Background: and Purpose Treatments for neuromyelitis optica spectrum disorder (NMOSD) such as eculizumab, ravulizumab, satralizumab, and inebilizumab have significantly advanced relapse prevention, but they remain expensive. Rituximab is an off-label yet popular alternative that offers a cost-effective solution, but its real-world efficacy needs better quantification for guiding the application of newer approved NMOSD treatments (ANTs). This study aimed to determine real-world rituximab failure rates to anticipate the demand for ANTs and aid in resource allocation. Methods: We conducted a nationwide retrospective study involving 605 aquaporin-4-antibody-positive NMOSD patients from 22 centers in South Korea that assessed the efficacy and safety of rituximab over a median follow-up of 47 months. Results: The 605 patients treated with rituximab included 525 (87%) who received continuous therapy throughout the follow-up period (median=47 months, interquartile range=15–87 months). During this period, 117 patients (19%) experienced at least 1 relapse. Notably, 68 of these patients (11% of the total cohort) experienced multiple relapses or at least 1 severe relapse.Additionally, 2% of the patients discontinued rituximab due to adverse events, which included severe infusion reactions, neutropenia, and infections. Conclusions This study has confirmed the efficacy of rituximab in treating NMOSD, as evidenced by an 87% continuation rate among patients over a 4-year follow-up period. Nevertheless, the occurrence of at least one relapse in 19% of the cohort, including 11% who experienced multiple or severe relapses, and a 2% discontinuation rate due to adverse events highlight the urgent need for alternative therapeutic options.

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.

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.

Background: and Purpose Treatments for neuromyelitis optica spectrum disorder (NMOSD) such as eculizumab, ravulizumab, satralizumab, and inebilizumab have significantly advanced relapse prevention, but they remain expensive. Rituximab is an off-label yet popular alternative that offers a cost-effective solution, but its real-world efficacy needs better quantification for guiding the application of newer approved NMOSD treatments (ANTs). This study aimed to determine real-world rituximab failure rates to anticipate the demand for ANTs and aid in resource allocation. Methods: We conducted a nationwide retrospective study involving 605 aquaporin-4-antibody-positive NMOSD patients from 22 centers in South Korea that assessed the efficacy and safety of rituximab over a median follow-up of 47 months. Results: The 605 patients treated with rituximab included 525 (87%) who received continuous therapy throughout the follow-up period (median=47 months, interquartile range=15–87 months). During this period, 117 patients (19%) experienced at least 1 relapse. Notably, 68 of these patients (11% of the total cohort) experienced multiple relapses or at least 1 severe relapse.Additionally, 2% of the patients discontinued rituximab due to adverse events, which included severe infusion reactions, neutropenia, and infections. Conclusions This study has confirmed the efficacy of rituximab in treating NMOSD, as evidenced by an 87% continuation rate among patients over a 4-year follow-up period. Nevertheless, the occurrence of at least one relapse in 19% of the cohort, including 11% who experienced multiple or severe relapses, and a 2% discontinuation rate due to adverse events highlight the urgent need for alternative therapeutic options.

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.

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.

Background: and Purpose Treatments for neuromyelitis optica spectrum disorder (NMOSD) such as eculizumab, ravulizumab, satralizumab, and inebilizumab have significantly advanced relapse prevention, but they remain expensive. Rituximab is an off-label yet popular alternative that offers a cost-effective solution, but its real-world efficacy needs better quantification for guiding the application of newer approved NMOSD treatments (ANTs). This study aimed to determine real-world rituximab failure rates to anticipate the demand for ANTs and aid in resource allocation. Methods: We conducted a nationwide retrospective study involving 605 aquaporin-4-antibody-positive NMOSD patients from 22 centers in South Korea that assessed the efficacy and safety of rituximab over a median follow-up of 47 months. Results: The 605 patients treated with rituximab included 525 (87%) who received continuous therapy throughout the follow-up period (median=47 months, interquartile range=15–87 months). During this period, 117 patients (19%) experienced at least 1 relapse. Notably, 68 of these patients (11% of the total cohort) experienced multiple relapses or at least 1 severe relapse.Additionally, 2% of the patients discontinued rituximab due to adverse events, which included severe infusion reactions, neutropenia, and infections. Conclusions This study has confirmed the efficacy of rituximab in treating NMOSD, as evidenced by an 87% continuation rate among patients over a 4-year follow-up period. Nevertheless, the occurrence of at least one relapse in 19% of the cohort, including 11% who experienced multiple or severe relapses, and a 2% discontinuation rate due to adverse events highlight the urgent need for alternative therapeutic options.

