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Baseline markers of inflammation, lipids, glucose, and Dietary Inflammatory Index scores do not differ between adults willing to participate in an intensive inflammation reduction intervention and those who do not

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Nutrition and Health 2019年第1期25卷 9-19页

作者： Crimarco, Anthony Turner-McGrievy, Gabrielle M Wirth, Michael D Shivappa, Nitin Dunn, Caroline Vyas, Shraddha Mandes, Trisha Hurley, Thomas G West, Delia Hébert, James R Department of Health Promotion Education and Behavior Arnold School of Public Health University of South Carolina BC United States Department of Epidemiology and Biostatistics Arnold School of Public Health University of South Carolina BC United States Cancer Prevention and Control Program Arnold School of Public Health University of South Carolina BC United States Connecting Health Innovations LLC BC United States Department of Exercise Science Arnold School of Public Health University of South Carolina BC United States

Background: Chronic inflammation is associated with numerous chronic diseases and can be managed with diet. Aim: The purpose of this study was to examine differences in baseline characteristics and plasma inflammation levels between two groups of participants that participated in an intensive, lifestyle intervention or a remotely delivered intervention. This work also assessed the association between Dietary Inflammatory Index (DII)^® scores and participants’ inflammatory and metabolic biomarkers at baseline. Method: Ninety-five participants (61 intervention, 34 control) chose to enroll in either a 12-month intervention consisting of a face-to-face nutrition, physical activity, and stress management intervention or a remotely-delivered intervention (control group) focusing on general cancer prevention. The intervention group met at the University of South Carolina for classes and the control group had materials emailed to them. A quantile regression was used to compare participants’ high-sensitivity C-reactive protein and interleukin-6 levels. Multiple linear regression was used to determine the association between DII scores and biomarkers. Results: There were significant differences in age, body mass index, body fat percentage, and blood pressure between groups, but there were no differences in levels of inflammatory biomarkers. Values of interleukin-6 at the 90th percentile of its distribution were 8.31 pg/ml higher among those in DII quartile 4 compared with quartile 1 (p = 0.02). All other outcomes were not significant. Conclusion: Given similar levels of inflammatory biomarkers, participants opting for the control group would also have benefited from a more intensive lifestyle intervention focusing on reducing inflammation. © The Author(s) 2018.

关键词： Dietary Inflammatory Index Inflammation self-selection trial stress vegetarian diet

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Highly processed foods intake is positively associated with pro-inflammatory potential of diet: cross-sectional analysis from the Moli-sani Study

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Nutrition, Metabolism and Cardiovascular Diseases 2021年第11期31卷 3253-3253页

作者： C. Mignogna S. Costanzo E. Ruggiero N. Shivappa J.R. Hebert S. Esposito A. Di Castelnuovo M. Persichillo C. Cerletti M.B. Donati G. De Gaetano L. Iacoviello M. Bonaccio Dep. Medicine and Surgery Research Center in Epidemiology and Preventive Medicine EPIMED University of Insubria Varese Italy Dep. Epidemiology and Prevention IRCCS NEUROMED Pozzilli Italy Cancer Prevention and Control Program Dep. Epidemiology and Biostatistics Arnold School of Public Health University of South Carolina Columbia US Dep. Nutrition Connecting Health Innovations LLC Columbia US Mediterranea Cardiocentro Napoli Italy

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A Practice Change to Eliminate Prophylactic Antibiotics for Elective Percutaneous Nephrostomy Tube Exchanges in Low-Risk Outpatients: A Retrospective Review on Risk for Infection

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Journal of Radiology Nursing 2019年第4期38卷 241-249页

作者： Brick, Jamie Michele Dail, Robin B. Priode, Kimberly Wirth, Michael Yamada, Ricardo University of South Carolina College of Nursing Columbia SC United States Associate Dean for Faculty Affairs and Professor University of South Carolina College of Nursing Columbia SC United States Clinical Assistant Professor University of South Carolina College of Nursing Columbia SC United States Research Assistant Professor College of Nursing and Department of Epidemiology and Biostatistics Cancer Prevention and Control Program University of South Carolina Columbia SC United States Assistant Professor Vascular and Interventional Radiology Medical University of South Carolina Charleston SC United States

The purpose of this project was to determine if evidence-based practice change related to antibiotic administration criteria for outpatients receiving percutaneous nephrostomy tube exchanges implemented by a medical center's Vascular and Interventional Radiology department impacted hospital admission rates for infection in these patients. The 2017 practice change was based on 2010 guidelines from the Society of Interventional Radiology (SIR), stating that outpatients with a low risk of acquiring infection did not need to receive a perioperative antibiotic, as evidence has shown prophylactic therapy has no significant effect on infection rates for this population. Using a retrospective review design, 1 year of data before and after the practice change were collected and analyzed using the repeated measures generalized estimating equation (GEE) model with a binomial output by Liang & Zeger. Fisher's exact test was used to evaluate demographic variables by level of risk of infection. Data included 493 procedural events for 126 outpatients. The mean number of events per patient was 3.91 (SD: 4.15;median: 2;interquartile range: 3). Admission and infection criteria within thirty days of the event and infection risk factors were collected for each patient. Age, sex, and race were the variables that had a significant relationship with risk level of infection. Due to sample size, the GEE model could not be run using risk level (high/low) to predict admissions before or after the practice change. The relationship between the number of risk factors (0-5) and the odds of admission for infection was the same regardless of the practice change (before: odds ratio [OR] = 2.17, 95% confidence interval [CI] = 1.19-3.95;after: OR = 1.9, 95% CI = 1.12-3.22, p_interaction =. 67). For every increase in a patient's number of risk factors, the odds of developing an infection would be expected to increase by almost 90% (OR = 1.9, 95% CI = 1.27-2.84). Although it was not possible t

