Association of interferon-based therapy with risk of autoimmune diseases in patients with chronic hepatitis C virus infection: A population-based Taiwanese cohort study.

Background: Interferon in combination with ribavirin has been the standard of care for chronic hepatitis C virus infection (HCV) for the past few decades. However, its effect on the risk of autoimmune diseases (ADs) among patients with HCV infection remains unclear. We assessed the potential association between interferon-based therapy (IBT) and AD risk in patients with HCV infection. Methods: This retrospective cohort study identified patients diagnosed with HCV infection between January 1, 2006, and December 31, 2015, from Taiwan's National Health Insurance Research Database. In total, 16,029 patients with HCV infection who received IBT and 141,214 patients with HCV infection who did not receive IBT were included. Both cohorts were followed up to assess the development of ADs. Hazard ratios (HRs) were calculated using the Cox proportional hazards regression model, which was adjusted for potential confounders. Results: The median follow-up period for IBT and non-IBT users was 4.53 and 3.34 years, respectively. No significant difference in the risk of overall ADs (adjusted HR [aHR]: 0.96, 95% confidence interval [CI]: 0.81-1.14) or systemic ADs (aHR: 0.88, 95% CI: 0.71-1.10) was noted during the study period. However, a slight increase in the risk of organ-specific ADs was noted among IBT users (incidence rate ratio: 1.33, 95% CI: 1.02-1.72). Furthermore, analysis of AD subgroups revealed a significant increase in the risks of Graves' disease (aHR: 6.06, 95% CI: 1.27-28.8) and Hashimoto's thyroiditis (aHR 1.49, 95% CI 1.01-2.21) among IBT users. Conclusions: IBT use increases the risk of autoimmune thyroid diseases (Hashimoto's thyroiditis and Graves' disease) in patients with HCV infection to a greater extent than non-IBT use.

Functional Plasmon-Activated Water Increases Akkermansia muciniphila Abundance in Gut Microbiota to Ameliorate Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) is associated with dysbiosis and intestinal barrier dysfunction, as indicated by epithelial hyperpermeability and high levels of mucosal-associated bacteria. Changes in gut microbiota may be correlated with IBD pathogenesis. Additionally, microbe-based treatments could mitigate clinical IBD symptoms. Plasmon-activated water (PAW) is known to have an anti-inflammatory potential. In this work, we studied the association between the anti-inflammatory ability of PAW and intestinal microbes, thereby improving IBD treatment. We examined the PAW-induced changes in the colonic immune activity and microbiota of mice by immunohistochemistry and next generation sequencing, determined whether drinking PAW can mitigate IBD induced by 2,4,6-trinitrobenzene sulfonic acid (TNBS) and dysbiosis through mice animal models. The effects of specific probiotic species on mice with TNBS-induced IBD were also investigated. Experimental results indicated that PAW could change the local inflammation in the intestinal microenvironment. Moreover, the abundance of Akkermansia spp. was degraded in the TNBS-treated mice but elevated in the PAW-drinking mice. Daily rectal injection of Akkermansia muciniphila, a potential probiotic species in Akkermansia spp., also improved the health of the mice. Correspondingly, both PAW consumption and increasing the intestinal abundance of Akkermansia muciniphila can mitigate IBD in mice. These findings indicate that increasing the abundance of Akkermansia muciniphila in the gut through PAW consumption or other methods may mitigate IBD in mice with clinically significant IBD.

Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features.

Colorectal cancer is the leading cause of cancer-associated morbidity and mortality worldwide. One of the causes of developing colorectal cancer is untreated colon adenomatous polyps. Clinically, polyps are detected in colonoscopy and the malignancies are determined according to the biopsy. To provide a quick and objective assessment to gastroenterologists, this study proposed a quantitative polyp classification via various image features in colonoscopy. The collected image database was composed of 1991 images including 1053 hyperplastic polyps and 938 adenomatous polyps and adenocarcinomas. From each image, textural features were extracted and combined in machine learning classifiers and machine-generated features were automatically selected in deep convolutional neural networks (DCNN). The DCNNs included AlexNet, Inception-V3, ResNet-101, and DenseNet-201. AlexNet trained from scratch achieved the best performance of 96.4% accuracy which is better than transfer learning and textural features. Using the prediction models, the malignancy level of polyps can be evaluated during a colonoscopy to provide a rapid treatment plan.

Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound.

BACKGROUND AND OBJECTIVE: Emergency physicians (EPs) frequently deal with abdominal pain, including that is caused by either gallstones or acute cholecystitis. Easy access and low cost justify point-of-care ultrasound (POCUS) use as a first-line test to detect these diseases; yet, the detection performance of POCUS by EPs is unreliable, causing misdiagnoses with serious impacts. This study aimed to develop a machine learning system to detect and localize gallstones and to detect acute cholecystitis by ultrasound (US) still images taken by physicians or technicians for preliminary diagnoses. METHODS: Abdominal US images (> 89,000) were collected from 2386 patients in a hospital database. We constructed training sets for gallstones with or without cholecystitis (N = 10,971) and cholecystitis with or without gallstones (N = 7348) as positives. Validation sets were also constructed for gallstones (N = 2664) and cholecystitis (N = 1919). We applied a single-shot multibox detector (SSD) and a feature pyramid network (FPN) to classify and localize objects using image features extracted by ResNet-50 for gallstones, and MobileNet V2 to classify cholecystitis. The deep learning models were pretrained using the COCO-2017 and ILSVRC-2012 datasets. RESULTS: Using the validation sets, the SSD-FPN-ResNet-50 and MobileNet V2 achieved areas under the receiver operating characteristics curve of 0.92 and 0.94, respectively. The inference speeds were 21 (47.6 frames per second, fps) and 7 ms (142.9 fps). CONCLUSIONS: A machine learning system was developed to detect and localize gallstones, and to detect cholecystitis, with acceptable discrimination and speed. This is the first study to develop this system for either gallstone or cholecystitis detection with absence or presence of each one. After clinical trials, this system may be used to assist EPs, including those in remote areas, for detecting these diseases.

