Photodynamic therapy-generated vaccines: relevance of tumour cell death expression
Bibliographic record
Abstract
Recent investigations have established that tumour cells treated in vitro by photodynamic therapy (PDT) can be used for generating potent vaccines against cancers of the same origin. In the present study, cancer vaccines were prepared by treating mouse SCCVII squamous cell carcinoma cells with photosensitiser chlorin e6-based PDT and used against poorly immunogenic SCCVII tumours growing in syngeneic immunocompetent mice. The vaccine potency increased when cells were post-incubated in culture after PDT treatment for 16 h before they were injected into tumour-bearing mice. Interfering with surface expression of phosphatidylserine (annexin V treatment) and apoptosis (caspase inhibitor treatment) demonstrated that this post-incubation effect is affiliated with the expression of changes associated with vaccine cell death. The cured mice acquired resistance to re-challenge with the same tumour, while the engagement of cytotoxic T lymphocytes was demonstrated by detection of high numbers of degranulating CD8+ cells in vaccinated tumours. The vaccines prepared from ex vivo PDT-treated SCCVII tumour tissue were also highly effective, implying that surgically removed tumour tissue can be directly used for PDT vaccines. This opens attractive prospects for employing PDT vaccines tailored for individual patients targeting specific antigens of the patient's tumour.
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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".