Inducing Targeted Mild Hyperthermia in Murine Tumor Models through Photothermal Conversion of Near-infrared Light by Intratumoral Gold Nanorods
Bibliographic record
Abstract
Mild hyperthermia (42-48 °C) is a well-established therapeutic modality that can induce controlled tumor cell death and stimulate anti-cancer immune responses. However, delivering heat precisely to tumor tissue while sparing surrounding healthy tissue remains a significant challenge. Traditional hyperthermia methods, such as isolated limb perfusion, require complex, invasive procedures and carry substantial risk of local toxicity and patient morbidity. In contrast, photothermal therapy using gold nanoparticles activated by near-infrared (NIR) light has emerged as a promising, less invasive strategy for achieving localized hyperthermia. Gold nanorods (GNRs), in particular, exhibit tunable optical properties and high photothermal conversion efficiency, making them ideal candidates for precise thermal modulation of tumor sites. Although this technique has shown considerable promise, especially for superficial and accessible tumors, reproducible delivery, spatial confinement, and safety remain active areas of refinement. In this protocol, we present our validated and optimized method for achieving localized, mild hyperthermia using intratumoral injection of biocompatible GNRs followed by short-duration, targeted NIR laser exposure. This approach enables rapid, controllable heating within the therapeutic range, promoting immunogenic tumor cell death and stimulating innate immune responses, mechanisms particularly relevant for immunologically "cold" tumors. Real-time temperature monitoring and local delivery ensure reproducibility, safety, and minimal systemic exposure. This streamlined protocol offers a robust and accessible platform for preclinical studies, supporting broader efforts to harness mild hyperthermia in cancer immunotherapy.
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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) | 0.001 | 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 | 0.001 |
| 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".