Bibliographic record
Abstract
Macrophages are key players in various inflammatory disorders and pathological conditions via phagocytosis and orchestrating immune responses. They are highly heterogeneous in terms of their phenotypes and functions by adaptation to different organs and tissue environments. Upon damage or infection, monocytes are rapidly recruited to tissues and differentiate into macrophages. Transglutaminases (TGs) are a family of structurally and functionally related enzymes with Ca2+-dependent transamidation and deamidation activity. Numerous studies have shown that TGs, particularly TG2 and Factor XIII-A, are extensively involved in monocyte- and macrophage-mediated physiological and pathological processes. In the present review, we outline the current knowledge of the role of TGs in the adhesion and extravasation of monocytes, the expression of TGs during macrophage differentiation, and the regulation of TG2 expression by various pro- and anti-inflammatory mediators in macrophages. Furthermore, we summarize the role of TGs in macrophage phagocytosis and the understanding of the mechanisms involved. Finally, we review the roles of TGs in tissue-specific macrophages, including monocytes/macrophages in vasculature, alveolar and interstitial macrophages in lung, microglia and infiltrated monocytes/macrophages in central nervous system, and osteoclasts in bone. Based on the studies in this review, we conclude that monocyte- and macrophage-derived TGs are involved in inflammatory processes in these organs. However, more in vivo studies and clinical studies during different stages of these processes are required to determine the accurate roles of TGs, their substrates, and the mechanisms-of-action.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".