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Panjla A, Joshi S, Singh G, Bamford SE, Mechler A, Verma S. Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance. Chem Asian J 2024:e202400102. [PMID: 38948939 DOI: 10.1002/asia.202400102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 06/30/2024] [Accepted: 07/01/2024] [Indexed: 07/02/2024]
Abstract
Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antibiotic drug discovery. Hence, new approaches need to be developed to accelerate the rate of drug discovery process and meet the demands for new antibiotics, while reducing the cost of their development. Machine learning holds immense promise of becoming a useful tool, especially since in the last two decades, exponential growth has occurred in computational power and biological big data analytics. Recent advancements in machine learning algorithms for drug discovery have provided significant clues for potential antibiotic classes. Apart from discovery of new scaffolds, the machine learning protocols will significantly impact prediction of AMR patterns and drug metabolism. In this review, we outline power of machine learning in antibiotic drug discovery, metabolic fate, and AMR prediction to support researchers engaged and interested in this field.
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Affiliation(s)
- Apurva Panjla
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India
| | - Saurabh Joshi
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India
| | - Geetanjali Singh
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India
| | - Sarah E Bamford
- Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria, 3086, Australia
| | - Adam Mechler
- Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria, 3086, Australia
| | - Sandeep Verma
- Mehta Family Center for Engineering in Medicine, Center for Nanoscience, Gangwal School of Medical Sciences and Technology, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India
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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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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [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: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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Principi N, Esposito S. Specific and Nonspecific Effects of Influenza Vaccines. Vaccines (Basel) 2024; 12:384. [PMID: 38675766 PMCID: PMC11054884 DOI: 10.3390/vaccines12040384] [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: 03/04/2024] [Revised: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024] Open
Abstract
With the introduction of the influenza vaccine in the official immunization schedule of most countries, several data regarding the efficacy, tolerability, and safety of influenza immunization were collected worldwide. Interestingly, together with the confirmation that influenza vaccines are effective in reducing the incidence of influenza virus infection and the incidence and severity of influenza disease, epidemiological data have indicated that influenza immunization could be useful for controlling antimicrobial resistance (AMR) development. Knowledge of the reliability of these findings seems essential for precise quantification of the clinical relevance of influenza immunization. If definitively confirmed, these findings can have a relevant impact on influenza vaccine development and use. Moreover, they can be used to convince even the most recalcitrant health authorities of the need to extend influenza immunization to the entire population. In this narrative review, present knowledge regarding these particular aspects of influenza immunization is discussed. Literature analysis showed that the specific effects of influenza immunization are great enough per se to recommend systematic annual immunization of younger children, old people, and all individuals with severe chronic underlying diseases. Moreover, influenza immunization can significantly contribute to limiting the emergence of antimicrobial resistance. The problem of the possible nonspecific effects of influenza vaccines remains unsolved. The definition of their role as inducers of trained immunity seems essential not only to evaluate how much they play a role in the prevention of infectious diseases but also to evaluate whether they can be used to prevent and treat clinical conditions in which chronic inflammation and autoimmunity play a fundamental pathogenetic role.
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Affiliation(s)
| | - Susanna Esposito
- Pediatric Clinic, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
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Zada S, Khan M, Su Z, Sajjad W, Rafiq M. Cryosphere: a frozen home of microbes and a potential source for drug discovery. Arch Microbiol 2024; 206:196. [PMID: 38546887 DOI: 10.1007/s00203-024-03899-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 04/02/2024]
Abstract
The world is concerned about the emergence of pathogens and the occurrence and spread of antibiotic resistance among pathogens. Drug development requires time to combat these issues. Consequently, drug development from natural sources is unavoidable. Cryosphere represents a gigantic source of microbes that could be the bioprospecting source of natural products with unique scaffolds as molecules or drug templates. This review focuses on the novel source of drug discovery and cryospheric environments as a potential source for microbial metabolites having potential medicinal applications. Furthermore, the problems encountered in discovering metabolites from cold-adapted microbes and their resolutions are discussed. By adopting modern practical approaches, the discovery of bioactive compounds might fulfill the demand for new drug development.
