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50 results for “Parasitic disease”
Data used in the article "Cryptic disease-induced mortality may cause host extinction in an apparently-stable host-parasite system"
<p>These data files include all the capture-history matrices that were used in the article, including two matrices with the age of captured individuals (adults or juveniles) according to the definition presented in the main text. Infection intensity (zoospore equivalents per swab) is provided in separate files for all <em>Rhinoderma darwini</em>i and<em> Eupsophus contulmoensis</em> individuals that tested positive for <em>Batrachochytrium dendrobatidis</em> infection. Also, the R code used for the fully paramaterized matrix population model 1 (including figures) is provided. Other codes used to analyze our data, especifically capture-recapture models, were obtained from Kéry and Schaub 2012 (<em>Bayesian population analysis using WinBUGS. A hierarchical perspective</em>. Waltham, USA: Academic Press.)</p>
How does parasite environmental transmission stage concentration change before, during, and after disease outbreaks?
<p>Outbreaks of environmentally transmitted parasites require that susceptible hosts encounter transmission stages in the environment and become infected, but we also know that transmission stages can be in the environment without triggering disease outbreaks. One challenge for understanding the relationship between environmental transmission stages and disease outbreaks is that the distribution and abundance of transmission stages outside of their hosts have been difficult to quantify. Thus, we have limited data about how changes in transmission stage abundance influence disease dynamics; moreover, we do not know whether the relationship between transmission stages and outbreaks differs among parasite species. We used digital PCR to quantify environmental transmission stages of five parasites in six lakes in southeastern Michigan every two weeks from June to November 2021. At the same time, we quantified infection prevalence in hosts and host density. Our study focused on eight zooplankton host species (<em>Daphnia</em> spp. and <em>Ceriodaphnia</em> <em>dubia</em>) and five of their parasites from diverse taxonomic groups (bacteria, yeast, microsporidia, and oomycete) with different infection mechanisms. We found that parasite transmission stage concentration increased prior to disease outbreaks for all parasites. However, parasites differed significantly in the relative timing of peaks in transmission stage concentration and infection outbreaks. The 'continuous shedder' parasites had transmission stage peaks at the same time as or slightly after the outbreak peaks. In contrast, parasites relying on host death for transmission ('obligate killers') had transmission stage peaks before outbreak peaks. For most parasites, lakes with outbreaks had higher spore concentrations than those without outbreaks, especially once an outbreak began; the exception was for a parasite, <em>Pasteuria</em> <em>ramosa</em>, with very strong genotypic specificity of infection. Overall, our results show that disease outbreaks are tightly linked to transmission stage concentration; outbreaks were preceded by increases in transmission stage concentration in the environment and then were fueled by the production of more transmission stages during the outbreak itself, with concentrations decreasing to pre-outbreak levels as outbreaks waned. Thus, tracking transmission stages in the environment improves our understanding of the drivers of disease outbreaks and reveals how parasite traits may affect these dynamics.</p>
Figure 4 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 4. Identification of PCR inhibitors in 18 biological samples positive for Aspergillus. Graph A: Ct values obtained from pure and diluted DNA samples (dilution rate 1/20). Graph B: Ct values obtained with 20 copies of a plasmid DNA systematically added to the same biological samples (undiluted and diluted) and a negative control sample (NC).
Figure 2 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 2. Influence of proteinase K digestion (56 °C overnight) on DNA extraction. Graph A shows the Ct values obtained by quantifying THP1 cell DNA derived from direct extraction with the NucliSENS easyMAG system and extraction performed on the same quantity of cells following overnight (ON) digestion with Proteinase K. Graph B shows Leishmania quantification after extraction with the NucliSENS easyMAG system both with and without PK and quantification after extraction using a QIAamp DNA Mini kit after ON digestion with PK.
Figure 7 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 7. Variation of the ratio between kinetoplastic DNA and nuclear DNA extraction with various Leishmania quantities in the presence of 103 THP1 cells.
Figure 5 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 5. Yield of DNA extraction from Leishmania and THP1 cells using the NucliSENS easyMAG system.
Figure 3 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 3. Results of the extraction experiments performed on yeast (Candida albicans) and filamentous fungi (Aspergillus fumigatus). A presents the kinetics of the extraction process after vortexing and glass-bead treatment. B shows the differences in DNA quantity obtained from fungal cells using the FastPrep system (with) compared to the same process without grinding.
Figure 6 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 6. Influence of the quantity of human cells (THP1 cells) on Leishmania quantification at various concentrations of host cells and parasites.
Fig. 5 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 5. Phylogenetic trees based on 28S (left) and cox1 (right) sequences of Strongyloides eggs. Sequences obtained from the eggs are bold type and indicated with an *. Numbers at the nodes represent posterior probabilities from Bayesian inference.
Fig. 2 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 2. Egg morphotypes found in the faeces of Mexican primates. A) Trypanoxyuris sp., B) Controrchis biliophilus, arrow pointing to the two eyespot remnants; C) trematode, diagnosed as C. biliophilus by molecular data; D) unidentified ancylostomatid; E) Strongyloides sp.; F) unidentified ascarid. Scale bar is equal to 15 Mm.
