Chitin and β-Glucan Polysaccharides as Immunomodulators of Airway Inflammation and Atopic Disease
Bibliographic record
Abstract
Polysaccharides are receiving increased attention due to their clinical applications in tissue engineering, vaccine development, nutritional supplementation and antimicrobial biopolymer engineering. The most abundant polysaccharides include fungal cell wall components chitin and β-1,3-glucans. Recent evidence has shown that these polysaccharides modulate airway inflammation, making them the basis of several drug discovery platforms. Small to intermediate chitin fragments (< 70 μm) are protective in allergic inflammatory models, skewing T cell immunity towards Th1 responses, and reducing the production of Th2 cytokines. As such, chitin prevents the development of the quintessential features of asthmatic disease including chronic airway inflammation, airway hyperresponsiveness and pathological remodeling changes in mouse models of allergy. In contrast, the in vivo effects of β-glucans in animal models of airway inflammation are often contradictory, and the number of human studies is limited. β-1,3- glucans are both pro and anti-inflammatory, preventing and enhancing allergic inflammation depending on the preparation, purity and species origin of the β-glucans. This review summarizes recent studies of chitin and β-glucans in models of atopy and airway inflammation and examines the possible reasons for the apparently contradictory observations. Recent relevant patents are also highlighted.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".