Characterization of Z/E11- and Z9-desaturases from the obliquebanded leafroller moth, Choristoneura rosaceana

A ▵11-desaturase gene was cloned from the sex pheromone gland of the obliquebanded leafroller moth, Choristoneura rosaceana. The desaturase cDNA sequence spans 1300 nucleotides with an open reading frame encoding a 335 amino-acid protein, which has 81% identity to a Z/E11-desaturase of the redbanded leafroller moth, Argyrotaenia velutinana. A functional assay with a pYES2 yeast expression system demonstrated that the ▵11-desaturase exhibits unusual substrate and stereospecificities in producing a Z/E11 mixture (7:1) of only C14 acids. A metabolic Z9-desaturase also was cloned from fat body of this species, and proved to be in the class that produces more Z9-16:Acid than Z9-18:Acid

Study Design and Data Analysis of Artificial Pancreas Device Systems with Closed-Loop Glucose-Sensing Insulin Delivery

Objective: The objective of this article is to provide a high-profile review and discussion on the study design and statistical analysis of pivotal clinical trials conducted to demonstrate the safety and effectiveness of closed-loop investigational artificial pancreas device systems (APDSs) in premarket approval applications. Methods: The United States Food and Drug Administration (FDA) guidance on the content of investigational device exemption and premarket approval applications for APDSs is reviewed with special emphasis on study design and statistical analysis of the pivotal clinical trials. The two pivotal studies for the MiniMed 670G hybrid closed-loop system by Medtronic in their premarket approval application are summarized and discussed. Results: The United States FDA established detailed recommendations on the study design and statistical analysis of pivotal clinical trials for the industry that seek market investigational APDSs and for FDA scientific reviewers that regulate the device applications. The recommendations cover specifics regarding patient population, clinical endpoints, and strategies for data analysis. However, the two pivotal studies that demonstrated the effectiveness of the FDA-approved MiniMed 670G hybrid closed-loop system were not typical randomized controlled trials as per FDA recommendations. Conclusion: The development and regulation of investigational APDSs require careful and sophisticated clinical study designs and data analysis in premarket approval applications. The regulatory evaluation process of the APDSs is rather complicated since the devices consist of multiple components that collaboratively function to mimic human pancreases

7-Chloro-5-(chloro­meth­yl)pyrazolo­[1,5-a]pyrimidine-3-carbonitrile

All non-H atoms of the title compound, C8H4Cl2N4, are essentially coplanar, with an r.m.s. deviation of 0.011 Å. In the crystal, weak C—H⋯N hydrogen bonds link the mol­ecules into infinite sheets parallel to the bc plane

Solvothermal Synthesis of Gd

Uniform Gd2O3 : Eu3+ luminescent nanowires were prepared on a large scale by a facile solvothermal method using polyethylene glycol (PEG-2000) as template and ethanol as solvent; the properties and the structure were characterized. X-ray diffraction (XRD) patterns and Fourier transform infrared spectrometry (FTIR) showed that the precursors are hexagonal phase Gd(OH)3 crystals, and the samples calcined at 800C° are cubic phase Gd2O3. Transmission Electron Microscopy (TEM) images indicated that the samples are nanowires with a diameter of 30 nm and a length of a few microns. Photoluminescence (PL) spectra showed that the ratio of D50→F72 to D50→F71 transition peak of the calcined samples is stronger than that of the precursors, which confirmed that the color purity of the Gd2O3 : Eu3+ is better than that of the precursors. The as-obtained Gd2O3 : Eu3+ luminescent nanowires show a strong red emission corresponding to D50→F72 transition (610 nm) of Eu3+ under ultraviolet excitation (250 nm), which have potential application in red-emitting phosphors and field emission display devices

Network-Based Methods for Prediction of Drug-Target Interactions

Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology

Research on CVDs Prediction and Early Warning Techniques in Healthcare Monitoring System

Abstract-Chronic diseases are gradually becoming the principal factors of harm to people's health. Fortunately, the development of e-health provides a novel thought for chronic disease prevention and treatment. This paper focuses on the research of cardiovascular disease (CVDs) prevention and early warning techniques using e-health and data mining. In this paper, we will use weighted associative classification algorithm to model the data in healthcare database to determine the level of cardiovascular risk. Besides, on the basis of data mining and knowledge discovery, intelligent warning mechanisms are proposed to provide different services to patients with different levels of risk. The experimental results show that the used classification algorithm is a more effective mining algorithm in the field of healthcare with higher accuracy and better comprehension. Our study is of definite significance to help control risk level of CVDs patients

Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study

Despite high global prevalence of hepatic steatosis, no automated diagnostics demonstrated generalizability in detecting steatosis on multiple international datasets. Traditionally, hepatic steatosis detection relies on clinicians selecting the region of interest (ROI) on computed tomography (CT) to measure liver attenuation. ROI selection demands time and expertise, and therefore is not routinely performed in populations. To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements. AI segmentation and AI-ROI method were evaluated on 1,014 non-contrast enhanced chest CT images from eight international datasets: LIDC-IDRI, NSCLC-Lung1, RIDER, VESSEL12, RICORD-1A, RICORD-1B, COVID-19-Italy, and COVID-19-China. AI segmentation achieved a mean dice coefficient of 0.957. Attenuations measured by AI-ROI showed no significant differences (p = 0.545) and a reduction of 71% time compared to expert measurements. The area under the curve (AUC) of the steatosis classification of AI-ROI is 0.921 (95% CI: 0.883 - 0.959). If performed as a routine screening method, our AI protocol could potentially allow early non-invasive, non-pharmacological preventative interventions for hepatic steatosis. 1,014 expert-annotated liver segmentations of patients with hepatic steatosis annotations can be downloaded here: https://drive.google.com/drive/folders/1-g_zJeAaZXYXGqL1OeF6pUjr6KB0igJX