Inhaled nitric oxide prevents NSAID-induced renal impairment in pseudo-normovolaemic piglets
Bibliographic record
Abstract
OBJECTIVE: Inhaled nitric oxide (iNO) is commonly used as a treatment of pulmonary hypertension. Its action is purported to be specific to the lung, but extrapulmonary effects have been reported. The objective of this study was to evaluate if iNO could compensate the renal impairment induced by ketoprofen, a conventional non-steroidal anti-inflammatory drug (NSAID), during general anaesthesia. METHODS: Under pseudo-normovolaemic condition, thirty piglets were randomly assigned into 5 equal groups and equipped for renal and systemic parameters measurements. A first experiment was carried out to validate methods and reproduce the renal effects of iNO (40 ppm) in comparison with a placebo (100% oxygen). In a second experiment, iNO was inhaled for 120 minutes right after NSAID treatment (ketoprofen 2 mg×kg-1 IV, and 40 ppm iNO; group KiNO) and its effects were compared to ketoprofen alone (2 mg×kg-1 IV; group K) and placebo (saline; group C). RESULTS: In this model, iNO increased significantly renal blood flow measured by ultrasonic (RBFUL: +53.2±17.2%; p = 0.008) and by PAH clearance (RBFPAH:+78.6±37.6%; p = 0.004) methods, glomerular filtration rate (GFR: +72.6±32.5%; p = 0.006) and urinary output (UO: +47.4±24.2%; p = 0.01). In the second experiment, no significant temporal variation was noted for renal parameters in groups KiNO and C, whereas a significant and constant decrease was observed in the group K for RBFUL (max -19.0±7.1%), GFR (max -26.6±10.4%) and UO (max -30.3±10.5%). CLINICAL SIGNIFICANCE: Our experiments show that iNO, released from its transport forms after its inhalation, can improve renal safety of NSAIDs. This result is promising regarding the use of NSAIDs in critical conditions, but needs to receive clinical confirmation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".