Lung gene therapy—How to capture illumination from the light already present in the tunnel
Bibliographic record
Abstract
Gene therapy has been considered as the most ideal medical intervention for genetic diseases because it is intended to target the cause of diseases instead of disease symptoms. Availability of techniques for identification of genetic mutations and for in vitro manipulation of genes makes it practical and attractive. After the initial hype in 1990s and later disappointments in clinical trials for more than a decade, light has finally come into the tunnel in recent years, especially in the field of eye gene therapy where it has taken big strides. Clinical trials in gene therapy for retinal degenerative diseases such as Leber's congenital amaurosis (LCA) and choroideremia demonstrated clear therapeutic efficacies without apparent side effects. Although these successful examples are still rare and sporadic in the field, they provide the proof of concept for harnessing the power of gene therapy to treat genetic diseases and to modernize our medication. In addition, those success stories illuminate the path for the development of gene therapy treating other genetic diseases. Because of the differences in target organs and cells, distinct barriers to gene delivery exist in gene therapy for each genetic disease. It is not feasible for authors to review the current development in the entire field. Thus, in this article, we will focus on what we can learn from the current success in gene therapy for retinal degenerative diseases to speed up the gene therapy development for lung diseases, such as cystic fibrosis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".