Fruit extracts to control pathogenic Escherichia coli: A sweet solution
Bibliographic record
Abstract
Escherichia coli is a major cause of diarrhea and is as well responsible for extraintestinal infections in humans and animals. Many pathotypes have been defined for this ubiquitous microorganism on the basis of the virulence attributes. For the last 70 years, antibiotics have been used to control infections caused by E. coli . However, with the resistance observed with many strains these drugs are less recommended. Plant extracts, in particular fruit, represent a source of bioactive compounds that could be beneficial in the control of infectious diseases caused by E. coli . These could have bacteriostatic or bactericidal potential or could be used as synergic agents to amplify the activity of antibiotics for which the germs present some level of resistance. Certain studies also revealed that fruit extracts could act directly on virulence characters to attenuate the pathogenic capacity of microorganisms. This review intent to expose the scant but rapidly growing information available that shows that fruit, used as crude extracts or purified molecules, should be considered to manage diverse types of infections caused by E. coli .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".