-03607" class="html-disp-formula">3</a>) on (<math display="inline"><semantics> <msub> <mi>I</mi> <mi>h</mi> </msub> </semantics></math>) to the key parameters that affect the dynamics of the disease.</p>
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<p>Results of Latin hypercube sampling of <math display="inline"><semantics> <msub> <mi>R</mi> <mn>0</mn> </msub> </semantics></math>, varying the key model parameters: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>β</mi> <mi>h</mi> </msub> </semantics></math> (disease transmission probability from mosquito to human), (<b>b</b>) <math display="inline"><semantics> <msub> <mi>β</mi> <mi>v</mi> </msub> </semantics></math> (disease transmission probability from human to mosquito), (<b>c</b>) <math display="inline"><semantics> <msub> <mi>δ</mi> <mi>v</mi> </msub> </semantics></math> (mosquito biting rate), (<b>d</b>) <math display="inline"><semantics> <msub> <mi>μ</mi> <mi>v</mi> </msub> </semantics></math> (mosquito mortality rate), (<b>e</b>) <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> (effectiveness of insecticides), and (<b>f</b>) <math display="inline"><semantics> <mi>ω</mi> </semantics></math> (effectiveness of preventive measures such as mosquito nets).</p>
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<p>Contour plots of <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math> (<b>a</b>) as the function of the use of insecticides (<math display="inline"><semantics> <mi>ϵ</mi> </semantics></math>) and the natural recovery rate of exposed humans <math display="inline"><semantics> <msub> <mi>θ</mi> <mi>h</mi> </msub> </semantics></math>, as well as (<b>b</b>) the function of the use of insecticides (<math display="inline"><semantics> <mi>ϵ</mi> </semantics></math>) and health education campaigns (<math display="inline"><semantics> <mi>ω</mi> </semantics></math>).</p>
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<p>Simulations of model system (<a href="#FD3-mathematics-12-03607" class="html-disp-formula">3</a>) at <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <mspace width="0.277778em"/> <mn>0.3</mn> <mo>,</mo> <mspace width="0.277778em"/> <mn>0.5</mn> <mo>,</mo> <mspace width="0.277778em"/> <mn>0.7</mn> </mrow> </semantics></math>. Simulations were carried out using the parameter values shown in <a href="#mathematics-12-03607-t001" class="html-table">Table 1</a>.</p>
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<p>Simulations of model system (<a href="#FD3-mathematics-12-03607" class="html-disp-formula">3</a>) at <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <mspace width="0.277778em"/> <mn>0.3</mn> <mo>,</mo> <mspace width="0.277778em"/> <mn>0.5</mn> <mo>,</mo> <mn>0.7</mn> </mrow> </semantics></math>. Simulations were carried out using the parameter values shown in <a href="#mathematics-12-03607-t001" class="html-table">Table 1</a>.</p>
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<p>Simulation of system (<a href="#FD3-mathematics-12-03607" class="html-disp-formula">3</a>) to investigate the effect of insecticide on the spread of malaria disease.</p>
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<p>Effects of varying <math display="inline"><semantics> <mi>ϵ</mi> </semantics></math> on reductions in new cases of malaria infection generated in the population.</p>
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<p>Simulation of system (<a href="#FD3-mathematics-12-03607" class="html-disp-formula">3</a>) to investigate the effect of insecticide on the spread of malaria disease.</p>
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<p>Effects of varying <math display="inline"><semantics> <mi>ω</mi> </semantics></math> on reductions in new malaria cases generated in the population.</p>
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15 pages, 1757 KiB  
Article
Severe Malaria in Angola: The Clinical Profile and Disease Outcome Among Adults from a Low-Endemic Area
by Inês Morais, Soraia Rodrigues, Aida Mas, Serguei Escalon, Adalzira Borrego, Fatima Nogueira and Maria Lina Antunes
Biomedicines 2024, 12(11), 2639; https://doi.org/10.3390/biomedicines12112639 - 19 Nov 2024
Viewed by 324
Abstract
Background/Objectives: Severe malaria poses a significant public health concern in Angola, particularly among adults. This study assessed the clinical manifestations and outcomes of severe Plasmodium falciparum malaria in adult patients admitted to Hospital Central Dr. António Agostinho Neto of Lubango (HCL), Angola. Methods: [...] Read more.
Background/Objectives: Severe malaria poses a significant public health concern in Angola, particularly among adults. This study assessed the clinical manifestations and outcomes of severe Plasmodium falciparum malaria in adult patients admitted to Hospital Central Dr. António Agostinho Neto of Lubango (HCL), Angola. Methods: The study retrospectively reviewed medical records of patients over 14 years old admitted with severe malaria during the first quarter of 2021 and 2022, coinciding with the peak transmission season. The World Health Organization (WHO) criteria were used to clarify the disease severity. The cohort included 640 patients—167 in 2021 and 473 in 2022—distributed across the following departments: the Intensive Care Unit (ICU; n = 81), Medicine (MED; n = 458) and Infectiology (INF; n = 101). Results: The median age was 26 years and 59.4% were males. Renal impairment was the most frequent severe manifestation, affecting 37.4% of cases. The mortality rate across the study period was 7%, showing a notable decrease from 10.2% in 2021 to 5.9% in 2022. The higher mortality rate in 2021 may reflect the impact of the COVID-19 pandemic, which limited hospital access and delayed care, resulting in more critical cases being admitted at a later stage. In 2022, with reduced COVID-19 pressures, earlier access to treatment may have improved outcomes, contributing to the lower mortality rate. Conclusions: This study emphasizes the need to assess the clinical burden of severe malaria in low-endemic regions, where shifting patterns may signal emerging threats such as antimalarial drug resistance. Further research is essential to optimize control strategies and strengthen surveillance systems, reducing morbidity and mortality. Full article
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<p>Map of Angola provinces highlighting the study site. Dark blue, Angola; light blue, Angola’s provinces; medium blue, Huíla Province; star, Lubango city; HCL, Hospital Central do Lubango Hospital Central Dr. António Agostinho Neto. The figure was created and edited using Inkscape software (version 1.2, Inkscape Project, Boston, MA, USA).</p>
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<p>Overall severe malaria profile of patients included in this study. Numbers beside bars represent the % of patients that presented the correspondent severe malaria criteria. Pulmonary edema was not recorded in any of the patients.</p>
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<p>Severe malaria profile of patients admitted to HCL and included in this study during 2021 and 2022. Numbers inside boxes represent the % of patients that presented the correspondent severe malaria criteria admitted to each department: ICU, Intensive Care Unit; INF, Infectiology; MED, Internal Medicine; Overall, aggregated data from all three departments. Pulmonary edema was not recorded in any of the patients.</p>
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<p>Clinical outcome of severe malaria patients. * Three (3) patients admitted to MED during 2021 abandoned the hospital before receiving medical discharge.</p>
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<p>Severe malaria features on admittance amongst non-survivors. (<b>A</b>) Numbers represent the % of patients admitted to each department: ICU, Intensive Care Unit; INF, Infectiology; MED, Internal Medicine; Overall, aggregated data from all three departments. Pulmonary edema was not recorded in any of the patients. There were no fatalities in INF during 2021. (<b>B</b>) Parasitemia at admission distributed by department and outcome. Black bars, non-survivors; white bars, survivors. (<b>C</b>,<b>D</b>) Creatinine and bilirubin levels and (<b>E</b>) Glasgow coma score (GCS). Dotted line indicates cut-off values for each parameter.</p>
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15 pages, 918 KiB  
Article
Community Knowledge, Attitudes and Practices About Malaria: Insights from a Northwestern Colombian Endemic Locality
by Paola Muñoz-Laiton, Juan C. Hernández-Valencia and Margarita M. Correa
Trop. Med. Infect. Dis. 2024, 9(11), 281; https://doi.org/10.3390/tropicalmed9110281 - 18 Nov 2024
Viewed by 372
Abstract
Malaria prevention and control programs are mainly oriented to vector control, timely diagnosis and adequate treatment. Malaria transmission is influenced by several factors, including biological and social aspects. Thus, it is relevant to consider community beliefs and practices to ensure sustainable prevention and [...] Read more.
Malaria prevention and control programs are mainly oriented to vector control, timely diagnosis and adequate treatment. Malaria transmission is influenced by several factors, including biological and social aspects. Thus, it is relevant to consider community beliefs and practices to ensure sustainable prevention and control strategies. This study aimed to determine knowledge, attitudes and practices (KAP) towards malaria in an endemic locality in northwestern Colombia. Preliminary data were collected through a focus group discussion. Subsequently, a KAP survey was administered to the community. KAP scores were associated with both sociodemographic characteristics and with previous malaria infection. Focus group data revealed knowledge gaps and the absence of or having worn-out nets. Survey results showed that participants recognized a mosquito bite as the transmission mode (72.09%), followed by dirty water (44.19%), high fever (86.05%) and headache (79.07%) as the main symptoms. Regarding attitudes, 44.19% of the people would go to the hospital in the case of having symptoms. The most recognized practices for disease prevention were the use of mosquito nets (65.12%) and fans (23.26%). The results showed that some people had misconceptions about the disease transmission mode. The analysis showed significant associations of either female gender and homemaker occupation with a good knowledge [OR = 3.74, (p = 0.04), OR = 3.55, (p = 0.04), respectively] or female with a positive attitude towards malaria control and prevention [OR = 4.80, (p = 0.04)]. These results showed that the identified gaps in KAP require increasing education among the community in addition to applying public health prevention efforts. The data may be useful in designing malaria control strategies that involve community participation. Full article
(This article belongs to the Section Vector-Borne Diseases)
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<p>Study area. The left square shows the map of Colombia in relation to South America and the Bajo Cauca subregion in northwestern Colombia. The right square shows the Villa Grande locality in the BC subregion.</p>
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<p>Diagram of categories and subcategories established after qualitative analysis of the focus group discussion.</p>
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10 pages, 784 KiB  
Article
Severity of Vessel Color Changes and Macular and Peripheral Whitening in Malarial Retinopathy Are Associated with Higher Total Body and Sequestered Parasite Burdens
by Chiadika Nwanze, Daniel Muller, Priscilla Suleman, Mrinmayee Takle, John R. Barber, Kyle J. Wilson, Nicholas A. V. Beare, Karl B. Seydel and Douglas G. Postels
Trop. Med. Infect. Dis. 2024, 9(11), 279; https://doi.org/10.3390/tropicalmed9110279 - 16 Nov 2024
Viewed by 336
Abstract
Two-thirds of children with cerebral malaria (CM) exhibit retinopathy characterized by whitening, vessel color changes, and/or hemorrhages. The pathogenesis of malarial retinopathy is not fully understood. This study aimed to assess the relationship between malarial retinopathy and the severity of its components (macular [...] Read more.
