Ex Vivo Modification of Cells to Induce a Muscle-Based Expression
Bibliographic record
Abstract
Ex vivo gene therapy is a possible treatment for several muscular dystrophies. The best transgene to be expressed and the appropriate cell type to be used currently remain the subject of many investigations. The most adequate gene modification technique also remains to be established. Different transgenes have already been tested in animal models and transgenic mice. Several cell types were evaluated during the last decades and several vectors or transfection methods were analysed. From these essays, over time, several proofs of principles were made to demonstrate the feasibility of this type of therapy. For DMD, it is possible to express several truncated versions of dystrophin or exon skipping molecules. It is also possible to express other molecules that would mitigate the phenotype. Different cell types are also available. From the well documented myoblasts to the AC133 positive cells, the choice of cell types is exploding. Gene modification also evolved during the last decade. Efficient transfection technique and viral vectors are currently available. Given all these possibilities, the researcher has to make several choices. This review is trying to give clues of how to make those choices.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".