Short-term variability of blood pressure: effects of lower-body negative pressure and long-duration bed rest
Bibliographic record
Abstract
Mild lower-body negative pressure (LBNP) has been utilized to selectively unload cardiopulmonary baroreceptors, but there is evidence that arterial baroreceptors can be transiently unloaded after the onset of mild LBNP. In this paper, a black box mathematical model for the prediction of diastolic blood pressure (DBP) variability from multiple inputs (systolic blood pressure, R-R interval duration, and central venous pressure) was applied to interpret the dynamics of blood pressure maintenance under the challenge of LBNP and in long-duration, head-down bed rest (HDBR). Hemodynamic recordings from seven participants in the WISE (Women's International Space Simulation for Exploration) Study collected during an experiment of incremental LBNP (-10 mmHg, -20 mmHg, -30 mmHg) were analyzed before and on day 50 of a 60-day-long HDBR campaign. Autoregressive spectral analysis focused on low-frequency (LF, ~0.1 Hz) oscillations of DBP, which are related to fluctuations in vascular resistance due to sympathetic and baroreflex regulation of vasomotor tone. The arterial baroreflex-related component explained 49 ± 13% of LF variability of DBP in spontaneous conditions, and 89 ± 9% (P < 0.05) on day 50 of HDBR, while the cardiopulmonary baroreflex component explained 17 ± 9% and 12 ± 4%, respectively. The arterial baroreflex-related variability was significantly increased in bed rest also for LBNP equal to -20 and -30 mmHg. The proposed technique provided a model interpretation of the proportional effect of arterial baroreflex vs. cardiopulmonary baroreflex-mediated components of blood pressure control and showed that arterial baroreflex was the main player in the mediation of DBP variability. Data during bed rest suggested that cardiopulmonary baroreflex-related effects are blunted and that blood pressure maintenance in the presence of an orthostatic stimulus relies mostly on arterial control.
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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".