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A novel dual-functional probe FSH from dipeptide (Ser-His-NH) and 5-carboxy fluorescein (5-FAM) fluorophore was developed for the relay detection of copper ions (Cu) and glyphosate (Glyp). As design, FSH exhibited high selectivity to Cu using colorimetric and fluorimetric methods, and formed non-fluorescence FSH-Cu ensemble. Further, the FSH-Cu ensemble responded to glyphosate with notable selectivity through fluorescence enhancement effect and colorimetric changes. The limit of detections (LODs) for Cu and glyphosate were calculated as 40.4 nM and 15.9 nM, respectively. Notably, FSH was successfully applied to the continuous detection and imaging of Cu and glyphosate in real water samples, test strips, living cells and zebrafish larvae. Moreover, we constructed the molecular logic gate for high sensitivity analysis, and the applications of FSH for "naked-eye" monitoring in food, plants and soil. More importantly, a portable smartphone-assisted RGB analysis method was designed to allow semi-quantitative detection of Cu and glyphosate.
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http://dx.doi.org/10.1016/j.foodchem.2025.145244 | 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 PDFJMIR Med Inform
September 2025
College of Medical Informatics, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China, 86 13500303273.
Background: Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health care outcomes. However, the quality and integrity of real-world electronic health records (EHRs) limit their utility in developing risk assessment tools.
View Article and Find Full Text PDFPhys Rev Lett
August 2025
Southern University of Science and Technology, Department of Physics, State Key Laboratory of Quantum Functional Materials, and Guangdong Basic Research Center of Excellence for Quantum Science, Shenzhen 518055, China.
Quantum computing is expected to provide an exponential speedup in machine learning. However, optimizing the data loading process, commonly referred to as "quantum data embedding," to maximize classification performance remains a critical challenge. In this Letter, we propose a neural quantum embedding (NQE) technique based on deterministic quantum computation with one qubit (DQC1).
View Article and Find Full Text PDFPLoS One
September 2025
School of Computer Science, CHART Laboratory, University of Nottingham, Nottingham, United Kingdom.
Background And Objective: Male fertility assessment through sperm morphology analysis remains a critical component of reproductive health evaluation, as abnormal sperm morphology is strongly correlated with reduced fertility rates and poor assisted reproductive technology outcomes. Traditional manual analysis performed by embryologists is time-intensive, subjective, and prone to significant inter-observer variability, with studies reporting up to 40% disagreement between expert evaluators. This research presents a novel deep learning framework combining Convolutional Block Attention Module (CBAM) with ResNet50 architecture and advanced deep feature engineering (DFE) techniques for automated, objective sperm morphology classification.
View Article and Find Full Text PDFSci Adv
September 2025
NSF National Center for Atmospheric Research, Boulder, CO, USA.
The El Niño-Southern Oscillation (ENSO) is a key driver of global climate variability. Early-season westerly wind bursts (WWBs) have long been suggested to be important for ENSO evolution and diversity, with the Madden-Julian Oscillation (MJO) among the main sources of WWBs. However, MJO's contribution to ENSO evolution has been difficult to quantify.
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