Focal adhesions are involved in simulated-microgravity-induced basilar and femoral arterial remodelling in rats
Bibliographic record
Abstract
Recent studies have suggested that microgravity-induced arterial remodelling contributes to post-flight orthostatic intolerance and that multiple mechanisms are involved in arterial remodelling. However, the initial mechanism by which haemodynamic changes induce arterial remodelling is unknown. Focal adhesions (FAs) are dynamic protein complexes that have mechanotransduction properties. This study aimed to investigate the role of FAs in simulated-microgravity-induced basilar and femoral arterial remodelling. A 4-week hindlimb-unweighted (HU) rat model was used to simulate the effects of microgravity, and daily 1-hour intermittent artificial gravity (IAG) was used to prevent arterial remodelling. After 4-week HU, wall thickness, volume of smooth muscle cells (SMCs) and collagen content were increased in basilar artery but decreased in femoral artery (P < 0.05). Additionally, the expression of p-FAK Y397 and p-Src Y418 was increased and reduced in SMCs of basilar and femoral arteries, respectively, by HU (P < 0.05). The number of FAs was increased in basilar artery and reduced in femoral artery by HU (P < 0.05). Furthermore, daily 1-hour IAG prevented HU-induced differential structural adaptations and changes in FAs of basilar and femoral arteries. These results suggest that FAs may act as mechanosensors in arterial remodelling by initiating intracellular signal transduction in response to altered mechanical stress induced by microgravity.
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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".