Nanoparticles based image-guided thermal therapy and temperature feedback
Bibliographic record
Abstract
nanomaterials, such as hydrodynamic radii and core sizes at sub-tissue levels, are elucidated. The review then delves into specific modalities of thermally induced therapy, including magnetically induced hyperthermia and luminescent-based thermal treatments. Magnetic hyperthermia treatment is explored alongside its imaging and relaxometric properties, emphasizing the implications of imaging formulations on biotransformation and biodistribution. This review also provides an overview of the magnetic hyperthermia treatment using magnetic nanoparticles to induce localized heat in tissues. Similarly, optical and thermal imaging techniques utilizing luminescent nanomaterials are discussed, highlighting their potential for light-induced thermal therapy and cellular-level temperature monitoring. Finally, the application landscape of diagnosis and photothermal therapy (PTT) is surveyed, encompassing diverse areas such as cancer treatment, drug delivery, antibacterial therapy, and immunotherapy. The utility of nanothermometers in elucidating thermal relaxation dynamics as a diagnostic tool is underscored, alongside discussions on PTT hyperthermia protocols. Moreover, the advancements in nanoparticle magnetic imaging and implications of imaging formulations especially in creating positive MRI contrast agents are also presented. This comprehensive review offers insights into the evolving landscape of nanoparticle-based image-guided thermal therapies, promising advancements in precision medicine and targeted interventions, underscoring the importance of continued research in optimization for the full potential of magnetic hyperthermia to improve its efficacy and clinical translation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".