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Comparison of endoscopic third ventriculostomy versus cerebrospinal fluid shunt procedures for the treatment of pediatric hydrocephalus in Taiwan

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Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery 2024年第9期40卷 2883-2891页

作者： Shu-Mei Chen Li-Ying Chen Jiann-Her Lin Nicole Salazar Tu-Hsueh Yeh Wei-Lun Lo Tai-Ngar Lui Yi-Chen Hsieh Li-Nien Chien Department of Neurosurgery Taipei Medical University Hospital Taipei Medical University Taipei Taiwan. Department of Surgery School of Medicine Taipei Medical University Taipei Taiwan. Health Data Analytics and Statistics Center Office of Data Science Taipei Medical University Taipei Taiwan. Julius L. Chambers Biomedical/Biotechnology Institute and Department of Biological & Biomedical Sciences North Carolina Central University Durham NC USA. Department of Neurology Taipei Medical University Hospital Taipei Taipei Medical University Taipei Taiwan. Department of Neurology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Department of Neurosurgery Shuang Ho Hospital Taipei Medical University Taipei Taiwan. The PhD Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University 250 Wu-Hsing Street Taipei Taiwan. ychsieh@tmu.edu.tw. Institute of Health and Welfare Policy College of Medicine National Yang Ming Chiao Tung University No.155 Sec. 2 Linong St. Beitou District Taipei Taiwan. linien.chien@nycu.edu.tw. Graduate Institute of Data Science College of Management Taipei Medical University Taipei Taiwan. linien.chien@nycu.edu.tw.

PURPOSE:Pediatric hydrocephalus is the most common cause of surgically treatable neurological disease in children. Controversies exist whether endoscopic third ventriculostomy (ETV) or cerebrospinal fluid (CSF) shunt placement is the most appropriate treatment for pediatric hydrocephalus. This study aimed to compare the risk of re-operation and death between the two procedures. METHODS:We performed a retrospective population-based cohort study and included patients younger than 20-years-old who underwent CSF shunt or ETV for hydrocephalus from the Taiwan National Health Insurance Research database. RESULTS:A total of 3,555 pediatric patients from 2004 to 2017 were selected, including 2,340 (65.8%) patients that received CSF shunt placement and 1215 (34.2%) patients that underwent ETV. The incidence of all-cause death was 3.31 per 100 person-year for CSF shunt group and 2.52 per 100 person-year for ETV group, with an adjusted hazard ratio (HR) of 0.79 (95% confidence interval [CI] = 0.66-0.94, p = 0.009). The cumulative incidence competing risk for reoperation was 31.2% for the CSF shunt group and 26.4% for the ETV group, with an adjusted subdistribution HR of 0.82 (95% CI = 0.70-0.96, p = 0.015). Subgroup analysis showed that ETV was beneficial for hydrocephalus coexisting with brain or spinal tumor, central nervous system infection, and intracranial hemorrhage. CONCLUSION:Our data indicates ETV is a better operative procedure for pediatric hydrocephalus when advanced surgical techniques and instruments are available.

关键词： Cerebrospinal shunt Pediatric hydrocephalus Reoperation Third ventriculostomy

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Investigation of structural and electronic properties of boron carbide based on SCAN meta-GGA functional

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AIP Conference Proceedings 2023年第1期2604卷

作者： Muhammad R. Ramadhan Imam M. Al-Khafiiz Jihan Anavisha Jofim Y. Napitupulu Wanda R. Tiana Fahmi Astuti M. Zuhnir Piliang Physics Study Program Republic of Indonesia Defense University Kawasan Indonesia Peace and Security Center Sentul Bogor 16810 Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia

Boron Carbide (B4C) is one of the known superhard material that can be utilized in many engineering and scientific applications. Here we investigated B4C system in its rhombohedral structure (a=5.17 Å, α=65.52°) by using the meta-GGA functional, SCAN, within density functional theory framework. We observed that the optimized lattice parameters given by the SCAN functional is more realistic compared to the LDA and GGA calculations. From the optimized structure of B4C, we determined the bulk modulus value of 255 GPa which only differs by ∼3 % from experimental data. Furthermore, the underestimation of band gap in LDA and GGA implementations is also remedied, where the meta-GGA, SCAN, functional estimates 1.98 eV insulating gap from density of states calculation. This value is very close to the known experimental observations (2.09 eV).

