Objective: aaWilson’s disease (WD) is a rare genetic disorder of copper metabolism, and longitudinal follow-up studies are limited. We performed a retrospective analysis to determine the clinical characteristics and long-term outcomes in a large WD cohort. Methods: aaMedical records of WD patients diagnosed from 2006–2021 at National Taiwan University Hospital were retrospectively evaluated for clinical presentations, neuroimages, genetic information, and follow-up outcomes. Results: aaThe present study enrolled 123 WD patients (mean follow-up: 11.12 ± 7.41 years), including 74 patients (60.2%) with hepatic features and 49 patients (39.8%) with predominantly neuropsychiatric symptoms. Compared to the hepatic group, the neuropsychiatric group exhibited more Kayser-Fleischer rings (77.6% vs. 41.9%, p < 0.01), lower serum ceruloplasmin levels (4.9 ± 3.9 vs. 6.3 ± 3.9 mg/dL, p < 0.01), smaller total brain and subcortical gray matter volumes (p < 0.0001), and worse functional outcomes during follow-up (p = 0.0003). Among patients with available DNA samples (n = 59), the most common mutations were p.R778L (allelic frequency of 22.03%) followed by p.P992L (11.86%) and p.T935M (9.32%). Patients with at least one allele of p.R778L had a younger onset age (p = 0.04), lower ceruloplasmin levels (p < 0.01), lower serum copper levels (p = 0.03), higher percentage of the hepatic form (p = 0.03), and a better functional outcome during follow-up (p = 0.0012) compared to patients with other genetic variations. Conclusion aaThe distinct clinical characteristics and long-term outcomes of patients in our cohort support the ethnic differences regarding the mutational spectrum and clinical presentations in WD.

Chronic inflammation is thought to be the leading cause of colorectal cancer, and interleukin-10 (IL10) has been identified as a potent immunomodulatory cytokine that regulates inflammatory responses in the gastrointestinal tract. Although several single nucleotide polymorphisms (SNPs) in IL10 have been associated with the risk of colorectal cancer, their prognostic significance has not been determined. Two hundred and eighty-two colorectal cancer patients were genotyped for two candidate cancer-associated SNPs in IL10. The associations of these SNPs with distant metastasis-free survival and overall survival were evaluated by Kaplan-Meier analysis and Cox regression model. The minor homozygote GG genotype of IL10 rs3021094 was significantly associated with a 3.30-fold higher risk of death compared with the TT+TG genotypes (P=0.011). The patients with IL10 rs3021094 GG genotype also had a poorer overall survival in Kaplan-Meier analysis (log-rank P=0.007) and in multivariate Cox regression model (P=0.044) adjusting for age, gender, carcinoembryonic antigen levels, tumor differentiation, stage, lymphovascular invasion, and perineural invasion. In conclusion, our results suggest that IL10 rs3021094 might be a valuable prognostic biomarker for colorectal cancer patients.
Aged ; Alleles ; Carcinoembryonic Antigen/blood ; Cell Differentiation ; Colorectal Neoplasms/*genetics/mortality/pathology ; Female ; Genotype ; Homozygote ; Humans ; Interleukin-10/*genetics ; Kaplan-Meier Estimate ; Lymphatic Metastasis ; Male ; Middle Aged ; Neoplasm Staging ; *Polymorphism, Single Nucleotide ; Regression Analysis ; Tumor Markers, Biological/genetics

OBJECTIVE: Logistic regression analysis (LRA), Support Vector Machine (SVM) and a neural network (NN) are commonly used statistical models in computer-aided diagnostic (CAD) systems for breast ultrasonography (US). The aim of this study was to clarify the diagnostic ability of the use of these statistical models for future applications of CAD systems, such as three-dimensional (3D) power Doppler imaging, vascularity evaluation and the differentiation of a solid mass. MATERIALS AND METHODS: A database that contained 3D power Doppler imaging pairs of non-harmonic and tissue harmonic images for 97 benign and 86 malignant solid tumors was utilized. The virtual organ computer-aided analysis-imaging program was used to analyze the stored volumes of the 183 solid breast tumors. LRA, an SVM and NN were employed in comparative analyses for the characterization of benign and malignant solid breast masses from the database. RESULTS: The values of area under receiver operating characteristic (ROC) curve, referred to as Az values for the use of non-harmonic 3D power Doppler US with LRA, SVM and NN were 0.9341, 0.9185 and 0.9086, respectively. The Az values for the use of harmonic 3D power Doppler US with LRA, SVM and NN were 0.9286, 0.8979 and 0.9009, respectively. The Az values of six ROC curves for the use of LRA, SVM and NN for non-harmonic or harmonic 3D power Doppler imaging were similar. CONCLUSION: The diagnostic performances of these three models (LRA, SVM and NN) are not different as demonstrated by ROC curve analysis. Depending on user emphasis for the use of ROC curve findings, the use of LRA appears to provide better sensitivity as compared to the other statistical models.
Adolescent ; Adult ; Aged ; Aged, 80 and over ; *Artificial Intelligence ; Breast Neoplasms/*ultrasonography ; Diagnosis, Computer-Assisted ; Diagnosis, Differential ; Female ; Humans ; Image Interpretation, Computer-Assisted ; Imaging, Three-Dimensional/*statistics & numerical data ; Logistic Models ; Middle Aged ; *Neural Networks (Computer) ; Predictive Value of Tests ; ROC Curve ; Sensitivity and Specificity ; Ultrasonography, Doppler/*statistics & numerical data ; Ultrasonography, Mammary/*statistics & numerical data

