An Antibacterial Nanorobotic Approach for the Specific Targeting and Removal of Multiple Drug‐Resistant <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) causes diseases ranging from skin infections to lethal sepsis and has become a serious threat to human health due to multiple-drug resistance (MDR). Therefore, a resistance-free antibacterial therapy is necessary to overcome MDR MRSA infections. In this study, an antibacterial nanorobot (Ab-nanobot) is developed wherein a cell wall-binding domain (CBD)-endolysin, acting as a sensor, is covalently conjugated with an actuator consisting of an iron oxide/silica core-shell. The CBD-endolysin sensor shows an excellent specificity to detect, bind, and accumulate on the S. aureus USA300 cell surface even in a bacterial consortium, and in host cell infections. Ab-nanobot specifically captures and kills MRSA in response to medically approved radiofrequency (RF) electromagnetic stimulation (EMS) signal. When Ab-nanobot receives the RF-EMS signal on the cell surface, actuator induces cell death in MRSA with 99.999% removal within 20 min by cell-wall damage via generation of localized heat and reactive oxygen species. The in vivo efficacy of Ab-nanobot is proven using a mice subcutaneous skin infection model. Collectively, this study offers a nanomedical resistance-free strategy to overcome MDR MRSA infections by providing a highly specific nanorobot for S. aureus.
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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".