Molecular investigation to RNA and protein based interaction induced <i>in vivo</i> biocompatibility of phytofabricated AuNP with embryonic zebrafish
Bibliographic record
Abstract
Implication of gold nanoparticles in industrial and day-to-day life products at extensive scale has raised concern about their toxicity to environment and human health. Moreover, quest of new technologies for production of biocompatible nanoparticles increased. This study explores the molecular toxicology of AuNP with enlightenment of their green synthesis using medicinal plant extract as reducing and stabilizing agent. Synthesized CAuNP were characterized for their physiochemical properties by standard techniques like FESEM, TEM, DLS, UV-Vis spectroscopy and FTIR. GCMS analysis revealed the involvement of -OH compounds for CAuNP synthesis. Determined size and zeta potential of CAuNP was found to be 21 ± 08 nm and -24 ± 11 mV with SPR peak at 554 nm. LC50 of CAuNP with zebrafish embryos was 69 ± 12 µg/ml compared to 52 ± 06 µg/ml of AuNP. Gold nanoparticles were found to exhibit concentration dependent morphological abnormalities with acute effect at cellular and molecular level. Experimental and computational analysis depicted the nanotoxicity of gold nanoparticles as a consequence of oxidative stress generation leading to apoptosis due to their influential interaction with Sod1, He1a and tp53 mRNA and proteins. The investigation deciphered the nanotoxicity of gold nanoparticles and suggested the implication of new green methodology for their future productions.
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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".