When glycobiology meets inflammasome activation: Insights and implications
Bibliographic record
Abstract
BACKGROUND: Glycobiology focuses mainly on the study of glycan structures and their biological functions. Glycans not only provide a basic energy supply through the tricarboxylic acid cycle and glycolysis but also serve as important immune regulators during pathogen invasion and homeostasis maintenance. Inflammasomes are critical multiprotein complexes of the immune system that detect both exogenous pathogenic threats and endogenous danger signals to mediate inflammatory responses. Glycobiology has revealed significant insights into the mechanisms of immune responses, particularly in the context of inflammasome activation. AIM OF REVIEW: This review summarizes the multifaceted relationships between glycobiology and inflammasome activation, highlighting how glycan structures, glycosylation patterns, and glycan-binding proteins influence inflammasome pathways. This review sheds light on novel targets for drug development aimed at modulating inflammatory pathways through the targeting of specific glycan structures. KEY SCIENTIFIC CONCEPTS OF REVIEW: Glycans directly or indirectly provide prime and activation signals for inflammasomes, glycosylation of inflammasome-related proteins by glycan structures modulates inflammasome activation and downstream inflammation, and the interaction between glycans and lectins also provides regulatory signals for inflammasome activation. This intersection of glycobiology and inflammasome activation presents a unique opportunity to elucidate the molecular mechanisms underlying inflammatory responses and their potential therapeutic implications.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".