Sex-Specific T-Cell Regulation of Angiotensin II–Dependent Hypertension
Bibliographic record
Abstract
Studies suggest T cells modulate arterial pressure. Because robust sex differences exist in the immune system and in hypertension, we investigated sex differences in T-cell modulation of angiotensin II-induced increases in mean arterial pressure in male (M) and female (F) wild-type and recombination-activating-gene-1-deficient (Rag1(-/-)) mice. Sex differences in peak mean arterial pressure in wild-type were lost in Rag1(-/-) mice (mm Hg: wild-type-F, 136±4.9 versus wild-type-M, 153±1.7; P<0.02; Rag1(-/-)-F, 135±2.1 versus Rag1(-/-)-M, 141±3.8). Peak mean arterial pressure was 13 mm Hg higher after adoptive transfer of male (CD3(M)→Rag1(-/-)-M) versus female (CD3(F)→Rag1(-/-)-M) T cells. CD3(M)→Rag1(-/-)-M mice exhibited higher splenic frequencies of proinflammatory interleukin-17A (2.4-fold) and tumor necrosis factor-α (2.2-fold)-producing T cells and lower plasma levels (13-fold) and renal mRNA expression (2.4-fold) of interleukin-10, whereas CD3(F)→Rag1(-/-)-M mice displayed a higher activation state in general and T-helper-1-biased renal inflammation. Greater T-cell infiltration into perivascular adipose tissue and kidney associated with increased pressor responses to angiotensin II if the T cell donor was male but not female and these sex differences in T-cell subset expansion and tissue infiltration were maintained for 7 to 8 weeks within the male host. Thus, the adaptive immune response and role of pro- and anti-inflammatory cytokine signaling in hypertension are distinct between the sexes and need to be understood to improve therapeutics for hypertension-associated disease in both men and women.
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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".