In-vivo Effect of Probiotics on Escherichia coli O157:H7 Isolated from Salad Vegetables
Bibliographic record
Abstract
This study was undertaken to isolate Escherichia coli O157:H7 from salad vegetables, determine its pathogenicity and effect on the gastrointestinal tract of mice and sensitivity to probiotics; Lactobacillus lactis ATCC12315 and Lactobacillus acidophilus ATCC4356 in-vivo. Twenty seven samples made up of eight different types of salad vegetables which includes carrot, cabbage, cucumber, lettuce, peas, green pepper, green beans and spring onions were collected from different locations; markets, farm and street vendors in Lagos, Nigeria. Sampling was done between 13th October 2009 and 17th March 2010. In all experiments with mice the protocol for care of animal was carried out according to National Institute of Health (NIH). The mice given only E. coli O157:H7 developed diarrhea which led to loss of body weight and death of majority of them. On the other hand mice that were not given any microbial suspension (control), those given only probiotics, combination of E. coli O157:H7 and probiotics showed no symptom of diarrhea and there was increase in their body weight. The histopathology of the intestines of this group of mice showed that their intestines were not damaged, while those given only E. coli O157:H7 showed lymphoid hyperplasia, mucosal sloughing and inflammation (enteritis). The maintenance of good health by probiotics has again been re-emphasized.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".