Bibliographic record
Abstract
Recognizing the importance of leukocyte trafficking in inflammation led to some therapeutic breakthroughs. However, many inflammatory pathologies remain without specific therapy. This review discusses leukocytes in the context of sterile inflammation, a process caused by sterile (non-microbial) molecules, comprising damage-associated molecular patterns (DAMPs). DAMPs bind specific receptors to activate inflammation and start a highly optimized sequence of immune cell recruitment of neutrophils and monocytes to initiate effective tissue repair. When DAMPs are cleared, the recruited leukocytes change from a proinflammatory to a reparative program, a switch that is locally supervised by invariant natural killer T cells. In addition, neutrophils exit the inflammatory site and reverse transmigrate back to the bloodstream. Inflammation persists when the program switch or reverse transmigration fails, or when the coordinated leukocyte effort cannot clear the immunostimulatory molecules. The latter causes inappropriate leukocyte activation, a driver of many pathologies associated with poor lifestyle choices. We discuss lifestyle-associated inflammatory diseases and their corresponding immunostimulatory lifestyle-associated molecular patterns (LAMPs) and distinguish them from DAMPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".