Objective: The long-term efficacy of radiofrequency ablation (RFA) for the treatment of benign thyroid nodules remains unclear. We aimed to evaluate the long-term efficacy, emphasizing single-session RFA, and identify the factors associated with cases requiring additional RFA sessions to achieve a comparable volume reduction rates (VRR). Materials and Methods: We retrospectively evaluated benign thyroid nodules treated with RFA between 2008 and 2018.Treatment efficacy at the 5- and 10-year follow-ups was analyzed. Additionally, subgroup analysis comparing technique efficacy, such as the final VRR, between the single- and multi-session RFA groups was performed. Continuous variables were analyzed using the two-sample t-test or Mann–Whitney U test, and categorical variables were analyzed using the Chi-square or Fisher’s exact test. Results: A total of 267 nodules from 237 patients (age: 46.3 ± 15.0 years; female: 210/237 [88.6%]) were included. Of these, 60 were analyzed for the 5-year follow-up (mean follow-up duration ± standard deviation: 5.8 ± 0.4 years) and 29 for the 10-year follow-up (10.9 ± 0.9 years). Single-session RFA showed a median VRR of 95.7% (5th year) and 98.8% (10th year), while multi-session RFA showed comparable median VRRs of 97.4% (5th year) and 96.9% (10th year). The vascularity type, demographic factors, nodular components, and locations did not significantly differ between the single-session and multisession RFA groups. However, nodules with pre-RFA volume <10 mL were more prevalent in the single-session RFA group than in the multi-session RFA group (5th year: 64.3% [18/28] vs. 34.4% [11/32], P = 0.040; 10th year: 75.0% [12/16] vs. 23.1% [3/13], P = 0.016). Conclusion Single-session RFA may be sufficient for achieving adequate volume reduction during long-term follow-up for small-volume benign thyroid nodules. A high VRR was maintained regardless of the nodular component, location, demographic factors, or vascularity type. However, large-volume nodules may require multiple RFA sessions to achieve a comparable VRR.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objectives: Excessive sedentary behavior in youth is a major global issue, contributing to the rise in childhood obesity and metabolic diseases. International public health authorities have issued guidelines recommending that children and adolescents limit their daily sedentary time, including screen time. However, to date, no studies have explored the relationship between sedentary behavior as an exposure factor and skeletal muscle strength and mass as outcomes in this population. The present study investigated the association of sedentary behavior with handgrip strength (HGS) and appendicular lean mass (ALM) among United States adolescents. Methods: A total of 1449 adolescent participants from the National Health and Nutrition Examination Survey (2011-2014) were included. Information on sedentary behavior, specifically daily sedentary time, was obtained through a self-reported questionnaire. Muscular parameters, including HGS and ALM, were measured. To adjust for differences in body size, these parameters were divided by body mass index (BMI) and weight. Linear regression analyses were performed to evaluate the associations between daily sedentary time and each muscular parameter, adjusting for age, sex, ethnicity, annual family income, and moderate-to-vigorous physical activity (MVPA). Results: The linear regression analyses revealed negative associations between daily sedentary time and all muscular parameters, apart from absolute ALM. These included HGS (β, -0.265; standard error [SE], 0.074; p=0.001), HGS/BMI (β, -0.021; SE, 0.004; p<0.001), HGS/weight (β, -0.008; SE, 0.002; p<0.001), ALM/BMI (β, -0.008; SE, 0.003; p=0.010), and ALM/weight (β, -0.003; SE, 0.001; p=0.005). Conclusions After adjusting for MVPA, daily sedentary time was inversely associated with HGS, HGS/BMI, HGS/weight, ALM/BMI, and ALM/weight in United States adolescents.

Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

Acute Respiratory Distress Syndrome (ARDS) is a severe condition characterized by extensive lung inflammation and increased alveolar-capillary permeability, often triggered by infections or systemic inflammatory responses. Mesenchymal stem cells (MSCs)-based therapy holds promise for treating ARDS, as MSCs manifest immunomodulatory and regenerative properties that mitigate inflammation and enhance tissue repair. Primed MSCs, modified to augment specific functionalities, demonstrate superior therapeutic efficacy in targeted therapies compared to naive MSCs. This study explored the immunomodulatory potential of MSCs using mixed lymphocyte reaction (MLR) assays and co-culture experiments with M1/M2 macrophages. Additionally, RNA sequencing was employed to identify alterations in immune and inflammation-related factors in primed MSCs. The therapeutic effects of primed MSCs were assessed in an LPS-induced ARDS mouse model, and the underlying mechanisms were investigated through spatial transcriptomics analysis. The study revealed that MSCs primed with IFN-γ and IL-1β significantly enhanced the suppression of T cell activity compared to naive MSCs, concurrently inhibiting TNF-α while increasing IL-10 production in macrophages. Notably, combined treatment with these two cytokines resulted in a significant upregulation of immune and inflammation-regulating factors. Furthermore, our analyses elucidated the mechanisms behind the therapeutic effects of primed MSCs, including the inhibition of inflammatory cell infiltration in lung tissue, modulation of immune and inflammatory responses, and enhancement of elastin fiber formation. Signaling pathway analysis confirmed that efficacy could be enhanced by modulating NFκB and TNF-α signaling. In conclusion, in early-phase ARDS, primed MSCs displayed enhanced homing capabilities, improved lung function, and reduced inflammation.

