ICG@ZIF@HA Nanoparticles and GO-Loaded Freeze-Drying Microneedles Mediated PDT/PTT Dual-Modal Phototherapeutic for Efficient Bacterial Biofilm Elimination
Bibliographic record
Abstract
Biofilms, which are dense bacterial aggregates that form protective adhesion layers on surfaces, are associated with chronic wound infections, severe acne, and various other bacterial infection sites. However, the increasing antibiotic resistance of bacteria within biofilms and the limited penetration capacity of conventional treatments pose significant therapeutic challenges. To address these issues, we engineered a freeze-drying microneedle system integrating indocyanine green-loaded ZIF-8/hyaluronic acid nanoparticles (ICG@ZIF@HA NPs) and graphene oxide (GO) for dual-modal phototherapy. Notably, the microneedles demonstrated robust mechanical strength, enabling penetration through 140 μm-thick Staphylococcus aureus biofilms. Subsequently, the disruption of the biofilm’s structural integrity facilitated the release of ICG@ZIF@HA NPs from the microneedle tips into deeper biofilm regions. Moreover, the nanoparticles exhibited pH-triggered release kinetics, with over 80% of ICG released at pH 5.5 within 2 h, and generated reactive oxygen species (ROS) under 808 nm near-infrared (NIR) irradiation. Meanwhile, the GO in the backing provided localized hyperthermia, reaching 55 °C within 500 s under NIR exposure. By combining ROS generation and bacterial membrane disruption, the microneedle system achieved a synergistic antimicrobial effect, effectively inhibiting bacterial growth and eliminating biofilms under NIR irradiation. Thus, this study establishes a robust foundation for integrating antimicrobial therapy with microneedle technology, offering a safer and more efficient strategy for biofilm eradication.
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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".