Investigating Sex-Dependent Differences in Tensional Homeostasis
Bibliographic record
Abstract
Sex-dependent variances in cellular and tissue mechanics are poorly understood despite evidence showing females' increased susceptibility to soft tissue injuries and degenerative diseases compared to that of males.This project outlines a collaborative effort between the Smith and Connizzo Labs to explore sex-specific differences in cellular mechanosensing, focusing on how cells respond to external stimuli to maintain a resting level of mechanical stress (tensional homeostasis) and extracellular matrix (ECM) remodeling.Using traction force microscopy (TFM) and a novel tissue bioreactor system, this study aims to quantify differences in tensional homeostasis between male and female tendon cells (tenocytes) and investigate strain transfer through the ECM in mouse flexor digitorum longus (FDL) tendons.The process involves both cellular and tissue components.At the cellular level, male and female mouse tenocytes are derived through digestion, seeded onto polyacrylamide gels, and then imaged to determine the coefficient of variation in traction forces.On the tissue level, tenocyte nuclei and cellular membranes are stained, loaded into a confocal-mounted bioreactor, stretched, and imaged.Results reveal efficient traction force experimentation setup with challenges in cell culture viability, but successful staining protocols for live tissue imaging have been obtained with images from male and female tendons subjected to strains of 0%, 3%, 6%, and 9%.Using these images, subsequent MATLAB image analysis calculated nuclear strain transfer, nuclear strain, and cellular strain for both sexes.The study provides quantitative measures of sex-dependent differences in tissue mechanosensing mechanisms, carrying potential implications for gender-specific therapeutics.
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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) | 0.004 | 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".