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Record W3214879156 · doi:10.18805/ijar.b-4722

Detection and Genomic Characterization of Senecavirus from Indian Pigs

2021· article· en· W3214879156 on OpenAlexaboutno aff
Sushila Maan, Kanisht Batra, Deepika Chaudhary, Monika Punia, Vijay Kadian, Vinay G. Joshi, Aman Kumar

Bibliographic record

VenueIndian Journal of Animal Research · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
FundersHaryana State Council for Science and Technology
KeywordsGenBankGenomeMetagenomicsBiologyPopulationVeterinary medicineWhole genome sequencingVirologyMedicineGeneticsGeneEnvironmental health

Abstract

fetched live from OpenAlex

Background: Senecavirus A (SVA), is a positive sense small non-enveloped RNA virus which belongs to Picornaviridae family and is responsible for porcine vesicular disease. The disease has been reported in many countries since late 2014, 2015 and 2016 like USA, Canada, Brazil, China and Thailand. Methods: In this study, the metagenomic study was performed on faecal samples of pigs/piglets suffering from diarrhea in Haryana, India with the help of next generation sequencing. The cDNA library was prepared from the faecal samples and run on the Illumina MiSeq instrument followed by identification and genomic characterization. Result: This study revealed the presence of SVA in the samples. The characterization of complete genome sequence of this strain showed complete nucleotide identity (100%) with SVA genomes reported from Canada, however, the polyprotein shares 98-99% amino acid sequence similarity with the genomes currently available in the GenBank. To the best of our knowledge this is the first report of SVA infection in pigs/piglets of Haryana, India. It demonstrates that an active and urgent surveillance of the swine population is required in the region. Additionally, the veterinarians must pay immediate attention to this vesicular disease and adopt preventive measures for its control.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.080
GPT teacher head0.317
Teacher spread0.237 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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