Effects of estrogen and progesterone on cerebrovascular responses to euoxic hypercapnia in women
Bibliographic record
Abstract
OBJECTIVES: To determine the cerebral blood flow response to step changes in end-tidal Pco(2) in premenopausal women (n = 10; mean age±standard deviation 27.0±6.4 years) during the follicular (FP), mid-cycle (MC) and luteal (LP) phases of the menstrual cycle. METHODS: Transcranial Doppler ultrasound was used to measure beat-by-beat averaged peak blood flow velocity (V(p)) in the middle cerebral artery in response to 20 min of euoxic hypercapnia (end-tidal PO(2) = 88 Torr; end-tidal PCO(2) = 7.0 Torr above resting values). The V(p) responses to euoxic hypercapnia were fitted to a simple mathematical model that included gain terms for the on (G(on)) and off (G(off)) responses, time constants for the on (τ(on)) and off (τ(off)) responses, baseline terms and a time delay (T(d)). RESULTS: Serum progesterone levels were significantly greater for LP compared to FP and MC (40.6±13.2 vs. 32.6±1.4 nmol/l (p < 0.001) and 8.8±3.8 nmol/l (p < 0.001), respectively). Serum estrogen concentrations were significantly lower in FP compared to MC and LP (150.9±51.2 vs. 506.5±220.5 pmol/l (p = 0.002) and 589.1±222.8 pmol/l (p < 0.001), respectively). Arterial PCO(2) was significantly greater in MC compared to LP (35.0±2.1 and 32.6±1.4 Torr, respectively; p = 0.02). There was a significant increase in G(off) during LP compared with FP and MC (3.38±0.68 vs. 2.79±0.82 cm s(-1) Torr(-1) (p = 0.021) and 2.74±0.90 (p = 0.018) cm s(-1) Torr2(1), respectively). Progesterone and the estrogen/progesterone ratio contributed to the observed differences in G(off). CONCLUSION: There is an increase in G(off) during LP that is explained, at least in part, by increases in serum progesterone and estrogen and a decrease in arterial PCO(2).
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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 | 0.002 |
| 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) | 0.001 | 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".