Therapeutic time window for angiotensin‐(1–7) in acute lung injury
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There is presently no proven pharmacological therapy for the acute respiratory distress syndrome. Recently, we and others discovered that the heptapeptide angiotensin-(1-7) [Ang-(1-7)] shows significant beneficial effects in preclinical models of acute lung injury (ALI). Here, we aimed to identify the best time window for Ang-(1-7) administration to protect rats from oleic acid (OA) induced ALI. EXPERIMENTAL APPROACH: The effects of i.v. infused Ang-(1-7) were examined over four different time windows before or after induction of ALI in male Sprague-Dawley rats. Haemodynamic effects were continuously monitored, and loss of barrier function, inflammation and lung peptidase activities were measured as experimental endpoints. KEY RESULTS: Ang-(1-7) infusion provided the best protection against experimental ALI when administered by continuous infusion starting immediately after 30 min OA infusion till the end of the experiment (30-240 min). Both pretreatment (-60 to 0 min before OA) and short-term therapy (30-90 min) also had beneficial effects although less pronounced than the effects achieved with the optimal therapy window. Starting infusion of Ang-(1-7) 60 min after the end of OA treatment (90-240 min) did not protect barrier function or haemodynamics but still reduced myeloperoxidase activity and increased ACE2/ACE activity ratio respectively. CONCLUSIONS AND IMPLICATIONS: Our findings indicate that early initiation of therapy after ALI and continuous drug delivery are most beneficial for optimal therapeutic efficiency of Ang-(1-7) treatment in experimental ALI and, presumably accordingly, in clinical acute respiratory distress syndrome.
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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 | 0.001 |
| 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".