Great advancements have been made in understanding the basic mechanisms of ictogenesis using single-cell electrophysiology (e.g., patch clamp, sharp electrode), large-scale electrophysiology (e.g., electroencephalography [EEG], field potential recording), and large-scale imaging (magnetic resonance imaging [MRI], positron emission tomography [PET], calcium imaging of acetoxymethyl ester [AM] dye-loaded tissue). Until recently, it has been challenging to study experimentally how population rhythms emerge from cellular activity. Newly developed optical imaging technologies hold promise for bridging this gap by making it possible to simultaneously record the many cellular elements that comprise a neural circuit. Furthermore, easily accessible genetic technologies for targeting expression of fluorescent protein-based indicators make it possible to study, in animal models of epilepsy, epileptogenic changes to neural circuits over long periods. In this review, we summarize some of the latest imaging tools (fluorescent probes, gene delivery methods, and microscopy techniques) that can lead to the advancement of cell- and circuit-level understanding of epilepsy, which in turn may inform and improve development of next generation antiepileptic and antiepileptogenic drugs.
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http://dx.doi.org/10.1111/epi.12939 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
Biomed Phys Eng Express
January 2025
University of Gothenburg, Bruna stråket 13, Goteborg, 405 30, SWEDEN.
Dual-polarity readout is a simple and robust way to mitigate Nyquist ghosting in diffusion-weighted echo-planar imaging but imposes doubled scan time. We here propose how dual-polarity readout can be implemented with little or no increase in scan time by exploiting an observed b-value dependence and signal averaging. The b-value dependence was confirmed in healthy volunteers with distinct ghosting at low b-values but of negligible magnitude at b = 1000 s/mm2.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
School of Engineering and Computing, University of the West of Scotland, University of the West of Scotland - Paisley Campus, Paisley PA1 2BE, UK, City, Paisley, PA1 2BE, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Cancer grade classification is a challenging task identified from the cell structure of healthy and abnormal tissues. The partitioner learns about the malignant cell through the grading and plans the treatment strategy accordingly. A major portion of researchers used DL models for grade classification.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
Department of Ophthalmology, Hospital Universitario de Canarias, Carretera Ofra S/N, La Laguna, Santa Cruz de Tenerife, 38320, SPAIN.
This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
Shandong University of Traditional Chinese Medicine, Qingdao Academy of Chinese Medical Sciences, Jinan, Shandong, 250355, CHINA.
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease, and it can be used as an important indicator of disease progression. However, many existing methods focus mainly on the image itself when processing brain imaging data, ignoring other non-imaging data (e.g.
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