Imaging the Modulation of Adenoviral Kinetics and Biodistribution for Cancer Gene Therapy
Bibliographic record
Abstract
To explore systemic utilization of Epstein-Barr virus (EBV)-specific transcriptionally targeted adenoviruses, three vectors were constructed to examine kinetics, specificity, and biodistribution: adv.oriP.luc, expressing luciferase under EBV-specific control; adv.SV40luc, expressing luciferase constitutively; and adv.oriP.E1A.oriP.luc, a conditionally replicating adenovirus, expressing both luciferase and E1A. Bioluminescence imaging (BLI) was conducted on tumor-bearing severe combined immunodeficient (SCID) mice (C666-1, EBV-positive human nasopharyngeal cancer) treated intravenously (i.v.) with 3 x 10(8) infectious units (ifu) of the adenoviral vectors. At 72 hours, adv.oriPluc demonstrated an 8.4-fold higher tumor signal than adv.SV40luc; adv.oriP.E1A.oriP.luc was 26.7-fold higher; however, a significant liver signal was also observed, necessitating further action to improve biodistribution. Several compounds were examined to this end, including norepinephrine, serotonin, clodronate liposomes, and STI571, to determine whether any of these measures could improve adenoviral biodistribution. Each of these interventions was assessed using BLI in mice i.v. injected with adv.oriP.luc. STI571 achieved the highest increase in tumor-to-liver ratio (TLR; 6.6-fold), which was associated with a 59% reduction in tumor interstitial fluid pressure (IFP) along with a decrease in platelet-derived growth factor-beta receptor (PDGF beta R) activation. This study reports the favorable modulation by STI571 of the biodistribution of adenoviral vectors, providing a potential approach to improving therapeutic outcome.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".