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The Role of Lactate Metabolism in Heart Failure and Cardiogenic Shock: Clinical Insights and Therapeutic Implications

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Journal of Cardiac Failure 2025年

作者： YUZEFPOLSKAYA, MELANA SCHWARTZ, SARAH LADANYI, ANNAMARIA ABRAHAM, JACOB GALE, CHRIS P. GRINSTEIN, JONATHAN KLEIN, LIVIU MAJURE, DAVID T. PHANCAO, ANITA SHEIKH, FAROOQ H. COLOMBO, PAOLO C. JANUZZI, JAMES L. MOLINA, EZEQUIEL J. Division of Cardiovascular Medicine Department of Cardiology New York Presbyterian Hospital Columbia University New York NY United States Center for Cardiovascular Analytics Research & Data Science (CARDS) Providence St. Joseph Research Network Portland OR United States Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds Leeds UK Leeds Institute for Data Analytics University of Leeds Leeds UK Leeds Teaching Hospitals NHS Trust Leeds United Kingdom Division of Cardiovascular Medicine Department of Cardiology University of Chicago Chicago IL United States Division of Cardiovascular Medicine Department of Cardiology University of San Francisco San Francisco CA United States Division of Cardiovascular Medicine Department of Cardiology New York Presbyterian Hospital Weill Cornell Medical College New York NY United States Division of Cardiovascular Medicine Department of Cardiology University of Miami Miami FL United States Advanced Heart Failure Program MedStar Health Georgetown University School of Medicine Washington D.C. United States Cardiology Division Massachusetts General Hospital Harvard Medical School Baim Institute for Clinical Research Boston MA United States Division of Cardiothoracic Surgery Department of Surgery Piedmont Heart Institute Atlanta GA United States

Heart failure (HF) is associated with poor prognosis, especially when it progresses to cardiogenic shock (CS), where survival rates substantially decline. A key area of interest is the role of blood lactate as a biomarker in these conditions. Lactate is produced under normal physiological conditions but increases with impaired tissue perfusion, a hallmark of HF and CS. Elevated lactate levels result from increased production, reduced clearance or both and are often associated with worse outcomes. Traditionally considered a byproduct of anaerobic metabolism, lactate is now recognized as an important energy substrate, particularly in myocardial tissue during periods of metabolic stress. Recent studies suggest that dynamic lactate monitoring, including lactate clearance (LC), may provide critical insights into patients’ prognoses and responses to therapy. Serial measurements of lactate have been shown to predict survival in critically ill patients, including those with HF and CS. In CS, elevated lactate levels correlate with increased mortality risk, and LC is emerging as an important parameter in treatment protocols. Despite growing evidence of lactate's clinical relevance, research is needed to establish standardized thresholds and optimal monitoring timelines. Understanding the complexities of lactate metabolism and its role in HF and CS could lead to improved risk stratification and more personalized treatment approaches. © 2025

关键词： cardiogenic shock Heart failure lactate levels

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An overview of facial micro-expression analysis: data, methodology and challenge

arXiv

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arXiv 2020年

作者： Xie, Hong-Xia Lo, Ling Shuai, Hong-Han Cheng, Wen-Huang The Institute of of Electronics National Chiao Tung Univresity Hsinchu300 Taiwan The Department of Electrical and Computer Engineering National Chiao Tung Univresity Hsinchu300 Taiwan The Institute of Electronics National Chiao Tung Univresity Hsinchu300 Taiwan The Artificial Intelligence and Data Science Program National Chung Hsing University Taichung400 Taiwan

Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g. police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Generative models of simultaneously heavy-tailed distributions of inter-event times on nodes and edges

arXiv

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arXiv 2020年

作者： dos Reis, Elohim Fonseca Li, Aming Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY United States Department of Zoology University of Oxford Oxford United Kingdom Department of Biochemistry University ofOxford Oxford United Kingdom Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States

