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Efficacy and safety of finerenone in patients with an acute change in estimated glomerular filtration rate in the prespecified FIDELITY pool analysis

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Kidney International 2025年第1期108卷 136-144页

作者： Sankar D. Navaneethan Stefan D. Anker Gerasimos Filippatos Bertram Pitt Peter Rossing Luis M. Ruilope Phyllis August Meike Brinker Andrea Lage Luke Roberts Charlie Scott Pantelis Sarafidis Section of Nephrology Baylor College of Medicine Houston Texas USA Department of Cardiology (CVK) of German Heart Center Charité Institute of Health Center for Regenerative Therapies (BCRT) German Centre for Cardiovascular Research (DZHK) partner site Berlin Charité Universitätsmedizin Berlin Germany Department of Cardiology School of Medicine National and Kapodistrian University of Athens Attikon University Hospital Athens Greece Department of Medicine University of Michigan School of Medicine Ann Arbor Michigan USA Steno Diabetes Center Copenhagen Herlev Denmark Department of Clinical Medicine University of Copenhagen Copenhagen Denmark Cardiorenal Translational Laboratory and Hypertension Unit Institute of Research Imas12 Madrid Spain CIBER-CV Hospital Universitario 12 de Octubre Madrid Spain Faculty of Sport Sciences European University of Madrid Madrid Spain Division of Nephrology and Hypertension Weill Cornell Medicine New York City New York USA Cardiology and Nephrology Clinical Development Bayer AG Wuppertal Germany Cardiology and Nephrology Clinical Development Bayer SA São Paulo Brazil Clinical Development Bayer PLC Reading UK Data Science and Analytics Bayer PLC Reading UK Department of Nephrology University of Thessaloniki Thessaloniki Greece

Introduction The efficacy and safety of finerenone (a nonsteroidal mineralocorticoid receptor antagonist) versus placebo were assessed according to different changes in estimated glomerular filtration rate (eGFR) using data from FIDELITY, a pooled individual-level analysis of two clinical trials. Methods Patients had chronic kidney disease (eGFR of 25 ml/min/1.73 m 2 or greater) and type 2 diabetes with optimized renin-angiotensin system blockade. Risk of composite cardiovascular and composite kidney outcomes was analyzed by baseline eGFR change at month one in the total population and by treatment group. Results Of 12,798 patients, 25.1% had a >10% eGFR decline, 31.2% had a >0–10% decline, 26.8% had a 0–10% increase, and 16.8% had a >10% increase after one month of treatment. Factors associated with acute eGFR decline included higher baseline urine albumin-to-creatinine ratio, eGFR, systolic blood pressure, diuretic or beta-blocker use, and finerenone use. Finerenone significantly reduced composite cardiovascular and kidney outcomes overall and had similar beneficial effect across eGFR subgroups of >10% decline, >0–10% decline, 0–10% increase, and >10% increase for composite cardiovascular (hazard ratio [95% Confidence Interval] of 0.74 [0.61–0.90], 0.87 [0.73–1.04], 1.06 [0.87–1.28], and 0.78 [0.61–0.99], respectively) and kidney outcomes (0.67 [0.53–0.85], 0.78 [0.61–1.01], 0.56 [0.40–0.77], and 0.75 [0.50–1.14], respectively) (P interaction 0.048 and 0.23, respectively). When modeled as a continuous variable, finerenone reduced the risk of cardiovascular and kidney outcomes, irrespective of acute eGFR change (P interaction 0.58 and 0.36, respectively). Conclusions The cardiovascular and kidney benefits of finerenone were not modified by an acute eGFR change after drug initiation.

关键词： acute eGFR decline cardiovascular outcomes finerenone kidney outcomes nonsteroidal mineralocorticoid receptor antagonist

