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Search Results (218)

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Keywords = waterborne pathogens

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15 pages, 2770 KiB  
Article
Influence of Physiographic Region on Pathogen Concentrations Between Stream Types
by E. A. Bradley, B. Graeme Lockaby, Todd Steury and Steven Madere
Water 2024, 16(22), 3218; https://doi.org/10.3390/w16223218 - 8 Nov 2024
Viewed by 660
Abstract
Predicting public health risk associated with exposure to and recreational use of surface waters is often challenging due to substantial variability in concentrations of pathogenic bacteria, even among seemingly similar streams. In this study, we document significant differences in the surface water concentrations [...] Read more.
Predicting public health risk associated with exposure to and recreational use of surface waters is often challenging due to substantial variability in concentrations of pathogenic bacteria, even among seemingly similar streams. In this study, we document significant differences in the surface water concentrations of the common bacteria indicators Escherichia coli and fecal coliform between two major stream types—blackwater and redwater streams (p < 0.001). We propose and present evidence that these findings result from natural biogeochemical variation between physiographic regions (p < 0.001). These findings suggest that physiographic stream type may influence the degree of exposure to waterborne pathogens and risk of waterborne disease. Future research is needed to assess whether the inclusion of stream type in risk assessments can improve public health modeling and mapping. Full article
(This article belongs to the Section Water and One Health)
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<p>The physiographic regions of the East Gulf Coastal Plain, Alabama by Sophia M. Rutledge, 2016, property of the Geological Survey of Alabama. First appeared as “Plate 81 Physiographic Region of the East Gulf Coastal Plain, Alabama” in the Geological Survey of Alabama Bulletin 186: “Assessment of groundwater resources in Alabama, 2010–2016”.</p>
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<p>Average precipitation (mm) by stream type throughout duration of study. Data retrieved from PRISM Climate Group, Oregon State University, <a href="https://prism.oregonstate.edu" target="_blank">https://prism.oregonstate.edu</a> (accessed 28 June 2023).</p>
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<p>Average temperature (°C) by stream type throughout duration of study. Data retrieved from PRISM Climate Group, Oregon State University, <a href="https://prism.oregonstate.edu" target="_blank">https://prism.oregonstate.edu</a> (accessed 28 June 2023).</p>
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<p>Box and whiskers plot depicting impact of stream type on concentrations of <span class="html-italic">E. coli</span> log(MPN).</p>
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<p>Box and whiskers plot depicting impact of stream type on concentrations of fecal coliform logMPN.</p>
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13 pages, 1880 KiB  
Article
Depuration of Aliarcobacter butzleri and Malaciobacter molluscorum in Comparison with Escherichia coli in Mussels (Mytilus galloprovincialis) and Oysters (Crassostrea gigas)
by Nuria Salas-Massó, Ana Fernández-Bravo, Edgar Bertomeu, Karl B. Andree, Maria José Figueras and Dolors Furones
Pathogens 2024, 13(11), 973; https://doi.org/10.3390/pathogens13110973 - 7 Nov 2024
Viewed by 396
Abstract
Arcobacter-related species are considered emerging food-borne and waterborne pathogens, with shellfish being a suggested reservoir. In a published study that investigated 204 shellfish samples and 476 isolates, the species Arcobacter butzleri (now known as Aliarcobacter butzleri) and Arcobacter molluscorum (now known [...] Read more.
Arcobacter-related species are considered emerging food-borne and waterborne pathogens, with shellfish being a suggested reservoir. In a published study that investigated 204 shellfish samples and 476 isolates, the species Arcobacter butzleri (now known as Aliarcobacter butzleri) and Arcobacter molluscorum (now known as Malaciobacter molluscorum) have been isolated as the most dominant species. However, the efficiency of depuration for eliminating A. butzleri and M. molluscorum in comparison with Escherichia coli from mussels and oysters is unknown and is therefore the objective of this investigation. The shellfish depuration process was evaluated in the laboratory, in summer and winter, using mussels and oysters collected from the Ebro Delta harvesting areas after performing a natural contamination and an artificial contamination using the same conditions for both mollusk and seasons. The natural contamination was performed by exposing the shellfish to a freshwater channel that receives untreated sewage from the village of Poble Nou (PNC) and that had a salinity of 10.7–16.8‰. The artificial contamination exposed the shellfish to A. butzleri and E. coli (in one tank) and to M. molluscorum and E. coli in another tank under controlled conditions of salinity (34.5‰) and temperature (20 °C summer and 14 °C winter). When evaluating the reduction in the bacteria load (every 24 h) throughout 120 h, the naturally contaminated shellfish at the PNC showed a higher reduction than the shellfish contaminated at the laboratory, with the exception of M. molluscorum, that at 24 h could not be detected in summer, neither in mussels nor in oysters. This may be attributed to the fact that the bacteria from the PNC were less adapted to the conditions of high salinity (34.5‰) in which the depuration process was performed. Although temperature did not statistically make a difference in depuration, at 20 °C a higher elimination of all bacteria was recorded relative to 14 °C. In general, E. coli survived more in mussels than in oysters, and M. molluscorum suffered in both mollusks a higher reduction than A. butzleri. New studies are required to determine further the safety of bivalves regarding the presence of Arcobacter-related species. Full article
(This article belongs to the Special Issue Current Research on Host–Pathogen Interaction in 2024)
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<p>Scheme of the different experimental conditions that were used in this study.</p>
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<p>(<b>a</b>–<b>f</b>) Bar representation of the log MPN/100 g of <span class="html-italic">Escherichia coli</span> (Ec) and <span class="html-italic">Arcobacter</span>-related species (Ab, <span class="html-italic">Aliarcobacter butzleri</span> and Mm, <span class="html-italic">Malaciobacter molluscorum</span>) found in the naturally contaminated (at the Poble Nou channel, PNC) and the artificially contaminated mussels, in summer and winter, in the different trial depuration tanks (<b>a</b>–<b>f</b>) and obtained from time 0 (t0) every 24 h up to 120 h. The different colors indicate the standards of the four categories (A, B, C and D) established by the European Union for shellfish harvesting areas based on the MPN results of <span class="html-italic">E. coli</span>/100 g [<a href="#B12-pathogens-13-00973" class="html-bibr">12</a>,<a href="#B13-pathogens-13-00973" class="html-bibr">13</a>]: class A (green), ≤230 MPN/100 g (log 2.36)—shellfish do not require depuration; class B (orange), ≤4600 MPN/100 g (log 3.66); class C (red), ≤46,000 MPN/100 g (log 4.66) and class D (brown), ≥46,000 MPN/100 g—these shellfish are prohibited for consumption. For human consumption Class B to C require depuration to reach class A.</p>
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<p>(<b>a</b>–<b>f</b>) Bar representation of the log MPN/100 g of <span class="html-italic">Escherichia coli</span> (Ec) and <span class="html-italic">Arcobacter</span>-related species (Ab, <span class="html-italic">Aliarcobacter butzleri</span> and Mm, <span class="html-italic">Malaciobacter molluscorum</span>) found in the naturally contaminated (at the Poble Nou channel, PNC) and the artificially contaminated oysters, in summer and winter, in the different trial depuration tanks (<b>a</b>–<b>f</b>) and obtained from time 0 (t0) every 24 h up to 120 h. The different colors indicate the standards of the four categories (A, B, C and D) established by the European Union for shellfish harvesting areas based on the MPN results of <span class="html-italic">E. coli</span>/100 g [<a href="#B12-pathogens-13-00973" class="html-bibr">12</a>,<a href="#B13-pathogens-13-00973" class="html-bibr">13</a>]: class A (green), ≤230 MPN/100 g (log 2.36)—shellfish do not require depuration; class B (orange), ≤4600 MPN/100 g (log 3.66); class C (red), ≤46,000 MPN/100 g (log 4.66) and class D (brown), ≥46,000 MPN/100 g—these shellfish are prohibited for consumption. For human consumption Class B to C require depuration to reach class A.</p>
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14 pages, 2999 KiB  
Article
AI-Aided Robotic Wide-Range Water Quality Monitoring System
by Ameen Awwad, Ghaleb A. Husseini and Lutfi Albasha
Sustainability 2024, 16(21), 9499; https://doi.org/10.3390/su16219499 - 31 Oct 2024
Viewed by 546
Abstract
Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially [...] Read more.
Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-consuming conventional monitoring protocol by automating sample collection, sample processing, and pathogen detection. Furthermore, it provides a safer processing method against the spillage of contaminated liquids and potential resultant aerosols during the heat fixation of specimens. A morphological image processing technique of light microscopic images is used to segment images, assisting in selecting a unified appropriate input segment size based on individual blob areas of different bacterial cultures. The dataset included harmful pathogenic bacteria (A. baumanii, E. coli, and P. aeruginosa) and harmless ones found in drinking water and wastewater (E. faecium, L. paracasei, and Micrococcus spp.). The segmented labeled dataset was used to train deep convolutional neural networks to automatically detect pathogens in microscopic images. To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks’ architecture and training settings. Different convolutional networks were tested in accordance with different required output labels. The neural network used to classify bacterial cultures as harmful or harmless achieved an accuracy of 99.7%. The neural network used to identify the specific types of bacteria achieved a cumulative accuracy of 93.65%. Full article
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<p>(<b>a</b>) An airborne water sample collector, (<b>b</b>) water drip kit, (<b>c</b>) staining drip kit, (<b>d</b>) sample slide, (<b>e</b>) base moving the slide, (<b>f</b>) microscope, (<b>g</b>) camera, (<b>h</b>) image processing, and (<b>i</b>) deep neural network.</p>
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<p>From <b>left</b> to <b>right</b>: examples of segments of Gram-stained microscopic images that are used as inputs for the CNN from the DIBas dataset. In the upper row are the pathogenic ones: <span class="html-italic">A. baumanii</span>, <span class="html-italic">E. coli</span>, and <span class="html-italic">P. aeruginosa</span>, whereas in the lower row are the harmless ones: <span class="html-italic">E. faecium</span>, <span class="html-italic">L. paracasei</span>, and <span class="html-italic">Micrococcus</span> spp.</p>
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<p>(<b>a</b>) A simplified example of a flight profile between the waypoints and (<b>b</b>) a sample collector fixed on an aircraft. A miniature prototype of the device on a small drone with an HC-SR04 ultrasonic sensor to automate the water trap movement fixed to a servomotor through an arm in (<b>c</b>). (<b>d</b>) A sample collector controller’s program flowchart.</p>
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<p>Parts of the automated sample processor prototype. The servomotors’ arms of the dispensers are attached to a rubber piece to open and close the pressure relief hole on the top of each container, as required for solution flow from the hole in the bottom. Five dispensers are used for crystal violet, iodine, ethyl alcohol, safranin, and distilled water for washing between staining stages.</p>
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<p>Stages of the processing of a microscopic image of <span class="html-italic">Micrococcus</span> spp. morphotypes, starting with the original image in (<b>a</b>), converted to grey-scale and reversed to the image in (<b>b</b>), on which top-hat transform was applied to produce the version shown in (<b>c</b>), which was binarized to create (<b>d</b>). Blobs, shown in white in the binarized version in (<b>d</b>) are isolated and measured individually as illustrated by the example shown in the red rectangle.</p>
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<p>Histograms of the areas of blobs in microscopic images of different bacteria as per the title on the top of each graph, where the y-axis represents the number of blobs in each area range on the horizontal axis.</p>
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<p>A diagram of building blocks of the CNN, where N is the number of cascaded hidden layers in each convolutional block, and M is the number of outputs for the fully connected layer (2 in the general classification network and 6 in the specific classification network). The kernel size of convolutional (conv.) and pooling layers is written between parenthesis.</p>
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<p>Training progress graphs for the CNN with two output labels for the input of microscopic images: harmful and harmless in (<b>a</b>) and six output labels for specific species in (<b>c</b>). The blue and lighter blue lines represent the real and smoothed accuracy lines, respectively, while the dots along the dashed line represent the validated accuracy values. Charts in (<b>b</b>,<b>d</b>) show the confusion matrices for each CNN resulting from the training progress graphs above. Each bacterium is listed by the first part of its name in the confusion matrix in (<b>d</b>), namely <span class="html-italic">Acinetobacter</span> for (<span class="html-italic">A. baumanii</span>), <span class="html-italic">Enterococcus</span> for (<span class="html-italic">E. faecium</span>), <span class="html-italic">Escherichia</span> for (<span class="html-italic">E. coli</span>), <span class="html-italic">Lacticaseibacillus</span> for (<span class="html-italic">L. paracasei</span>), <span class="html-italic">Pseudomonas</span> for (<span class="html-italic">P. aeruginosa</span>), and <span class="html-italic">Micrococcus</span>.</p>
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16 pages, 2262 KiB  
Article
Decontamination Potential of Ultraviolet Type C Radiation in Water Treatment Systems: Targeting Microbial Inactivation
by Abayomi Olusegun Adeniyi and Modupe Olufunmilayo Jimoh
Water 2024, 16(19), 2725; https://doi.org/10.3390/w16192725 - 25 Sep 2024
Viewed by 1305
Abstract
Access to safe water and sanitation is a critical global challenge, posing significant health risks worldwide due to waterborne diseases. This study investigates the efficacy of ultraviolet type C radiation as a disinfection method for improving water quality. The research elucidates UV-C’s mechanism [...] Read more.
