Nasal Brushing Sampling and Processing using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic
Bibliographic record
Abstract
Primary Ciliary Dyskinesia (PCD) is a genetic motile ciliopathy, leading to significant otosinopulmonary disease. PCD diagnosis is often missed or delayed due to challenges with different diagnostic modalities. Ciliary videomicroscopy, using Digital High-Speed Videomicroscopy (DHSV), one of the diagnostic tools for PCD, is considered the optimal method to perform ciliary functional analysis (CFA), comprising of ciliary beat frequency (CBF) and beat pattern (CBP) analysis. However, DHSV lacks standardized, published operating procedure for processing and analyzing samples. It also uses living respiratory epithelium, a significant infection control issue during the COVID-19 pandemic. To continue providing a diagnostic service during this health crisis, the ciliary videomicroscopy protocol has been adapted to include adequate infection control measures. Here, we describe a revised protocol for sampling and laboratory processing of ciliated respiratory samples, highlighting adaptations made to comply with COVID-19 infection control measures. Representative results of CFA from nasal brushing samples obtained from 16 healthy subjects, processed and analyzed according to this protocol, are described. We also illustrate the importance of obtaining and processing optimal quality epithelial ciliated strips, as samples not meeting quality selection criteria do now allow for CFA, potentially decreasing the diagnostic reliability and the efficiency of this technique.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".