Objective: To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists’ performance for pulmonary nodule detection on chest radiographs (CXRs). Materials and Methods: A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed. Results: BSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively; p ≤ 0.005). In the person-wise analysis, BSp-DL with an area under the ROC curve (AUROC) of 0.984 and 0.931 for readers 1 and 2, respectively, showed better performance than standard CXR (AUROC of 0.915 and 0.798, respectively; p ≤ 0.011) and comparable performance to BSt-DE (AUROC of 0.988 and 0.974; p ≥ 0.064). BSt-DE and BSp-DL were superior to standard CXR for detecting nodules overlapping with bones (p < 0.017) or in the upper/middle lung zone (p < 0.017). BSt-DE was superior (p < 0.017) to BSp-DL in detecting peripheral and sub-centimeter nodules. Conclusion BSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists’ performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.

In this study, the effect of particle size of genistein-loaded solid lipid particulate systems on drug dissolution behavior and oral bioavailability was investigated. Genistein-loaded solid lipid microparticles and nanoparticles were prepared with glyceryl palmitostearate. Except for the particle size, other properties of genistein-loaded solid lipid microparticles and nanoparticles such as particle composition and drug loading efficiency and amount were similarly controlled to mainly evaluate the effect of different particle sizes of the solid lipid particulate systems on drug dissolution behavior and oral bioavailability. The results showed that genistein-loaded solid lipid microparticles and nanoparticles exhibited a considerably increased drug dissolution rate compared to that of genistein bulk powder and suspension. The microparticles gradually released genistein as a function of time while the nanoparticles exhibited a biphasic drug release pattern, showing an initial burst drug release, followed by a sustained release. The oral bioavailability of genistein loaded in solid lipid microparticles and nanoparticles in rats was also significantly enhanced compared to that in bulk powders and the suspension. However, the bioavailability from the microparticles increased more than that from the nanoparticles mainly because the rapid drug dissolution rate and rapid absorption of genistein because of the large surface area of the genistein-solid lipid nanoparticles cleared the drug to a greater extent than the genistein-solid lipid microparticles did. Therefore, the findings of this study suggest that controlling the particle size of solid-lipid particulate systems at a micro-scale would be a promising strategy to increase the oral bioavailability of genistein.
Absorption* ; Animals ; Biological Availability* ; Drug Liberation ; Genistein* ; Nanoparticles* ; Particle Size* ; Powders ; Rats

Purpose: To find biomarkers for disease, there have been constant attempts to investigate the genes that differ from those in the disease groups. However, the values that lie outside the overall pattern of a distribution, the outliers, are frequently excluded in traditional analytical methods as they are considered to be ‘some sort of problem.’ Such outliers may have a biologic role in the disease group. Thus, this study explored new biomarker using outlier analysis, and verified the suitability of therapeutic potential of two genes (TM4SF4 and LRRK2). Materials and Methods: Modified Tukey’s fences outlier analysis was carried out to identify new biomarkers using the public gene expression datasets. And we verified the presence of the selected biomarkers in other clinical samples via customized gene expression panels and tissue microarrays. Moreover, a siRNA-based knockdown test was performed to evaluate the impact of the biomarkers on oncogenic phenotypes. Results: TM4SF4 in lung cancer and LRRK2 in breast cancer were chosen as candidates among the genes derived from the analysis. TM4SF4 and LRRK2 were overexpressed in the small number of samples with lung cancer (4.20%) and breast cancer (2.42%), respectively. Knockdown of TM4SF4 and LRRK2 suppressed the growth of lung and breast cancer cell lines. The LRRK2 overexpressing cell lines were more sensitive to LRRK2-IN-1 than the LRRK2 under-expressing cell lines Conclusion Our modified outlier-based analysis method has proved to rescue biomarkers previously missed or unnoticed by traditional analysis showing TM4SF4 and LRRK2 are novel target candidates for lung and breast cancer, respectively.

Purpose: NUT carcinoma (NC) is a solid tumor caused by the rearrangement of NUTM1 that usually develops in midline structures, such as the thorax. No standard treatment has been established despite high lethality. Thus, we investigated whether targeting the junction region of NUTM1 fusion breakpoints could serve as a potential treatment option for NC. Materials and Methods: We designed and evaluated a series of small interfering RNAs (siRNAs) targeting the junction region of BRD4-NUTM1 fusion (B4N), the most common form of NUTM1 fusion. Droplet digital polymerase chain reaction using the blood of patients was also tested to evaluate the treatment responses by the junction sequence of the B4N fusion transcripts. Results: As expected, the majority of NC fusion types were B4N (12 of 18, 67%). B4N fusion-specific siRNA treatment on NC cells showed specific inhibitory effects on the B4N fusion transcript and fusion protein without affecting the endogenous expression of the parent genes, resulting in decreased relative cell growth and attenuation of tumor size. In addition, the fusion transcript levels in platelet-rich-plasma samples of the NC patients with systemic metastasis showed a negative correlation with therapeutic effect, suggesting its potential as a measure of treatment responsiveness. Conclusion This study suggests that tumor-specific sequences could be used to treat patients with fusion genes as part of precision medicine for a rare but deadly disease.