Objective: The long-term efficacy of radiofrequency ablation (RFA) for the treatment of benign thyroid nodules remains unclear. We aimed to evaluate the long-term efficacy, emphasizing single-session RFA, and identify the factors associated with cases requiring additional RFA sessions to achieve a comparable volume reduction rates (VRR). Materials and Methods: We retrospectively evaluated benign thyroid nodules treated with RFA between 2008 and 2018.Treatment efficacy at the 5- and 10-year follow-ups was analyzed. Additionally, subgroup analysis comparing technique efficacy, such as the final VRR, between the single- and multi-session RFA groups was performed. Continuous variables were analyzed using the two-sample t-test or Mann–Whitney U test, and categorical variables were analyzed using the Chi-square or Fisher’s exact test. Results: A total of 267 nodules from 237 patients (age: 46.3 ± 15.0 years; female: 210/237 [88.6%]) were included. Of these, 60 were analyzed for the 5-year follow-up (mean follow-up duration ± standard deviation: 5.8 ± 0.4 years) and 29 for the 10-year follow-up (10.9 ± 0.9 years). Single-session RFA showed a median VRR of 95.7% (5th year) and 98.8% (10th year), while multi-session RFA showed comparable median VRRs of 97.4% (5th year) and 96.9% (10th year). The vascularity type, demographic factors, nodular components, and locations did not significantly differ between the single-session and multisession RFA groups. However, nodules with pre-RFA volume <10 mL were more prevalent in the single-session RFA group than in the multi-session RFA group (5th year: 64.3% [18/28] vs. 34.4% [11/32], P = 0.040; 10th year: 75.0% [12/16] vs. 23.1% [3/13], P = 0.016). Conclusion Single-session RFA may be sufficient for achieving adequate volume reduction during long-term follow-up for small-volume benign thyroid nodules. A high VRR was maintained regardless of the nodular component, location, demographic factors, or vascularity type. However, large-volume nodules may require multiple RFA sessions to achieve a comparable VRR.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Objective: The long-term efficacy of radiofrequency ablation (RFA) for the treatment of benign thyroid nodules remains unclear. We aimed to evaluate the long-term efficacy, emphasizing single-session RFA, and identify the factors associated with cases requiring additional RFA sessions to achieve a comparable volume reduction rates (VRR). Materials and Methods: We retrospectively evaluated benign thyroid nodules treated with RFA between 2008 and 2018.Treatment efficacy at the 5- and 10-year follow-ups was analyzed. Additionally, subgroup analysis comparing technique efficacy, such as the final VRR, between the single- and multi-session RFA groups was performed. Continuous variables were analyzed using the two-sample t-test or Mann–Whitney U test, and categorical variables were analyzed using the Chi-square or Fisher’s exact test. Results: A total of 267 nodules from 237 patients (age: 46.3 ± 15.0 years; female: 210/237 [88.6%]) were included. Of these, 60 were analyzed for the 5-year follow-up (mean follow-up duration ± standard deviation: 5.8 ± 0.4 years) and 29 for the 10-year follow-up (10.9 ± 0.9 years). Single-session RFA showed a median VRR of 95.7% (5th year) and 98.8% (10th year), while multi-session RFA showed comparable median VRRs of 97.4% (5th year) and 96.9% (10th year). The vascularity type, demographic factors, nodular components, and locations did not significantly differ between the single-session and multisession RFA groups. However, nodules with pre-RFA volume <10 mL were more prevalent in the single-session RFA group than in the multi-session RFA group (5th year: 64.3% [18/28] vs. 34.4% [11/32], P = 0.040; 10th year: 75.0% [12/16] vs. 23.1% [3/13], P = 0.016). Conclusion Single-session RFA may be sufficient for achieving adequate volume reduction during long-term follow-up for small-volume benign thyroid nodules. A high VRR was maintained regardless of the nodular component, location, demographic factors, or vascularity type. However, large-volume nodules may require multiple RFA sessions to achieve a comparable VRR.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.