Efficient generation of human induced pluripotent stem cells from urine samples of patients with Fragile X syndrome
Bibliographic record
Abstract
Human induced pluripotent stem cells (iPSCs) are a valuable tool for studying human development and diseases. iPSCs can be generated by reprogramming from any somatic cells, however establishing primary cell cultures can involve invasive procedures (e.g., skin biopsy) and be labor-intensive. In this paper, we describe an efficient, reliable, and non-invasive method for cultivating primary urine-derived cells (UDCs) and efficiently reprogram them into iPSCs using a feeder-free and non-integrative system. This approach has several advantages: (i) UDCs collection and culture are non-invasive, straightforward, and do not require medical personnel; (ii) reprogramming UDCs using commercially available Sendai viruses is highly efficient and reliable; and (iii) iPSCs generated from UDCs demonstrate strong differentiation potential. To showcase the effectiveness of this method, we generated iPSC lines from UDCs of three control individuals and three patients with Fragile X syndrome.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".