Methods, potentials, and limitations of gene delivery to regenerate central nervous system cells
Bibliographic record
Abstract
Arvind Kumar1, Tryambak D Singh2, Santosh K Singh3, Satya Prakash41School of Biotechnology, Faculty of Science; 2Department of Medicinal Chemistry, Institute of Medical Sciences; 3Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India; 4Biomedical Technology and Cell Therapy Research Laboratory, Department of Biomedical Engineering and Physiology, Artificial Cells and Organs Research Centre, Faculty of Medicine, McGill University, Montreal, Québec, CanadaAbstract: This review evaluates methods, success and limitations of transgenes delivery in central nervous system (CNS). Both viral and nonviral (such as liposome mediated) methods, expression and stability of transgenes have been discussed. The controlled expression and delivery techniques of transgene at the injured or diseased sites have also been discussed. Mifepristone (RU486) and tetracycline-based switch system for controlled expression could be a very useful tool for clinical purposes. Here we emphasized the importance and consequences of viral- and nonviral-mediated transgenes transfer and therapeutic ability along with advantages of controlled expressions. Keywords: transgenes, viral vector, nonviral vector, RU486, tetracycline, CNS
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".