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Affiliation(s)
- Sahib Zada
- Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou, China
| | - Mohsin Khan
- Department of Biological Sciences, Ohio University Athens, Athens, OH, USA
| | - Zheng Su
- Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou, China
| | - Wasim Sajjad
- State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China.
| | - Muhammad Rafiq
- Department of Microbiology, Faculty of Life Sciences and Informatics, Balochistan University of IT, Engineering and Management Sciences, Quetta, 87650, Pakistan.
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Periferakis A, Periferakis AT, Troumpata L, Dragosloveanu S, Timofticiuc IA, Georgatos-Garcia S, Scheau AE, Periferakis K, Caruntu A, Badarau IA, Scheau C, Caruntu C. Use of Biomaterials in 3D Printing as a Solution to Microbial Infections in Arthroplasty and Osseous Reconstruction. Biomimetics (Basel) 2024; 9:154. [PMID: 38534839 DOI: 10.3390/biomimetics9030154] [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: 01/26/2024] [Revised: 02/23/2024] [Accepted: 02/25/2024] [Indexed: 03/28/2024] Open
Abstract
The incidence of microbial infections in orthopedic prosthetic surgeries is a perennial problem that increases morbidity and mortality, representing one of the major complications of such medical interventions. The emergence of novel technologies, especially 3D printing, represents a promising avenue of development for reducing the risk of such eventualities. There are already a host of biomaterials, suitable for 3D printing, that are being tested for antimicrobial properties when they are coated with bioactive compounds, such as antibiotics, or combined with hydrogels with antimicrobial and antioxidant properties, such as chitosan and metal nanoparticles, among others. The materials discussed in the context of this paper comprise beta-tricalcium phosphate (β-TCP), biphasic calcium phosphate (BCP), hydroxyapatite, lithium disilicate glass, polyetheretherketone (PEEK), poly(propylene fumarate) (PPF), poly(trimethylene carbonate) (PTMC), and zirconia. While the recent research results are promising, further development is required to address the increasing antibiotic resistance exhibited by several common pathogens, the potential for fungal infections, and the potential toxicity of some metal nanoparticles. Other solutions, like the incorporation of phytochemicals, should also be explored. Incorporating artificial intelligence (AI) in the development of certain orthopedic implants and the potential use of AI against bacterial infections might represent viable solutions to these problems. Finally, there are some legal considerations associated with the use of biomaterials and the widespread use of 3D printing, which must be taken into account.
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Affiliation(s)
- Argyrios Periferakis
- Department of Physiology, The "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Akadimia of Ancient Greek and Traditional Chinese Medicine, 16675 Athens, Greece
- Elkyda, Research & Education Centre of Charismatheia, 17675 Athens, Greece
| | - Aristodemos-Theodoros Periferakis
- Department of Physiology, The "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Elkyda, Research & Education Centre of Charismatheia, 17675 Athens, Greece
| | - Lamprini Troumpata
- Department of Physiology, The "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
| | - Serban Dragosloveanu
- Department of Orthopaedics and Traumatology, The "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Department of Orthopaedics, "Foisor" Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania
| | - Iosif-Aliodor Timofticiuc
- Department of Physiology, The "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
| | - Spyrangelos Georgatos-Garcia
- Tilburg Institute for Law, Technology, and Society (TILT), Tilburg University, 5037 DE Tilburg, The Netherlands
- Corvers Greece IKE, 15124 Athens, Greece
| | - Andreea-Elena Scheau
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
| | - Konstantinos Periferakis
- Akadimia of Ancient Greek and Traditional Chinese Medicine, 16675 Athens, Greece
- Pan-Hellenic Organization of Educational Programs (P.O.E.P.), 17236 Athens, Greece
| | - Ana Caruntu
- Department of Oral and Maxillofacial Surgery, "Carol Davila" Central Military Emergency Hospital, 010825 Bucharest, Romania
- Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, Titu Maiorescu University, 031593 Bucharest, Romania
| | - Ioana Anca Badarau
- Department of Physiology, The "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
| | - Cristian Scheau
- Department of Physiology, The "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Department of Radiology and Medical Imaging, "Foisor" Clinical Hospital of Orthopaedics, Traumatology and Osteoarticular TB, 021382 Bucharest, Romania
| | - Constantin Caruntu
- Department of Physiology, The "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania
- Department of Dermatology, "Prof. N.C. Paulescu" National Institute of Diabetes, Nutrition and Metabolic Diseases, 011233 Bucharest, Romania
