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POS0080-HPR LONG-TERM EFFECTIVENESS OF A LIFESTYLE PROGRAM FOR RHEUMATOID ARTHRITIS: ONE-YEAR FOLLOW-UP OF THE “PLANTS FOR JOINTS” RANDOMIZED CLINICAL TRIAL

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Annals of the Rheumatic Diseases 2023年 82卷 251-252页

作者： C. Wagenaar W. Walrabenstein M. Van der Leeden F. Turkstra J. Twisk M. Boers H. Van Middendorp P. Weijs D. Van Schaardenburg Reade Rheumatology Amsterdam Netherlands Amsterdam UMC locatie AMC Rheumatology and immunology center Amsterdam Netherlands Amsterdam University of Applied Sciences Nutrition & Dietetics Amsterdam Netherlands Amsterdam UMC locatie VUmc Rehabilitation Medicine Amsterdam Netherlands Amsterdam Movement Sciences Research Institute Movement Sciences Amsterdam Netherlands Amsterdam UMC locatie VUmc Epidemiology and Data Science Amsterdam Netherlands Leiden University Institute of Psychology Health Medical & Neuropsychology unit Leiden Netherlands Amsterdam UMC locatie VUmc Nutrition & Dietetics Amsterdam Netherlands

Background The 16-week Plants for Joints (PFJ) multidisciplinary lifestyle program, based on a whole-food plant-based diet, physical activity, and stress management, significantly reduced 28-joint Disease Activity Score (DAS28) score compared to usual care in patients with rheumatoid arthritis (RA). [1,2] Objectives To determine the long-term effectiveness of the PFJ lifestyle program on disease activity in patients with RA. Methods In the PFJ assessor-blind randomized clinical trial, patients with RA and a DAS28 score ≥ 2.6 and ≤ 5.1 were randomized to receive the PFJ program in addition to usual care, or the control group which received usual care. After this 16-week period the control group also received the PFJ program. After completion of the PFJ program all participants were followed-up for one year with biannual visits and 6 adherence-promoting webinars. If participants had a DAS28 score <2.6 they were instructed to taper anti-rheumatic medication following a pre-specified protocol. Medication changes were assessed at one year as an “increase,” “stable,” or “decrease” compared to baseline by an independent committee. Secondary outcomes included anthropometric and metabolic markers. A paired sample T-Test between 1-year follow-up and start of intervention was performed. Results Of 77 participants who completed the initial 16-week clinical trial 92% were female with a mean (SD) age of 55 (12) and body mass index of 26 (4) kg/m² at baseline. The 65 (84%) participants who completed the one-year follow-up had a greater average change in DAS28 score (–0.9 vs. –0.05) compared to non-completers. In the year after completing the PFJ program the DAS28 score continued to improve slightly whereby a mean 1.01-point difference was observed after one year for the completed cases compared to baseline values (95% CI 0.76 to 1.26; p < 0.001) (Figure 1). All components of the DAS28 improved significantly compared to before the program (Table 1). Significant improvements were fo

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POS0825 CANCER RISK IN PATIENTS WITH RHEUMATOID ARTHRITIS TREATED WITH JANUS KINASE INHIBITORS: A NATIONWIDE DANISH REGISTER-BASED COHORT STUDY

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Annals of the Rheumatic Diseases 2023年 82卷 708-709页

作者： R. Westermann R.L. Cordtz K. Duch L. Mellemkjær M.L. Hetland A.M. Burden O. Hejlesen M. Bøgsted L. Dreyer Aalborg University Hospital Center of Rheumatic Research Aalborg (CERRA) Department of Rheumatology Aalborg Denmark Aalborg University Hospital Clinical Cancer Research Unit Aalborg Denmark Aalborg University Hospital Unit of Clinical Biostatistics Aalborg Denmark Danish Cancer Society Danish Cancer Society Research Center (DCRC) Copenhagen Denmark Rigshospitalet Glostrup The DANBIO registry Center for Rheumatology and Spine Diseases Centre of Head and Orthopedics Glostrup Denmark University of Copenhagen Faculty of Health and Medical Sciences Department of Clinical Medicine Copenhagen Denmark Rigshospitalet Glostrup Copenhagen Center for Arthritis Research (COPECARE) Center for Rheumatology and Spine Diseases Centre of Head and Orthopedics Glostrup Denmark ETH Zurich Institute of Pharmaceutical Sciences Pharmacoepidemiology Group Department of Chemistry and Applied Biosciences Zurich Switzerland Aalborg University Dept of Health Science and Technology Aalborg Denmark UiT The Arctic University of Norway Department of Computer Science Tromsø Norway Aalborg University Hospital Center for Clinical Data Science Department of Clinical Medicine Aalborg Denmark

