Management of the human mucosal defensive barrier: evidence for glycan legislation
Bibliographic record
Abstract
The human gastrointestinal barrier comprises several layers which enable protection against the external environment. The mucosal epithelium, lamina propria, glycocalyx and secreted mucus each make a contribution to barrier protection. Glycocalyx and secreted mucins constitute a glycosylated environment which interacts with the enteric microflora. Turnover of the mucus layer and the creation of binding ligands for bacteria are significant factors in gut homeostasis. The gut microbiota is composed of many bacterial species, but improved technology has allowed detection of populations present at different stages of development and in disease. Interaction of the microflora with the gut occurs from birth onwards and enables maturation of gut angiogenesis and glycosylation as demonstrated in mouse models. Glycan legislation regulates the ongoing interaction between the microflora and the host mucosa. This accounts for host glycosylation mechanisms providing a dynamic response to fluctuations in the gut microflora. Evidence for glycan legislation is based on a surgical model where intact mucosa can be compared with and without contact to the faecal microflora. In addition, mucosal cell glycosylation is assessed using inhibitors of O-glycan synthesis. These inhibitors lead to growth arrest in cultured colorectal cancer cell lines through the induction of apoptosis and downregulation of proliferation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".