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89 results for “helminth infection”
A droplet digital polymerase chain reaction assay to detect rare helminth parasites infecting natural host populations (Vancouver Island 2023, University of Wisconsin Madison Laboratory colony 2024)
Helminth infections represent a significant challenge to human, livestock, and wildlife health, yet they remain relatively under-studied, especially in terms of their ecological impacts. Better understanding of how these parasites spread in wildlife populations could improve our ability to predict and manage disease transmission across various species. Traditional detection methods, such as visually identifying parasites in environmental samples or infected hosts, often fall short, especially during the early stages of infection when parasite loads are minimal. In this study, we introduce a highly sensitive and precise droplet digital PCR (ddPCR) assay that quantifies helminth DNA in aquatic habitats, focusing on the 18S rRNA gene as a marker. These data utilize the model host-parasite system between the tapeworm Schistocephalus solidus, and its cyclopoid copepod host, Acanthocyclops robustus. The molecular assays are built around creating an infection standard in the lab, where copepods were singly infected with a single tapeworm parasite. We extracted DNA from 100 infected adults and used this as a standard to translate gene copy numbers from the ddPCR reactions to actual animal values. After creating a known lab standard, we then use the generated probes and primers to detect (and quantify!) infection burdens in field samples, which include both water filter samples (eDNA) and zooplankton tows from several lakes around Vancouver Island, B.C. The data presented here include well-specific data from ddPCR runs (amplitude of individual level oil droplets in the reaction) as well as each ddPCR analysis in its entirety. In order to prove the specificity of probes and probe-primers, we include here ddPCR runs of closely related helminth species, Schistocephalus cotti and Schistocephalus pungitii. We also consider the binding to another genera of copepod, the calanoid Eurytomora. All of the data wrangling, analysis, and data visualization are included as .Rmd files in th
Fig. 3 in A comparison of helminth infections as assessed through coprological analysis and adult worm burdens in a wild host
Fig. 3. The relationship between adult lungworm (A. falciformis) burden and number of larvae per g of faeces in Irish badgers (n = 289).
Fig. 2 in A comparison of helminth infections as assessed through coprological analysis and adult worm burdens in a wild host
Fig. 2. The relationship between adult hookworm (U. criniformis) burden and faecal egg counts (e.p.g) in Irish badgers (n = 289).
Fig. 4 in Predictors of helminth parasite infection in female chacma baboons (Papio ursinus)
Fig. 4. An interaction between host reproductive state and progestagen concentrations in the infection intensity of Protospirura. Pregnant females (blue) exhibit increased infection intensity of Protospirura with rising progestagen concentrations. Non-pregnant females (red) exhibit decreased infection intensity of Protospirura with rising progestagen concentrations. Confidence intervals are in gray. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Predictors of helminth parasite infection in female chacma baboons (Papio ursinus)
Fig. 2. Plots showing associations between log Protospirura intensity (eggs per gram; epg) in female baboons and marginal effects of each predictor variable. Plots are (A) pregnant (no or yes); (B) log progestagen concentrations (low = below median; high = above median; ng/g); (C) season (dry or wet); (D) presence/absence of Oesophagostomum; and (E) log Trichuris intensity (epg). Points and whiskers on the plot represent the mean and confidence intervals. For Fig. 2E, the values of each fixed effect are divided into tertiles. Numbers above each bar indicate sample size.
Fig. 3 in Predictors of helminth parasite infection in female chacma baboons (Papio ursinus)
Fig. 3. Plots showing associations between log Trichuris intensity (eggs per gram; epg) in female baboons and marginal effects of each predictor variable. Plots are (A) cycling (no or yes); (B) log fecal glucocorticoid concentrations (ng/g); (C) season (dry or wet); (D) presence/absence of Oesophagostomum; and (E) log Protospirua intensity (epg). Points and whiskers on the plot represent the mean and confidence intervals. For Fig. 3B and E, the values of each fixed effect are divided into tertiles. Numbers above each bar indicate sample size. Photograph by Bobby Habig.
Fig. 1 in Predictors of helminth parasite infection in female chacma baboons (Papio ursinus)
Fig. 1. Population and host level processes proposed to drive within group variation in helminth infection risk among female chacma baboon hosts (partially adapted from Akinyi et al., 2019; Habig et al., 2019). Four key drivers of parasite risk are examined: environmental conditions; reproductive stage; steroid hormones; and patterns of coinfection.
Fig. 4 in New hosts for a snake's helminth: First report of intermediate and definitive hosts naturally infected by Ophidascaris arndti (Ascarididae) in the wild
Fig. 4. Scanning electron micrographs of Ophidascaris arndti larva from Akodon montensis. (A) Body; (B) Anterior end; (C) Apical view, amphids (am) and double papillae (dp); (D) Posterior end lateral view, anus (A).
