Diabetes-induced atrophy is associated with a muscle-specific alteration in NF-κB activation and expression
Bibliographic record
Abstract
NF-kappaB is a transcription factor implicated in pathological responses that develop during diabetes mellitus, including skeletal muscle atrophy. Given that NF-kappaB activation, protein composition, and content within diabetic skeletal muscle remain generally uncharacterized, a streptozotocin (STZ) model was used to assess NF-kappaB activation, composition, and content. Sprague-Dawley rats were injected with STZ (55 mg/kg) and after 30 days the soleus (SOL), plantaris (PL), red gastrocnemius (RG), and white gastrocnemius (WG) muscles were assessed by electrophoresis mobility shift assay and western blotting. NF-kappaB activation was detected in all muscles examined, but was reduced in RG muscles from diabetic animals. Supershifts indicated NF-kappaB was composed primarily of p50 in diabetic and control animals. The content of both p65 and p52 was elevated in SOL and PL muscles, while p52 was decreased in RG. The coactivating protein, Bcl-3, was increased in WG and RG, but decreased in PL. Both p50 and RelB remained unchanged in all tissues examined. All muscles from diabetic animals demonstrated reduced mass when compared to controls, but only the gastrocnemius demonstrated atrophy as reflected by a reduced muscle-to-body mass ratio. In conclusion, diabetic alterations to the contents and activation of the NF-kappaB protein were tissue-specific, but did not appear to alter dimer composition of constitutively bound NF-kappaB. These results indicate that diabetes may alter NF-kappaB activity and expression in a muscle-specific manner.
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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) | 0.000 | 0.000 |
| 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".