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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Machine Learning - Algorithms, Models and Applications

arXiv

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arXiv 2022年

作者： Sen, Jaydip Sen, Rajdeep Dutta, Abhishek Mehtab, Sidra Kherwa, Pooja Singh, Sonali Ahmed, Saheel Berry, Pranay Khurana, Sahil Akinbo, Racheal S. Daramola, Oladunni A. Ost, Kalum J. Anderson, David W. Cadotte, David W. Lainjo, Bongs Department of Data Science Praxis Business School Kolkata India School of Computing and Analytics NASHM Knowledge Campus Kolkata India Maharaja Surajmal Institute of Technology New Delhi India Federal University of Technology Akure Nigeria Hotchkiss Brain Institute CalgaryAB Canada Cumming School of Medicine CalgaryAB Canada Division of Neurosurgery Department of Clinical Neurosciences University of Calgary AB Canada Division of Neurosurgery Department of Radiology University of Calgary AB Canada Combined Orthopedic and Neurosurgery Spine Program University of Calgary AB Canada Cybermatic International MontréalQC Canada

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence. © 2022, CC BY-SA.

关键词： Learning algorithms

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A future for the world's children? A WHO–UNICEF–Lancet Commission

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The Lancet 2020年第10224期395卷 605-658页

作者： Clark, Helen Coll-Seck, Awa Marie Banerjee, Anshu Peterson, Stefan Dalglish, Sarah L Ameratunga, Shanthi Balabanova, Dina Bhan, Maharaj Kishan Bhutta, Zulfiqar A Borrazzo, John Claeson, Mariam Doherty, Tanya El-Jardali, Fadi George, Asha S Gichaga, Angela Gram, Lu Hipgrave, David B Kwamie, Aku Meng, Qingyue Mercer, Raúl Narain, Sunita Nsungwa-Sabiiti, Jesca Olumide, Adesola O Osrin, David Powell-Jackson, Timothy Rasanathan, Kumanan Rasul, Imran Reid, Papaarangi Requejo, Jennifer Rohde, Sarah S Rollins, Nigel Romedenne, Magali Singh Sachdev, Harshpal Saleh, Rana Shawar, Yusra R Shiffman, Jeremy Simon, Jonathon Sly, Peter D Stenberg, Karin Tomlinson, Mark Ved, Rajani R Costello, Anthony The Helen Clark Foundation Auckland New Zealand Partnership for Maternal Newborn & Child Health Geneva Switzerland Senegal Presidency Dakar Senegal Department of Maternal Newborn Child and Adolescent Health WHO Geneva Switzerland Department of Health Systems Governance and Financing WHO Geneva Switzerland Health Division HIV Data and Analytics Division UNICEF New York NY United States HIV Data and Analytics Division UNICEF New York NY United States Department of International Health Johns Hopkins School of Public Health Baltimore MD United States School of Population Health University of Auckland Auckland New Zealand Department of Global Health and Development London School of Hygiene and Tropical Medicine London United Kingdom Health Systems Transformation Platform Tata Trusts Mumbai India Centre for Global Child Health Hospital for Sick Children Toronto OT Canada Global Financing Facility World Bank Washington DC United States Health Systems Research Unit South African Medical Research Council Cape Town South Africa Department of Health Management and Policy Beirut Lebanon Knowledge to Policy Center American University of Beirut Beirut Lebanon School of Public Health University of Western Cape Bellville South Africa Financing Alliance for Health Nairobi Kenya Institute for Global Health London United Kingdom Department of Economics London United Kingdom University College London London United Kingdom Health Policy and Systems Research Consultant Accra Ghana China Center for Health Development Studies Peking University Beijing China Program of Social Sciences and Health Latin American School of Social Sciences Buenos Aires Argentina Centre for Science and Environment New Delhi India Department of Maternal and Child Health Uganda Ministry of Health Kampala Uganda Institute of Child Health University of Ibadan Ibadan Nigeria Department of Health Systems WHO Phnom Penh Cambodia West and Central Regional Office UNICEF Dakar Senegal P

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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MOOCs – international information and education phenomenon?

