Control the Epithelial Barrier: A Pivotal First Line of Defense
Bibliographic record
Abstract
Lumen-derived material gains access to the mucosa by permeating between adjacent epithelial cells (ie, paracellular pathway), by transcytosis across the apical and basolateral cell membranes (ie, transcellular pathway) or by exploiting breaks or erosions in the epithelium that may, for example, result from inflammation. Increased epithelial permeability (or decreased barrier function) has repeatedly been demonstrated in a variety of gut disturbances; notably, in inflammatory bowel disease (IBD). There has been an exponential increase in our knowledge of the structural elements that comprise the epithelial barrier, and of the intrinsic factors (eg, cytokines) and external stimuli (eg, bacterial toxins) that can either perturb or enhance epithelial permeability. Canadian researchers have been very active in the study of epithelial permeability and have been responsible for major advances in the field, documenting increased permeability in patients with ulcer disease and IBD and some of their first degree relatives (as well as before onset of overt inflammation), and elucidating mechanisms of stress-induced and cytokine-induced increases in permeability (1-8). A recent study from Scott et al (9) continues this impressive tradition.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".