Dye-enhanced selective photothermal laser-tissue interaction and photodynamic therapy in combination with immunoadjuvant for cancer treatment
Bibliographic record
Abstract
Immunoadjuvants have been used to stimulate host immune responses. However, immunoadjuvants alone have not been very successful in treating metastatic tumors. Following the principle of combined therapy in AIDS treatment and in combination chemotherapy, immunoadjuvants have been used in conjunction with other treatment modalities. The current study is an attempt to use both selective photothermal and selective photochemical interactions to accompany a new immunoadjuvant in the treatment of metastatic tumors. The immunoadjuvant, glycated chitosan (GC), has been shown in the previous studies to be effective in inducing immune responses when combined with the treatment of laser irradiation after the intratumoral injection of indocyanine green solution. When glycated chitosan was used with photodynamic therapy (PDT), the treatment effect was significantly increased. Specifically, when glycated chitosan was injected peritumorally after Photofrin-based PDT treatment of EMT6 mammary sarcoma in mice, the tumor-free rate of the treated mice was increased from 38% to 75% using 1.5% GC solution. In mTHPC-based PDT treatment of Line-1 lung adenocarcinoma in mice, the tumor-free rates of treated mice reached 38% while PDT alone did not result in any tumor free mouse. The combination of the immunoadjuvant and selective photophysical interaction may become an effective method to treat tumors with an induced anti-tumor immunity.
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 | 0.000 | 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) | 0.001 | 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".