Background: Cardiovascular diseases are the leading cause of mortality after liver transplantation (LT). Although the impact of secondary tricuspid regurgitation (TR) with severe pulmonary hypertension (PH) is well investigated, the impact of primary TR with tricuspid valve incompetence (TVI) on LT outcomes remains unclear. We aimed to investigate the prevalence and impact of primary TR with TVI on LT outcomes in a large-volume LT center. Methods: We retrospectively examined 5 512 consecutive LT recipients who underwent routine pretransplant echocardiography between 2008 and 2020. Patients were categorized based on the presence of anatomical TVI, specifically defined by incomplete coaptation, coaptation failure, prolapse, and flail leaflets of tricuspid valve (TV). Propensity score (PS)-based inverse probability weighting (IPW) was used to balance clinical and cardiovascular risk variables. The outcomes were one-year cumulative all-cause mortality and 30-day major adverse cardiovascular events (MACE). Results: Anatomical TVI was identified in 14 patients (0.3%). Although rare, these patients exhibited significantly lower post-LT one-year survival rates (64.3% vs. 91.5%, P < 0.001) and higher 30-day MACE rates (42.9% vs. 16.9%, P = 0.026) than patients without TVI. They also had worse survival irrespective of echocardiographic evidence of PH (P < 0.001) and exhibited higher one-year mortality (IPW-adjusted hazard ratio: 4.09, P = 0.002) and increased 30-day MACE rates (IPW-adjusted odds ratio: 1.24, P = 0.048). Conclusions Primary TR with anatomical TVI was associated with significantly reduced one-year survival and increased post-LT MACE rates. These patients should be prioritized similarly to those with secondary TR with severe PH, with appropriate pretransplant evaluations and treatments to improve survival outcomes.

Background: The World Health Organization has declared the end of the coronavirus disease 2019 (COVID-19) public health emergency. However, this did not indicate the end of COVID-19. Several months after the infection, numerous patients complain of respiratory or nonspecific symptoms; this condition is called long COVID. Even patients with mild COVID-19 can experience long COVID, thus the burden of long COVID remains considerable. Therefore, we conducted this study to comprehensively analyze the effects of long COVID using multi-faceted assessments. Materials and Methods: We conducted a prospective cohort study involving patients diagnosed with COVID-19 between February 2020 and September 2021 in six tertiary hospitals in Korea. Patients were followed up at 1, 3, 6, 12, 18, and 24 months after discharge. Long COVID was defined as the persistence of three or more COVID-19-related symptoms. The primary outcome of this study was the prevalence of long COVID after the period of COVID-19. Results: During the study period, 290 patients were enrolled. Among them, 54.5 and 34.6% experienced long COVID within 6 months and after more than 18 months, respectively. Several patients showed abnormal results when tested for post-traumatic stress disorder (17.4%) and anxiety (31.9%) after 18 months. In patients who underwent follow-up chest computed tomography 18 months after COVID-19, abnormal findings remained at 51.9%. Males (odds ratio [OR], 0.17; 95% confidence interval [CI], 0.05–0.53; P=0.004) and elderly (OR, 1.04; 95% CI, 1.00–1.09; P=0.04) showed a significant association with long COVID after 12–18 months in a multivariable logistic regression analysis. Conclusion Many patients still showed long COVID after 18 months post SARS-CoV-2 infection. When managing these patients, the assessment of multiple aspects is necessary.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that lacks hormone receptor and Her2 (ERBB2) expression, leaving chemotherapy as the only treatment option. The urgent need for targeted therapy for TNBC patients has led to the investigation of small interfering RNAs (siRNAs), which can target genes in a sequence-specific manner, unlike other drugs. However, the clinical translation of siRNAs has been hindered by the lack of an effective delivery system, except in the case of liver diseases. The MYC oncogene is commonly overexpressed in TNBC compared to other breast cancer subtypes. In this study, we used siRNA to target MYC in MDA-MB-231, MDA-MB-157, MDA-MB-436 and Hs-578T cells. We designed various symmetric and asymmetric (asiRNAs), screened them for in vitro efficacy, modified them for enhanced nuclease resistance and reduced off-target effects, and conjugated them with cholesterol (ChoL) and docosanoic acid (DCA) as a delivery system. DCA was conjugated to the 3’ end of asiRNA by a cleavable phosphodiester linker for in vivo delivery. Our findings demonstrated that asiRNA-VP and Mod_asiRNA10-6 efficiently downregulated MYC and its downstream targets, including RRM2, RAD51 and PARP1. Moreover, in a tumor xenograft model, asiRNA-VP-DCA effectively knocked down MYC mRNA and protein expression. Remarkably, durable knockdown persisted for at least 46 days postdosing in mouse tumor xenografts, with no visible signs of toxicity, underscoring the safety of DCA-conjugated asiRNAs. In conclusion, this study developed novel asiRNAs, design platforms, validated modification patterns, and in vivo, delivery systems specifically targeting MYC in TNBC.