Fig. 4 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 4. Phylogenetic tree based on 28S sequences of Controrchis biliophilus. Sequences obtained from the eggs are bold type and indicated with an *. Numbers at the nodes represent posterior probabilities from Bayesian inference. Host species are indicated within parenthesis.
Fig. 1 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 1. Surveyed sites for parasites in Mexican primates. Dots indicate sampling sites, black: Alouatta palliata; white: A. pigra; and grey: Atetes geoffroyi. Polygons indicate the primate distribution range in Mexico, diagonal lines: A. palliata; dashes: A. pigra; and grey: A. geoffroyi.
Fig. 3 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 3. Phylogenetic trees based on 28S (left) and cox1 (right) sequences of Trypanoxyuris sp. Sequences obtained from the eggs are bold type and indicated with an *. Numbers at the nodes represent posterior probabilities from Bayesian inference.
Fig. 2. Flow diagrams showing a in "Weight of evidence" as a tool for evaluating disease in wildlife: An example assessing parasitic infection in Northern bobwhite (Colinus virginianus)
Fig. 2. Flow diagrams showing a weight of evidence framework using the (A) 7 questions proposed by Burkhardt-Holm and Scheurer (2007) and the (B) modified questions for addressing disease(s) in wildlife.
Fig. 1 in Review on parasites of wild and captive giant pandas (Ailuropoda melanoleuca): Diversity, disease and conservation impact
Fig. 1. Distribution of wild giant pandas in six mountain regions (Qinling, Minshan, Qionglai, Liangshan, Daxiangling and Xiaoxiangling) in three Provinces (Gansu, Shaanxi, and Sichuan) of China. Adapted from Wang et al. (2018).
Fig. 1 in "Weight of evidence" as a tool for evaluating disease in wildlife: An example assessing parasitic infection in Northern bobwhite (Colinus virginianus)
Fig. 1. Timeline depicting the history of wildlife diseases in the United States: blue boxes are for disease reports and outbreaks, green for improvements to disease research, and red for events that hindered disease research. Abbreviations: foot-and-mouth disease (FMD), Smoot-Hawley Tariff Act (SHTA), State-Federal Cooperative Brucellosis Eradication Program (SFCBER), Bear River Research Station (BRRS), Wildlife Disease Investigations Laboratory (WDIL), Southeastern Cooperative Wildlife Disease Study (SCWDS), epizootic hemorrhagic disease (EHD), World Organisation for Animal Health's (OIE), National Wildlife Research Center (NWRC), U. S. Fish and Wildlife Service (USFWS). References: 1. Antolin et al. (2002), 2. Creel (1941), 3. Anderson (1978), 4. Locke and Friend (1987), 5. McCoy and Chapin (1912), 6. Wherry and Lamb (1914), 7. Meagher and Meyer (1994), 8. Clements (2007), 9, Bachrach (1968), 10. Busch and Parker (1972), 11. USFWS (1991), 12. Tunnicliff and Marsh (1935), 13. Brooks and Buchanan (1970), 14. Elton (1931), 15. Brown (2007), 16. CDFW 2019, 17. Friend (2014), 18. SCWDS 2019, 19. Shope et al. (1960), 20. Cohen (2000), 21. Cross et al. (2013), 22. Samuel et al. (2007), 23. Carvalho et al. (2017), 24. Dobson and Hudson (1986), 25. Jones et al. (2008), 26. Berger et al. (1998), 27. Laurance et al. (1996), 28. Collins and Crump (2009), 29. OIE 2008, 30. Voyles et al. (2015), 31. Fagerstone (2014), 32. USFWS (2016), 33. Scheele et al. (2019). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Data for: Parasite prevalence depends on female preference: Integrating parasite-mediated sexual selection and infectious disease dynamics
Open the record for dataset details and reuse information.
How does parasite environmental transmission stage concentration change before, during, and after disease outbreaks?
Open the record for dataset details and reuse information.
The effect of parasite dose on disease severity in the rodent malaria Plasmodium chabaudi
<p>Experiments were designed to look at the relationship between infective dose and disease severity using two clones of <em>Plasmodium chabaudi</em> that differ in virulence. We asked whether there were dose–severity relationships, whether clone differences in virulence were maintained over a range of doses, and whether disease severity could be accounted for by parasite dynamics. Groups of mice were infected with parasite doses differing by an order of magnitude, ranging from 100 to 1×10<sup><sup>8</sup></sup> parasites. Infective dose affected the probability of death, but only with the more virulent clone. Dose also affected morbidity. For both clones, higher doses induced greater anaemia. Larger doses caused greater weight loss, but only for infections with the more virulent clone. Here, for a given dose, mice lost a fixed amount of weight, irrespective of their initial weight. Larger doses induced earlier mortality and morbidity than did lower dose treatments. Finally, dose affected parasite dynamics, with earlier and higher peak parasite densities in larger dose infections. All these effects were small relative to clone differences in disease severity, which were apparent across the range of doses. Dose effects were manifested through the timing and/or magnitude of peak parasite densities, broadly supporting the idea that dose affects disease severity by altering the time the host has to control parasite densities and ameliorate the effects of parasites. We discuss the possible efficacy of intervention strategies aimed at reducing human disease severity by reducing infective parasite dose.</p>
Figure 1 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 1. Study design.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.