Chronic probiotic supplementation with or without glutamine does not influence the eHsp72 response to a multi-day ultra-endurance exercise event
Bibliographic record
Abstract
Probiotic and glutamine supplementation increases tissue Hsp72, but their influence on extracellular Hsp72 (eHsp72) has not been investigated. The aim of this study was to investigate the effect of chronic probiotic supplementation, with or without glutamine, on eHsp72 concentration before and after an ultramarathon. Thirty-two participants were split into 3 independent groups, where they ingested probiotic capsules (PRO; n = 11), probiotic + glutamine powder (PGLn; n = 10), or no supplementation (CON; n = 11), over a 12-week period prior to commencement of the Marathon des Sables (MDS). eHsp72 concentration in the plasma was measured at baseline, 7 days pre-race, 6–8 h post-race, and 7 days post-race. The MDS increased eHsp72 concentrations by 124% (F[1,3] = 22.716, p < 0.001), but there was no difference in the response between groups. Additionally, PRO or PGLn supplementation did not modify pre- or post-MDS eHsp72 concentrations compared with CON (p > 0.05). In conclusion, the MDS caused a substantial increase in eHsp72 concentration, indicating high levels of systemic stress. However, chronic PRO or PGLn supplementation did not affect eHsp72 compared with control pre- or post-MDS. Given the role of eHsp72 in immune activation, the commercially available supplements used in this study are unlikely to influence this cascade.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".