Access to safe water and sanitation is a critical global challenge, posing significant health risks worldwide due to waterborne diseases. This study investigates the efficacy of ultraviolet type C radiation as a disinfection method for improving water quality. The research elucidates UV-C’s mechanism of action, highlighting its ability to disrupt DNA and RNA replication, thereby inactivating pathogens. Furthermore, the study analyses the influence of key factors on UV-C disinfection effectiveness, including water turbidity and the presence of dissolved organic matter, which can attenuate UV-C penetration and reduce treatment efficiency. The experimental results demonstrate a substantial reduction in microbial content following UV-C treatment. River water samples exhibited a 57.143% reduction in microbial load, while well water samples showed a 50% reduction. Notably, Escherichia coli (E. coli) concentrations decreased significantly, with an 83.33% reduction in well water and a 62.5% reduction in borehole water. This study makes a novel contribution to the understanding of UV-C disinfection by identifying the presence of resistant organisms, including Adenoviruses, Bacterial spores, and the Protozoan Acanthamoeba, in water samples. This finding expands the scope of UV-C research beyond easily culturable bacteria. To address this challenge, future investigations should explore synergistic disinfection strategies, such as combining UV-C treatment with advanced oxidation processes. Optimising UV-C system designs and developing robust, real-time monitoring systems capable of detecting and quantifying known and emerging UV-resistant pathogens are crucial for ensuring comprehensive water decontamination. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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<p>Electromagnetic spectrum showing the different ranges. Source: [<a href="#B29-water-16-02725" class="html-bibr">29</a>].</p>
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<p>Spectrum of solar radiation. Source: [<a href="#B32-water-16-02725" class="html-bibr">32</a>].</p>
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<p>Typical ozone density profile (red line) and UVR. Source: [<a href="#B33-water-16-02725" class="html-bibr">33</a>].</p>
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<p>Inactivation of DNA structure by ultraviolet radiation. Source: [<a href="#B38-water-16-02725" class="html-bibr">38</a>].</p>
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<p>Map of Ibadan North Local Government. Source: [<a href="#B47-water-16-02725" class="html-bibr">47</a>].</p>
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<p>Flow diagram showing water flow through UV sterilizer. Source: [<a href="#B52-water-16-02725" class="html-bibr">52</a>].</p>
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<p>(<b>a</b>) Chart showing before and after total coliform results vs. NSDWQ*; (<b>b</b>) chart showing before and after Escherichia Coliform results vs. NSDWQ.</p>
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12 pages, 1543 KiB  
Article
Photocatalytic Degradation of Levofloxacin and Inactivation of Enterococci Levofloxacin-Resistant Bacteria Using Pure Rare-Earth Oxides
by Lorenzo Saviano, Antonietta Mancuso, Alice Cardito, Olga Sacco, Vincenzo Vaiano, Maurizio Carotenuto, Giovanni Libralato and Giusy Lofrano
Separations 2024, 11(9), 272; https://doi.org/10.3390/separations11090272 - 18 Sep 2024
Viewed by 407
Abstract
In this study, La2O3 and CeO2 nanopowders were prepared using a simple and cost-effective precipitation method. Wide-angle X-ray diffraction (WAXD), UV-Visible reflectance diffuses (UV-Vis DRS), Raman spectroscopy, and specific surface area were used to characterize the photocatalysts, evidencing that [...] Read more.
In this study, La2O3 and CeO2 nanopowders were prepared using a simple and cost-effective precipitation method. Wide-angle X-ray diffraction (WAXD), UV-Visible reflectance diffuses (UV-Vis DRS), Raman spectroscopy, and specific surface area were used to characterize the photocatalysts, evidencing that the used preparation method was effective in the generation of crystalline CeO2 and La2O3. In particular, WAXD results showed that the average crystallite size of the achieved La2O3 and CeO2 samples were about 22 nm and 28 nm, respectively. The photocatalytic performances of the prepared catalysts were investigated in the degradation of levofloxacin (LEV) and the inactivation of a waterborne pathogen levofloxacin resistant (Enterococcus faecalis ATCC 29212) by using a photoreactor equipped with a solar simulator (SS). After 120 min, the CeO2 and La2O3 photocatalytic treatments allowed us to achieve between 75% and 83% of levofloxacin removal, respectively. A complete removal of 106 CFU/mL Enterococcus faecalis ATCC 29212 was achieved after 5 and 60 min of La2O3 and CeO2 photocatalytic processes, respectively. Full article
(This article belongs to the Section Environmental Separations)
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<p>Schematic flow chart for the preparation of La<sub>2</sub>O<sub>3</sub> andCeO<sub>2</sub> nanopowders.</p>
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<p>WAXD patterns of La<sub>2</sub>O<sub>3</sub> and CeO<sub>2</sub> photocatalysts.</p>
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<p>Band gap energy calculation using UV–VIS DRS spectra of CeO<sub>2</sub> and La<sub>2</sub>O<sub>3</sub> samples. (<b>a</b>) Eg<sub>d</sub> evaluation of CeO<sub>2</sub>. (<b>b</b>) Eg<sub>i</sub> evaluation of CeO<sub>2</sub>. (<b>c</b>) Eg<sub>d</sub> evaluation of La<sub>2</sub>O<sub>3</sub>. (<b>d</b>) Eg<sub>i</sub> evaluation of La<sub>2</sub>O<sub>3</sub>.</p>
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<p>Raman spectra of CeO<sub>2</sub> and La<sub>2</sub>O<sub>3</sub> photocatalysts.</p>
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<p>FTIR spectra of CeO<sub>2</sub> and La<sub>2</sub>O<sub>3</sub> photocatalysts.</p>
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<p>Reduction in LEV as a function of time during photocatalytic treatment at pH 7 in the presence of the catalysts CeO<sub>2</sub> (<b>A</b>) and La<sub>2</sub>O<sub>3</sub> (<b>B</b>). Average results of duplicate measurements are shown.</p>
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<p>Effect of simulated solar radiation coupled with 0.5 g/L of REE catalysts on the inactivation of levofloxacin-resistant <span class="html-italic">Enterococcus faecalis</span> ATCC 29212 strain. (<b>A</b>) CeO<sub>2</sub> and (<b>B</b>) La<sub>2</sub>O<sub>3</sub>. Results are shown as the logarithm of CFU mL<sup>−1</sup>.</p>
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12 pages, 3242 KiB  
Article
Electrochemical Impedance Spectroscopy-Based Microfluidic Biosensor Using Cell-Imprinted Polymers for Bacteria Detection
by Shiva Akhtarian, Satinder Kaur Brar and Pouya Rezai
Biosensors 2024, 14(9), 445; https://doi.org/10.3390/bios14090445 - 18 Sep 2024
Viewed by 896
Abstract
The rapid and sensitive detection of bacterial contaminants using low-cost and portable point-of-need (PoN) biosensors has gained significant interest in water quality monitoring. Cell-imprinted polymers (CIPs) are emerging as effective and inexpensive materials for bacterial detection as they provide specific binding sites designed [...] Read more.