Background: Cardiovascular diseases are the leading cause of mortality after liver transplantation (LT). Although the impact of secondary tricuspid regurgitation (TR) with severe pulmonary hypertension (PH) is well investigated, the impact of primary TR with tricuspid valve incompetence (TVI) on LT outcomes remains unclear. We aimed to investigate the prevalence and impact of primary TR with TVI on LT outcomes in a large-volume LT center. Methods: We retrospectively examined 5 512 consecutive LT recipients who underwent routine pretransplant echocardiography between 2008 and 2020. Patients were categorized based on the presence of anatomical TVI, specifically defined by incomplete coaptation, coaptation failure, prolapse, and flail leaflets of tricuspid valve (TV). Propensity score (PS)-based inverse probability weighting (IPW) was used to balance clinical and cardiovascular risk variables. The outcomes were one-year cumulative all-cause mortality and 30-day major adverse cardiovascular events (MACE). Results: Anatomical TVI was identified in 14 patients (0.3%). Although rare, these patients exhibited significantly lower post-LT one-year survival rates (64.3% vs. 91.5%, P < 0.001) and higher 30-day MACE rates (42.9% vs. 16.9%, P = 0.026) than patients without TVI. They also had worse survival irrespective of echocardiographic evidence of PH (P < 0.001) and exhibited higher one-year mortality (IPW-adjusted hazard ratio: 4.09, P = 0.002) and increased 30-day MACE rates (IPW-adjusted odds ratio: 1.24, P = 0.048). Conclusions Primary TR with anatomical TVI was associated with significantly reduced one-year survival and increased post-LT MACE rates. These patients should be prioritized similarly to those with secondary TR with severe PH, with appropriate pretransplant evaluations and treatments to improve survival outcomes.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that lacks hormone receptor and Her2 (ERBB2) expression, leaving chemotherapy as the only treatment option. The urgent need for targeted therapy for TNBC patients has led to the investigation of small interfering RNAs (siRNAs), which can target genes in a sequence-specific manner, unlike other drugs. However, the clinical translation of siRNAs has been hindered by the lack of an effective delivery system, except in the case of liver diseases. The MYC oncogene is commonly overexpressed in TNBC compared to other breast cancer subtypes. In this study, we used siRNA to target MYC in MDA-MB-231, MDA-MB-157, MDA-MB-436 and Hs-578T cells. We designed various symmetric and asymmetric (asiRNAs), screened them for in vitro efficacy, modified them for enhanced nuclease resistance and reduced off-target effects, and conjugated them with cholesterol (ChoL) and docosanoic acid (DCA) as a delivery system. DCA was conjugated to the 3’ end of asiRNA by a cleavable phosphodiester linker for in vivo delivery. Our findings demonstrated that asiRNA-VP and Mod_asiRNA10-6 efficiently downregulated MYC and its downstream targets, including RRM2, RAD51 and PARP1. Moreover, in a tumor xenograft model, asiRNA-VP-DCA effectively knocked down MYC mRNA and protein expression. Remarkably, durable knockdown persisted for at least 46 days postdosing in mouse tumor xenografts, with no visible signs of toxicity, underscoring the safety of DCA-conjugated asiRNAs. In conclusion, this study developed novel asiRNAs, design platforms, validated modification patterns, and in vivo, delivery systems specifically targeting MYC in TNBC.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.