Evolving Concepts and Translational Relevance of Enteroendocrine Cell Biology
Bibliographic record
Abstract
CONTEXT: Classical enteroenteroendocrine cell (EEC) biology evolved historically from identification of scattered hormone-producing endocrine cells within the epithelial mucosa of the stomach, small and large intestine. Purification of functional EEC hormones from intestinal extracts, coupled with molecular cloning of cDNAs and genes expressed within EECs has greatly expanded the complexity of EEC endocrinology, with implications for understanding the contribution of EECs to disease pathophysiology. EVIDENCE ACQUISITION: Pubmed searches identified manuscripts highlighting new concepts illuminating the molecular biology, classification and functional role(s) of EECs and their hormonal products. EVIDENCE SYNTHESIS: Molecular interrogation of EECs has been transformed over the past decade, raising multiple new questions that challenge historical concepts of EEC biology. Evidence for evolution of the EEC from a unihormonal cell type with classical endocrine actions, to a complex plurihormonal dynamic cell with pleiotropic interactive functional networks within the gastrointestinal mucosa is critically assessed. We discuss gaps in understanding how EECs sense and respond to nutrients, cytokines, toxins, pathogens, the microbiota, and the microbial metabolome, and highlight the expanding translational relevance of EECs in the pathophysiology and therapy of metabolic and inflammatory disorders. CONCLUSIONS: The EEC system represents the largest specialized endocrine network in human physiology, integrating environmental and nutrient cues, enabling neural and hormonal control of metabolic homeostasis. Updating EEC classification systems will enable more accurate comparative analyses of EEC subpopulations and endocrine networks in multiple regions of the gastrointestinal tract.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".