Background Concerns regarding the risk of cancer with janus kinase inhibitor (JAKi) use in patients with rheumatoid arthritis (RA) escalated after the release of results from Pfizer's clinical trial, ORAL Surveillance . [1] The trial showed increased risks of major adverse cardiovascular events and cancer in tofacitinib compared with tumour necrosis factor inhibitor recipients. Precautionary considerations on JAKi use in high-risk subsets of patients with RA have since been issued by the European Medicines Agency. Objectives We aimed to investigate the risk of first primary cancer in patients with RA treated JAKi (tofacitinib and baricitinib) compared with those who received biologic disease-modifying anti-rheumatic drugs (bDMARDs) in a real-world setting. Methods We performed an observational cohort study using the nationwide registers in Denmark. Patients with RA aged 18+ years, without a previous cancer diagnosis, and who initiated treatment with JAKi or bDMARDs from 1 January 2017 to 31 December 2020 were identified in the Danish Rheumatology Quality Register (DANBIO) and followed for any cancer (except non-melanoma skin cancer). We applied inverse probability of treatment weighting (IPTW) to account for covariate differences between treatment groups. IPTW-generated weights were used with cause-specific Cox (CSC) models to calculate hazard ratios (HRs) for cancer incidence in JAKi-treated compared with bDMARD-treated patients with RA. Multiple subgroup and sensitivity analyses were performed. Results We identified 875 and 4247 RA patients treated with JAKi and bDMARDs, respectively. The JAKi group contributed with 1315 person years (PYRS) and 19 cancers, while the bDMARD group contributed with 8597 PYRS and 111 cancers. The corresponding crude incidence rates per 1000 PYRS for cancer were 14.4 (JAKi) and 12.9 (bDMARD). Comparing the two groups using weighted CSC models, a HR of 1.41 (95%CI 0.76 to 2.37, 95% confidence intervals) was seen for JAKi- versus bDMARD-

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Correction: Integrated approaches for immunotoxicity risk assessment: challenges and future directions

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Discover Toxicology 2024年第1期1卷 1-2页

作者： Chandrasekar, Vaisali Panicker, Anu Jayanthi Dey, Arindam K. Mohammad, Syed Chakraborty, Aparajita Samal, Shailesh Kumar Dash, Alisha Bhadra, Jolly Suar, Mrutunjay Khare, Manish Dakua, Sarada Prasad Singh, Ajay Vikram Department of Surgery Hamad Medical Corporation Doha Qatar Qatar University Young Scientists Center (QUYSC) Qatar University Doha Qatar STEMAZE Heilbronn Germany Department of Chemistry Dartmouth College Hanover USA Unit of Immunology and Chronic Disease Institute of Environmental Medicine Karolinska Institute Stockholm Sweden Centre for Applied Research in Data Science Indian Institute of Technology-Ropar Rupnagar India School of Biotechnology KIIT University Bhubaneswar India MIT Academy of Engineering Pune India Clinical Advancement Department Hamad Medical Corporation Doha Qatar Department of Chemical and Product Safety German Federal Institute for Risk Assessment (BfR) Berlin Germany

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A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

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Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

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Learner Action Patterns in the Problem-Solving Process Related to Program Code Composition Based on Tracking System Activities

Learner Action Patterns in the Problem-Solving Process Relat...

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International Conference on Informatics and Computing (ICIC)

作者： Aulia Akhrian Syahidi Ahmad Afif Supianto Tsukasa Hirashima Yutaka Watanobe Smart City Information System Applied Undergraduate Program Politeknik Negeri Banjarmasin Banjarmasin City Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung City Indonesia Learning Engineering Laboratory Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Higashihiroshima Japan Department of Computer Science and Engineering Division of Information Systems University of Aizu Aizu-Wakamatsu City Fukushima Japan

ISBN: (纸本)9798350345728

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the “if-then” rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process.

关键词： Visualization Codes Media data models Recording data mining Problem-solving

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Citizen science in environmental and ecological sciences

NATURE REVIEWS METHODS PRIMERS

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NATURE REVIEWS METHODS PRIMERS 2022年第1期2卷 1-20页