Two-thirds of children with cerebral malaria (CM) exhibit retinopathy characterized by whitening, vessel color changes, and/or hemorrhages. The pathogenesis of malarial retinopathy is not fully understood. This study aimed to assess the relationship between malarial retinopathy and the severity of its components (macular whitening, retinal hemorrhages, and vessel color changes) with the total, circulating, or sequestered parasite load in children with CM. Total parasite burden was estimated by measuring plasma levels of Plasmodium falciparum histidine-rich protein 2 (PfHRP2), while the sequestered load was calculated as the difference between the total burden and circulating parasitemia. Children with retinopathy-positive CM (n = 172) had higher total and sequestered parasite burdens compared to retinopathy-negative children (n = 42) (both p = 0.049). In a subgroup with detailed retinopathy grading (n = 52), more extensive vessel color changes correlated with higher total, sequestered, and circulating parasite loads (p = 0.0057, p = 0.0068, and p = 0.0433, respectively). Peripheral retinal whitening was also associated with increased total and sequestered loads (p = 0.0017 and p = 0.0012). No association was found between retinal hemorrhages and parasite burden, indicating that other factors may influence their pathogenesis. Full article
(This article belongs to the Special Issue Recent Progress in Mosquito-Borne Diseases)
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<p>Association of total body parasite burden with severity of vessel color changes (<b>A</b>), peripheral whitening (<b>B</b>), macular whitening (<b>C</b>), and retinal hemorrhages (<b>D</b>). log (tbp qHRP-2): logarithm of total body parasite burden as determined by analysis of blood quantitative histidine-rich protein 2. <sup>1</sup> Vessel changes are graded as no (0) or yes (1) in each quadrant of both eyes, divided by the number of quadrants visualized, with a mean taken between the eyes. <sup>2</sup> Peripheral whitening is graded as absent (0), mild (1), moderate (2), or severe (3). Grade 3 peripheral whitening must be a widespread mosaic or have patches of confluence. <sup>3</sup> Macular whitening is graded as absent (0), less than one-third disc (optic nerve) area in diameter (1), one-third to one disc area in diameter (2), greater than one disc area in diameter (3), with a mean taken between the two eyes. <sup>4</sup> Retinal hemorrhages are graded as absent (0), 1–5 hemorrhages (1), 6–20 hemorrhages (2), 21–50 hemorrhages (3), or 51 or more hemorrhages (4).</p>
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<p>Association of sequestered parasite burden with severity of vessel color changes (<b>A</b>), peripheral whitening (<b>B</b>), macular whitening (<b>C</b>), and retinal hemorrhages (<b>D</b>). Log (seq qHRP-2): logarithm of sequestered parasite load as determined by estimating total body parasite burden using quantitative histidine-rich protein 2 and subtracting circulating parasite burden. <sup>1</sup> Vessel changes are graded as no (0) or yes (1) in each quadrant of both eyes, divided by the number of quadrants visualized, with a mean taken between the eyes. <sup>2</sup> Peripheral whitening is graded as absent (0), mild (1), moderate (2), or severe (3). Grade 3 peripheral whitening must be a widespread mosaic or have patches of confluence. <sup>3</sup> Macular whitening is graded as absent (0), less than one-third disc (optic nerve) area in diameter (1), one-third to one disc area in diameter (2), greater than one disc area in diameter (3), with a mean taken between the two eyes. <sup>4</sup> Retinal hemorrhages are graded as absent (0), 1–5 hemorrhages (1), 6–20 hemorrhages (2), 21–50 hemorrhages (3), or 51 or more hemorrhages (4).</p>
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<p>Association of sequestered parasite burden with severity of vessel color changes (<b>A</b>), peripheral whitening (<b>B</b>), macular whitening (<b>C</b>), and retinal hemorrhages (<b>D</b>). Log (seq qHRP-2): logarithm of sequestered parasite load as determined by estimating total body parasite burden using quantitative histidine-rich protein 2 and subtracting circulating parasite burden. <sup>1</sup> Vessel changes are graded as no (0) or yes (1) in each quadrant of both eyes, divided by the number of quadrants visualized, with a mean taken between the eyes. <sup>2</sup> Peripheral whitening is graded as absent (0), mild (1), moderate (2), or severe (3). Grade 3 peripheral whitening must be a widespread mosaic or have patches of confluence. <sup>3</sup> Macular whitening is graded as absent (0), less than one-third disc (optic nerve) area in diameter (1), one-third to one disc area in diameter (2), greater than one disc area in diameter (3), with a mean taken between the two eyes. <sup>4</sup> Retinal hemorrhages are graded as absent (0), 1–5 hemorrhages (1), 6–20 hemorrhages (2), 21–50 hemorrhages (3), or 51 or more hemorrhages (4).</p>
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<p>Association of circulating parasite burden with severity of vessel color changes (<b>A</b>), peripheral whitening (<b>B</b>), macular whitening (<b>C</b>), and retinal hemorrhages (<b>D</b>). Log (parasitemia): logarithm of circulating parasite burden. <sup>1</sup> Vessel changes are graded as no (0) or yes (1) in each quadrant of both eyes, divided by the number of quadrants visualized, with a mean taken between the eyes. <sup>2</sup> Peripheral whitening is graded as absent (0), mild (1), moderate (2), or severe (3). Grade 3 peripheral whitening must be a widespread mosaic or have patches of confluence. <sup>3</sup> Macular whitening is graded as absent (0), less than one-third disc (optic nerve) area in diameter (1), one-third to one disc area in diameter (2), greater than one disc area in diameter (3), with a mean taken between the two eyes. <sup>4</sup> Retinal hemorrhages are graded as absent (0), 1–5 hemorrhages (1), 6–20 hemorrhages (2), 21–50 hemorrhages (3), or 51 or more hemorrhages (4).</p>
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15 pages, 8448 KiB  
Review
The J Domain Proteins of Plasmodium knowlesi, a Zoonotic Malaria Parasite of Humans
by Michael O. Daniyan, Harpreet Singh and Gregory L. Blatch
Int. J. Mol. Sci. 2024, 25(22), 12302; https://doi.org/10.3390/ijms252212302 - 16 Nov 2024
Viewed by 551
Abstract
Plasmodium knowlesi is a zoonotic form of human malaria, the pathology of which is poorly understood. While the J domain protein (JDP) family has been extensively studied in Plasmodium falciparum, and shown to contribute to malaria pathology, there is currently very limited [...] Read more.
Plasmodium knowlesi is a zoonotic form of human malaria, the pathology of which is poorly understood. While the J domain protein (JDP) family has been extensively studied in Plasmodium falciparum, and shown to contribute to malaria pathology, there is currently very limited information on the P. knowlesi JDPs (PkJDPs). This review provides a critical analysis of the literature and publicly available data on PkJDPs. Interestingly, the P. knowlesi genome encodes at least 31 PkJDPs, with well over half belonging to the most diverse types which contain only the signature J domain (type IIIs, 19) or a corrupted version of the J domain (type IVs, 2) as evidence of their membership. The more typical PkJDPs containing other domains typical of JDPs in addition to the J domain are much fewer in number (type IIs, 8; type Is, 2). This study indentifies PkJDPs that are potentially involved in: folding of newly synthesized or misfolded proteins within the P. knowlesi cytosol (a canonical type I and certain typical type IIs); protein translocation (a type III) and folding (a type II) in the ER; and protein import into mitochondria (a type III). Interestingly, a type II PkJDP is potentially exported to the host cell cytosol where it may recruit human HSP70 for the trafficking and folding of other exported P. knowlesi proteins. Experimental studies are required on this fascinating family of proteins, not only to validate their role in the pathology of knowlesi malaria, but also because they represent potential anti-malarial drug targets. Full article
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<p>JDPs are a structurally diverse family of proteins that network with the major chaperones, HSP70s and HSP90s. (<b>A</b>) Chaperones assist newly synthesized or unfolded/misfolded proteins (black coil) to fold into a functional state (black helix/sheet) [<a href="#B17-ijms-25-12302" class="html-bibr">17</a>]. JDPs (purple) recognize a defined range of protein substrates which they target to partner HSP70s (green), thereby stimulating ATP hydrolysis and locking the substrate in the HSP70 SBD [<a href="#B18-ijms-25-12302" class="html-bibr">18</a>,<a href="#B19-ijms-25-12302" class="html-bibr">19</a>]. Nucleotide exchange triggers the release of substrate to fold, or in the case of certain specialized HSP90 client proteins, HSP70 delivers the substrate to HSP90 (blue) with the assistance of HOP (red), resulting in another ATP-dependent chaperone cycle, before the substrate is released to fold or re-enter the cycle if necessary [<a href="#B20-ijms-25-12302" class="html-bibr">20</a>]. Abbreviations used: HSP, heat shock protein; JDP, J domain protein; SBD, substrate-binding domain; J, J domain; NBD, nucleotide-binding domain; N, N-terminal domain; M, middle domain; C, C-terminal domain; HOP, HSP70-HSP90 organizing protein; TPR1 and TPR2, tetratricopeptide repeat domains 1 and 2, respectively. This graphic was created with BioRender (<a href="http://biorender.com" target="_blank">biorender.com</a>). (<b>B</b>) The domain organization in the various types or classes of JDPs. The J domain is the signature domain (green) of JDPs, with types I-III J domains containing the highly conserved histidine-proline-aspartic acid (HPD) motif (indicated by the solid box) [<a href="#B21-ijms-25-12302" class="html-bibr">21</a>,<a href="#B22-ijms-25-12302" class="html-bibr">22</a>,<a href="#B23-ijms-25-12302" class="html-bibr">23</a>], and type IV J domains containing a corrupted HPD motif (indicated by the dashed box) [<a href="#B8-ijms-25-12302" class="html-bibr">8</a>]. The J domain occurs near the N-terminus of type I and II JDPs, while in type III and IV JDPs it can be found anywhere along the sequence (green dashed double-headed arrow). The other domains are as follows: the glycine and phenylalanine rich region (G/F; brick red); the C-terminal substrate-binding domain I (CTD-I; sky blue) with an embedded zinc-finger-like region (ZFLR; yellow); and the C-terminal substrate-binding domain II (CTD-II; sky blue). The letters N and C indicate the N-terminal and C-terminal ends of the proteins.</p>