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Ramezani M, Takian A, Bakhtiari A, Rabiee HR, Ghazanfari S, Sazgarnejad S. Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework. BioData Min 2023; 16:31. [PMID: 37904172 PMCID: PMC10617108 DOI: 10.1186/s13040-023-00346-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 10/07/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND The governance of health systems is complex in nature due to several intertwined and multi-dimensional factors contributing to it. Recent challenges of health systems reflect the need for innovative approaches that can minimize adverse consequences of policies. Hence, there is compelling evidence of a distinct outlook on the health ecosystem using artificial intelligence (AI). Therefore, this study aimed to investigate the roles of AI and its applications in health system governance through an interpretive scoping review of current evidence. METHOD This study intended to offer a research agenda and framework for the applications of AI in health systems governance. To include shreds of evidence with a greater focus on the application of AI in health governance from different perspectives, we searched the published literature from 2000 to 2023 through PubMed, Scopus, and Web of Science Databases. RESULTS Our findings showed that integrating AI capabilities into health systems governance has the potential to influence three cardinal dimensions of health. These include social determinants of health, elements of governance, and health system tasks and goals. AI paves the way for strengthening the health system's governance through various aspects, i.e., intelligence innovations, flexible boundaries, multidimensional analysis, new insights, and cognition modifications to the health ecosystem area. CONCLUSION AI is expected to be seen as a tool with new applications and capabilities, with the potential to change each component of governance in the health ecosystem, which can eventually help achieve health-related goals.
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Affiliation(s)
- Maryam Ramezani
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Amirhossein Takian
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran.
- Department of Global Health and Public Policy, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
| | - Ahad Bakhtiari
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid R Rabiee
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Sadegh Ghazanfari
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Saharnaz Sazgarnejad
- School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Samantray D, Tanwar AS, Murali TS, Brand A, Satyamoorthy K, Paul B. A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:445-460. [PMID: 37861712 DOI: 10.1089/omi.2023.0140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.
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Affiliation(s)
- Debyani Samantray
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Ankit Singh Tanwar
- United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht, The Netherlands
- Faculty of Health, Medicine and Life Sciences (FHML), Maastricht University, Maastricht, The Netherlands
| | - Thokur Sreepathy Murali
- Department of Biotechnology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Angela Brand
- United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht, The Netherlands
- Faculty of Health, Medicine and Life Sciences (FHML), Maastricht University, Maastricht, The Netherlands
- Department of Health Information, Prasanna School of Public Health (PSPH), Manipal Academy of Higher Education, Manipal, India
| | - Kapaettu Satyamoorthy
- SDM College of Medical Sciences and Hospital, Shri Dharmasthala Manjunatheshwara (SDM) University, Dharwad, India
| | - Bobby Paul
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
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Blondeau J, Charles MK, Loo V, Adam H, Gonzalez Del Vecchio M, Ghakis C, O'Callaghan E, El Ali R. A nested cohort 5-year Canadian surveillance of Gram-negative antimicrobial resistance for optimized antimicrobial therapy. Sci Rep 2023; 13:14142. [PMID: 37644048 PMCID: PMC10465604 DOI: 10.1038/s41598-023-40012-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/03/2023] [Indexed: 08/31/2023] Open
Abstract
We analyzed 5 years (2016-2020) of nested Canadian data from the Study for Monitoring Antimicrobial Resistance Trends (SMART) to identify pathogen predominance and antimicrobial resistance (AMR) patterns of adult Gram-negative infections in Canadian health care and to complement other public surveillance programs and studies in Canada. A total of 6853 isolates were analyzed from medical (44%), surgical (18%), intensive care (22%) and emergency units (15%) and from respiratory tract (36%), intra-abdominal (25%), urinary tract (24%) and bloodstream (15%) infections. Overall, E. coli (36%), P. aeruginosa (18%) and K. pneumoniae (12%) were the most frequent isolates and P. aeruginosa was the most common respiratory pathogen. 18% of Enterobacterales species were ESBL positive. Collective susceptibility profiles showed that P. aeruginosa isolates were highly susceptible (> 95%) to ceftolozane/tazobactam and colistin, though markedly less susceptible (58-74%) to other antimicrobials tested. Multi-drug resistance (MDR) was present in 10% of P. aeruginosa isolates and was more frequent in those from respiratory infections and from ICU than non-ICU locations. Of P. aeruginosa isolates that were resistant to combinations of ceftazidime, piperacillin/tazobactam and meropenem, 73-96% were susceptible to ceftolozane/tazobactam over the period of the study. These national data can now be combined with clinical prediction rules and genomic data to enable expert antimicrobial stewardship applications and guide treatment policies to optimize adult patient care.