Intervals between discrete events representing human activities, as well as other types of events, often obey heavy-tailed distributions, and their impacts on collective dynamics on networks such as contagion processes have been intensively studied. The literature supports that such heavy-tailed distributions are present for inter-event times associated with both individual nodes and individual edges in networks. However, the simultaneous presence of heavy-tailed distributions of inter-event times for nodes and edges is a non-trivial phenomenon, and its origin has been elusive. In the present study, we propose a generative model and its variants to explain this phenomenon. We assume that each node independently transits between a high-activity and low-activity state according to a continuous-time two-state Markov process and that, for the main model, events on an edge occur at a high rate if and only if both end nodes of the edge are in the high-activity state. In other words, two nodes interact frequently only when both nodes prefer to interact with others. The model produces distributions of inter-event times for both individual nodes and edges that resemble heavy-tailed distributions across some scales. It also produces positive correlation in consecutive inter-event times, which is another stylized observation for empirical data of human activity. We expect that our modeling framework provides a useful benchmark for investigating dynamics on temporal networks driven by non-Poissonian event sequences. Copyright © 2020, The Authors. All rights reserved.

关键词： Continuous time systems

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Deep learning-based frozen section to FFPE translation

arXiv

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arXiv 2021年

作者： Ozyoruk, Kutsev Bengisu Can, Sermet Gokceler, Guliz Irem Başak, Kayhan Demir, Derya Serin, Gurdeniz Hacisalihoglu, Uguray Payam Kurtuluş, Emirhan Darbaz, Berkan Lu, Ming Y. Chen, Tiffany Y. Williamson, Drew F.K. Yilmaz, Funda Mahmood, Faisal Turan, Mehmet Institute of Biomedical Engineering Bogazici University Istanbul Turkey Uk Biocentre Tilbrook United Kingdom Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and Mit CambradgeMA United States Saǧlik Bilimleri University Kartal Dr.Lütfi Kirdar City Hospital Department of Pathology Istanbul Turkey Faculty of Medicine Department of Pathology Ege University Izmir Turkey Istanbul Yeni Yuzyil University Medical Faculty Gaziosmanpasa Hospital Pathology Department Turkey

Frozen sectioning (FS) is the preparation method of choice for microscopic evaluation of tissues during surgical operations. The high speed of procedure allows pathologists to rapidly assess the key microscopic features, such as tumor margins and malignant status to guide surgical decision-making and minimise disruptions to the course of the operation. However, FS is prone to introducing many misleading artificial structures (histological artefacts), such as nuclear ice crystals, compression, and cutting artefacts, hindering timely and accurate diagnostic judgement of the pathologist. Additional training and prolonged experience is often required to make highly effective and time critical diagnosis on frozen sections. On the other hand, the gold standard tissue preparation technique of formalin-fixation and paraffin-embedding (FFPE) provides significantly superior image quality, but is a very time-consuming process (12-48 hours), making it unsuitable for intra-operative use. In this paper, we propose an artificial intelligence (AI) method that improves FS image quality by computationally transforming frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style images in minutes. AI-FFPE rectifies FS artefacts with the guidance of an attention-mechanism that puts a particular emphasis on artefacts while utilising a self-regularization mechanism established between FS input image and synthesized FFPE-style image that preserves clinically relevant features. As a result, AI-FFPE method successfully generates FFPE-style images without significantly extending tissue processing time and consequently improves diagnostic accuracy. We demonstrate the efficacy of AI-FFPE on lung and brain frozen sections using a variety of different qualitative and quantitative metrics including visual Turing tests from 20 board certified pathologists. Additionally, we demonstrate that deep models trained on FFPE tissue can adapt to frozen sections which a much higher efficacy whe

关键词： Surgery

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Concurrency measures in the era of temporal network epidemiology: A review

arXiv

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arXiv 2020年

作者： Masuda, Naoki Miller, Joel C. Holme, Petter Department of Mathematics State University of New York Buffalo United States Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States School of Engineering and Mathematical Sciences La Trobe University Australia Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan

Diseases spread over temporal networks of interaction events between individuals. Structures of these temporal networks hold the keys to understanding epidemic propagation. One early concept of the literature to aid in discussing these structures is concurrency-quantifying individuals' tendency to form time-overlapping "partnerships". Although conflicting evaluations and an overabundance of operational definitions have marred the history of concurrency, it remains important, especially in the area of sexually transmitted infections. Today, much of theoretical epidemiology uses more direct models of contact patterns, and there is an emerging body of literature trying to connect methods to the concurrency literature. In this review, we will cover the development of the concept of concurrency and these new approaches. © 2020, CC BY.

