The effect of STAT1, miR-99b, and MAP2K1 in alcoholic liver disease (ALD) mouse model and hepatocyte.

Alcoholic liver disease (ALD) serves as the leading cause of chronic liver diseases-related morbidity and mortality, which threatens the life of millions of patients in the world. However, the molecular mechanisms underlying ALD progression remain unclear. Here, we applied microarray analysis and experimental approaches to identify miRNAs and related regulatory signaling that associated with ALD. Microarray analysis identified that the expression of miR-99b was elevated in the ALD mouse model. The AML-12 cells were treated with EtOH and the expression of miR-99b was enhanced in the cells. The expression of miR-99b was positively correlated with ALT levels in the ALD mice. The microarray analysis identified the abnormally expressed mRNAs in ALD mice and the overlap analysis was performed with based on the differently expressed mRNAs and the transcriptional factors of miR-99b, in which STAT1 was identified. The elevated expression of STAT1 was validated in ALD mice. Meanwhile, the treatment of EtOH induced the expression of STAT1 in the AML-12 cells. The expression of STAT1 was positively correlated with ALT levels in the ALD mice. The positive correlation of STAT1 and miR-99b expression was identified in bioinformatics analysis and ALD mice. The expression of miR-99b and pri-miR-99b was promoted by the overexpression of STAT1 in AML-12 cells. ChIP analysis confirmed the enrichment of STAT1 on miR-99b promoter in AML-12 cells. Next, we found that the expression of mitogen-activated protein kinase kinase 1 (MAP2K1) was negatively associated with miR-99b. The expression of MAP2K1 was downregulated in ALD mice. Consistently, the expression of MAP2K1 was reduced by the treatment of EtOH in AML-12 cells. The expression of MAP2K1 was negative correlated with ALT levels in the ALD mice. We identified the binding site of MAP2K1 and miR-99b. Meanwhile, the treatment of miR-99b mimic repressed the luciferase activity of MAP2K1 in AML-12 cells. The expression of MAP2K1 was suppressed by miR-99b in the cells. We observed that the expression of MAP2K1 was inhibited by the overexpression of STAT1 in AML-12 cells. Meanwhile, the apoptosis of AML-12 cells was induced by the treatment of EtOH, while miR-99b mimic promoted but the overexpression of MAP2K1 attenuated the effect of EtOH in the cells. In conclusion, we identified the correlation and effect of STAT1, miR-99b, and MAP2K1 in ALD mouse model and hepatocyte. STAT1, miR-99b, and MAP2K1 may serve as potential therapeutic target of ALD.

Regression-based Chinese norms of number connection test A and digit symbol test for diagnosing minimal hepatic encephalopathy.

Number connection test A (NCT-A) and digit symbol test (DST), the preferential neuropsychological tests to detect minimal hepatic encephalopathy (MHE) in China, haven't been standardized in Chinese population. We aimed to establish the norms based on a multi-center cross-sectional study and to detect MHE in cirrhotic patients. NCT-A and DST were administered to 648 healthy controls and 1665 cirrhotic patients. The regression-based procedure was applied to develop demographically adjusted norms for NCT-A and DST based on healthy controls. Age, gender, education, and age by education interaction were all predictors of DST, while age, gender, and education by gender interaction were predictors of log10 NCT-A. The predictive equations for expected scores of NCT-A and DST were established, and Z-scores were calculated. The norm for NCT-A was set as Z ≤ 1.64, while the norm for DST was set as Z ≥ - 1.64. Cirrhotic patients with concurrent abnormal NCT-A and DST results were diagnosed with MHE. The prevalence of MHE was 8.89% in cirrhotic patients, and only worse Child-Pugh classification (P = 0.002, OR = 2.389) was demonstrated to be the risk factor for MHE. The regression-based normative data of NCT-A and DST have been developed to detect MHE in China. A significant proportion of Chinese cirrhotic patients suffered from MHE, especially those with worse Child-Pugh classification.

Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design.

Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts have been made to design customized architectures specifically for this purpose. This paper presents a comprehensive evaluation on transfer learning based solutions and automatically designed networks, analyzing the accuracy and robustness of different recognition models in three folds. First, we develop six different DCNN models (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) based on transfer learning. Second, we adapt the Bayesian optimization method to optimize a CNN network (BONet) for classifying breast lesions. A retrospective dataset of 3034 US images collected from various hospitals is then used for evaluation. Extensive tests show that the BONet outperforms other models, exhibiting higher accuracy (83.33%), lower generalization gap (1.85%), shorter training time (66 min), and less model complexity (approximately 0.5 million weight parameters). We also compare the diagnostic performance of all models against that by three experienced radiologists. Finally, we explore the use of saliency maps to explain the classification decisions made by different models. Our investigation shows that saliency maps can assist in comprehending the classification decisions.

Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images.

Thyroid cancer is one of the common types of cancer worldwide, and Ultrasound (US) imaging is a modality normally used for thyroid cancer diagnostics. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TIRADS) has been widely adopted to identify and classify US image characteristics for thyroid nodules. This paper presents novel methods for detecting the characteristic descriptors derived from TIRADS. Our methods return descriptions of the nodule margin irregularity, margin smoothness, calcification as well as shape and echogenicity using conventional computer vision and deep learning techniques. We evaluate our methods using datasets of 471 US images of thyroid nodules acquired from US machines of different makes and labeled by multiple radiologists. The proposed methods achieved overall accuracies of 88.00%, 93.18%, and 89.13% in classifying nodule calcification, margin irregularity, and margin smoothness respectively. Further tests with limited data also show a promising overall accuracy of 90.60% for echogenicity and 100.00% for nodule shape. This study provides an automated annotation of thyroid nodule characteristics from 2D ultrasound images. The experimental results showed promising performance of our methods for thyroid nodule analysis. The automatic detection of correct characteristics not only offers supporting evidence for diagnosis, but also generates patient reports rapidly, thereby decreasing the workload of radiologists and enhancing productivity.

ENAS-B: Combining ENAS With Bayesian Optimization for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification From Ultrasound Images.

Efficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell structures for Convolutional Neural Network (CNN) design. It has been successfully used in various applications including ultrasound image classification for breast lesions. However, the existing ENAS approach only optimizes cell structures rather than the whole CNN architecture nor its trainable hyperparameters. This paper presents a novel framework for automatic design of CNN architectures by combining strengths of ENAS and Bayesian Optimization in two-folds. Firstly, we use ENAS to search for optimal normal and reduction cells. Secondly, with the optimal cells and a suitable hyperparameter search space, we adopt Bayesian Optimization to find the optimal depth of the network and optimal configuration of the trainable hyperparameters. To test the validity of the proposed framework, a dataset of 1522 breast lesion ultrasound images is used for the searching and modeling. We then evaluate the robustness of the proposed approach by testing the optimized CNN model on three external datasets consisting of 727 benign and 506 malignant lesion images. We further compare the CNN model with the default ENAS-based CNN model, and then with CNN models based on the state-of-the-art architectures. The results (error rate of no more than 20.6% on internal tests and 17.3% on average of external tests) show that the proposed framework generates robust and light CNN models.

Integrated analysis of single-cell and bulk RNA-sequencing reveals tumor heterogeneity and a signature based on NK cell marker genes for predicting prognosis in hepatocellular carcinoma.

