A biosynthesized gold nanoparticle from Staphylococcus aureus – as a functional factor in muscle tissue engineering
Bibliographic record
Abstract
Nano-biosynthesis for gold nanoparticle (AuNP) using bacteria can produce the tailored functional AuNP because of the different bioactive molecules coating on the AuNP derived from different bacteria. However, the biosynthesis for the biocompatible AuNP from the harmful bacteria is still challenging, and the extensive application of the bacteria-derived AuNP in tissue repair is lacking. In this study, without other auxiliary chemical molecules, the gradient centrifugation was used to successfully remove the toxic part of the pristine AuNPs biosynthesized from Staphylococcus aureus (S. aureus). The purified S. aureus-derived AuNPs were proved to be beneficial for the muscle cells’ viability and could even protect the cells against the cardiotoxin damages. Furthermore, the S. aureus-derived AuNPs were assembled into an elastic scaffold to form the AuNPs-incorporated cardiac patch. The in vivo study in rat myocardial infarction (MI) models demonstrated that these S. aureus-derived AuNPs could be taken as a functional factor in the cardiac patch to promote MI repair, through decreasing the infarct area and improving the cardiac function of the infarct heart. This study provides a functional S. aureus-derived AuNP with tissue repair potential, which can be extensively applied in muscle tissue engineering.
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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".