Training-induced alterations of skeletal muscle mitochondrial biogenesis proteins in non-insulin-dependent type 2 diabetic men
Bibliographic record
Abstract
This study investigates whether regular physical activity (moderate endurance or resistance training twice a week for 3 months) influences the key regulatory molecules of mitochondrial biogenesis (peroxisome proliferator-activated receptor gamma coactivator-1α (PGC1α), nuclear respiratory factor-1 (NRF1), and mitochondrial transcription factor A (TFAM)) in patients suffering from non-insulin-dependent type 2 diabetes mellitus (T2DM) (n = 16, years = 62 ± 7, body mass index (BMI) = 30 ± 4 kg/m(2)). Seven T2DM men took part in endurance training, and 9 men participated in resistance training. BMI-matched non-diabetic male control subjects (CON) (n = 7, years = 53 ± 6, BMI = 30 ± 4 kg/m(2)) were studied for comparison. The protein contents of PGC1α, NRF1, and TFAM were determined using immunohistochemical staining methods on biopsies taken from the musculus vastus lateralis. At baseline, no differences were observed in NRF1-density between the T2DM men and the CON, while the contents of PGC1α and TFAM were decreased in the T2DM men. PGC1α and TFAM contents were not changed in the T2DM patients after the training period, but NRF1 was decreased. The down-regulation of mitochondrial signaling molecules might explain the patho-physiological reduction in mitochondrial biogenesis found in T2DM. Physical training, as performed in our study, did not reverse the down-regulation of mitochondrial signaling molecules--at least not after 3 months. [corrected].
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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) | 0.001 | 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".