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Record W2916203996 · doi:10.1249/mss.0000000000001490

Response

2018· letter· en· W2916203996 on OpenAlexaffabout
Jamie Whitfield, George J. F. Heigenhauser, Luc J. C. van Loon, Lawrence L. Spriet, A. Russell Tupling, Graham P. Holloway

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2018
Typeletter
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsUniversity of GuelphMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsAssertionIngestionSkeletal muscleDietary NitrateMedicineNitratePhysiologyChemistryInternal medicineBiologyComputer scienceEcologyNitrite

Abstract

fetched live from OpenAlex

To the Editor-in-Chief, We would like to thank Dr. Coggan for a positive and succinct summary of our findings, and for concurring with our main assertion that protein modifications, and not protein expression, likely contribute to the ergogenic effects of nitrate consumption within skeletal muscle. Nevertheless, Dr. Coggan has taken exception to the statement in our published work that “future studies should also include an assessment of changes in function after acute ingestion.” However, the following sentence in that work gets at the main point we were trying to highlight, namely, that to our knowledge, mechanistic work has not been performed after a single bolus of nitrate consumption. Specifically, previous work examining calcium handling (1,2), mitochondrial coupling (3–5), and mitochondrial reactive oxygen species emission (4) have all been conducted after repeated intake (i.e., ≥3 d) of oral nitrate. Therefore, although there are numerous examples showing that a single intake of oral nitrates can improve human exercise performance (only some of these seminal articles are referenced within our manuscript [6,7], because of the journal’s restriction on the number of references), we stand by our assertion that future research is required to delineate the specific mechanisms associated with an acute intake of nitrate. In our recent work, we hypothesized that changes in human skeletal muscle contractile function after nitrate supplementation could be explained by structural and/or redox-mediated changes, but we were unable to detect a change in the redox status of skeletal muscle (2). However, we acknowledge that the measurements made represent a snapshot of cellular oxidative stress and may therefore lack the sensitivity to accurately determine redox signaling. As such, future research is required in all potential aspects of signaling after nitrate supplementation, and the utilization of advanced techniques and -omics strategies may prove beneficial in delineating the physiological significance of the previously observed increase in H2O2 emission (4). Furthermore, given the similarities in muscle function with acute and chronic nitrate consumption, the logical progression in our minds would be to use an experimental design focused on the changes seen after an acute bolus of nitrate. We feel that the findings of such a study would be important, because it may further our understanding of the mechanistic underpinnings of nitrate supplementation, as well as help inform researchers and practitioners how to appropriately tailor supplementation protocols across different populations. Jamie Whitfield Mary Mackillop Institute for Health Research Australian Catholic University Melbourne, VIC AUSTRALIA George J. F. Heigenhauser Department of Medicine McMaster University Hamilton, ON CANADA Luc J. C. van Loon Department of Human Movement Sciences Nutrition and Toxicology Research Institute Maastricht University Maastricht, the NETHERLANDS Lawrence L. Spriet Department of Human Health and Nutritional Sciences University of Guelph Guelph, ON CANADA A. Russell Tupling Department of Kinesiology University of Waterloo Waterloo, ON CANADA Graham P. Holloway Department of Human Health and Nutritional Sciences University of Guelph Guelph, ON CANADA

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.357
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.030
GPT teacher head0.323
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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