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Affiliation(s)
- Joseph Blondeau
- Clinical Microbiology, Royal University Hospital and the Saskatchewan Health Authority, and the Departments of Pathology and Laboratory Medicine, Microbiology, Immunology and Biochemistry, and Ophthalmology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Marthe Kenny Charles
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver General Hospital, Vancouver, BC, Canada
| | - Vivian Loo
- Division of Infectious Diseases, Department of Medicine, McGill University and McGill University Health Centre, Montreal, Canada
| | - Heather Adam
- Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba and Diagnostic Services, Shared Health, Winnipeg, MB, Canada
| | | | - Christiane Ghakis
- Medical and Scientific Affairs, Merck Canada Inc., Kirkland, QC, Canada
| | - Emma O'Callaghan
- Formerly affiliated With Merck Canada Inc., Kirkland, QC, Canada
| | - Radwan El Ali
- Medical and Scientific Affairs, Merck Canada Inc., Kirkland, QC, Canada.
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10
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Amin D, Garzόn-Orjuela N, Garcia Pereira A, Parveen S, Vornhagen H, Vellinga A. Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review. Antibiotics (Basel) 2023; 12:1293. [PMID: 37627713 PMCID: PMC10451640 DOI: 10.3390/antibiotics12081293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023] Open
Abstract
Introduction: The use of antibiotics leads to antibiotic resistance (ABR). Different methods have been used to predict and control ABR. In recent years, artificial intelligence (AI) has been explored to improve antibiotic (AB) prescribing, and thereby control and reduce ABR. This review explores whether the use of AI can improve antibiotic prescribing for human patients. Methods: Observational studies that use AI to improve antibiotic prescribing were retrieved for this review. There were no restrictions on the time, setting or language. References of the included studies were checked for additional eligible studies. Two independent authors screened the studies for inclusion and assessed the risk of bias of the included studies using the National Institute of Health (NIH) Quality Assessment Tool for observational cohort studies. Results: Out of 3692 records, fifteen studies were eligible for full-text screening. Five studies were included in this review, and a narrative synthesis was carried out to assess their findings. All of the studies used supervised machine learning (ML) models as a subfield of AI, such as logistic regression, random forest, gradient boosting decision trees, support vector machines and K-nearest neighbours. Each study showed a positive contribution of ML in improving antibiotic prescribing, either by reducing antibiotic prescriptions or predicting inappropriate prescriptions. However, none of the studies reported the engagement of AB prescribers in developing their ML models, nor their feedback on the user-friendliness and reliability of the models in different healthcare settings. Conclusion: The use of ML methods may improve antibiotic prescribing in both primary and secondary settings. None of the studies evaluated the implementation process of their models in clinical practices. Prospero Registration: (CRD42022329049).