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Bulletin of the Association for Information science and Technology 2014年第5期40卷

作者： Lee Wilson Anatoliy Gruzd Graduate Student School of Information Management in the master of library and information studies program at Dalhousie University. He is currently working as a research assistant at the Dalhousie Social Media Lab working on a SSHRC funded Insight grant project entitled “Learning Analytics for the Social Media Age” Associate Professor School of Information Management director of the Social Media Lab at the Faculty of Management and cross-appointed to the Faculty of Computer Science at Dalhousie University Canada. He is also a co-editor of a new multidisciplinary journal on Big Data and Society to be published by Sage and a member of the Institute for Big Data Analytics at Dalhousie University

EDITOR'S SUMMARY Since the 1990s massive open online courses (MOOCs) have offered web-based learning on a large scale and with open access. The leading MOOC providers in 2014 – Udemy, Coursera and edX – vary in detail but share the goal of facilitating learning for unlimited audiences at no cost or minimal charge, overcoming socioeconomic hurdles and opening education to all. The potential is strong, and data shows promising registration figures from India and economically developing countries. Yet MOOCs fall short of their goal of widespread and readily accessed education, impeded by technology challenges, lack of basic education and predominance of English as the language of instruction. Maintaining a high standard of educational quality is challenging, and attrition rates are very high. Those in library and information science can facilitate learning through MOOCs and also benefit by using the platform to build awareness of the professional field.

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Outcomes with Femoral IABP in Heart failure and Acute Myocardial Infarction Related Cardiogenic Shock

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Journal of cardiac failure 2025年

作者： Arthur Reshad Garan Arvind Bhimaraj Rachna Kataria Aniket Rali E Wilson Grandin Alvaro Delgado Ajar Kochar Borui Li Manreet K Kanwar Shashank S Sinha Jaime Hernandez-Montfort Jacob Abraham Song Li Alexandra Arias-Mendoza Hiram Bezerra Vanessa Blumer I-Hui Chiang Mary Jane Farr Justin Fried Ann Gage Shelley Hall Gavin W Hickey Onyedika Ilonze Maya Guglin Wissam Khalife Ju Kim Scott Lundgren Jeffrey Marbach Joseph Mishkin Sandeep Nathan Mohit Pahuja Andrew Schwartzman Van-Khue Ton Oleg Alec Vishnevsky Esther Vorovich Joyce Wald Elric Zweck Qiuyue Kong Paavni Sangal Karol D Walec Peter Zazzali Neil M Harwani Kevin John Claudius Mahr Daniel Burkhoff Navin K Kapur Beth Israel Deaconess Medical Center and Harvard Medical School Boston MA. Methodist DeBakey Cardiology Associates Houston Methodist Hospital Houston TX. Lifespan Cardiovascular Institute Brown University Providence RI. Vanderbilt University Nashville TN. Brigham and Women's Hospital and Harvard Medical School Boston MA. The Cardiovascular Center Tufts Medical Center Boston MA. Cardiovascular Institute at Allegheny Health Network Pittsburgh PA. Inova Heart and Vascular Institute Inova Fairfax Campus Falls Church VA. Baylor Scott & White Health Advanced Heart Failure Program Clinic Temple TX. Center for Cardiovascular Analytics Research and Data Science Providence Heart Institute Portland OR. Institute for Advanced Cardiac Care Medical City Healthcare Dallas TX. Instituto Nacional de Cardiologia Mexico City Mexico. Tampa General Hospital Tampa FL. Banner University Medical Center Phoenix AZ. University of Texas Southwestern Medical Center Dallas TX. Columbia University Irving Medical Center New York NY. TriStar Centennial Medical Center Nashville TN. Baylor University Medical Center Dallas TX. University of Pittsburgh Medical Center Pittsburgh PA. Indiana University School of Medicine IN. Robert Wood Johnson Medical Center New Brunswick NJ. University of Texas Medical Branch Galveston TX. University of Nebraska Medical Center Omaha NE. Oregon Health State University Portland OR. Atrium Health Sanger Heart and Vascular Institute (SHVI) Charlotte NC. University of Chicago Chicago IL. University of Oklahoma Health Science Center Oklahoma City OK. Maine Medical Center Portland ME. Massachusetts General Hospital and Harvard Medical School Boston MA. Thomas Jefferson University Hospital Philadelphia PA. Northwestern Medicine Chicago IL. Hospital of the University of Pennsylvania Philadelphia PA. Department of Cardiology Pulmonology and Vascular Medicine Medical Faculty and University Hospital Düsseldorf Heinrich-Heine-University Düsseldorf Germ

