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57 results for “host detection”
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
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
Figure 5 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts
Figure 5. Phylogenetic tree based on 16S rRNA sequences of Cardinium, constructed by a neighbor-joining procedure. Cardinium strains are depicted by the host name. The accession numbers are shown after the host name. Numbers on the nodes indicate bootstrap percent confidence values.
Figure 2 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts
Figure 2. Neighbor-joining tree of COI sequences of the Erythraeidae mites of the present study and the Genbank sequence data. Numbers above/below nodes represent bootstrap values.
Figure 1 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts
Figure 1. Erythraeus (Erythraeus) pistacicus Haitlinger, Mehrnejad & Šundić, 2016 larva (Black arrow)inside the gall, feeding on the aphid, Forda hirsuta Mordvilko, 1928, on pistachio trees. June 2022, Mashhad, Northeast of Iran.
Figure 7 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts
Figure 7. Phylogenetic relationship of Planomicrobium symbiont identified from Erythraeus (Erythraeus) pistacicus with related sequences retrieved from GenBank. The tree was constructed using neighbor-joining procedure. The sequence obtained from E. (E.) pistacicus in this study is in red box. Sequence from Bacillus subtilis was used as an
Fig. 3 in Detection of haemosporidian parasites in wild and domestic birds in northern and central provinces of Iran: Introduction of new lineages and hosts
Fig. 3. Median joining haplotype network of Haemoproteus lineages. Detected sequences in this study are in bold.
Fig. 2. Bayesian tree reconstructed using 478 in Detection of haemosporidian parasites in wild and domestic birds in northern and central provinces of Iran: Introduction of new lineages and hosts
Fig. 2. Bayesian tree reconstructed using 478-bp mitochondrial cytb gene for avian blood parasites lineages. The amplified sequences in the current study are highlighted in bold. Posterior probability support of>0.8 is displayed for each branch. Schematic tree is summarized in section A and separated clade for each genus is given in sections of B (Plasmodium), C (Haemoproteus), and D (Leucocytozoon).
Fig. 2 in Sensitive, quantitative detection of Besnoitia darlingi and related parasites in intermediate hosts and to assess felids as definitive hosts for known and as-yet undescribed related parasite species
Fig. 2. Coccidia-specific PCR to confirm the presence of DNA of various parasite species used to test the analytic specificity of the BdanjoRT1 real-time PCR. (1) Besnoitia darlingi, (2) B. neotomofelis, (3) B. oryctofelisi, (4) B. besnoiti (Evora isolate), (5) B. bennetti (Texas), (6) B. tarandi (Bt-CA-Quebec1), (7) Toxoplasma gondii, (8) Hammondia hammondi, (9) Neospora caninum, (10) H. heydorni, (11) Cystoisospora felis, (12) C. rivolta, (13) C. burrowsi, (14) C. canis, (15) Sarcocystis cruzi and (16) Crytosporidium parvum. Presence of (17) Giardia duodenalis and (18) Tritrichomonas foetus DNA was shown by amplification using species or genus-specific primers, respectively. C, negative control; M, marker.
Fig. 3 in Sensitive, quantitative detection of Besnoitia darlingi and related parasites in intermediate hosts and to assess felids as definitive hosts for known and as-yet undescribed related parasite species
Fig. 3. Analytic sensitivity (A) and standard curve (B) obtained for the threshold cycle (Cq) values obtained in the BdanjoRT1 real-time PCR using varying amounts of genomic Besnoitia darlingi DNA (approximately equivalent to the DNA content of 10.000 [blue], 1000 [green], 100 [red], 10 [brown] and 1 [black] B. darlingi tachyzoites) diluted in 100 ng/μl mouse DNA. Cq values used for regression are displayed as circles. Results on samples resembling DNA of 0.1 tachyzoite were not included in regression, since only two of four samples had reacted with a Cq value of 37.8 or 38.3 (displayed grey in A and as crosses in B). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Sensitive, quantitative detection of Besnoitia darlingi and related parasites in intermediate hosts and to assess felids as definitive hosts for known and as-yet undescribed related parasite species
Fig. 1. Location of the primers and the probe of the Besnoitia darlingi/B.neotomofelis /B. oryctofelisi-specific real-time PCR assay BdanjoRT1 within the ITS-1 region of the rRNA gene. The sequences of the ITS-1 region of B. akodoni (AY545987, bold), B. jellisoni (AF076860, bold), B. neotomofelis (HQ909085, bold), B. darlingi (AF489696, bold) and B. oryctofelisi (AY182000, bold), were aligned relative to sequences of other Besnoitia spp. including B. besnoiti from Portugal, Spain and Germany, B. bennetti reported from the USA and Belgium, B. tarandi from Canada and Finland and those of Neospora caninum, Hammondia heydorni, Toxoplasma gondii and H. hammondi by using Clustal V (DNAStar, Madisin, Wisconsin, USA). Deletions and substitutions in the sequences relative to and within the clade of B. acodoni, B. jellisoni, B. neotomofelis, B. darlingi and B. oryctofelisi are indicated by black background. Sequences of the primer BdanjoRev and the Probe Bb11-12 are displayed in their complementary form. The probe Bb11-12 was established for a real-time PCR to detect B. besnoiti, but it is universal and can be used for the detection of all Besnoitia spp. mentioned here.