关键词： Antibiotic stewardship Infection Interventional radiology Percutaneous nephrostomy Practice change

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Correction to: The dietary inflammatory index is associated with anti and pro‑inflammatory adipokines in Brazilian schoolchildren

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European journal of nutrition 2021年第5期60卷 2851-2853页

作者： Lara Gomes Suhett H H M Hermsdorff Sarah Aparecida Vieira Ribeiro Mariana De Santis Filgueiras Nitin Shivappa James R Hébert Juliana Farias de Novaes Department of Nutrition and Health Universidade Federal de Viçosa (UFV) Av. P.H. Rolfs s/n Campus Universitário Viçosa Minas Gerais 36570-000 Brazil. nutrilarasuhett@***. Department of Nutrition and Health Universidade Federal de Viçosa (UFV) Av. P.H. Rolfs s/n Campus Universitário Viçosa Minas Gerais 36570-000 Brazil. Department of Epidemiology and Biostatistics Arnold School of Public Health University of South Carolina Columbia SC USA. Statewide Cancer Prevention and Control Program (CPCP) Arnold School of Public Health University of South Carolina 915 Greene Street Columbia SC 29208 USA. Department of Nutrition Connecting Health Innovations (CHI) 1417 Gregg Street Columbia SC 29201 USA.

A correction to this paper has been published: https://***/10.1007/s00394-021-02544-w

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Primary and secondary prevention strategies to reduce the impact of lung cancer in women: where are we now and where are we going?

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Archivos de Bronconeumología (English Edition) 2020年第5期56卷 340-340页

作者： Adrián González-Marrón Juan Carlos Martín-Sánchez Nuria Matilla-Santander Àurea Cartanyà-Hueso Jose M. Martínez-Sánchez Ferrán Garcia-Alemany Encarna Martínez-Martín Carmen Vidal Montse García Group of Evaluation of Health Determinants and Health Policies Universitat Internacional de Catalunya Sant Cugat del Vallès Spain Screening Cancer Unit Cancer Prevention and Control Program Institut Català d’Oncologia L'Hospitalet de Llobregat Barcelona Spain

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Potential complications of monkeypox

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The Lancet. Neurology 2022年第10期21卷 872页

作者： B Jeanne Billioux Oliver Tshiani Mbaya James Sejvar Avindra Nath National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda MA 20892 USA. Institut National de Recherche Biomédicale Kinshasa Democratic Republic of Congo Clinical Monitoring Research Program Directorate Frederick National Laboratory for Cancer Research Frederick MD USA. National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda MA 20892 USA Centers for Disease Control and Prevention Atlanta GA USA. Electronic address: avindra.nath@nih.gov.

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Estrategias de prevención primaria y secundaria para reducir el impacto del cáncer de pulmón en las mujeres: ¿dónde nos encontramos y hacia dónde vamos?

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Archives of Bronchology (Archivos de Bronconeumología, English Edition) 2020年第5期56卷 340-340页

作者： González-Marrón, Adrián Martín-Sánchez, Juan Carlos Garcia-Alemany, Ferrán Martínez-Martín, Encarna Matilla-Santander, Nuria Cartanyà-Hueso, Àurea Vidal, Carmen García, Montse Martínez-Sánchez, Jose M. Group of Evaluation of Health Determinants and Health Policies Universitat Internacional de Catalunya Sant Cugat del Vallès Barcelona España Screening Cancer Unit Cancer Prevention and Control Program Institut Català d’Oncologia L’Hospitalet de Llobregat Barcelona España

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The dietary inflammatory index in women diagnosed with breast cancer after 12 months of dietary. Treatment with a Mediterranean diet low in glycemic index: data from DEDICA randomized controlled trial

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Nutrition, Metabolism and Cardiovascular Diseases 2021年第11期31卷 3256-3256页

作者： S. Vitale V. Mattioli N. Shivappa J.R. Hebert E. Palumbo G. Porciello C. Montagnese I. Calabrese R. Pica M. Prete A. Luongo M. Grimaldi A. Crispo L. Falzone M. Libra J. Polesel D. Serraino L. Poletto L.S.A. Augustin National Cancer Institute Istituto Nazionale Tumori IRCCS “Fondazione Giovanni Pascale” Naples Italy National Cancer Institute Centro di Riferimento Oncologico (CRO) IRCCS Epidemiology Unit Aviano Italy Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics Arnold School of Public Health University of South Carolina Columbia SC USA Department of Nutrition Connecting Health Innovations LLC Columbia SC USA Department of Clinical Medicine and Surgery Federico II University Naples Italy Department of Biomedical and Biotechnological Sciences Oncologic Clinical and General Pathology Section University of Catania Italy

