Simplified Characterization of Heat-Localizing Nanocomposite Gels to Determine Their Clinical Relevance for Laser Tissue Welding
Bibliographic record
Abstract
Nanocomposite gels (NCGs) are photo-responsive materials intended for medical applications where heat localization is desired, such as in cancer therapy, targeted drug delivery, and as biosolders in laser tissue welding (LTW) for e.g. wound closure and accelerated healing or tear repair. When NCGs are selectively coated onto a surface, the coated region heats under irradiation to a greater extent than the uncoated surroundings. Current characterization methods for measuring the selective heating performance of these NCGs can have limited precision and relevance, as they place limited focus on quantifying heating localization, can sometimes rely on the use of infrared imaging, which is susceptible to emissivity effects, or oftentimes employ sample dimensions that are unrepresentative of their clinical applications. We report a simplified photothermal characterization method for NCGs. The method relies on a custom measurement system that consists of two parallel glass plates transparent to 800 nm light, separated by fixed spacers to allow for the confinement of gel samples to clinically relevant thicknesses as thin as 100 μm. A platinum thin film resistive temperature detector (RTD) in direct contact to the gel between the plates sufficiently provides precise measurements with low enough response time. Custom NCGs were prepared by dissolving hyaluronic acid and guar gum to a final polymeric concentration of 3% w/v in aqueous solutions with up to 1.4 nM gold nanorods (GNRs). The GNRs were tuned to plasmonically heat under ≈800 nm wavelength light. A total of 30 s of 2.4 W 808 nm near-infrared (NIR) laser irradiation resulted in a rise in RTD temperature of higher than 34 °C, sufficient for e.g. LTW. Serving as a safety metric, a plasmonic heat amplification factor, ξ, was defined for the NCG as the ratio of the temperature increase with GNRs by the increase without. The ξ of our NCGs was found to reach values above 1.5 under these lasing conditions, indicating their thermal suitability for administration to tissue. The reported method and concepts allow for effective characterization of novel laser-activated NCGs for LTW.
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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".