Lysis of Dysplastic but not Normal Oral Keratinocytes and Tissue-Engineered Epithelia with Conditionally Replicating Adenoviruses
Bibliographic record
Abstract
There is no effective medical treatment for oral precancer, and surgery to remove these lesions is imprecise because abnormal mucosa extends beyond the visible lesion. Development of vectors for tumor-selective viral replication has been a significant advance, and viral lysis is well suited to destruction of oral precancerous mucosa. To facilitate evaluation of new treatments, we engineered dysplastic oral epithelium using keratinocytes isolated from dysplastic lesions. We show that these model systems recapitulate the key characteristics of the clinical lesions closely, and that topical delivery of the conditionally replicating adenovirus (CRAd) dl922-947 can lyse tissue-engineered epithelia that show mild, moderate, or severe dysplasia, but normal oral epithelia are very resistant to this treatment. The lytic effect is determined by various factors, including the grade and proliferation index of the dysplastic epithelia. The presence of suprabasal cycling cells, expression of the coxsackie adenovirus receptor (CAR), the transcription cofactor p300, and other aberrations that affect the regulation of the cell cycle or apoptosis and promote viral replication may also be important. The ability of dl922-947 to destroy engineered oral dysplasia was significantly greater than that observed using wild-type adenovirus, d/1520, or viruses modified to bypass cell entry dependent on the presence of CAR. Evidence of infection in clinical dysplastic lesions was also shown ex vivo using tissue explants. We conclude that dl922-947 may provide an efficient molecular cytotoxic to dissolve oral dysplastic lesions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".