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<p>The <span class="html-italic">P. knowlesi</span> type II JDP, PKNH_0216100, is predicted to be exported into the human host cell cytosol and is homologous to other plasmodial type II JDPs known or predicted to be exported. The proteins in the multiple sequence alignment are defined by their PlasmoDB accession number in the first column: <span class="html-italic">P. chabaudi</span> type II JDP, PCHAS_0213300; <span class="html-italic">P. yoelii</span> type II JDP, PY17X_0216500; <span class="html-italic">P. berghei</span> type II JDP, PBANKA_0214800; <span class="html-italic">P. knowlesi</span> type II JDP, PKNH_0216100; <span class="html-italic">P. falciparum</span> type II JDPs, PFE0055c/PF3D7_0501100, PFB0090c/PF3D7_0201800, and PFA0066w/PF3D7_0113700. The protein sequences for all JDPs are listed in the <a href="#app1-ijms-25-12302" class="html-app">Supplementary Materials (List S1)</a>. Colored in black are identical amino acids (in at least 50% of the aligned sequences), colored in light gray are similar amino acids (in at least 50% of the aligned sequences), and colored in white are the amino acids with no identity or similarity. The default categories for similar amino acids were applied to the multiple sequence alignment (ILV, FWY, KRH, DE, GAS, P, C and TNQM). In <span class="html-italic">P. yoelli</span>, the <span class="html-italic">Plasmodium</span> Lipophilic And Secondary structure Mediated Export Domain (PLASMED) motif (purple shading) has been identified as required for export [<a href="#B42-ijms-25-12302" class="html-bibr">42</a>]. In <span class="html-italic">P. falciparum</span>, most exported proteins contain a <span class="html-italic">Plasmodium</span> export element (PEXEL, also known as the host-targeting signal), a pentameric motif (RxLxE/Q/D; yellow shading) generally located downstream of a recessed signal sequence [<a href="#B43-ijms-25-12302" class="html-bibr">43</a>,<a href="#B44-ijms-25-12302" class="html-bibr">44</a>]. During export, the PEXEL sequence is recognized and cleaved between the third and fourth residues by Plasmepsin V. The J domain (green shading), the G/F-rich region (brick red shading), and the C-Terminal Domain (CTD containing CTD-I and II; blue shading) containing the substrate binding site, are all indicated by colored shading. The numbers in the last column are the residue positions within the full-length sequence. The alignments were created using Clustal Omega [<a href="#B45-ijms-25-12302" class="html-bibr">45</a>] and rendered with box shading using Multiple Align Show [<a href="#B46-ijms-25-12302" class="html-bibr">46</a>].</p>
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<p>Predicted three-dimensional (3D) structure of PKNH_0216100. The 3D structures of the near-full-length protein, J domain (green)-glycine/phenylalanine (G/F)-rich region (brick red)-C-terminal-domain (CTD; sky blue) (left-hand side), and the J domain alone (green; right-hand side) were determined using the AlphaFold 3 Server (<a href="http://alphafoldserver.com" target="_blank">alphafoldserver.com</a> [<a href="#B47-ijms-25-12302" class="html-bibr">47</a>]). The predicted template modeling (pTM) scores were above the threshold (0.5) for the predicted structures to be considered potentially similar to the true structure. For the J domain structure, helicies I–IV and the Loop region are labeled, and conserved residues previously identified in other JDPs as important for functional interaction with HSP70 [<a href="#B19-ijms-25-12302" class="html-bibr">19</a>,<a href="#B22-ijms-25-12302" class="html-bibr">22</a>,<a href="#B35-ijms-25-12302" class="html-bibr">35</a>,<a href="#B48-ijms-25-12302" class="html-bibr">48</a>,<a href="#B49-ijms-25-12302" class="html-bibr">49</a>,<a href="#B50-ijms-25-12302" class="html-bibr">50</a>,<a href="#B51-ijms-25-12302" class="html-bibr">51</a>,<a href="#B52-ijms-25-12302" class="html-bibr">52</a>], have been highlighted as sticks (gray); the highly conserved basic residues on helix II (K102, K103, K106 and K107) and the invariant histidine-proline-aspartate (HPD) motif in the Loop region. The protein sequence for PKNH_0216100 is listed in the <a href="#app1-ijms-25-12302" class="html-app">Supplementary Materials (List S1)</a>. The predicted structures were rendered graphically using PyMol 3.0.4 (PyMOL Molecular Graphics System, Version 3.0 Schrödinger, LLC, New York, NY, USA).</p>
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<p>Predicted 3D structure of the complex of the J domain of PKNH_0216100 and human HSP70 (HSPA1A). The complex was rendered in two different orientations, to show the J domain (green) bound to human HSP70 (N-terminal nucleotide-binding domain, NBD, sky blue; C-terminal substrate-binding domain, SBD, orange), with the J domain antiparallel helices II and III front-on (left-hand side) and side-on (middle and right-hand side). The structure predicted that two conserved basic residues of helix II (K102 and K106) and two conserved acidic residues on the underside cleft of human HSP70 NBD (D213 and D214) projected into the binding interface of the complex. These key residues are shown as sticks colored by element type (middle and right-hand side). The two basic residues of helix II are equivalent to previously identified conserved JDP residues (see the legend of <a href="#ijms-25-12302-f003" class="html-fig">Figure 3</a>) that make key contacts with conserved acidic residues on the underside cleft of the NBD of HSP70s (acidic residues topologically equivalent to D213 and D214 of human HSP70 [<a href="#B57-ijms-25-12302" class="html-bibr">57</a>,<a href="#B58-ijms-25-12302" class="html-bibr">58</a>,<a href="#B59-ijms-25-12302" class="html-bibr">59</a>]). The predicted 3D complex was determined using the AlphaFold 3 Server (<a href="http://alphafoldserver.com" target="_blank">alphafoldserver.com</a>; [<a href="#B47-ijms-25-12302" class="html-bibr">47</a>]). The interface predicted template modeling (ipTM) score was above the threshold (0.8) for the predicted complex to be considered potentially similar to the true structure. The protein sequences for PKNH_0216100 and human HSP70 (HSPA1A) are listed in the <a href="#app1-ijms-25-12302" class="html-app">Supplementary Materials (List S1)</a>. The complex was graphically rendered using PyMol 3.0.4 (PyMOL Molecular Graphics System, Version 3.0 Schrödinger, LLC).</p>
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<p>Schematic representation of a <span class="html-italic">P. knowlesi</span>-infected red blood cell showing the potential sub-cellular localization and chaperone partnerships of certain PkJDPs. The PkJDPs, PkHSP70s, PkHSP90s and PkHOP have been given common names based on their homology with chaperones and co-chaperones of <span class="html-italic">P. falciparum</span>. The PkJDPs include the following members: PkHSP40 (PKNH_0424600); Pkj2 (PKNH_0906300); Pkj4 (PKNH_1311500); PkSec63 (PKNH_1419600); PkTIM14 (PKNH_0319800); and Exported PkJDP (PKNH_0216100). The PkHSP70s include: PkHSP70-1 (PKNH_1312700); PkHSP70-2 (PKNH_0715900); PkHSP70-3 (PKNH_0932200); PkHSP70-y (PKNH_1257200); and PkHSP70-z (PKNH_0107400). The PkHSP90s include: PkHSP90 (PKNH_0107000); PkGRP94 (PKNH_1441400); PkTRAP1 (PKNH_0915900); and PkHSP90_A (PKNH_1238400). There is only one PkHOP protein (PKNH_0420900). Refer to the main text and <a href="#app1-ijms-25-12302" class="html-app">Supplementary Materials (List S1 and Table S1)</a> for the details of each protein. The infected red blood cell (iRBC) cytosol is shown in pink, and the parasite cytosol is shown in pale yellow. Unlike <span class="html-italic">P. falciparum</span>-iRBCs, <span class="html-italic">P. knowlesi</span>-iRBCs do not appear to have knobs or Maurer’s Clefts; however, there is evidence for cytoahherence ligands in the iRBC membrane (iRM) [<a href="#B73-ijms-25-12302" class="html-bibr">73</a>], and cleft-like structures, vesicles and a tubovesicular network (TVN) within the iRBC cytosol [<a href="#B74-ijms-25-12302" class="html-bibr">74</a>]. The abbreviations used for other sub-cellular features include PV, parasitophorous vacuole; PVM, parasitophorous vacuole membrane; PPM, parasite plasma membrane; AP, apicoplast; ER, endoplasmic reticulum; EV, endocytic vesicle; FV, food vacuole; M, mitochondrion; and N, nucleus.</p>
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6 pages, 200 KiB  
Case Report
False-Positive Malaria Rapid Diagnostic Test Likely Due to African Tick Bite Fever: A Case Report
by Rahel T. Zewude, Syed Zain Ahmad, Tom Joseph and Andrea K. Boggild
Reports 2024, 7(4), 100; https://doi.org/10.3390/reports7040100 - 16 Nov 2024
Viewed by 331
Abstract
Background and Clinical Significance: Fever in the returning traveler is a medical emergency warranting prompt exclusion of potentially life-threatening infections such as malaria. Case Presentation: We describe a case of a febrile returned traveler to South Africa whose prompt initial diagnostic [...] Read more.
