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.

Glycogen synthase kinase-3β (GSK-3β) is an important serine/threonine kinase that implicates in multiple cellular processes and links with the neurodegenerative diseases including Alzheimer’s disease (AD). In this study, structure-based virtual screening was performed to search database for compounds targeting GSK-3β from Enamine’s screening collection. Of the top-ranked compounds, 7 primary hits underwent a luminescent kinase assay and a cell assay using human neuroblastoma SH-SY5Y cells expressing Tau repeat domain (TauRD) with pro-aggregant mutation ΔK280. In the kinase assay for these 7 compounds, residual GSK-3β activities ranged from 36.1% to 90.0% were detected at the IC50 of SB-216763. In the cell assay, only compounds VB-030 and VB-037 reduced Tau aggregation in SH-SY5Y cells expressing ΔK280 TauRD-DsRed folding reporter. In SH-SY5Y cells expressing ΔK280 TauRD, neither VB-030 nor VB-037 increased expression of GSK-3α Ser21 or GSK-3β Ser9. Among extracellular signal-regulated kinase (ERK), AKT serine/threonine kinase 1 (AKT), mitogen-activated protein kinase 14 (P38) and mitogenactivated protein kinase 8 (JNK) which modulate Tau phosphorylation, VB-037 attenuated active phosphorylation of P38 Thr180/ Tyr182, whereas VB-030 had no effect on the phosphorylation status of ERK, AKT, P38 or JNK. However, both VB-030 and VB-037 reduced endogenous Tau phosphorylation at Ser202, Thr231, Ser396 and Ser404 in neuronally differentiated SH-SY5Y expressing ΔK280 TauRD. In addition, VB-030 and VB-037 further improved neuronal survival and/or neurite length and branch in mouse hippocampal primary culture under Tau cytotoxicity. Overall, through inhibiting GSK-3β kinase activity and/or p-P38 (Thr180/Tyr182), both compounds may serve as promising candidates to reduce Tau aggregation/cytotoxicity for AD treatment.

Objective: To quantitatively assess the pulmonary vasculature using non-contrast computed tomography (CT) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) pre- and post-treatment and correlate CT-based parameters with right heart catheterization (RHC) hemodynamic and clinical parameters. Materials and Methods: A total of 30 patients with CTEPH (mean age, 57.9 years; 53% female) who received multimodal treatment, including riociguat for ≥ 16 weeks with or without balloon pulmonary angioplasty and underwent both noncontrast CT for pulmonary vasculature analysis and RHC pre- and post-treatment were included. The radiographic analysis included subpleural perfusion parameters, including blood volume in small vessels with a cross-sectional area ≤ 5 mm 2 (BV5) and total blood vessel volume (TBV) in the lungs. The RHC parameters included mean pulmonary artery pressure (mPAP), pulmonary vascular resistance (PVR), and cardiac index (CI). Clinical parameters included the World Health Organization (WHO) functional class and 6-minute walking distance (6MWD). Results: The number, area, and density of the subpleural small vessels increased after treatment by 35.7% (P < 0.001), 13.3% (P = 0.028), and 39.3% (P < 0.001), respectively. The blood volume shifted from larger to smaller vessels, as indicated by an 11.3% increase in the BV5/TBV ratio (P = 0.042). The BV5/TBV ratio was negatively correlated with PVR (r = -0.26; P = 0.035) and positively correlated with CI (r = 0.33; P = 0.009). The percent change across treatment in the BV5/TBV ratio correlated with the percent change in mPAP (r = -0.56; P = 0.001), PVR (r = -0.64; P < 0.001), and CI (r = 0.28; P = 0.049).Furthermore, the BV5/TBV ratio was inversely associated with the WHO functional classes I–IV (P = 0.004) and positively associated with 6MWD (P = 0.013). Conclusion Non-contrast CT measures could quantitatively assess changes in the pulmonary vasculature in response to treatment and were correlated with hemodynamic and clinical parameters.

