Data mining of magnetocardiograms for prediction of ischemic heart disease

Ischemic Heart Disease (IHD) is a major cause of death. Early and accurate detection of IHD along with rapid diagnosis are important for reducing the mortality rate. Magnetocardiogram (MCG) is a tool for detecting electro-physiological activity of the myocardium. MCG is a fully non-contact method, which avoids the problems of skin-electrode contact in the Electrocardiogram (ECG) method. However, the interpretation of MCG recordings is time-consuming and requires analysis by an expert. Therefore, we propose the use of machine learning for identification of IHD patients. Back-propagation neural network (BPNN), the Bayesian neural network (BNN), the probabilistic neural network (PNN) and the support vector machine (SVM) were applied to develop classification models for identifying IHD patients. MCG data was acquired by sequential measurement, above the torso, of the magnetic field emitted by the myocardium using a J-T interval of 125 cases. The training and validation data of 74 cases employed 10-fold cross-validation methods to optimize support vector machine and neural network parameters. The predictive performance was assessed on the testing data of 51 cases using the following metrics: accuracy, sensitivity, and specificity and area under the receiver operating characteristic (ROC) curve. The results demonstrated that both BPNN and BNN displayed the highest and the same level of accuracy at 78.43 %. Furthermore, the decision threshold and the area under the ROC curve was -0.2774 and 0.9059, respectively, for BPNN and 0.0470 and 0.8495, respectively, for BNN. This indicated that BPNN was the best classification model, BNN was the best performing model with sensitivity of 96.65 %, and SVM employing the radial basis function kernel displayed the highest specificity of 86.36 %

Paper-based acetylcholinesterase inhibition assay combining a wet system for organophosphate and carbamate pesticides detection

A dramatic increase in pesticide usage in agriculture highlights the need for on-site monitoring for public health and safety. Here, a paper-based sensor combined with a wet system was developed for the simple and rapid screening of organophosphate (OP) and carbamate (CM) pesticides based on the inhibition of acetylcholinesterase (AChE). The paper-based sensor was designed as a foldable device consisting of a cover and detection sheets pre-prepared with indoxyl acetate and AChE, respectively. The paper-based sensor requires only the incubation of a sample on the test zone for 10 minutes, followed by closing of the foldable sheet to initiate the enzymatic reaction. Importantly, the buffer loading hole was additionally designed on the cover sheet to facilitate the interaction of the coated substrate and the immobilized enzyme. This subsequently facilitates the mixing of indoxyl acetate with AChE, resulting in the improved analytical performance of the sensor. The absence or decrease in blue color produced by the AChE hydrolysis of indoxyl acetate can be observed in the presence of OPs and CMs. Under optimized conditions and using image analysis, the limit of detection (LOD) of carbofuran, dichlorvos, carbaryl, paraoxon, and pirimicarb are 0.003, 0.3, 0.5, 0.6, and 0.6 ppm, respectively. The assay could be applied to determine OP and CM residues in spiked food samples. Visual interpretation of the color signal was clearly observed at the concentration of 5 mg/kg. Furthermore, a self-contained sample pre-concentration approach greatly enhanced the detection sensitivity. The paper-based device developed here is low-cost, requires minimal reagents and is easy to handle. As such, it would be practically useful for pesticide screening by non-professional end-users

Appropriate Technology for the Bioconversion of Water Hyacinth (Eichhornia crassipes) to Liquid Ethanol

This article appraises the need for introducing appropriate technology to improve the production of renewable energy, particularly on the community basis and social aspect of sustainability. Using two-sequential steps of acid hydrolysis (10% sulfuric acid) and yeast (Candida shehatae; xylose-fermenting yeast) fermentation, bioconversion of water hyacinth (Eichhornia crassipes; a noxious weed and fast growing aquatic plant widely distributed in many tropical regions of the world) to liquid ethanol has successfully been performed. The maximum ethanol yield coefficient of 0.19 g g-1 WH with the productivity of 0.008 g l-1 h-1 was achieved. This is as well comparable to those obtained from the enzymatic saccharification and/or the fermentation of acid-pretreated water hyacinth hydrolysate using fully-equipped fermenter reported elsewhere. More importantly, determinations of xylose and ethanol contents can potentially be performed using two reliable colorimetric approaches (Phloroglucinol and Dichromate assays, respectively) in conjunction with home-made portable photometer. The technology presented herein can be transferred and implemented to gain opportunity of becoming self-reliance of community in the third world countries

