Non-invasive sources of cells with primary cilia from pediatric and adult patients
Bibliographic record
Abstract
BACKGROUND: Ciliopathies give rise to a multitude of organ-specific pathologies; obtaining relevant primary patient material is useful for both diagnostics and research. However, acquisition of primary ciliated cells from patients, particularly pediatric patients, presents multiple difficulties. Biopsies and blood samples are invasive, and patients (and their parents) may be reluctant to travel to medical centers, especially for research purposes. We sought to develop non-invasive methods of obtaining viable and ciliated primary cells from ciliopathy patients which could be obtained in the home environment. FINDINGS: We introduce two methods for the non-invasive acquisition of primary ciliated cells. In one approach, we collected spontaneously shed deciduous (milk) teeth from children. Fibroblast-like cells were observed after approximately 2 weeks of culture of fragmented teeth. Secondly, urine samples were collected from children or adults. Cellular content was isolated and after approximately 1 week, renal epithelial cells were observed. Both urine and tooth-derived cells ciliate and express ciliary proteins visible with immunofluorescence. Urine-derived renal epithelial cells (URECs) are amenable to 3D culturing, siRNA knockdown, and ex vivo drug testing. CONCLUSIONS: As evidence continues to accumulate showing that the primary cilium has a central role in development and disease, the need for readily available and ciliated patient cells will increase. Here, we introduce two methods for the non-invasive acquisition of cells with primary cilia. We believe that these cells can be used for further ex vivo study of ciliopathies and in the future, for personalized medicine.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".