Background: Natural killer (NK) cells are a type of innate immune cell that recognize and eliminate tumor cells and infected cells, without prior sensitization or activation. Herein, we aimed to construct a predictive model based on NK cell-related genes for hepatocellular carcinoma (HCC) patients and assess the feasibility of utilizing this model for prognosis prediction. Methods: Single-cell RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database to identify marker genes of NK cells. Univariate Cox and lasso regression were performed to further establish a signature in the TCGA dataset. Subsequently, qPCR and immunohistochemistry (IHC) staining were employed to validate the expression levels of prognosis signature genes in HCC. The effectiveness of the model was further validated using two external cohorts from the GEO and ICGC datasets. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, and biological function were compared for different genetic subtypes and risk groups. Finally, molecular docking was performed to evaluate the binding affinity between the hub gene and chemotherapeutic drugs. Results: A total of 161 HCC-related NK cell marker genes (NKMGs) were identified, 28 of which were significantly associated with overall survival in HCC patients. Based on differences in gene expression characteristics, HCC patients were classified into three subtypes. Ten prognosis genes (KLRB1, CD7, LDB2, FCER1G, PFN1, FYN, ACTG1, PABPC1, CALM1, and RPS8) were screened to develop a prognosis model. The model not only demonstrated excellent predictive performance on the training dataset, but also were successfully validated on two independent external datasets. The risk scores derived from the model were shown to be an independent prognosis factor for HCC and were correlated with pathological severity. Moreover, qPCR and IHC staining confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis. Finally, molecular docking revealed favorable binding energies between the hub gene ACTG1 and chemotherapeutic drugs. Conclusion: In this study, we developed a model for predicting the prognosis of HCC based on NK cells. The utilization of NKMGs as innovative biomarkers showed promise in the prognosis assessment of HCC.

Association between leptin and NAFLD: a two-sample Mendelian randomization study.

BACKGROUND: The etiology of nonalcoholic fatty liver disease (NAFLD) involves a complex interaction of genetic and environmental factors. Previous observational studies have revealed that higher leptin levels are related to a lower risk of developing NAFLD, but the causative association remains unknown. We intended to study the causal effect between leptin and NAFLD using the Mendelian randomization (MR) study. METHODS: We performed a two-sample Mendelian randomization (TSMR) analysis using summary GWAS data from leptin (up to 50,321 individuals) and NAFLD (8,434 cases and 770,180 controls) in a European population. Instrumental variables (IVs) that satisfied the three core assumptions of Mendelian randomization were selected. The TSMR analysis was conducted using the inverse variance weighted (IVW) method, MR-Egger regression method, and weighted median (WM) method. To ensure the accuracy and stability of the study results, heterogeneity tests, multiple validity tests, and sensitivity analyses were conducted. RESULTS: The findings of the TSMR correlation analysis between NAFLD and leptin were as follows: IVW method (odds ratio (OR) 0.6729; 95% confidence interval (95% CI) 0.4907-0.9235; P = 0.0142), WM method (OR 0.6549; 95% CI 0.4373-0.9806; P = 0.0399), and MR-Egger regression method (P = 0.6920). Additionally, the findings of the TSMR correlation analysis between NAFLD and circulating leptin levels adjusted for body mass index (BMI) were as follows: IVW method (OR 0.5876; 95% CI 0.3781-0.9134; P = 0.0181), WM method (OR 0.6074; 95% CI 0.4231-0.8721; P = 0.0069), and MR-Egger regression method (P = 0.8870). It has also been shown that higher levels of leptin are causally linked to a lower risk of developing NAFLD, suggesting that leptin may serve as a protective factor for NAFLD. CONCLUSIONS: Using TSMR analysis and the GWAS database, we investigated the genetic relationship between elevated leptin levels and lowered risk of NAFLD in this study. However, further research is required to understand the underlying mechanisms.

Template Based Synthesis of Porous Graphdiyne Nanosheet for Reversible and Fast NO2 Detection by UV Irradiation.

Two-dimensional graphdiyne (GDY) formed by sp and sp2 hybridized carbon has been found to be an efficient toxic gas sensing material by density functional theory (DFT). However, little experimental research concerning its gas sensing capability has been reported owing to the complex preparation process and harsh experimental conditions. Herein, porous GDY nanosheets are successfully synthesized through a facile solvothermal synthesis technique by using CuO microspheres (MSs) as both template and source of catalyst. The porous GDY nanosheets exhibit a broadband optical absorption, rendering it suitable for the light-driven optoelectronic gas sensing applications. The GDY-based gas sensor was demonstrated to have excellent reversible to NO2 behaviors at 25 °C for the first time. More importantly, higher response value and faster response-recovery time once exposed to NO2 gas molecules are achieved by the illumination of UV light. In this way, our work paves the way for the exploration of GDY-based gas detection experimentally.