Polyacrylamide hydrogel encapsulated E. coli expressing metal-sensing green fluorescent protein as a potential tool for copper ion determination

A simple, inexpensive and field applicable metal determination system would be a powerful tool for the efficient control of metal ion contamination in various sources e.g. drinking-water, water reservoir and waste discharges. In this study, we developed a cell-based metal sensor for specific and real-time detection of copper ions. E. coli expressing metal-sensing green fluorescent protein (designated as TG1/(CG)6GFP and TG1/H6CdBP4GFP) were constructed and served as a metal analytical system. Copper ions were found to exert a fluorescence quenching effect, while zinc and cadmium ions caused minor fluorescence enhancement in the engineered bacterial suspension. To construct a user-friendly and reagentless metal detection system, TG1/H6CdBP4GFP and TG1/(CG)6GFP were encapsulated in polyacrylamide hydrogels that were subsequently immobilized on an optical fiber equipped with a fluorescence detection module. The sensor could be applied to measure metal ions by simply dipping the encapsulated bacteria into a metal solution and monitoring fluorescence changes in real time as a function of the metal concentration in solution. The sensor system demonstrated high specificity toward copper ions. The fluorescence intensities of the encapsulated TG1/(CG)6GFP and TG1/H6CdBP4GFP were quenched by approximately 70 % and 80 % by a high-dose of copper ions (50mM), respectively. The level of fluorescence quenching exhibited a direct correlation with the copper concentration, with a linear correlation coefficient (r) of 0.99. The cell-based metal sensor was able to efficiently monitor copper concentrations ranging between 5 µM and 50 mM, encompassing the maximum allowed copper contamination in drinking water (31.15 µM) established by the WHO. Furthermore, the cell-based metal sensor could undergo prolonged storage for at least 2 weeks without significantly influencing the copper sensitivity

Prediction of selectivity index of pentachlorophenol-imprinted polymers

A data set comprising of the selectivity index of pentachlorophenol-imprinted polymers against 53 pentachlorophenol and related compounds was obtained from the excellent work of Baggiani et al. Molecular descriptors of the phenol compounds were calculated with E-DRAGON to obtain a total of 1,666 descriptors spanning 20 categories of molecular properties. Multivariate analysis of the data set was performed using multiple linear regression, partial least squares regression, and principal component regression. Partial least squares regression was found to deliver an excellent predictive model and was chosen for further investigation. The descriptor dimension was reduced by the combined use of partial least squares and Unsupervised Forward Selection algorithm. The obtained Quantitative Structure-Property Relationship (QSPR) model based on the smaller subset of the molecular descriptors displayed substantial gain in predictive ability when compared to the model of Baggiani et al. Such QSPR model can help in the computational design of MIPs with predefined selectivity toward template molecules of interest

Native and chimeric metal-binding lactate dehydrogenase as detection and protection tools for oxidative stress induced by Fenton's reaction