The rapid and sensitive detection of bacterial contaminants using low-cost and portable point-of-need (PoN) biosensors has gained significant interest in water quality monitoring. Cell-imprinted polymers (CIPs) are emerging as effective and inexpensive materials for bacterial detection as they provide specific binding sites designed to capture whole bacterial cells, especially when integrated into PoN microfluidic devices. However, improving the sensitivity and detection limits of these sensors remains challenging. In this study, we integrated CIP-functionalized stainless steel microwires (CIP-MWs) into a microfluidic device for the impedimetric detection of E. coli bacteria. The sensor featured two parallel microchannels with three-electrode configurations that allowed simultaneous control and electrochemical impedance spectroscopy (EIS) measurements. A CIP-MW and a non-imprinted polymer (NIP)-MW suspended perpendicular to the microchannels served as the working electrodes in the test and control channels, respectively. Electrochemical spectra were fitted with equivalent electrical circuits, and the charge transfer resistances of both cells were measured before and after incubation with target bacteria. The charge transfer resistance of the CIP-MWs after 30 min of incubation with bacteria was increased. By normalizing the change in charge transfer resistance and analyzing the dose–response curve for bacterial concentrations ranging from 0 to 107 CFU/mL, we determined the limits of detection and quantification as 2 × 102 CFU/mL and 1.4 × 104 CFU/mL, respectively. The sensor demonstrated a dynamic range of 102 to 107 CFU/mL, where bacterial counts were statistically distinguishable. The proposed sensor offers a sensitive, cost-effective, durable, and rapid solution for on-site identification of waterborne pathogens. Full article
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<p>Impedimetric microfluidic bacteria sensor design and fabrication. (<b>A</b>) Upper and lower PDMS layers with integrated MWs. (<b>B</b>) Final microfluidic device post-plasma bonding of PDMS layers onto a glass slide. (<b>C</b>) Schematic of the sensor design illustrating flow directions and concurrent test and control measurement microchannels with CIP-MW and NIP-MW WEs, respectively. For REs and CEs, Ag-MWs and SS-MWs were used, respectively.</p>
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<p>Experimental setup used to test the impedimetric microfluidic bacteria sensor.</p>
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<p>Electrochemical impedance spectroscopy (EIS) measurements and equivalent electrical circuits of the microfluidic sensor with CIP-MWs as the working electrode (WE) in the presence of K<sub>3</sub>[Fe(CN)<sub>6</sub>]/K<sub>4</sub>[Fe(CN)<sub>6</sub>] as the redox probe. (<b>A</b>) Standard Randles circuit diagram fit. (<b>B</b>) Modified Randles circuit diagram fit. Insets show the goodness of fit values. The blue lines represent the experimental data, while the red lines correspond to the fitted curves from the circuit models.</p>
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<p>Electrochemical impedance spectroscopy (EIS) curves of microfluidic devices in 0.1 M KCl containing 5 mM K<sub>3</sub>[Fe(CN)<sub>6</sub>] with NIP-MWs and CIP-MWs serving as working electrodes. Minus and plus signs in the legend denote measurements obtained pre-and post-bacteria incubation, respectively. The inset shows an enlarged view of the NIP-MW (− and +) and CIP-MW data.</p>
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<p>Charge transfer resistance (R<sub>CT</sub>) values for microfluidic devices in 0.1 M KCl containing 5 mM K<sub>3</sub>[Fe(CN)<sub>6</sub>] with NIP-MWs and CIP-MWs serving as working electrodes. (<b>A</b>) R<sub>CT</sub> values obtained before normalization and (<b>B</b>) normalized R<sub>CT</sub> change values. The minus and plus signs in the x axis indicate pre-and post-bacteria incubation measurements, respectively. The error bars are standard deviations (SD). *: <span class="html-italic">p</span>-value &lt; 0.05; ***: <span class="html-italic">p</span>-value &lt; 0.001.</p>
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<p>EIS-based microfluidic bacteria sensor characterization. (<b>A</b>) Normalized post-incubation charge transfer resistance shift of the microfluidic sensor with CIP-MWs and parallel control experiments utilizing NIP-MWs, when exposed to different bacteria counts. (<b>B</b>) The dose–response ΔR/R<sub>CT,1</sub> curve established for the CIP-MW-based sensor. Error bars are standard deviations (SD). ns: non-significant; *: <span class="html-italic">p</span>-value &lt; 0.05; **: <span class="html-italic">p</span>-value &lt; 0.01; ***: <span class="html-italic">p</span>-value &lt; 0.001.</p>
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13 pages, 2594 KiB  
Article
Evaluation of an Enzyme-Linked Magnetic Electrochemical Assay for Hepatitis a Virus Detection in Drinking and Vegetable Processing Water
by Cristine D’Agostino, Rocco Cancelliere, Antonio Ceccarelli, Danila Moscone, Loredana Cozzi, Giuseppina La Rosa, Elisabetta Suffredini and Laura Micheli
Chemosensors 2024, 12(9), 188; https://doi.org/10.3390/chemosensors12090188 - 14 Sep 2024
Viewed by 1189
Abstract
Globally, waterborne viral infections significantly threaten public health. While current European Union regulations stipulate that drinking water must be devoid of harmful pathogens, they do not specifically address the presence of enteric viruses in water used for irrigation or food production. Traditional virus [...] Read more.
Globally, waterborne viral infections significantly threaten public health. While current European Union regulations stipulate that drinking water must be devoid of harmful pathogens, they do not specifically address the presence of enteric viruses in water used for irrigation or food production. Traditional virus detection methods rely on molecular biology assays, requiring specialized personnel and laboratory facilities. Here, we describe an electrochemical sandwich enzyme-linked immunomagnetic assay (ELIME) for the detection of the hepatitis A virus (HAV) in water matrices. This method employed screen-printed electrodes as the sensing platform and utilized commercially available pre-activated magnetic beads to provide a robust foundation for the immunological reaction. The ELIME assay demonstrated exceptional analytical performance in only 185 min achieving a detection limit of 0.5 genomic copies per milliliter (g.c./mL) and exhibiting good reproducibility with a relative standard deviation (RSD) of 7% in HAV-spiked drinking and processing water samples. Compared with the real-time RT-qPCR method described in ISO 15216-1, the ELIME assay demonstrated higher sensitivity, although the overall linearity of the method was moderate. These analytical attributes highlight the potential of the ELIME assay as a rapid and viable alternative for HAV detection in water used for agriculture and food processing. Full article
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<p>Protocol used in the ELIME detection.</p>
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<p>Extraction method for HAV from drinking water and water for vegetable processing.</p>
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<p>General schematic of ELIME functioning. (<b>a</b>) Immobilization of Goat Anti-Mouse IgG magnetic nanoparticles (MN-MAb1) on SPE, (<b>b</b>) antigen–antibody interaction, (<b>c</b>) immunocomplex detection using Mab-AP, and (<b>d</b>) electrochemical detection using DPV.</p>
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<p>Mab1 optimization. (<b>a</b>) The effect of the MAbI concentration on the signal. Protocol: 3% dry milk (DM); [MAbI] 0, 5, 10, and 15 μg/mL; [HAV] 10<sup>−8</sup> UI/mL; [MAb-AP] 1:10,000 (<span class="html-italic">v</span>/<span class="html-italic">v</span>) in PBS; and 5 mg/mL 1-NPP; in red circle the selected concentration is underlined. (<b>b</b>) The DPV measurement potential range 0–600 mV, the pulse width of 50 ms, pulse amplitude of 70 mV, and a scan rate of 100 mV/s.</p>
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<p>Study of the blocking reagent at different concentrations of MAb-AP. Three percent BSA, 3% DM, and 1% PVA; [MAb-AP] 1:5000, 1:10,000, and 1:25,000 (<span class="html-italic">v</span>/<span class="html-italic">v</span>) in PBS; 5 mg/mL 1-NPP; DPV measurement potential range 0–600 mV; pulse width of 50 ms; pulse amplitude of 70 mV; and scan rate of 100 mV/s.</p>
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<p>Electrochemical LbL-EC of ELIME assembly. (<b>a</b>) cv voltammograms and (<b>b</b>) EIS spectra. At least three experiments for each curve were conducted.</p>
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<p>Calibration curve for HAV. [MAb<sub>I</sub>] 10 μg/mL, 3% BSA, [HAV] 0–10 g.c./mL, [MAb-AP] 1:25,000 (<span class="html-italic">v</span>/<span class="html-italic">v</span>), and 5 mg/mL 1-NPP. DPV measurement potential range 0–600 mV, pulse width of 50 ms, pulse amplitude of 70 mV, and a scan rate of 100 mV/s. (<b>a</b>) 4-parameter logistic calibration curv. Parameters: <span class="html-italic">a</span> = 96.53, <span class="html-italic">b</span> = −3.03, <span class="html-italic">x</span><sub>0</sub> = 0.89, <span class="html-italic">y</span><sub>0</sub> = −0.18. LOD = 0.5 g.c./mL, RSD% 7%. (<b>b</b>) DPV measures used for the calibration curve.</p>
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16 pages, 3006 KiB  
Article
Biomonitoring of Waters and Tambacu (Colossoma macropomum × Piaractus mesopotamicus) from the Amazônia Legal, Brazil
by Karuane Saturnino da Silva Araújo, Thiago Machado da Silva Acioly, Ivaneide Oliveira Nascimento, Francisca Neide Costa, Fabiano Corrêa, Ana Maria Gagneten and Diego Carvalho Viana
Water 2024, 16(18), 2588; https://doi.org/10.3390/w16182588 - 12 Sep 2024
Viewed by 710
Abstract
Fish farming is increasingly important globally and nationally, playing a crucial role in fish production for human consumption. Monitoring microbiological and chemical contaminants from water discharge is essential to mitigate the risk of contaminating water and fish for human consumption. This study analyzes [...] Read more.
