Long-term heart transplant recipients: heart rate-related effects on augmented transfer function coherence during repeated squat-stand maneuvers in males
Bibliographic record
Abstract
Previous research has highlighted that squat-stand maneuvers (SSMs) augment coherence values within the cerebral pressure-flow relationship to ∼0.99. However, it is not fully elucidated if mean arterial pressure (MAP) leads to this physiological entrainment independently, or if heart rate (HR) and/or the partial pressure of carbon dioxide (Pco2) also have contributing influences. A 2:1 control-to-case model was used in the present investigation [participant number ( n) = 40; n = 16 age-matched (AM); n = 16 donor control (DM); n = 8 heart transplant recipients (HTRs)]. The latter group was used to mechanistically isolate the extent to which HR influences the cerebral pressure-flow relationship. Participants completed 5 min of squat-stand maneuvers at 0.05 Hz (10 s) and 0.10 Hz (5 s). Linear transfer function analysis (TFA) examined the relationship between different physiological inputs (i.e., MAP, HR, and Pco2) and output [cerebral blood velocity (CBV)] during SSM; and cardiac baroreceptor sensitivity (BRS). Compared with DM, cardiac BRS was reduced in AM ( P < 0.001), which was further reduced in HTR ( P < 0.045). In addition, during the SSM, HR was elevated in HTR compared with both control groups ( P < 0.001), but all groups had near-maximal coherence metrics ≥0.98 at 0.05 Hz and ≥0.99 at 0.10 Hz ( P ≥ 0.399). In contrast, the mean HR-CBV/Pco2-CBV relationships ranged from 0.38 (HTR) to 0.81 (DM). Despite near abolishment of BRS and blunted HR following heart transplantation, long-term HTR exhibited near-maximal coherence within the MAP-CBV relationship, comparable with AM and DM. Therefore, these results show that the augmented coherence with SSM is driven by blood pressure, whereas elevations in TFA coherence as a result of HR contribution are likely correlational in nature.
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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) | 0.002 | 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".