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VO microcubes as an alternative to peroxidase/TMB for colorimetric detection of HO: Development of glucose sensing method.
Spectrochim Acta A Mol Biomol Spectrosc
January 2025
Department of Chemistry, University of Kurdistan, Sanandaj 66177-15175 Iran; Research Center for Nanotechnology, University of Kurdistan, Sanandaj 66177-15175 Iran. Electronic address:
The study focuses on the synthesis of VO microcubes for the non-enzymatic colorimetric determination of HO.Vanadium oxide nanostructures are known for their redox activity and layered structures, making VO a valuable material for sensing applications. The characterization of the prepared sample was done using XPS, XRD, Raman spectroscopy, and SEM techniques.
View Article and Find Full Text PDFObjectives: The Assessment of Burden of Chronic Conditions (ABCC) tool is developed to facilitate a personalised approach to care through assessment and visualisation of a patient's experienced burden of disease, and integrating this in the conversation based on shared decision-making and individualised care plans. An indispensable step in the implementation process is an understanding of the context. The aim of this study is to perform a context analysis to identify barriers and facilitators to the implementation of the ABCC tool by healthcare providers (HCPs) in Dutch primary care.
View Article and Find Full Text PDFDVPT: Dynamic Visual Prompt Tuning of large pre-trained models for medical image analysis.
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Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 138632, Singapore.
Pre-training and fine-tuning have become popular due to the rich representations embedded in large pre-trained models, which can be leveraged for downstream medical tasks. However, existing methods typically either fine-tune all parameters or only task-specific layers of pre-trained models, overlooking the variability in input medical images. As a result, these approaches may lack efficiency or effectiveness.
View Article and Find Full Text PDFRapid and automated interpretation of CRISPR-Cas13-based lateral flow assay test results using machine learning.
Sens Diagn
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Department of Bioengineering, Rice University Houston TX 77030 USA
CRISPR-Cas-based lateral flow assays (LFAs) have emerged as a promising diagnostic tool for ultrasensitive detection of nucleic acids, offering improved speed, simplicity and cost-effectiveness compared to polymerase chain reaction (PCR)-based assays. However, visual interpretation of CRISPR-Cas-based LFA test results is prone to human error, potentially leading to false-positive or false-negative outcomes when analyzing test/control lines. To address this limitation, we have developed two neural network models: one based on a fully convolutional neural network and the other on a lightweight mobile-optimized neural network for automated interpretation of CRISPR-Cas-based LFA test results.
View Article and Find Full Text PDFGJFocuser: a Gaussian difference and joint learning-based autofocus method for whole slide imaging.
Biomed Opt Express
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School of Computer Science and Technology, Hainan University, Haikou 570228, China.
Whole slide imaging (WSI) provides tissue visualization at the cellular level, thereby enhancing the effectiveness of computer-aided diagnostic systems. High-precision autofocusing methods are essential for ensuring the quality of WSI. However, the accuracy of existing autofocusing techniques can be notably affected by variations in staining and sample heterogeneity, particularly without the addition of extra hardware.
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