Possible Solutions to the Associated Cytotoxicity of Silver Nanoparticles
Bibliographic record
Abstract
Nanotechnology is the emerging field because of its antimicrobial and antibacterial activities. Because of these activities they can be used in different industries now-a-days. Because of this reason silver nanoparticles can be used in the drugs which may treat many fungal, bacterial and microbial diseases in human beings. Besides of these application, its concern related to the biological impacts of silver nanoparticles are also studied. Also their emerging risks and their problems in environment and living organisms are also studied. Many studies have been published in which their cytotoxicity effects on human health and on environment are investigated. An important factor related to the cytotoxicity of nanoparticles is that they may also increase the damage of genetic material as they can cross the cell membrane and nucleus because of their small size. So, there is an interest in studying the cytotoxicity of silver nanoparticles and also gives its possible solution to reduce its toxicity which may harm the human body or environment. For this reason, difference sizes of nanoparticles were used and synthesized to check their effect on environment and human body. Many studies are required for the solutions of the toxicity related to silver nanoparticles. This review is related to the recent works and publications and researches on the solutions of the toxicity related to silver nanoparticles and also about the more and safe application of silver nanoparticles. This study tells us that the silver nanoparticles which are naturally occurring are not toxic to the human body and environment instead of the silver nanoparticles which are artificially synthesized. This review also tells that that the human cells shows resistivity towards the cytotoxicity of silver nanoparticles while the silver nanoparticles toxicity may affect other organs very much. Also it concludes that how we can resist the toxicity of the chemically synthesized silver nanoparticles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".