Population and virulence factor dynamics in fecal<i>Escherichia coli</i>from healthy adults consuming weight control diets
Bibliographic record
Abstract
Individual susceptibility to gastrointestinal infection is seen commonly in food poisoning outbreaks, but factors (such as diet) which may modulate this variability are understood poorly. Similarly, factors altering the population dynamics of enteric non-pathogenic Escherichia coli or of pathogenic E. coli containing toxin-signature DNA sequences in the colonic flora of healthy individuals are largely unknown. Feces were collected 4 times over a 12 week period from 41 healthy volunteer adults on a weight control diet (high or low in fiber). E. coli strains were examined by conventional culture followed by PCR for virulence genes stx1, stx2, eae and hlyA, and polymorphic beta-glucuronidase. Total E. coli counts ranged from undetectable to 8.75 log10 CFU/g feces and were unaffected by dietary fiber consumption or gender. Total E. coli counts were correlated positively with age (r = 0.401, P < 0.05). Fifty-eight percent (n = 24) of study individuals harboured more than 1 morph of beta-glucuronidase, indicating the presence of more than 1 strain of E. coli. Virulence genes were detected in 12 of 41 adults, comprising 10 stx1, 3 stx2, 3 eae, and 0 hlyA, but occurrence was not associated with diet, gender, or age. Factors influencing strain mobility over time did not appear to include diet or gender, while the positive relationship between total E. coli numbers and increasing age suggests that some older individuals are "more permissive" to mobile E. coli, including those with toxin genes.
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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 | 0.000 | 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".