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Record W2500874561

Prevalence of Cryptosporidium, Giardia, Salmonella, and Cephalosporin-Resistant E. coli Strains in Canada goose Feces Urban and Peri-Urban Sites in Central Ohio

2015· article· en· W2500874561 on OpenAlexaboutno aff
Laura Binkley

Bibliographic record

VenueOhioLink ETD Center (Ohio Library and Information Network) · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsCryptosporidiumFecesGiardiaBiologyVeterinary medicineSalmonellaPopulationOutbreakWildlifeGeographyMicrobiologyEnvironmental healthEcologyVirologyMedicineBacteria
DOInot available

Abstract

fetched live from OpenAlex

Large populations of resident geese can pose a pathogen exposure hazard and disease risk to humans and animals in urban areas.Evidence suggests that waterfowl play a role in pathogen dissemination and disease transmission to humans, however, more definitive data are often needed.This exploratory study sought to identify potential exposure hazards, the first step in risk assessment.This research also discusses doseresponse for protozoan organisms and initiated the exposure assessment process by measuring environmental variables that may be associated with exposure.A total of 199 Canada goose fecal samples were collected from 5 peri-urban and 7 urban sites throughout the Greater Columbus, Ohio area.Samples were collected during two time periods: during 4-11 June, 2013 when geese had just begun their molt, and 16-30 August, 2013 after geese regained flight.Juveniles were distinguished from adults only during the first sample period.Antigen capture enzyme-linked immunosorbent assay (ELISA) was used to detect presence of Giardia and Cryptosporidium.Selective media were used to culture Salmonella and cephalosporin-resistant E. coli strains.Cryptosporidium was the most prevalent pathogen with 44.7% of samples testing positive.Feces collected from urban sites during the first period were 1.86 times more likely to be positive for Cryptosporidium than peri-urban sites (P = 0.10).Forward model selection methods determined that prevalence was positively associated with human population density iii surrounding collection sites, proportion of each site defined as pasture by the National Land Cover Database (NLCD), and distance of each site from nearest wastewater treatment plant (WWTP).Feces collected from urban sites during the second period were no more likely to be positive for Cryptosporidium than peri-urban sites (P = 1.00,Odds Ratio= 1.00).Forward stepwise model selection determined that prevalence was positively associated with human population density within study sites, distance of each site from nearest livestock farm, and distance of each site from nearest wastewater treatment plant.Giardia was present in only 3.5% of samples.None were positive for Giardia in the first period.Feces collected from urban sites during the second period were 1.9 times more likely to be positive for Giardia than peri-urban sites (P = 0.74).Prevalence of cephalosporin-resistant E. coli strains was 10.8%.Only one positive was detected during the first period.Feces collected from urban sites during the second period were 6.7 times more likely to be positive for cephalosporin-resistant E.coli than peri-urban sites (P = 0.10).No Salmonella was detected in the fecal samples.All pathogens tested, with the exception of Salmonella, showed a similar trend where a greater percentage of positives were detected at urban sites than peri-urban sites.The exposure potential of goose feces to the human population appears to be high for Cryptosporidium but low for Giardia and Salmonella.The discovery of cephalosporin-resistant strains of E.coli in fecal samples poses a potential exposure hazard because antibiotic-resistant strains are difficult to treat if infection occurs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.193
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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