A Perspective on Therapeutic Applications and Strategies to Mitigate Toxicityof Metallic Nanoparticles
Bibliographic record
Abstract
Metallic nanoparticles (MNPs) have been widely used for diagnostic and therapeutic purposes in clinical practice. A number of MNP formulations are being investigated in clinical trials for various applications. This increase in the use of NPs results in higher exposure to humans, leading to toxicity issues. Hence, it is necessary to determine the possible undesirable effects of the MNPs after in-vivo application and exposure. One of the main reasons for the toxicity of MNPs is the release of their respective metallic ions throughout the body. Many research studies are in progress investigating the various strategies to reduce the toxicity of MNPs. These research studies aim to change the size, dose, agglomeration, release, and excretion rates of MNPs. In this perspective review, we discussed the possible strategies to improve the therapeutic effects of MNPs through various processes, with lessons learned from the studies involving silver nanoparticles (AgNPs). We also discussed the ways to manage the toxicity of MNPs by purification, surface functionalization, synergistic effect, and targeted therapy approach. All these strategies could reduce the dose of the MNPs without compromising their therapeutic benefits, which could decrease the toxicity of MNPs. Additionally, we briefly discussed the market and toxicology testing for FDA-regulated MNPs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".