BACKGROUND:Intra-aortic balloon pump (IABP) insertion has not been shown to improve mortality in acute myocardial infarction-related cardiogenic shock (AMI-CS) but is increasingly used in heart failure-related cardiogenic shock (HF-CS). OBJECTIVE:We sought to compare IABP-related outcomes in HF-CS and AMI-CS patients. METHODS:The Cardiogenic Shock Working Group registry was queried for CS patients receiving femoral IABP as the first temporary mechanical circulatory support (tMCS) device. Patients were divided into those with AMI-CS or HF-CS and were excluded if they received the IABP in conjunction with VA-ECMO or another device concomitantly. Outcomes including rates of native heart survival (NHS, i.e. weaned from IABP and discharged), heart replacement therapy (HRT, i.e. bridge to durable left ventricular assist device or heart transplant), need for another tMCS device, and death were recorded and compared between the two cohorts. RESULTS:In total, 886 patients were supported by IABP as the first tMCS device; of these, 407 (45.6%) had HF-CS and 384 (43.3%) AMI-CS. Those with HF-CS were younger but had a higher burden of cardiovascular co-morbidities than those with AMI-CS. Among the HF-CS cohort, 33.2% had NHS and 26.7% were bridged to HRT without another tMCS device. In the AMI-CS cohort, 43.4% had NHS and 2.1% were bridged to HRT without another tMCS device. Mortality was higher in AMI-CS (36.4% vs 20.6%, p<0.001). Complication rates were higher in AMI-CS and those needing another tMCS device. CONCLUSION:Patients with HF-CS were more likely to have a favorable outcome with IABP than those with AMI-CS.

关键词： Cardiogenic shock Heart Failure Hemodynamics IABP acute myocardial infarction

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Modeling the number of confirmed cases of COVID-19 in East Java using negative binomial regression based on least square spline estimator

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AIP Conference Proceedings 2024年第1期3083卷

作者： Arip Ramadan Nur Chamidah I. Nyoman Budiantara Student of Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Information System School of Industrial and System Engineering Telkom University Surabaya 60231 Indonesia. Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Statistics Faculty of Sciences and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia.

The East Java Province has experienced a significant surge in number of confirmed cases of COVID-19. This study endeavors to investigate the potential correlation between weather conditions and the incidence of number of confirmed cases of COVID-19 in East Java. To achieve this, a nonparametric regression model, specifically, the Negative Binomial Regression (NBR) model based on the least squares spline estimator, was utilized. The outcomes of the study indicate that the Mean Absolute Percentage Error (MAPE) of nonparametic regression model is 0.30. Meanwhile, the MAPE for the parametric regression model is 0.34. The results show that a nonparametric regression model approach is better than parametric regression model approach. The study establishes that the truncated spline estimator based NBR model represents the best fit, with an MLCV value of -256.71. The findings of the study suggest that a temperature less than 21.75°C is associated with a decrease of 13.28 number of confirmed cases of COVID-19 per each 1°C increase, while a temperature between 21.75°C and 25.78°C is linked to an increase of 6.85 number of confirmed cases of COVID-19 per each 1°C increase. In contrast, a temperature greater than 25.78°C is associated with a decrease of 139.42 number of confirmed cases of COVID-19 per each 1°C increase. Similarly, a wind speed less than 5.57 m/s is related to a decrease of 12.99 number of confirmed cases of COVID-19 per each 1 m/s increase, whereas a wind speed between 5.57 m/s and 8.99 m/s is associated with a decrease of 10.29 number of confirmed cases of COVID-19 per each 1 m/s increase. Furthermore, a wind speed greater than 8.99 m/s is linked to a decrease of 19.16 number of confirmed cases of COVID-19 per each 1 m/s increase. The study provides evidence that higher temperatures and wind speeds result in a slower rise in the incidence of the number of confirmed cases of COVID-19. Consequently, it is recommended that the local government remains vigilant du

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Correction to: Select, route and schedule: optimizing community paramedicine service delivery with mandatory visits and patient prioritization

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Health care management science 2023年第4期26卷 747页

作者： Shima Azizi Özge Aygül Brenton Faber Sharon Johnson Renata Konrad Andrew C Trapp Business Analytics and Information Systems Peter J. Tobin College of Business St. John's University Queens NY USA. azizis@stjohns.edu. Data Science Program Worcester Polytechnic Institute 100 Institute Road Worcester MA USA. Biomedical Engineering Worcester Polytechnic Institute 100 Institute Road Worcester MA USA. The Business School Worcester Polytechnic Institute 100 Institute Road Worcester MA USA.

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