Evaluation of treadmill exercise effect on muscular lipid profiles of diabetic fatty rats by nanoflow liquid chromatography–tandem mass spectrometry
Bibliographic record
Abstract
We compare comprehensive quantitative profiling of lipids at the molecular level from skeletal muscle tissues (gastrocnemius and soleus) of Zucker diabetic fatty rats and Zucker lean control rats during treadmill exercise by nanoflow liquid chromatography-tandem mass spectrometry. Because type II diabetes is caused by decreased insulin sensitivity due to excess lipids accumulated in skeletal muscle tissue, lipidomic analysis of muscle tissues under treadmill exercise can help unveil the mechanism of lipid-associated insulin resistance. In total, 314 lipid species, including phospholipids, sphingolipids, ceramides, diacylglycerols (DAGs), and triacylglycerols (TAGs), were analyzed to examine diabetes-related lipid species and responses to treadmill exercise. Most lysophospholipid levels increased with diabetes. While DAG levels (10 from the gastrocnemius and 13 from the soleus) were >3-fold higher in diabetic rats, levels of most of these decreased after exercise in soleus but not in gastrocnemius. Levels of 5 highly abundant TAGs (52:1 and 54:3 in the gastrocnemius and 48:2, 50:2, and 52:4 in the soleus) displaying 2-fold increases in diabetic rats decreased after exercise in the soleus but not in the gastrocnemius in most cases. Thus, aerobic exercise has a stronger influence on lipid levels in the soleus than in the gastrocnemius in type 2 diabetic rats.
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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) | 0.000 | 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".