PCD Diagnosis by Super resolution (3DSIM) microscopy
Bibliographic record
Abstract
Primary ciliary dyskinesia (PCD) is a heterogeneous recessive genetic disease characterized by impaired mucociliary clearance. PCD is currently diagnosed by analysis of transmission electron microscopy micrographs or genome sequencing of biallelic pathogenic variants in one of 37 known PCD genes. Despite recent advances in the genetics of PCD, there are suspected PCD cases that remain unresolved. Methods: We used immunofluorescence microscopy together with 3DSIM super-resolution microscopy (SRM) on airway epithelial cells freshly isolated from nostril of patients or airway cells grown on air liquid interphase to confirm or refute cases of PCD where transmission electron microscopy was non-diagnostic or where variants of unknown significance were identified in known PCD genes by genetic testing. In all cases, the laboratory technician developing and analyzing the images was blind to the TEM and genetic results of the patient. Results: To date, 7 patients with possible or confirmed PCD have been recruited and imaged by super-resolution microscopy. In all cases, SRM was helpful in either confirming or refuting the diagnosis of PCD. This includes cases with DNAH11, CCDC39, CCDC40, DNAH5, RPGR and CCNO variants. Conclusions: SRM can be a useful diagnostic tool to either confirm or refute the diagnosis of PCD in cases that cannot be solved with either transmission electron microscopy or current clinical genetic testing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".