TSH Pulses Finely Tune Thyroid Hormone Release and TSH Receptor Transduction
Bibliographic record
Abstract
Detection of circulating TSH is a first-line test of thyroid dysfunction, a major health problem (affecting about 5% of the population) that, if untreated, can lead to a significant deterioration of quality of life and adverse effects on multiple organ systems. Human TSH levels display both pulsatile and (nonpulsatile) basal TSH secretion patterns; however, the importance of these in regulating thyroid function and their decoding by the thyroid is unknown. Here, we developed a novel ultra-sensitive ELISA that allows precise detection of TSH secretion patterns with minute resolution in mouse models of health and disease. We characterized the patterns of ultradian TSH pulses in healthy, freely behaving mice over the day-night cycle. Challenge of the thyroid axis with primary hypothyroidism because of iodine deficiency, a major cause of thyroid dysfunction worldwide, results in alterations of TSH pulsatility. Induction in mouse models of sequential TSH pulses that mimic ultradian TSH profiles in periods of minutes were more efficient than sustained rises in basal TSH levels at increasing both thyroid follicle cAMP levels, as monitored with a genetically encoded cAMP sensor, and circulating thyroid hormone. Hence, this mouse TSH assay provides a powerful tool to decipher how ultradian TSH pulses encode thyroid outcomes and to uncover hidden parameters in the TSH-thyroid hormone set-point in health and disease.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".