Mreg Activity in Tumor Response to Photodynamic Therapy and Photodynamic Therapy-Generated Cancer Vaccines
Bibliographic record
Abstract
Myeloid regulatory cells (Mregs) are, together with regulatory T cells (Tregs), a dominant effector population responsible for restriction of the duration and strength of antitumor immune response. Photodynamic therapy (PDT) and cancer vaccines generated by PDT are modalities whose effectiveness in tumor destruction is closely dependent on the associated antitumor immune response. The present study investigated whether the immunodepletion of granulocytic Mregs in host mice by anti-GR1 antibody would improve the response of tumors to PDT or PDT vaccines in these animals. Anti-GR1 administration immediately after Temoporfin-PDT of mouse SCCVII tumors abrogated curative effect of PDT. The opposite effect, increasing PDT-mediated tumor cure-rates was attained by delaying anti-GR1 treatment to 1 h post PDT. With PDT vaccines, multiple anti-GR1 administrations (days 0, 4, and 8 post vaccination) improved the therapy response with SCCVII tumors. The results with PDT suggest that neutrophils (boosting antitumor effect of this therapy) that are engaged immediately after photodynamic light treatment are within one hour replaced with a different myeloid population, presumably Mregs that hampers the therapy-mediated antitumor effect. Anti-GR1 antibody, when used with optimal timing, can improve the efficacy of both PDT of tumors in situ and PDT-generated cancer vaccines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".