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Li X, Zheng T, Xiao Y, Zhao Y, Wu P. Field-Deployable Colorimetric Array for On-Site Diagnosis of Urinary Tract Infection and Identification of Causative Pathogens. Anal Chem 2024; 96:14679-14687. [PMID: 39190031 DOI: 10.1021/acs.analchem.4c03617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/28/2024]
Abstract
Urinary tract infection (UTI) is a common and prevalent disease caused by a spectrum of pathogens. Lack of access to rapid, portable, and high-quality diagnostics in resource-limited settings aggravates the improper treatment of UTIs, which is also a major driver of antibiotic misuse worldwide. Here, we describe a custom-made portable colorimetric array (PoCA) for reading out polymerase chain reaction (PCR) amplicons, the rationale of which is to transfer the previously developed dsDNA-based photosensitization colorimetric assay (solution) onto paper discs for detection. By integrating mini-LED irradiation and paper discs, the PoCA can read out 96 PCR tests in one pot, thus allowing diagnosis and identification of 12 prevailing UTI pathogens in less than 2 h, coupled with a portable thermal cycler for PCR. After analyzing 200 clinical urine samples, the pathogen profiling accuracy of the PoCA was demonstrated to be higher than the standard urine culture (confirmed with metagenomic next-generation sequencing). The PoCA platform could be used in primary care for rapid UTI diagnosis and pathogen identification.
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Affiliation(s)
- Xianming Li
- Analytical & Testing Center, Sichuan University, Chengdu 610064, Sichuan, China
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Ting Zheng
- Analytical & Testing Center, Sichuan University, Chengdu 610064, Sichuan, China
| | - Yuling Xiao
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yi Zhao
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Peng Wu
- Analytical & Testing Center, Sichuan University, Chengdu 610064, Sichuan, China
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Tejeda MI, Fernández J, Valledor P, Almirall C, Barberán J, Romero-Brufau S. Retrospective validation study of a machine learning-based software for empirical and organism-targeted antibiotic therapy selection. Antimicrob Agents Chemother 2024:e0077724. [PMID: 39194206 DOI: 10.1128/aac.00777-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 07/16/2024] [Indexed: 08/29/2024] Open
Abstract
Errors in antibiotic prescriptions are frequent, often resulting from the inadequate coverage of the infection-causative microorganism. The efficacy of iAST, a machine-learning-based software offering empirical and organism-targeted antibiotic recommendations, was assessed. The study was conducted in a 12-hospital Spanish institution. After model fine-tuning with 27,531 historical antibiograms, 325 consecutive patients with acute infections were selected for retrospective validation. The primary endpoint was comparing each of the top three of iAST's antibiotic recommendations' success rates (confirmed by antibiogram results) with the antibiotic prescribed by the physicians. Secondary endpoints included examining the same hypothesis within specific study population subgroups and assessing antibiotic stewardship by comparing the percentage of antibiotics recommended that belonged to different World Health Organization AWaRe groups within each arm of the study. All of iAST first three recommendations were non-inferior to doctor prescription in the primary endpoint analysis population as well as the secondary endpoint. The overall success rate of doctors' empirical treatment was 68.93%, while that of the first three iAST options was 91.06% (P < 0.001), 90.63% (P < 0.001), and 91.06% (P < 001), respectively. For organism-targeted therapy, the doctor's overall success rate was 84.16%, and that of the first three ranked iAST options was 97.83% (P < 0.001), 94.09% (P < 0.001), and 91.30% (P < 0.001), respectively. In empirical therapy, compared to physician prescriptions, iAST demonstrated a greater propensity to recommend access antibiotics, fewer watch antibiotics, and higher reserve antibiotics. In organism-targeted therapy, iAST advised a higher utilization of access antibiotics. The present study demonstrates iAST accuracy in predicting antibiotic susceptibility, showcasing its potential to promote effective antibiotic stewardship. CLINICAL TRIALS This study is registered with ClinicalTrials.gov as NCT06174519.