Fish farming is increasingly important globally and nationally, playing a crucial role in fish production for human consumption. Monitoring microbiological and chemical contaminants from water discharge is essential to mitigate the risk of contaminating water and fish for human consumption. This study analyzes the physicochemical and E. coli parameters of water and tambacu fish muscles (Colossoma macropomum × Piaractus mesopotamicus) in Western Maranhão, Brazil. It also includes a qualitative characterization of zooplankton in the ponds. Samples were collected from tambacu ponds in a dam system fed by natural watercourses from the Tocantins River tributaries, located at the connection of the Brazilian savanna and Amazon biomes. The physicochemical and E. coli parameters of water did not meet national standards. The zooplankton community included Rotifera, Cladocera, Copepoda, and Protozoa representatives, with no prior studies on zooplankton in the region, making these findings unprecedented. The biological quality of freshwater is crucial in fish farming, as poor quality can lead to decreased productivity and fish mortality, raising significant food safety concerns. The water quality studied is related to the potential influence of untreated wastewater as a source of contamination, leaving the studied region still far from safe water reuse practices. The findings on chemical and E. coli contamination of fish farming waters concern human health and emphasize the need for appropriate regulations. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Location and key characteristics of the study area, Maranhão, Brazil.</p>
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<p>Specimen of tambacu (<span class="html-italic">Colossoma macropomum × Piaractus mesopotamicus</span>) from the Amazônia legal, Brazil.</p>
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<p>Zooplankton species sampled in the water of tambacu fish farming tanks.</p>
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10 pages, 529 KiB  
Communication
Short Communication: Rotavirus Group A Occurrence in Rural Water Source Samples in a Midwest Region State of Brazil, Comparing Wet and Dry Seasons
by Graziela Picciola Bordoni, Lucas Candido Gonçalves Barbosa, Fernando Santos Lima, Mônica de Oliveira Santos, José Daniel Gonçalves Vieira, Thais Reis Oliveira, Paulo Sérgio Scalize and Lilian Carla Carneiro
Viruses 2024, 16(9), 1452; https://doi.org/10.3390/v16091452 - 12 Sep 2024
Viewed by 631
Abstract
Identified as a potential reference pathogen by the WHO Guidelines for Drinking-Water Quality, Rotavirus (RV) is among the main enteric viruses that cause waterborne diseases. The aim of this study was to identify and correlate the presence of RV in collective and individual [...] Read more.
Identified as a potential reference pathogen by the WHO Guidelines for Drinking-Water Quality, Rotavirus (RV) is among the main enteric viruses that cause waterborne diseases. The aim of this study was to identify and correlate the presence of RV in collective and individual water sources of rural communities in the state of Goiás, within the seasons in which the collections were made (rainy and dry seasons). For this, 86 water samples in the dry period and 160 samples in the rainy period were collected. Concentration of water samples, extraction of viral genetic material and molecular tests were performed. When analyzing the presence of RV in the samples, taking into consideration the period studied, RV was found to be more prevalent in the dry season (54.7%) than in the rainy season (20%), showing a strong statistical association with the dry season (p-value < 0.001). The presence of pathogenic microorganisms in water is a public risk issue, enabling the emergence of outbreaks, endemics and epidemics. In the present research, there was an association between the presence of Rotavirus and the dry period of the year when compared to the rainy period. Full article
(This article belongs to the Special Issue The 9th Edition of the European Rotavirus Biology Meeting (ERBM-9))
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<p>A presentation of the percentage of types of water sources where the research samples were collected. Each pie chart shows the percentages for each period, the dry period and the rainy period. Dry season (<span class="html-italic">n</span> = 86); rainy season (<span class="html-italic">n</span> = 160).</p>
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13 pages, 1311 KiB  
Article
In the Depths of Wash Water: Isolation of Opportunistic Bacteria from Fresh-Cut Processing Plants
by Piotr Kanarek, Barbara Breza-Boruta and Tomasz Bogiel
Pathogens 2024, 13(9), 768; https://doi.org/10.3390/pathogens13090768 - 6 Sep 2024
Viewed by 878
Abstract
The fruit and vegetable industry in post-harvest processing plants is characterized by a substantial consumption of water resources. Wash waters may serve as an environment for the periodic or permanent habitation of microorganisms, particularly if biofilm forms on the inner walls of tanks [...] Read more.
The fruit and vegetable industry in post-harvest processing plants is characterized by a substantial consumption of water resources. Wash waters may serve as an environment for the periodic or permanent habitation of microorganisms, particularly if biofilm forms on the inner walls of tanks and flushing channels. Despite the implementation of integrated food safety monitoring systems in numerous countries, foodborne pathogens remain a global public health and food safety concern, particularly for minimally processed food products such as vegetables and fruits. This necessitates the importance of studies that will explore wash water quality to safeguard minimally processed food against foodborne pathogen contamination. Therefore, the current study aimed to isolate and identify bacteria contaminating the wash waters of four fresh-cut processing plants (Poland) and to evaluate the phenotypic antibiotic resistance profiles in selected species. Bacteria were isolated using membrane filtration and identified through mass spectrometry, followed by antibiotic susceptibility testing according to EUCAST guidelines. The results revealed that the level of contamination with total aerobic bacteria in the water ranged from 1.30 × 106 cfu/mL to 2.54 × 108 cfu/mL. Among the isolates, opportunistic pathogens including Enterococcus faecalis, Pseudomonas aeruginosa, Klebsiella oxytoca, Klebsiella pneumoniae, Serratia marcescens, and Proteus vulgaris strains were identified. An especially noteworthy result was the identification of cefepime-resistant K. oxytoca isolates. These findings highlight the importance of monitoring the microbial microflora in minimally processed foods and the need for appropriate sanitary control procedures to minimize the risk of pathogen contamination, ensuring that products remain safe and of high quality throughout the supply chain. Full article
(This article belongs to the Section Bacterial Pathogens)
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<p>Summary of factors influencing possible pre-release contamination of fruit and vegetables.</p>
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<p>Location of fresh-cut processing plants.</p>
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<p>Levels of microbiological contamination of water after fruit and vegetable wash processes (A, B, C, D—locations of fresh-cut processing plants).</p>
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21 pages, 567 KiB  
Systematic Review
Systematic Review of the Impact of Natural Resource Management on Public Health Outcomes: Focus on Water Quality
by Mohammed Elmadani, Evans Kasmai Kiptulon, Simon Klára and Máté Orsolya
Resources 2024, 13(9), 122; https://doi.org/10.3390/resources13090122 - 30 Aug 2024
Viewed by 2159
Abstract
Natural resource management (NRM) plays a pivotal role in ensuring the sustainability of ecosystems, which are essential for human health and well-being. This systematic review examines the impact of various NRM practices on water quality and their subsequent effects on public health. Specifically, [...] Read more.
