Bibliographic record
Abstract
Lung transplantation is effective life-saving therapy for the treatment of a variety of end-stage lung diseases. However, the application of lung transplantation is hindered by multiple factors such as the shortage of organ donors, early graft failure and chronic graft dysfunction. These problems are related to various lung injuries before and after transplantation including donor brain-death-related lung injury, ischemia, reperfusion and immune-mediated injuries. Gene transfection presents a potential molecular therapeutic solution to modify the transplanted organ such that it is better able to deal with these obstacles. In fact, in many ways lung transplantation is an ideal situation for gene therapy in that: 1) the targeted injuries are predictable (e.g. IR injury), 2) only transient gene expression is needed in many instances, 3) the immunosuppressive regimen necessary to prevent rejection of the transplanted organ attenuates vector-induced inflammation and the immune response to the vectors or the transgene products, and thus effectively augments and prolongs gene expression; 4) the anatomical structure of the lung enables trans-airway access and local gene delivery - as well as re-transfection. A number of issues need to be considered to develop a strategy of gene delivery in lung transplantation: administration route (intra-airway, trans-vascular, intravenous, intramuscular), timing (donor in-vivo, ex-vivo organ transfection or recipient), vector selection and gene selection. Based on our work and the work of others, over the last decade, we present the state of art of in gene therapy in lung transplantation and exciting future directions in the field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".