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Postmenopausal Hypertension

2010· article· en· W2149916063 on OpenAlexaff
Licy L. Yanes, Damián G. Romero, Radu Iliescu, Huimin Zhang, Deborah Davis, Jane F. Reckelhoff

Bibliographic record

VenueHypertension · 2010
Typearticle
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsMonsanto (Canada)
FundersNational Heart, Lung, and Blood Institute
KeywordsLosartanInternal medicineEndocrinologyBlood pressureMedicineMenopauseRenin–angiotensin systemPlasma renin activityAngiotensin IIPostmenopausal women

Abstract

fetched live from OpenAlex

After menopause, blood pressure increases in women. However, the underlying mechanisms responsible for postmenopausal hypertension are not completely understood. This study was conducted to determine the role that the renin-angiotensin system (RAS) plays in post-menopausal hypertension. Post-estrous cycling (postmenopausal) spontaneously hypertensive rats or young female controls were treated with losartan, an angiotensin (Ang) II type 1 receptor blocker, for 25 days. Mean arterial pressure was recorded continuously by radiotelemetry. Losartan significantly decreased blood pressure in postmenopausal rats and young female controls but failed to normalize blood pressure in postmenopausal rats to levels found in young controls. Plasma renin activity and plasma angiotensinogen were significantly elevated, and intrarenal Ang II type 1 receptor and renin mRNA expression were significantly downregulated in postmenopausal rats. Therefore, RAS only partially contributes to hypertension in postcycling spontaneously hypertensive rats, whereas hypertension in young females is mediated mainly by the RAS. The data suggest that other mechanisms besides activation of the RAS are likely involved in postmenopausal hypertension.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.025
GPT teacher head0.253
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations84
Published2010
Admission routes1
Has abstractyes

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