Radiodynamic Therapy with Photosensitizers: Mini-Review of Experimental and Clinical Studies
Bibliographic record
Abstract
Photodynamic Therapy (PDT) is a light-based method that uses photo-reactive molecules, such as different types of photosensitizers (PS), to destroy malignant tumors. As some authors testify, PS was shown to act as a radio-reactive molecule by enhancing generation of reactive oxygen species upon X-ray irradiation. The method of treatment, which is based on the combined use of PS and ionizing radiation, is called «Radiodynamic therapy» (RDT). The advantage of RDT over PDT is the X-ray’s penetrability through tissues, which will find many applications for treatment of deep malignant tumors. The authors of a number of research centers in Japan, Germany, Israel and Lithuania presented the experience of using RDT in experiments on tumor cell lines and animals with transplanted tumors. A clinical approbation of the method has been started in patients with unresectable forms of bladder and cervical cancer, with gliomas and other forms of malignant tumors. The data obtained in experiments on cultures of tumor cells and animals with transplanted tumors indicate a high antitumor efficacy of the RDT with various types of PS. This fact is confirmed by a statistically significant decrease in viable tumor cells with the combined use of RT and PS, as well as a pronounced inhibition of the growth of transplanted tumors compared with the control groups, including the group of radiation therapy in mono mode. The preliminary data obtained show good tolerability of the method in clinical oncology (no serious adverse reactions) and satisfactory antitumor efficacy (an increase in the frequency of objective responses and an increase in the % reduction in tumor volume, which made it possible to transfer them to a resectable state).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".