Gastrointestinal Inflammation and Repair: Role of Microbiome, Infection, and Nutrition
Bibliographic record
Abstract
Gastrointestinal inflammation is a complex biological response to injury as a result of different stimuli such as pathogens, damaged cells, or irritants.Symbiotic microbiome in digestive tract is considered to protect gut by removing harmful stimuli and to enhance healing process.Alteration or absence of microbiome can lead to exacerbated type 2 immunity and allergic/infectious and inflammatory complications including parasitic diseases.Thus, the microbiota regulates type 2 responses and acts as a key element in harmonizing immune responses at mucosal surfaces.While the mechanism by which microbiota regulates type 2 immunity is unclear, it is known as a strong inducer of proinflammatory T helper 17 cells and regulatory T cells (Tregs) in the intestine.The signals at the sites of inflammation mediate rapid cell recruitment and differentiation in order to remove inflammatory inducers and promote tissue homeostasis restoration.However, persistent inflammatory stimuli or dysregulation of mechanisms of the restoration can lead to chronic inflammation.Different stressors can affect immune system and increase risk for infectious diseases, such as gastritis in postinfectious irritable bowel syndrome (IBS), and vice versa, as IBS patients have increased susceptibility to develop infectious gastroenteritis.Various viral (e.g., norovirus), microbial (e.g., Campylobacter jejuni, Clostridium, Mycobacterium), and parasitic agents (e.g., Giardia, helminthes) are known to be involved in the development of chronic inflammatory bowel diseases.Yet, the mechanisms of action are not well known and there is no available cure.Additionally, nutritional elements, such as n-3 polyunsaturated fatty acids, antioxidants, probiotics, and prebiotics directly and indirectly modulate GI immunity.Diets
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".