Enhancing laser thermal-therapy using ultrasound-microbubbles and gold nanorods: In vitro investigation
Bibliographic record
Abstract
Gold nanorods (GNR) in laser-induced thermal therapy can significantly increase light absorption, leading to a local temperature increase and causing irreversible cell damage. One of the key challenges in using GNR as a thermal therapy agent is to deliver a concentration of GNR to generate sufficient heat and cause cell death. In this study, ultrasound and microbubble induced sonoporation is used to enhance intracellular uptake of GNR and improve the therapeutic outcome of laserinduced thermal therapy. Acute myeloid leukemia (AML) cells in suspension (0.6 mL) were treated with ultrasound and microbubbles (USMB) at 1 MHz frequency, 16 microseconds pulse duration, 1 kHz pulse repetition frequency, 1 minute insonation time, varying acoustic pressures (0, 1.26 and 1.73 MPa) and 10 μL Definity microbubble agent with and without GNR (12 nm × 48 nm) at varying concentration (1.0×1010 to 2.5×1011 GNR/mL). The GNR were manufactured through wet chemical synthesis process and measured using Transmission Electron Microscopy (TEM) and Atomic Absorption Spectroscopy (AAS) for size and concentration respectively. Following ultrasound and microbubble treatment, cells were centrifuged to remove excess gold nanorods and treated in suspension with an 810 nm laser (Diomed 60 NIR) at 4 W for 5 minutes. A thermal camera (FLIR Thermovision A40) was positioned to monitor the sample temperature throughout laser treatment and cell viability was assessed using flow cytometry with propidium iodide. Cell viability of 18±2% was achieved with GNR+USMB (1.26 MPa) compared to 72±3% with GNR alone (12 hour incubation) and 99±0.2% with USMB (1.26 MPa) alone. With increasing GNR concentration during ultrasound and microbubble treatment, laser induced sample temperature increased and consequently cell viability decreased. Cell viability decreased from 92±1% at 1.0×1011 GNR/mL to 29±5% at 1.5×1011 GNR/mL concentration with corresponding maximum temperatures of 50°C and 54°C, respectively. The combined treatment of ultrasound-microbubble and gold nanorod laser induced thermal-therapy showed a synergistic enhancement of cell death in vitro. This study shows promise for an enhanced therapeutic effect with the combined treatment in vivo.
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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".