Inflammation: The Mother of All Diseases Meets the Mother of All Therapies.
Bibliographic record
Abstract
Introduction: Incretins are small peptides secreted by the gastrointestinal tract. These peptides exert their action by binding to G-protein-coupled receptors that are widely distributed in the pancreas, throughout the gastrointestinal tract, and the brain. The physiological role of incretins (such as GLP-1) is to regulate glucose levels by increasing insulin secretion, delaying gastric emptying, and decreasing appetite, leading to weight loss. Method: In this review, we aimed to report the effects of inflammation on human health and how GLP-1 and GLP-1 receptor agonists, which are now being used as first-line agents to control obesity, can have a broader effect on human diseases. Results: The literature shows the benefits of these drugs in diseases other than obesity, including in diseases of many organs such as heart, kidneys, liver, blood vessels, and in neurodegenerative and psychiatric conditions. These diverse beneficial effects are attributed to the anti-inflammatory activities of these new drugs. Conclusions: The physiological actions of incretins have recently been better understood. The surprisingly diverse therapeutic activities of this class of new drugs suggest that they will likely play central roles not only in the management of type-2 diabetes but for the treatment of obesity and a wide spectrum of diseases for which inflammation is a major factor.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".