Background and Clinical Significance: Fever in the returning traveler is a medical emergency warranting prompt exclusion of potentially life-threatening infections such as malaria. Case Presentation: We describe a case of a febrile returned traveler to South Africa whose prompt initial diagnostic work-up was notable for a false-positive malaria rapid diagnostic test (RDT), and who nevertheless responded quickly to oral atovaquone-proguanil, despite an ultimate diagnosis of African tick bite fever. Subsequent RDT and malaria thick- and thin-film blood examination failed to corroborate a diagnosis of malaria and all other microbiological testing other than rickettsial serology remained non-contributory. Conclusions: The case presented highlights important points regarding diagnostic test performance characteristics and premature diagnostic closure. Full article
(This article belongs to the Collection Health Threats of Climate Change)
48 pages, 9198 KiB  
Review
Illuminating Malaria: Spectroscopy’s Vital Role in Diagnosis and Research
by Bayden R. Wood, John A. Adegoke, Thulya Chakkumpulakkal Puthan Veettil, Ankit Dodla, Keith Dias, Neha Mehlawat, Callum Gassner, Victoria Stock, Sarika Joshi, Magdalena Giergiel, Diana E. Bedolla and Philip Heraud
Spectrosc. J. 2024, 2(4), 216-263; https://doi.org/10.3390/spectroscj2040015 - 15 Nov 2024
Viewed by 308
Abstract
Spectroscopic techniques have emerged as crucial tools in the field of malaria research, offering immense potential for improved diagnosis and enhanced understanding of the disease. This review article pays tribute to the pioneering contributions of Professor Henry Mantsch in the realm of clinical [...] Read more.
Spectroscopic techniques have emerged as crucial tools in the field of malaria research, offering immense potential for improved diagnosis and enhanced understanding of the disease. This review article pays tribute to the pioneering contributions of Professor Henry Mantsch in the realm of clinical biospectroscopy, by comprehensively exploring the diverse applications of spectroscopic methods in malaria research. From the identification of reliable biomarkers to the development of innovative diagnostic approaches, spectroscopic techniques spanning the ultraviolet to far-infrared regions have played a pivotal role in advancing our knowledge of malaria. This review will highlight the multifaceted ways in which spectroscopy has contributed to the field, with a particular emphasis on its impact on diagnostic advancements and drug research. By leveraging the minimally invasive and highly accurate nature of spectroscopic techniques, researchers have made significant strides in improving the detection and monitoring of malaria parasites. These advancements hold the promise of enhancing patient outcomes and aiding in the global efforts towards the eradication of this devastating disease. Full article
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Graphical abstract

Graphical abstract
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<p>Asexual and sexual phases of the malaria parasite in RBC. After sporozoites enter the bloodstream, they travel to the liver, where they invade hepatocytes and develop into schizonts, each containing thousands of merozoites. These merozoites are then released and invade erythrocytes, initiating the intraerythrocytic asexual phase. During this phase, the parasites grow and divide within the food vacuole, progressing through three distinct morphological stages: ring, trophozoite, and schizont. When schizonts rupture, they release merozoites, continuing the erythrocytic cycle. Some merozoites, instead of replicating, differentiate into male and female gametocytes capable of transmission to mosquitoes. The digestion of hemoglobin by the parasite leads to the accumulation of Hz. In the circulation, only ring-stage parasites and late-stage gametocytes are observed. Reproduced with permission from the Royal Society of Chemistry [<a href="#B7-spectroscj-02-00015" class="html-bibr">7</a>].</p>
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<p>Hematin and β-hematin structure. (<b>A</b>) Schematic representation of hematin, the monomeric precursor of β-hematin. (<b>B</b>) Structure and packing arrangement of β-hematin (synthetic malaria pigment) viewed along the c-axis. Some (h,k,l) planes are indicated. Reprinted with permission from the American Chemical Society [<a href="#B19-spectroscj-02-00015" class="html-bibr">19</a>].</p>
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<p>Raman excitation wavelength measurements recorded of β-hematin. The asterisks (*) highlight the bands enhanced relative to the other excitation wavelengths at 830 nm. Reproduced with permission from the American Chemical Society [<a href="#B20-spectroscj-02-00015" class="html-bibr">20</a>].</p>
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<p>(<b>A</b>) FTIR spectrum of β-hematin. (<b>B</b>) FTIR spectrum of hemozoin extracted from malaria trophozoites. Reproduced with permission from the American Chemical Society [<a href="#B20-spectroscj-02-00015" class="html-bibr">20</a>].</p>
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<p>Absorbance spectra recorded during the acidification of hemin to form β-hematin. Reproduced with permission from the American Chemical Society [<a href="#B20-spectroscj-02-00015" class="html-bibr">20</a>].</p>
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<p>Representative second derivative spectra, (<b>a</b>) β-hematin (green), (<b>b</b>) dry hemozoin isolated from infected red blood cells (red), (<b>c</b>) dry crystalline hemozoin purchased from Invivogen (blue). Reproduced with permission from the American Chemical Society [<a href="#B5-spectroscj-02-00015" class="html-bibr">5</a>].</p>
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<p>FTIR averaged normalized spectra of the C-H stretching region and fingerprint region from the Australian Synchrotron of RBCs (control) and the three stages of the parasitic life cycle (ring, trophozoite, and schizont) within a fixed RBC. Standard deviation spectra are shown below each spectrum for both spectral regions. Reproduced with permission from the American Chemical Society [<a href="#B31-spectroscj-02-00015" class="html-bibr">31</a>].</p>
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<p>(<b>A</b>) Visible image of the thick film of malaria-infected RBCs. (<b>B</b>) Partial dark-field effect visible micrograph highlighting the trophozoites. (<b>C</b>) Chemical map of the area outlined by the red square (ROI) in (<b>B</b>), generated by integrating the region between 1680 and 1620 cm<sup>−1</sup>, with lighter colors indicating hemozoin deposits within the trophozoites. (<b>D</b>) UHCA of ROI using D-values algorithm in the range of 1700–1300 cm<sup>−1</sup> revealing two clusters: Blue cluster, hemozoin, and red cluster, hemoglobin. (<b>E</b>) UHCA of ROI showing five clusters where the pink cluster spectrum is like hemozoin in the late-stage trophozoites, while green and grey clusters represent a mix of hemoglobin and hemozoin. The light blue cluster corresponds well with the hemoglobin present within RBCs, along with red cluster present as submicron dots (300 nm) corresponding to the hemozoin throughout the stages of <span class="html-italic">P. falciparum</span> life cycle. (<b>F</b>) Mean spectra corresponding to each cluster shown in (<b>E</b>). Reproduced with permission from the Royal Society of Chemistry [<a href="#B68-spectroscj-02-00015" class="html-bibr">68</a>].</p>
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<p>Raman acoustic levitation spectroscopy (RALS). (<b>A</b>) A droplet of isolated red blood cells levitated using a piezo-electric transducer and reflective plate. (<b>B</b>) Schematic showing acoustic levitator coupled to a Raman microscope using a right-angled adaptor. (<b>C</b>) Spectra of trophozoite lysate from lysed red blood cells (<b>top</b>) and micro-Raman spectrum of hemozoin (<b>bottom</b>). Reproduced with permission from the Royal Society of Chemistry [<a href="#B69-spectroscj-02-00015" class="html-bibr">69</a>].</p>
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<p>(<b>A</b>) <span class="html-italic">Graphium weiskei</span> butterfly wings. (<b>B</b>) Schematic cross-sectional view of a gold-coated wing showing typical chitinous conical protrusion dimensions and spacings based on SEM images. (<b>C</b>–<b>F</b>) SEM images of chitinous nano-structured conical arrays found on the wings of the <span class="html-italic">G. weiskei</span> butterfly. (<b>C</b>,<b>D</b>) SEM images acquired after deposition with <span class="html-italic">P. falciparum</span>-infected RBC lysate. (<b>E</b>,<b>F</b>) Control butterfly wings without lysate deposition. (<b>G</b>–<b>I</b>) SERS spectra of 0.0005%, 0.005%, and 0% (control) malarial-infected RBC lysate, respectively. (<b>J</b>) Conventional Raman spectrum of hemozoin at 785 nm. Reproduced with permission from the Royal Society of Chemistry [<a href="#B81-spectroscj-02-00015" class="html-bibr">81</a>].</p>
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<p>(<b>A</b>) <span class="html-italic">Graphium weiskei</span> butterfly wings. (<b>B</b>) Schematic cross-sectional view of a gold-coated wing showing typical chitinous conical protrusion dimensions and spacings based on SEM images. (<b>C</b>–<b>F</b>) SEM images of chitinous nano-structured conical arrays found on the wings of the <span class="html-italic">G. weiskei</span> butterfly. (<b>C</b>,<b>D</b>) SEM images acquired after deposition with <span class="html-italic">P. falciparum</span>-infected RBC lysate. (<b>E</b>,<b>F</b>) Control butterfly wings without lysate deposition. (<b>G</b>–<b>I</b>) SERS spectra of 0.0005%, 0.005%, and 0% (control) malarial-infected RBC lysate, respectively. (<b>J</b>) Conventional Raman spectrum of hemozoin at 785 nm. Reproduced with permission from the Royal Society of Chemistry [<a href="#B81-spectroscj-02-00015" class="html-bibr">81</a>].</p>
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<p>Infrared images of trophozoites inside infected erythrocytes. (<b>a</b>) Three-dimensional representation of an infected and an uninfected cell. (<b>b</b>) False color images of 6 erythrocytes infected with trophozoites and their visible images. Color scale corresponding to the integration area underneath each spectrum (pixel). Reproduced with permission from the Royal Society of Chemistry [<a href="#B93-spectroscj-02-00015" class="html-bibr">93</a>].</p>
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<p>A diagram of the instrumentation and operation of an O-PTIR microscope (Photothermal Inc., Santa Barbara, CA, USA). This figure is from an open-access article distributed under the terms of the Creative Commons CC-BY license [<a href="#B109-spectroscj-02-00015" class="html-bibr">109</a>].</p>
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<p>Trends in miniaturization of near-infrared spectrometers. This article containing this figure is distributed under the terms of the Creative Commons Attribution-Non-commercial 4.0 License [<a href="#B119-spectroscj-02-00015" class="html-bibr">119</a>].</p>