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Affiliation(s)
- Maria Isabel Tejeda
- Infectious Diseases Unit, Hospital Universitario HM Montepríncipe, Madrid, Spain
| | - Javier Fernández
- Research and Innovation Department, Pragmatech AI Solutions, Oviedo, Spain
- Microbiology Department, Hospital Universitario Central de Asturias, Oviedo, Spain
- Microbiology and Infectious Pathology, ISPA, Oviedo, Spain
- Functional Biology Department, Universidad de Oviedo, Oviedo, Spain
| | - Pablo Valledor
- Research and Innovation Department, Pragmatech AI Solutions, Oviedo, Spain
| | | | - José Barberán
- Infectious Diseases Unit, Hospital Universitario HM Montepríncipe, Madrid, Spain
- HM Faculty of Health Sciences, University Camilo Jose Cela, Madrid, Spain
| | - Santiago Romero-Brufau
- Research and Innovation Department, Pragmatech AI Solutions, Oviedo, Spain
- Department of Otorhinolaryngology-Head & Neck Surgery, Mayo Clinic, Rochester, Minnesota, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
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Giacobbe DR, Marelli C, Guastavino S, Signori A, Mora S, Rosso N, Campi C, Piana M, Murgia Y, Giacomini M, Bassetti M. Artificial intelligence and prescription of antibiotic therapy: present and future. Expert Rev Anti Infect Ther 2024:1-15. [PMID: 39155449 DOI: 10.1080/14787210.2024.2386669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 07/28/2024] [Indexed: 08/20/2024]
Abstract
INTRODUCTION In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription. AREAS COVERED In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024. EXPERT OPINION Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Marelli
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Alessio Signori
- Section of Biostatistics, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Sara Mora
- UO Information and Communication Technologies, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Nicola Rosso
- UO Information and Communication Technologies, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Campi
- Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
- Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Michele Piana
- Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
- Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Ylenia Murgia
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Mauro Giacomini
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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Giacobbe DR, Marelli C, Guastavino S, Mora S, Rosso N, Signori A, Campi C, Giacomini M, Bassetti M. Explainable and Interpretable Machine Learning for Antimicrobial Stewardship: Opportunities and Challenges. Clin Ther 2024; 46:474-480. [PMID: 38519371 DOI: 10.1016/j.clinthera.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/24/2024]
Abstract
There is growing interest in exploiting the advances in artificial intelligence and machine learning (ML) for improving and monitoring antimicrobial prescriptions in line with antimicrobial stewardship principles. Against this background, the concepts of interpretability and explainability are becoming increasingly essential to understanding how ML algorithms could predict antimicrobial resistance or recommend specific therapeutic agents, to avoid unintended biases related to the "black box" nature of complex models. In this commentary, we review and discuss some relevant topics on the use of ML algorithms for antimicrobial stewardship interventions, highlighting opportunities and challenges, with particular attention paid to interpretability and explainability of employed models. As in other fields of medicine, the exponential growth of artificial intelligence and ML indicates the potential for improving the efficacy of antimicrobial stewardship interventions, at least in part by reducing time-consuming tasks for overwhelmed health care personnel. Improving our knowledge about how complex ML models work could help to achieve crucial advances in promoting the appropriate use of antimicrobials, as well as in preventing antimicrobial resistance selection and dissemination.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Department of Health Sciences, University of Genoa, Genoa, Italy; UO Clinica Malattie Infettive, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico San Martino, Genoa, Italy.
| | - Cristina Marelli
- UO Clinica Malattie Infettive, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Sara Mora
- UO Information and Communication Technologies, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico San Martino, Genoa, Italy
| | - Nicola Rosso
- UO Information and Communication Technologies, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico San Martino, Genoa, Italy
| | - Alessio Signori
- Section of Biostatistics, Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Cristina Campi
- Department of Mathematics, University of Genoa, Genoa, Italy; Life Science Computational Laboratory, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico San Martino, Genoa, Italy
| | - Mauro Giacomini
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Department of Health Sciences, University of Genoa, Genoa, Italy; UO Clinica Malattie Infettive, Istituto di Ricovero e Cura a Carattere Scientifico Ospedale Policlinico San Martino, Genoa, Italy
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Yang J, Eyre DW, Lu L, Clifton DA. Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections. NPJ ANTIMICROBIALS AND RESISTANCE 2023; 1:14. [PMID: 38686216 PMCID: PMC11057209 DOI: 10.1038/s44259-023-00015-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 10/04/2023] [Indexed: 05/02/2024]
Abstract
Urinary tract infections are one of the most common bacterial infections worldwide; however, increasing antimicrobial resistance in bacterial pathogens is making it challenging for clinicians to correctly prescribe patients appropriate antibiotics. In this study, we present four interpretable machine learning-based decision support algorithms for predicting antimicrobial resistance. Using electronic health record data from a large cohort of patients diagnosed with potentially complicated UTIs, we demonstrate high predictability of antibiotic resistance across four antibiotics - nitrofurantoin, co-trimoxazole, ciprofloxacin, and levofloxacin. We additionally demonstrate the generalizability of our methods on a separate cohort of patients with uncomplicated UTIs, demonstrating that machine learning-driven approaches can help alleviate the potential of administering non-susceptible treatments, facilitate rapid effective clinical interventions, and enable personalized treatment suggestions. Additionally, these techniques present the benefit of providing model interpretability, explaining the basis for generated predictions.
