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Stenosis-induced thrombosis and abandonment of the hemodialysis synthetic graft is an important cause of morbidity and mortality. The graft vascular circuit is a unique low-resistance shunt that has not yet been systematically evaluated. In this study, we developed a mathematical model of this circuit. Pressure losses (deltaPs) were measured in an in vitro experimental apparatus and compared with losses predicted by equations from the engineering literature. We considered the inflow artery, arterial and venous anastomoses, graft, stenosis, and outflow vein. We found significant differences between equations and experimental results, and attributed these differences to the transitional nature of the flow. Adjustment of the equations led to good agreement with experimental data. The resulting mathematical model predicts relations between stenosis, blood flow, intragraft pressure, and important clinical variables such as mean arterial blood pressure and hematocrit. Application of the model should improve understanding of the hemodynamics of the stenotic graft vascular circuit.  相似文献   
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There is extensive experimental data showing that the final pH and buffer composition after protein diafiltration (DF), particularly with monoclonal antibodies, can be considerably different than that in the DF buffer due to electrostatic interactions between the charged protein and the charged ions. Previous models for this behavior have focused on the final (equilibrium) partitioning and are unable to explain the complex pH and concentration profiles during the DF process. The objective of this study is to develop a new model for antibody DF based on solution of the transient mass balance equations, with the permeate concentrations of the charged species evaluated assuming Donnan equilibrium across the semipermeable membrane in combination with electroneutrality constraints. Model predictions are in excellent agreement with experimental data obtained during DF of both acidic and basic monoclonal antibodies, with the protein charge determined from independent electrophoretic mobility measurements. The model is able to predict the entire pH/histidine concentration profiles during DF, providing a framework for the development of DF processes that yield the desired antibody formulation.  相似文献   
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Glucuronoxylans with a backbone of 1,4-linked β-D-xylosyl residues are ubiquitous in the secondary walls of gymnosperms and angiosperms. Xylans have been reported to be present in hornwort cell walls, but their structures have not been determined. In contrast, the presence of xylans in the cell walls of mosses and liverworts remains a subject of debate. Here we present data that unequivocally establishes that the cell walls of leafy tissue and axillary hair cells of the moss Physcomitrella patens contain a glucuronoxylan that is structurally similar to glucuronoxylans in the secondary cell walls of vascular plants. Some of the 1,4-linked β-D-xylopyranosyl residues in the backbone of this glucuronoxylan bear an α-D-glucosyluronic acid (GlcpA) sidechain at O-2. In contrast, the lycopodiophyte Selaginella kraussiana synthesizes a glucuronoxylan substituted with 4-O-Me-α-D-GlcpA sidechains, as do many hardwood species. The monilophyte Equisetum hyemale produces a glucuronoxylan with both 4-O-Me-α-D-GlcpA and α-D-GlcpA sidechains, as does Arabidopsis. The seedless plant glucuronoxylans contain no discernible amounts of the reducing-end sequence that is characteristic of gymnosperm and eudicot xylans. Phylogenetic studies showed that the P. patens genome contains genes with high sequence similarity to Arabidopsis CAZy family GT8, GT43 and GT47 glycosyltransferases that are likely involved in xylan synthesis. We conclude that mosses synthesize glucuronoxylan that is structurally similar to the glucuronoxylans present in the secondary cell walls of lycopodiophytes, monilophytes, and many seed-bearing plants, and that several of the glycosyltransferases required for glucuronoxylan synthesis evolved before the evolution of tracheophytes.  相似文献   
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Endothelial cell junctions are thought to be preferential sites for transmigration. However, the factors that determine the site of transmigration are not well defined. Our data show that the preferential role of endothelial cell junctions is not limited to transmigration but extends to earlier steps of leukocyte recruitment, such as rolling and arrest. We used primary mouse neutrophils and mouse aortic endothelium in a flow chamber system to compare adhesive interactions near endothelial cell junctions to interactions over endothelial cell centers. We found differences in both rolling velocity and arrest frequency for neutrophils at endothelial cell junctions vs. more central areas of endothelial cells. Differences were governed by adhesion molecule interactions, not local topography. Interestingly, the role of particular adhesion molecules depended on their location on the endothelial cell surface. Although ICAM-1 stabilized and slowed rolling over central areas of the cell, it did not influence rolling velocity over endothelial cell junctions. P-selectin and VCAM-1 were more important for rolling near endothelial cell junctions than E-selectin. This demonstrates that adhesive properties of endothelial cell junctions influence early events in the adhesion cascade, which may help explain how leukocytes are localized to sites of eventual transmigration. endothelial cells; rolling; selectins; integrins  相似文献   
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During times of environmental insult, Bacillus subtilis undergoes developmental changes leading to biofilm formation, sporulation and competence. Each of these states is regulated in part by the phosphorylated form of the master response regulator Spo0A (Spo0A~P). The phosphorylation state of Spo0A is controlled by a multi‐component phosphorelay. RicA, RicF and RicT (previously YmcA, YlbF and YaaT) have been shown to be important regulatory proteins for multiple developmental fates. These proteins directly interact and form a stable complex, which has been proposed to accelerate the phosphorelay. Indeed, this complex is sufficient to stimulate the rate of phosphotransfer amongst the phosphorelay proteins in vitro. In this study, we demonstrate that two [4Fe‐4S]2+ clusters can be assembled on the complex. As with other iron‐sulfur cluster‐binding proteins, the complex was also found to bind FAD, hinting that these cofactors may be involved in sensing the cellular redox state. This work provides the first comprehensive characterization of an iron‐sulfur protein complex that regulates Spo0A~P levels. Phylogenetic and genetic evidence suggests that the complex plays a broader role beyond stimulation of the phosphorelay.  相似文献   
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The successful invasion of Plasmodium is an essential step in their life cycle. The parasite reticulocyte‐binding protein homologues (RHs) and erythrocyte‐binding like proteins are two families involved in the invasion leading to merozoite‐red blood cell (RBC) junction formation. Ca2+ signaling has been shown to play a critical role in the invasion. RHs have been linked to Ca2+ signaling, which triggers the erythrocyte‐binding like proteins release ahead of junction formation, consistent with RHs performing an initial sensing function in identifying suitable RBCs. RH5, the only essential RHs, is a highly promising vaccine candidate. RH5‐basigin interaction is essential for merozoite invasion and also important in determining host tropism. Here, we show that RH5 has a distinct function from the other RHs. We show that RH5‐Basigin interaction on its own triggers a Ca2+ signal in the RBC resulting in changes in RBC cytoskeletal proteins phosphorylation and overall alterations in RBC cytoskeleton architecture. Antibodies targeting RH5 that block the signal prevent invasion before junction formation consistent with the Ca2+ signal in the RBC leading to rearrangement of the cytoskeleton required for invasion. This work provides the first time a functional context for the essential role of RH5 and will now open up new avenues to target merozoite invasion.  相似文献   
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Conventional protein–protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, structure-based virtual screening. Existing deep learning methods for protein–protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multitrack iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments, GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. On the Database of Interacting Protein Structures (DIPS) test set, GeoDock achieves a 43% top-1 success rate, outperforming all other tested methods. However, in the standard DIPS train/test splits, we discovered contamination of close homologs in the training set. After decontaminating the training set, the success rate is 31%. On the DB5.5 test set and a benchmark dataset of antibody–antigen complexes, GeoDock outperforms the deep learning models trained using the same dataset but falls behind most of the conventional methods and AlphaFold-Multimer. GeoDock attains an average inference speed of under 1 s on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.  相似文献   
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