Alzheimer’s disease (AD) is a neurodegenerative disease with progressive memory loss and the cognitive decline. AD is mainly caused by abnormal accumulation of misfolded amyloid β (Aβ), which leads to neurodegeneration via a number of possible mechanisms such as down-regulation of brain-derived neurotrophic factor-tropomyosin-related kinase B (BDNF-TRKB) signaling pathway. 7 ,8-Dihydroxyflavone (7,8-DHF), a TRKB agonist, has demonstrated potential to enhance BDNF-TRKB pathway in various neurodegenerative diseases. T o expand the capacity of flavones as TRKB agonists, two natural flavones quercetin and apigenin, were evaluated. With tryptophan fluorescence quenching assay, we illustrated the direct interaction between quercetin/ apigenin and TRKB extracellular domain. Employing Aβ folding reporter SH-SY5Y cells, we showed that quercetin and apigenin reduced Aβ-aggregation, oxidative stress, caspase-1 and acetylcholinesterase activities, as well as improved the neurite outgrowth. Treatments with quercetin and apigenin increased TRKB Tyr516 and Tyr817 and downstream cAMP-response-element binding protein (CREB) Ser133 to activate transcription of BDNF and BCL2 apoptosis regulator (BCL2), as well as reduced the expression of pro-apoptotic BCL2 associated X protein (BAX). Knockdown of TRKB counteracted the improvement of neurite outgrowth by quercetin and apigenin. Our results demonstrate that quercetin and apigenin are to work likely as a direct agonist on TRKB for their neuroprotective action, strengthening the therapeutic potential of quercetin and apigenin in treating AD.

Objective: The impact of serotonergic system on obsessive-compulsive disorder (OCD) is well studied. However, the correlation between OC presentations and autonomic nervous system (ANS) is still unclear. Furthermore, whether the correlation might be modulated by serotonin is also uncertain. Methods: We recruited eighty-nine healthy subjects. Serotonin transporter (SERT) availability by [ 123 I]ADAM and heart rate variability (HRV) tests were measured. Symptoms checklist-90 was measured for the OC presentations. The interaction between HRV and SERT availability were calculated and the correlation between HRV and OC symptoms were analyzed after stratified SERT level into two groups, split at medium. Results: The interactions were significant in the factors of low frequency (LF), high frequency (HF), and root mean square of successive differences (RMSSD). Furthermore, the significantly negative correlations between OC symptoms and the above HRV indexes existed only in subjects with higher SERT availability. Conclusion OC symptoms might be correlated with ANS regulations in subjects with higher SERT availability.

Purpose: The aim of this study was to investigate the efficacy of various epidermal growth factor receptor (EGFR)–tyrosine kinase inhibitors (TKIs) plus bevacizumab in advanced EGFR-mutant lung adenocarcinoma patients. Materials and Methods: From August 2016 to October 2020, we enrolled advanced lung adenocarcinoma patients harboring exon 19 deletion or L858R receiving gefitinib, erlotinib and afatinib plus bevacizumab as the first-line treatment for the purposes of analysis. Results: A total of 36 patients were included in the final analysis. Three patients received gefitinib, 17 received erlotinib, and 16 received afatinib combined with bevacizumab as the first-line treatment. The objective response rate was 77.8%, and disease control rate was 94.4%. The overall median progression-free survival (PFS) was 16.4 months, while the median PFS was 17.1 months in patients with exon 19 deletion, and 16.2 months in patients with L858R mutation (p=0.311). Regarding the use of different EGFR-TKIs, the median PFS was 17.1 months in the erlotinib group and 21.6 months in the afatinib group (p=0.617). In patients with brain metastasis at baseline, the median PFS was 18.9 months in the erlotinib group and 16.4 months in the afatinib group (p=0.747). Amongst patients harboring exon 19 deletion, the median PFS was 16.2 months in the erlotinib group and not-reached in the afatinib group (p=0.141). In patients with L858R mutation, the median PFS was 18.9 months in the erlotinib group and 16.2 months in the afatinib group (p=0.481). Conclusion Our research demonstrates that not only erlotinib combined with bevacizumab, but also afatinib plus bevacizumab as first-line treatment, provides solid clinical efficacy in advanced EGFR-mutant lung adenocarcinoma patients.