In the present study, a simple and reliable antioxidant screening technique based on lactate dehydrogenase (LDH) oxidation by Cu2+-mediated Fenton’s reaction has successfully been developed. Oxidation of LDH by hydroxyl radical consequently leads to enzymatic inactiva-tion, while addition of antioxidants can protect and regain enzyme activity. This method dem-onstrated a high feasibility on detecting of antioxidative activity of lipophilic (e. g. alpha-Tocopherol and beta-Carotene) and hydrophilic compounds (e. g. glutathione, mannitol and thiourea) in a single assay. Results from linear correlation curves revealed that the IC50 were in the order of beta-carotene (3.45 µg/ml) > alpha-Tocopherol (52.31 µg/ml) > Mn(II)-bacitracin (109.37 µg/ml) > glutathione (122.63 µg/ml). Detailed investigations revealed that oxidation of LDH resulted in enzyme degradation, which was metal- and time-dependent mechanism. Therefore, further experiments were conducted to determine whether extension of the N-terminus of LDH with metal-binding regions possesses protective effect against the inactivation process. Genetic construction of chimeric LDH carrying two and four repetitive se-quences of cadmium binding peptide (CdBP), designated as CdBP2LDH and CdBP4LDH, has been carried out. From our findings, the CdBP2LDH and the CdBP4LDH exhibited protective action and enzyme activity regained 20-30 % and 70 % higher than that of the native LDH, respectively. Two possible mechanisms have been proposed to play important role in protec-tion against metal-mediated Fenton’s reaction: i) changing in redox potential of Cu2+ in metal-peptide complex, and ii) taking away of Cu2+ ion from the crucial amino acids by metal satu-ration at the cadmium-binding peptides

Pesticide Aptasensors—State of the Art and Perspectives

Contamination by pesticides in the food chain and the environment is a worldwide problem that needs to be actively monitored to ensure safety. Unfortunately, standard pesticide analysis based on mass spectrometry takes a lot of time, money and effort. Thus, simple, reliable, cost-effective and field applicable methods for pesticide detection have been actively developed. One of the most promising technologies is an aptamer-based biosensor or so-called aptasensor. It utilizes aptamers, short single-stranded DNAs or RNAs, as pesticide recognition elements to integrate with various innovative biosensing technologies for specific and sensitive detection of pesticide residues. Several platforms for aptasensors have been dynamically established, such as colorimetry, fluorometry, electrochemistry, electrochemiluminescence (ECL) and so forth. Each platform has both advantages and disadvantages depending on the purpose of use and readiness of technology. For example, colorimetric-based aptasensors are more affordable than others because of the simplicity of fabrication and resource requirements. Electrochemical-based aptasensors have mainly shown better sensitivity than others with exceedingly low detection limits. This paper critically reviews the progression of pesticide aptasensors throughout the development process, including the selection, characterization and modification of aptamers, the conceptual frameworks of integrating aptamers and biosensors, the ASSURED (affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free and deliverable to end users) criteria of different platforms and the future outlook

Lifestyle behaviors and serum vitamin C in the Thai population in Bangkok Metropolitan

This study aimed to investigate the influence of lifestyle behaviors on the vitamin C levels in the circulating blood of the Thai population in Bangkok Metropolitan. The participants (n=250) included community workers (i.e., construction and business office workers) from the Bangkok Metropolitan, and the participants were placed in various behavior and lifestyle groups (Group I: reference; Group II: alcohol drinkers; Group III: outdoor workers; Group IV: smokers; and Group V: combined). The results showed that the lowest and highest vitamin C intakes were 7 and 27 mg/day in Groups IV and III, respectively. Group I (indoor workers free of smoking and drinking), had the highest total serum vitamin C level (39.7 μmol/L), while Group V (outdoor workers with smoking and drinking), had the lowest value (12.5 μmol/L). Furthermore, Group V had the highest prevalence (44 %) of total serum vitamin C deficiency (<11 μmol/L), while Group I had the lowest deficient indication (8 %). The vitamin C dietary intake and total serum levels were positively correlated in the reference group (Spearman’s correlation=0.402, p < 0.05) but not in the other four groups. The significant adjusted odds ratio of inadequate total serum vitamin C (< 23 μmol/L) was 2.90 (CI: 1.15, 7.31) in Group IV and 3.73 (CI: 1.42, 9.81) in Group V. Moreover, the tendency to have an inadequate total serum vitamin C level was demonstrated in the following order: Group I < II < III < IV < V. Our results indicated that outdoor workers (Group III) and smokers (Group IV) had a greater likelihood of having a vitamin C deficiency than the reference group. A high percentage of deficiency was clearly observed among the outdoor workers with smoking and drinking behaviors (Group V)