Cell nanomechanics and focal adhesions are regulated by retinol and conjugated linoleic acid in a dose-dependent manner
Bibliographic record
Abstract
Retinol and conjugated linoleic acid (CLA) have previously been shown to have an important role in gene expression and various cellular processes, including differentiation, proliferation and cell death. In this study we have investigated the effect of retinol and CLA, both individually and in combination, on the intracellular cytoskeleton, focal adhesions (FAs) and the nanomechanical properties of 3T3 fibroblasts. We observed a dose-dependent decrease in the formation of FAs following treatment with either compound, which was directly correlated to an increase in cell height (>30%) and a decrease in the measured Young's modulus (approximately 28%). Furthermore, treatments with both compounds demonstrated an increased effect and led to a reduction of >70% in the average number of FAs per cell and a decrease of >50% in average cell stiffness. These data reveal that retinol and CLA disrupt FA formation, leading to an increase in cell height and a significant decrease in stiffness. These results may broaden our understanding of the interplay between cell nanomechanics and cellular contact with the external microenvironment, and help to shed light on the important role of retinoids and CLA in health and disease.
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 | 0.000 |
| 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".