Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it