The Effects of Poststroke Captopril and Losartan Treatment on Cerebral Blood Flow Autoregulation in SHRsp with Hemorrhagic Stroke
Bibliographic record
Abstract
The ability of captopril and losartan treatment to restore cerebral blood flow (CBF) autoregulation after intracerebral hemorrhagic stroke (HS) was assessed in Kyoto-Wistar stroke-prone hypertensive rats (SHRsp). Laser Doppler techniques assessed CBF autoregulation in the middle cerebral artery (MCA) perfusion domain and a pressure myograph was used to measure pressure-dependent constriction (PDC) in isolated MCAs before and after stroke and after 13, 33, and 63 days of poststroke captopril or losartan treatment. The treatments did not lower blood pressure (BP) and equally suppressed plasma aldosterone after HS. The HS development was associated with the loss of CBF autoregulation, high CBF, increased CBF conductance to elevations in BP, and the loss of PDC in the MCAs. Both treatments restored these functions to prestroke levels within 13 days. The PDC and CBF autoregulation subsequently deteriorated after 63 days of captopril treatment while being maintained at prestroke levels over all durations of losartan treatment. The SHRsp subjected to 35 days of poststroke losartan treatment exhibited less blood-brain barrier (BBB) disruption and brain herniation than captopril-treated SHRsp. The superior ability of losartan to restore CBF autoregulation and myogenic function may have contributed to the more effective attenuation of cerebral damage after HS.
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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) | 0.001 | 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 | 0.001 |
| 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".