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There is no data on changes of PCT on the background of ABT in pregnant with not severe CAP. Aim: to study the role of dynamic of PCT in pregnant with not severe CAP as a marker of effective ABT. Materials: we studied PCT levels in 35 pregnant with CAP which formed the main group (age (Med [25–75%]) 25 [19–30] yrs, gestational age 30 [27–33] wks) and in a control group of 15 healthy pregnant (age 31 [25–37] yrs, gestational age 35 [34–37] wks). Methods: in pregnant with CAP PCT was measured on admission, before starting ABT (PCT-1) and on the 5th day of therapy (PCT-2). We used SMRT-CO scale to estimate the severity of CAP and respiratory rate (RR), body temperature (BT) and leukocyte count – to monitor clinical status. Results: on admission all patients had high BT (37,9 [37–38,2]°C ), increased RR (28 [24–31] per min.) and leukocytosis (11,9 [8,3–14,3]*109/l), also they had 2 or less points on the SMRT-CO scale which corresponds to not severe CAP. They were prescribed standard ABT with amoxicillin/clavulonate. On the 5th day of ABT we observed a significant decrease of BT, RR and leukocyte count. Dynamics of PCT is shown in [Table 1][1]. View this table: Table 1 Changes of PCT on the background of ABT Conclusion: dynamics of PCT can be used as a marker of effectiveness of ABT in pregnant with not severe CAP. [1]: #T1

Background and Purpose— Numerous imaging techniques have been developed and applied to evaluate brain hemodynamics. Among these are positron emission tomography, single photon emission computed tomog

Enzyme-linked immune-sorbent assay (ELISA) for the determination of PCT in human serum, plasma, tissue homogenates or cell culture supernatants. For research use only, not for use in diagnostic procedures.

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Increased Temporal Resolution for Dynamic Perfusion CT (PCT) with C-arm Flat-Detector CT, SST15-04, 9008589, Marcel Beister,

Purpose Dynamic myocardial perfusion computed tomography (DM-PCT) imaging offers benefits over quantitative assessment of myocardial blood flow (MBF) for diagnosis and risk stratification of coronar...

While provisional patent applications are never published and cannot become prior art, recent decisions from the U.S. Court of Appeals for the Federal Circuit illustrate that parties can nonetheless m...

Although transcriptomic profiling of kidney tissues or isolated glomeruli has provided insights into broad changes in the pathogenesis of diabetic kidney disease, the presence of multiple cell types in kidney samples limits what can be discerned about changes in specific types of cells. To better elucidate the mechanisms of early diabetic kidney injury, the authors used single-cell RNA sequencing technology to perform a gene expression analysis of individual kidney glomerular cells of a diabetic mouse model. A comparison of gene expression in normal versus diabetic mouse kidney cells at a single-cell level showed dynamic changes in the pattern of expressed genes. This approach may help identify important factors underlying the pathophysiology of diabetic kidney disease progression and point to potential new therapeutic approaches. Background Recent single-cell RNA sequencing (scRNA-seq) analyses have offered much insight into cell-specific gene expression profiles in normal kidneys. However, in diseased kidneys, understanding of changes in specific cells, particularly glomerular cells, remains limited. Methods To elucidate the glomerular cell–specific gene expression changes in diabetic kidney disease, we performed scRNA-seq analysis of isolated glomerular cells from streptozotocin-induced diabetic endothelial nitric oxide synthase (eNOS)–deficient (eNOS−/−) mice and control eNOS−/− mice. Results We identified five distinct cell populations, including glomerular endothelial cells, mesangial cells, podocytes, immune cells, and tubular cells. Using scRNA-seq analysis, we confirmed the expression of glomerular cell–specific markers and also identified several new potential markers of glomerular cells. The number of immune cells was significantly higher in diabetic glomeruli compared with control glomeruli, and further cluster analysis showed that these immune cells were predominantly macrophages. Analysis of differential gene expression in endothelial and mesangial cells of diabetic and control mice showed dynamic changes in the pattern of expressed genes, many of which are known to be involved in diabetic kidney disease. Moreover, gene expression analysis showed variable responses of individual cells to diabetic injury. Conclusions Our findings demonstrate the ability of scRNA-seq analysis in isolated glomerular cells from diabetic and control mice to reveal dynamic changes in gene expression in diabetic kidneys, with variable responses of individual cells. Such changes, which might not be apparent in bulk transcriptomic analysis of glomerular cells, may help identify important pathophysiologic factors contributing to the progression of diabetic kidney disease.