Natural resource management (NRM) plays a pivotal role in ensuring the sustainability of ecosystems, which are essential for human health and well-being. This systematic review examines the impact of various NRM practices on water quality and their subsequent effects on public health. Specifically, it focuses on interventions such as watershed management, pollution control, land use management, water treatment, and ecosystem restoration. We conducted a comprehensive search across PubMed, Scopus, and Web of Science, supplemented by gray literature from Google Scholar, WHO reports, and government and NGO publications, covering studies published between 2014 and 2024. A total of 42 studies met the inclusion criteria, encompassing diverse geographical regions with significant representation from developing countries. The findings indicate that effective NRM practices, particularly those aimed at reducing pollutants, managing watersheds, and promoting sustainable land use, significantly improve water quality by lowering levels of chemical contaminants, microbial pathogens, and physical pollutants. Improved water quality directly correlates with reduced incidences of waterborne diseases, chronic health conditions from long-term chemical exposure, and acute health effects from immediate pollutant exposure. The review underscores the need for tailored NRM strategies that consider local environmental and socio-economic contexts. It also highlights the importance of community involvement, regulatory frameworks, and continuous monitoring to enhance the effectiveness of NRM interventions. Despite the positive impacts, barriers such as limited financial resources, technical expertise, and community engagement pose challenges to the implementation of these practices. In conclusion, the systematic review demonstrates that comprehensive and context-specific NRM practices are crucial for improving water quality and public health outcomes. Policymakers and practitioners are encouraged to adopt integrated water resource management approaches, prioritize sustainable practices, and engage local communities to achieve long-term health and environmental benefits. Full article
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<p>PRISMA flowchart illustrating the results of the literature search and screening procedure for the most recent studies on water quality management’s impact on public health.</p>
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14 pages, 1746 KiB  
Review
A Review of Winter Ulcer Disease and Skin Ulcer Outbreaks in Atlantic Salmon (Salmo salar)
by Maryam Ghasemieshkaftaki
Hydrobiology 2024, 3(3), 224-237; https://doi.org/10.3390/hydrobiology3030015 - 11 Aug 2024
Cited by 1 | Viewed by 1121
Abstract
Winter ulcer disease (WUD) is widely recognized as a serious threat to animal welfare and a major contributor to revenue loss within the aquaculture sector, particularly affecting the salmon-farming industry. This highlights the significant impact of WUD on both animal well-being and the [...] Read more.
Winter ulcer disease (WUD) is widely recognized as a serious threat to animal welfare and a major contributor to revenue loss within the aquaculture sector, particularly affecting the salmon-farming industry. This highlights the significant impact of WUD on both animal well-being and the economic sustainability of fish farming. WUD causes hemorrhagic signs and results in dermal lesions and ulcers. This disease can lead to higher mortality rates and a considerable decline in the fish’s market value. Moritella viscosa, a Gram-negative bacterium, is predominantly, but not exclusively, correlated with the emergence of WUD, mostly during the colder seasons. Waterborne transmission is the primary way for spreading the bacterium within a population. However, there is remarkable variation in the prevalence and characteristics of WUD in different regions. In Europe, this disease often occurs in the winter, and the intensity and occurrence of outbreaks are influenced by water temperature and salinity. In contrast, outbreaks are typically observed in the summer and mid-autumn in Eastern Canada. Despite the administration of various polyvalent vaccines, outbreaks of skin ulcers have been documented in Canada, and studies have highlighted the possible roles of other bacterial pathogens in Atlantic salmon. This review discusses the etiology, pathogenesis, and potential mitigation or prevention strategies for WUD, mainly in Atlantic salmon. Moreover, it underscores the necessity of conducting further investigations to discover the potential unknown causative agents of ulcerative disease and design appropriate vaccines or preventive strategies for these pathogens. Full article
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<p>(<b>A</b>) Mortality and clinical signs reported in Atlantic salmon after challenge with <span class="html-italic">M. viscosa</span> [<a href="#B28-hydrobiology-03-00015" class="html-bibr">28</a>,<a href="#B29-hydrobiology-03-00015" class="html-bibr">29</a>]. This bacterium can affect Atlantic salmon at lower temperatures [<a href="#B30-hydrobiology-03-00015" class="html-bibr">30</a>]. (<b>B</b>) Atlantic salmon were vaccinated and then challenged with <span class="html-italic">M. viscosa</span>. A significant increase in WBC levels was observed after the challenge [<a href="#B1-hydrobiology-03-00015" class="html-bibr">1</a>]. Increasing levels of lymphocytes and low mortality rates indicated the appropriate functioning of the adaptive immune response in Atlantic salmon immunized with ALPHA JECT micro IV vaccine. This illustration was created by the author using BioRender (<a href="https://biorender.com/" target="_blank">https://biorender.com/</a>) (accessed on 17 April 2024).</p>
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<p>The occurrence of ulcer disease is different in European countries and Eastern Canada. In European cases, when the water temperature rises above 8 °C, the ulcers disappear, and fish can survive [<a href="#B38-hydrobiology-03-00015" class="html-bibr">38</a>]. However, in Eastern Canada, the ulcerative disease occurs at between around 10 and 13 °C and will be persistent until summer or mid-autumn [<a href="#B8-hydrobiology-03-00015" class="html-bibr">8</a>]. In both scenarios, bacterial adhesion leads to lesions and ulcers. This figure was designed by the author utilizing BioRender (<a href="https://biorender.com/" target="_blank">https://biorender.com/</a>) (accessed on 28 February 2024).</p>
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<p>This map shows the distribution of <span class="html-italic">M. viscosa</span> clades in different regions. ‘Typical’ <span class="html-italic">M. viscosa</span> has mostly been isolated from Atlantic salmon farmed in Norway, Scotland, and the Faroe Islands. ‘Variant’ <span class="html-italic">M. viscosa</span> has been observed in Atlantic salmon cultured in Canada and Iceland [<a href="#B63-hydrobiology-03-00015" class="html-bibr">63</a>]. This figure was designed by the author utilizing BioRender (<a href="https://biorender.com/" target="_blank">https://biorender.com/</a>) (accessed on 9 April 2024).</p>
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18 pages, 679 KiB  
Review
Advancing Pathogen Identification: The Role of Digital PCR in Enhancing Diagnostic Power in Different Settings
by Alessia Mirabile, Giuseppe Sangiorgio, Paolo Giuseppe Bonacci, Dalida Bivona, Emanuele Nicitra, Carmelo Bonomo, Dafne Bongiorno, Stefania Stefani and Nicolò Musso
Diagnostics 2024, 14(15), 1598; https://doi.org/10.3390/diagnostics14151598 - 25 Jul 2024
Viewed by 4004
Abstract
Digital polymerase chain reaction (dPCR) has emerged as a groundbreaking technology in molecular biology and diagnostics, offering exceptional precision and sensitivity in nucleic acid detection and quantification. This review highlights the core principles and transformative potential of dPCR, particularly in infectious disease diagnostics [...] Read more.
