Prediction of Coronary Artery Disease Risk Based on Multiple Longitudinal Biomarkers

In the last decade, few topics in the area of cardiovascular disease (CVD) research have received as much attention as risk prediction. One of the well-documented risk factors for CVD is high blood pressure (BP). Traditional CVD risk prediction models consider BP levels measured at a single time and such models form the basis for current clinical guidelines for CVD prevention. However, in clinical practice, BP levels are often observed and recorded in a longitudinal fashion. Information on BP trajectories can be powerful predictors for CVD events. We consider joint modeling of time to coronary artery disease and individual longitudinal measures of systolic and diastolic BPs in a primary care cohort with up to 20 years of follow-up. We applied novel prediction metrics to assess the predictive performance of joint models. Predictive performances of proposed joint models and other models were assessed via simulations and illustrated using the primary care cohort

Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome

Joint models are statistical tools for estimating the association between time-to-event and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models include a two-stage method, Bayesian and maximum-likelihood methods. In this work, we consider joint models of a time-to-event outcome and multiple longitudinal processes and develop a maximum-likelihood estimation method using the expectation–maximization algorithm. We assess the performance of the proposed method via simulations and apply the methodology to a data set to determine the association between longitudinal systolic and diastolic blood pressure measures and time to coronary artery disease

Predictive role of blood-based indicators in neuromyelitis optica spectrum disorders

IntroductionThis study aimed to assess the predictive role of blood markers in neuromyelitis optica spectrum disorders (NMOSD).MethodsData from patients with NMOSD, multiple sclerosis (MS), and healthy individuals were retrospectively collected in a 1:1:1 ratio. The expanded disability status scale (EDSS) score was used to assess the severity of the NMOSD upon admission. Receiver operating characteristic (ROC) curve analysis was used to distinguish NMOSD patients from healthy individuals, and active NMOSD from remitting NMOSD patients. Binary logistic regression analysis was used to evaluate risk factors that could be used to predict disease recurrence. Finally, Wilcoxon signed-rank test or matched-sample t-test was used to analyze the differences between the indicators in the remission and active phases in the same NMOSD patient.ResultsAmong the 54 NMOSD patients, neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) (platelet × NLR) were significantly higher than those of MS patients and healthy individuals and positively correlated with the EDSS score of NMOSD patients at admission. PLR can be used to simultaneously distinguish between NMOSD patients in the active and remission phase. Eleven (20.4%) of the 54 patients had recurrence within 12 months. We found that monocyte-to-lymphocyte ratio (MLR) (AUC = 0.76, cut-off value = 0.34) could effectively predict NMOSD recurrence. Binary logistic regression analysis showed that a higher MLR at first admission was the only risk factor for recurrence (p = 0.027; OR = 1.173; 95% CI = 1.018–1.351). In patients in the relapsing phase, no significant changes in monocyte and lymphocyte count was observed from the first admission, whereas patients in remission had significantly higher levels than when they were first admitted.ConclusionHigh PLR is a characteristic marker of active NMOSD, while high MLR is a risk factor for disease recurrence. These inexpensive indicators should be widely used in the diagnosis, prognosis, and judgment of treatment efficacy in NMOSD

Overexpression of AtBMI1C, a Polycomb Group Protein Gene, Accelerates Flowering in Arabidopsis

