Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Background and Objectives: SH3 and multiple ankyrin repeat domains 3 (Shank3) proteins play crucial roles as neuronal postsynaptic scaffolds. Alongside neuropsychiatric symptoms, individuals with SHANK3 mutations often exhibit symptoms related to dysfunctions in other organs, including the heart. However, detailed insights into the cardiac functions of Shank3 remain limited. This study aimed to characterize the cardiac phenotypes of Shank3-overexpressing transgenic mice and explore the underlying mechanisms. Methods: Cardiac histological analysis, electrocardiogram and echocardiogram recordings were conducted on Shank3-overexpressing transgenic mice. Electrophysiological properties, including action potentials and L-type Ca2+ channel (LTCC) currents, were measured in isolated cardiomyocytes. Ca2+ homeostasis was assessed by analyzing cytosolic Ca2+transients and sarcoplasmic reticulum Ca2+ contents. Depolarization-induced cell shortening was examined in cardiomyocytes. Immunoprecipitation followed by mass spectrometrybased identification was employed to identify proteins in the cardiac Shank3 interactome.Western blot and immunocytochemical analyses were conducted to identify changes in protein expression in Shank3-overexpressing transgenic cardiomyocytes. Results: The hearts of Shank3-overexpressing transgenic mice displayed reduced weight and increased fibrosis. In vivo, sudden cardiac death, arrhythmia, and contractility impairments were identified. Shank3-overexpressing transgenic cardiomyocytes showed prolonged action potential duration and increased LTCC current density. Cytosolic Ca2+ transients were increased with prolonged decay time, while sarcoplasmic reticulum Ca2+ contents remained normal. Cell shortening was augmented in Shank3-overexpressing transgenic cardiomyocytes. The cardiac Shank3 interactome comprised 78 proteins with various functions. Troponin I levels were down-regulated in Shank3-overexpressing transgenic cardiomyocytes. Conclusions This study revealed cardiac dysfunction in Shank3-overexpressing transgenic mice, potentially attributed to changes in Ca2+ homeostasis and contraction, with a notable reduction in troponin I.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Background and Objectives: SH3 and multiple ankyrin repeat domains 3 (Shank3) proteins play crucial roles as neuronal postsynaptic scaffolds. Alongside neuropsychiatric symptoms, individuals with SHANK3 mutations often exhibit symptoms related to dysfunctions in other organs, including the heart. However, detailed insights into the cardiac functions of Shank3 remain limited. This study aimed to characterize the cardiac phenotypes of Shank3-overexpressing transgenic mice and explore the underlying mechanisms. Methods: Cardiac histological analysis, electrocardiogram and echocardiogram recordings were conducted on Shank3-overexpressing transgenic mice. Electrophysiological properties, including action potentials and L-type Ca2+ channel (LTCC) currents, were measured in isolated cardiomyocytes. Ca2+ homeostasis was assessed by analyzing cytosolic Ca2+transients and sarcoplasmic reticulum Ca2+ contents. Depolarization-induced cell shortening was examined in cardiomyocytes. Immunoprecipitation followed by mass spectrometrybased identification was employed to identify proteins in the cardiac Shank3 interactome.Western blot and immunocytochemical analyses were conducted to identify changes in protein expression in Shank3-overexpressing transgenic cardiomyocytes. Results: The hearts of Shank3-overexpressing transgenic mice displayed reduced weight and increased fibrosis. In vivo, sudden cardiac death, arrhythmia, and contractility impairments were identified. Shank3-overexpressing transgenic cardiomyocytes showed prolonged action potential duration and increased LTCC current density. Cytosolic Ca2+ transients were increased with prolonged decay time, while sarcoplasmic reticulum Ca2+ contents remained normal. Cell shortening was augmented in Shank3-overexpressing transgenic cardiomyocytes. The cardiac Shank3 interactome comprised 78 proteins with various functions. Troponin I levels were down-regulated in Shank3-overexpressing transgenic cardiomyocytes. Conclusions This study revealed cardiac dysfunction in Shank3-overexpressing transgenic mice, potentially attributed to changes in Ca2+ homeostasis and contraction, with a notable reduction in troponin I.