Identification of pseudo-immune tolerance for chronic hepatitis B patients: Development and validation of a non-invasive prediction model.

Background and aims: Patients with chronic hepatitis B (CHB) in the immune tolerant (IT) phase were previously thought to have no or slight inflammation or fibrosis in the liver. In fact, some CHB patients with normal ALT levels still experience liver fibrosis. This study aimed to develop and validate a non-invasive model for identifying pseudo-immune tolerance (pseudo-IT) of CHB by predicting significant liver fibrosis. Methods: This multi-center study enrolled a total of 445 IT-phase patients who had undergone liver biopsy for the training cohort (n = 289) and validation cohort (n = 156) during different time periods. A risk model (IT-3) for predicting significant liver fibrosis (Ishak score ≥ 3) was developed using high-risk factors which were identified using multivariate stepwise logistic regression. Next, an online dynamic nomogram was created for the clinical usage. The receiver operating characteristic (ROC) curve, net reclassification improvement and integrated discrimination improvement were used to assess the discrimination of the IT-3 model. Calibration curves were used to evaluate the models' calibration. The clinical practicability of the model was evaluated using decision curve analysis and clinical impact curves. Results: 8.8% (39 of 445) patients presented with significant liver fibrosis in this study. Aspartate aminotransferase (AST), hepatitis B e-antigen (HBeAg), and platelet (PLT) were included in the prediction model (IT-3). The IT-3 model showed good calibration and discrimination both in the training and validation cohorts (AUC = 0.888 and 0.833, respectively). The continuous NRI and IDI showed that the IT-3 model had better predictive accuracy than GPR, APRI, and FIB-4 (p < 0.001). Decision curve analysis and clinical impact curves were used to demonstrate the clinical usefulness. At a cut-off value of 106 points, the sensitivity and specificity were 91.7 and 70.2%, respectively. Conclusion: The IT-3 model proved an accurate non-invasive method in identifying pseudo-IT of CHB, which can help to formulate more appropriate treatment strategies.

A Sequential Membrane Process of Ultrafiltration Forward Osmosis and Reverse Osmosis for Poultry Slaughterhouse Wastewater Treatment and Reuse.

To address some challenges of food security and sustainability of the poultry processing industry, a sequential membrane process of ultrafiltration (UF), forward osmosis (FO), and reverse osmosis (RO) is proposed to treat semi-processed poultry slaughterhouse wastewater (PSWW) and water recovery. The pretreatment of PSWW with UF removed 36.7% of chemical oxygen demand (COD), 38.9% of total phosphorous (TP), 24.7% of total solids (TS), 14.5% of total volatile solids (TVS), 27.3% of total fixed solids (TFS), and 12.1% of total nitrogen (TN). Then, the PSWW was treated with FO membrane in FO mode, pressure retarded osmosis (PRO) mode, and L-DOPA coated membrane in the PRO mode. The FO mode was optimal for PSWW treatment by achieving the highest average flux of 10.4 ± 0.2 L/m2-h and the highest pollutant removal efficiency; 100% of COD, 100% of TP, 90.5% of TS, 85.3% of TVS, 92.1% of TFS, and 37.2% of TN. The performance of the FO membrane was entirely restored by flushing the membrane with 0.1% sodium dodecyl sulfate solution. RO significantly removed COD, TS, TVS, TFS, and TP. However, TN was reduced by only 62% because of the high ammonia concentration present in the draw solution. Overall, the sequential membrane process (UF-FO-RO) showed excellent performance by providing high rejection efficiency for pollutant removal and water recovery.

Is it possible to intervene early cirrhosis by targeting toll-like receptors to rebalance the intestinal microbiome?

Cirrhosis is a progressive chronic liver disease caused by one or more causes and characterized by diffuse fibrosis, pseudolobules, and regenerated nodules. Once progression to hepatic decompensation, the function of the liver and other organs is impaired and almost impossible to reverse and recover, which often results in hospitalization, impaired quality of life, and high mortality. However, in the early stage of cirrhosis, there seems to be a possibility of cirrhosis reversal. The development of cirrhosis is related to the intestinal microbiota and activation of toll-like receptors (TLRs) pathways, which could regulate cell proliferation, apoptosis, expression of the hepatomitogen epiregulin, and liver inflammation. Targeting regulation of intestinal microbiota and TLRs pathways could affect the occurrence and development of cirrhosis and its complications. In this paper, we first reviewed the dynamic change of intestinal microbiota and TLRs during cirrhosis progression. And further discussed the interaction between them and potential therapeutic targets to reverse early staged cirrhosis.

