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Background: The SNP rs671 of Human aldehyde dehydrogenase (ALDH) is G-A transition at 1510th nucleotides, which is an important clinical indicator of alcoholic liver disease, digestive tract cancer and some drug efficiency. The commonly used genotyping assay of this polymorphism is relatively time-consuming and costly.
Finding: This study develops a rapid and accurate one-step CRISPR/Cas12b assay to distinguish the G1510A polymorphism of human ALDH2 free of DNA amplification. The method we established requires only one step of adding 1 μl genomic DNA sample to premixed system, and waiting for the acquisition of fluorescent signal, taking approximate 30 min.
Conclusions: This method provides a potential tool for more accurate and reliable nucleic acid detection with a single base difference and supports the relevant disease diagnosis and personalized medicine.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464061 | PMC |
http://dx.doi.org/10.1186/s13008-023-00095-6 | DOI Listing |
J Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFPLoS One
September 2025
State Key Laboratory of Precision Blasting Engineering, Jianghan University, Wuhan, Hubei, PR China.
Numerous parameters influence the slotting performance of slotted cartridge, to facilitate rapid, efficient, and accurate predictions of the slitting performance, statistical analysis of PMMA blasting experiments with six different slitted cartridge parameters yielded 12 evaluation indicators. Subsequently, a principal component analysis (PCA) method was introduced to reduce the dimensionality of the data associated with these indicators, and three new comprehensive indicators were extracted for a comprehensive assessment of the slotting performance. The PCA scores ranked the influence of the six slotted cartridge parameters on slotting performance as follows: decoupling coefficient, slotting width, slotting angle, slotting tube thickness, slotting tube material, and charge amount.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
September 2025
The rapid advancement of single-cell sequencing technology has generated vast amounts of multi-omics data, presenting unprecedented opportunities for single-cell multi-omics clustering analysis. However, existing single-cell clustering algorithms focus on extracting shared representations, overlooking the interactions and correlations among cells. This oversight inevitably leads to biased or confounded cell clustering results.
View Article and Find Full Text PDFACS Sens
September 2025
METU MEMS Center, Ankara 06530, Türkiye.
Cardiovascular diseases (CVDs) remain a leading cause of death, particularly in developing countries, where their incidence continues to rise. Traditional CVD diagnostic methods are often time-consuming and inconvenient, necessitating more efficient alternatives. Rapid and accurate measurement of cardiac biomarkers released into body fluids is critical for early detection, timely intervention, and improved patient outcomes.
View Article and Find Full Text PDFCereb Cortex
August 2025
Section on Functional Imaging Methods & Functional MRI Core Facility, National Institute of Mental Health, 10 Center Drive, Rm 1D80, Bethesda, MD 20892, United States.
Statistical Parametric Mapping (SPM) has been profoundly influential to neuroimaging as it has fostered rigorous, statistically grounded structure for model-based inferences that have led to mechanistic insights about the human brain over the past 30 years. The statistical constructs shared with the world through SPM have been instrumental for deriving meaning from neuroimaging data; however, they require simplifying assumptions which can provide results that, while statistically sound, may not accurately reflect the mechanisms of brain function. A platform that fosters the exploration of the rich and varying neuronal and physiologic underpinnings of the measured signals and their associations to behavior and physiologic measures needs a different set of tools.
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