In this work, an automatic multi-channel ink-jet for chemiluminescence (CL) analysis was developed. The four-channel ink-jet device was controlled by a home-made circuit. Differing from the classic flow injection CL, the whole procedure for CL analysis was automatically completed on a hydrophobic glass side. CL reaction of luminal and hydrogen peroxide for the determination of horseradish peroxidase (HRP) was selected as an application to automatic CL analysis platform. All solutions delivered by different channels were precisely ejected to the same position of the glass slide for the CL analysis. The consumption of reaction solution was reduced to nanoliter level. The whole CL analysis could be completed in less than 4min, which was benefited from the prompt solution mixing in small size of droplet. The CL intensity increased linearly with HRP concentration in the range from 0.01 to 0.5μgmL(-1). The limit of detection (LOD) (S/N=3) was 0.005μgmL(-1). Finally, the automatic CL system could also be used for the detection of HRP in HRP-protein conjugates, which showed its practical application in immunoassay.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.aca.2012.06.022 | DOI Listing |
Micromachines (Basel)
December 2024
Department of Engineering and System Science, National Tsing Hua University, Hsinchu 30013, Taiwan.
(1) Background: Fetal chromosomal examination is a critical component of modern prenatal testing. Traditionally, maternal serum biomarkers such as free β-human chorionic gonadotropin (Free β-HCG) and pregnancy-associated plasma protein A (PAPPA) have been employed for screening, achieving a detection rate of approximately 90% for fetuses with Down syndrome, albeit with a false positive rate of 5%. While amniocentesis remains the gold standard for the prenatal diagnosis of chromosomal abnormalities, including Down syndrome and Edwards syndrome, its invasive nature carries a significant risk of complications, such as infection, preterm labor, or miscarriage, occurring at a rate of 7 per 1000 procedures.
View Article and Find Full Text PDFBrain Sci
December 2024
West China Institute of Children's Brain and Cognition, Chongqing University of Education, Chongqing 400065, China.
Background: Emotions play a crucial role in people's lives, profoundly affecting their cognition, decision-making, and interpersonal communication. Emotion recognition based on brain signals has become a significant challenge in the fields of affective computing and human-computer interaction.
Methods: Addressing the issue of inaccurate feature extraction and low accuracy of existing deep learning models in emotion recognition, this paper proposes a multi-channel automatic classification model for emotion EEG signals named DACB, which is based on dual attention mechanisms, convolutional neural networks, and bidirectional long short-term memory networks.
Brain Sci
November 2024
Department of Neurology, Beth Isreal Deaconess Medical Center, Harvard Medical School, Harvard University, Cambridge, MA 02215, USA.
: Manually labeling sleep stages is time-consuming and labor-intensive, making automatic sleep staging methods crucial for practical sleep monitoring. While both single- and multi-channel data are commonly used in automatic sleep staging, limited research has adequately investigated the differences in their effectiveness. In this study, four public data sets-Sleep-SC, APPLES, SHHS1, and MrOS1-are utilized, and an advanced hybrid attention neural network composed of a multi-branch convolutional neural network and the multi-head attention mechanism is employed for automatic sleep staging.
View Article and Find Full Text PDFFront Neuroinform
December 2024
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
Introduction: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.
Methods: We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability.
Front Comput Neurosci
December 2024
School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!