Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss the recent work examining the importance of taurine in skeletal muscle and outline the discrepancy that exists between research findings in rodent vs. human skeletal muscle. RECENT FINDINGS: There is clear evidence that a normal taurine level is important for the normal functioning of skeletal muscle. Taurine is believed to be involved in many cellular functions, but in skeletal muscle its main roles are to facilitate Ca2+ dependent excitation-contraction processes, contribute to the regulation of cellular volume, and aid in antioxidant defense from stress responses. Most research has studied the importance of taurine in rodent skeletal muscle by downregulating and upregulating the muscle taurine content and examining the effects on the functioning of skeletal muscle at rest and during the stress of contractions (exercise). One successful research approach is to supplement the diet with taurine, which leads to increases in muscle taurine content and contractile function in rodents. However, this approach does not work in human skeletal muscle as the processes involved in the transport of taurine into the muscle are resistant to large and prolonged increases in plasma taurine following oral taurine supplementation. At present, attempts to influence muscle function with taurine supplementation can only occur through interactions outside the muscle cell in humans. SUMMARY: Future research should target the mechanisms responsible for the transport of taurine into human skeletal muscle and determine why the muscle defends the normal taurine content in the face of elevated plasma taurine levels, as opposed to the results in rodent muscle. This may lead to more fruitful usage of taurine as a skeletal muscle enhancing nutrient in athletic and clinical populations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".