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<p>Scanning of mice and blood spots using NIRS. Panel (<b>A</b>,<b>B</b>) illustrate how mice were held and non-invasively scanned. Panel (<b>C</b>) illustrates scanning of dry blood spots on the slides. Panel (<b>D</b>) shows the resultant raw spectral signatures from various body parts of a mouse and spectral signatures from blood spots. The figure is from an open-access article distributed under the terms of the Creative Commons Attribution License [<a href="#B131-spectroscj-02-00015" class="html-bibr">131</a>].</p>
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<p>Partial least squares regression plots for malaria-diluted samples ranging from (<b>A</b>) 6% to 0.00001%, (<b>B</b>) 0.1% to 0.00001%, where the actual parasitemia plotted on the <span class="html-italic">x</span>-axis and the predicted parasitemia on the <span class="html-italic">y</span>-axis. (<b>C</b>) PCA Score plots (PC1 vs. PC2) for 6% to 0.00001% range. (<b>D</b>) Comparison between control samples and those with the lowest parasitemia (0.000001%). Reproduced with permission from the American Chemical Society [<a href="#B5-spectroscj-02-00015" class="html-bibr">5</a>].</p>
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<p>The image illustrates PCA applied to visible spectra of single cells. (<b>A</b>) A 3D scores plot for various RBC samples (control, rings, trophozoites, schizonts), demonstrating clear separation of control RBCs from infected cells along PC 2. (<b>B</b>) The PC1 loadings plot, highlighting significant positive and negative loadings. (<b>C</b>) The PC2 loadings plot. (<b>D</b>) A 3D scores plot comparing RBCs infected with rings (R) and trophozoites (T). (<b>E</b>) Schizonts (S) and trophozoites (T). (<b>F</b>) Rings (R) and schizonts (S). (<b>G</b>–<b>I</b>) Presents the computed confusion matrices (CM) that illustrate the accuracy of the SVM models developed in this study. (<b>G</b>) Shows multiclass models, including datasets from control, rings, schizonts, and trophozoites. Panels (<b>B</b>–<b>D</b>) display binary classifications comparing infected classes with the control: (<b>B</b>) control vs. trophozoites, (<b>C</b>) control vs. rings, and (<b>D</b>) control vs. schizonts. The numbers in each class indicate the spectra count used for testing. Reproduced with permission from the American Chemical Society [<a href="#B6-spectroscj-02-00015" class="html-bibr">6</a>].</p>
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<p>Analysis of IR and Raman spectra from a single isolated red blood cell (RBC). For the IR analysis: (<b>a</b>) An unsupervised hierarchical cluster analysis (UHCA) cluster image was generated, and (<b>b</b>) shows a visible image of a Giemsa-stained cell. For the Raman analysis: (<b>c</b>) presents the UHCA cluster image, and (<b>d</b>,<b>e</b>) the average spectra for each cluster were displayed for the IR and Raman analysis, respectively, while (<b>f</b>) displays the integration map of the Raman band for hemozoin, in the range of 1629–1599 cm<sup>−1</sup>, using baselines set at 1585–1575 cm<sup>−1</sup> and 1652–1643 cm<sup>−1</sup>. The Raman spectra provided precise localization of hemozoin bands, which was not possible to identify directly in the IR spectra of the RBC. Note the colors of the spectra match the classes in the UHCA maps. Reproduced with permission from Elsevier [<a href="#B94-spectroscj-02-00015" class="html-bibr">94</a>].</p>
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<p>(<b>A</b>–<b>C</b>) PLS-R results for WB samples spiked with R-staged parasites. PLS-R predicted versus reference plots for the higher parasitemia models (1–0.25%) are shown for: (<b>A</b>) the lower wavelength range (200–700 nm), (<b>B</b>) the higher wavelength range (1000–2500 nm), and (<b>C</b>) the entire wavelength range (200–2500 nm). (<b>D</b>) The PLS regression vector for the lower parasitemia models highlights key marker bands associated with both infected and control aqueous blood. Reproduced with permission from the American Chemical Society [<a href="#B132-spectroscj-02-00015" class="html-bibr">132</a>].</p>
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<p>AFM-IR imaging of a <span class="html-italic">P. falciparum</span> trophozoite inside a red blood cell. (<b>a</b>) AFM topography. (<b>b</b>) AFM deflection map showing the location of the points where spectra were measured, inside (blue) and outside (red) of the protrusion. (<b>c</b>,<b>d</b>) Spectra measured from the signal of the IR intensity peak (V) showing different bands for the red and blue spots in the 1450–950 and 1800–1450 cm<sup>−1</sup> regions, respectively. (<b>e</b>,<b>f</b>) IR peak maps obtained at 1207 and 1660 cm<sup>−1</sup>, respectively. (<b>g</b>,<b>h</b>) Score and loading plots from the PCA applied to the 3100–2800 cm<sup>−1</sup> region. Replicated from [<a href="#B156-spectroscj-02-00015" class="html-bibr">156</a>].</p>
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<p>(<b>A</b>–<b>C</b>) AFM images recorded of sectioned cells prior to TERS acquisition. (<b>A</b>) A 30 × 30 μm AFM image recorded of a population of infected red blood cells showing a potential cell target highlighted by the blue square. (<b>B</b>) A high-resolution image of the cell highlighted in (<b>A</b>) showing hemozoin crystals aligned in the digestive vacuole. (<b>C</b>) An even higher resolution AFM image of the digestive vacuole of the parasite showing single crystals of hemozoin that can be selectively targeted with the TERS active tip. (<b>D</b>) TERS spectrum recorded of the edge of a hemozoin crystal. The spectrum was recorded with a laser power of 600 μW and exposure time of 5 s. (<b>E</b>) After recording a spectrum, the tip was retracted by several micrometers, and a further spectrum recorded to ensure the tip had not been contaminated by the sample. (<b>F</b>) Surface-enhanced Raman spectrum recorded of β-hematin prepared using SERS active Ag-particles. Spectra were recorded using a 532 nm laser and 10 s acquisition time. (<b>G</b>) Resonance Raman spectrum of β-hematin recorded at 532 nm with 10 s acquisition time. Reproduced with permission from the American Chemical Society [<a href="#B157-spectroscj-02-00015" class="html-bibr">157</a>].</p>
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<p>A partial least squares discriminant analysis (PLS-DA) prediction plot showing the classification of either malaria positive (&lt;0.5) or negative (&gt;0.5); spectra color-coded malaria positive (red) or negative (green) by PCR. (<b>B</b>) Same as in (<b>A</b>), except support vector machine (SVM) learning is used for the classification. (<b>C</b>) Receiver operating characteristic (ROC) curves showing the diagnostic of the PLS-DA and SVM classification. (<b>D</b>) ROC curve for data where samples were assigned positive- and negative, based on PCR versus randomized models. (<b>E</b>) Average spectra over the three spectral ranges used for PLS-DA classification. Superimposed is a color code showing the regression loadings for malaria positive (“warm colors”) or negative (“cool colors”) classification for each absorbance value. This figure is reproduced from an open-access article published by Biomedical Central (BMC), a part of Springer Nature [<a href="#B57-spectroscj-02-00015" class="html-bibr">57</a>].</p>
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22 pages, 1954 KiB  
Review
Dihydroartemisinin–Piperaquine Combination in the Treatment of Uncomplicated Plasmodium falciparum Malaria: Update on Clinical Failures in Africa and Tools for Surveillance
by Océane Delandre, Bruno Pradines and Emilie Javelle
J. Clin. Med. 2024, 13(22), 6828; https://doi.org/10.3390/jcm13226828 - 13 Nov 2024
Viewed by 558
Abstract
Dihydroartemisinin (or artenimol)–piperaquine is one of the six artemisinin-based combination therapies recommended in uncomplicated malaria treatment. However, artemisinin partial resistance has been reported in Cambodia, Laos, Vietnam, India, and, recently, in Africa. Polymorphisms in the Pfk13 gene have been described as molecular markers [...] Read more.
Dihydroartemisinin (or artenimol)–piperaquine is one of the six artemisinin-based combination therapies recommended in uncomplicated malaria treatment. However, artemisinin partial resistance has been reported in Cambodia, Laos, Vietnam, India, and, recently, in Africa. Polymorphisms in the Pfk13 gene have been described as molecular markers of artemisinin resistance and the amplification of the plasmepsine II/III (Pfpmp2/Pfpmp3) gene has been associated with piperaquine resistance. However, some therapeutic failures with this combination remain unexplained by strains’ characterization. We provide an overview on the use of dihydroartemisinin–piperaquine in malaria treatment and discuss tools available to monitor its efficacy. Full article
(This article belongs to the Special Issue Clinical Advances in Malaria)
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<p>Dihydroartemisinin and piperaquine structure.</p>
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<p>Targets and mechanisms of dihydroartemisinin (DHA) and piperaquine (PPQ) molecules in the parasite <span class="html-italic">Plasmodium</span>. ER: endoplasmic reticulum. (Created with BioRender.com).</p>
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<p>RSA-PSA tests template. The initial parasitemia must be between 0.5 and 1% with 100% of young trophozoite (rings). This figure was developed from a template document generated by the WorldWide Antimalarial Resistance Network (WWARN). The original document is available on the WWARN website <a href="http://www.wwarn.org" target="_blank">www.wwarn.org</a> (accessed on 9 November 2024) (Created with BioRender.com).</p>
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<p>Chemosusceptibility test template. The test should be carried out with parasitemia adjusted to 0.05% and a minimum of 80% young trophozoites. Incubation with drugs lasts 72 h in a controlled atmosphere at 37 °C with 10% O<sub>2</sub> and 5% CO<sub>2</sub>. After 72 h of incubation, the plates are frozen to allow lysis of the red blood cells, HRP2-based ELISA assays are used to measure the parasites’ growth, and the 50% inhibitory concentration (IC<sub>50</sub>) is determined via non-linear regression. (Created with BioRender.com).</p>
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<p>WHO definitions for antimalarial drug responses.</p>
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13 pages, 1208 KiB  
Review
The Critical Role of Host and Bacterial Extracellular Vesicles in Endometriosis
by Michaela Wagner, Chloe Hicks, Emad El-Omar, Valery Combes and Fatima El-Assaad
Biomedicines 2024, 12(11), 2585; https://doi.org/10.3390/biomedicines12112585 - 12 Nov 2024
Viewed by 574
Abstract
Endometriosis is a chronic, inflammatory, oestrogen-dependent disorder that is defined by the presence of endometrium-like tissue in the extra-uterine environment. It is estimated to affect approximately 10% of women of reproductive age, and the cause is still largely unknown. The heterogenous nature and [...] Read more.
