Resolution of Inflammation: Leukocytes and Molecular Pathways as Potential Therapeutic Targets
Bibliographic record
Abstract
trafficking also modulates neutrophil apoptosis, another control point in resolving inflammation.Data emerging from a variety of experimental models demonstrated that resolvins and protectins limit adaptive immunity in asthma, attenuate adipose tissue inflammation and metabolic dysfunctions, including type-2 diabetes mellitus-associated insulin resistance and non-alcoholic fatty acid liver disease, and protect the kidney against diabetic nephropathy.The glucocorticoid-regulated protein annexin A1 may act in concert with lipoxins to limit inflammation underlying myocardial ischemia/reperfusion, arthritis, multiple sclerosis, and sepsis.Altering local adenosine signaling affects leukocyte activation and trafficking, and may restore tissue homeostasis in liver injury and rheumatoid arthritis.The nuclear proteins proliferating cell nuclear antigen (PCNA) and myeloid nuclear differentiation antigen (MNDA) play opposing roles in modulating neutrophil apoptosis in patient with sepsis.Targeting the complement C5a receptors C5Ra and C5L2 may attenuate excessive inflammation in sepsis and autoimmune diseases.Intracellular molecular mechanisms have also been implicated in dampening inflammation.The NOD-like receptors NOD1 and NOD2 recognize intracellular pathogens and activate transcriptional responses that would lead to restriction of infection.Suppression of IFNγ production in T lymphocytes by allopurinol may attenuate cell-mediated inflammatory diseases.This Research Topic focuses on articles that discuss the molecular mechanisms governing the function and fate of leukocytes during inflammation and identify potential targets for development of innovative therapeutic approaches for the treatment of inflammatory pathologies based on mimicking and/or activating endogenous pro-resolution mechanisms.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".