Background: and Purpose This study evaluated the diagnostic utility of an anti-signal-recognition particle 54 (anti-SRP54) antibody-based enzyme-linked immunosorbent assay (ELISA) as well as the clinical, serological, and pathological characteristics of patients with SRP immune-mediated necrotizing myopathy (IMNM). Methods: We evaluated 87 patients with idiopathic inflammatory myopathy and 107 healthy participants between January 2002 and December 2023. The sensitivity and specificity of the ELISA for anti-SRP54 antibodies were assessed, and the clinical profiles of patients with antiSRP54 antibodies were determined. Results: The ELISA for anti-SRP54 antibodies had a sensitivity and specificity of 88% and 99%, respectively, along with a test–retest reliability of 0.92 (p<0.001). The 32 patients diagnosed with anti-SRP IMNM using a line-blot immunoassay included 28 (88%) who tested positive for anti-SRP54 antibodies using the ELISA, comprising 12 (43%) males and 16 (57%) females whose median ages at symptom onset and diagnosis were 43.0 years and 43.5 years, respectively. Symptoms included proximal muscle weakness in all 28 (100%) patients, neck weakness in 9 (32%), myalgia in 15 (54%), dysphagia in 5 (18%), dyspnea in 4 (14%), dysarthria in 2 (7%), interstitial lung disease in 2 (7%), and myocarditis in 2 (7%). The median serum creatine kinase (CK) level was 7,261 U/L (interquartile range: 5,086–10,007 U/L), and the median anti-SRP54 antibody level was 2.0 U/mL (interquartile range: 1.0–5.6 U/mL). The serum CK level was significantly higher in patients with coexisting anti-Ro-52 antibodies. Conclusions This study has confirmed the reliability of the ELISA for anti-SRP54 antibodies and provided insights into the clinical, serological, and pathological characteristics of South Korean patients with anti-SRP IMNM.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: Dapagliflozin, a sodium-glucose cotransporter 2 inhibitor, reduces hyperglycemia and obesity by inhibiting renal glucose reabsorption. This post hoc study evaluated clinical factors influencing patient response to dapagliflozin. Methods: The analysis focused on patients treated with dapagliflozin (10 mg/day for 52 weeks) within the randomized, double-blind, parallel-group BEYOND trial. Adequate glycemic control (GC) was defined as a reduction in glycated hemoglobin (HbA1c) of ≥ 1.0% or the achievement of an HbA1c level <7.0% at week 52. Significant weight loss (WL) referred to a reduction in body weight of ≥3.0% at week 52. Participants were classified into four groups based on their GC and WL responses: GC+/WL+, GC+/WL−, GC−/WL+, and GC−/WL−. Results: Among dapagliflozin recipients (n=56), at 52 weeks, HbA1c had decreased by 1.0%±0.8% from baseline, while body weight had declined by 2.4±3.1 kg. Overall, 69.6% of participants achieved GC+, and 57.1% achieved WL+. Male sex and shorter diabetes duration were significantly associated with achieving GC+. Conversely, higher estimated glomerular filtration rate was significantly linked to WL+. The only factor significantly associated with both GC+ and WL+ was shorter diabetes duration (odds ratio, 0.81; 95% confidence interval, 0.68 to 0.97; P=0.023). The GC+ and WL+ groups exhibited favorable responses beginning soon after dapagliflozin therapy was initiated. Furthermore, HbA1c decline was more strongly associated with reduction in visceral fat than with WL. Conclusion A short duration of diabetes and early response to treatment appear to represent key factors in maximizing the benefits of dapagliflozin for blood glucose and weight management.

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: Antithyroid drug (ATD) treatment is the preferred initial treatment for Graves’ disease (GD) in South Korea, despite higher treatment failure rates than radioactive iodine (RAI) therapy or thyroidectomy. This study aimed to evaluate the incidence of treatment failure associated with the primary modalities for GD treatment in real-world practice. Methods: We included 452,001 patients diagnosed with GD between 2004 and 2020 from the Korean National Health Insurance Service-National Health Information Database. Treatment failure was defined as switching from ATD, RAI, or thyroidectomy treatments, and for ATD specifically, inability to discontinue medication for over 2 years. Results: Mean age was 46.2 years, with females constituting 70.8%. Initial treatments for GD included ATDs (98.0%), thyroidectomy (1.3%), and RAI (0.7%), with a noted increment in ATD application from 96.2% in 2004 to 98.8% in 2020. During a median follow- up of 8.5 years, the treatment failure rates were 58.5% for ATDs, 21.3% for RAI, and 2.1% for thyroidectomy. Multivariate analysis indicated that the hazard ratio for treatment failure with ATD was 2.81 times higher than RAI. RAI treatments ≥10 mCi had 37% lower failure rates than doses <10 mCi. Conclusion ATDs are the most commonly used for GD in South Korea, followed by thyroidectomy and RAI. Although the risk of treatment failure for ATD is higher than that of RAI therapy, initial RAI treatment in South Korea is relatively limited compared to that in Western countries. Further studies are required to evaluate the cause of low initial RAI treatment rates in South Korea.