Downregulation of ST6GAL1 Promotes Liver Inflammation and Predicts Adverse Prognosis in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the most malignant tumors worldwide. The ST6 ß-galactoside α-2, 6-sialyltransferase 1 (ST6GAL1) has been found aberrantly expressed in a variety of cancers including HCC, but its function and mechanism in regulating liver inflammation remain to be investigated. This study aimed to explore the role of ST6GAL1 in HCC. The data of ST6GAL1 expression, prognosis, and clinical parameters were collected and further analyzed from the public databases including The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), and Gene Expression Omnibus (GEO). The HCC rat model was constructed by intraperitoneal injection of diethylnitrosamine. The mRNA and protein expression levels of ST6GAL1 in rat liver tissues were detected by real-time quantitative polymerase chain reaction, capillary electrophoresis, and Western blot. Results: The ST6GAL1 mRNA and protein expression levels were both lower in HCC tissues compared with normal liver tissues in the public databases and HCC rat model. The survival analysis showed that upregulation of ST6GAL1 was an independent prognostic factor for good prognosis in HCC patients. The ST6GAL1 mRNA expression showed a negative correlation with ST6GAL1 methylation levels. Enrichment analysis showed that ST6GAL1 expression was most associated with metabolic, cancer, estrogen, axon guidance, cAMP, and PI3K-AKT signaling pathways. The ST6GAL1 mRNA expression negatively correlated with liver inflammation status and proportion of NK CD56bright, NK CD56dim, pDC, and CD8+ T cells in liver. Conclusion: Compared with normal tissues, ST6GAL1 was lower expressed in HCC tumor tissues, and the downregulation of ST6GAL1 was associated with a poor prognosis in HCC patients. ST6GAL1 could further affect the infiltration of immune cells to exert anti-inflammation function in liver. Our study indicated that ST6GAL1 could be a potential biomarker and therapeutic target to assess the prognosis and regulate the immune cells infiltration level of HCC.

Network pharmacology and in vitro experiments-based strategy to investigate the mechanisms of KangXianYiAi formula for hepatitis B virus-related hepatocellular carcinoma.

The Chinese traditional medicine KangXianYiAi formula (KXYA) is used to treat hepatic disease in the clinic. Here we aim to confirm the therapeutic effects and explore the pharmacological mechanisms of KXYA on hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). We first collected and analyzed clinical data of 40 chronic hepatitis B (CHB) patients with precancerous liver lesions under KXYA treatment. Then, the cell viability, migration, cell cycle, and apoptosis of HepAD38 cells with KXYA treatment were examined. Next, we performed network pharmacological analysis based on database mining to obtain the key target pathways and genes of KXYA treatment on HBV-related HCC. We finally analyzed the expression of the key genes between normal and HBV-related HCC tissues in databases and measured the mRNA expression of the key genes in HepAD38 cells after KXYA treatment. The KXYA treatment could reduce the liver nodule size of CHB patients, suppress the proliferation and migration capabilities, and promote apoptosis of HepAD38 cells. The key pathways of KXYA on HBV-related HCC were Cancer, Hepatitis B, Viral carcinogenesis, Focal adhesion, and PI3K-Akt signaling, and KXYA treatment could regulate the expression of the key genes including HNF4A, MAPK8, NR3C1, PTEN, EGFR, and HDAC1. The KXYA exhibited a curative effect via inhibiting proliferation, migration, and promoting apoptosis of HBV-related HCC and the pharmacological mechanism was related to the regulation of the expression of HNF4A, MAPK8, NR3C1, PTEN, EGFR, and HDAC1.

PEG-rhG-CSF for prophylaxis of neutropenia after chemotherapy in patients with non-small cell lung cancer: A multicenter, prospective, randomized study.

