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Record W4394262071 · doi:10.6084/m9.figshare.11999562

Biological characteristics associated with virulence in <i>Clostridioides difficile</i> ribotype 002 in Hong Kong

2020· dataset· en· W4394262071 on OpenAlexaff
Ka Yi Kong, Thomas N.Y. Kwong, Hung Chan, Kristine Wong, Samuel S. Y. Wong, Anu P. Chaparala, Raphael C.Y. Chan, Lin Zhang, Joseph J.�Y. Sung, Jun Yu, Peter M. Hawkey, Margaret Ip, William Ka Kei Wu, Sunny H. Wong

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsClostridioidesVirulenceBiologyMicrobiologyGeographyVeterinary medicineGeneticsMedicineGene

Abstract

fetched live from OpenAlex

Clostridioides difficile infection (CDI) is a common cause of nosocomial diarrhea and can sometimes lead to pseudo-membranous colitis and toxic megacolon. We previously reported that the PCR ribotype 002 was a common C. difficile ribotype in Hong Kong that was associated with increased mortality. In this study, we assessed in vitro bacteriological characteristics and in vivo virulence of ribotype 002 compared to other common ribotypes, including ribotypes 012, 014 and 046. We observed significantly higher toxin A (p < 0.05) and toxin B (p < 0.05) production, sporulation (p < 0.001) and germination rates (p < 0.0001) in ribotype 002 than other common ribotypes. In a murine model of C. difficile infection, ribotype 002 caused significantly more weight loss (p < 0.001) and histological damage (p < 0.001) than other common ribotypes. These findings may have contributed to the higher prevalence and mortality observed, and provided mechanistic insights that can help public surveillance and develop novel therapeutics to combat against this 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.066
GPT teacher head0.293
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

Explore more

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