Bacterial Adherence, Colonization, and Invasion of Mucosal Surfaces
Bibliographic record
Abstract
This chapter provides a brief overview of the pathogenic strategies of some bacteria that infect the mucosal surface of the intestinal tract. It focuses on two model systems for the study of bacterial pathogens that cause disease by colonizing (enteropathogenic Escherichia coli [EPEC]) or penetrating (Salmonella species) the intestinal epithelium. Mucosal surfaces have many physiological defenses against pathogenic bacteria. These include entrapment in a thick blanket of mucus and clearance by peristalsis in the gut or ciliary movement in the airways. Adhesins on the bacterial surface provide specificity for interaction with target host cells. For example, M cells of the intestinal epithelium have cell surface glycosylation patterns that vary between species and tissue location. Adhesion is often a prerequisite for penetration of the mucosal surface, though different pathogens penetrate this barrier by different means and with different ends. EPEC provides a suitable model for understanding A/E pathogens and has largely been studied in vitro by infection of epithelial tissue cell cultures. Salmonella species infect a broad range of animals and can cause different diseases in different hosts. For example Salmonella enterica serovar Typhi causes typhoid fever in humans, which can be fatal. Recent progress has revealed the mechanisms by which the translocated effectors of SPI-1 mediate invasion by Salmonella serovar Typhimurium. Pathogenic bacteria have evolved different strategies to initiate infection at mucosal surfaces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".