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Affiliation(s)
- Doaa Amin
- School of Public Health, Physiotherapy & Sports Science, University College Dublin, Belfield, Dublin 4, Dublin, Ireland; (N.G.-O.); (S.P.); (A.V.)
| | - Nathaly Garzόn-Orjuela
- School of Public Health, Physiotherapy & Sports Science, University College Dublin, Belfield, Dublin 4, Dublin, Ireland; (N.G.-O.); (S.P.); (A.V.)
| | - Agustin Garcia Pereira
- Insight Centre for Data Analytics, University of Galway, H91 AEX4 Galway, Ireland; (A.G.P.); (H.V.)
| | - Sana Parveen
- School of Public Health, Physiotherapy & Sports Science, University College Dublin, Belfield, Dublin 4, Dublin, Ireland; (N.G.-O.); (S.P.); (A.V.)
| | - Heike Vornhagen
- Insight Centre for Data Analytics, University of Galway, H91 AEX4 Galway, Ireland; (A.G.P.); (H.V.)
| | - Akke Vellinga
- School of Public Health, Physiotherapy & Sports Science, University College Dublin, Belfield, Dublin 4, Dublin, Ireland; (N.G.-O.); (S.P.); (A.V.)
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11
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Principi N, Autore G, Argentiero A, Esposito S. Short-term antibiotic therapy for the most common bacterial respiratory infections in infants and children. Front Pharmacol 2023; 14:1174146. [PMID: 37346296 PMCID: PMC10279853 DOI: 10.3389/fphar.2023.1174146] [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: 02/25/2023] [Accepted: 05/25/2023] [Indexed: 06/23/2023] Open
Abstract
Overuse and misuse of antibiotics have strongly accelerated the progressive increase in bacterial antimicrobial resistance (AMR). The evidence that antimicrobial selective pressure was greater the longer the antibiotic therapy was continued has led some experts to reconsider duration of antibiotic therapy testing the use of short-term drug administration. If as effective as long-term therapy, short-term therapy could have been an easy measure to limit AMR emergence. In the present narrative review, whether present knowledge on short-term therapy of acute streptococcal pharyngitis (ASF), acute otitis media (AOM) and mild to moderate community-acquired pneumonia (CAP) allows systematic use of short-term therapy in infants and children with these diseases is discussed. Literature analysis showed that reducing the duration of antibiotic therapy for some of the most common pediatric respiratory infections could be a valid measure to contain the antibiotic abuse and the consequent impact on the emergence of AMR. Several data seem to indicate that this type of intervention is possible, as short-term therapy has been found as effective as the traditionally recommended long-term therapy in some cases of ASF, AOM and mild to moderate CAP. However, further studies are needed to better characterize infants and children who can have benefit with short-term antibiotic therapy in common bacterial respiratory infections.
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Affiliation(s)
| | - Giovanni Autore
- Pediatric Clinic, Pietro Barilla Children’s Hospital, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Alberto Argentiero
- Pediatric Clinic, Pietro Barilla Children’s Hospital, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Susanna Esposito
- Pediatric Clinic, Pietro Barilla Children’s Hospital, Department of Medicine and Surgery, University of Parma, Parma, Italy
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12
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Artificial Intelligence in Biological Sciences. Life (Basel) 2022; 12:life12091430. [PMID: 36143468 PMCID: PMC9505413 DOI: 10.3390/life12091430] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/25/2022] [Accepted: 09/10/2022] [Indexed: 12/03/2022] Open
Abstract
Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.
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13
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Meoli A, Ciavola L, Rahman S, Masetti M, Toschetti T, Morini R, Dal Canto G, Auriti C, Caminiti C, Castagnola E, Conti G, Donà D, Galli L, La Grutta S, Lancella L, Lima M, Lo Vecchio A, Pelizzo G, Petrosillo N, Simonini A, Venturini E, Caramelli F, Gargiulo GD, Sesenna E, Sgarzani R, Vicini C, Zucchelli M, Mosca F, Staiano A, Principi N, Esposito S. Prevention of Surgical Site Infections in Neonates and Children: Non-Pharmacological Measures of Prevention. Antibiotics (Basel) 2022; 11:antibiotics11070863. [PMID: 35884117 PMCID: PMC9311619 DOI: 10.3390/antibiotics11070863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/19/2022] [Accepted: 06/23/2022] [Indexed: 12/04/2022] Open
Abstract
A surgical site infection (SSI) is an infection that occurs in the incision created by an invasive surgical procedure. Although most infections are treatable with antibiotics, SSIs remain a significant cause of morbidity and mortality after surgery and have a significant economic impact on health systems. Preventive measures are essential to decrease the incidence of SSIs and antibiotic abuse, but data in the literature regarding risk factors for SSIs in the pediatric age group are scarce, and current guidelines for the prevention of the risk of developing SSIs are mainly focused on the adult population. This document describes the current knowledge on risk factors for SSIs in neonates and children undergoing surgery and has the purpose of providing guidance to health care professionals for the prevention of SSIs in this population. Our aim is to consider the possible non-pharmacological measures that can be adopted to prevent SSIs. To our knowledge, this is the first study to provide recommendations based on a careful review of the available scientific evidence for the non-pharmacological prevention of SSIs in neonates and children. The specific scenarios developed are intended to guide the healthcare professional in practice to ensure standardized management of the neonatal and pediatric patients, decrease the incidence of SSIs and reduce antibiotic abuse.
