A new paradigm in respiratory hygiene: modulating respiratory secretions to contain cough bioaerosol without affecting mucus clearance
Bibliographic record
Abstract
BACKGROUND: Several strategies and devices have been designed to protect health care providers from acquiring transmissible respiratory diseases while providing care. In modulating the physical characteristics of the respiratory secretions to minimize the aerosolization that facilitates transmission of airborne diseases, a fundamental premise is that the prototype drugs have no adverse effect on the first line of respiratory defense, clearance of mucus by ciliary action. METHODS: To assess and demonstrate the primary mechanism of our mucomodulators (XLs), we have built our evidence moving from basic laboratory studies to an ex-vivo model and then to an in-vivo large animal model. We exposed anesthetized dogs without hypersecretion to different dose concentrations of aerosolized XL "B", XL "D" and XL "S". We assessed: cardio-respiratory pattern, tracheal mucus clearance, airway patency, and mucus viscoelastic changes. RESULTS: Exposure of frog palate mucus to XLs did not affect the clearance of mucus by ciliary action. Dogs maintained normal cardio-respiratory pattern with XL administration. Tracheal mucociliary clearance in anesthetized dogs indicated a sustained 40% mean increase. Tracheal mucus showed increased filance, and there was no mucus retention in the airways. CONCLUSION: The ex-vivo frog palate and the in-vivo mammalian models used in this study, appear to be appropriate and complement each other to better assess the effects that our mucomodulators exert on the mucociliary clearance defence mechanism. The physiological function of the mucociliary apparatus was not negatively affected in any of the two epithelial models. Airway mucus crosslinked by mucomodulators is better cleared from an intact airway and normally functioning respiratory system, either due to enhanced interaction with cilia or airflow-dependent mechanisms. Data obtained in this study allow us to assure that we have complied with the fundamental requirement criteria established in the initial phase of developing the concept of mucomodulation: Can we modulate the physical characteristics of the respiratory secretions to reduce aerosolization without affecting normal mucociliary clearance function, or even better improving it?
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".