Background: The effectiveness of intravenous tissue plasminogen activator (IV tPA) in patients with large-vessel occlusion (LVO) receiving endovascular treatment (EVT) for acute ischemic stroke (AIS) has been questioned. We investigated IV tPA effectiveness in real-world AIS patients, including those with intracranial LVO receiving EVT. Methods: We identified patients with AIS who presented to hospital with National Institutes of Health Stroke Scale ≥4 within 8 hours of symptom onset from the institutional stroke registry. The association of IV tPA use with effectiveness and safety outcomes was analyzed in overall enrolled AIS patients; LVO patients; and patients treated with EVT. The effect of IV tPA was assessed using multiple logistic regression. Results: Among the 654 patients meeting study entry criteria, 238 (36.4%) received IV tPA and 416 (63.6%) did not. Multiple logistic regression analysis and shift analysis revealed IV tPA was associated with improved outcomes in overall enrolled AIS population, LVO, and EVT-treated subgroups. Among EVT-treated patients, IV tPA was associated with higher likelihood of ambulatory or better outcome (modified Rankin Scale 0–3) with odds ratio of 1.95 (P=0.03). Conclusions In this real-world study, IV tPA use was associated with improved outcomes for patients with AIS, including among LVO patients treated and not treated with EVT, in the contemporary mechanical thrombectomy era.

Background: We aimed to investigate the predictive values of plasma C-peptide levels and the continuous glucose monitoring (CGM)-defined coefficient of variation (CV) in risk prediction for hypoglycemia in Korean people with diabetes with normal and impaired kidney function. Methods: We analyzed data from 1,185 participants diagnosed with type 1 and type 2 diabetes who underwent blinded professional CGM between January 2009 and May 2021 at outpatient clinics. We explored correlations among CGM-defined CV, plasma C-peptide levels, and time below range at <70 and 54 mg/dL across different kidney function categories. Results: In patients with chronic kidney disease (CKD) stages 1–2 (n=934), 89.3% who had a random plasma C-peptide level higher than 600 pmol/L exhibited a CV of ≤36%. Among those in CKD stage 3 (n=161) with a random plasma C-peptide level exceeding 600 pmol/L, 66.7% showed a CV of ≤36%. In stages 4–5 of CKD (n=90), the correlation between random C-peptide levels and CV was not significant (r=–0.05, P=0.640), including cases with a CV greater than 36% despite very high random plasma C-peptide levels. Random plasma C-peptide levels and CGM-assessed CV significantly predicted hypoglycemia in CKD stages 1–2 and 1–5, respectively. Conclusion The established C-peptide criteria in Western populations are applicable to Korean people with diabetes for hypoglycemic risk prediction, unless kidney function is impaired equivalent to CKD stage 3–5. The CGM-defined CV is informative for hypoglycemic risk prediction regardless of kidney function.

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.

Purpose: This study investigated the impact of nusinersen on health-related quality of life (HRQoL), functional performance, and caregiver burden in patients with infantile-onset spinal muscular atrophy (SMA), addressing a growing interest in disease-modifying treatments. Methods: A 14-month observational study was conducted to evaluate changes in HRQoL and functional performance using the Pediatric Quality of Life Inventory (PedsQL) Infant Scales and the Pediatric Evaluation of Disability Inventory Computer Adaptive Test (PEDI-CAT). Caregiver burden was assessed through the Assessment of Caregiver Experience with Neuromuscular Disease (ACEND). Motor function was evaluated using the Children’s Hospital of Philadelphia Infant Test of Neuromuscular Disorders (CHOP INTEND). Correlations between CHOP INTEND scores, functional performance, and caregiver burden were analyzed. Results: Eight patients with infantile-onset SMA and their caregivers participated, with a median treatment initiation age of 4.6 months (range, 1.1 to 15.1). CHOP INTEND scores showed significant improvement (P<0.001), whereas all PedsQL Infant Scale scores declined. Conversely, the PEDI-CAT revealed significant enhancements in daily activities, mobility, and social-cognitive domains (all P<0.001). Caregiver burden lessened across most dimensions (P<0.001), with the exception of the time-related burden (P=0.731). Higher CHOP INTEND scores correlated with improvements in PEDI-CAT domains and a reduction in caregiver burden related to sitting/play and transfer activities. Conclusion The study demonstrates the positive effects of nusinersen on functional performance and caregiver burden in patients with infantile-onset SMA. However, discrepancies were observed in HRQoL outcomes, suggesting a need for further research that includes SMA-specific outcome measures to comprehensively assess the treatment's impact on patients' lives.

