Background: Lipoprotein (a) [Lp(a)] is a critical factor in cardiovascular health, composed of low-density lipoprotein-like particles bound to apolipoprotein (a). Elevated Lp(a) levels are associated with an increased risk of cardiovascular diseases (CVD), accelerating disease progression and raising CVD-related mortality. However, the lack of standardized measurement methods for Lp(a) contributes to diagnostic uncertainties in this area.
Method: A quantitative measurement method for serum Lp(a) was developed using fully automated latex-enhanced particle immunoturbidimetry, marking a significant advancement in diagnostic capabilities. Key parameters, including repeatability, stability, linearity, detection limit, interference, and method comparison, were evaluated to ensure the assay's reliability and accuracy.
Result: Lp(a) in samples was detected by carboxylated latex particles (95 nm in diameter) covalently coated with anti-Lp(a) antibodies. Lp(a) concentration was quantified by measuring the turbidity changes caused by agglutination at 600 nm. This method provides rapid, accurate, and fully automated measurements on the Hitachi 7100 automatic biochemical analyzer. With intra-batch precision CV% of 1.10% and inter-batch precision CV% of 1.79%, the method demonstrates reliable performance with Randox biochemical quality control samples. It has a detection limit of 7 mg/L and a high correlation coefficient (R = 0.9946) within the 0-1500 mg/L range. Minimal interference from bilirubin, fat emulsion, hemoglobin, and ascorbic acid was observed. Additionally, it shows strong correlation (R = 0.9972) with a commercially available latex-enhanced immunoturbidimetric Lp(a) assay reagent, confirming its comparability and clinical suitability.
Conclusion: The quantitative serum Lp(a) determination method based on latex-enhanced immunoturbidimetry offers numerous advantages. It provides rapid, accurate, and automated results, making it ideal for routine clinical testing. The method effectively measures Lp(a) in serum samples by leveraging the interaction between Lp(a) and latex particles.
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http://dx.doi.org/10.1007/s10529-025-03564-w | DOI Listing |
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School of Mechatronic Engineering and automation, Shanghai University, Shanghai, China.
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Robust perception systems allow farm robots to recognize weeds and vegetation, enabling the selective application of fertilizers and herbicides to mitigate the environmental impact of traditional agricultural practices. Today's perception systems typically rely on deep learning to interpret sensor data for tasks such as distinguishing soil, crops, and weeds. These approaches usually require substantial amounts of manually labeled training data, which is often time-consuming and requires domain expertise.
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LAI, CNRS, INSERM, Turing Center for Living Systems, Aix Marseille Univ, Marseille, France.
Experiments with gradients of soluble bioactive species have significantly advanced with microfluidic developments that enable cell observation and stringent control of environmental conditions. While some methodologies rely on flow to establish gradients, others opt for flow-free conditions, which is particularly beneficial for studying non-adherent and/or shear-sensitive cells. In flow-free devices, bioactive species diffuse either through resistive microchannels in microchannel-based devices, through a porous membrane in membrane-based devices, or through a hydrogel in gel-based devices.
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Vrije Universiteit Brussel, Department of Chemical Engineering, Pleinlaan 2, 1050 Brussel, Belgium. Electronic address:
Chromatographic problem solving, commonly referred to as method development (MD), is hugely complex, given the many operational parameters that must be optimized and their large effect on the elution times of individual sample compounds. Recently, the use of reinforcement learning has been proposed to automate and expedite this process for liquid chromatography (LC). This study further explores deep reinforcement learning (RL) for LC method development.
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February 2025
College of Food Science and Engineering, Henan University of Technology, Zhengzhou 450001, China; Academy of National Food and Strategic Reserves Administration, NFSRA Key Laboratory of Grain and oil quality and safety, Beijing 100037, China. Electronic address:
A programmable multichannel chemiluminescence immunoassay sensor was developed based on the biotin-streptavidin system for the automatic quantification of the total amount of fumonisin B, B and B in maize samples. First, the key parameters, such as the reaction time and reagent dosage were optimized to improve the sensitivity. All reagents were subsequently added to the corresponding wells of the reagent strips, which were further sealed to create a total fumonisin quantification kit.
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