An investigation of the distribution and location of mast cells affected by the stiffness of substrates as a mechanical niche
Bibliographic record
Abstract
The distribution and location of mast cells are closely related to their physiological and pathological functions, such as allergic responses, immunity, and fibrosis, and are used in acupuncture.In this study, the distribution of mast cells in vivo was observed, and mechanical clues for understanding their distribution based on mechanical niches were explored.By toluidine blue staining and immunohistochemical staining, we examined the distribution and location of mast cells in rat skin and found that mast cells are distributed in a spatially nonuniform manner, preferring to locate at regions in the tissue and extracellular matrix with stiffness changes.In vitro experiments for studying the distribution of rat basophilic leukemia (RBL-2H3) mast cell line on poly-di-methyl-siloxane (PDMS) substrates with stiffness variations were performed.It was found that RBL-2H3 cells migrate and tend to remain in the areas with stiffness variations.The present research suggests that changing the stiffness of local tissues may stimulate mast cell recruitment, which may be the method by which some traditional Chinese medicine treatments, such as acupuncture.On the basis of the origin of mast cells and our experimental results, we predict that mast cells exist in tissues that contain permeable capillaries and prefer regions with stiffness changes.We discussed this prediction using examples of specific tissues from some cases.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".