Nanocluster Mediated Photothermia Improves Eradication Efficiency and Antibiotic Sensitivity of Helicobacter Pylori
Bibliographic record
Abstract
Abstract Helicobacter pylori (H. pylori) eradication plays a crucial role in gastric cancer prevention, but the antimicrobial resistance of H. pylori is obstructing this elimination process. In this study, we developed nanoclusters (NCs) from Zn0.3Fe2.7O4 nanoparticles using a poly(ethylene glycol)-b-poly(ε-caprolactone)-based nanocarrier as an innovative antibiotic-independent H. pylori management. The nanocluster showed minimal toxicity and maximal biocompatibility. With a low concentration (50 µg/mL) of NCs under a short time period (～2 min) of near-infrared (808nm) irradiation, we kept the culture medium temperature to 41 °C for 20 minutes with continuous irradiation. The heated NCs exhibited efficient photothermal effects and resulted in an excellent inhibition of H. pylori growth, adhesion ability and cell vacuolization ability in in vitro investigation. Transmission electron microscopy showed a dramatic morphologic change after NCs photothermia on H. pylori, including cell wall and membrane rupture, as well as ribosome damage. Besides, levofloxacin and clarithromycin resistance were improved after photothermal treatment in H. pylori NCTC 11637 and/or clinical strains, however metronidazole resistance was unchanged. We also discovered a significant decrease in the biofilm formation of H. pylori under the NCs-based photothermal application, while efflux pump function was unchanged. In summary, using this novel NCs-based photothermal approach, we were able to demonstrate in vitro a significant inhibition of both H. pylori growth and molecular toxicity, and its improvement in antibiotic resistance alone with the eradication of H. pylori biofilms previously believed to be resistant to conventional antibiotics.
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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".