Enhancement of Laser Cancer Treatment by a Chitosan‐derived Immunoadjuvant<sup>¶</sup>
Bibliographic record
Abstract
ABSTRACT A chitosan derivative, glycated chitosan (GC), has been used as an immunostimulant for cancer treatment in laser immunotherapy. The function of GC is to enhance the host immune response after direct cancer cell destruction by a selective laser photothermal interaction. To further test its effects, laser immunotherapy was extended to include several different adjuvants for immunological stimulation and to include photodynamic therapy (PDT) as a different tumor‐destruction mechanism. Complete Freund (CF) adjuvant, incomplete Freund (IF) adjuvant and Corynebacterium parvum (CP) were selected for treatment of metastatic mammary tumors in rats, in combination with a selective photothermal interaction. The solution of the immunoadjuvants admixed with indocyanine green (ICG), a light‐absorbing dye, was injected directly into the tumors, followed by noninvasive irradiation of an 805 nm laser. Combined with PDT, in the treatment of tumors in mice, GC was administered peritumorally immediately after laser irradiation. The survivals of treated animals were compared with untreated control animals. In the treatment of rat tumors, CF, IF and CP raised the cure rates from 0% to 18%, 7% and 9%, respectively. In comparison, GC resulted in a 29% long‐term survival. In the treatment of EMT6 mammary sarcoma in mice, GC of 0.5% and 1.5% concentrations increased the cure rates of Photofrin‐based PDT treatment from 38% to 63% and 75%, respectively. In the treatment of Line 1 lung adenocarcinoma in mice, a 1.67% GC solution enabled a noncurative meso ‐substituted tetra( meta ‐hydroxy‐phenyl)chlorin–based PDT to cure 37% of the tumor‐bearing mice. The experimental results of this study confirmed our previous studies, showing that immunoadjuvants played an active role in laser‐related cancer treatment and that GC significantly enhanced the efficacy of laser cancer treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".