Multifunctional Gold Nanorod for Therapeutic Applications and Pharmaceutical Delivery Considering Cellular Metabolic Responses, Oxidative Stress and Cellular Longevity
Bibliographic record
Abstract
Multifunctional gold nanorods (GNR) have drawn growing interest in biomedical fields because of their excellent biocompatibility, ease of alteration, and special optical properties. The great advantage of using GNR in medicine is their application to Photothermal therapy (PPTT), which is possible thanks to their ability to turn luminous energy into heat to cause cellular hyperthermia. For this purpose, the relevant articles between 1988 and 2020 were searched in databases such as John Wiley, Free paper, Scopus, Science Direct, and Springer to obtain the latest findings on multifunctional gold nanorods for therapeutic applications and pharmaceutical delivery. In this article, we review recent progress in diagnostic and therapeutic applications of multifunctional GNR, highlighting new information about their toxicity to various cellular categories, oxidative stress, cellular longevity, and their metabolic effects, such as the effect on the energy cycles and genetic structures. The methods for the synthesis and functionalization of GNR were surveyed. This review includes new information about GNR toxicity to various cellular categories and their metabolic effects.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".