High-intensity interval resistance training (HIIRT) improves liver gluconeogenesis from lactate in Swiss mice
Bibliographic record
Abstract
High-intensity physical exercise favors anaerobic glycolysis and increases lactatemia. Lactate is converted back to glucose in the liver, so that the lactate threshold, an indicator of physical performance, must be related to the gluconeogenic capacity of the liver. This research assessed the effect of a high-intensity interval resistance training (HIIRT) on liver gluconeogenesis from lactate. Swiss mice were trained (groups T) on vertical ladder with overload of 90% of their maximal load. Control animals remained untrained (groups C0 and C8). In situ liver perfusion with lactate and adrenaline was performed in rested mice after 6 hours of food deprivation. There were larger outputs of glucose (T6, 71.90%; T8, 54.53%) and pyruvate (T8, 129.28%) (representative values for 4 mM lactate) in the groups trained for 6 or 8 weeks (T6 and T8), and of glucose in the presence of adrenaline in group T8 (280%). The content of PEPCK, an important regulatory enzyme of the gluconeogenic pathway, was 69.13% higher in group T8 than in the age-matched untrained animals (C8). HIIRT augmented liver gluconeogenesis from lactate and this might improve the lactate threshold. Novelty: The liver metabolizes lactate from muscle into glucose. Physical training may enhance the gluconeogenic capacity of the liver. As lactate clearance by the liver improves, lactate threshold is displaced to higher exercise intensities.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".