Recent advances in the use of CRISPR/Cas for understanding the early development of molecular gaps in glial cells
Bibliographic record
Abstract
Glial cells are non-neuronal elements of the nervous system (NS) and play a central role in its development, maturation, and homeostasis. Glial cell interest has increased, leading to the discovery of novel study fields. The CRISPR/Cas system has been widely employed for NS understanding. Its use to study glial cells gives crucial information about their mechanisms and role in the central nervous system (CNS) and neurodegenerative disorders. Furthermore, the increasingly accelerated discovery of genes associated with the multiple implications of glial cells could be studied and complemented with the novel screening methods of high-content and single-cell screens at the genome-scale as Perturb-Seq, CRISP-seq, and CROPseq. Besides, the emerging methods, GESTALT, and LINNAEUS, employed to generate large-scale cell lineage maps have yielded invaluable information about processes involved in neurogenesis. These advances offer new therapeutic approaches to finding critical unanswered questions about glial cells and their fundamental role in the nervous system. Furthermore, they help to better understanding the significance of glial cells and their role in developmental biology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".