Exploring an Impaired Sphingosine-1-Phosphate Response to Skeletal Muscle Damage in a Murine Model of Type 1 Diabetes Mellitus
Bibliographic record
Abstract
Skeletal muscle is an adaptive tissue that possesses an innate ability to fully regenerate from a damaging stimulus. Type 1 diabetes mellitus (T1DM) elicits a pathophysiological environment that prevents normal skeletal muscle regeneration by dysregulating key events in the regenerative process. It has been shown that the sphingosine-1-phosphate (S1P) response to skeletal muscle damage is blunted in murine models of T1DM. S1P content normally increases in skeletal muscle acutely (within seven days) following damage to promote regeneration, and an absence of this response results in inadequate recovery. Thus, the lack of S1P accumulation seen in skeletal muscle of diabetic rodents following damage has the potential to contribute to impaired muscle regeneration. This investigation aimed to elucidate the mechanisms underlying this response by assessing: 1) S1P content via Liquid-Chromatography Mass-Spectrometry and 2) expression level of proteins that regulate S1P content via SDS-PAGE and Western Blot analysis. Results from this study show a blunted S1P response to skeletal muscle damage in a T1DM model as S1P content is reduced in Akita mice five days into regeneration. Furthermore, it was found that while sphingosine lyase (SPL) expression increased in both the T1DM models and WT mice following muscle damage, this expression was significantly greater in the diabetic condition. Total sphingosine kinase 1 content was also found to be increased five days following damage, but there was no significant effect of diabetes. Thus, the greater expression of SPL in the T1DM model suggests that S1P is degraded at a faster rate, preventing the normal accumulation of S1P following skeletal muscle damage. Future research should aim to identify the cause of this overexpression and the impact it has on skeletal muscle regeneration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| 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".