Objective: Several miRNAs have been identified as differentially expressed in patients with poor ovarian response (POR) compared to those with normal responses. This study aims to assess the potential of serum miR-329-3p as a biomarker for diagnosing POR. Methods: We conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to confirm the target genes of miR-329-3p. KGN cells were transfected with both miR-329-3p mimic and inhibitor to assess the differential expression of these target genes. In accordance with the Bologna criteria, we enrolled 16 control patients and 16 patients with POR. We collected patient samples, including serum from day 2 and the human chorionic gonadotropin (hCG) day, as well as granulosa and cumulus cells, to validate the expression of miR-329-3p using quantitative real-time polymerase chain reaction. Results: KEGG pathway analysis revealed that miR-329-3p targeted adenylyl cyclase 9 (ADCY9) and protein kinase A subunit beta (PRKACB), both of which are involved in ovarian steroidogenesis. In KGN cells treated with a miR-329-3p mimic, ADCY9 and PRKACB expression levels were significantly reduced (p<0.05). Elevated levels of miR-329-3p suppressed aromatase expression and 17β-estradiol production by modulating ADCY9 and PRKACB in KGN cells. These effects were also observed in POR patients. Follicle-stimulating hormone receptor (FSHR) expression was diminished in the granulosa cells of POR patients. On day 2, on hCG day, and in granulosa cells, miR-329-3p exhibited high expression levels in the serum of POR patients. Conclusion miR-329-3p exhibited increased expression in granulosa cells and in the sera of POR patients. Consequently, we propose that miR-329-3p may be a potential biomarker for the diagnosis of POR.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: Dapagliflozin, a sodium-glucose cotransporter 2 inhibitor, reduces hyperglycemia and obesity by inhibiting renal glucose reabsorption. This post hoc study evaluated clinical factors influencing patient response to dapagliflozin. Methods: The analysis focused on patients treated with dapagliflozin (10 mg/day for 52 weeks) within the randomized, double-blind, parallel-group BEYOND trial. Adequate glycemic control (GC) was defined as a reduction in glycated hemoglobin (HbA1c) of ≥ 1.0% or the achievement of an HbA1c level <7.0% at week 52. Significant weight loss (WL) referred to a reduction in body weight of ≥3.0% at week 52. Participants were classified into four groups based on their GC and WL responses: GC+/WL+, GC+/WL−, GC−/WL+, and GC−/WL−. Results: Among dapagliflozin recipients (n=56), at 52 weeks, HbA1c had decreased by 1.0%±0.8% from baseline, while body weight had declined by 2.4±3.1 kg. Overall, 69.6% of participants achieved GC+, and 57.1% achieved WL+. Male sex and shorter diabetes duration were significantly associated with achieving GC+. Conversely, higher estimated glomerular filtration rate was significantly linked to WL+. The only factor significantly associated with both GC+ and WL+ was shorter diabetes duration (odds ratio, 0.81; 95% confidence interval, 0.68 to 0.97; P=0.023). The GC+ and WL+ groups exhibited favorable responses beginning soon after dapagliflozin therapy was initiated. Furthermore, HbA1c decline was more strongly associated with reduction in visceral fat than with WL. Conclusion A short duration of diabetes and early response to treatment appear to represent key factors in maximizing the benefits of dapagliflozin for blood glucose and weight management.

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: Antithyroid drug (ATD) treatment is the preferred initial treatment for Graves’ disease (GD) in South Korea, despite higher treatment failure rates than radioactive iodine (RAI) therapy or thyroidectomy. This study aimed to evaluate the incidence of treatment failure associated with the primary modalities for GD treatment in real-world practice. Methods: We included 452,001 patients diagnosed with GD between 2004 and 2020 from the Korean National Health Insurance Service-National Health Information Database. Treatment failure was defined as switching from ATD, RAI, or thyroidectomy treatments, and for ATD specifically, inability to discontinue medication for over 2 years. Results: Mean age was 46.2 years, with females constituting 70.8%. Initial treatments for GD included ATDs (98.0%), thyroidectomy (1.3%), and RAI (0.7%), with a noted increment in ATD application from 96.2% in 2004 to 98.8% in 2020. During a median follow- up of 8.5 years, the treatment failure rates were 58.5% for ATDs, 21.3% for RAI, and 2.1% for thyroidectomy. Multivariate analysis indicated that the hazard ratio for treatment failure with ATD was 2.81 times higher than RAI. RAI treatments ≥10 mCi had 37% lower failure rates than doses <10 mCi. Conclusion ATDs are the most commonly used for GD in South Korea, followed by thyroidectomy and RAI. Although the risk of treatment failure for ATD is higher than that of RAI therapy, initial RAI treatment in South Korea is relatively limited compared to that in Western countries. Further studies are required to evaluate the cause of low initial RAI treatment rates in South Korea.