Mechanosensing in macrophages and dendritic cells in steady-state and disease
Bibliographic record
Abstract
Macrophages and dendritic cells are myeloid cells that play critical roles in immune responses. Macrophages help to maintain homeostasis through tissue regeneration and the clearance of dead cells, but also mediate inflammatory processes against invading pathogens. As the most potent antigen-presenting cells, dendritic cells are important in connecting innate to adaptive immune responses via activation of T cells, and inducing tolerance under physiological conditions. While it is known that macrophages and dendritic cells respond to biochemical cues in the microenvironment, the role of extracellular mechanical stimuli is becoming increasingly apparent. Immune cell mechanotransduction is an emerging field, where accumulating evidence suggests a role for extracellular physical cues coming from tissue stiffness in promoting immune cell recruitment, activation, metabolism and inflammatory function. Additionally, many diseases such as pulmonary fibrosis, cardiovascular disease, cancer, and cirrhosis are associated with changes to the tissue biophysical environment. This review will discuss current knowledge about the effects of biophysical cues including matrix stiffness, topography, and mechanical forces on macrophage and dendritic cell behavior under steady-state and pathophysiological conditions. In addition, we will also provide insight on molecular mediators and signaling pathways important in macrophage and dendritic cell mechanotransduction.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".