Design, Characterization and Evaluation of Metallic Nano Biocomposites of Neomycin
Bibliographic record
Abstract
Neomycin is formulated into nanoparticles in order to increase the therapeutic efficacy, decrease the dose of drug and to decrease the topical dose related toxic effects. The present study was aimed at the preparation of zinc nanoparticles (ZN), chitosan nanoparticles (CN), zinc neomycin nanoparticles (ZNN) and zinc chitosan neomycin nanoparticles (ZCNN) in order to compare their antibacterial activity. Nanoparticles were prepared by subjecting the nano suspension containing the specified ingredients to stirring at 40oC for 4-5 h. The prepared nanoparticles were evaluated for particle size and surface morphology by Transmission Electron Microscopy (TEM), mean particle size and particle size distribution by DLS, percentage yield, loading efficiency, in vitro drug release by diffusion technique and agar cup plate method. TEM microphotographs and zeta sizer analysis revealed that the prepared nanoparticles were in the nanometric range, the particle size and particle size range of ZCNN was less compared to ZNN indicated more surface area of ZCNN. Among all the nanoparticles prepared, percentage yield, loading efficiency, in vitro drug release and zone of inhibition was found to be more for ZCNN. Thus, the results suggested that ZCNN act as promising drug delivery systems with better in vitro characteristics compared to other nanoparticles with increased therapeutic activity of neomycin.
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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) | 0.000 | 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".