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Affiliation(s)
- Jenny Yang
- Institute of Biomedical Engineering, Department Engineering Science, University of Oxford, Oxford, UK
| | - David W. Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lei Lu
- Institute of Biomedical Engineering, Department Engineering Science, University of Oxford, Oxford, UK
| | - David A. Clifton
- Institute of Biomedical Engineering, Department Engineering Science, University of Oxford, Oxford, UK
- Oxford-Suzhou Centre for Advanced Research (OSCAR), Suzhou, China
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Wang J, Tian Y, Zhou T, Tong D, Ma J, Li J. A survey of artificial intelligence in rheumatoid arthritis. RHEUMATOLOGY AND IMMUNOLOGY RESEARCH 2023; 4:69-77. [PMID: 37485476 PMCID: PMC10362600 DOI: 10.2478/rir-2023-0011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/14/2023] [Indexed: 07/25/2023]
Abstract
The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption.
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Affiliation(s)
- Jiaqi Wang
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou310027, Zhejiang Province, China
| | - Tianshu Zhou
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Danyang Tong
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Jing Ma
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Jingsong Li
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou310027, Zhejiang Province, China
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7
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Li B, Yan T. Metagenomic next generation sequencing for studying antibiotic resistance genes in the environment. ADVANCES IN APPLIED MICROBIOLOGY 2023; 123:41-89. [PMID: 37400174 DOI: 10.1016/bs.aambs.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
Bacterial antimicrobial resistance (AMR) is a persisting and growing threat to human health. Characterization of antibiotic resistance genes (ARGs) in the environment is important to understand and control ARG-associated microbial risks. Numerous challenges exist in monitoring ARGs in the environment, due to the extraordinary diversity of ARGs, low abundance of ARGs with respect to the complex environmental microbiomes, difficulties in linking ARGs with bacterial hosts by molecular methods, difficulties in achieving quantification and high throughput simultaneously, difficulties in assessing mobility potential of ARGs, and difficulties in determining the specific AMR determinant genes. Advances in the next generation sequencing (NGS) technologies and related computational and bioinformatic tools are facilitating rapid identification and characterization ARGs in genomes and metagenomes from environmental samples. This chapter discusses NGS-based strategies, including amplicon-based sequencing, whole genome sequencing, bacterial population-targeted metagenome sequencing, metagenomic NGS, quantitative metagenomic sequencing, and functional/phenotypic metagenomic sequencing. Current bioinformatic tools for analyzing sequencing data for studying environmental ARGs are also discussed.
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Affiliation(s)
- Bo Li
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Tao Yan
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI, United States.
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Sakagianni A, Koufopoulou C, Feretzakis G, Kalles D, Verykios VS, Myrianthefs P, Fildisis G. Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review. Antibiotics (Basel) 2023; 12:antibiotics12030452. [PMID: 36978319 PMCID: PMC10044642 DOI: 10.3390/antibiotics12030452] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/30/2023] Open
Abstract
Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.