关键词： Epidemiology

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Perspectives on How To Conduct Responsible Anti-Human Trafficking Research in Operations and Analytics

arXiv

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arXiv 2020年

作者： Konrad, Renata A. Maass, Kayse Lee Dimas, Geri L. Trapp, Andrew C. School of Business Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Mechanical and Industrial Engineering Northeastern University 360 Huntington Ave BostonMA02115 United States Data Science Program Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States

Human trafficking, the commercial exploitation of individuals, is a gross violation of human rights;harming societies, economies, health and development. The related disciplines of Operations Research (OR) and Analytics are uniquely positioned to support trafficking prevention and intervention efforts by efficiently evaluating a plethora of decision alternatives and providing quantitative, actionable insights. As operations and analytical efforts in the counter-trafficking field emerge, it is imperative to grasp subtle, yet distinctive, nuances associated with human trafficking. This paper is intended to inform those practitioners working in the Operations and Analytics fields by highlighting key features of human trafficking activity. We grouped ten themes around two broad categories: (1) representation of human trafficking and (2) consideration of survivors and communities. These insights are derived from our collective experience in working in this area and substantiated by domain expertise. Based on these areas, we then suggest avenues for future work. © 2020, CC BY-NC-SA.

关键词： Operations research

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The EMPOWER blended digital intervention for relapse prevention in schizophrenia: a feasibility cluster randomised controlled trial in Scotland and Australia

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The Lancet Psychiatry 2022年第6期9卷 477-486页

作者： Gumley, Andrew I Bradstreet, Simon Ainsworth, John Allan, Stephanie Alvarez-Jimenez, Mario Aucott, Lorna Birchwood, Maximillian Briggs, Andrew Bucci, Sandra Cotton, Sue M Engel, Lidia French, Paul Lederman, Reeva Lewis, Shôn Machin, Matthew MacLennan, Graeme McLeod, Hamish McMeekin, Nicola Mihalopoulos, Cathy Morton, Emma Norrie, John Schwannauer, Matthias Singh, Swaran P Sundram, Suresh Thompson, Andrew Williams, Chris Yung, Alison R Farhall, John Gleeson, John Glasgow Institute of Health and Wellbeing University of Glasgow Glasgow United Kingdom Division of Psychology and Mental Health School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Orygen Melbourne Melbourne VIC Australia Centre for Youth Mental Health University of Melbourne Melbourne VIC Australia School of Computing and Information Systems Faculty of Engineering and Information Technology University of Melbourne Melbourne VIC Australia Centre for Healthcare Randomised Trials (CHaRT) University of Aberdeen Aberdeen United Kingdom Centre for Mental Health and Wellbeing Research Warwick Medical School University of Warwick Warwick United Kingdom Department of Health Services Research and Policy London School of Hygiene & Tropical Medicine London United Kingdom Greater Manchester Mental Health NHS Foundation Trust Manchester United Kingdom School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Department of Psychiatry School of Clinical Sciences Monash University Melbourne VIC Australia School of Medicine Deakin University Melbourne VIC Australia Department of Psychiatry Manchester Metropolitan University Manchester United Kingdom Department of Psychiatry University of British Columbia Vancouver BC Canada The Usher Institute University of Edinburgh Edinburgh United Kingdom School for Health in Social Sciences University of Edinburgh Edinburgh United Kingdom Mental Health Program Monash Health Melbourne VIC Australia Department of Psychology and Counselling La Trobe University Melbourne VIC Australia NorthWestern Mental Health The Royal Melbourne Hospital Epping VIC Australia Healthy Brain and Mi