Background and Objectives: SH3 and multiple ankyrin repeat domains 3 (Shank3) proteins play crucial roles as neuronal postsynaptic scaffolds. Alongside neuropsychiatric symptoms, individuals with SHANK3 mutations often exhibit symptoms related to dysfunctions in other organs, including the heart. However, detailed insights into the cardiac functions of Shank3 remain limited. This study aimed to characterize the cardiac phenotypes of Shank3-overexpressing transgenic mice and explore the underlying mechanisms. Methods: Cardiac histological analysis, electrocardiogram and echocardiogram recordings were conducted on Shank3-overexpressing transgenic mice. Electrophysiological properties, including action potentials and L-type Ca2+ channel (LTCC) currents, were measured in isolated cardiomyocytes. Ca2+ homeostasis was assessed by analyzing cytosolic Ca2+transients and sarcoplasmic reticulum Ca2+ contents. Depolarization-induced cell shortening was examined in cardiomyocytes. Immunoprecipitation followed by mass spectrometrybased identification was employed to identify proteins in the cardiac Shank3 interactome.Western blot and immunocytochemical analyses were conducted to identify changes in protein expression in Shank3-overexpressing transgenic cardiomyocytes. Results: The hearts of Shank3-overexpressing transgenic mice displayed reduced weight and increased fibrosis. In vivo, sudden cardiac death, arrhythmia, and contractility impairments were identified. Shank3-overexpressing transgenic cardiomyocytes showed prolonged action potential duration and increased LTCC current density. Cytosolic Ca2+ transients were increased with prolonged decay time, while sarcoplasmic reticulum Ca2+ contents remained normal. Cell shortening was augmented in Shank3-overexpressing transgenic cardiomyocytes. The cardiac Shank3 interactome comprised 78 proteins with various functions. Troponin I levels were down-regulated in Shank3-overexpressing transgenic cardiomyocytes. Conclusions This study revealed cardiac dysfunction in Shank3-overexpressing transgenic mice, potentially attributed to changes in Ca2+ homeostasis and contraction, with a notable reduction in troponin I.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Background and Objectives: SH3 and multiple ankyrin repeat domains 3 (Shank3) proteins play crucial roles as neuronal postsynaptic scaffolds. Alongside neuropsychiatric symptoms, individuals with SHANK3 mutations often exhibit symptoms related to dysfunctions in other organs, including the heart. However, detailed insights into the cardiac functions of Shank3 remain limited. This study aimed to characterize the cardiac phenotypes of Shank3-overexpressing transgenic mice and explore the underlying mechanisms. Methods: Cardiac histological analysis, electrocardiogram and echocardiogram recordings were conducted on Shank3-overexpressing transgenic mice. Electrophysiological properties, including action potentials and L-type Ca2+ channel (LTCC) currents, were measured in isolated cardiomyocytes. Ca2+ homeostasis was assessed by analyzing cytosolic Ca2+transients and sarcoplasmic reticulum Ca2+ contents. Depolarization-induced cell shortening was examined in cardiomyocytes. Immunoprecipitation followed by mass spectrometrybased identification was employed to identify proteins in the cardiac Shank3 interactome.Western blot and immunocytochemical analyses were conducted to identify changes in protein expression in Shank3-overexpressing transgenic cardiomyocytes. Results: The hearts of Shank3-overexpressing transgenic mice displayed reduced weight and increased fibrosis. In vivo, sudden cardiac death, arrhythmia, and contractility impairments were identified. Shank3-overexpressing transgenic cardiomyocytes showed prolonged action potential duration and increased LTCC current density. Cytosolic Ca2+ transients were increased with prolonged decay time, while sarcoplasmic reticulum Ca2+ contents remained normal. Cell shortening was augmented in Shank3-overexpressing transgenic cardiomyocytes. The cardiac Shank3 interactome comprised 78 proteins with various functions. Troponin I levels were down-regulated in Shank3-overexpressing transgenic cardiomyocytes. Conclusions This study revealed cardiac dysfunction in Shank3-overexpressing transgenic mice, potentially attributed to changes in Ca2+ homeostasis and contraction, with a notable reduction in troponin I.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Background: University students have been known as having a higher rate of problem drinking than the general population in South Korea. In particular, the university freshmen may experience problem drinking from increased occasions of frequent social gatherings at the first school year as they have increased time to freely use compared to the high school days. Problem drinking among university freshmen may be influenced by multiple factors. The present study aimed to examine the prevalence of problem drinking and to identify individualand family-level factors associated with university freshmen's problem drinking. Methods: A cross-sectional, correlation study was conducted. Participants were 227 university freshmen (70 men and 157 women) under the parenting of mother and father from 10 universities in Seoul. The problem drinking was evaluated by using the Alcohol Use Disorder Identification Test. Alcohol Abstinence Self-Efficacy Scale and Parental Authority Questionnaire for parenting behaviors were used. Results: The prevalence of problem drinking was 58.6% among university freshmen. An individual-level factor of alcohol abstinence self-efficacy was significantly associated with the prevalence of problem drinking (odds ratio, 0.94; 95% confidence interval, 0.91-0.96) among university freshmen. However, any of family-level factors including types of parenting behaviors were not significantly associated with the prevalence of problem drinking. Conclusions Alcohol abstinence self-efficacy may be a strong protective factor against university freshmen's problem drinking. University-based alcohol abstinence programs should comprise of nursing strategies including the enhancement of abstinence self-efficacy at the first school year.