Endometriosis is a chronic, inflammatory, oestrogen-dependent disorder that is defined by the presence of endometrium-like tissue in the extra-uterine environment. It is estimated to affect approximately 10% of women of reproductive age, and the cause is still largely unknown. The heterogenous nature and complex pathophysiology of the disease results in diagnostic and therapeutic challenges. This review examines the emerging role of host extracellular vesicles (EVs) in endometriosis development and progression, with a particular focus on bacterial extracellular vesicles (BEVs). EVs are nano-sized membrane-bound particles that can transport bioactive molecules such as nucleic acids, proteins, and lipids, and therefore play an essential role in intercellular communication. Due to their unique cargo composition, EVs can play a dual role, both in the disease pathogenesis and as biomarkers. Both host and bacterial EVs (HEVs and BEVs) have been implicated in endometriosis, by modulating inflammatory responses, angiogenesis, tissue remodelling, and cellular proliferation within the peritoneal microenvironment. Understanding the intricate mechanisms underlying EVs in endometriosis pathophysiology and modulation of the lesion microenvironment may lead to novel diagnostic tools and therapeutic targets. Future research should focus on uncovering the specific cargo, the inter-kingdom cell-to-cell interactions, and the anti-inflammatory and anti-microbial mechanisms of both HEVs and BEVs in endometriosis in the hope of discovering translational findings that could improve the diagnosis and treatment of the disease. Full article
(This article belongs to the Special Issue Advanced Research in Endometriosis 4.0)
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<p>Extracellular vesicles as targets or tools in endometriosis? Proposed mechanism of extracellular vesicle (EV) and bacterial EV (BEV) involvement in endometriosis. The diagram illustrates how a dysbiotic gut may facilitate the translocation of BEVs across the gut mucosa into systemic circulation. These vesicles, along with host EVs, can migrate to the pelvic peritoneal cavity, contributing to the pathophysiology of endometriosis by promoting angiogenesis, immune evasion by endometrial cells, tissue remodelling, and adhesion. The associated increase in inflammatory markers (IL-17, TNF-α, IL-6, etc.) in the peritoneal fluid (PF) and serum is also indicated, highlighting their potential role in disease progression. ↑ = increased.</p>
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<p>Extracellular vesicles play a role in various pathophysiological functions. A leaky ‘dysbiotic’ gut allows extracellular vesicles (EVs) and bacterial extracellular vesicles (BEVs) to enter the circulation to act locally or systematically.</p>
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31 pages, 18235 KiB  
Article
Geospatial Analysis of Malaria and Typhoid Prevalence Due to Waste Dumpsite Exposure in Kinshasa Districts with and Without Waste Services: A Case Study of Bandalungwa and Bumbu, Democratic Republic of Congo
by Yllah Kang Okin, Helmut Yabar, Karume Lubula Kevin, Takeshi Mizunoya and Yoshiro Higano
Int. J. Environ. Res. Public Health 2024, 21(11), 1495; https://doi.org/10.3390/ijerph21111495 - 11 Nov 2024
Viewed by 746
Abstract
Municipal solid waste (MSW) management poses substantial challenges in rapidly urbanizing areas, with implications for both the environment and public health. This study focuses on the city of Kinshasa in the Democratic Republic of Congo, investigating whether the presence or absence of solid [...] Read more.
Municipal solid waste (MSW) management poses substantial challenges in rapidly urbanizing areas, with implications for both the environment and public health. This study focuses on the city of Kinshasa in the Democratic Republic of Congo, investigating whether the presence or absence of solid waste collection services results in varying health and economic impacts, and additionally, seeking to establish a correlation between residing in proximity to dumpsites and the prevalence of diseases like malaria and typhoid, thereby providing a comprehensive understanding of the health implications tied to waste exposure. Health data were collected through survey questionnaires, and the geospatial distribution of 19 dumpsites was analyzed using Google Earth Pro 7.3.1 for satellite imagery and GIS software 10.3.1 to map dumpsites and define 1 km buffer zones around the largest dumpsites for household sampling. Statistical analyses were conducted using R Version 4.2.3, employing Chi-square tests for disease prevalence and logistic regression to assess associations between waste management practices and health outcomes. A multivariate regression was used to evaluate correlations between discomfort symptoms (e.g., nasal and eye irritation) and waste activities. The geospatial analysis revealed significant variation in dumpsite size and location, with larger dumpsites near water bodies and flood-prone areas. The study contributes valuable insights into waste-related health risks, emphasizing the need for improved waste management policies in rapidly urbanizing areas like Kinshasa. The socio-demographic analysis reveals distinct traits within the surveyed populations of two communes, Bandalungwa (Bandal) and Bumbu. Bumbu, characterized by larger open dumpsites and limited waste collection services, exhibits a higher prevalence of certain diseases, particularly typhoid fever, and malaria. This discrepancy is statistically significant (p < 2.2 × 10−16), suggesting a potential link between waste exposure and disease prevalence. In Bandal, self-waste collection is a high risk of exposure to typhoid (OR = 4.834 and p = 0.00001), but the implementation of a waste collection service shows protective effect (OR = 0.206 and p = 0.00001). The lack of waste collection services in Bumbu increases the risk of exposure, although not significantly (OR = 2.268 and p = 0.08). Key findings indicate that waste disposal methods significantly differ between Bandal and Bumbu. Bumbu’s reliance on burning and dumping creates environments conducive to disease vectors, contributing to elevated disease transmission risks. However, an in-depth correlation analysis reveals that specific waste management practices, such as burning, burying, and open dumping, do not exhibit statistically significant associations with disease prevalence, underlining the complexity of disease dynamics. This study contributes valuable insights into the importance for urban public health, particularly in rapidly urbanizing cities like Kinshasa, where inadequate waste management exacerbates health risks. By investigating the correlation between proximity to unregulated dumpsites and the prevalence of diseases such as malaria and typhoid fever, the research underscores the urgent need for targeted waste management policies. The stark health disparities between Bandal, with better waste services, and Bumbu, where services are lacking, highlight the protective effect of organized waste collection. These findings suggest that expanding public waste services and enforcing stricter regulations on waste disposal could reduce disease prevalence in vulnerable areas. Additionally, the study supports integrating waste management into urban planning as a critical public health measure. Its evidence-based approach offers valuable insights for policymakers in Kinshasa and other cities facing similar challenges, emphasizing the broader health implications of environmental governance in urban settings. Full article
(This article belongs to the Collection Environmental Risk Assessment)
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<p>Map of study areas in Kinshasa.</p>
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<p>R studio data analysis process.</p>
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<p>Surveyed dumpsites in Bumbu and Bandal.</p>
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<p>Onsite photo (<b>left</b>) and Google earth image (<b>right</b>) of dumpsite number 7 in Bumbu.</p>
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<p>Onsite photo (<b>left</b>) and Google earth image (<b>right</b>) of dumpsite number 10 in Bandal.</p>
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<p>Survey buffer zone in Bandal.</p>
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<p>Survey buffer zone in Bumbu.</p>
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<p>The yellow pinpoint shows dumpsite 10 in Bandal in 2018 (on the <b>left</b>) and in 2021 (on the <b>right</b>).</p>
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<p>The yellow pinpoint shows dumpsite 7 in Bumbu in 2018 (on the <b>left</b>) and in 2021 (on the <b>right</b>).</p>
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33 pages, 5653 KiB  
Protocol
A Reproducible Protocol for the Isolation of Malaria-Derived Extracellular Vesicles by Differential Centrifugation
by Tosin Opadokun and Petra Rohrbach
Methods Protoc. 2024, 7(6), 92; https://doi.org/10.3390/mps7060092 - 9 Nov 2024
Viewed by 377
Abstract
Over the last few decades, malaria-derived extracellular vesicles (EVs) have gained increasing interest due to their role in disease pathophysiology and parasite biology. Unlike other EV research fields, the isolation of malaria EVs is not standardized, hampering inter-study comparisons. Most malaria EV studies [...] Read more.
