Peritoneal cells from starch-injected Swiss mice were propagated in plastic petri dishes and on cover slips in a mouse L-cell-conditioned medium for 12 to 24 h and then infected with various multiplicities of lactate dehydrogenase-elevating virus (LDV). Over 95% of the cells in these cultures phagocytosed latex particles and were, therefore, considered macrophages. Infected and mock infected macrophage cultures were supplemented with [3H]uridine at various times after infection and with actinomycin D 30 min before addition of the [3H]uridine. After 1 or 2 h of further incubation, plate cultures were analyzed for LDV-specific RNA, and cover slip cultures were investigated by autoradiography. Other cultures were labeled in the absence of actinomycin D, and the culture fluid was analyzed for labeled LDV. There was a good correlation between the production of LDV-specific RNA and LDV and the number of heavily labeled cells in these cultures. The labeled cells in these cultures. The labeled cells, therefore, were equated with productively infected cells. Only a maximum of about 20% of the macrophages, however, became heavily labeled regardless of the multiplicity of infection or the time, after infection, at which the cells were exposed to [3H]uridine. Only background labeling was observed in the remainder of the cells and in mock-infected cells treated with actinomycin D. The highest proportion of labeled cells was observed when the cells were infected with a multiplicity of infection of about 2,000 mouse infectious units per cell and labeled from 6 to 8 h after infection. Thereafter, the proportion of productively infected cells decreased progressively, concomitant with a decrease in the amounts of viral specific RNA and of LDV produced by the cultures. The results indicate that the majority of the macrophages in primary macrophage cultures do not support LDV replication. Their nonpermissiveness may depend on the physiological state of the cells or reflect the presence of subpopulations of macrophages, but no morphological differences between productively infected an uninfected cells were detectable.
A partially ordered Fe16N2 thin film, which exhibits a higher saturation magnetization than a bcc-Fe thin film, was grown on a Au(001) texture on a GaAs(001) substrate for studies of crystalline structure, electronic structure, and magnetic properties. Fe 2p3/2 and 2p1/2 X-ray photoelectron spectroscopies (XPS) reveal the electronic hybridization between the Fe atoms and the adjacent N atoms, whereas a multipeak analysis suggests the charge-transfer-induced electronic rearrangement of electronic configuration in Fe(8h) and Fe(4e) geometrical sites. These results are consistent with the previous model and help explain the saturation magnetization enhancement in the α-FeN system.
Osteogenesis imperfecta (OI) is a genetic disease with an estimated prevalence of 1 in 13,500 and 1 in 9700. The classification into subtypes of OI is important for prognosis and management. In this study, we established a clinical severity prediction model depending on multiple features of variants in COL1A1/2 genes. Introduction Ninety percent of OI cases are caused by pathogenic variants in the COL1A1/COL1A2 gene. The Sillence classification describes four OI types with variable clinical features ranging from mild symptoms to lethal and progressively deforming symptoms. Methods We established a prediction model of the clinical severity of OI based on the random forest model with a training set obtained from the Human Gene Mutation Database, including 790 records of the COL1A1/COL1A2 genes. The features used in the prediction model were respectively based on variant-type features only, and the optimized features. Results With the training set, the prediction results showed that the area under the receiver operating characteristic curve (AUC) for predicting lethal to severe OI or mild/moderate OI was 0.767 and 0.902, respectively, when using variant-type features only and optimized features for COL1A1 defects, 0.545 and 0.731, respectively, for COL1A2 defects. For the 17 patients from our hospital, prediction accuracy for the patient with the COL1A1 and COL1A2 defects was 76.5% (95% CI: 50.1-93.2%) and 88.2% (95% CI: 63.6-98.5%), respectively. Conclusion We established an OI severity prediction model depending on multiple features of the specific variants in COL1A1/2 genes, with a prediction accuracy of 76-88%. This prediction algorithm is a promising alternative that could prove to be valuable in clinical practice.
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Next generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed mvPPT (Pathogenicity Prediction Tool for missense variants), a highly sensitive and accurate missense variant classifier based on gradient boosting. MvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, allele, amino acid and genotype frequencies, and genomic context. Compared with established predictors, mvPPT achieved superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights of variant pathogenicity.
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