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| http://dx.doi.org/10.3389/fpsyg.2022.905811 | DOI Listing |
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Evaluating AI performance in nephrology triage and subspecialty referrals.
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Division of Nephrology and Hypertension, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
Artificial intelligence (AI) has shown promise in revolutionizing medical triage, particularly in the context of the rising prevalence of kidney-related conditions with the aging global population. This study evaluates the utility of ChatGPT, a large language model, in triaging nephrology cases through simulated real-world scenarios. Two nephrologists created 100 patient cases that encompassed various aspects of nephrology.
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Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Dusseldorf, Germany.
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View Article and Find Full Text PDFSTAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration.
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Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan.
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View Article and Find Full Text PDFmsiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis.
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Institute for Experimental Immunology and Imaging, University Hospital Essen, Essen, Germany.
Multimodal imaging by matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI MSI) and microscopy holds potential for understanding pathological mechanisms by mapping molecular signatures from the tissue microenvironment to specific cell populations. However, existing software solutions for MALDI MSI data analysis are incomplete, require programming skills and contain laborious manual steps, hindering broadly applicable, reproducible, and high-throughput analysis to generate impactful biological discoveries. Here, we present msiFlow, an accessible open-source, platform-independent and vendor-neutral software for end-to-end, high-throughput, transparent and reproducible analysis of multimodal imaging data.
View Article and Find Full Text PDFLocalized Nanopore Fabrication in Silicon Nitride Membranes by Femtosecond Laser Exposure and Subsequent Controlled Breakdown.
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Division of Micro and Nanosystems (MST), School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, Stockholm SE-10044, Sweden.
Controlled breakdown has emerged as an effective method for fabricating solid-state nanopores in thin suspended dielectric membranes for various biomolecular sensing applications. On an unpatterned membrane, the site of nanopore formation by controlled breakdown is random. Nanopore formation on a specific site on the membrane has previously been realized using local thinning of the membrane by lithographic processes or laser-assisted photothermal etching under immersion in an aqueous salt solution.
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