Pharmaceutical Gene Delivery Systems
Bibliographic record
Abstract
Introduction to gene therapy and guidelines to pharmaceutical development, Sean M. Sullivan sustaining transgene expression in vivo, Nelson S. Yew and Seng H. Cheng expression systems - regulated gene expression, Jeffrey L. Nordstrom mechanismsfor cationic lipids in gene transfer, Lisa S. Uyechi-O'Brien and Francis C. Szoka, Jr polymer-based gene delivery systems, Lionel Wightman, Ralf Kircheis, and Ernst Wagner chimeric gene delivery systems, Yasufumi Kaneda adenoviral vectors for genedelivery, Jonathan L. Bramson and Robin J. Parks gene delivery technology - adeno-associated virus, Barrie J. Carter retrovectors go forward, Jean-Christophe Pages and Olivier Danos device-mediated gene delivery - a review, Fiona McLaughlin and Alain Rolland nonviral approaches for cancer gene delivery, Vivian Wai-Yan Lui and Leaf Huang replicating adenoviral vectors for cancer therapy, Murali Ramachandra, John A. Howe, and G. William Demers cardiovascular gene therapy, Mikko P. Turunen, Mikko O.Hiltunen, Seppo Yla-Herttuala pulmonary gene therapy, Jane C. Davies, Duncan M. Geddes, and Eric W.F.W. Alton artificial chromosomes, Jonathan Black and Jean-Michel Vos.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.076 |
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".