A new paradigm in respiratory hygiene: increasing the cohesivity of airway secretions to improve cough interaction and reduce aerosol dispersion
Bibliographic record
Abstract
BACKGROUND: Infectious respiratory diseases are transmitted to non-infected subjects when an infected person expels pathogenic microorganisms to the surrounding environment when coughing or sneezing. When the airway mucus layer interacts with high-speed airflow, droplets are expelled as aerosol; their concentration and size distribution may each play an important role in disease transmission. Our goal is to reduce the aerosolizability of respiratory secretions while interfering only minimally with normal mucus clearance using agents capable of increasing crosslinking in the mucin glycoprotein network. METHODS: We exposed mucus simulants (MS) to airflow in a simulated cough machine (SCM). The MS ranged from non-viscous, non-elastic substances (water) to MS of varying degrees of viscosity and elasticity. Mucociliary clearance of the MS was assessed on the frog palate, elasticity in the Filancemeter and the aerosol pattern in a "bulls-eye" target. The sample loaded was weighed before and after each cough maneuver. We tested two mucomodulators: sodium tetraborate (XL"B") and calcium chloride (XL "C"). RESULTS: Mucociliary transport was close to normal speed in viscoelastic samples compared to non-elastic, non-viscous or viscous-only samples. Spinnability ranged from 2.5 +/- 0.6 to 50.9 +/- 6.9 cm, and the amount of MS expelled from the SCM increased from 47 % to 96 % adding 1.5 microL to 150 microL of XL "B". Concurrently, particles were inversely reduced to almost disappear from the aerosolization pattern. CONCLUSION: The aerosolizability of MS was modified by increasing its cohesivity, thereby reducing the number of particles expelled from the SCM while interfering minimally with its clearance on the frog palate. An unexpected finding is that MS crosslinking increased "expectoration".
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".