A to Z of nutritional supplements: dietary supplements, sports nutrition foods and ergogenic aids for health and performance—Part 32
Bibliographic record
Abstract
The letter P brings together two of the most talked about supplement families: proteins, which have been perennially popular since the time of the ancient Olympians and prohormones, which will be dealt with in a later issue. Both supplement families include products which range from simple and relatively inexpensive, to exotic, expensive and emotively marketed. Part 32 also includes information on proline, a non-essential amino acid which is marketed for growth and repair of soft tissue based on its importance in the make-up of collagen. ### S M Phillips L Breen Skeletal muscle protein turnover rates are ∼1%–2%/d and exist in dynamic, usually balanced, equilibrium between muscle protein breakdown (MPB) and muscle protein synthesis (MPS). For example, in the fasted state, MPB>MPS, whereas in response to ingestion of protein-containing meals, MPS>MPB.1 Thus, in healthy adults, muscle mass remains relatively stable due to ‘fed-gain’ being balanced by fasted-loss, so daily protein flux, while it may be 3–4 times greater than net intake and loss, is in tight balance. Fasted-state protein losses are typically about 40–60 g/d for a sedentary person weighing 70–90 kg and it is debatable what the losses would be in athletes, be they aerobically or resistance trained. Dietary protein for athletic populations can serve as signal and substrate for MPS, resulting in protein accretion for hypertrophy, repair of damaged proteins or assisting the maintenance of lean mass. There are important messages for athletes, who differ from sedentary individuals, in terms of quantity, timing and quality of protein intake in relation to an athlete's training stimulus. The molecular changes underpinning these adaptations are gene transcription and mRNA translational signalling and are highlighted in a review.2 The general consensus is that adults need no more than 0.8–0.9 g/kg/d of protein to meet their needs. However, the notion of consumption of ‘extra’ protein above these …
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.036 |
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".