Thirty-day experimental diabetes impairs contractility and increases fatigue resistance in rat diaphragm muscle associated with increased anti-oxidative activity
Bibliographic record
Abstract
Diabetes mellitus is a metabolic disorder that can generate tissue damage through several pathways. Alteration and dysfunction of skeletal muscle are reported including respiratory muscles, which may compromise respiratory parameters in diabetic patients. We have aimed to evaluate the diaphragm muscle contractility, tissue remodeling, oxidative stress, and inflammatory parameters from 30 day streptozotocin-treated rats. The diaphragm contractility was assessed using isolated muscle, tissue remodeling using histology and zymography techniques, and tissue oxidative stress and inflammatory parameters by enzyme activity assay. Our data revealed in the diabetes mellitus group an increase in maximum tetanic force (4.82 ± 0.13 versus 4.24 ± 0.18 N/cm2 (p = 0.015)) and fatigue resistance (139.16 ± 10.78 versus 62.25 ± 4.45 s (p < 0.001)), reduction of 35.4% in muscle trophism (p < 0.001), increase of 32.6% of collagen deposition (p = 0.007), reduction of 21.3% in N-acetylglucosaminidase activity (p < 0.001), and increase of 246.7% of catalase activity (p = 0.002) without changes in reactive oxygen species (p = 0.518) and tissue lipid peroxidation (p = 0.664). All observed changes are attributed to the poor glycemic control (471.20 ± 16.91 versus 80.00 ± 3.42 mg/dL (p < 0.001)), which caused defective tissue regeneration and increased catalase activity as a compensatory mechanism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".