Current in vitro pancreatic lipase inhibitor screenings are based on previous spectrophotometric lipase assays. Nevertheless, they are with little evaluation of assay conditions. This study focuses ...on the impacts of experimental factors on enzyme activity in the assay with p-nitrophenyl palmitate as substrate by monitoring their effects on the hydrolysis rates. On the results, experimental conditions for lipase inhibitory assay were proposed. Notably, 5 mM sodium deoxycholate as emulsifier not only maintains the assay homogeneity but also enhances lipase activity. Organic co-solvents to dissolve organic inhibitors including DMSO, EtOH, MeOH, IPA, AcCN 0–30% (v/v) was found well tolerated by the enzyme. With 10% (v/v) glycerol, lipase solutions can be stored at –20°C for up to one month without significant loss of activity. The results reported here provide researchers the assay condition sets in which most inhibitors can be dissolved, and lipase activity is not severely affected. This could accelerate the rational development of novel lipase inhibitors.
Abstract
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning ...algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set.
Preliminary to the development a new program supporting perinatal HIV prevention, this assessment was conducted to evaluate Vietnam's national prevention of mother-to-child HIV transmission (PMTCT) ...program by estimating HIV prevalence among prenatal women and analyzing the healthcare system capacity to deliver services. In 2002–03, a technical team reviewed existing national and local surveillance and program data and conducted on-site interviews and observations at maternal-child health (MCH) programs in the seven provinces with highest HIV rates. The team found that despite high (85%) prenatal service utilization and widespread availability of HIV testing and dissemination of prevention protocols, few HIV-infected mothers were identified in time to allow effective perinatal HIV prevention. Program deficits clustered around the general areas of provider misunderstanding of occupational HIV risk and MTCT, impractical PMTCT policies, and practices hampering effective use of prevention and treatment protocols. Existing problems were significant but modifiable, and will require implementation of practical and appropriate guidelines, enhanced clinical and laboratory capacity, and continued program management and monitoring.
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for ...the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available (https://www.physionet.org/content/vindr-cxr/1.0.0/) in DICOM format along with the labels of both the training set and the test set.
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