PubMed · 5456835
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D R Mattison. 1970. Amniotic fluid osmolality.. https://pubmed.ncbi.nlm.nih.gov/5456835/
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3'-Azido-2',3'-dideoxyuridine (AZDU, Azddu, CS-87) is a nucleoside analog of 3'-azido-3'-deoxythymidine (zidovudine, AZT) that has been shown to inhibit human immunodeficiency virus (HIV-1). AZDU is a potential candidate for treatment of pregnant mothers to prevent prenatal transmission of HIV/AIDS to their unborn children. A rapid and efficient high-performance liquid chromatography (HPLC) method for the determination of AZDU concentrations in rat maternal plasma, amniotic fluid, placental and fetal tissue samples has been developed and validated. Tissue samples were homogenized in distilled water, protein precipitated and extracted using a C-18 solid-phase extraction (SPE) method prior to analysis. Plasma and amniotic fluid samples were protein precipitated with 2 M perchloric acid prior to analysis. Baseline resolution was achieved using a 4.5% acetonitrile in 40 mM sodium acetate (pH 7) buffer mobile phase for amniotic fluid, placenta and fetus samples and with a 5.5% acetonitrile in buffer solution for plasma at flow-rates of 2.0 ml/min. The HPLC system consists of a Hypersil ODS column (150x4.6 mm) with a Nova-Pak C-18 guard column with detection at 263 nm. The method yields retention times of 6.2 and 12.2 min for AZDU and AZT in plasma and 8.3 and 17.6 min for AZDU and AZT in amniotic fluid, fetal and placental tissues. Limits of detection ranged from 0.01 to 0.075 microg/ml. Recoveries ranged from 81 to 96% for AZDU and from 82 to 96% for AZT in the different matrices. Intra-day (n=6) and inter-day (n=9) precision (% RSD) and accuracy (% Error) ranged from 1.48 to 6.25% and from 0.50 to 10.07%, respectively.
In order to clarify the pathological outcome of congenital diaphragmatic hernia (CDH), we devised an animal model of CDH by administration of 2,4-dichlorophenyl-p-nitrophenyl ether (nitrofen) to pregnant rats, and determined the level and distribution of lung surfactant using the monoclonal antibody toward sphingomyelin and disaturated phosphatidylcholine (disat-PC). In control rats, the concentration of disat-PC was found to increase greatly from 16 to 18 days of gestation. Intragastric administration of nitrofen to pregnant rats at day 9 of gestation resulted in CDH in 42.7% of fetuses delivered after 20 days of gestation. In nitrofen-treated fetuses, the concentration of disat-PC in the lungs was lower than those in control fetuses, and surfactant apoprotein SP-A was similarly reduced in nitrofen-treated fetuses. However, the concentration of disat-PC in nitrofen-treated fetuses was higher than that in control fetuses at 18 days of gestation, indicating a synthetic potential of surfactant in nitrofen-treated fetuses comparable to that at the late stage of normal gestation. Immunohistochemical study with the antibody revealed that surfactant phospholipid was mainly in the form of intracellular granules in nitrofen-treated fetuses, probably causing the hypoplastic lungs and then CDH, in contrast to the uniform distribution on the pulmonary alveolar surface in control fetuses.
BACKGROUND: Previous systems for dot (signal) counting in fluorescence in situ hybridization (FISH) images have relied on an auto-focusing method for obtaining a clearly defined image. Because signals are distributed in three dimensions within the nucleus and artifacts such as debris and background fluorescence can attract the focusing method, valid signals can be left unfocused or unseen. This leads to dot counting errors, which increase with the number of probes. METHODS: The approach described here dispenses with auto-focusing, and instead relies on a neural network (NN) classifier that discriminates between in and out-of-focus images taken at different focal planes of the same field of view. Discrimination is performed by the NN, which classifies signals of each image as valid data or artifacts (due to out of focusing). The image that contains no artifacts is the in-focus image selected for dot count proportion estimation. RESULTS: Using an NN classifier and a set of features to represent signals improves upon previous discrimination schemes that are based on nonadaptable decision boundaries and single-feature signal representation. Moreover, the classifier is not limited by the number of probes. Three classification strategies, two of them hierarchical, have been examined and found to achieve each between 83% and 87% accuracy on unseen data. Screening, while performing dot counting, of in and out-of-focus images based on signal classification suggests an accurate and efficient alternative to that obtained using an auto-focusing mechanism.