Over the last few decades, malaria-derived extracellular vesicles (EVs) have gained increasing interest due to their role in disease pathophysiology and parasite biology. Unlike other EV research fields, the isolation of malaria EVs is not standardized, hampering inter-study comparisons. Most malaria EV studies isolate vesicles by the “gold-standard” technique of differential (ultra)centrifugation (DC). Here, we describe in detail an optimized and reproducible protocol for the isolation of malaria-derived EVs by DC. The protocol begins with a description of cultivating high-parasitemia, synchronous P. falciparum cultures that are the source of EV-containing conditioned culture media. The isolation protocol generates two EV subtypes, and we provide details of characterizing these distinct subtypes by analyzing human and parasite proteins by Western blot analysis. We identify some of these proteins as suitable markers for malaria EV subpopulations and subtypes. Full article
(This article belongs to the Section Molecular and Cellular Biology)
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<p>Life cycle of <span class="html-italic">P. falciparum</span>. Sporozoites injected into the host during the blood meal of an infected female <span class="html-italic">Anopheles</span> mosquito develop into liver schizonts that rupture to release merozoites. Merozoites invade RBCs, where they develop cyclically over the course of 48 h through 3 asexual life stages, namely rings (~24 h), trophozoites (~14 h), and schizonts (~10 h). Repeated rupture of infected RBCs releases toxins and parasite waste products into the bloodstream that induce immune responses, resulting in the clinical manifestation of the disease in infected individuals.</p>
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<p>EVs may be generated from viable cells either as microvesicles by directly budding off the cell membrane (CM) or as exosomes by the fusion of multivesicular bodies (MVBs) with the CM. EVs are heterogenous in their size and biomolecular composition.</p>
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<p>High-parasitemia, synchronous cultures of <span class="html-italic">P. falciparum</span> for EV studies can be cultivated within a week of thawing frozen young rings at ≥10% parasitemia, using a combination of sorbitol and Percoll synchronization techniques to enrich ring and mature stages, respectively. To maintain healthy parasites, cultures must be checked, and media must be changed every 24 h. EV-containing CCM from ring-, trophozoite-, and schizont-stage iRBCs can be harvested at 20–22 h, 36–38 h, and 44–46 h post invasion (PI), respectively, for up to 3 invasion cycles, after which synchronization may be repeated to continue CCM harvest and parasites are frozen. CCM = conditioned culture media.</p>
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<p>Schematic of synchronization techniques for achieving synchronous high-parasitemia <span class="html-italic">P. falciparum</span> cultures for EV isolation. The tubes on the left and right of images A and B represent before and after centrifugation, respectively. (<b>A</b>) Asynchronous cultures contain RBCs infected with rings, trophozoites, and schizonts. Sorbitol lyses mature-stage iRBCs, while ring-iRBCs and RBCs are preserved. These cells are separated from the sorbitol after centrifugation (pellet). (<b>B</b>) Percoll synchronization recovers mature-stage iRBCs, separating them from uninfected RBCs and early-stage iRBCs after centrifugation.</p>
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<p>Malaria EV isolation protocol by differential centrifugation. CCM is harvested from a <span class="html-italic">P. falciparum</span> culture by centrifugation at 300× <span class="html-italic">g</span> for 5 min. The cell pellet is returned to culture and the supernatant is passed through 2 further centrifugations at an increasing speed and time, as well as a filtration step to remove potential contaminants, such as <span class="html-italic">P. falciparum</span> merozoites, dead cells, debris, and large EVs. An EV pellet (P1) is isolated from the supernatant at 10,000× <span class="html-italic">g</span>. The supernatant can then be concentrated to reduce its volume. A second EV pellet (P2) is isolated from the supernatant at 100,000× <span class="html-italic">g</span>. Centrifugation of the pellets in a tabletop ultracentrifuge rotor such as TLA100.3 is highly recommended for the recovery of concentrated EVs.</p>
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<p>Giemsa-stained smears of <span class="html-italic">P. falciparum</span> iRBC cultures. (<b>A</b>) Asynchronous culture showing RBCs infected with ring forms (r) at different stages of development, trophozoites (t), and schizonts (s). The smear was made from a culture with 22% parasitemia. (<b>B</b>) Late rings at 20–22 h. (<b>C</b>) Late trophozoites at 36–38 h. (<b>D</b>) Mature schizonts at 44–46 h and two young rings at &lt;6 h can be seen (red arrow heads). Images B, C, and D are of smears from the same culture with 15% parasitemia at the time of CCM harvest.</p>
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<p>Two EV subtypes with differing protein concentration and composition are isolated using an optimized DC protocol. (<b>A</b>) The protein concentration of P2, as determined by a BCA protein assay, is significantly higher than that of P1; data show 3 biological replicates. (<b>B</b>,<b>C</b>) WBA of proteins in EVs. Here, 1 µg of protein for each EV and control was loaded onto pre-cast tris-glycine gels. SDS-PAGE was performed under reducing conditions except for EBA-175, which was performed under non-reducing conditions. Antibody dilutions for analyzed proteins were: band 3—1:5000; GPA, flotillin 2 and PfGRP78—1:1000; hemoglobin and HAP—1:2000; EBA-175—1:400. Molecular weights of identified proteins are shown to the left of the blots. Ghost membranes and cytosol of healthy RBCs, and cell lysates (CL) of rings obtained by saponin lysis of ring-iRBCs, are used as controls. (<b>B</b>) Band 3, GPA, and flotillin 2 are significantly enriched in P1 EVs. A 40-kDa band is detected in P1 while a 60-kDa band is detected slightly in P2. Two dimers of GPA are detected in P1, and a single low-molecular-weight dimer is detected in P2. The ~65 kDa GPA band in RBC ghosts is not detected in EVs. Flotillin 2 is strongly detected in P1 EVs relative to P2. Hemoglobin, which is a known component of RBC EVs, is detected in P1 and P2 iRBC-EVs. (<b>C</b>) The pro-enzyme (faint 51-kDa band) and mature form (37-kDa) of histoaspartic protease (HAP) is detected in malaria EVs. Erythrocyte binding antigen 175 (EBA-175) is detected faintly in P2 but not in P1 EVs, while glucose regulated protein 78 (PfGRP78) is not detected in either EV subtypes. R—Rings.</p>
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<p>The use of 2 Percoll concentrations (65% and 35%) allows debris to be collected at the interface between the culture medium and 35% Percoll [<a href="#B67-mps-07-00092" class="html-bibr">67</a>] (<a href="#mps-07-00092-f004" class="html-fig">Figure 4</a>). Ideally, the iRBC pellet should be layered on top of the 35% Percoll solution with a clear interface (<b>A</b>). We observed, however, that in some cases, layering the RBC pellet over 35% Percoll with a clear interface cannot be achieved, and the cells mix with the 35% Percoll (<b>B</b>). This may be due to the density of the donor blood cells. In such a situation, proceeding with centrifugation immediately still separates the mature-stage iRBCs from dead cells and debris (<b>C</b>), yielding synchronized parasites when cells are returned to culture. An alternative protocol, where cells are layered over a single Percoll concentration (60 to 70%), may also be used to achieve synchronization.</p>
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<p>(related to <a href="#mps-07-00092-f005" class="html-fig">Figure 5</a>). To accommodate larger starting volumes of CCM and promote ease of handling to isolate the first malaria EV pellet, Intellifuge software was used to transfer the protocol for the JA-25.50 rotor, which has a nominal capacity of 400 mL (<b>A</b>, left), to the JLA-16.250 rotor with a nominal capacity of 1500 mL (<b>A</b>, right). EVs are isolated using the JLA-16.250 rotor by centrifugation at a higher speed of ~30,000× <span class="html-italic">g</span> and shorter run time of 40 min (<b>B</b>, shaded area) compared to centrifugation at 10,000× <span class="html-italic">g</span> for 1 h in a JA-25.50 rotor to obtain qualitatively and quantitatively comparable EV isolates.</p>
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<p>NTA (<b>A</b>), FC (<b>B</b>) and TEM (<b>C</b>). NTA of ring-iRBC-EVs shows that the particle concentration of P2 is significantly more than P1; data show 2 biological replicates (<b>A</b>). We have previously reported similar findings for trophozoite- and schizont-iRBC EVs [<a href="#B51-mps-07-00092" class="html-bibr">51</a>]. Conversely, FC of ring-iRBC EVs with glycophorin A (GPA) showed that GPA positive particles were more abundant in P1 EVs than in P2 EVs; these data are from a single experimental run (<b>B</b>); however, the results mirror those of WBA, whereby several RBC membrane proteins that serve as EV markers are consistently detected in higher abundance in P1 compared to P2 EVs (see <a href="#mps-07-00092-f007" class="html-fig">Figure 7</a>B and <a href="#app5-mps-07-00092" class="html-app">Appendix E</a>). The higher particle concentration of P2 as seen in (<b>A</b>) is likely due to higher amounts of co-isolating non-EV proteins in P2 than P1. This is evident in the TEM images, whereby background protein aggregates are observed in P2 EVs compared to P1; the large images are wide-field representations of at least 4 captures of each EV sample, while the insets show enlarged single EVs (small red boxes) (<b>C</b>). TEM reveals that malaria-derived EVs are generally small-sized at ≤200 nm in diameter. Scale bar: 200 nm at 23,000×.</p>
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<p>WBA of band 3 and glycophorin A (GPA). P1 and P2 EVs isolated from ring-, trophozoite-, and schizont-stage iRBCs show similar results from different biological replicates (data show 2 biological replicates), indicating reproducibility of the EV isolation protocol described; this is related to <a href="#mps-07-00092-f007" class="html-fig">Figure 7</a>B. Here, 1 µg of protein for each EV and control was loaded onto pre-cast tris-glycine gels. SDS-PAGE was performed under reducing conditions. Antibody dilutions for analyzed proteins were band 3—1:5000; GPA—1:1000. Molecular weights of identified proteins are shown to the left of the blots. As a control, EVs were isolated from parasite-free RBCs (U—uninfected RBCs) maintained under the same culture conditions as iRBCs and included in downstream analyses to determine baseline vesiculation in uninfected RBCs (uRBCs). Ghost membranes (G) and cytosol (C) of healthy RBCs are used as controls. In (<b>C</b>,<b>D</b>), cell lysates (CL) of parasites obtained by saponin lysis of iRBCs are used as additional controls. Band 3 (<b>A</b>,<b>B</b>) and GPA (<b>C</b>,<b>D</b>) are significantly enriched in P1 EVs. Notably, 2 dimers of GPA are detected in P1 EVs, and a single lower molecular weight dimer is detected in P2. R—Rings, T—trophozoites, S—schizonts.</p>
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<p>HAP is detected at 51 kDa and 37 kDa in EVs isolated from ring-, trophozoite-, and schizont-iRBCs. The observed bands in schizont P1 and P2 are faint compared to ring and trophozoite EVs (related to <a href="#mps-07-00092-f007" class="html-fig">Figure 7</a>C). The last column (rings) contains the lysate of <span class="html-italic">P. falciparum</span> rings from ring-infected RBCs and serves as a positive control for HAP. R—rings, T—trophozoites, S—schizonts, U—uninfected RBCs.</p>
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13 pages, 1622 KiB  
Case Report
A Severe Case of Plasmodium falciparum Malaria in a 44-Year-Old Caucasian Woman on Return to Western Romania from a Visit to Nigeria
by Alin Gabriel Mihu, Rodica Lighezan, Daniela Adriana Oatis, Ovidiu Alexandru Mederle, Cristina Petrine-Mocanu, Cristina Petrescu, Mirandolina Eugenia Prisca, Laura Andreea Ghenciu, Cecilia Roberta Avram, Maria Alina Lupu, Adelaida Bica and Tudor Rareș Olariu
Life 2024, 14(11), 1454; https://doi.org/10.3390/life14111454 - 9 Nov 2024
Viewed by 589
Abstract
Malaria is currently the most prevalent life-threatening infectious disease in the world. In this case report, we present a 44-year-old Caucasian woman with a low level of education and no significant past medical history who presented to the emergency room of the Emergency [...] Read more.
