NIR‐Responsive ZIF‐8 Metal‐Organic Framework Nanohybrids with Photothermal, Antimicrobial, and Osteoinductive Properties to Prevent Implant Infection
Bibliographic record
Abstract
Current treatments for bone injuries face notable limitations such as adverse reactions to implant materials and increased risks of infection. There is an essential need for a therapeutic that will address these issues and decrease recovery times. Herein, a multifunctional nanohybrid zinc-based metal-organic framework integrated with gold nanoparticles (Au@ZIF-8) is synthesized to promote antibacterial and osteogenic benefits. Au@ZIF-8 is capable of converting light energy into heat and has demonstrated its ability to increase the surrounding temperature by ≈30 °C. As a result, Au@ZIF-8 has exhibited bactericidal activity against methicillin-resistant Staphylococcus aureus (MRSA) upon exposure to near-infrared (NIR) irradiation. Concurrently, Au@ZIF-8 sustains the release of zinc ions from the nanohybrid for the potential of bone repair. When combined with a gelatin-based hydrogel, Au@ZIF-8 significantly elevated osteogenic gene expression and promoted preosteoclast differentiation through the sustained zinc ion release, as opposed to a gel-only control. The potential of the multifunctional nanohybrid is further demonstrated as a coating material for titanium orthopedic implants to introduce antibacterial properties and promote osteogenic differentiation of preosteoblasts for bone healing. Given its excellent antibacterial in response to NIR irradiation and osteogenic abilities, Au@ZIF-8 is a promising photothermal therapy for bone injuries.
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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".