Photoimmunotherapy for Cancer Treatment
Bibliographic record
Abstract
Phototherapy, with its effective dose control and light delivery, has become a promising modality for treating malignant and nonmalignant diseases. Photochemical interaction, specifically photodynamic therapy (PDT), and photothermal interaction have been the primary mechanisms for direct cell destruction in the treatment of cancers. Preclinical studies demonstrate that, in addition to direct local cytotoxicity, PDT can also induce systemic immune responses, which may enhance therapeutic effects on primary tumors and on metastases at distant sites. Selective photothermal therapy, using an in situ application of light-absorbing dye, has also proven to be an effective method for local treatment of tumors. When combined with immunotherapy, the effects of phototherapy can be amplified, potentially making the photoimmunotherapy a systemic treatment modality. This phototherapy-immunotherapy combination, particularly in conjunction with immunoadjuvant, has been used in preclinical studies. The efficacy and long-term effects of such a combination are summarized and the recent experimental results are presented. A new immunoadjuvant, glycated chitosan (GC), has been used to enhance photochemical and photothermal therapies. The PDT-GC combination in the treatment of mammary tumors and lung tumors in mice provided significant improvement in the long-term survival of tumor-bearing animals. The use of GC in dye-assisted laser photothermal therapy also provided long-term curative effects and antitumor immune responses in the treatment of metastatic tumors in rats. The immune responses induced by phototherapy and enhanced by immunotherapy could become important mechanism in the control of metastatic tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".