Physiological control of pituitary hormone secretory-burst mass, frequency, and waveform: a statistical formulation and analysis
Bibliographic record
Abstract
The present study investigates the time-varying control of pituitary hormone secretion over the day and night (D/N). To this end, we implemented an analytical platform designed to reconstruct simultaneously 1) basal (nonpulsatile) secretion, 2) single or dual secretory-burst waveforms, 3) random effects on burst amplitude, 4) stochastic pulse-renewal properties, 5) biexponential elimination kinetics, and 6) experimental uncertainty. The statistical solution is conditioned on a priori pulse-onset times, which are estimated in the first stage. Primary data composed of thyrotropin (TSH) concentrations were monitored over 24 h in 27 healthy adults. According to statistical criteria, 21/27 profiles favored a dual compared with single secretory-burst waveform. An objectively defined waveform change point (D/N boundary) emerged at 2046 (+/-23 min), after which 1) the mass of TSH released per burst increases by 2.1-fold (P < 0.001), 2) TSH secretory-burst frequency rises by 1.2-fold (P < 0.001), 3) the latency to maximal TSH secretion within a burst decreases by 67% (P < 0.001), 4) variability in secretory-burst shape diminishes by 50% (P < 0.001), and 5) basal TSH secretion declines by 17% (P < 0.002). In contrast, the regularity of successive burst times and the slow-phase half-life are stable. In conclusion, nycthemeral mechanisms govern TSH secretory-burst mass, frequency, waveform, and variability but not evidently TSH elimination kinetics or the pulse-timing process. Further studies will be required to assess the generality of the foregoing distinctive control mechanisms in other hypothalamo-pituitary axes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".