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However, inflammation response, enteric neurotransmitter 5-HT and major gut peptides might not be involved in this pathological process. Together, these findings provide valuable insights into the novel mechanism of TiO2NP-induced neurotoxicity. Understanding the microbiota-gut-brain axis will provide the foundation for potential therapeutic or prevention approaches against TiO2NP-induced gut and brain-related disorders.Amorphous silica nanoparticles are widely used as pharmaceutical excipients and food additive (E551). Despite the potential human health risks of mineral nanoparticles, very few data regarding their oral toxicity are currently available. This study aims to evaluate and to understand the interactions of silica particles at 1 and 10 mg mL-1 with the intestinal barrier using a Caco-2 monolayer and a Caco-2/HT29-MTX co-culture. A size- and concentration-dependent reversible increase of the paracellular permeability is identified after a short-term exposure to silica nanoparticles. Nanoparticles of 30 nm induce the highest transepithelial electrical resistance drop whereas no effect is observed with 200 nm particles. Additive E551 affect the Caco-2 monolayer permeability. Mucus layer reduces the permeability modulation by limiting the cellular uptake of silica. After nanoparticle exposure, tight junction expression including Zonula occludens 1 (ZO-1) and Claudin 2 is not affected, whereas the actin cytoskeleton disruption of enterocytes and the widening of ZO-1 staining bands are observed. A complete permeability recovery is concomitant with the de novo filament actin assembly and the reduction of ZO-1 bands. These findings suggest the paracellular modulation by small silica particles is directly correlated to the alteration of the ZO-actin binding strongly involved in the stability of the tight junction network.Rosetta and Damietta are the main branches of the Nile River in Egypt. They provide the required freshwater for different usage for about 20 million people. In the present study, chemical and biological indices were used to assess the water quality and provide a full image of the environmental status in the investigated area. Generally, the chemical parameters, except the dissolved oxygen, were at higher levels in Rosetta Branch when compared to Damietta Branch. Also, Damietta Branch frequently showed the presence of the macroinvertebrate families that are bioindicators of moderate and good water quality. Contrarily, the most resistant species to pollution were frequently recorded in the Rosetta Branch. According to Canadian WQI, the water of Rosetta Branch is classified from "marginal" to "poor" for the drinking and aquatic life uses and "fair" to "good" for irrigation usage. On the other side, the water quality of Damietta Branch is classified as "fair" with respect to drinking water and "good" to aquatic life and irrigation. Based on using macroinvertebrate families as bioindicators, the Biological Monitoring Working Party (BMWP) index and the Nile Biotic Pollution Index (NBPI) indicated that the water quality of the Damietta Branch was within "moderate" class, while Rosetta Branch is categorized from "very polluted" to "extremely polluted" classes. The results proved that both BMWP and NBPI have coincided with the CWQI for the drinking and aquatic life indices (p  less then  0.0001) indicating the validity of BMWP and NBPI to assess the water quality of the investigated area.BACKGROUND AND PURPOSE Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features could improve the accuracy of survival predictions for lower grade gliomas over clinical and IDH status. MATERIALS AND METHODS Radiomic features (n = 250) were extracted from preoperative MRI data of 296 lower grade glioma patients from databases at our institutional (n = 205) and The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA) (n = 91) datasets. For predicting overall survival, random survival forest models were trained with radiomic features; non-imaging prognostic factors including age, resection extent, WHO grade, and IDH status on the institutional dataset, and validated on the TCGA/TCIA dataset. The performance of the random survival forest (RSF) model and incremental value of radiomic features were assessed by time-dependent receiver operating characteristics. selleck inhibitor RESULTS The radiomics RSF model identified 71 radiomic features to predict overall survival, which were successfully validated on TCGA/TCIA dataset (iAUC, 0.620; 95% CI, 0.501-0.756). Relative to the RSF model from the non-imaging prognostic parameters, the addition of radiomic features significantly improved the overall survival prediction accuracy of the random survival forest model (iAUC, 0.627 vs. 0.709; difference, 0.097; 95% CI, 0.003-0.209). CONCLUSION Radiomic phenotyping with machine learning can improve survival prediction over clinical profile and genomic data for lower grade gliomas. KEY POINTS • Radiomics analysis with machine learning can improve survival prediction over the non-imaging factors (clinical and molecular profiles) for