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Affiliation(s)
| | - Christina Koufopoulou
- 1st Anesthesiology Department, Aretaieio Hospital, National and Kapodistrian University of Athens Medical School, 11528 Athens, Greece
| | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
- Department of Quality Control, Research and Continuing Education, Sismanogleio General Hospital, 15126 Marousi, Greece
| | - Dimitris Kalles
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
| | - Vassilios S Verykios
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
| | - Pavlos Myrianthefs
- Faculty of Nursing, School of Health Sciences, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Georgios Fildisis
- Faculty of Nursing, School of Health Sciences, National and Kapodistrian University of Athens, 11527 Athens, Greece
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Development of a Prediction Model for Antibiotic-Resistant Urinary Tract Infections Using Integrated Electronic Health Records from Multiple Clinics in North-Central Florida. Infect Dis Ther 2022; 11:1869-1882. [PMID: 35908268 PMCID: PMC9617983 DOI: 10.1007/s40121-022-00677-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/13/2022] [Indexed: 02/03/2023] Open
Abstract
INTRODUCTION Urinary tract infections (UTIs) are common infections for which initial antibiotic treatment decisions are empirically based, often without antibiotic susceptibility testing to evaluate resistance, increasing the risk of inappropriate therapy. We hypothesized that models based on electronic health records (EHR) could assist in the identification of patients at higher risk for antibiotic-resistant UTIs and help guide the selection of antimicrobials in hospital and clinic settings. METHODS EHR from multiple centers in North-Central Florida, including patient demographics, previous diagnoses, prescriptions, and antibiotic susceptibility tests, were obtained for 9990 patients diagnosed with a UTI during 2011-2019. Decision trees, boosted logistic regression (BLR), and random forest models were developed to predict resistance to common antibiotics used for UTI management [sulfamethoxazole-trimethoprim (SXT), nitrofurantoin (NIT), ciprofloxacin (CIP)] and multidrug resistance (MDR). RESULTS There were 6307 (63.1%) individuals with a UTI caused by a resistant microorganism. Overall, the population was majority female, white, non-Hispanic, and older aged (mean = 60.7 years). The BLR models yielded the highest discriminative ability, as measured by the out-of-bag area under the receiver-operating curve (AUROC), for the resistance outcomes [AUROC = 0.58 (SXT), 0.62 (NIT), 0.64 (CIP), and 0.66 (MDR)]. Variables in the best performing model were sex, history of UTIs, catheterization, renal disease, dementia, hemiplegia/paraplegia, and hypertension. CONCLUSIONS The discriminative ability of the prediction models was moderate. Nonetheless, these models based solely on EHR demonstrate utility for the identification of patients at higher risk for resistant infections. These models, in turn, may help guide clinical decision-making on the ordering of urine cultures and decisions regarding empiric therapy for these patients.
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Luo W, Gong L, Chen X, Gao R, Peng B, Wang Y, Luo T, Yang Y, Kang B, Peng C, Ma L, Mei M, Liu Z, Li Q, Yang S, Wang Z, Hu J. Lifestyle and chronic kidney disease: A machine learning modeling study. Front Nutr 2022; 9:918576. [PMID: 35938107 PMCID: PMC9355159 DOI: 10.3389/fnut.2022.918576] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/27/2022] [Indexed: 12/23/2022] Open
Abstract
Background Individual lifestyle varies in the real world, and the comparative efficacy of lifestyles to preserve renal function remains indeterminate. We aimed to systematically compare the effects of lifestyles on chronic kidney disease (CKD) incidence, and establish a lifestyle scoring system for CKD risk identification. Methods Using the data of the UK Biobank cohort, we included 470,778 participants who were free of CKD at the baseline. We harnessed the light gradient boosting machine algorithm to rank the importance of 37 lifestyle factors (such as dietary patterns, physical activity (PA), sleep, psychological health, smoking, and alcohol) on the risk of CKD. The lifestyle score was calculated by a combination of machine learning and the Cox proportional-hazards model. A CKD event was defined as an estimated glomerular filtration rate <60 ml/min/1.73 m2, mortality and hospitalization due to chronic renal failure, and self-reported chronic renal failure, initiated renal replacement therapy. Results During a median of the 11-year follow-up, 13,555 participants developed the CKD event. Bread, walking time, moderate activity, and vigorous activity ranked as the top four risk factors of CKD. A healthy lifestyle mainly consisted of whole grain bread, walking, moderate physical activity, oat cereal, and muesli, which have scored 12, 12, 10, 7, and 7, respectively. An unhealthy lifestyle mainly included white bread, tea >4 cups/day, biscuit cereal, low drink temperature, and processed meat, which have scored −12, −9, −7, −4, and −3, respectively. In restricted cubic spline regression analysis, a higher lifestyle score was associated with a lower risk of CKD event (p for linear relation < 0.001). Compared to participants with the lifestyle score < 0, participants scoring 0–20, 20–40, 40–60, and >60 exhibited 25, 42, 55, and 70% lower risk of CKD event, respectively. The C-statistic of the age-adjusted lifestyle score for predicting CKD events was 0.710 (0.703–0.718). Conclusion A lifestyle scoring system for CKD prevention was established. Based on the system, individuals could flexibly choose healthy lifestyles and avoid unhealthy lifestyles to prevent CKD.
