Effects of acute hypovolaemia by furosemide on tracheal transepithelial potential difference and mucus in dogs
Bibliographic record
Abstract
Furosemide is a potent diuretic that affects water transfer across the respiratory epithelium, which is closely related to the transepithelial potential difference (PD). Water is a critical factor that determines mucus transport; an important lung defence mechanism that removes particles and microorganisms from the respiratory system. The aim of the present study was to investigate the acute effects of furosemide and hypovolaemia on tracheal PD and mucus properties. A total of 36 male mixed-breed dogs were submitted to anaesthesia, mechanical ventilation and haemodynamic monitoring. They were randomly assigned to three groups consisting of: a control group, a furosemide (40 mg i.v.) + hypovolaemia group, and a furosemide (40 mg i.v.) + volume replacement group. Tracheal PD and mucus samples were collected at time 0, 1 and 2 h after intervention. Mucus properties were analysed by means of a magnetic microrheometer and in vitro mucociliary transportability on the frog palate. Compared to controls, furosemide decreased PD to intermediate values, and only significantly when associated with hypovolaemia (-13+/-5 and -8+/-2 mV, time 0 and 2 h, respectively). In addition to the direct effect of furosemide, these results indicate that hypovolaemia also affects ion transport in the tracheal membrane. Furosemide and hypovolemia have no acute effects on respiratory mucus properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".