Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.

Background/Aims: Oral EDP-514 is a potent core protein inhibitor of hepatitis B virus (HBV) replication, which produced a >4-log viral load reduction in HBV-infected chimeric mice with human liver cells. This study evaluated the safety, pharmacokinetics, and antiviral activity of three doses of EDP-514 in treatment-naive viremic patients with HBeAgpositive or -negative chronic HBV infection. Methods: Patients with HBsAg detectable at screening and at least 6 months previously were eligible. HBeAg-positive and -negative patients had a serum/plasma HBV DNA level ≥20,000 and ≥2,000 IU/mL, respectively. Twenty-five patients were randomized to EDP-514 200 (n=6), 400 (n=6) or 800 mg (n=7) or placebo (n=6) once daily for 28 days. Results: A dose-related increase in EDP-514 exposure (AUClast and Cmax) was observed across doses. At Day 28, mean reductions in HBV DNA were –2.9, –3.3, –3.5 and –0.2 log10 IU/mL with EDP-514 200 mg, 400 mg, 800 mg, and placebo groups, respectively. The corresponding mean change from baseline for HBV RNA levels was –2.9, –2.4, –2.0, and –0.02 log10 U/mL. No virologic failures were observed. No clinically meaningful changes from baseline were observed for HBsAg, HBeAg or HBcrAg. Nine patients reported treatment emergent adverse events of mild or moderate severity with no discontinuations, serious AEs or deaths. Conclusions In treatment-naïve viremic patients, oral EDP-514 was generally safe and well-tolerated, displayed PK profile supportive of once-daily dosing, and markedly reduced HBV DNA and HBV RNA.