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Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

arXiv

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arXiv 2020年

作者： Dou, Yingtong Liu, Zhiwei Sun, Li Deng, Yutong Peng, Hao Yu, Philip S. Department of Computer Science University of Illinois at Chicago United States School of Computer Science Beijing University of Posts and Telecommunications China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in recent years, revealing the suspiciousness of nodes by aggregating their neighborhood information via different relations. However, few prior works have noticed the camouflage behavior of fraudsters, which could hamper the performance of GNN-based fraud detectors during the aggregation process. In this paper, we introduce two types of camouflages based on recent empirical studies, i.e., the feature camouflage and the relation camouflage. Existing GNNs have not addressed these two camouflages, which results in their poor performance in fraud detection problems. Alternatively, we propose a new model named CAmouflage-REsistant GNN (CARE-GNN), to enhance the GNN aggregation process with three unique modules against camouflages. Concretely, we first devise a label-aware similarity measure to find informative neighboring nodes. Then, we leverage reinforcement learning (RL) to find the optimal amounts of neighbors to be selected. Finally, the selected neighbors across different relations are aggregated together. Comprehensive experiments on two real-world fraud datasets demonstrate the effectiveness of the RL algorithm. The proposed CARE-GNN also outperforms state-of-the-art GNNs and GNN-based fraud detectors. We integrate all GNN-based fraud detectors as an open-source toolbox1. The CARE-GNN code and datasets are available at https://***/YingtongDou/CARE-GNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction

arXiv

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arXiv 2020年

作者： Ye, Haishan Xiong, Wei Zhang, Tong Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen Hong Kong Department of Probability and Statistics University of Science and Technology of China China Hong Kong University of Science and Technology Hong Kong

This paper considers the decentralized composite optimization problem. We propose a novel decentralized variance reduction proximal-gradient algorithmic framework, called PMGT-VR, which is based on a combination of several techniques including multi-consensus, gradient tracking, and variance reduction. The proposed framework relies on an imitation of centralized algorithms and we demonstrate that algorithms under this framework achieve convergence rates similar to that of their centralized counterparts. We also describe and analyze two representative algorithms, PMGT-SAGA and PMGT-LSVRG, and compare them to existing state-of-the-art proximal algorithms. To the best of our knowledge, PMGT-VR is the first linearly convergent decentralized stochastic algorithm that can solve decentralized composite optimization problems. Numerical experiments are provided to demonstrate the effectiveness of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimization

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Same same but different:Neuroanatomic and connectomic profiles of early-onset schizophrenia and autism spectrum disorder

Medicine Plus

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Medicine Plus 2024年第1期1卷 13-25页

作者： Bin Lu Xue Li Xue-Ping Gao Jing Chen Yi Liu Jian-Ping Lu Siu-Ching Kat Zeng-Hui Ma Li-Yang Zhao Ting-Ni Yin Xing Su Fan-Chao Meng Ya-Ru Zhang Shu-Xian Wu Wen-Jie Yan Ya-Song Du Xue-Rong Luo Jing Liu Chao-Gan Yan CAS Key Laboratory of Behavioral Science Institute of PsychologyBeijing 100101China Department of Psychology University of Chinese Academy of SciencesBeijing 100101China Peking University Sixth Hospital Peking University Institute of Mental HealthNHC Key Laboratory of Mental Health(Peking University)National Clinical Research Center for Mental Disorders and National Center for Mental Disorders(Peking University Sixth Hospital)Beijing 100191China Department of Psychiatry National Clinical Research Center for Mental Disordersand National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangsha 410011China Shanghai Mental Health Center Shanghai Jiao Tong University School of MedicineShanghai 200030China Department of Child and Adolescent Psychiatry Shenzhen Kangning Hospital Shenzhen 518118China International Big-Data Center for Depression Research Institute of PsychologyChinese Academy of SciencesBeijing 100101China Magnetic Resonance Imaging Research Center Institute of PsychologyChinese Academy of SciencesBeijing 100101China

Background:Early-onset schizophrenia(EOS)and autism spectrum disorder(ASD)are two mental disorders with abnormal neurodevelopment and overlapping clinical ***,few prospective multi-center imaging studies explored their pathophysiological differences and similarities ***:We utilized a large cohort of functional magnetic resonance imaging data with standardized scanning protocols from 594 participants(276 participants with high-functioning ASD,187 participants with EOS,and 131 normal controls(NCs))recruited at five centers.A comprehensive evaluation of anatomical and connectomic features,in-cluding cortical thickness,subcortical nuclei volume,functional connectivity,functional gradients,and network efficiency,was conducted,culminating in the construction of imaging-based neuro-profiles for individuals with EOS and ***:The derived neuro-profiles revealed that EOS is characterized by more pro-nounced reductions in cortical thickness and subcortical nuclei *** contrast,ASD exhibited more pronounced abnormalities in connectomic *** de-creased network functional connectivity and compressed functional gradients in ASD are likely to contribute to reduced network *** based on imaging profiles successfully distinguished individuals with EOS,ASD,and NCs with com-mendable performance in leave-site-out cross-validation(area under the operating characteristic curve(AUC)for ASD ***:0.769,AUC for ASD ***:0.792,AUC for EOS ***:0.854).Conclusion:The neuro-profiles revealed that EOS and ASD exhibit overlapping as well as unique imaging characteristics;however,EOS exhibits greater structural variation,while ASD presents with more subtle connectomic *** findings offer valuable insights into the distinct neurobiological mechanisms of EOS and ASD and enhance the prospect of early diagnosis through objective imaging-based biomarkers.

