Bibliographic record
Abstract
In our opponents’ view (Reid et al. 2014), the exact answer to the question of what is the dominant mechanism remains unknown and, as they conclude, ‘…proteolysis is an essential component of disuse atrophy.’ Importantly, we agree with both points. However, our position remains that the answer as to which is the dominant mechanism cannot merely be obtained from static protein/gene measurements or from rodent knock out models. As we in our original manuscript, and others (Cunningham, 2002) have highlighted, there are marked interspecies differences between humans and rodents that we propose have a significant bearing on this debate. While important proof of concept mechanistic information can be derived from rodent models they are still methodologically flawed. For example, the continued use of ex vivo muscle preparation is inherently biased towards showing elevated rates of muscle protein breakdown (MPB) and proteolytic markers as the tissue is essentially dying (albeit slowly) during the experiment. Our opponents cite only a single human study that attempted to measure MPB to support their argument (Tesch et al. 2008), which relied on an indirect proteolytic marker, and which did not include concomitant assessments of muscle protein synthesis (MPS). Even in recent papers (Bodine & Baehr, 2014) where large volumes of data were reviewed, in very few cited studies were actual rates of protein synthesis and/or breakdown even reported; instead, the change in muscle mass was estimated, proteolytic ‘markers’ were measured and a ‘conclusion’ reached as to the mechanism. To clarify, we do not dismiss our opponents’ argument that at least in the early phase of muscle disuse (<10 days), increases in MPB contribute to the decline in skeletal muscle size (Wall et al. 2013). There is evidence (with indirect markers) for this assertion, even in humans (Tesch et al. 2008). Nonetheless, the decline in human muscle size with disuse is predominantly driven by a reduction in the rate of MPS, especially in response to contraction and nutrition. Our opponents’ use of a graphic word cloud to support their thesis, while intriguing, is not in our view evidence that they are correct. Instead, we view this graphic ‘evidence’ as a depiction of the number of animal- versus human-based researchers in this area. Since animal-based researchers have generated most of the data in this field, the use of a word cloud in this instance is simply a reflection of the conclusion of the majority of researchers, using the same model, in this field. Thus, the word cloud is analogous to someone shouting the loudest in an argument and believing that approach renders their argument correct. Readers are invited to give their views on this and the accompanying CrossTalk articles in this issue by submitting a brief (250 word) comment. Comments may be submitted up to 6 weeks after publication of the article, at which point the discussion will close and the CrossTalk authors will be invited to submit a ‘Last Word’. Please email your comment to journals@physoc.org. Disclaimer: Supplementary materials have been peer-reviewed but not copyedited. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. The authors report no conflict of interest, financial or otherwise. This work was supported by grants to S.M.P. from the National Science and Engineering Research Council of Canada and the Canadian Institutes of Health Research, as well as the Canadian Diabetes Association.
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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.029 | 0.065 |
| Insufficient payload (model declined to judge) | 0.020 | 0.023 |
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".