Psoriasis is a common chronic inflammatory disease in which keratinocytes proliferate abnormally due to excessive immune action. Psoriasis can be associated with various comorbidities and has a significant impact on health-related quality of life. Although many systemic treatments, including biologic agents, have been developed, topical treatment remains the main option for psoriasis management. Consequently, there is an urgent need to develop topical treatments with minimal side effects and high efficacy. Mesenchymal stem cells (MSCs) exhibit excellent immune regulation, anti-inflammatory activities, and therapeutic effects, and MSC-derived extracellular vesicles (EVs) can serve as crucial mediators of functional transfer from MSCs. Therefore, this study aimed to develop a safe and easy-to-use emulsion cream for treating psoriasis using MSC conditioned media (CM) containing EVs. We developed an enhanced Wharton’s jelly MSC (WJ-MSC) culture method through a three-dimensional (3D) culture containing exogenous transforming growth factor-β3. Using the 3D culture system, we obtained CM from WJ-MSCs, which yielded a higher EV production compared to that of conventional WJ-MSC culture methods, and investigated the effect of EV-enriched 3D-WJMSC-CM cream on psoriasis-related inflammation. Administration of the EV-enriched 3D-WJ-MSC-CM cream significantly reduced erythema, thickness, and scaling of skin lesions, alleviated imiquimod-induced psoriasiform lesions in mice, and ameliorated histopathological changes in mouse skin. The upregulated mRNA expression of inflammatory cytokines, including IL-17a, IL-22, IL-23, and IL-36, decreased in the lesions. In conclusion, we present here a new topical treatment for psoriasis using an MSC EV-enriched cream.

Purpose: The role of allogeneic stem cell transplantation (alloSCT) in multiple myeloma (MM) treatment remains controversial. We conducted a retrospective, multicenter, nationwide study in Korea to evaluate the outcomes of alloSCT in Asian patients with MM. Materials and Methods: Overall, 109 patients with MM who underwent alloSCT between 2003 and 2020 were included in this study. Data were collected from the Korean Multiple Myeloma Working Party Registry. Results: The overall response rate and stringent complete response plus complete response (CR) rates were 67.0 and 46.8%, respectively, after alloSCT. At a median follow-up of 32.5 months, the 3-year probability of progression-free survival (PFS) and overall survival (OS) rates were 69.3% and 71.8%, respectively. The 3-year probabilities of OS rates in the upfront alloSCT, tandem auto-alloSCT, and later alloSCT groups were 75.0%, 88.9%, and 61.1%, respectively. Patients who achieved CR before or after alloSCT had significantly longer OS (89.8 vs. 18 months and 89.8 vs. 15.2 months, respectively). Even though patients who did not achieve CR prior to alloSCT, those who achieve CR after alloSCT had improved PFS and OS compared to those who had no achievement of CR both prior and after alloSCT. Patients who underwent alloSCT with 1-2 prior treatment lines had improved PFS (22.4 vs. 4.5 months) and OS (45.6 vs. 15.3 months) compared to those with three or more prior treatment lines. Conclusion AlloSCT may be a promising therapeutic option especially for younger, chemosensitive patients with earlier implementation from relapse.

Background/Aims: Shifts in the gut microbiota and metabolites are interrelated with liver cirrhosis progression and complications. However, causal relationships have not been evaluated comprehensively. Here, we identified complication-dependent gut microbiota and metabolic signatures in patients with liver cirrhosis. Methods: Microbiome taxonomic profiling was performed on 194 stool samples (52 controls and 142 cirrhosis patients) via V3-V4 16S rRNA sequencing. Next, 51 samples (17 controls and 34 cirrhosis patients) were selected for fecal metabolite profiling via gas chromatography mass spectrometry and liquid chromatography coupled to timeof-flight mass spectrometry. Correlation analyses were performed targeting the gut-microbiota, metabolites, clinical parameters, and presence of complications (varices, ascites, peritonitis, encephalopathy, hepatorenal syndrome, hepatocellular carcinoma, and deceased). Results: Veillonella bacteria, Ruminococcus gnavus, and Streptococcus pneumoniae are cirrhosis-related microbiotas compared with control group. Bacteroides ovatus, Clostridium symbiosum, Emergencia timonensis, Fusobacterium varium, and Hungatella_uc were associated with complications in the cirrhosis group. The areas under the receiver operating characteristic curve (AUROCs) for the diagnosis of cirrhosis, encephalopathy, hepatorenal syndrome, and deceased were 0.863, 0.733, 0.71, and 0.69, respectively. The AUROCs of mixed microbial species for the diagnosis of cirrhosis and complication were 0.808 and 0.847, respectively. According to the metabolic profile, 5 increased fecal metabolites in patients with cirrhosis were biomarkers (AUROC >0.880) for the diagnosis of cirrhosis and complications. Clinical markers were significantly correlated with the gut microbiota and metabolites. Conclusions Cirrhosis-dependent gut microbiota and metabolites present unique signatures that can be used as noninvasive biomarkers for the diagnosis of cirrhosis and its complications.