CHEMICAL GENETICS REVEALS A COMPLEX FUNCTIONAL GROUND STATEOF NEURAL STEM CELLS
Bibliographic record
Abstract
The identification of self-renewing and multipotent neural stem cells (NSCs) in the mammalian brain holds promise for the treatment of neurological diseases and has yielded newinsight into brain cancer. However, the complete repertoire of signaling pathways that governs the proliferation and self-renewal of NSCs, which we refer to as the ‘ground state’, remains largely uncharacterized. Although the candidate gene approach has uncovered vital pathways in NSC biology, so far only a few highly studied pathways have been investigated. Based on the intimate relationship between NSC self-renewal and neurosphere proliferation,we undertook a chemical genetic screen for inhibitors of neurosphere proliferation in order to probe the operational circuitry of the NSC. The screen recovered small molecules known to affect neurotransmission pathways previously thought to operate primarily in the mature central nervous system; these compounds also had potent inhibitory effects on cultures enriched for brain cancer stem cells. These results suggest that clinically approved neuromodulators may remodel the mature central nervous system and find application in the treatment of brain cancer. (colour figure available in PDF version)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".