Mucus dispersal by Dithiothreitol (DTT) reduces ciliary dyskinesia
Bibliographic record
Abstract
Primary Ciliary Dyskinesia (PCD) is predominantly an autosomal recessive disease that reduces normal cilia movement and impairs the mucociliary escalator leading to lung disease. Nasal brush biopsy samples often contain high levels of mucous that can interfere with ciliary beat frequency (CBF) and ciliary beat pattern (CBP). These secondary changes can hinder diagnostic testing for PCD using high-speed video analysis. To determine if DTT treatment reduced the effect of mucus overlying the respiratory epithelium on ciliary function (CBP and CBF). 5 Nasal brush biopsies from non-PCD subjects were taken and ciliated epithelium was resuspended in M199 with or without 10mM DTT. High speed video microscopy (HSVM) was used to determine the ciliary function and mucous. Samples that contained well-ciliated epithelium and mucous, that was unable to be cleared, were chosen for DTT treatment. DTT was added for 30 minutes and HSVM performed at 37°C. Movies were replayed in slow motion and CBF and CBP (% dyskinetic cilia) were determined. Total number of edges studied was 60, and the number of readings taken for evaluation of CBP and CBF was 246. DTT significantly (p<0.05) improved the CBP (dyskinetic cilia 84±8 cf 33±4%). CBF was not significantly (p>0.05) changed by DTT treatment (9.6±4 cf 10.6±0.6Hz). The effect of DTT treatment on cilia structure and epithelial histopathology will be presented. DTT is effective for mucous dispersal in ciliated biopsy samples, and in the samples studied, increased the proportion of cilia with a normal beat pattern. We are testing this approach on samples from PCD patients and healthy controls to see if it can improve our PCD diagnostic pathway for resolving mucous-induced dyskinesia.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".