Digital polymerase chain reaction (dPCR) has emerged as a groundbreaking technology in molecular biology and diagnostics, offering exceptional precision and sensitivity in nucleic acid detection and quantification. This review highlights the core principles and transformative potential of dPCR, particularly in infectious disease diagnostics and environmental surveillance. Emphasizing its evolution from traditional PCR, dPCR provides accurate absolute quantification of target nucleic acids through advanced partitioning techniques. The review addresses the significant impact of dPCR in sepsis diagnosis and management, showcasing its superior sensitivity and specificity in early pathogen detection and identification of drug-resistant genes. Despite its advantages, challenges such as optimization of experimental conditions, standardization of data analysis workflows, and high costs are discussed. Furthermore, we compare various commercially available dPCR platforms, detailing their features and applications in clinical and research settings. Additionally, the review explores dPCR’s role in water microbiology, particularly in wastewater surveillance and monitoring of waterborne pathogens, underscoring its importance in public health protection. In conclusion, future prospects of dPCR, including methodological optimization, integration with innovative technologies, and expansion into new sectors like metagenomics, are explored. Full article
(This article belongs to the Special Issue Diagnosis and Prognosis of Sepsis)
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<p>Graphical workflow of ddPCR (above) and dPCR (below). Independently created by an author (P.G.B.) using <a href="http://BioRender.com" target="_blank">BioRender.com</a> accessed on 25 May 2024, freely inspired by Kokkoris et al. [<a href="#B16-diagnostics-14-01598" class="html-bibr">16</a>].</p>
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14 pages, 1134 KiB  
Article
Development of Polymerase Chain Reaction–High-Resolution Melt Assay for Waterborne Pathogens Legionella pneumophila, Vibrio parahaemolyticus, and Camplobacter jejuni
by Shannon M. Carr and Kelly M. Elkins
Microorganisms 2024, 12(7), 1366; https://doi.org/10.3390/microorganisms12071366 - 3 Jul 2024
Viewed by 948
Abstract
Legionella pneumophila is the waterborne pathogen primarily responsible for causing both Pontiac Fever and Legionnaire’s Disease in humans. L. pneumophila is transmitted via aerosolized water droplets. The purpose of this study was to design and test primers to allow for rapid polymerase chain [...] Read more.
Legionella pneumophila is the waterborne pathogen primarily responsible for causing both Pontiac Fever and Legionnaire’s Disease in humans. L. pneumophila is transmitted via aerosolized water droplets. The purpose of this study was to design and test primers to allow for rapid polymerase chain reaction (PCR) melt detection and identification of this infectious agent in cases of clinical or emergency response detection. New PCR primers were designed for this species of bacteria; the primer set was purchased from IDT and the target bacterial DNA was purchased from ATCC. The L. pneumophila primers targeted the macrophage infectivity potentiator gene (mip), which inhibits macrophage phagocytosis. The primers were tested for specificity, repeatability, and sensitivity using PCR–high-resolution melt (HRM) assays. The primer set was found to be specific to the designated bacteria and did not amplify the other twenty-one species from the panel. The L. pneumophila assay was able to be multiplexed. The duplex assay consists of primers for L. pneumophila and Vibrio parahaemolyticus, which are both waterborne pathogens. The triplex assay consists of primers for L. pneumophila, V. parahaemolyticus, and Campylobacter jejuni. The unique melting temperature for the L. pneumophila primer assay is 82.84 ± 0.19 °C, the C. jejuni assay is 78.10 ± 0.58 °C, and the V. parahaemolyticus assay is 86.74 ± 0.65 °C. Full article
(This article belongs to the Special Issue Advances in Research on Waterborne Pathogens)
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<p>PCR melt curves of (<b>a</b>) <span class="html-italic">L. pneumophila mip</span>, (<b>b</b>) <span class="html-italic">C. jejuni cadF</span>, and (<b>c</b>) <span class="html-italic">V. parahaemolyticus thl</span> primers as single primer assays. Melt curves of <span class="html-italic">L. pneumophila mip</span> primers as a duplex assay with <span class="html-italic">V. parahaemolyticus thl</span> primers (<b>d</b>) testing bacterial DNA individually as a duplex assay and (<b>e</b>) testing a bacterial DNA mixture as a duplex assay. Melt curves of <span class="html-italic">L. pneumophila mip</span> primers as a triplex assay with <span class="html-italic">V. parahaemolyticus thl</span> and <span class="html-italic">C. jejuni cadF</span> primers (<b>f</b>) testing bacterial DNA individually as a triplex assay and (<b>g</b>) testing a bacterial DNA mixture as a triplex assay.</p>
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<p>PCR–HRM results from repeatability testing of the primers sets for <span class="html-italic">C. jejuni</span> (green line), <span class="html-italic">L. pneumophila</span> (red line), and <span class="html-italic">V. parahaemolyticus</span> (blue line) and the triplex assay (solid black line). The negative amplification controls are shown with the gray lines (primers and master mix). The initial hold was 95 °C for 10 min followed by 40 cycles of 15 s each at 60 °C, 72 °C, and 95 °C, including the first ten cycles of touchdown from 60 °C to 55 °C in 0.5 °C increments, hold at 72 °C for 5 min, hold at 45 °C for 60 s, and melt from 65 °C to 95 °C, increasing by 0.3 °C every 3 s with gain optimization and detecting HRM.</p>
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<p>Results from 3% agarose gel with primer sets for <span class="html-italic">L. pneumophila</span> (LP), <span class="html-italic">V. parahaemolyticus</span> (VP), and <span class="html-italic">C. jejuni</span> (CJ) and target DNA each (lanes 1, 2, and 3, respectively), ThermoFisher TrackIt 100 bp DNA ladder (sizes labelled) (lane 4), Fast Ruler Ultra-Low Range DNA ladder (lane 5), triplex primer set with all three target DNAs (lane 6, species labelled), and the negative ampflification control with the triplex primers was run in lane 7.</p>
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<p>Sensitivity of <span class="html-italic">L. pneumophila mip</span> primers as a (<b>a</b>) single primer assay, (<b>b</b>) duplex assay with <span class="html-italic">V. parahaemolyticus thl</span> primers (left peak) testing a mixture of <span class="html-italic">L. pneumophila</span> and <span class="html-italic">V. parahaemolyticus</span> DNA with concentrations as shown for both, and (<b>c</b>) triplex assay with <span class="html-italic">V. parahaemolyticus thl</span> and <span class="html-italic">C. jejuni cadF</span> primers testing a mixture of <span class="html-italic">L. pneumophila</span>, <span class="html-italic">V. parahaemolyticus</span>, and <span class="html-italic">C. jejuni</span> DNA in triplicate (1 ng/µL, solid black line; 0.1 ng/µL, dashed line; 0.05 ng/µL, dotted line).</p>
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<p>Specificity of <span class="html-italic">L. pneumophila mip</span> primers tested against 21 other bacterial strains (gray lines) as a (<b>a</b>) single primer assay (solid black trace), (<b>b</b>) duplex assay with <span class="html-italic">V. parahaemolyticus thl</span> primers (dot black trace), and (<b>c</b>) triplex assay with <span class="html-italic">V. parahaemolyticus thl</span> (dash dotted black trace) and <span class="html-italic">C. jejuni cadF</span> (dot black trace) primers.</p>
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54 pages, 4151 KiB  
Review
Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
by Franca Rossi, Serena Santonicola, Carmela Amadoro, Lucio Marino and Giampaolo Colavita
Appl. Sci. 2024, 14(12), 5339; https://doi.org/10.3390/app14125339 - 20 Jun 2024
Cited by 1 | Viewed by 3202
Abstract
This narrative review was aimed at collecting updated knowledge on the risk factors, illnesses caused, and measures for the prevention of protozoan infections transmitted by food and drinking water. Reports screened dated from 2019 to the present and regarded global prevalence in food [...] Read more.