Malaria is currently the most prevalent life-threatening infectious disease in the world. In this case report, we present a 44-year-old Caucasian woman with a low level of education and no significant past medical history who presented to the emergency room of the Emergency County Hospital of Arad, Romania, with a general affected state, a fever of 38.5 °C, chills, weakness, headache, muscle pain, nausea, icterus, and watery diarrheal stool. A viral infection was initially suspected, and the patient was transferred to the Infectious Diseases Department. The anamnesis revealed that the patient traveled to Nigeria (Ado Ekiti) and returned to Romania 14 days before presenting to the hospital without following antimalarial prophylaxis. A peripheral blood smear was conducted and revealed parasitemia with ring forms of Plasmodium falciparum (P. falciparum) of 10–15% within the red blood cells. Parasitemia increased within a day to 15–18%, and her health rapidly deteriorated. She was transferred to the Victor Babeș Infectious Disease Hospital in Bucharest for the urgent initiation of antimalarial treatment. The patient’s condition continued to worsen rapidly, and she succumbed to her illness due to multi-organ failure. This report details the first documented case of malaria imported from Nigeria to Romania. People traveling to malaria-endemic areas should be educated about preventing this parasitic infection, both by adopting measures to reduce the risk of mosquito bites and by using appropriate chemoprophylaxis. In the context of resuming travel after the COVID-19 pandemic, understanding and adhering to prophylactic measures is crucial to avoid tragic situations, as highlighted in this case report. Full article
(This article belongs to the Special Issue Trends in Microbiology 2024)
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<p>(<b>A</b>,<b>B</b>) May–Grunwald Giemsa-stained thin peripheral blood smear showing severe parasitemia with several ring forms of <span class="html-italic">Plasmodium falciparum </span>(×1000).</p>
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<p>May–Grunwald Giemsa-stained thin peripheral blood smear showing severe parasitemia with ring forms of <span class="html-italic">Plasmodium falciparum</span>. (<b>A</b>) A band neutrophil with toxic vacuoles. (<b>B</b>) Two metamyelocytes with toxic granules and vacuoles. (<b>C</b>) Two band neutrophils with toxic granules and vacuoles. (<b>D</b>) A giant metamyelocyte with several vacuoles, toxic granules, and an unsegmented neutrophil with toxic granules (×1000).</p>
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<p>May–Grunwald Giemsa-stained thin peripheral blood smear showing severe parasitemia with ring forms of <span class="html-italic">Plasmodium falciparum</span> with an (<b>A</b>) unsegmented neutrophil and (<b>B</b>) metamyelocyte, both with the malaria pigment (hemozoin) (×1000).</p>
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21 pages, 2662 KiB  
Article
Impact of Climate Variability and Interventions on Malaria Incidence and Forecasting in Burkina Faso
by Nafissatou Traoré, Ourohiré Millogo, Ali Sié and Penelope Vounatsou
Int. J. Environ. Res. Public Health 2024, 21(11), 1487; https://doi.org/10.3390/ijerph21111487 - 8 Nov 2024
Viewed by 633
Abstract
Background: Malaria remains a climate-driven public health issue in Burkina Faso, yet the interactions between climatic factors and malaria interventions across different zones are not well understood. This study estimates time delays in the effects of climatic factors on malaria incidence, develops forecasting [...] Read more.
Background: Malaria remains a climate-driven public health issue in Burkina Faso, yet the interactions between climatic factors and malaria interventions across different zones are not well understood. This study estimates time delays in the effects of climatic factors on malaria incidence, develops forecasting models, and assesses their short-term forecasting performance across three distinct climatic zones: the Sahelian zone (hot/arid), the Sudano-Sahelian zone (moderate temperatures/rainfall); and the Sudanian zone (cooler/wet). Methods: Monthly confirmed malaria cases of children under five during the period 2015–2021 were analyzed using Bayesian generalized autoregressive moving average negative binomial models. The predictors included land surface temperature (LST), rainfall, the coverage of insecticide-treated net (ITN) use, and the coverage of artemisinin-based combination therapies (ACTs). Bayesian variable selection was used to identify the time delays between climatic suitability and malaria incidence. Wavelet analysis was conducted to understand better how fluctuations in climatic factors across different time scales and climatic zones affect malaria transmission dynamics. Results: Malaria incidence averaged 9.92 cases per 1000 persons per month from 2015 to 2021, with peak incidences in July and October in the cooler/wet zone and October in the other zones. Periodicities at 6-month and 12-month intervals were identified in malaria incidence and LST and at 12 months for rainfall from 2015 to 2021 in all climatic zones. Varying lag times in the effects of climatic factors were identified across the zones. The highest predictive power was observed at lead times of 3 months in the cooler/wet zone, followed by 2 months in the hot/arid and moderate zones. Forecasting accuracy, measured by the mean absolute percentage error (MAPE), varied across the zones: 28% in the cooler/wet zone, 53% in the moderate zone, and 45% in the hot/arid zone. ITNs were not statistically important in the hot/arid zone, while ACTs were not in the cooler/wet and moderate zones. Conclusions: The interaction between climatic factors and interventions varied across zones, with the best forecasting performance in the cooler/wet zone. Zone-specific intervention planning and model development adjustments are essential for more efficient early-warning systems. Full article
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Figure 1
<p>Geographical distribution of monthly average malaria incidence by climatic zone. The range of values in the legend indicates the minimum and maximum values within the zones.</p>
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<p>Temporal trend of monthly (<b>A</b>) LST, (<b>B</b>) rainfall, (<b>C</b>) ACT coverage, (<b>D</b>) bednet use coverage, and (<b>E</b>) malaria incidence.</p>
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<p>Overall model fitting and predictive performance in the three climatic regions: (<b>A</b>) hot/arid zone, (<b>B</b>) cooler/wet zone, and (<b>C</b>) Moderate zone. The blue, green, and red lines represent actual, forecasted, and fitted cases, respectively.</p>
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<p>Model predictive performance for each lead time (1 to 12 months) of the forecasting data segment: (<b>A</b>) hot/arid zone, (<b>B</b>) cooler/wet zone, (<b>C</b>) moderate zone.</p>
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<p>Wavelet power levels of malaria incidence (<b>A</b>,<b>D</b>,<b>G</b>), LST (<b>B</b>,<b>E</b>,<b>H</b>), and rainfall (<b>C</b>,<b>F</b>,<b>I</b>) in the hot/arid (left plots), moderate (middle plots), and cooler/wet zones (right plots), respectively. The cone of influence (COI), where edge effects might influence the analysis, is depicted as a lighter shade. Patterns below the cross-hatched region are considered statistically significant. The color code ranges from blue (low values) to red (high values), indicating increasing significance levels. The white lines outline areas of significance.</p>
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<p>Cross-coherence of LST (<b>A</b>,<b>C</b>,<b>E</b>) and rainfall (<b>B</b>,<b>D</b>,<b>F</b>) with malaria incidence in the hot/arid (left plots), moderate (middle plots), and cooler/wet (right plots) zones, respectively. The arrows indicate the relative phasing.</p>
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Article
Setting Up an NGS Sequencing Platform and Monitoring Molecular Markers of Anti-Malarial Drug Resistance in Djibouti
by Nasserdine Papa Mze, Houssein Yonis Arreh, Rahma Abdi Moussa, Mahdi Bachir Elmi, Mohamed Ahmed Waiss, Mohamed Migane Abdi, Hassan Ibrahim Robleh, Samatar Kayad Guelleh, Abdoul-ilah Ahmed Abdi, Hervé Bogreau, Leonardo K. Basco and Bouh Abdi Khaireh
Biology 2024, 13(11), 905; https://doi.org/10.3390/biology13110905 - 6 Nov 2024
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Abstract
Djibouti is confronted with malaria resurgence, with malaria having been occurring in epidemic proportions since a decade ago. The current epidemiology of drug-resistant Plasmodium falciparum is not well known. Molecular markers were analyzed by targeted sequencing in 79 P. falciparum clinical isolates collected [...] Read more.
Djibouti is confronted with malaria resurgence, with malaria having been occurring in epidemic proportions since a decade ago. The current epidemiology of drug-resistant Plasmodium falciparum is not well known. Molecular markers were analyzed by targeted sequencing in 79 P. falciparum clinical isolates collected in Djibouti city in 2023 using the Miseq Illumina platform newly installed in the country. The objective of the study was to analyze the key codons in these molecular markers associated with antimalarial drug resistance. The prevalence of the mutant Pfcrt CVIET haplotype (92%) associated with chloroquine resistance and mutant Pfdhps-Pfdhfr haplotypes (7.4% SGEA and 53.5% IRN, respectively) associated with sulfadoxine-pyrimethamine resistance was high. By contrast, Pfmdr1 haplotypes associated with amodiaquine (YYY) or lumefantrine (NFD) resistance were not observed in any of the isolates. Although the “Asian-type” PfK13 mutations associated with artemisinin resistance were not observed, the “African-type” PfK13 substitution, R622I, was found in a single isolate (1.4%) for the first time in Djibouti. Our genotyping data suggest that most Djiboutian P. falciparum isolates are resistant to chloroquine and sulfadoxine-pyrimethamine but are sensitive to amodiaquine, lumefantrine, and artemisinin. Nonetheless, the presence of an isolate with the R622I PfK13 substitution is a warning signal that calls for a regular surveillance of molecular markers of antimalarial drug resistance. Full article
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<p>(<b>A</b>) Djibouti’s geographical position in the Horn of Africa. This map illustrates the proximity of Djibouti to Ethiopian cities where the “African-specific” R622I PfK13 amino acid substitution associated with artemisinin resistance has recently been observed, as indicated by blue dots [<a href="#B22-biology-13-00905" class="html-bibr">22</a>,<a href="#B23-biology-13-00905" class="html-bibr">23</a>]. (<b>B</b>) Map of Djibouti city showing the location of health centers and hospitals where blood samples from malaria-infected patients were collected. Ambouli health center, indicated by a blue dot, was the only Djiboutian study site where R622I was found in the present study. The locations of other health centers and hospitals where R622I was not observed are shown with black dots.</p>
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<p>Comparison of the prevalence of mutant alleles in the molecular markers between Djiboutian isolates collected in 1998–2002 or 2023. <sup>1</sup> In the study conducted by Rogier et al. [<a href="#B3-biology-13-00905" class="html-bibr">3</a>], <span class="html-italic">P. falciparum</span> isolates were collected at different periods from 1998 to 2002 (a total of 139 samples). A few isolates presented mixed alleles (i.e., both wild-type and mutant alleles). Mixed isolates were not taken into consideration to calculate the prevalence of mutant isolates. NS, not significant; <span class="html-italic">Pfdhfr</span>, <span class="html-italic">P. falciparum</span> dihydrofolate reductase; <span class="html-italic">Pfdhps</span>, <span class="html-italic">P. falciparum</span> dihydropteroate synthase; <span class="html-italic">Pfcrt</span>, <span class="html-italic">P. falciparum</span> chloroquine resistance transporter.</p>
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