Fig. 3 in New hosts for a snake's helminth: First report of intermediate and definitive hosts naturally infected by Ophidascaris arndti (Ascarididae) in the wild
Fig. 3. Scanning electron micrographs of Ophidascaris arndti from Bothrops freitasi: (A) Male ventral view of the posterior end papillae (arrow); (B) Male cloaca (c) and papillae (arrow); (C) Male ventral view of tail, cloaca (c), papillae (arrow), and phasmid (ph, arrowhead); (D) Female ventro-lateral view of tail.
Fig. 2 in New hosts for a snake's helminth: First report of intermediate and definitive hosts naturally infected by Ophidascaris arndti (Ascarididae) in the wild
Fig. 2. Scanning electron micrographs of Ophidascaris arndti from Bothrops freitasi adult female. (A) Anterior end lateral view showing the excretory pore (arrow); (B) Dorsal view of lips, dorsal lip (D), double papillae (dp), lateroventral lips (LV), amphids (am), pointed depression (arrowhead); (C) Detail of denticles; (D) Detailed amphids.
Fig. 1 in New hosts for a snake's helminth: First report of intermediate and definitive hosts naturally infected by Ophidascaris arndti (Ascarididae) in the wild
Fig. 1. Light microscopy drawing of adult Ophidascaris arndti from Bothrops freitasi. (A) Female anterior end lateral view; (B) Female lips apical view; (C) Male posterior end lateral view, spicule, and ejaculatory duct; (D) Female posterior end ventral view; (E) Spicules; (F) Lateral view of the vulva, vagina, and uterine branches.
Fig. 5 in New hosts for a snake's helminth: First report of intermediate and definitive hosts naturally infected by Ophidascaris arndti (Ascarididae) in the wild
Fig. 5. Photomicrography of Ophidascaris arndti larva from Akodon montensis. (A) Anterior end lateral view; (B) Posterior end lateral view.
Fig. 6 in New hosts for a snake's helminth: First report of intermediate and definitive hosts naturally infected by Ophidascaris arndti (Ascarididae) in the wild
Fig. 6. Phylogenetic relationships Bayesian tree based on partial MT-CO1 gene sequences of ascaridoid specimens, including Ophidascaris and outgroup. The numbers at the nodes are BPPs (left) and aLRT (right). The scale bar represents the number of substitutions per site.
Fig. 2 in Ecological drivers of helminth infection patterns in the Virunga Massif mountain gorilla population
Fig. 2. Principal component analysis output showing associations between variables and the first two principal components PC1 and PC2. Each variable contribution to principal components and its quality are represented by length of vector and its color, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Ecological drivers of helminth infection patterns in the Virunga Massif mountain gorilla population
Fig. 1. Location of study gorilla groups during the Virunga Massif 2015–2016 surveys (Hickey et al., 2019) expressed as centroids of their 500-m buffered minimum-convex polygon. Vegetation data were adopted according to WWF-Germany and IGCP 2017; boundaries of protected areas were derived from ProtectedP lanet.net database. Map was created using ArcGIS Desktop 10.8 (ESRI, 2020. ArcGIS Desktop: Release 10.8. Redlands, CA: Environmental Systems Research Institute; esri.com).
Fig. 4. Predicted lines from a in Ecological drivers of helminth infection patterns in the Virunga Massif mountain gorilla population
Fig. 4. Predicted lines from a generalized linear mixed model for significant effects of (a) the second principal component (PC2), (b) interaction between the first principal component (PC1) and MCP = area of 500-m buffered minimum convex polygon of detected nest sites per gorilla group, (see Minimum convex polygon calculation and Statistical analyses for details) and (c) interaction between monitoring (habituation) status and MCP on tapeworm infection (egg counts per gram in fecal sample). Principal components were computed from 10 correlated environmental variables (see Material and methods for details).
Fig. 3. Predicted lines from a in Ecological drivers of helminth infection patterns in the Virunga Massif mountain gorilla population
Fig. 3. Predicted lines from a generalized linear mixed model for significant effects of (a) the first (PC1) and (b) second principal component (PC2), (c) Density = mean relative density of gorillas per MCP and (d) MCP = area of 500-m buffered minimum convex polygon of detected nest sites per gorilla group, (see Minimum convex polygon calculation and Statistical analyses for details) on strongylid infection (egg counts per gram in fecal sample). Principal components were computed from 10 correlated environmental variables (see Material and methods for details).
Fig. 5 in Influence of seasonality and biological activity on infection by helminths in Cantabrian bear
Fig. 5. Seasonal kinetics of helminths egg-output in Cantabrian brown bears (n = 248) according to their activity periods. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Influence of seasonality and biological activity on infection by helminths in Cantabrian bear
Fig. 2. Seasonal variations in the prevalence of helminth infection in feces of brown bears (n = 248) from the western part of the Cantabrian Mountains (Asturias and Le´on provinces, Spain). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Influence of seasonality and biological activity on infection by helminths in Cantabrian bear
Fig. 4. Seasonal variations in the prevalence of helminth infection in feces of brown bears (n = 248) from Cantabrian Mountains (Spain) according to their activity periods. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
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