Background: Early warning signs monitoring by service users with schizophrenia has shown promise in preventing relapse but the quality of evidence is low. We aimed to establish the feasibility of undertaking a definitive randomised controlled trial to determine the effectiveness of a blended digital intervention for relapse prevention in schizophrenia. Methods: This multicentre, feasibility, cluster randomised controlled trial aimed to compare Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery (EMPOWER) with treatment as usual in community mental health services (CMHS) in Glasgow and Melbourne. CMHS were the unit of randomisation, selected on the basis of those that probably had five or more care coordinators willing to participate. Participants were eligible if they were older than 16 years, had a schizophrenia or related diagnosis confirmed via case records, were able to provide informed consent, had contact with CMHS, and had had a relapse within the previous 2 years. Participants were randomised within stratified clusters to EMPOWER or to continue their usual approach to care. EMPOWER blended a smartphone for active monitoring of early warning signs with peer support to promote self-management and clinical triage to promote access to relapse prevention. Main outcomes were feasibility, acceptability, usability, and safety, which was assessed through face-to-face interviews. App usage was assessed via the smartphone and self-report. Primary end point was 12 months. Participants, research assistants and other team members involved in delivering the intervention were not masked to treatment conditions. Assessment of relapse was done by an independent adjudication panel masked to randomisation group. The study is registered at ISRCTN (99559262). Findings: We identified and randomised eight CMHS (six in Glasgow and two in Melbourne) comprising 47 care coordinators. We recruited 86 service users between Jan 19 and Au

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An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter 3

An Improved Version of Texture-based Foreground Segmentation...

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3rd International Conference on Computer science and Computational Intelligence, ICCSCI 2018

作者： Muchtar, Kahlil Rahman, Faris Cenggoro, Tjeng Wawan Budiarto, Arif Pardamean, Bens Nodeflux Inc. Jakarta Indonesia Syiah Kuala University Aceh Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Foreground segmentation is one of moving object detection techniques of computer vision applications. To date, modern moving object detection methods require complex background modeling and thresholds tuning to confront illumination changes. This paper proposes an adaptive approach based on non-overlapping block texture representation. It aims to design a computationally light and efficient solution to improve the robustness of detection. We evaluate our proposed method on internal and public sequences and provide the quantitative and qualitative measurements. Experimental results show that the proposed method can improve the results of previous method and suitable for real-time challenges. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Textures

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Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy

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Procedia Computer science 2019年 157卷 31-37页

作者： Puspita Anggraini Kaban Robert Kurniawan Rezzy Eko Caraka Bens Pardamean Budi Yuniarto Sukim Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia College of Informatics Chaoyang University of Technology Taichung City 41349 Taiwan (R.O.C.) Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Geographically, Indonesia is a meeting point of three continental plates. Scilicet, the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. Therefore, Indonesia is part of the infamous volcanic zone called the ”Ring of Fire” and one of the areas prone to natural disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. This study aims to capture the spatial pattern and identify the causes of social vulnerability in the districts/cities in Indonesia using the biclustering method. The data is extracted from the Indonesian National Socio-Economic Survey (SUSENAS) by BPS-Statistics in 2014. The biclustering result indicates that each district/city has its own social vulnerability characteristics and shows that the vulnerable aspects of each district/city are different. The adjacent observations tend to have social vulenrability characteristics. The results of this study can be used as a reference for national disaster mitigation policy in Indonesia.

关键词： biclustering disaster mitigation social vulnerability SoVI spatial pattern

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SWEET: a single-sample network inference method for deciphering individual features in disease

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Briefings in bioinformatics 2023年第2期24卷 bbad032页

作者： Hsin-Hua Chen Chun-Wei Hsueh Chia-Hwa Lee Ting-Yi Hao Tzu-Ying Tu Lan-Yun Chang Jih-Chin Lee Chun-Yu Lin Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan. TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan. Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan. Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. Center for Intelligent Drug Systems and Smart Bio-devices National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan.

Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.

关键词： drug repurposing gene expression network inference network medicine precision medicine single-sample network

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