关键词： Early onset schizophrenia Autism spectrum disorder Neuroanatomy Connectome Functional gradient Functional magnetic resonance imaging

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Indonesia Infrastructure Development Topic Discovery on Online News with Latent Dirichlet Allocation

引用

IOP Conference Series: Materials science and Engineering 2021年第1期1077卷

作者： Ahmad Fathan Hidayatullah Muhammad Rifqi Ma'arif Muhammad Habibie Siti Khomsah Department of Informatics Universitas Islam Indonesia Yogyakarta Indonesia Center of Data Analytics Studies and Services Universitas Jenderal Achmad Yani Yogyakarta Indonesia Department of Data Science Institut Teknologi Telkom Purwokerto Indonesia

In the period of 2014-2019, the Indonesian government has defined infrastructure development as a priority. Related to this, various media, in both online and offline, routinely report the infrastructure development news to the public. However, it is quite difficult for people to obtain a summary of information from the internet about the infrastructure development that have been carried out by the government. This study aims to provide a brief summary about infrastructure development in Indonesia by performing topic modeling approach using Latent Dirichlet Allocation. We found that the use of bigram language model could help identify phrase from the corpus. Therefore, the keywords contained in the topics are more interpretable and acceptable. Moreover, a coherence score measurement was applied to find the best number of topics from our dataset. Based on the experiments, we obtained 40 topic models from our dataset. From those 40 topics, we inferred several topic labels, such as, oil and gas infrastructure; power plant infrastructure; information technology infrastructure and internet networks; road infrastructure; reservoir infrastructure, irrigation networks and water resources; railway infrastructure; and airport infrastructure.

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Thingview Free: Application of Smart Greenhouse for Chilli Plant on Android

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Journal of Physics: Conference Series 2021年第1期1951卷

作者： I Fatimah B Indarto D Anggoro B Arfianto Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia

Monitoring and controlling of the greenhouse climate are carried out so that the climate in the greenhouse is in accordance with the growth requirements of the chili plants in it. Automation is carried out using a humidifier, plant watering can, space heater, and fan. Microclimates that are controlled include soil humidity, temperature, and humidity. The actuator is connected to the SHT11 sensor and soil moisture sensor. The sensor is connected to the Arduino Uno so that it can give commands to the actuator. Then the ESP-01 wifi module is connected to the Arduino Uno so that the sensor reading data will be sent to the Thingspeak database to be monitored. The results of this study are humidity and temperature data before and after automation. The data after automation shows the values that match the growing requirements of chilies, namely air temperature of 20°C to 30°C, humidity of 60% to 80% and soil moisture> 50%. Monitoring of microclimate conditions in the greenhouse can be done through the Thingview Free application on android which is limited to 8 graphs in real time. Microclimate control using several actuators in the greenhouse shows that the greenhouse automation system is already running according to the microclimate conditions required by chili plants.

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SCPM-Net: An Anchor-Free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching

arXiv

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arXiv 2021年

作者： Luo, Xiangde Song, Tao Wang, Guotai Chen, Jieneng Chen, Yinan Li, Kang Metaxas, Dimitris N. Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China SenseTime Research Shanghai China College of Electronics and Information Engineering Tongji University Shanghai China West China Biomedical Big Data Center Sichuan University West China Hospital Chengdu China Department of Computer Science Rutgers University PiscatawayNJ08854 United States

