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Record W3124624765

Molecular detection of canine parvovirus from haemorrhagic enteric affections of dog in Orathanadu region, Tamil Nadu, South India

2021· article· en· W3124624765 on OpenAlexaboutno aff
B. Puvarajan, T. Lurthureetha, S Murugavel, R. Manickam

Bibliographic record

VenueJournal of Entomology and Zoology Studies · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsCanine parvovirusBreedFecesPolymerase chain reactionVeterinary medicineBiologyVirologyParvovirusVirusMedicineMicrobiologyAnimal scienceGene
DOInot available

Abstract

fetched live from OpenAlex

The present study deals with the rapid detection of Canine Parvo Viral (CPV) infection in various breed of dogs of Cauvery Delta Region, Tamilnadu by molecular means of diagnosis employing the polymerase chain reaction (PCR) method. A total of 168 haemorrhagic fecal samples were collected and out of those 112 were found positive by PCR. Among different age groups, 154/80,55/67 and 3/21 dogs were positive in pups aged 0-3 weeks,4–8 and 9–12 weeks respectively. All positive samples were from unvaccinated dogs. The breed of Labrador was found to be the most susceptible breed (n = 43) to Parvo viral infection. The positivity of confirmation (112/168: 66%) concludes PCR being a reliable one for early diagnosis and the study on variants of CPV-2b was ascertained by PCR and being the first report on molecular detection of CPV infection in dogs in this region. Type CPV-2c was not detected among the examined samples. On review of literature and on observance of recent reports the CPV Variant (CPV-2b) in India is gaining importance and mortality of pups dure to CPV infection is high and precise and early diagnosis of CPV infection warrants early intervention of prior vaccination in all breeds of dogs against the deadly viral infection.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.472

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 communication0.0000.000
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 head0.022
GPT teacher head0.305
Teacher spread0.283 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

Explore more

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