NANO-GOLD AND NANO-ZINC OXIDE: EFFECT ON GUSTATORY RECEPTOR GENES IN FLIES
Bibliographic record
Abstract
Drinking colloidal gold as elixir of life is an age-old practice worldwide. A large body of data containing patients' experiences after intake of colloidal gold for long duration would be available in the medical records of hospitals. ZnO has been approved by FDA for topical use and not for oral intake. Drosophila melanogaster (wild type) strains were fed with physiologically relevant concentrations of nano-gold and nano- ZnO along with appropriate controls. Citrate-capped nano-gold (average particle size is 15–20 nm) synthesized by reducing hydrogen tetrachloroaurate with 1% trisodium citrate and custom-made nano- ZnO , purchased from M K Implex, Canada (average particle size 50 nm) were used as treatments. Microarray studies revealed that fly trehalose receptor genes, Tre and Tre1, are both unaffected after nano-gold and nano- ZnO treatment. Gr64 subfamily members (encoding sugar receptors like glucose, sucrose, and maltose), for example, Gr64a-b become downregulated, but Gr64c, Gr64d, Gr64f remain unaltered in case of both the treatments. Among bitter receptor genes, Gr66a is the most well studied and shows significant downregulation by nano-gold and not by nano- ZnO . Ppk11 and Ppk19 are gustatory ion channel genes which modulate salt perception. Ppk11 was found to be downregulated by both nano-gold and nano- ZnO , while ppk19 expression is suppressed by nano-gold treatment but not by nano- ZnO . The effects of these two nanoparticles on pheromone receptors (Gr32a, Gr39a, and Gr68a) and CO 2 receptors (Gr21a and Gr63a) are presented. To the best of our knowledge, this is the first report on the effect of pure nanoparticles on gustation. Data has been analyzed in the light of the age-old tradition of oral administration of the nano-gold viz-a-viz topical use of nano- ZnO . These results would have far reaching implications in the design of nano-gold mediated oral drug delivery of cancer and other drugs as well as nano- ZnO coated drugs/cosmetics and nano- ZnO carrier based drug delivery in skins and in other topical applications.
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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".