Thyroid-stimulating hormone acutely increases monocyte gene expression in vivo.
Bibliographic record
Abstract
OBJECTIVES: Thyroid-stimulating hormone (TSH) acts in an extra-thyroidal fashion and induces a pro-inflammatory, pro-coagulant state. Blood monocytes can be activated by vascular stress, but it is not known if this occurs upon TSH administration. Our aim was to determine if recombinant human (rh) TSH, administered acutely to patients being screened for thyroid cancer recurrence, alters blood monocyte gene expression. DESIGN AND SETTING: Patients (14 women, 1 man) had a mean (±SD) age of 48±10 years, a body mass index of 26±6 kg/m2, a history of total thyroidectomy and radioablation for thyroid cancer, and were on L-thyroxine therapy at a university teaching hospital. They received 2 intramuscular doses of rhTSH (0.9 mg), administered on days 1 and 2. Blood samples were obtained at baseline on day1, and on days 3 and 5. RESULTS: Monocyte MCP-1 mRNA (mean±SE) increased significantly by 1.7±0.3 fold on day 5 following rhTSH stimulation (p=0.03, n=15). IL-1β and CD36 mRNA expression also increased on day 5 (1.9±0.4 fold, p=0.07, n=14) and 2.5±0.4 fold, p=0.1, n=10), respectively, although did not quite reach statistical significance. Significant correlations were detected between the BMI of patients and their TSH-stimulated monocyte mRNA responses at day 5 for CD11a, (r=0.66, n=14, p=0.01); CD14 (r=0.638, n=13, p=0.019), and CD16, r=0.84, n=13, p=0.0003). CONCLUSION: TSH administration increases pro-atherogenic monocyte gene expression.
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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 | 0.000 | 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".