Water Supply and Sanitation in Peri-Urban Areas of Developing Country: A Case Study of Faisalabad, Punjab, Pakistan
Bibliographic record
Abstract
Airway clearance of pathogens and particulates relies on motile cilia. Impaired cilia motility can lead to reduction in lung function, lung transplant, or death in some cases. More than 50 proteins regulating cilia motility are linked to primary ciliary dyskinesia (PCD), a heterogeneous, mainly recessive genetic lung disease. Accurate PCD molecular diagnosis is essential for identifying therapeutic targets and for initiating therapies that can stabilize lung function, thereby reducing socioeconomic impact of the disease. To date, PCD diagnosis has mainly relied on nonquantitative methods that have limited sensitivity or require a priori knowledge of the genes involved. Here, we developed a quantitative super-resolution microscopy workflow: (i) to increase sensitivity and throughput, (ii) to detect structural defects in PCD patients' cells, and (iii) to quantify motility defects caused by yet to be found PCD genes. Toward these goals, we built a localization map of PCD proteins by three-dimensional structured illumination microscopy and implemented quantitative image analysis and machine learning to detect protein mislocalization, we analyzed axonemal structure by stochastic optical reconstruction microscopy, and we developed a high-throughput method for detecting motile cilia uncoordination by rotational polarity. Together, our data show that super-resolution methods are powerful tools for improving diagnosis of motile ciliopathies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".