Associations between osteocalcin, metabolic phenotypes and type II diabetes: a systematic review and meta-analysis

The initial discovery in experimental research showed osteocalcin (OC), a bone protein, might regulate glucose homeostasis. However, the investigations in humans have found conflicting evidence. Therefore, we conducted a systematic review with meta-analyses to investigate the association of type 2 diabetes (T2DM) with the total osteocalcin (TOC) and undercarboxylated osteocalcin (ucOC). Three major databases were searched. We included 43,366 unique participants from 104 studies:15,027 T2DM and 23,680 controls. The main ethnicities were Asian and Caucasian. About 48% female and 52% male aged 36-84 were identified. The risk of bias was 30% for selection, 29% for comparability, 36% for exposure/outcomes. We found a lower mean levels ofOC in T2DM patients compared with non-diabetic controls (TOC: -3.35ng/ml [95% CI: -4.22, -2.48]; ucOC -0.67ng/ml [95% CI: -1.10, -0.24]). Furthermore, TOC and fasting plasma glucose (FPG) was inversely correlated (-0.23 [95%CI: -0.30, -0.18]). Results also showed that with per SD increase in TOC, the incidence of T2DM decreased (OR: 0.76 [0.63,0.92]). However, the relationship did not exist in the analysis of ucOC (OR: 0.79 [0.56,1.11]). The sources of heterogeneity for the mean difference in TOC between T2DM patients and controls was partially explained by the assays of TOC (R2= 32%). Interestingly, the correlation between TOC and FPG for controls was the weakest (−0.16 [95% CI: −0.26, −0.06]) while the correlation for T2DM was stronger (−0.23 [95% CI: −0.28, −0.18]). In conclusion, we found a low OC level in patients with impaired glucose metabolism; TOC was correlated with glucose metabolic indices in T2DM. Hence, patients with T2DM might be in a low bone status. It is still unclear whether OC could be a suitable marker measuring bone status for T2DM; whether TOC/ ucOC could predict the risk of T2DM. Further study is called for investigating the causality of the lower OC status in T2DM patients

Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time

Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database

Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs are one of most common causes to withdraw some drugs from market. Prescription event monitoring (PEM) is an important approach to detect the adverse drug reactions. The main problem to deal with this method is how to automatically extract the medical events or side effects from high-throughput medical events, which are collected from day to day clinical practice. In this study we propose a novel concept of feature matrix to detect the ADRs. Feature matrix, which is extracted from big medical data from The Health Improvement Network (THIN) database, is created to characterize the medical events for the patients who take drugs. Feature matrix builds the foundation for the irregular and big medical data. Then feature selection methods are performed on feature matrix to detect the significant features. Finally the ADRs can be located based on the significant features. The experiments are carried out on three drugs: Atorvastatin, Alendronate, and Metoclopramide. Major side effects for each drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on computerized methods, further investigation is needed.Comment: International Journal of Information Technology and Computer Science (IJITCS), in print, 201

Detect adverse drug reactions for drug Atorvastatin

Adverse drug reactions (ADRs) are big concern for public health. ADRs are one of most common causes to withdraw some drugs from markets. Now two major methods for detecting ADRs are spontaneous reporting system (SRS), and prescription event monitoring (PEM). The World Health Organization (WHO) defines a signal in pharmacovigilance as "any reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously". For spontaneous reporting systems, many machine learning methods are used to detect ADRs, such as Bayesian confidence propagation neural network (BCPNN), decision support methods, genetic algorithms, knowledge based approaches, etc. One limitation is the reporting mechanism to submit ADR reports, which has serious underreporting and is not able to accurately quantify the corresponding risk. Another limitation is hard to detect ADRs with small number of occurrences of each drug-event association in the database. In this paper we propose feature selection approach to detect ADRs from The Health Improvement Network (THIN) database. First a feature matrix, which represents the medical events for the patients before and after taking drugs, is created by linking patients' prescriptions and corresponding medical events together. Then significant features are selected based on feature selection methods, comparing the feature matrix before patients take drugs with one after patients take drugs. Finally the significant ADRs can be detected from thousands of medical events based on corresponding features. Experiments are carried out on the drug Atorvastatin. Good performance is achieved.Comment: Fifth International Symposium on Computational Intelligence and Design (ISCID), 213-216, 2012. arXiv admin note: substantial text overlap with arXiv:1308.514

Optimal control-based inverse determination of electrode distribution for electroosmotic micromixer

This paper presents an optimal control-based inverse method used to determine the distribution of the electrodes for the electroosmotic micromixers with external driven flow from the inlet. Based on the optimal control method, one Dirichlet boundary control problem is constructed to inversely find the optimal distribution of the electrodes on the sidewalls of electroosmotic micromixers and achieve the acceptable mixing performance. After solving the boundary control problem, the step-shaped distribution of the external electric potential imposed on the sidewalls can be obtained and the distribution of electrodes can be inversely determined according to the obtained external electric potential. Numerical results are also provided to demonstrate the effectivity of the proposed method

Emerald Ash Borer and the application of biological control in Virginia

The emerald ash borer (Agrilus planipennis; EAB) is an invasive wood-boring beetle whose larvae feed on ash phloem. After only 1-5 years of infestation, the larvae create extensive tunnels under the bark that disrupt the tree’s ability to transport water and nutrients, which eventually girdles and kills the tree. Since 2008, EAB has spread to all but the eastern-most counties in Virginia. Bological control is one strategy to limit EAB populations. In this project we study control by native agents (woodpeckers) and imported agents (parasitoid wasps). Mathematical models of host-parasitoid interactions and simulations based on both models and field studies will be presented. Our novel contribution extends the basic Nicholson-Bailey model to a partial refuge system, realized in Virginia where EAB infests both ash and white fringetrees with fringetrees less attractive to the parasitoids. We determine ranges for model parameters that result in stable equilibrium populations