Telomerase and neuronal marker status of differentiated NT2 and SK‐N‐SH human neuronal cells and primary human neurons
Bibliographic record
Abstract
Upon treatment with retinoic acid, NTera-2 (NT2) human teratocarcinoma and SK-N-SH neuroblastoma cells can be induced to terminally differentiate into postmitotic neuronal cells. The neuronal cell yield obtained from the NT-2 cells is partially dependent on the time of differentiation (24-55 days). SK-N-SH cells differentiate into a mixed population of neuronal and epithelium-like cells. Here we report modified protocols that increase the number of differentiated NT-2 and SK-N-SH cells and that establish an enriched neuronal SK-N-SH-derived cell population essentially devoid of nonneuronal cells. Differentiated cells express the cytoskeleton-associated protein tau and other typical neuronal markers, such as Map2, Ngn1, NeuroD, Mash1, and GluR which are also expressed in primary human fetal neurons. Telomerase activity is down-regulated in differentiated cells, which is consistent with the telomerase status of primary fetal human neurons. Thus, differentiated NT2 and SK-N-SH cells may represent an excellent source for studies investigating the role of telomerase or other survival-promoting activities in protecting human neuronal cells from cell death-mediating stresses associated with neurodegenerative diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".