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In acoustic droplet vaporization (ADV), a cavitated bubble grows and collapses depending on the pressure amplitude of the acoustic pulse. During the bubble collapse, the surrounding liquid is compressed to high pressure, and liquid compressibility can have a significant impact on bubble behavior and ADV threshold. In this work, a one-dimensional numerical model considering liquid compressibility is presented for ADV of a volatile microdroplet, extending our previous Rayleigh-Plesset based model [Ultrason. Chem. 71 (2021) 105361]. The numerical results for bubble motion and liquid energy change in ADV show that the liquid compressibility highly inhibits bubble growth during bubble collapse and rebound, especially under high acoustic frequency conditions. The liquid compressibility effect on the ADV threshold is quantified with varying acoustic frequencies and amplitudes.

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The major function of adipocytes is to store fat in the form of triglycerides. One of the signaling pathways known to affect adipogenesis, i.e. fat formation, is the WNT/β-catenin pathway which inhibits the expression and activity of key regulators of adipogenesis. The purpose of this research is to find genes among the WNT/β-catenin pathway which regulate adipogenesis by using small interfering (si) RNA and to find the association of single nucleotide polymorphisms (SNPs) of the gene with serum triglyceride levels in the human population. To elucidate the effects of β-catenin siRNA on adipogenesis key factors, PPARγ and C/EBPα, we performed real-time PCR and western blotting experiments for the analyses of mRNA and protein levels. It was found that the siRNA-mediated knockdown of β-catenin upregulates adipogenesis key factors. However, upstream regulators of the WNT/β-catenin pathway, such as DVL2 and LRP6, had no significant effects compared to β-catenin. These results indicate that β-catenin is a candidate gene for human fat accumulation. In general, serum triglyceride level is a good indicator of fat accumulation in humans. According to statistical analyses of the association between serum triglyceride level and SNPs of β-catenin,-10,288 C>T SNP (rs7630377) in the promoter region was significantly associated with serum triglyceride levels (p<0.05) in 290 Korean subjects. On the other hand, serum cholesterol levels were not significantly associated with SNPs of the β-catenin gene. The results of this study showed that β-catenin is associated with fat accumulation both in vitro and in the human population.

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Obtaining an integral depth-dose (IDD) curve using a recently developed acrylic-disk radiation sensor (ADRS) is time-consuming because its single structure requires point-by-point measurements in a water phantom. The goal of this study was to verify the ability of a newly designed multilayer ADRS, composed of 20 layers, to measure the energy of proton pencil beam scanning (PBS) in patient-specific quality assurance (QA). Materials and methods: The multilayer ADRS consisted of a disk-type transmitter, with a diameter of 15 cm and with a thickness of 1 mm, surrounded by a thin optical fiber; this ADRS provided a higher spatial resolution than the single ADRS, which was 2 mm. The dosimetric characteristics of the multilayer ADRS were determined to accurately measure the energy delivered layer-by-layer. We selected five patients to verify the energy measured using the multilayer ADRS from the actual clinical proton therapy plans. The accuracy of the results measured using the multilayer ADRS was compared with that of measurements by a Bragg peak ionization chamber (IC) and that calculated by a Monte Carlo TOPAS simulation. Results: The difference between the multilayer ADRS measurements and those of the TOPAS simulation was within 1% for all patients. The ranges, corresponding to the beam energies for each patient, measured using the multilayer ADRS were closer to those calculated using the TOPAS simulation than those measured using the Bragg peak IC. Conclusions: The multilayer ADRS is well suited to verifying the energy of a pencil beam. The acrylic materials used in its configuration make this device easier to use and more cost-effective than conventional detectors. This device, with its high extensibility and stability, may be applicable as a new dosimetry tool for PBS.

Purpose The objective of this study was to fabricate an anthropomorphic multimodality pelvic phantom to evaluate a deep‐learning‐based synthetic computed tomography (CT) algorithm for magnetic resonance (MR)‐only radiotherapy. Methods Polyurethane‐based and silicone‐based materials with various silicone oil concentrations were scanned using 0.35 T MR and CT scanner to determine the tissue surrogate. Five tissue surrogates were determined by comparing the organ intensity with patient CT and MR images. Patient‐specific organ modeling for three‐dimensional printing was performed by manually delineating the structures of interest. The phantom was finally fabricated by casting materials for each structure. For the quantitative evaluation, the mean and standard deviations were measured within the regions of interest on the MR, simulation CT (CTsim), and synthetic CT (CTsyn) images. Intensity‐modulated radiation therapy plans were generated to assess the impact of different electron density assignments on plan quality using CTsim and CTsyn. The dose calculation accuracy was investigated in terms of gamma analysis and dose‐volume histogram parameters. Results For the prostate site, the mean MR intensities for the patient and phantom were 78.1 ± 13.8 and 86.5 ± 19.3, respectively. The mean intensity of the synthetic image was 30.9 Hounsfield unit (HU), which was comparable to that of the real CT phantom image. The original and synthetic CT intensities of the fat tissue in the phantom were −105.8 ± 4.9 HU and −107.8 ± 7.8 HU, respectively. For the target volume, the difference in D95% was 0.32 Gy using CTsyn with respect to CTsim values. The V65Gy values for the bladder in the plans using CTsim and CTsyn were 0.31% and 0.15%, respectively. Conclusion This work demonstrated that the anthropomorphic phantom was physiologically and geometrically similar to the patient organs and was employed to quantitatively evaluate the deep‐learning‐based synthetic CT algorithm.