Time‐coordinated prevalence of extracellular <scp>HGF</scp>, <scp>FGF</scp>2 and <scp>TGF</scp>‐β3 in crush‐injured skeletal muscle
Bibliographic record
Abstract
Successful regeneration and remodeling of neuromuscular junctions are critical for restoring functional capacities and properties of skeletal muscle after damage, and axon-guidance molecules may be involved in the signaling that regulates such restoration. Recently, we found that early-differentiated satellite cells up-regulate a secreted neural chemorepellent Sema3A upon in vivo muscle-crush injury. The study also revealed that Sema3A expression is up-regulated in primary satellite-cell cultures in response to hepatocyte growth factor (HGF) and basic fibroblast growth factor (FGF2) and is prevented by transforming growth factor (TGF)-β2, 3. In order to verify the physiological significance of this regulation in vitro, the present study was designed to estimate the time-course of extracellular HGF, FGF2 and TGF-β3 concentrations after crush-injury of Gastrocnemius muscle in the rat lower hind-limb, using a combination of a non-homogenization/non-spin extraction of extracellular wound fluids and enhanced chemiluminescence-Western blotting analyses. Results clearly demonstrated that active HGF and FGF2 are prevalent in 2-8 days post-crush, whereas active TGF-β3 increases after 12 days, providing a better understanding of the time-coordinated levels of HGF, FGF2 and TGF-β3 that drive regulation of Sema3A expression during regenerative intramuscular moto-neuritogenesis.
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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".