Cerebral, cardiac and skeletal muscle stress associated with a series of static and dynamic apnoeas
Bibliographic record
Abstract
PURPOSE: This study sought to explore, for the first time, the effects of repeated maximal static and dynamic apnoeic attempts on the physiological milieu by assessing cerebral, cardiac and striatal muscle stress-related biomarkers in a group of elite breath-hold divers (EBHD). METHODS: Sixteen healthy males were recruited (EBHD = 8; controls = 8). On two separate occasions, EBHD performed two sets of five repeated maximal static apnoeas (STA) or five repeated maximal dynamic apnoeas (DYN). Controls performed a static eupnoeic protocol to negate any effects of water immersion and diurnal variation on haematology (CTL). Venous blood samples were drawn at 30, 90, and 180 min after each protocol to determine S100β, neuron-specific enolase (NSE), myoglobin, and high sensitivity cardiac troponin T (hscTNT) concentrations. RESULTS: S100β and myoglobin concentrations were elevated following both apnoeic interventions (p < 0.001; p ≤ 0.028, respectively) but not after CTL (p ≥ 0.348). S100β increased from baseline (0.024 ± 0.005 µg/L) at 30 (STA, +149%, p < 0.001; DYN, +166%, p < 0.001) and 90 min (STA, +129%, p < 0.001; DYN, +132%, p = 0.008) following the last apnoeic repetition. Myoglobin was higher than baseline (22.3 ± 2.7 ng/ml) at 30 (+42%, p = 0.04), 90 (+64%, p < 0.001) and 180 min (+49%, p = 0.013) post-STA and at 90 min (+63%, p = 0.016) post-DYN. Post-apnoeic S100β and myoglobin concentrations were higher than CTL (STA, p < 0.001; DYN, p ≤ 0.004). NSE and hscTNT did not change from basal concentrations after the apnoeic (p ≥ 0.146) nor following the eupnoeic (p ≥ 0.553) intervention. CONCLUSIONS: This study suggests that a series of repeated maximal static and dynamic apnoeas transiently disrupt the blood-brain barrier and instigate muscle injury but do not induce neuronal-parenchymal damage or myocardial damage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".