Bibliographic record
Abstract
Bacterial enterotoxin receptors. Enteric bacterial toxins display a great diversity in their structure, molecular weight and mechanism of action. The interaction of enterotoxins with the intestinal mucosa either leads to a direct effect on the cell membrane or an effect on signal transduction within eukaryotic cells. However, before a toxin can affect a cell, it must after its secretion by a microorganism, recognise and bind to a specific surface molecule, its receptor. Membrane receptors of bacterial enterotoxins have been identified as protein, glycoprotein or glycolipid in nature. The chemical nature of the molecules acting as receptors is crucial and during evolution they have been carefully selected. Some toxins, after their interaction with a receptor molecule, will transduce a signal across the cell membrane while remaining at the cell surface. Other toxins, after this initial binding step with a receptor, will be internalised. Others can form pores leading to leakage of cellular components and cell lysis. Receptors that have been identified often comprise a saccharidic chain that is directly involved in the recognition and binding of the toxin. Today, models explaining toxin-receptor interactions are more complex, including multistep events. This review summarises the knowledge of the interactions between bacterial toxins and membrane receptors present on intestinal mucosa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".