Decreased Satellite Cell Number and Function in Humans and Mice With Type 1 Diabetes Is the Result of Altered Notch Signaling
Bibliographic record
Abstract
Type 1 diabetes (T1D) negatively influences skeletal muscle health; however, its effect on muscle satellite cells (SCs) remains largely unknown. SCs from samples from rodents (Akita) and human subjects with T1D were examined to discern differences in SC density and functionality compared with samples from their respective control subjects. Examination of the Notch pathway was undertaken to investigate its role in changes to SC functionality. Compared with controls, Akita mice demonstrated increased muscle damage after eccentric exercise along with a decline in SC density and myogenic capacity. Quantification of components of the Notch signaling pathway revealed a persistent activation of Notch signaling in Akita SCs, which could be reversed with the Notch inhibitor DAPT. Similar to Akita samples, skeletal muscle from human subjects with T1D displayed a significant reduction in SC content, and the Notch ligand, DLL1, was significantly increased compared with control subjects, supporting the dysregulated Notch pathway observed in Akita muscles. These data indicate that persistent activation in Notch signaling impairs SC functionality in the T1D muscle, resulting in a decline in SC content. Given the vital role played by the SC in muscle growth and maintenance, these findings suggest that impairments in SC capacities play a primary role in the skeletal muscle myopathy that characterizes T1D.
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".