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Affiliation(s)
- Aniello Meoli
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Lorenzo Ciavola
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Sofia Rahman
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Marco Masetti
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Tommaso Toschetti
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Riccardo Morini
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Giulia Dal Canto
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
| | - Cinzia Auriti
- Neonatology and Neonatal Intensive Care Unit, IRCCS Bambino Gesù Children’s Hospital, 00165 Rome, Italy;
| | - Caterina Caminiti
- Research and Innovation Unit, University Hospital of Parma, 43126 Parma, Italy;
| | - Elio Castagnola
- Infectious Diseases Unit, IRCCS Giannina Gaslini, 16147 Genoa, Italy;
| | - Giorgio Conti
- Pediatric ICU and Trauma Center, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00165 Rome, Italy;
| | - Daniele Donà
- Division of Paediatric Infectious Diseases, Department for Woman and Child Health, University of Padua, 35100 Padua, Italy;
| | - Luisa Galli
- Infectious Disease Unit, Meyer Children’s Hospital, 50139 Florence, Italy; (L.G.); (E.V.)
| | - Stefania La Grutta
- Institute of Translational Pharmacology IFT, National Research Council, 90146 Palermo, Italy;
| | - Laura Lancella
- Paediatric Infectious Disease Unit, Academic Department of Pediatrics, IRCCS Bambino Gesù Children’s Hospital, 00165 Rome, Italy;
| | - Mario Lima
- Pediatric Surgery, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Andrea Lo Vecchio
- Department of Translational Medical Science, Section of Pediatrics, University of Naples “Federico II”, 80138 Naples, Italy; (A.L.V.); (A.S.)
| | - Gloria Pelizzo
- Pediatric Surgery Department, “Vittore Buzzi” Children’s Hospital, 20154 Milano, Italy;
| | - Nicola Petrosillo
- Infection Prevention and Control—Infectious Disease Service, Foundation University Hospital Campus Bio-Medico, 00128 Rome, Italy;
| | - Alessandro Simonini
- Pediatric Anesthesia and Intensive Care Unit, Salesi Children’s Hospital, 60123 Ancona, Italy;
| | - Elisabetta Venturini
- Infectious Disease Unit, Meyer Children’s Hospital, 50139 Florence, Italy; (L.G.); (E.V.)
| | - Fabio Caramelli
- Pediatric Intensive Care Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Gaetano Domenico Gargiulo
- Department of Cardio-Thoracic and Vascular Medicine, Adult Cardiac Surgery, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Enrico Sesenna
- Maxillo-Facial Surgery Unit, Head and Neck Department, University Hospital of Parma, 43126 Parma, Italy;
| | - Rossella Sgarzani
- Servizio di Chirurgia Plastica, Centro Grandi Ustionati, Ospedale M. Bufalini, AUSL Romagna, 47521 Cesena, Italy;
| | - Claudio Vicini
- Head-Neck and Oral Surgery Unit, Department of Head-Neck Surgery, Otolaryngology, Morgagni Piertoni Hospital, 47121 Forli, Italy;
| | - Mino Zucchelli
- Pediatric Neurosurgery, IRCCS Istituto delle Scienze Neurologiche di Bologna, 40138 Bologna, Italy;
| | - Fabio Mosca
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Department of Mother, Child and Infant, 20122 Milan, Italy;
| | - Annamaria Staiano
- Department of Translational Medical Science, Section of Pediatrics, University of Naples “Federico II”, 80138 Naples, Italy; (A.L.V.); (A.S.)
| | | | - Susanna Esposito
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (A.M.); (L.C.); (S.R.); (M.M.); (T.T.); (R.M.); (G.D.C.)
