Bacterial Interactions with Host Epithelium In Vitro
Bibliographic record
Abstract
During recent years there has been a resurgence of interest in the role that bacteria, both pathogenic and commensal, play in the pathogenesis and pathophysiology of disease. For example, antibiotics can be a useful therapy to relieve the symptomatology experienced by a cohort of patients with Crohn′s disease, a major class of inflammatory bowel disease (1). Traditionally, assessment of the impact of bacteria on the health status of an individual has focused on the role of their noxious products (e.g., lipopolysaccharide [endotoxin], exotoxins, and enterotoxins [e.g., cholera toxin, superantigens]) and recruitment and/or activation of immune cells. However, it is now clear that the direct interaction of bacteria with epithelial cells can mobilize intraepithelial signaling molecules, resulting in altered epithelial physiology. Studies using co-culture models of epithelial cell lines with bacteria have shown unequivocally that bacterial attachment to enterocytes affects a plethora of epithelial functions including vectorial ion transport, barrier function, and the production of immune mediators and chemokines; the latter response allowing participation in, and modulation of, mucosal immune reactions (2,6). Indeed, awareness that bacteria can directly affect the host cells to which they attach has led to a new discipline in microbiology-namely, cellular microbiology (7).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".