98%
921
2 minutes
20
In recent years, the frequent outbreaks of Phaeocystis globosa (P. globosa) bloom have seriously threatened the safety of coastal nuclear power. However, existing detection technologies cannot meet the demand for the early warning of P. globose blooms. In this study, a DNA electrochemical biosensor for the specific detection of P. globosa was developed based on gold-loaded carbon cube (Au-CC) electrode modification materials and an exonuclease III (Exo III)-assisted amplification strategy. The biosensor was then successfully applied to the early warning and dynamic monitoring of P. globosa blooms. The limit of detection (LOD) and limit of quantitation (LOQ) of the biosensor were 32.25 fg/μL (926 cells/L) and 97.36 fg/μL (2456 cells/L), respectively, which were significantly lower than the outbreak threshold of P. globosa (1.0 × 10 cells/L). The excellent detection performance of the biosensor was due to the sensitive response of Au-CC to electrode interface disturbance and the indirect amplification of P. globosa DNA by the Exo III-assisted target cycling process. In addition, compared to ddPCR and traditional microscopy methods, the biosensor exhibited good accuracy and reliability (P > 0.05). In analyzing actual samples, this biosensor achieved consistent results with the standard survey method and successfully predicted the low outbreak risk of P. globosa in the sampling area. Therefore, this biosensor offers great potential in the dynamic detection and early warning of P. globosa bloom and may serve as a powerful tool for ensuring coastal nuclear power safety and protecting marine ecology.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.talanta.2025.127759 | DOI Listing |
BMC Nephrol
September 2025
School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, China.
Ultrasonics
September 2025
Faculty of Land Resource Engineering, Kunming University of Science and Technology, Yunnan 650093, China; Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, Ministry of Natural Resources of the People's Republic of China, Yunnan Province, Kunming, Yunnan
Identifying and predicting the catastrophic failure of brittle rock remains a challenging task, yet it is crucial for developing early warning systems and preventing dynamic rock hazards. In this study, we employed the propagative parameters of ultrasonic waves and information from acoustic emission (AE) events to characterize the brittle failure of a flawed sandstone sample under uniaxial compression. A sliding event window method was developed to obtain the temporal b-value, effectively revealing microcrack growth based on the frequency-magnitude distribution of AE events.
View Article and Find Full Text PDFDriven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFJ Agric Food Chem
September 2025
Department of Food Science and Engineering, Ningbo University, Ningbo 315211, P.R. China.
Sleep deprivation (SD) is a major contributor to cognitive impairment, often accompanied by central neuroinflammation and gut microbiota dysbiosis. The tryptophan (TRP) pathway, activated via indoleamine 2,3-dioxygenase (IDO), serves as a critical link between immune activation and neuronal damage. Umbelliferone (UMB), a naturally occurring coumarin compound, possesses anti-inflammatory, antioxidant, and microbiota-modulating properties.
View Article and Find Full Text PDFClin Transl Gastroenterol
September 2025
Department of Internal Medicine, School of Medicine, University of Medicine and Pharmacy at Ho Cho Minh City, Vietnam.
Background: Severe acute pancreatitis (SAP) is a life-threatening condition requiring early risk stratification. While the Bedside Index for Severity in Acute Pancreatitis (BISAP) is widely used, its reliance on complex parameters limits its applicability in resource-constrained settings. This study introduces a decision tree model based on Classification and Regression Tree (CART) analysis, utilizing Neutrophil-to-Lymphocyte Ratio (NLR) and C-reactive Protein (CRP), as a simpler alternative for early SAP prediction.
View Article and Find Full Text PDF