BACKGROUND: To evaluate the efficacy and safety of pegylated recombinant human granulocyte colony-stimulating factor (PEG-rhG-CSF) in preventing neutropenia during multiple cycles of chemotherapy in patients with non-small cell lung cancer (NSCLC). METHOD: In a multicenter, prospective, randomized trial, patients with NSCLC were randomly assigned in a 2:1 ratio to treatment group (PEG-rhG-CSF as primary prophylactic therapy) or control group. Patients in the control group were administered rhG-CSF when white blood cell count was <2.0 × 109 /L or absolute neutrophil count <1.0 × 109 /L. The primary endpoint was the incidence of grade 3/4 neutropenia. Secondary endpoints included the incidence and duration of grade 3/4 neutropenia in each cycle, the incidence of febrile neutropenia (FN), delay rate of chemotherapy, prolonged time of chemotherapy, and safety. RESULTS: Between January 2019 and July 2021, 130 patients were enrolled (treatment group: n = 87, control group: n = 43). The incidence of grade 3/4 neutropenia in the treatment group was significantly lower than that in the control group (1.15% vs. 11.63%, p < 0.05). The mean duration of grade 3/4 neutropenia for the treatment and control group was 2.00 and 3.75 days, respectively. There were no statistical differences in the incidence of FN, delay rate of chemotherapy, prolonged time of chemotherapy, and antibiotic use between the two groups (all p > 0.05). Adverse events were reported in 47.13% of patients in the treatment group and 48.84% patients in the control group. CONCLUSIONS: Primary prophylactic treatment with PEG-rhG-CSF could reduce the incidence of neutropenia in patients with NSCLC during multiple cycles of chemotherapy, with acceptable safety and tolerability.

Countermeasures for the Development of Data Integration of the Internet Ice and Snow Tourism Industry under the Background of Artificial Intelligence.

Under the background of "Internet + Ice and Snow Tourism," the tourism industry has ushered in new development opportunities and external challenges. "Internet + tourism" breeds a new huge tourism market. This new market consists of netizens + purchasing power + purchasing desire. This paper mainly selects the integration of mobile ice and snow tourism and tourism and leisure industry as the main research subject, focusing on the in-depth study of the future development trend of the integration of ice and snow tourism and leisure industry and the mobile Internet industry. The analysis found that the tourism industry in the ice and snow area mainly has the following problems: the division of functional areas is not clear, there is a large gap between service innovation capabilities and industrial demand, and the level of industrial informatization is inconsistent with development goals and the lack of market service credit. On the basis of combining tourism Internet service platform and big data marketing experience, creatively propose to create a new development pattern of "one network, three places and one platform" for ice and snow tourism, promote the "Three New Coverage Projects" of the tourism network, and build a new cross-business cooperation mechanism of "Internet + Tourism"; rely on Internet marketing and publicity measures to enhance the development of tourism service integrity and other countermeasures; use Internet thinking to solve the problems of tourism industry development; and provide suggestions for the innovative development of the tourism industry in the ice and snow regions and the entire autonomous region. The implementation of these countermeasures predicts that the local economy will increase by at least 51.28%, so the development of the "Internet + ice and snow tourism" type industry in combination with the actual situation is not waiting.

Statins in Hepatitis B or C Patients Is Associated With Reduced Hepatocellular Carcinoma Risk: A Systematic Review and Meta-Analysis.