- Correspondence: ; Tel.: +39-0521-903524
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14
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Rabaan AA, Alhumaid S, Mutair AA, Garout M, Abulhamayel Y, Halwani MA, Alestad JH, Bshabshe AA, Sulaiman T, AlFonaisan MK, Almusawi T, Albayat H, Alsaeed M, Alfaresi M, Alotaibi S, Alhashem YN, Temsah MH, Ali U, Ahmed N. Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates. Antibiotics (Basel) 2022; 11:antibiotics11060784. [PMID: 35740190 PMCID: PMC9220767 DOI: 10.3390/antibiotics11060784] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 05/31/2022] [Accepted: 06/07/2022] [Indexed: 11/16/2022] Open
Abstract
Artificial intelligence (AI) is a branch of science and engineering that focuses on the computational understanding of intelligent behavior. Many human professions, including clinical diagnosis and prognosis, are greatly useful from AI. Antimicrobial resistance (AMR) is among the most critical challenges facing Pakistan and the rest of the world. The rising incidence of AMR has become a significant issue, and authorities must take measures to combat the overuse and incorrect use of antibiotics in order to combat rising resistance rates. The widespread use of antibiotics in clinical practice has not only resulted in drug resistance but has also increased the threat of super-resistant bacteria emergence. As AMR rises, clinicians find it more difficult to treat many bacterial infections in a timely manner, and therapy becomes prohibitively costly for patients. To combat the rise in AMR rates, it is critical to implement an institutional antibiotic stewardship program that monitors correct antibiotic use, controls antibiotics, and generates antibiograms. Furthermore, these types of tools may aid in the treatment of patients in the event of a medical emergency in which a physician is unable to wait for bacterial culture results. AI’s applications in healthcare might be unlimited, reducing the time it takes to discover new antimicrobial drugs, improving diagnostic and treatment accuracy, and lowering expenses at the same time. The majority of suggested AI solutions for AMR are meant to supplement rather than replace a doctor’s prescription or opinion, but rather to serve as a valuable tool for making their work easier. When it comes to infectious diseases, AI has the potential to be a game-changer in the battle against antibiotic resistance. Finally, when selecting antibiotic therapy for infections, data from local antibiotic stewardship programs are critical to ensuring that these bacteria are treated quickly and effectively. Furthermore, organizations such as the World Health Organization (WHO) have underlined the necessity of selecting the appropriate antibiotic and treating for the shortest time feasible to minimize the spread of resistant and invasive resistant bacterial strains.
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Affiliation(s)
- Ali A. Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran 31311, Saudi Arabia
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
- Department of Public Health and Nutrition, The University of Haripur, Haripur 22610, Pakistan
- Correspondence: (A.A.R.); (N.A.)
