Translating the combination of gene therapy and tissue engineering for treating recessive dystrophic epidermolysis bullosa
Bibliographic record
Abstract
The combination of gene therapy and tissue engineering is one of the most promising strategies for the treatment of recessive dystrophic epidermolysis bullosa (RDEB). RDEB is a rare genetic disease characterised by mutations in the COL7A1 gene, encoding type VII collagen (COLVII), which forms anchoring fibrils at the dermal-epidermal junction of the skin. This disease causes severe blistering and only palliative treatments are offered. In this study, the base of a strategy combining gene therapy and a tissue-engineered skin substitute (TES), which would be suitable for the permanent closure of skin wounds, was set-up. As a high transduction efficiency into fibroblasts and/or keratinocytes seems to be a prerequisite for a robust and sustained correction of RDEB, different envelope pseudotyped retroviral vectors and the transduction enhancer EF-C were tested. When green fluorescent protein (GFP) was used as a reporter gene to evaluate the retroviral-mediated gene transfer, the fibroblast infection efficiency was 30 % higher with the Ampho pseudotyped vector as compared with the other pseudotypes. At least a 3.1-fold and a 1.3-fold increased transduction were obtained in fibroblasts and keratinocytes, respectively, with EF-C as compared with polybrene. A continuous and intense deposit of haemagglutinin (HA)-COLVII was observed at the dermal-epidermal junction of self-assembled TESs made of cells transduced with a HA-tagged COL7A1 vector. Furthermore, HA-tagged basal epidermal cells expressing keratin 19 were observed in TESs, suggesting stem cell transduction. This approach could be a valuable therapeutic option to further develop, in order to improve the long-term life quality of RDEB patients.
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 | 0.001 | 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".