A Monolayer Culture Method for Neural Induction of Human Pluripotent Stem Cells v1
Bibliographic record
Abstract
Multipotent neural progenitor cells (NPCs) generate the major cell types of the central nervous system (CNS): neurons, astrocytes and oligodendrocytes. Human pluripotent stem cells (hPSCs), including embryonic stem (ES) cells and induced pluripotent stem (iPS) cells, can be directed to differentiate into NPCs using a variety of methods. This process, known as ‘neural induction’, must be efficient and reliable, in order to generate high-quality NPCs for downstream applications. Although neural induction of hPSCs using embryoid body (EB) formation allows for visual assessment of induction success via the formation of neural rosettes, the whole process can take anywhere between 16 and 19 days to get single-cell neural progenitor cells (NPCs). As a result, monolayer culture-based neural induction methods have recently gained popularity, since they enable single-cell NPCs to be obtained in as few as six days. Here we describe a procedure for neural induction using STEMdiff™ Neural Induction Medium in a monolayer culture-based system to efficiently generate PAX6-positive NPCs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".