Purpose: This study investigated the impact of nusinersen on health-related quality of life (HRQoL), functional performance, and caregiver burden in patients with infantile-onset spinal muscular atrophy (SMA), addressing a growing interest in disease-modifying treatments. Methods: A 14-month observational study was conducted to evaluate changes in HRQoL and functional performance using the Pediatric Quality of Life Inventory (PedsQL) Infant Scales and the Pediatric Evaluation of Disability Inventory Computer Adaptive Test (PEDI-CAT). Caregiver burden was assessed through the Assessment of Caregiver Experience with Neuromuscular Disease (ACEND). Motor function was evaluated using the Children’s Hospital of Philadelphia Infant Test of Neuromuscular Disorders (CHOP INTEND). Correlations between CHOP INTEND scores, functional performance, and caregiver burden were analyzed. Results: Eight patients with infantile-onset SMA and their caregivers participated, with a median treatment initiation age of 4.6 months (range, 1.1 to 15.1). CHOP INTEND scores showed significant improvement (P<0.001), whereas all PedsQL Infant Scale scores declined. Conversely, the PEDI-CAT revealed significant enhancements in daily activities, mobility, and social-cognitive domains (all P<0.001). Caregiver burden lessened across most dimensions (P<0.001), with the exception of the time-related burden (P=0.731). Higher CHOP INTEND scores correlated with improvements in PEDI-CAT domains and a reduction in caregiver burden related to sitting/play and transfer activities. Conclusion The study demonstrates the positive effects of nusinersen on functional performance and caregiver burden in patients with infantile-onset SMA. However, discrepancies were observed in HRQoL outcomes, suggesting a need for further research that includes SMA-specific outcome measures to comprehensively assess the treatment's impact on patients' lives.

Background: We aimed to investigate the predictive values of plasma C-peptide levels and the continuous glucose monitoring (CGM)-defined coefficient of variation (CV) in risk prediction for hypoglycemia in Korean people with diabetes with normal and impaired kidney function. Methods: We analyzed data from 1,185 participants diagnosed with type 1 and type 2 diabetes who underwent blinded professional CGM between January 2009 and May 2021 at outpatient clinics. We explored correlations among CGM-defined CV, plasma C-peptide levels, and time below range at <70 and 54 mg/dL across different kidney function categories. Results: In patients with chronic kidney disease (CKD) stages 1–2 (n=934), 89.3% who had a random plasma C-peptide level higher than 600 pmol/L exhibited a CV of ≤36%. Among those in CKD stage 3 (n=161) with a random plasma C-peptide level exceeding 600 pmol/L, 66.7% showed a CV of ≤36%. In stages 4–5 of CKD (n=90), the correlation between random C-peptide levels and CV was not significant (r=–0.05, P=0.640), including cases with a CV greater than 36% despite very high random plasma C-peptide levels. Random plasma C-peptide levels and CGM-assessed CV significantly predicted hypoglycemia in CKD stages 1–2 and 1–5, respectively. Conclusion The established C-peptide criteria in Western populations are applicable to Korean people with diabetes for hypoglycemic risk prediction, unless kidney function is impaired equivalent to CKD stage 3–5. The CGM-defined CV is informative for hypoglycemic risk prediction regardless of kidney function.

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: We aimed to investigate the predictive values of plasma C-peptide levels and the continuous glucose monitoring (CGM)-defined coefficient of variation (CV) in risk prediction for hypoglycemia in Korean people with diabetes with normal and impaired kidney function. Methods: We analyzed data from 1,185 participants diagnosed with type 1 and type 2 diabetes who underwent blinded professional CGM between January 2009 and May 2021 at outpatient clinics. We explored correlations among CGM-defined CV, plasma C-peptide levels, and time below range at <70 and 54 mg/dL across different kidney function categories. Results: In patients with chronic kidney disease (CKD) stages 1–2 (n=934), 89.3% who had a random plasma C-peptide level higher than 600 pmol/L exhibited a CV of ≤36%. Among those in CKD stage 3 (n=161) with a random plasma C-peptide level exceeding 600 pmol/L, 66.7% showed a CV of ≤36%. In stages 4–5 of CKD (n=90), the correlation between random C-peptide levels and CV was not significant (r=–0.05, P=0.640), including cases with a CV greater than 36% despite very high random plasma C-peptide levels. Random plasma C-peptide levels and CGM-assessed CV significantly predicted hypoglycemia in CKD stages 1–2 and 1–5, respectively. Conclusion The established C-peptide criteria in Western populations are applicable to Korean people with diabetes for hypoglycemic risk prediction, unless kidney function is impaired equivalent to CKD stage 3–5. The CGM-defined CV is informative for hypoglycemic risk prediction regardless of kidney function.

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.