Three‐dimensional porous scaffolds at the crossroads of tissue engineering and cell‐based gene therapy
Bibliographic record
Abstract
In the last 20 years, more than 1,500 gene therapy clinical trials have been approved worldwide targeting a variety of indications, from inherited monogenic diseases to acquired conditions such as cancer, cardiovascular and infectious diseases. However, concerns about the safety and efficacy of gene therapy pharmaceuticals justify the development of alternative strategies to ensure the clinical translation of this still promising field. In particular, ex vivo gene therapy strategies using autologous adult stem cells coupled to three-dimensional (3D) porous scaffolds show great promises in preclinical studies. Developments in the fields of biomaterial sciences and tissue engineering have already helped understanding how we can harness to regenerative potential of many cell types to create artificial tissues and organs and vastly improve the engraftment of ex vivo manipulated adult stem cells. In this article, we will review the current state of the art in tissue engineering by exploring the various types of clinically available biomaterials and the methods used to process them into complex 3D scaffolds. We will then review how these technologies are applied in cell-based gene therapy and identify novel avenues of research that may benefit patients in the near future.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".