作者： Morneau, Dominique Novel Data Ecosystems for Sustainability Research Group Advancing Systems Analysis Program International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria Centre d’écologie et des sciences de la conservation Muséum national d’Histoire naturelle Paris France Faculté de droit Université de Namur Namur Belgium Citizen Science Lab Leiden University Leiden Netherlands Department of Mechanical Engineering University of Bath Bath UK Nordic Foundation for Development and Ecology (NORDECO) Copenhagen Denmark Department of Biology Brandeis University Waltham MA USA Ornamental Plant Pathology Department of Plant Pathology Puyallup Research and Extension Center Washington State University Puyallup WA USA Environmental and Sustainability Participatory Information Systems Group Institute of Marine Sciences (ICM-CSIC) Barcelona Spain Electron Microscopy Science Technology Platform The Francis Crick Institute London UK Departamento de Biologia Marina Facultad Ciencias del Mar Universidad Católica del Norte Coquimbo Chile Millennium Nucleus Ecology and Sustainable Management of Oceanic Island (ESMOI) Coquimbo Chile Centro de Estudios Avanzados en Zonas Áridas (CEAZA) Coquimbo Chile Department of Geography University College London (UCL) London UK

This PrimeView highlights the utility of citizen science across a range of ecological and environmental science research.

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The Influence of Ph on the Efficacy of Oxidation-Reduction Potential (Orp) to Predict Chlorine Disinfection of Surrogate Bacteria, Escherichia Coli O157:H7 and Listeria Monocytogenes in Oxidant Demand Free Conditions and Fresh Produce Wash Water

SSRN

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SSRN 2023年

作者： Gongora, Kimberly Vankerschaver, Joris Sampers, Imca Van Haute, Sam Laboratory of Food Microbiology and Biotechnology Department of Food Technology Safety and Health Faculty of Bioscience Engineering Ghent University Ghent9000 Belgium Center for Food Chemistry and Technology Ghent University Global Campus Incheon21985 Korea Republic of Center for Biosystems and Biotech Data Science Ghent University Global Campus Incheon21985 Korea Republic of Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium Research Unit VEG-i-TEC Department of Food Technology Safety and Health Faculty of Bioscience Engineering Ghent University Campus Kortrijk Sint-Martens-Latemlaan 2B Kortrijk8500 Belgium

Oxidation-reduction potential (ORP) is commonly used as a rapid measurement of the antimicrobial potential of free chlorine during industrial fresh produce washing. The current study tested the hypothesis that ORP can act as a "single variable" measurement of bacterial (vegetative and endospores) inactivation effectiveness with free chlorine, irrespective of the water pH value, which has on occasion been assumed but never confirmed nor disproven. Chlorine-dosed pH 6.5 and 8.5 phosphate buffer solutions were inoculated with Escherichia coli (E. coli), Listeria innocua (L. innocua), or Bacillus subtilis (B. subtilis) endospores. ORP, free chlorine (FC), and log reduction were monitored after 5 seconds (for E. coli and L. innocua) and up to 30 minutes (for B. subtilis spores) of disinfection. Logistic and exponential models were developed to describe how bacteria reduction varied in function of ORP at different pH levels. Validation tests were performed in phosphate buffered pH 6.5 and 8.5 cabbage wash water periodically dosed with FC, cabbage extract and a cocktail of Escherichia coli O157:H7 (E. coli O157:H7) and Listeria monocytogenes (L. monocytogenes). The results showed that, at equal ORP values, the reduction of bacteria was not consistent across pH values, with higher reductions at higher pH. This is the opposite of the well-known free chlorine-controlled bacterial inactivation, where the antibacterial effect is higher at lower pH. The validation test results indicated that in the cabbage wash water, the relationship between disinfection efficiency and ORP was consistent with the oxidant demand free systems. The study suggests that ORP cannot serve as a reliable single variable measurement to predict bacterial disinfection in buffered systems. When using ORP to monitor and control the antibacterial effectiveness of the chlorinated wash water, it is crucial to take into account (and control) the pH. © 2023, The Authors. All rights reserved.

关键词： Washing

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EEG signal analysis using deep learning: A systematic literature review 5

EEG signal analysis using deep learning: A systematic litera...

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5th International Conference on Intelligent Computing in data sciences, ICDS 2021

作者： Amrani, Ghita Adadi, Amina Berrada, Mohammed Souirti, Zouhayr Boujraf, Said Laboratory of Artificial Intelligence Data Science and Emerging Systems National School of Applied Sciences Fez Morocco Moulay Ismail University ISIC Research Team of High School of Technology LMMI Laboratory Meknes Morocco Sleep Center Hassan II University Hospital Clinical Neurosciences Laboratory Faculty of Medicine and Pharmacy Neurology Department Fez Morocco Hassan II University Hospital Clinical Neurosciences Laboratory Department of Biophysics and Clinical MRI Methods Faculty of Medicine Pharmacy Department of Radiology and Clinical Imaging Fez Morocco

