A viral transduction approach for iPSC differentiation into cortisol-producing steroidogenic cells
Bibliographic record
Abstract
Abstract Steroid hormones are important signaling molecules that are primarily produced by specialized cells. The ability to culture steroidogenic cells is critical to study this important process. While a few steroidogenic cell lines are available for study, they are typically derived from cancer patients and do not reflect genetic alterations in steroidogenic genes that cause human disease. In this regard, iPSCs are a powerful tool for modeling human disease, as patient-derived iPSCs can be differentiated into a variety of different cell types. While other approaches exist to differentiate iPSCs into steroidogenic cells, they are typically complex and time consuming. In contrast, a simple approach based on lentivirus transduction of SF1, the master regulator of steroidogenesis, can differentiate multiple types of cultured cells into a steroidogenic state. However, we show here that this approach does not work for iPSCs. To circumvent this limitation, we report a simple adaptation that first differentiates iPSCs to embryoid bodies prior of SF1 transduction. With this modified approach, we provide a straightforward cost-effective approach to differentiate iPSCs into actively steroidogenic cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".