OBJECTIVE: Hypermethylation of human papillomavirus (HPV) and host genes has been reported in cervical cancer. However, the degree of methylation of different HPV types relative to the severity of the cervical lesions remains controversial. Studies of the degree of methylation associated with the host gene and the HPV genome to the severity of cervical lesions are rare. We examined the association of methylation status between host genes and late gene 1 (L1) regions of HPV16, 18, 52, and 58 in cervical brushings. METHODS: Cervical brushings from 147 HPV-infected patients were obtained. The samples comprised normal (n=28), cervical intraepithelial neoplasia (CIN) 1 (n=45), CIN2 (n=13), and CIN3/carcinoma in situ (n=61). The methylation status of HPV and host genes was measured using bisulfite pyrosequencing and quantitative methylation-specific polymerase chain reaction (PCR). RESULTS: The degree of methylation of L1 in HPV16, 18, and 52 was associated with the severity of the cervical lesion. In HPV52, C-phosphate-G (CpG) sites 6368m, 6405m, and 6443m showed significantly higher methylation in lesions ≥CIN3 (p=0.005, 0.003, and 0.026, respectively). Methylation of most HPV types except HPV52 (r<−0.1) was positively correlated with the degree of methylation of host genes including PAX1 and SOX1 (0.4≤r≤0.7). Combining HPV methylation with PAX1 methylation improved the clustering for ≥CIN2. CONCLUSION: Our study showed that the degree of L1 methylation of HPV16, 18, and 52 but not 58 is associated with the severity of cervical lesions. The association between HPV methylation and host gene methylation suggests different responses of host cellular epigenetic machinery to different HPV genotypes.
Cervical Intraepithelial Neoplasia* ; DNA Methylation ; Epigenomics ; Genome ; Genotype ; Human papillomavirus 16 ; Humans ; Methylation* ; Papillomaviridae ; Polymerase Chain Reaction ; Uterine Cervical Neoplasms

PURPOSE: Epidermal growth factor receptor (EGFR) T790M mutation serves as an important predictor of osimertinib efficacy. However, little is known about how it works among patients with various timings of T790M emergence and treatment. MATERIALS AND METHODS: Advanced EGFR-mutant lung adenocarcinoma patients with positive T790M mutation in tumor were retrospectively enrolled and observed to determine the outcomes of osimertinib treatment. We evaluated the association between patients’ characteristics and the efficacy of osimertinib treatment, particularly with respect to the timing of T790M emergence and osimertinib prescription. RESULTS: A total of 91 patients were enrolled, including 14 (15.4%) with primary and 77 (84.6%) with acquired T790M mutation. The objective response rate and disease controlratewere 60.9% and 85.1%, respectively. The median progression-free survival (PFS) and overall survival were 11.5 months (95% confidence interval [CI], 9.0 to 14.0) and 30.4 months (95% CI, 11.3 to 49.5), respectively. There was no significant difference in response rate and PFS between primary and acquired T790M populations. In the acquired T790M subgroup, patientswho received osimertinib after T790M had been confirmed by rebiopsy had a longer PFS than those with intercalated treatments between rebiopsy and osimertinib prescription (14.0 months [95% CI, 9.0 to 18.9] vs. 7.2 months [95% CI, 3.7 to 10.8]; adjusted hazard ratio, 0.48 [95% CI, 0.24 to 0.98; p=0.043]). Rebiopsy timing did not influence the outcome. CONCLUSION: Osimertinib prescription with intercalated treatment following rebiopsy but not the timing of T790M emergence influenced the treatment outcome. We suggest that it is better to start osimertinib treatment once T790M mutation has been confirmed by biopsy.
Adenocarcinoma ; Biopsy ; Disease-Free Survival* ; Humans ; Lung ; Prescriptions ; Receptor, Epidermal Growth Factor ; Retrospective Studies ; Treatment Outcome

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.

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.