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STREAMLINE: AN AUTOMATED MACHINE LEARNING PIPELINE FOR BIOMEDICINE APPLIED TO EXAMINE THE UTILITY OF PHOTOGRAPHY-BASED PHENOTYPES FOR OSA PREDICTION ACROSS INTERNATIONAL SLEEP CENTERS

arXiv

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arXiv 2023年

作者： Urbanowicz, Ryan J. Bandhey, Harsh Keenan, Brendan T. Maislin, Greg Hwang, Sy Mowery, Danielle L. Lynch, Shannon M. Mazzotti, Diego R. Han, Fang Li, Qing Yun Penzel, Thomas Tufik, Sergio Bittencourt, Lia Gislason, Thorarinn de Chazal, Philip Singh, Bhajan McArdle, Nigel Chen, Ning-Hung Pack, Allan Schwab, Richard J. Cistulli, Peter A. Magalang, Ulysses J. Department of Computational Biomedicine Cedars Sinai Medical Center Los AngelesCA United States Division of Sleep Medicine Department of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Biomedical Informatics University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States Cancer Prevention and Control Program Fox Chase Cancer Center PhiladelphiaPA United States Division of Medical Informatics Department of Internal Medicine University of Kansas LawrenceKS United States Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine University of Kansas LawrenceKS United States Division of Sleep Medicine Peking University People’s Hospital Beijing China Department of Respiratory and Critical Care Medicine Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Interdisciplinary Center of Sleep Medicine Charite University Hospital Berlin Germany Department of Psychobiology Federal University of Sao Paulo Sao Paulo Brazil Department of Sleep Medicine Landspitali University Hospital Reykjavik Iceland University of Iceland Faculty of Medicine Reykjavik Iceland Charles Perkins Center Faculty of Engineering University of Sydney Sydney Australia Department of Pulmonary Physiology and Sleep Medicine Sir Charles Gairdner Hospital Nedlands Australia University of Western Australia Crawley Australia Department of Pulmonary and Critical Care Medicine Sleep Center Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Charles Perkins Centre Faculty of Medicine and Health University of Sydney Royal North Shore Hospital Sydney Australia Division of Pulmonary Critical Care and Sleep Medicine Department of Medicine Ohio State University Wexner Medical Center ColumbusOH United States 700 N. San Vicente Blvd. Pacific Design Center Suite G541E Los AngelesCA90069 United States

Objective: While machine learning (ML) includes a valuable array of tools for analyzing biomedical data with multivariate and complex underlying associations, significant time and expertise is required to assemble effective, rigorous, comparable, reproducible, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML application by automating a subset of analysis pipeline elements. In this study we develop and validate a Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to investigate the added utility of photography-based phenotypes for predicting obstructive sleep apnea (OSA);a common and underdiagnosed condition associated with a variety of health, economic, and safety consequences. Methods: STREAMLINE is designed to tackle biomedical binary classification tasks while (1) avoiding common mistakes, (2) accommodating complex associations and common data challenges, and (3) allowing scalability, reproducibility, and model interpretation. It automates the majority of established, generalizable, and reliably automatable aspects of an ML analysis pipeline while incorporating cutting edge algorithms and providing opportunities for human-in-the-loop customization. We present a broadly refactored and extended release of STREAMLINE, validating and benchmarking performance across simulated and real-world datasets. Then we applied STREAMLINE to evaluate the utility of demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and photography-based craniofacial (CF) and intraoral (IO) anatomy measures in predicting ‘any OSA’ or ‘moderate/severe OSA’ using 3,111 participants from Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Results: Benchmarking analyses validated the efficacy of STREAMLINE across data simulations with increasingly complex patterns of association including epistatic interactions and genetic heterogeneity. OSA analyses identified a significant increase in ROC-AUC when adding CF t

关键词： Machine learning

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Correction to: Cardio-oncology Preventative Care: Racial and Ethnic Disparities

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Current Cardiovascular Risk Reports 2022年第1期16卷 11-11页

作者： Prasad, Pooja Branch, Mary Asemota, Daniel Elsayed, Razan Addison, Daniel Brown, Sherry-Ann Department of Internal Medicine University of California Davis Health Sacramento USA Heart and Vascular Center of Excellence Wake Forest Baptist Health Winston Salem USA Academic Department of Internal Medicine North Shore University Hospital and Long Island Jewish Medical Center Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Manhasset USA Department of Internal Medicine Englewood Health Englewood USA Cardio-Oncology Program Division of Cardiology Ohio State University Medical Center Columbus USA Division of Cancer Prevention and Control Department of Internal Medicine College of Medicine Ohio State University Columbus USA Cardio-Oncology Program Division of Cardiovascular Medicine Medical College of Wisconsin Wauwatosa USA Department of Cardiovascular Diseases Mayo Clinic Rochester USA

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