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| http://dx.doi.org/10.3978/j.issn.2305-5839.2014.10.03 | DOI Listing |
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From bits to bedside: entering the age of digital twins in cardiac electrophysiology.
Europace
December 2024
Gottfried Schatz Research Center, Medical Physics and Biophysics, Medical University of Graz, Graz, Austria.
This State of the Future Review describes and discusses the potential transformative power of digital twins in cardiac electrophysiology. In this 'big picture' approach, we explore the evolution of mechanistic modelling based digital twins, their current and immediate clinical applications, and envision a future where continuous updates, advanced calibration, and seamless data integration redefine clinical practice of cardiac electrophysiology. Our aim is to inspire researchers and clinicians to embrace the extraordinary possibilities that digital twins offer in the pursuit of precision medicine.
View Article and Find Full Text PDFN6-methyladenosine regulators in hepatocellular carcinoma: investigating the precise definition and clinical applications of biomarkers.
Biol Direct
November 2024
Department of Hepatopancreatobiliary Surgery, Hangzhou Institute of Medicine (HIM), Zhejiang Cancer Hospital, Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
Background: Accurately identifying effective biomarkers and translating them into clinical practice have significant implications for improving clinical outcomes in hepatocellular carcinoma (HCC). In this study, our objective is to explore appropriate methods to improve the accuracy of biomarker identification and investigate their clinical value.
Methods: Concentrating on the N6-methyladenosine (m6A) modification regulators, we utilized dozens of multi-omics HCC datasets to analyze the expression patterns and genetic features of m6A regulators.
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension.
J Biomed Inform
November 2024
Department of Data Science and Big Data Technology, Hainan University, Haikou 570228, China; Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York 10065, USA. Electronic address:
Objective: Although deep learning techniques have shown significant achievements, they frequently depend on extensive amounts of hand-labeled data and tend to perform inadequately in few-shot scenarios. The objective of this study is to devise a strategy that can improve the model's capability to recognize biomedical entities in scenarios of few-shot learning.
Methods: By redefining biomedical named entity recognition (BioNER) as a machine reading comprehension (MRC) problem, we propose a demonstration-based learning method to address few-shot BioNER, which involves constructing appropriate task demonstrations.
Multidisciplinary approach to redefining thyroid hormone reference intervals with big data analysis.
Clin Biochem
December 2024
Alberta Precision Laboratories, Calgary, AB, Canada; Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. Electronic address:
Objectives: This study aimed to employ big data analysis to harmonize reference intervals (RI) for thyroid function tests, with refinement to the TSH upper reference limit, and to optimize the TSH reflex algorithm to improve clinical management and test utilization.
Design & Methods: TSH, free T4, and free T3 results tested in Alberta, Canada, on Roche Cobas and Siemens Atellica were extracted from the laboratory information system (N = 1,144,155 for TSH, N = 183,354 for free T4 and N = 92,632 for free T3). Results from specialists, inpatients, or repeat testing, as well as from positive thyroid disease, autoimmune disease, and pregnancy biomarkers were excluded.
Future of Allergy and Immunology: Is AI the Key in the Digital Era?
Ann Allergy Asthma Immunol
October 2024
School of Medicine, Department of Chest Diseases, Division of Allergy and Clinical Immunology, Hacettepe University, Ankara, Turkey.
Artificial Intelligence (AI) is reshaping allergy and immunology by integrating cutting-edge technology to enhance patient outcomes and redefine clinical practices and research. This review examines AI's evolving role, emphasizing its impact on diagnostic accuracy, personalized treatments, and innovative research methodologies. AI has advanced diagnostic tools, such as models predicting allergen sensitivity, and enhanced immunotherapy strategies.
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