Effective control of biofilms by photothermal therapy using a gold nanorod hydrogel
Bibliographic record
Abstract
Biofilms are matrices synthesized by bacteria containing polysaccharides, DNA, and proteins. The development of biofilms in infectious processes can induce a chronic inflammatory response that may progress to the destruction of tissues. The treatment of biofilms is difficult because they serve as a bacterial mechanism of defense and high doses of antibiotics are necessary to treat these infections with limited positive results. It has been demonstrated that photothermal therapy using gold nanorods (AuNRs) is an attractive treatment because of its anti-biofilm activity. The purpose of this work was to generate a novel chitosan-based hydrogel embedded with AuNRs to evaluate its anti-biofilm activity. AuNRs were synthesized by the seed-mediated growth method and mixed with the chitosan-based hydrogel. Hydrogels were characterized and tested against two bacterial strains by irradiating the produced biofilm in the presence of the nanoformulation with a laser adjusted at the near infrared spectrum. In addition, the safety of the nanoformulation was assessed with normal human gingival fibroblasts. Results showed that a significant bacterial killing was measured when biofilms were exposed to an increase of 10°C for a short time of 2 min. Moreover, no cytotoxicity was measured when normal gingival fibroblasts were exposed to the nanoformulation using the bactericidal conditions. The development of the reported formulation can be used as a direct application to treat periodontal diseases or biofilm-produced bacteria that colonize the oral cavity.
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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".