Infrared‐Emitting QDs for Thermal Therapy with Real‐Time Subcutaneous Temperature Feedback
Bibliographic record
Abstract
Nowadays, one of the most exciting applications of nanotechnology in biomedicine is the development of localized, noninvasive therapies for diverse diseases, such as cancer. Among them, nanoparticle‐based photothermal therapy (PTT), which destroys malignant cells by delivering heat upon optical excitation of nanoprobes injected into a living specimen, is emerging with great potential. Two main milestones that must be reached for PTT to become a viable clinical treatment are deep penetration of the triggering optical excitation and real‐time accurate temperature monitoring of the ongoing therapy, which constitutes a critical factor to minimize collateral damage. In this work, a yet unexplored capability of near‐infrared emitting semiconductor nanocrystals (quantum dots, QDs) is demonstrated. Temperature self‐monitored QD‐based PTT is presented for the first time using PbS/CdS/ZnS QDs emitting in the second biological window. These QDs are capable of acting, simultaneously, as photothermal agents (heaters) and high‐resolution fluorescent thermal sensors, making it possible to achieve full control over the intratumoral temperature increment during PTT. The differences observed between intratumoral and surface temperatures in this comprehensive investigation, through different irradiation conditions, highlight the need for real‐time control of the intratumoral temperature that allows for a dynamic adjustment of the treatment conditions in order to maximize the efficacy of the therapy.
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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) | 0.000 | 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".