Direct conversion of human pluripotent stem cells into cranial motor neurons using a piggyBac vector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human pluripotent stem cells (PSCs) are widely used for in vitro disease modeling. One of the challenges in the field is represented by the ability of converting human PSCs into specific disease-relevant cell types. The nervous system is composed of a wide variety of neuronal types with selective vulnerability in neurodegenerative diseases. This is particularly relevant for motor neuron diseases, in which different motor neurons populations show a different susceptibility to degeneration. Here we developed a fast and efficient method to convert human induced Pluripotent Stem Cells into cranial motor neurons of the branchiomotor and visceral motor subtype. These populations represent the motor neuron subgroup that is primarily affected by a severe form of amyotrophic lateral sclerosis with bulbar onset and worst prognosis. This goal was achieved by stable integration of an inducible vector, based on the piggyBac transposon, allowing controlled activation of Ngn2, Isl1 and Phox2a (NIP). The NIP module effectively produced electrophysiologically active cranial motor neurons. Our method can be easily extended to PSCs carrying disease-associated mutations, thus providing a useful tool to shed light on the cellular and molecular bases of selective motor neuron vulnerability in pathological conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it