Effect of in vitro gastrointestinal digestion on the chemical composition and antioxidant properties of Ginkgo biloba leaves decoction and commercial capsules

In this study Ginkgo biloba leaves (GBL) decoction and commercial capsules were digested using an in vitro model. Thirty-six active compounds were identified and quantified by HPLC-ESI-MS analysis based on the MS/MS patterns (precursor ions and product ions) and retention times, in comparison with reference standards. Most compounds in GBL showed a significant decrease during intestinal digestion, with an exception of vanillic acid and biflavonoids. Bioaccessibility values of chemical compositions varied between decoction and capsules samples. Also, significant reductions of total flavonoids and total phenolic content was observed after in vitro digestion. Both, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity decreased after gastric digestion, but increased during intestinal digestion. Nevertheless, different behaviour was observed in reducing antioxidant power (FRAP) assay. Compared to the pH of digestion, the influence of digestive enzymes on the chemical composition and antioxidant activity of GBL was relatively minor. Overall, these results may help provide a valid foundation for further investigations on bioactive compounds and the pharmacodynamics of GBL

Heterologous expression and characterization of a malathion-hydrolyzing carboxylesterase from a thermophilic bacterium, Alicyclobacillus tengchongensis

A carboxylesterase gene from thermophilic bacterium, Alicyclobacillus tengchongensis, was cloned and expressed in Escherichia coli BL21 (DE3). The gene coded for a 513 amino acid protein with a calculated molecular mass of 57.82 kDa. The deduced amino acid sequence had structural features highly conserved among serine hydrolases, including Ser204, Glu325, and His415 as a catalytic triad, as well as type-B carboxylesterase serine active site (FGGDPENITIGGQSAG) and type-B carboxylesterase signature 2 (EDCLYLNIWTP). The purified enzyme exhibited optimum activity with β-naphthyl acetate at 60 °C and pH 7 as well as stability at 25 °C and pH 7. One unit of the enzyme hydrolyzed 5 mg malathion l(−1) by 50 % within 25 min and 89 % within 100 min. The enzyme strongly degraded malathion and has a potential use for the detoxification of malathion residues

Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT

Objective: The purpose is to establish and validate a machine-learning-derived radiomics approach to deter-mine the existence of atrial fibrillation (AF) by analyzing epicardial adipose tissue (EAT) in CT images. Methods: Patients with AF based on electrocardio-graphic tracing who underwent contrast-enhanced (n = 200) or non-enhanced (n = 300) chest CT scans were analyzed retrospectively. After EAT segmentation and radiomics feature extraction, the segmented EAT yielded 1691 radiomics features. The most contributive features to AF were selected by the Boruta algorithm and machine-learning-based random forest algorithm, and combined to construct a radiomics signature (EAT-score). Multivariate logistic regression was used to build clinical factor and nested models. Results: In the test cohort of contrast-enhanced scanning (n = 60/200), the AUC of EAT-score for identifying patients with AF was 0.92 (95%CI: 0.84–1.00), higher than 0.71 (0.58–0.85) of the clinical factor model (total cholesterol and body mass index) (DeLong’s p = 0.01), and higher than 0.73 (0.61–0.86) of the EAT volume model (p = 0.01). In the test cohort of non-enhanced scanning (n = 100/300), the AUC of EAT-score was 0.85 (0.77–0.92), higher than that of the CT attenuation model (p 0.05). Conclusion: EAT-score generated by machine-learning-based radiomics achieved high performance in identifying patients with AF. Advances in knowledge: A radiomics analysis based on machine learning allows for the identification of AF on the EAT in contrast-enhanced and non-enhanced chest CT

Effects of Cl − on Cu 2 O nanocubes for direct epoxidation of propylene by molecular oxygen

Abstract(#br)Promoters have been widely used in industrial catalysis but understanding the mechanism behind their function is still a challenging subject. As ideal model catalysts, nanocrystal catalysts with well−defined exposing facets and relatively large specific surface have attracted intense attention. We explored the effect of Cl − loading on Cu 2 O nanocubes enclosed by (100) facets for the direct epoxidation of propylene by molecular oxygen. Interestingly, a volcano−type curve was obtained when the selectivity and conversion are plotted as a function of the loading amount of Cl − , indicating that optimal surface O/Cl − ratio is critical for desirable catalytic performance

Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma

Background: To establish a machine-learning-derived nomogram based on radiomic features and clinical factors to predict post-surgical 2-year progression-free survival (PFS) in patients with lung adenocarcinoma.Methods: Patients with &gt;2 years post-surgical prognosis results of lung adenocarcinoma were included in Hospital-1 for model training (n = 100) and internal validation (n = 50), and in Hospital-2 for external testing (n = 50). A total of 1,672 radiomic features were extracted from 3D segmented CT images. The Rad-score was established using random survival forest by accumulating and weighting the top-20 imaging features contributive to PFS. A nomogram for predicting PFS was established, which comprised the Rad-score and clinical factors highly relevant to PFS.Results: In the training, internal validation, and external test groups, 69/100 (69%), 37/50 (74%) and 36/50 (72%) patients were progression-free at two years, respectively. According to the Rad-score, the integral of area under the curve (iAUC) for discriminating high and low risk of progression was 0.92 (95%CI: 0.77-1.0), 0.70 (0.41-0.98) and 0.90 (0.65-1.0), respectively. The C-index of Rad-score was 0.781 and 0.860 in the training and external test groups, higher than 0.707 and 0.606 for TNM stage, respectively. The nomogram integrating Rad-score and clinical factors (lung nodule type, cM stage and histological type) achieved a C-index of 0.845 and 0.837 to predict 2-year PFS, respectively, significantly higher than by only radiomic features (all p &lt; 0.01).Conclusion: The nomogram comprising CT-derived radiomic features and risk factors showed a high performance in predicting post-surgical 2-year PFS of patients with lung adenocarcinoma, which may help personalize the treatment decisions.</p

Bicuspid Aortic Valve: An Update in Morphology, Genetics, Biomarker, Complications, Imaging Diagnosis and Treatment

The bicuspid aortic valve, a kind of heart disease that comes from parents, has been paid attention around the world. Although most bicuspid aortic valve (BAV) patients will suffer from some complications including aortic stenosis, aortic regurgitation, endocarditis, and heart dysfunction in the late stage of the disease, there is none symptom in the childhood, which restrains us to diagnose and treatment in the onset phase of BAV. Hemodynamic abnormalities induced by the malformations of the valves in BAV patients for a long time will cause BAV-associated aortopathy: including progress aortic dilation, aneurysm, dissection and rupture, cardiac cyst and even sudden death. At present, preventive surgical intervention is the only effective method used in this situation and the diameter of the aorta is the primary reference criterion for surgery. And the treatment effects are always not satisfactory for patients and clinicians. Therefore, we need more methods to evaluate the progression of BAV and the surgery value and the appropriate intervention time by combining basic research with clinical treatment. In this review, advances in morphology, genetic, biomarkers, diagnosis and treatments are summarized, which expects to provide an update about BAV. It is our supreme expectations to provide some evidences for BAV early screening and diagnosis, and in our opinion, personalized surgical strategy is the trend of future BAV treatment

Widely Targeted Metabolomics Analysis of Compositional Differences between Purple Black Rice and Common Rice at Different Harvest Periods

In this study, widely targeted metabolomics based on ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to analyze the types and contents of differential metabolites between purple black rice and common rice produced in Guangxi and to illustrate the significantly differential metabolic pathways. A total of 667 metabolites were identified, including lipids, flavonoids, phenolic acids, alkaloids, and organic acids. Principal component analysis (PCA), cluster analysis and overall metabolite profiling showed that 239 of the 278 differential metabolites identified in mature rice samples were up-regulated, and 235 of the 267 differential metabolites identified in the 20 day earlier harvest group were up-regulated when compared with common rice. The metabolite composition of purple black rice at different harvest periods was similar, indicating that porridge made from purple black rice harvested in advance will have soft and glutinous texture as well as complete nutritional structure. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that significantly up-regulated compounds such as rhamnose-3-O-glucoside, hesperidin-7-O-glucoside, homoeriodictyol, eriodictyol, hesperidin, catechin, quercetin, naringin and chalcones in purple black rice were involved in the biosynthetic pathways of flavonoids and flavonols, regulating the contents of flavonoids and flavonols, and improving the nutritional quality and antioxidant effect of purple black rice