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Affiliation(s)
- Wenjin Luo
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lilin Gong
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiangjun Chen
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Rufei Gao
- Laboratory of Reproductive Biology, School of Public Health and Management, Chongqing Medical University, Chongqing, China
| | - Bin Peng
- School of Public Health and Management, Chongqing Medical University, Chongqing, China
| | - Yue Wang
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ting Luo
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yi Yang
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bing Kang
- Department of Clinical Nutrition, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chuan Peng
- The Chongqing Key Laboratory of Translational Medicine in Major Metabolic Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Linqiang Ma
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mei Mei
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhiping Liu
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qifu Li
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shumin Yang
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhihong Wang
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Zhihong Wang
| | - Jinbo Hu
- Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Jinbo Hu
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Simeth NA, Kinateder T, Rajendran C, Nazet J, Merkl R, Sterner R, König B, Kneuttinger AC. Towards Photochromic Azobenzene-Based Inhibitors for Tryptophan Synthase. Chemistry 2021; 27:2439-2451. [PMID: 33078454 PMCID: PMC7898615 DOI: 10.1002/chem.202004061] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/16/2020] [Indexed: 01/25/2023]
Abstract
Light regulation of drug molecules has gained growing interest in biochemical and pharmacological research in recent years. In addition, a serious need for novel molecular targets of antibiotics has emerged presently. Herein, the development of a photocontrollable, azobenzene-based antibiotic precursor towards tryptophan synthase (TS), an essential metabolic multienzyme complex in bacteria, is presented. The compound exhibited moderately strong inhibition of TS in its E configuration and five times lower inhibition strength in its Z configuration. A combination of biochemical, crystallographic, and computational analyses was used to characterize the inhibition mode of this compound. Remarkably, binding of the inhibitor to a hitherto-unconsidered cavity results in an unproductive conformation of TS leading to noncompetitive inhibition of tryptophan production. In conclusion, we created a promising lead compound for combatting bacterial diseases, which targets an essential metabolic enzyme, and whose inhibition strength can be controlled with light.
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Affiliation(s)
- Nadja A. Simeth
- Institute for Organic ChemistryDepartment of Chemistry and PharmacyUniversity of RegensburgUniversitätsstrasse 3193040RegensburgGermany
- Stratingh Institute for ChemistryFaculty of Science and EngineeringUniversity of GroningenNijenborgh 49747AGGroningenThe Netherlands
| | - Thomas Kinateder
- Institute for Biophysics and Physical BiochemistryRegensburg Center for BiochemistryUniversity of RegensburgUniversitätsstrasse 3193040RegensburgGermany
| | - Chitra Rajendran
- Institute for Biophysics and Physical BiochemistryRegensburg Center for BiochemistryUniversity of RegensburgUniversitätsstrasse 3193040RegensburgGermany
| | - Julian Nazet
- Institute for Biophysics and Physical BiochemistryRegensburg Center for BiochemistryUniversity of RegensburgUniversitätsstrasse 3193040RegensburgGermany
| | - Rainer Merkl
- Institute for Biophysics and Physical BiochemistryRegensburg Center for BiochemistryUniversity of RegensburgUniversitätsstrasse 3193040RegensburgGermany
| | - Reinhard Sterner
- Institute for Biophysics and Physical BiochemistryRegensburg Center for BiochemistryUniversity of RegensburgUniversitätsstrasse 3193040RegensburgGermany
| | - Burkhard König
- Institute for Organic ChemistryDepartment of Chemistry and PharmacyUniversity of RegensburgUniversitätsstrasse 3193040RegensburgGermany
| | - Andrea C. Kneuttinger
- Institute for Biophysics and Physical BiochemistryRegensburg Center for BiochemistryUniversity of RegensburgUniversitätsstrasse 3193040RegensburgGermany
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