Photothermal Ablation of Human Lung Cancer by Low-power Near-Infrared Laser and Topical Injection of Indocyanine Green
Bibliographic record
Abstract
The present study was designed to evaluate the efficacy of photothermal ablation therapy for lung cancer by low-power near-infrared laser and topical injection of indocyanine green (ICG). In vitro study 1: an 808 nm laser with 250 mW was irradiated for 10 minutes using different dilutions of ICG and the temporal thermal effect was monitored. ICG (1 mL of 0.5 g/L) was heated to a temperature of >30°C from the base temperature by laser irradiation. In vitro study 2: the cytotoxic effect of hyperthermia on human lung cancer cells was examined in different temperature and time settings. Cell viability was quantified by both an MTS assay and reculturing. Fatal conditions evaluated by reculturing were as follows: thermal treatment at 55°C for 5 minutes, 53°C for 10 minutes, and 51°C for 15 minutes. The MTS assay study suggested that thermal treatment at 59°C for 5 minutes and 57°C for 20 minutes showed a severe cytotoxic effect. In vivo study: nude mouse subcutaneous NCI-H460 human lung cancer xenograft models were used for the study. Saline or 0.5 g/L of ICG was injected topically into the tumor (n=3/group). Tumors were irradiated with a laser at 500 mW for 10 minutes. Although the tumor diameter reached 1 cm within 24 days after treatment in all 3 mice using saline/laser, tumor sizes were gradually reduced in all 3 mice using the ICG/laser. In 2 of the 3 mice using ICG/laser, tumors had disappeared macroscopically. The efficacy of the photothermal ablation therapy by low-power near-infrared laser and a topical injection of ICG was clarified using a mouse subcutaneous a lung cancer xenograft model.
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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) | 0.001 | 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".