H epatocellular carcinoma (HCC) is the sixth most common type of cancer and ranks third as a cause of cancer-related death globally. Worldwide, approximately 630 000 new cases of HCC occur annually, with more than half of these cases occurring in China (1). This disease is related to hepatitis B virus (HBV) infection in almost 70%-90% of cases in the highly endemic Asia-Pacific regions, especially in China (2, 3). Partial hepatectomy and liver transplantation are the most effective curative treatments, albeit in a limited number of cases (4). Indeed, the 5-year recurrence rates after surgical treatment and liver transplantation are as high as 70% and 35%, respectively (5-7). It is, therefore, necessary to find effective biomarkers that can identify aggressive behavior and predict tumor recurrence after liver resection and transplantation.In HCC, the presence of microvascular invasion (MVI) is a histopathologic feature indicative of aggressive behavior (8). Previous studies have identified MVI as a major risk factor for early recurrence in the first two years after liver resection and transplantation (9). Precise identification of MVI involvement in patients with HCC is critical to develop treatment strategies and arrive at prognoses. Over the past decade, researchers have made persistent endeavors towards the preoperative prediction of MVI (10-12). Although several radiologic features on contrast-enhanced magnetic resonance imaging (MRI) and computed tomography (CT) images (such as tumor margin, internal arteries, and hypodense halos) are known PURPOSE We aimed to develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). METHODSA total of 304 eligible patients with HCC were randomly divided into training (n=184) and independent validation (n=120) cohorts. Portal venous and arterial phase computed tomography data of the HCCs were collected to extract radiomic features. Using the least absolute shrinkage and selection operator algorithm, the training set was processed to reduce data dimensions, feature selection, and construction of a radiomics signature. Then, a prediction model including the radiomics signature, radiologic features, and alpha-fetoprotein (AFP) level, as presented in a radiomics nomogram, was developed using multivariable logistic regression analysis. The radiomics nomogram was analyzed based on its discrimination ability, calibration, and clinical usefulness. Internal cohort data were validated using the radiomics nomogram. RESULTSThe radiomics signature was significantly associated with MVI status (P < 0.001, both cohorts). Predictors, including the radiomics signature, nonsmooth tumor margin, hypoattenuating halos, internal arteries, and alpha-fetoprotein level were reserved in the individualized prediction no- CONCLUSIONThe radiomics nomogram, as a noninvasive preoperative prediction method, shows a favorable predictive accuracy for MVI status in patients with HBV-related HCC.

Janus emulsion assays that rely on carbohydrate–lectin binding for the detection of Escherichia coli bacteria are described. Surfactants containing mannose are self-assembled at the surface of Janus droplets to produce particles with lectin binding sites. Janus droplets orient in a vertical direction as a result of the difference in densities between the hydrocarbon and fluorocarbon solvents. Binding of lectin to mannose(s) causes agglutination and a tilted geometry. The distinct optical difference between naturally aligned and agglutinated Janus droplets produces signals that can be detected quantitatively. The Janus emulsion assay sensitively and selectively binds to E. coli at 104 cfu/mL and can be easily prepared with long-time stability. It provides the basis for the development of inexpensive portable devices for fast, on-site pathogen detection.

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