Automatic and accurate lung nodule detection from 3D Computed Tomography (CT) scans plays a vital role in efficient lung cancer screening. Despite the state-of-the-art performance obtained by recent anchor-based detectors using Convolutional Neural Networks (CNNs) for this task, they require predetermined anchor parameters such as the size, number, and aspect ratio of anchors, and have limited robustness when dealing with lung nodules with a massive variety of sizes. To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network (SCPM-Net) that is anchor-free and automatically predicts the position, radius, and offset of nodules without manual design of nodule/anchor parameters. The SCPM-Net consists of two novel components: sphere representation and center points matching. First, to match the nodule annotation in clinical practice, we replace the commonly used bounding box with our proposed bounding sphere to represent nodules with the centroid, radius, and local offset in 3D space. A compatible sphere-based intersection over-union loss function is introduced to train the lung nodule detection network stably and efficiently. Second, we empower the network anchor-free by designing a positive center-points selection and matching (CPM) process, which naturally discards pre-determined anchor boxes. An online hard example mining and re-focal loss subsequently enable the CPM process to be more robust, resulting in more accurate point assignment and mitigation of class imbalance. In addition, to better capture spatial information and 3D context for the detection, we propose to fuse multi-level spatial coordinate maps with the feature extractor and combine them with 3D squeeze-and-excitation attention modules. Experimental results on the LUNA16 dataset showed that our proposed SCPM-Net framework achieves superior performance compared with existing anchor-based and anchor-free methods for lung nodule detection with the avera

关键词： Spheres

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Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction

arXiv

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arXiv 2020年

作者： Pian, Weiguo Wu, Yingbo Qu, Xiangmou Cai, Junpeng Kou, Ziyi Automotive Collaborative Innovation Center Chongqing University China School of Big Data & Software Engineering Chongqing University China Department of Computer Science and Engineering University of Notre Dame United States

Ride-hailing demand prediction is an essential task in spatial-temporal data mining. Accurate Ride-hailing demand prediction can help to pre-allocate resources, improve vehicle utilization and user experiences. Graph Convolutional Networks (GCN) is commonly used to model the complicated irregular non-Euclidean spatial correlations. However, existing GCN-based ride-hailing demand prediction methods only assign the same importance to different neighbor regions, and maintain a fixed graph structure with static spatial relationships throughout the timeline when extracting the irregular non-Euclidean spatial correlations. In this paper, we propose the Spatial-Temporal Dynamic Graph Attention Network (STDGAT), a novel ride-hailing demand prediction method. Based on the attention mechanism of GAT, STDGAT extracts different pair-wise correlations to achieve the adaptive importance allocation for different neighbor regions. Moreover, in STDGAT, we design a novel time-specific commuting-based graph attention mode to construct a dynamic graph structure for capturing the dynamic time-specific spatial relationships throughout the timeline. Extensive experiments are conducted on a real-world ride-hailing demand dataset, and the experimental results demonstrate the significant improvement of our method on three evaluation metrics RMSE, MAPE and MAE over state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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data sharing in learning analytics: how context and group discussion influence the individual willingness to share

Humanities and Social Sciences Communications

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Humanities and Social sciences Communications 2025年第1期12卷 1-13页

作者： Louis Longin Deisy Briceno Oleksandra Poquet Faculty of Philosophy Philosophy of Science and the Study of Religion Ludwig-Maximilians-Universität München Munich Germany LEAPS Lab—LEarning Analytics and Practices in Systems Department of Educational Sciences School of Social Sciences and Technology Technical University of Munich Munich Germany Munich Data Science Institute Technical University of Munich Munich Germany Centre for Change and Complexity in Learning University of South Australia Adelaide Australia

The ethical integration of the data generated by learners into educational practices is of great importance now that data-rich technologies are prevalent in education. Despite the common agreement that learners should have agency in deciding what to do with their data, existing ethical discussions focus on policies or algorithms, with limited attention to participatory learner practices. Participatory practices, particularly around informed consent, can support ethical and meaningful engagement with data sharing decisions. Using a novel experimental methodology, we explored how the decision context influences the perceived acceptability for sharing learning data. We found that participants became more cautious in sharing their data in and after a group discussion. The willingness to share was the lowest when these data were submitted to a government entity and for a collective benefit. Further network analysis of group discussions confirmed the observed attitude shifts: participants consistently discussed different aspects of sharing learning data based on the context such as sharing process vs outcome-related learning data. The results suggest that educational data consent is contextual and that perceptions of privacy in educational technology may differ from those in health contexts. The proposed method of interactive consent, therefore, not only contributes to theories explaining privacy and effective data collection but also represents a new way of conceptualising and realising participatory informed consent.

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