Pathogenicity and virulence of <i>Clostridium perfringens</i>
Bibliographic record
Abstract
Clostridium perfringens is an extremely versatile pathogen of humans and livestock, causing wound infections like gas gangrene (clostridial myonecrosis), enteritis/enterocolitis (including one of the most common human food-borne illnesses), and enterotoxemia (where toxins produced in the intestine are absorbed and damage distant organs such as the brain). The virulence of this Gram-positive, spore-forming, anaerobe is largely attributable to its copious toxin production; the diverse actions and roles in infection of these toxins are now becoming established. Most C. perfringens toxin genes are encoded on conjugative plasmids, including the pCW3-like and the recently discovered pCP13-like plasmid families. Production of C. perfringens toxins is highly regulated via processes involving two-component regulatory systems, quorum sensing and/or sporulation-related alternative sigma factors. Non-toxin factors, such as degradative enzymes like sialidases, are also now being implicated in the pathogenicity of this bacterium. These factors can promote toxin action in vitro and, perhaps in vivo, and also enhance C. perfringens intestinal colonization, e.g. NanI sialidase increases C. perfringens adherence to intestinal tissue and generates nutrients for its growth, at least in vitro. The possible virulence contributions of many other factors, such as adhesins, the capsule and biofilms, largely await future study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".