Molecular dynamics of inflammation resolution: therapeutic implications
Bibliographic record
Abstract
Inflammation is a critical part of innate immune response that is essential for exclusion of harmful stimuli and restoration of tissue homeostasis. Nonetheless, failure to resolve inflammation results in chronic inflammatory conditions, including autoimmune diseases. Conventionally, resolution of inflammation was deemed a passive process; however, evidence indicates that it entails active, highly regulated molecular and cellular events involving efferocytosis-driven macrophage reprogramming, post-transcriptional regulatory mechanisms and the production of specialized pro-resolving mediators (SPMs). These processes collectively restore tissue homeostasis and prevent chronic inflammation. Emerging therapeutic approaches targeting these pathways demonstrate promising results in preclinical studies and clinical trials, enhancing resolution and improving overall disease outcome. This resulted in a paradigm shift from conventional anti-inflammatory strategies to resolution-focused treatment. Yet, challenges remain due to the complexity of resolution mechanisms and tissue-specific differences. This review summarizes current advances in inflammation resolution, emphasizing emerging concepts of resolution pharmacology. By employing endogenous mechanisms facilitating resolution, novel therapeutic applications can effectively manage several chronic inflammatory disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".