Fig. 3 in Detection of Eumonospora henryae (Apicomplexa: Sarcocystidae) from Falco columbarius (Falconiformes: Aves): Comparison of host-parasite phylogram and comments on the family Sarcocystidae Poche, 1913
Fig. 3. Phylograms of the genus Eumonospora on the left and core land birds modified from McClure et al. (2019) on the right. The boxes under Eumonospora spp. represent detected host species and the shaded boxes encompass the Afroaves. The lines connect parasites and hosts encountered, with the dotted line indicating host switching across order boundaries.
Fig. 1 in Detection of Eumonospora henryae (Apicomplexa: Sarcocystidae) from Falco columbarius (Falconiformes: Aves): Comparison of host-parasite phylogram and comments on the family Sarcocystidae Poche, 1913
Fig. 1. Optical (A, B) and differential interference contrast photomicrographs (C, D) of oocysts and sporocysts of Eumonospora sp. detected from Falco columbarius. Fig. 1A. Sporulated oocyst with stout sporozoites (SZ) inside a sporocyst (SP). Fig. 1B. A collapsed oocyst with a compact sporocyst residuum (SR) within an SP. Fig. 1C. Randomly diffused SR within an SP. Fig. 1D. Eight SZs with diffused SR. Scale bars = 10 μm.
Fig. 2 in Detection of Eumonospora henryae (Apicomplexa: Sarcocystidae) from Falco columbarius (Falconiformes: Aves): Comparison of host-parasite phylogram and comments on the family Sarcocystidae Poche, 1913
Fig. 2. Phylogenetic trees based on three concatenated datasets (A: 18S + cox1, B: 18S + 28S, and C: 28S + cox1). Phylogenetic analyses are performed via Bayesian inference (BI) and maximum likelihood (ML) methods. Nodes are labelled with probability for BI method node support (left) and bootstrap value support for the ML method (right). Similar phylograms are illustrated with both methods in all datasets. Monophyletic clade of Eumonospora spp. branches off earlier than the clade of Besnoitia spp. and the clade comprising genera Hammondia, Heydornia, Neospora, and Toxoplasma. 18S: nuclear small subunit ribosomal DNA; 28S: nuclear large subunit ribosomal DNA; cox1: mitochondrial Cytochrome C oxidase subunit 1; NA: not available.
Host biomarkers and combinatorial scores for the detection of serious and invasive bacterial infection in pediatric patients with fever without source
<h3><span>Background </span></h3> <p><span>Improved tools are required to detect bacterial infection in children with fever without source (FWS), especially when younger than 3 years old. The aim of the present study was to investigate the diagnostic accuracy of a host signature combining for the first time two viral-induced biomarkers, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and interferon </span><span>γ</span><span>-induced protein-10 (IP-10), with a bacterial-induced one, C-reactive protein (CRP), to reliably predict bacterial infection in children with fever without source (FWS) and to compare its performance to routine individual biomarkers (CRP, procalcitonin (PCT), white blood cell and absolute neutrophil counts, TRAIL, and IP-10) and to the Labscore.</span></p> <h3><span>Methods</span></h3> <p><span>This was a prospective diagnostic accuracy study conducted in a single tertiary center in children aged less than 3 years old presenting with FWS. Reference standard etiology (bacterial or viral) was assigned by a panel of three independent experts. Diagnostic accuracy (AUC, sensitivity, specificity) of host individual biomarkers and combinatorial scores was evaluated in comparison to reference standard outcomes (expert panel adjudication and microbiological diagnosis). </span></p> <h3><span>Results </span></h3> <p><span>241 patients were included. 68 of them (28%) were diagnosed with a bacterial infection and 5 (2%) with invasive bacterial infection (IBI). Labscore, ImmunoXpert, and CRP attained the highest AUC values for the detection of bacterial infection, respectively 0.854 (0.804–0.905), 0.827 (0.764–0.890), and 0.807 (0.744–0.869). Labscore and ImmunoXpert outperformed the other single biomarkers with higher sensitivity and/or specificity and showed comparable performance to one another although slightly reduced sensitivity in children < 90 days of age.</span></p> <h3><span>Conclusion </span></h3> <p><span>Labscore and ImmunoXpert demonstrate high diagnostic accuracy for safely discriminating bacterial infection in children with FWS aged under and over 90 days, </span><span>supporting their adoption in the assessment of febrile patients. </span></p>
Figure 6 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts
Figure 6. Haplotype network of Cardinium endosymbionts based on 16S rDNA sequences.
Figure 4 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts
Figure 4. Haplotype network of Wolbachia endosymbionts based on wsp gene.
The global network of domestic mammal hosts and zoonotic bacteria: Implications for disease transmission and detection
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Host biomarkers and combinatorial scores for the detection of serious and invasive bacterial infection in pediatric patients with fever without source
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Melting curve analysis for detection and identification of ghost parasitoids in host carcasses a month after host death-fluorescence data
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Allen Brain Atlas
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OpenNeuro
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