This narrative review was aimed at collecting updated knowledge on the risk factors, illnesses caused, and measures for the prevention of protozoan infections transmitted by food and drinking water. Reports screened dated from 2019 to the present and regarded global prevalence in food handlers, occurrence in food and drinking water, impact on human health, and recently reported outbreaks and cases of severe infections attributable to the dietary route. Cryptosporidium spp., Cyclospora cayetanensis, Entamoeba histolytica, and Cystoisospora belli were the protozoans most frequently involved in recently reported waterborne and foodborne outbreaks and cases. Blastocystis hominis was reported to be the most widespread intestinal protozoan in humans, and two case reports indicated its pathogenic potential. Dientamoeba fragilis, Endolimax nana, and Pentatrichomonas hominis are also frequent but still require further investigation on their ability to cause illness. A progressive improvement in surveillance of protozoan infections and infection sources took place in developed countries where the implementation of reporting systems and the application of molecular diagnostic methods led to an enhanced capacity to identify epidemiological links and improve the prevention of foodborne and waterborne protozoan infections. Full article
(This article belongs to the Section Applied Microbiology)
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<p>Number of articles retrieved (normal character) and screened (bold character) for the construction of this review from the GoogleScholar (blue numbers) and Scopus (red numbers) databases. The search strings were “protozoan food (or drinking water) human infection case (or outbreak)” or the same strings with individual names of organisms in place of the word “protozoan”. The numbers comprise duplicate items from different searches.</p>
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<p>Number of waterborne or foodborne acute cases/outbreaks reported for pathogenic protozoans since 2019 [<a href="#B8-applsci-14-05339" class="html-bibr">8</a>,<a href="#B9-applsci-14-05339" class="html-bibr">9</a>,<a href="#B19-applsci-14-05339" class="html-bibr">19</a>,<a href="#B35-applsci-14-05339" class="html-bibr">35</a>,<a href="#B122-applsci-14-05339" class="html-bibr">122</a>,<a href="#B123-applsci-14-05339" class="html-bibr">123</a>,<a href="#B131-applsci-14-05339" class="html-bibr">131</a>,<a href="#B132-applsci-14-05339" class="html-bibr">132</a>,<a href="#B133-applsci-14-05339" class="html-bibr">133</a>,<a href="#B142-applsci-14-05339" class="html-bibr">142</a>,<a href="#B143-applsci-14-05339" class="html-bibr">143</a>,<a href="#B154-applsci-14-05339" class="html-bibr">154</a>,<a href="#B157-applsci-14-05339" class="html-bibr">157</a>,<a href="#B168-applsci-14-05339" class="html-bibr">168</a>,<a href="#B169-applsci-14-05339" class="html-bibr">169</a>,<a href="#B170-applsci-14-05339" class="html-bibr">170</a>,<a href="#B172-applsci-14-05339" class="html-bibr">172</a>,<a href="#B173-applsci-14-05339" class="html-bibr">173</a>,<a href="#B191-applsci-14-05339" class="html-bibr">191</a>,<a href="#B193-applsci-14-05339" class="html-bibr">193</a>,<a href="#B195-applsci-14-05339" class="html-bibr">195</a>,<a href="#B196-applsci-14-05339" class="html-bibr">196</a>,<a href="#B198-applsci-14-05339" class="html-bibr">198</a>,<a href="#B199-applsci-14-05339" class="html-bibr">199</a>,<a href="#B200-applsci-14-05339" class="html-bibr">200</a>,<a href="#B201-applsci-14-05339" class="html-bibr">201</a>,<a href="#B209-applsci-14-05339" class="html-bibr">209</a>,<a href="#B226-applsci-14-05339" class="html-bibr">226</a>,<a href="#B233-applsci-14-05339" class="html-bibr">233</a>,<a href="#B237-applsci-14-05339" class="html-bibr">237</a>,<a href="#B239-applsci-14-05339" class="html-bibr">239</a>,<a href="#B240-applsci-14-05339" class="html-bibr">240</a>,<a href="#B241-applsci-14-05339" class="html-bibr">241</a>,<a href="#B242-applsci-14-05339" class="html-bibr">242</a>,<a href="#B243-applsci-14-05339" class="html-bibr">243</a>,<a href="#B255-applsci-14-05339" class="html-bibr">255</a>,<a href="#B256-applsci-14-05339" class="html-bibr">256</a>,<a href="#B260-applsci-14-05339" class="html-bibr">260</a>,<a href="#B268-applsci-14-05339" class="html-bibr">268</a>,<a href="#B269-applsci-14-05339" class="html-bibr">269</a>,<a href="#B273-applsci-14-05339" class="html-bibr">273</a>,<a href="#B274-applsci-14-05339" class="html-bibr">274</a>,<a href="#B280-applsci-14-05339" class="html-bibr">280</a>,<a href="#B282-applsci-14-05339" class="html-bibr">282</a>,<a href="#B286-applsci-14-05339" class="html-bibr">286</a>]. Outbreaks are only shown for <span class="html-italic">Cryptosporidium</span> species.</p>
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<p>Schematic representation of the contamination sources at the origin of cases and outbreaks reported since 2019 for foodborne and waterborne protozoans [<a href="#B90-applsci-14-05339" class="html-bibr">90</a>,<a href="#B122-applsci-14-05339" class="html-bibr">122</a>,<a href="#B123-applsci-14-05339" class="html-bibr">123</a>,<a href="#B131-applsci-14-05339" class="html-bibr">131</a>,<a href="#B132-applsci-14-05339" class="html-bibr">132</a>,<a href="#B133-applsci-14-05339" class="html-bibr">133</a>,<a href="#B154-applsci-14-05339" class="html-bibr">154</a>,<a href="#B157-applsci-14-05339" class="html-bibr">157</a>,<a href="#B168-applsci-14-05339" class="html-bibr">168</a>,<a href="#B169-applsci-14-05339" class="html-bibr">169</a>,<a href="#B170-applsci-14-05339" class="html-bibr">170</a>,<a href="#B172-applsci-14-05339" class="html-bibr">172</a>,<a href="#B173-applsci-14-05339" class="html-bibr">173</a>,<a href="#B200-applsci-14-05339" class="html-bibr">200</a>,<a href="#B210-applsci-14-05339" class="html-bibr">210</a>,<a href="#B237-applsci-14-05339" class="html-bibr">237</a>,<a href="#B242-applsci-14-05339" class="html-bibr">242</a>,<a href="#B243-applsci-14-05339" class="html-bibr">243</a>,<a href="#B255-applsci-14-05339" class="html-bibr">255</a>,<a href="#B259-applsci-14-05339" class="html-bibr">259</a>,<a href="#B282-applsci-14-05339" class="html-bibr">282</a>].</p>
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