Endurance training alters basal erythrocyte MCT-1 contents and affects the lactate distribution between plasma and red blood cells in T2DM men following maximal exercise
Bibliographic record
Abstract
Chronic elevated lactate levels are associated with insulin resistance in patients with type 2 diabetes mellitus (T2DM). Furthermore, lactacidosis plays a role in limiting physical performance. Erythrocytes, which take up lactate via monocarboxylate transporter (MCT) proteins, may help transport lactate within the blood from lactate-producing to lactate-consuming organs. This study investigates whether cycling endurance training (3 times/week for 3 months) alters the basal erythrocyte content of MCT-1, and whether it affects lactate distribution kinetics in the blood of T2DM men (n = 10, years = 61 ± 9, body mass index = 31 ± 3 kg/m(2)) following maximal exercise (WHO step-incremental cycle ergometer test). Immunohistochemical staining indicated that basal erythrocyte contents of MCT-1 protein were up-regulated (+90%, P = 0.011) post-training. Erythrocyte and plasma lactate increased from before acute exercise (= resting values) to physical exhaustion pre- as well as post-training (pre-training: +309%, P = 0.004; +360%, P < 0.001; post-training: +318%, P = 0.008; +300%, P < 0.001), and did not significantly decrease during 5 min recovery. The lactate ratio (erythrocytes:plasma) remained unchanged after acute exercise pre-training, but was significantly increased after 5 min recovery post-training (compared with the resting value) (+22%, P = 0.022). The results suggest an increased time-delayed influx of lactate into erythrocytes following an acute bout of exercise in endurance-trained diabetic men.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".