BACKGROUND: Hepatocellular carcinoma is the world's leading cause of cancer-related death. Chronic hepatitis B virus and hepatitis C virus infection cause liver cancer. The aim of this study was to investigate the relationship between statins and the risk of hepatocellular carcinoma in patients with hepatitis B or C. METHODS: We systematically searched Web of Science, Embase, PubMed, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Database from their inception to January 2019. We included studies that reported the hepatocellular carcinoma incidence among hepatitis B virus- or hepatitis C virus-infected patients or hepatitis B virus- or hepatitis C virus-related cirrhotic patients, evaluated and clearly defined exposure to statins, provided effective comparison groups, and reported risk estimates. Inclusion was not otherwise restricted. Summary relative risk estimates with 95% CIs were calculated using a random-effects model. RESULTS: Meta-analysis of 10 studies showed that statin users had a significantly lower risk of hepatocellular carcinoma (relative risk = 0.47, 95% CI = 0.38-0.56) with significant heterogeneity. In 7 hepatitis studies, using statin was associated with a 53% reduction in the incidence of hepatocellular carcinoma (relative risk = 0.47, 95% CI = 0.43-0.50) with substantial heterogeneity. In 3 cirrhosis studies, the incidence of hepatocellular carcinoma in statin users was significantly reduced by 55% (relative risk = 0.45, 95% CI = 0.30-0.61) with no heterogeneity. CONCLUSION: Statins reduce the hepatocellular carcinoma risk among patients infected with hepatitis B virus or hepatitis C virus. This chemoprotective association is more pronounced in hepatitis B virus or hepatitis C virus-associated cirrhotic patients.

Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis: A Multi-Cohort Study.

BACKGROUND: This pilot study aims at exploiting machine learning techniques to extract color Doppler ultrasound (CDUS) features and to build an artificial neural network (ANN) model based on these CDUS features for improving the diagnostic performance of thyroid cancer classification. METHODS: A total of 674 patients with 712 thyroid nodules (TNs) (512 from internal dataset and 200 from external dataset) were randomly selected in this retrospective study. We used ANN to build a model (TDUS-Net) for classifying malignant and benign TNs using both the automatically extracted quantitative CDUS features (whole ratio, intranodular ratio, peripheral ratio, and number of vessels) and gray-scale ultrasound (US) features defined by the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS). Then, we compared the diagnostic performance of the model, the performance of another ANN model based on the gray-scale US features alone (TUS-Net), and that of radiologists. RESULTS: The TDUS-Net (0.898, 95% CI: 0.868-0.922) achieved a higher area under the curve (AUC) than that of TUS-Net (0.881, 95% CI: 0.850-0.908) in the internal tests. Compared with radiologists, TDUS-Net (AUC: 0.925, 95% CI: 0.880-0.958) performed better than radiologists (AUC: 0.810, 95% CI: 0.749-0.862) in the external tests. CONCLUSIONS: Applying a machine learning model by combining both gray-scale US features and CDUS features can achieve comparable or even higher performance than radiologists in classifying TNs.

A Non-invasive Model for Predicting Liver Inflammation in Chronic Hepatitis B Patients With Normal Serum Alanine Aminotransferase Levels.

Background and Aims: Chronic hepatitis B (CHB) patients with normal alanine aminotransferase (ALT) levels are at risk of disease progression. Currently, liver biopsy is suggested to identify this population. We aimed to establish a non-invasive diagnostic model to identify patients with significant liver inflammation. Method: A total of 504 CHB patients who had undergone liver biopsy with normal ALT levels were randomized into a training set (n = 310) and a validation set (n = 194). Independent variables were analyzed by stepwise logistic regression analysis. After the predictive model for diagnosing significant inflammation (Scheuer's system, G ≥ 2) was established, a nomogram was generated. Discrimination and calibration aspects of the model were measured using the area under the receiver operating characteristic curve (AUC) and assessment of a calibration curve. Clinical significance was evaluated by decision curve analysis (DCA). Result: The model was composed of 4 variables: aspartate aminotransferase (AST) levels, γ-glutamyl transpeptidase (GGT) levels, hepatitis B surface antigen (HBsAg) levels, and platelet (PLT) counts. Good discrimination and calibration of the model were observed in the training and validation sets (AUC = 0.87 and 0.86, respectively). The best cutoff point for the model was 0.12, where the specificity was 83.43%, the sensitivity was 77.42%, and the positive likelihood and negative likelihood ratios were 4.67 and 0.27, respectively. The model's predictive capability was superior to that of each single indicator. Conclusion: This study provides a non-invasive approach for predicting significant liver inflammation in CHB patients with normal ALT. Nomograms may help to identify target patients to allow timely initiation of antiviral treatment.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses.

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k (k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.