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Significant challenges to poultry health are posed by chicken anemia virus (CAV), which induces immunosuppression and causes increased susceptibility to secondary infections. The effective management and containment of CAV within poultry stocks require precise and prompt diagnosis. However, a deficiency persists in the availability of low-cost, rapid, and portable CAV detection devices. In this study, an immunochromatographic lateral-flow test strip-based assay was developed for CAV detection using in-house generated monoclonal antibodies (MABs) against CAV viral protein 1 (VP1). The recombinant truncated VP1 protein (Δ60VP1), with amino acid residues 1 to 60 of the native protein deleted, was produced via a prokaryotic expression system and utilized for immunizing BALB/c mice. Subsequently, high-affinity MABs against Δ60VP1 were generated and screened using conventional hybridoma technology combined with serial dilution assays. Two MABs, MAB1, and MAB3, both binding to distinct epitopes of Δ60VP1, were selected for the development of a lateral-flow assay. Sensitivity analysis demonstrated that the Δ60VP1 antigen could be detected by our homemade lateral-flow assay at concentrations as low as 625 ng/mL, and this sensitivity was maintained for at least 6 mo. The assay exhibited high specificity, as evidenced by its lack of reactivity with surrogate recombinant proteins and the absence of cross-reactivity with other chicken viruses and viral antigens. Comparative analysis with quantitative PCR data demonstrated substantial agreement, with a Kappa coefficient of 0.66, utilizing a sample set comprising 305 clinical chicken serum samples. In conclusion, the first lateral-flow assay for CAV detection was developed in this study, utilizing 2 specific anti-VP1 MABs. It is characterized by simplicity, rapidity, sensitivity, and specificity.

Polybrominated biphenyls (PBBs) and polybrominated biphenyl ethers (PBDEs) are substances used in electrical and electronic products. They are restricted substances due to their potential risk to human health and the environment. In this research work, a test method was evaluated for the quantitative analysis of polybrominated diphenyl ethers (PBDEs) and polybrominated biphenyls (PBBs) in acrylonitrile-butadiene–styrene (ABS) plastic and polyethylene materials, based on solvent extraction of a plastic sample with soxhlet extraction techniques and detection by gas chromatography/mass spectrometry. We have also developed a computer program for checking the suitability of the analytical method by evaluating validation characteristics, including accuracy, precision, specificity/selectivity, limit of detection (LOD), limit of quantification (LOQ), linearity, range and sensitivity. The computer program was successfully applied for evaluation of the analytical method for prohibited PBBs and PBDEs in plastics. Graphical charts for the evaluation of the analytical method are presented along with their values, and researchers can get insight into evaluating several parameters together. It was found that the analytical method was able to quantify monobrominated - decabrominated biphenyl and monobrominated diphenyl ether–nonabrominated diphenyl ether between 100-1750 mg/kg and decabrominated diphenyl ether between 100–8,000 mg/kg. In addition, ERM-EC590 Polyethylene certified reference material from the Institute for Reference Materials and Measurements (IRMM) was used to validate the analytical method for PBBs and PBDEs in plastics. The obtained accuracy and precision indicate that the method successfully meets the required accuracy expressed as % recovery (70-130%) and the required precision expressed as percentage relative standard deviation (less than 20% RSD) of the IEC 62321:2008 international standard method.

This paper proposed data mining techniques to improve efficiency and reliability in diabetes classification. The real data set collected from Sawanpracharak Regional Hospital, Thailand, was fist analyzed by using gain-ratio feature selection techniques. Three well known algorithms; naïve bayes, k-nearest neighbors and decision tree, were used to construct classification models on the selected features. Then, the popular ensemble learning; bagging and boosting were applied using the three base classifiers. The results revealed that the best model with the highest accuracy was bagging with base classifier decision tree algorithm (95.312%). The experiments also showed that ensemble classifier models performed better than the base classifiers alone.