Polycomb group protein (PcG)-mediated gene silencing is emerging as an essential developmental regulatory mechanism in eukaryotic organisms. PcGs inactivate or maintain the silenced state of their target chromatin by forming complexes, including Polycomb Repressive Complex 1 (PRC1) and 2 (PRC2). Three PRC2 complexes have been identified and characterized in Arabidopsis; of these, the EMF and VRN complexes suppress flowering by catalyzing the trimethylation of lysine 27 on histone H3 of FLOWER LOCUS T (FT) and FLOWER LOCUS C (FLC). However, little is known about the role of PRC1 in regulating the floral transition, although AtRING1A, AtRING1B, AtBMI1A, and AtBMI1B are believed to regulate shoot apical meristem and embryonic development as components of PRC1. Moreover, among the five RING finger PcGs in the Arabidopsis genome, four have been characterized. Here, we report that the fifth, AtBMI1C, is a novel, ubiquitously expressed nuclear PcG protein and part of PRC1, which is evolutionarily conserved with Psc and BMI1. Overexpression of AtBMI1C caused increased H2A monoubiquitination and flowering defects in Arabidopsis. Both the suppression of FLC and activation of FT were observed in AtBMI1C-overexpressing lines, resulting in early flowering. No change in the H3K27me3 level in FLC chromatin was detected in an AtBMI1C-overexpressing line. Our results suggest that AtBMI1C participates in flowering time control by regulating the expression of FLC; moreover, the repression of FLC by AtBMI1C is not due to the activity of PRC2. Instead, it is likely the result of PRC1 activity, into which AtBMI1C is integrated

Approximate Analytical Solutions of Fractional Perturbed Diffusion Equation by Reduced Differential Transform Method and the Homotopy Perturbation Method

The approximate analytical solutions of differential equations with fractional time derivative are obtained with the help of a general framework of the reduced differential transform method (RDTM) and the homotopy perturbation method (HPM). RDTM technique does not require any discretization, linearization, or small perturbations and therefore it reduces significantly the numerical computation. Comparing the methodology (RDTM) with some known technique (HPM) shows that the present approach is effective and powerful. The numerical calculations are carried out when the initial conditions in the form of periodic functions and the results are depicted through graphs. The two different cases have studied and proved that the method is extremely effective due to its simplistic approach and performance

A Physically Based Spatial Expansion Algorithm for Surface Air Temperature and Humidity

An algorithm was developed to expand the surface air temperature and air humidity to a larger spatial domain, based on the fact that the variation of surface air temperature and air humidity is controlled jointly by the local turbulence and the horizontal advection. This study proposed an algorithm which considers the advective driving force outside the thermal balance system and the turbulent driving force and radiant driving force inside the thermal balance system. The surface air temperature is determined by a combination of the surface observations and the regional land surface temperature observed from a satellite. The average absolute difference of the algorithm is 0.65 degree and 0.31 mb, respectively, for surface air temperature and humidity expansion, which provides a promising approach to downscale the two surface meteorological variables

Oligomerization of Cry9Aa in solution without receptor binding, is not related with insecticidal activity

Background: Bacillus thuringiensis Cry toxins bind with different insect midgut proteins leading to toxin oligomerization, membrane insertion and pore formation. However, different Cry toxins had been shown to readily form high molecular weight oligomers or aggregates in solution in the absence of receptor interaction. The role of Cry oligomers formed in solution remains uncertain. The Cry9A proteins show high toxicity against different Lepidoptera, and no-cross resistance with Cry1A. Results: Cry9Aa655 protein formed oligomers easily in solution mediated by disulfide bonds, according to SDS-PAGE analysis under non-reducing and reducing conditions. However, oligomerization is not observed if Cry9Aa655 is activated with trypsin, suggesting that cysteine residues, C14 and C16, located in the N-terminal end that is processed during activation participate in this oligomerization. To determine the role of these residues on oligomerization and in toxicity single and double alanine substitution were constructed. In contrast to single C14A and C16A mutants, the double C14A\u2013C16A mutant did not form oligomers in solution. Toxicity assays against Plutella xylostella showed that the C14A\u2013C16A mutant had a similar insecticidal activity as the Cry9Aa655 protein indicating the oligomers of Cry9Aa formed in solution in the absence of receptor binding are not related with toxicity. Conclusions: The aggregation of Cry9Aa655 polypeptides was mediated by disulfide bonds. Cry9Aa655 C14 and C16C are involved in oligomerization in solution. These aggregate forms are not related to the mode of action of Cry9Aa leading to toxicity