ISBN: (纸本)9781665442381

Objective: Electroencephalography (EEG) is very crucial for understanding the dynamic healthy and pathological complex processes in the brain. However, the manual analysis of the EEG signal is very complex, time-consuming, and depends on the expertise and experience of the users. Hence, nowadays research is conducted on automated EEG signal analysis using artificial intelligence and computer-aided technologies. This would allow fast and highly accurate results. The goal of this paper is to provide an extensive review of the EEG signal analysis using deep learning (DL).Methods: This systematic literature review of EEG processing using Deep Learning (DL) was achieved on Web of science, PubMed, and science Direct databases, resulting in 403 identified papers. All collected studies were analyzed based on main disorders studied, type of tasks performed, data source, stages of analysis, and DL ***: DL in EEG processing is promising in various research applications. It covered the common neurological disorders diagnosis such as epilepsy, movement disorder, depression, schizophrenia, autism, alcohol use, attention, memory, sleep, pain, etc. The main tasks covered by the included studies are detection and classification. The average range of data sources utilized by the included studies is 127 subjects with an EEG recording a total duration of 458 hours. In fact, we identified the use of a plethora of DL architecture for EEG analysis. 57% of papers used Convolutional Neural Networks (CNNs), whereas Recurrent Neural Networks (RNNs) were the architecture choice of about 12% of papers. Combinations of CNNs and Long Short-Term Memory (LSTM) were used in 13% of studies. Generative Adversarial Networks (GAN) and Autoencoder (AEs) were used in 5% and 4% of papers respectively. Restricted Boltzmann Machine (RBMs), Deep Belief Networks (DBNs), and other hybrid architectures appeared in 1% of ***: This systematic review showed that DL is a powerful tool

关键词： Electroencephalography

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

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AB0396 EFFECT OF A MULTIDISCIPLINARY LIFESTYLE PROGRAM IN PATIENTS WITH RHEUMATOID ARTHRITIS: THE PLANTS FOR JOINTS RANDOMIZED CONTROLLED TRIAL

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Annals of the Rheumatic Diseases 2022年 81卷 1326-1327页

作者： W. Walrabenstein C. Wagenaar M. van der Leeden F. Turkstra J. Twisk M. Boers H. van Middendorp P. Weijs D. van Schaardenburg Amsterdam UMC University of Amsterdam Amsterdam Rheumatology & Immunology Center Amsterdam Netherlands Reade Amsterdam Rheumatology & Immunology Center Amsterdam Netherlands Amsterdam UMC Vrije Universiteit Department of Rehabilitation Medicine Amsterdam Netherlands Vrije Universiteit Amsterdam Amsterdam Movement Sciences Research Institute Amsterdam Netherlands Amsterdam UMC Vrije Universiteit Department of Epidemiology and Data Science Amsterdam Netherlands Leiden University Institute of Psychology Health Medical & Neuropsychology Unit Leiden Netherlands Amsterdam University of Applied Sciences Department of Nutrition and Dietetics Center of Expertise Urban Vitality Amsterdam Netherlands

Background Lifestyle factors have been associated with the development and progression of rheumatoid arthritis (RA). Interventions involving whole food plant-based diets (WFPDs), physical activity or stress management have shown promising results for people with RA but were not yet evaluated in an integrated program. Objectives To determine the effect of a 16-week multidisciplinary lifestyle program on disease activity in patients with RA. Methods In the “Plants for Joints” (PFJ) parallel-arm, assessor-blind randomized clinical trial, patients with RA and a 28-joint Disease Activity Score [DAS28] score ≥ 2.6 and ≤ 5.1, were assigned to the PFJ group or the control group. The PFJ group followed a lifestyle program based on a WFPD, physical activity, and stress management in addition to usual care. The control group received usual care. Medication was kept stable three months before and during the trial. Secondary outcomes included anthropometric, and metabolic markers. An intention-to-treat analysis with a linear mixed model, adjusted for baseline values was used to analyze between-group differences of continuous outcomes. Results Of 115 people screened, 85 were randomized and 79 completed the study. Participants were 91% female with a mean (SD) age of 55 (12) and body mass index of 26 (4) kg/m 2 . After 16 weeks the PFJ group had a mean 0.85-point greater improvement of the DAS28 versus the control group (95% CI 0.40 to 1.30; p < 0.001) ( Figure 1 ). Subgroup analyses showed significant improvements in the seropositive as well as the seronegative subgroup, although the effect was more profound in the seronegative group. Weight, fat mass, HbA1c, LDL and triglycerides also showed significant improvements in the PFJ versus control group, while blood glucose and HDL remained unchanged (Table 1). No serious adverse events occurred. Download: Download high-res image (221KB) Download: Download full-size image Conclusion The 16-week PFJ lifestyle program substantially decre

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