Preliminary Study of S100B and Sema3A Expression Patterns in Regenerating Muscle Implicates P75-Expressing Terminal Schwann Cells and Muscle Satellite Cells in Neuromuscular Junction Restoration
Bibliographic record
Abstract
Terminal Schwann cells (TSCs) help regulate the formation, maintenance, function, and repair of neuromuscular junctions (NMJs) and axon guidance after muscle injury. Premature activation of muscle satellite cells (SCs), induced by isosorbide dinitrate (ISDN) before injury, accelerates myogenic regeneration, disrupts NMJ remodeling and maturation, decreases Sema3A protein-induced neuro-repulsion, and is accompanied by time-dependent changes in S100B protein levels. Here, to study the effects of premature SC activation on TSCs and SCs, both expressing P75 nerve growth-factor receptor, in situ hybridization was used to identify transcripts of S100B and Sema3A, and the number, intensity, and diameter of expression sites were analyzed. The number of sites/fields expressing S100B and Sema3A increased with regeneration time (both p < 0.001). Expression-site intensity (S100B) and diameter (S100B and Sema3A) decreased during regeneration (p = 0.005; p < 0.05, p = 0.006, respectively). P75 protein colocalized with a subset of S100B and Sema3A expression sites. Principal component analyses of gene expression, protein levels, and histological variables (fiber diameter, vascular density) in control and ISDN-pretreated groups explained 83% and 64% of the dataset variance, respectively. A very strong loading coefficient for colocalization of P75 protein with S100B and Sema3A mRNAs (0.91) in control regenerating muscle dropped markedly during regeneration disrupted by premature SC activation (−0.10 in Factor 1 to 0.55 in Factor 3). These findings strongly implicate the triple-expression profile by TSCs and/or SCs as a strong correlate of the important synchrony of muscle and nerve regeneration after muscle tissue injury. The results have the potential to focus future research on the complex interplay of TSCs and SCs in neuromuscular tissue repair and help promote effective function after traumatic muscle injury.
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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".