Deep learning for abdominal ultrasound: A computer-aided diagnostic system for the severity of fatty liver.

BACKGROUND: The prevalence of nonalcoholic fatty liver disease is increasing over time worldwide, with similar trends to those of diabetes and obesity. A liver biopsy, the gold standard of diagnosis, is not favored due to its invasiveness. Meanwhile, noninvasive evaluation methods of fatty liver are still either very expensive or demonstrate poor diagnostic performances, thus, limiting their applications. We developed neural network-based models to assess fatty liver and classify the severity using B-mode ultrasound (US) images. METHODS: We followed standards for reporting of diagnostic accuracy guidelines to report this study. In this retrospective study, we utilized B-mode US images from a consecutive series of patients to develop four-class, two-class, and three-class diagnostic prediction models. The images were eligible if confirmed by at least two gastroenterologists. We compared pretrained convolutional neural network models, consisting of visual geometry group (VGG)19, ResNet-50 v2, MobileNet v2, Xception, and Inception v2. For validation, we utilized 20% of the dataset resulting in >100 images for each severity category. RESULTS: There were 21,855 images from 2,070 patients classified as normal (N = 11,307), mild (N = 4,467), moderate (N = 3,155), or severe steatosis (N = 2,926). We used ResNet-50 v2 for the final model as the best ones. The areas under the receiver operating characteristic curves were 0.974 (mild steatosis vs others), 0.971 (moderate steatosis vs others), 0.981 (severe steatosis vs others), 0.985 (any severity vs normal), and 0.996 (moderate-to-severe steatosis/clinically abnormal vs normal-to-mild steatosis/clinically normal). CONCLUSION: Our deep learning models achieved comparable predictive performances to the most accurate, yet expensive, noninvasive diagnostic methods for fatty liver. Because of the discriminative ability, including for mild steatosis, significant impacts on clinical applications for fatty liver are expected. However, we need to overcome machine-dependent variation, motion artifacts, lacking of second confirmation from any other tools, and hospital-dependent regional bias.

Stool DNA test targeting methylated syndecan-2 (SDC2) as a noninvasive screening method for colorectal cancer.

Despite the steadily increasing worldwide incidence of colorectal cancer (CRC), an effective noninvasive approach for early detection of CRC is still under investigation. The guaiac-based fecal occult blood test (FOBT) and fecal immunochemical test (FIT) have gained popularity as noninvasive CRC screening tests owing to their convenience and relatively low costs. However, the FOBT and FIT have limited sensitivity and specificity. To develop a noninvasive tool for the detection of CRC, we investigated the sensitivity, specificity, and accuracy of a stool DNA test targeting methylated syndecan-2 (SDC2), which is frequently methylated in patients with CRC. The present study enrolled 62 patients diagnosed as having stage 0-IV CRC and 76 healthy participants between July 2018 and June 2019 from two institutions. Approximately 4.5 g of stool sample was collected from each participant for detection of human methylated SDC2 gene. In total, 48 of 62 (77.4%) patients with CRC showed positive results, whereas 67 out of 76 (88.2%) healthy participants showed negative results. The area under the curve of the receiver operating characteristic curve constructed was 0.872 for discrimination between patients with CRC and healthy individuals. The present study highlights the potential of the fecal methylated SDC2 test as a noninvasive detection method for CRC screening with a relatively favorable sensitivity of 77.4%, a specificity of 88.2% and a positive predictive value of 84.2% compared with other available fecal tests. Further multicenter clinical trials comprising subjects of varied ethnicities are required to validate this test for the mass screening of patients with CRC.

Red Blood Cell Aggregation-Associated Dietary Pattern Predicts Hyperlipidemia and Metabolic Syndrome.

Red blood cell (RBC) aggregation and iron status are interrelated and strongly influenced by dietary factors, and their alterations pose a great risk of dyslipidemia and metabolic syndrome (MetS). Currently, RBC aggregation-related dietary patterns remain unclear. This study investigated the dietary patterns that were associated with RBC aggregation and their predictive effects on hyperlipidemia and MetS. Anthropometric and blood biochemical data and food frequency questionnaires were collected from 212 adults. Dietary patterns were derived using reduced rank regression from 32 food groups. Adjusted linear regression showed that hepcidin, soluble CD163, and serum transferrin saturation (%TS) independently predicted RBC aggregation (all p < 0.01). Age-, sex-, and log-transformed body mass index (BMI)-adjusted prevalence rate ratio (PRR) showed a significant positive correlation between RBC aggregation and hyperlipidemia (p-trend < 0.05). RBC aggregation and iron-related dietary pattern scores (high consumption of noodles and deep-fried foods and low intake of steamed, boiled, and raw food, dairy products, orange, red, and purple vegetables, white and light-green vegetables, seafood, and rice) were also significantly associated with hyperlipidemia (p-trend < 0.05) and MetS (p-trend = 0.01) after adjusting for age, sex, and log-transformed BMI. Our results may help dieticians develop dietary strategies for preventing dyslipidemia and MetS.