Inflammatory cell death induced by 5-aminolevulinic acid-photodynamic therapy initiates anticancer immunity
Bibliographic record
Abstract
Background: Inflammatory cell death is a form of programmed cell death (PCD) that induces inflammatory mediators during the process. The production of inflammatory mediators during cell death is beneficial in standard cancer therapies as it can break the immune silence in cancers and induce anticancer immunity. Photodynamic therapy (PDT) is a cancer therapy with photosensitizer molecules and light sources to destroy cancer cells, which is currently used for treating different types of cancers in clinical settings. In this study, we investigated if PDT using 5-aminolevulinic (5-ALA-PDT) causes inflammatory cell death and, subsequently, increases the immunogenicity of cancer cells. Methods: Mouse breast cancer (4T1) and human colon cancer (DLD-1) cells were treated with 5-ALA for 4 hours and then irradiated with a light source. PCD induction was measured by western blot analysis and FACS. Morphological changes were determined by transmission electron microscopy (TEM). BALB/c mice were injected with cell-free media, supernatant of freeze/thaw cells or supernatant of PDT cells intramuscular every week for 4 weeks and then challenged with 4T1 cells at the right hind flank of BALB/c. Tumor growth was monitored for 12 days. Results: . Moreover, when mice were pretreated with 5-ALA-PDT culture supernatant, the growth of 4T1 tumors was significantly suppressed compared to those pretreated with freeze and thaw (F/T) 4T1 culture supernatant. Conclusion: .
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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) | 0.002 | 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".