Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The outcome of the treatment of solid tumors by photodynamic therapy (PDT) is critically dependent on the contribution from the host. This host response is provoked by the rapidly induced massive tumor tissue injury delivered by PDT that is experienced as a local trauma threatening the integrity and homeostasis at the affected site. STUDY DESIGN/MATERIALS AND METHODS: Mouse tumor models were extensively employed in pre-clinical studies investigating various aspects of host-tumor interaction following PDT, but important input was also derived from clinical data. RESULTS: The recognition of this PDT-inflicted insult by innate immune sensors detecting danger signals from the distressed/altered tumor tissue, triggers host-protecting responses dominantly manifested as acute inflammation that are elicited and orchestrated by the innate immune system. To secure the affected PDT-targeted site, the inflammatory reaction attacks tumor vasculature and then neutralizes the focal source of danger signals by eliminating the injured tumor cells. CONCLUSION: The provoked highly intensified phagocytosis of dead tumor cells occurring in the context of a vigorous innate immune reaction emerges as a key factor responsible for the development of tumor antigen-specific adaptive immune response that contributes to the eradication of PDT-treated cancers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".