Immunoregulatory Cell Depletion Improves the Efficacy of Photodynamic Therapy-Generated Cancer Vaccines
Bibliographic record
Abstract
Photodynamic therapy (PDT)-generated cancer vaccine represents an attractive potential application of PDT, therapeutic modality destroying targeted lesions by localized photooxidative stress. Since immunoregulatory cell activity has become recognized as a major obstacle to effective cancer immunotherapy, the present study examined their participation in the therapeutic effect of PDT cancer vaccine. Following protocols from previous studies, mouse with squamous cell carcinoma SCCVII tumors were vaccinated by SCCVII cells treated by PDT and response monitored by tumor size measurement. The effects of low-dose cyclophosphamide (50 mg/kg) and all-trans retinoic acid (ATRA) on the numbers of Tregs and myeloid-derived suppressor cells (MDSCs) were determined by antibody staining followed by flow cytometry, while their impact on PDT vaccine therapy was evaluated by monitoring changes in tumor responses. Cyclophosphamide effectively reduced the numbers of Tregs, which became elevated following PDT vaccine treatment, and this resulted in an increase in the vaccine's effectiveness. A similar benefit for the therapy outcome with PDT vaccine was attained by ATRA treatment. The activities of Tregs and MDSCs thus have a critical impact on therapy outcome with PDT vaccine and reducing their numbers substantially improves the vaccine's effectiveness.
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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 | 0.000 |
| 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".