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Among these biomarkers, 6 biomarkers (CD5, CCL23, CST5, IL-10RB, PD-L1, TNFRSF9) were inversely associated with eGFR in both diabetes types. The prospective analysis did not detect associations between inflammatory biomarkers and kidney function decline. No evidence of an interaction between diabetes type and inflammatory biomarkers was found.

Several biomarkers of inflammation associate with lower baseline eGFR in recent-onset type 1 and type 2 diabetes, but do not associate with kidney function loss during the first 5 years after the diagnosis of diabetes.

Several biomarkers of inflammation associate with lower baseline eGFR in recent-onset type 1 and type 2 diabetes, but do not associate with kidney function loss during the first 5 years after the diagnosis of diabetes.Understanding the health effects of protein intake is bedeviled by a number of factors, including protein quality and source. In addition, different units, including grams, grams per kilogram body weight (g/kg BW), and percent energy, may contribute to confusion about protein’s effects on health, especially BW-based units in increasingly obese populations. We aimed to review the literature and to conduct a modeling demonstration of various units of protein intake in relation to markers of cardiometabolic health. Data from the Framingham Heart Study Offspring (n = 1847; 60.3 y; 62.5% women) and Third Generation (n = 2548; 46.2 y; 55.3% women) cohorts and the NHANES 2003-04 (n = 1625; 46.2 y; 49.7% women) and 2005-06 (n = 1347; 43.7 y; 49.5% women) cycles were used to model cross-sectional associations between 7 protein units (grams, percent energy, g/kg ideal BW, g/kg actual BW, BW-adjusted g/kg actual BW, g/kg lean BW, and g/kg fat-free BW) and 9 cardiometabolic outcomes (fasting glucose, systolic and diastolic blood pressure, total and HDL cholesterol, triglycerides, BMI, waist circumference, and estimated glomerular filtration rate). The literature review indicated the use of myriad units of protein intake, with differential results on cardiometabolic outcomes. The modeling demonstration showed units expressed in BW were confounded by BW, irrespective of outcome. Units expressed in grams, percent energy, and ideal BW showed similar results, with or without adjustment for body size. After adjusting for BW, results of units expressed in BW aligned with results of grams, percent energy, and ideal BW. In conclusion, protein intake in cardiometabolic health appears to depend on protein’s unit of expression. Authors should be specific about the use of WHO (g/kg ideal BW) compared with US (g/kg actual BW) units, and ideally use gram or percent energy in observational studies. In populations where overweight/obesity are prevalent, intake based on actual BW should be reevaluated.

We compared the usefulness of single-photon emission computed tomography/computed tomography (SPECT/CT) and lung perfusion scintigraphy (LPS) for predicting postoperative lung function by comparing patients with borderline lung function.

A total of 274 patients who underwent simultaneous LPS and SPECT/CT and had a forced expiratory volume in 1 s (FEV1) or diffusing capacity for carbon monoxide (DLCO) under 80% were included. Deferoxamine Ferroptosis inhibitor The % uptake by LPS was calculated by the posterior-oblique method. The concordance and difference of the % uptake, predicted postoperative (ppo) FEV1 and ppoDLCO as determined by 2 methods were evaluated. The association between ppo values and actual postoperative FEV1 and DLCO was examined. Subgroup analysis was conducted in redo-operation cases.

The % uptake of each lobe, except the right middle lobe, showed fair concordance (concordance correlation coefficients for right upper, middle, lower, left upper and lower lobe = 0.61, 0.37, 0.71, 0.66 and 0.69, respectively). ppoFEV1 and ppoDLCO also revealed high concordance between both methods (concordance correlation coefficient = 0.93 for ppoFEV1 and concordance correlation coefficient = 0.92 for ppoDLCO) without a significant difference (P = 0.42 for ppoFEV1; P = 0.31 for ppoDLCO). Both ppoFEV1 and ppoDLCO showed a significantly high correlation with the actual FEV1 (r = 0.77, P < 0.01 for LPS, r = 0.77, P < 0.01 for SPECT/CT) and DLCO (r = 0.62, P < 0.01 for LPS, r = 0.62, P < 0.01 for SPECT/CT). High concordance of % uptake, ppoFEV1 and ppoDLCO was present in redo-operation patients.

Both LPS and SPECT/CT showed high predictability for actual postoperative lung function, and LPS showed good performance to estimate ppoFEV1 and ppoDLCO with reference to SPECT/CT, even in redo-operation cases.

Both LPS and SPECT/CT showed high predictability for actual postoperative lung function, and LPS showed good performance to estimate ppoFEV1 and ppoDLCO with reference to SPECT/CT, even in redo-operation cases.

OCSANA+ is a Cytoscape app for identifying nodes to drive the system toward a desired long-term behavior, prioritizing combinations of interventions in large-scale complex networks, and estimating the effects of node perturbations in signaling networks, all based on the analysis of the network’s structure. OCSANA+ includes an update to optimal combinations of interventions from network analysis software tool with cutting-edge and rigorously tested algorithms, together with recently developed structure-based control algorithms for non-linear systems and an algorithm for estimating signal flow. All these algorithms are based on the network’s topology. OCSANA+ is implemented as a Cytoscape app to enable a user interface for running analyses and visualizing results.

OCSANA+ app and its tutorial can be downloaded from the Cytoscape App Store or https//veraliconaresearchgroup.github.io/OCSANA-Plus/. The source code and computations are available in https//github.com/VeraLiconaResearchGroup/OCSANA-Plus_SourceCode.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data.

Here, we present a deep-learning-based method for the data association stage of particle tracking. The proposed method uses convolutional neural networks and long short-term memory networks to extract relevant dynamics features and predict the motion of a particle and the cost of linking detected particles from one time point to the next.