Scientific Reports - 3D Dental Subsurface Imaging Using Enhanced Truncated Correlation-Photothermal...

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Pressure switch and thermostat with two control outputs and two stages, operating in pressurization, depressurization, refrigeration, heating, or alarm mod...

Complete solutions for high power semiconductor device characterization

Did you know that there is more then one way to stretch out your muscles?    We are going to look at 2 different types in this blog post - Static and Dynamic

Amazon.com: PCT Facts: Alexa Skills

A new approach to dynamic allocation and pricing that blends dynamic paradigms from the operations research and management science literature with classical mechanism design methods.Dynamic allocation and pricing problems occur in numerous frameworks, including the pricing of seasonal goods in retail, the allocation of a fixed inventory in a given period of time, and the assignment of personnel to incoming tasks. Although most of these problems deal with issues treated in the mechanism design literature, the modern revenue management (RM) literature focuses instead on analyzing properties of restricted classes of allocation and pricing schemes. In this book, Alex Gershkov and Benny Moldovanu propose an approach to optimal allocations and prices based on the theory of mechanism design, adapted to dynamic settings.Drawing on their own recent work on the topic, the authors describe a modern theory of RM that blends the elegant dynamic models from the operations research (OR), management science, and computer science literatures with techniques from the classical mechanism design literature. Illustrating this blending of approaches, they start with well-known complete information, nonstrategic dynamic models that yield elegant explicit solutions. They then add strategic agents that are privately informed and then examine the consequences of these changes on the optimization problem of the designer. Their sequential modeling of both nonstrategic and strategic logic allows a clear picture of the delicate interplay between dynamic trade-offs and strategic incentives. Topics include the sequential assignment of heterogeneous objects, dynamic revenue optimization with heterogeneous objects, revenue maximization in the stochastic and dynamic knapsack model, the interaction between learning about demand and dynamic efficiency, and dynamic models with long-lived, strategic agents.

Trying to change a variable name of an XDF with rxSetVarInfo. I want to merge several data sets with common var names. (I know rxMerge can/will append to filenames where needed. I want to have m...

Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.

Sepsis is a major cause of mortality and morbidity in critically ill patients. Procalcitonin (PCT) and C-reactive protein (CRP) are the most frequently used biomarkers in sepsis. We investigated changes in PCT and CRP concentrations in critically ill patients with sepsis to determine which biochemical marker better predicts outcome. We retrospectively analyzed 171 episodes in 157 patients with severe sepsis and septic shock who were admitted to the Samsung Medical Center intensive care unit from March 2013 to February 2014. The primary endpoint was patient outcome within 7 days from ICU admission (treatment failure). The secondary endpoint was 28-day mortality. Severe sepsis was observed in 42 (25%) episodes from 41 patients, and septic shock was observed in 129 (75%) episodes from 120 patients. Fifty-five (32%) episodes from 42 patients had clinically-documented infection, and 116 (68%) episodes from 99 patients had microbiologically-documented infection. Initial peak PCT and CRP levels were not associated with treatment failure and 28-day mortality. However, PCT clearance (PCTc) and CRP (CRPc) clearance were significantly associated with treatment failure (p = 0.027 and p = 0.030, respectively) and marginally significant with 28-day mortality (p = 0.064 and p = 0.062, respectively). The AUC for prediction of treatment success was 0.71 (95% CI, 0.61–0.82) for PCTc and 0.71 (95% CI, 0.61–0.81) for CRPc. The AUC for survival prediction was 0.77 (95% CI, 0.66–0.88) for PCTc and 0.77 (95% CI, 0.67–0.88) for CRPc. Changes in PCT and CRP concentrations were associated with outcomes of critically ill septic patients. CRP may not be inferior to PCT in predicting outcome in these patients.