lower grade gliomas, across different institutions.OBJECTIVES To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). METHODS This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed. RESULTS Five radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better thing further workup and blind follow-up.OBJECTIVES To retrospectively evaluate the different performances of T1-SE and T1-GE sequences in detecting hypointense lesions in multiple sclerosis (MS), to quantify the degree of microstructural damage within lesions and to correlate them with patient clinical status. METHODS Sixty clinically isolated syndrome (CIS) and MS patients underwent brain magnetic resonance imaging (MRI) on 1.5-T and 3-T scanners. We identified T2 fluid-attenuated inversion recovery hyperintense lesions with no hypointense signal on T1-SE/T1-GE (a), hypointense lesions only on T1-GE (b), and hypointense lesions on both T1-SE and T1-GE sequences (c). We compared mean lesion number (LN) and volume (LV) identified on T1-SE and T1-GE sequences, correlating them with Expanded Disability Status Scale (EDSS); fractional anisotropy (FA) and mean diffusivity (MD) values inside each lesion type were extracted and normal-appearing white matter (NAWM). RESULTS Thirty-five patients were female. Mean age was 39.2 (± 7.8); median EDSS was 3 (± 2gher fields. • T1-weighted sequence type must be more carefully evaluated in clinical and research settings in the definition of "black holes" in MS, in order to avoid the overestimation of the effective severe tissue damage.OBJECTIVES Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the performance of a deep learning algorithm for detection of post-biopsy pneumothorax in chest radiographs (CRs), in consecutive cohorts reflecting actual clinical situation. METHODS We retrospectively included post-biopsy CRs of 1757 consecutive patients (1055 men, 702 women; mean age of 65.1 years) undergoing percutaneous lung biopsies from three institutions. A commercially available deep learning algorithm analyzed each CR to identify pneumothorax. We compared the performance of the algorithm with that of radiology reports made in the actual clinical practice. We also conducted a reader study, in which the performance of the algorithm was compared with those of four radiologists. Performances of the algorithm and radiologists were evaluated by area under receiver operating characteristic curves (AUROCs), sensitivity, and specificity, with reference standards definedThe deep learning algorithm has a potential role as a surveillance tool for accurate and timely diagnosis of post-biopsy pneumothorax.OBJECTIVES It is challenging to early differentiate biliary atresia from other causes of cholestasis. We aimed to develop an algorithm with risk stratification to distinguish biliary atresia from infantile cholestasis. METHODS In this study, we enrolled infants with cholestasis into 2 subgroups from January 2010 to April 2019. A prospective cohort (subgroup 2) of 187 patients (107 with biliary atresia and 80 without biliary atresia) underwent acoustic radiation force impulse elastography. Stepwise regression was used to identify significant predictors of biliary atresia. A sequential algorithm with risk stratification was constructed. RESULTS Among 187 patients, shear wave speed > 1.35 m/s and presence of the triangular cord sign were considered high risk for biliary atresia (red), in which 73 of 78 patients (accuracy of 93.6%) with biliary atresia were identified. Afterwards, γ-GT, abnormal gallbladder, and clay stool were introduced into the algorithm and 55 intermediate-risk infants were identified (yellow • Risk for biliary atresia was high (red), intermediate (yellow), or low (green). In the red and green group, we achieved an extremely high diagnostic performance (area under the curve, 0.983; sensitivity, 98.7%; specificity, 91.4%).The chemical composition of groundwater is a product of the evolution and transformation of major ions, which come from natural hydrogeochemical processes or from anthropogenic interference. The objective of this study was to identify the hydrogeochemical processes and the influence of anthropogenic activity on the variation of chemical composition in Toluca Valley groundwater. The type of water in the zone is fundamentally Mg-Ca-HCO3. Three groups with different evolutionary tendencies were identified one within a local recharge zone and two others in an intermediate region with anthropic activity. The latter, which show contamination by inorganic matter (fertilizers) and organic matter (urban or industrial wastewater). The content of N-NO3- (0.024-0.219 mEq L-1), N-NH4+ (0-0.022 mEq L-1), Porg (0.03-1.02 mEq L-1) and PO43- (0.0-0.28 mEq L-1) indicated contamination coming from inorganic and organic matter. These chemical compounds were identified by way of a 3D fluorescence technique. The results of this study demonstrate that the main processes that affect and control the chemical composition of the water in the Toluca Valley aquifer are weathering of silicates, the ion exchange and a mixture process generated by a source of anthropic contamination.