| | - Saad Alhumaid
- Administration of Pharmaceutical Care, Al-Ahsa Health Cluster, Ministry of Health, Al-Ahsa 31982, Saudi Arabia;
| | - Abbas Al Mutair
- Research Center, Almoosa Specialist Hospital, Alhassa, Al-Ahsa 36342, Saudi Arabia;
- Almoosa College of Health Sciences, Alhassa, Al-Ahsa 36342, Saudi Arabia
- School of Nursing, Wollongong University, Wollongong, NSW 2522, Australia
- Nursing Department, Prince Sultan Military College of Health Sciences, Dhahran 34313, Saudi Arabia
| | - Mohammed Garout
- Department of Community Medicine and Health Care for Pilgrims, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia;
| | - Yem Abulhamayel
- Specialty Internal Medicine Department, Johns Hopkins Aramco Healthcare, Dhahran 34465, Saudi Arabia;
| | - Muhammad A. Halwani
- Department of Medical Microbiology, Faculty of Medicine, Al Baha University, Al Baha 4781, Saudi Arabia;
| | - Jeehan H. Alestad
- Immunology and Infectious Microbiology Department, University of Glasgow, Glasgow G1 1XQ, UK;
- Microbiology Department, Collage of Medicine, Jabriya 46300, Kuwait
| | - Ali Al Bshabshe
- Adult Critical Care Department of Medicine, Division of Adult Critical Care, College of Medicine, King Khalid University, Abha 62561, Saudi Arabia;
| | - Tarek Sulaiman
- Infectious Diseases Section, Medical Specialties Department, King Fahad Medical City, Riyadh 12231, Saudi Arabia;
| | | | - Tariq Almusawi
- Infectious Disease and Critical Care Medicine Department, Dr. Sulaiman Alhabib Medical Group, Alkhobar 34423, Saudi Arabia;
- Department of Medicine, Royal College of Surgeons in Ireland-Medical University of Bahrain, Manama 15503, Bahrain
| | - Hawra Albayat
- Infectious Disease Department, King Saud Medical City, Riyadh 7790, Saudi Arabia;
| | - Mohammed Alsaeed
- Infectious Disease Division, Department of Medicine, Prince Sultan Military Medical City, Riyadh 11159, Saudi Arabia;
| | - Mubarak Alfaresi
- Department of Pathology and Laboratory Medicine, Sheikh Khalifa General Hospital, Umm Al Quwain 499, United Arab Emirates;
- Department of Pathology, College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai 505055, United Arab Emirates
| | - Sultan Alotaibi
- Molecular Microbiology Department, King Fahad Medical City, Riyadh 11525, Saudi Arabia;
| | - Yousef N. Alhashem
- Department of Clinical Laboratory Sciences, Mohammed AlMana College of Health Sciences, Dammam 34222, Saudi Arabia;
| | - Mohamad-Hani Temsah
- Pediatric Department, College of Medicine, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Urooj Ali
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore 54000, Pakistan;
| | - Naveed Ahmed
- Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia
- Correspondence: (A.A.R.); (N.A.)
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15
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Amaning Danquah C, Minkah PAB, Osei Duah Junior I, Amankwah KB, Somuah SO. Antimicrobial Compounds from Microorganisms. Antibiotics (Basel) 2022; 11:285. [PMID: 35326749 PMCID: PMC8944786 DOI: 10.3390/antibiotics11030285] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 01/27/2022] [Accepted: 02/07/2022] [Indexed: 02/06/2023] Open
Abstract
Antimicrobial resistance is an exigent public health concern owing to the emergence of novel strains of human resistant pathogens and the concurrent rise in multi-drug resistance. An influx of new antimicrobials is urgently required to improve the treatment outcomes of infectious diseases and save lives. Plant metabolites and bioactive compounds from chemical synthesis have found their efficacy to be dwindling, despite some of them being developed as drugs and used to treat human infections for several decades. Microorganisms are considered untapped reservoirs for promising biomolecules with varying structural and functional antimicrobial activity. The advent of cost-effective and convenient model organisms, state-of-the-art molecular biology, omics technology, and machine learning has enhanced the bioprospecting of novel antimicrobial drugs and the identification of new drug targets. This review summarizes antimicrobial compounds isolated from microorganisms and reports on the modern tools and strategies for exploiting promising antimicrobial drug candidates. The investigation identified a plethora of novel compounds from microbial sources with excellent antimicrobial activity against disease-causing human pathogens. Researchers could maximize the use of novel model systems and advanced biomolecular and computational tools in exploiting lead antimicrobials, consequently ameliorating antimicrobial resistance.
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Affiliation(s)
- Cynthia Amaning Danquah
- Department of Pharmacology, Faculty of Pharmacy and Pharmaceutical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, PMB, Kumasi, Ghana;
| | - Prince Amankwah Baffour Minkah
- Department of Pharmacology, Faculty of Pharmacy and Pharmaceutical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, PMB, Kumasi, Ghana;
- Global Health and Infectious Disease Research Group, Kumasi Centre for Collaborative Research in Tropical Medicine, College of Health Sciences, Kwame Nkrumah University of Science and Technology, PMB, Kumasi, Ghana
| | - Isaiah Osei Duah Junior
- Department of Optometry and Visual Science, College of Science, Kwame Nkrumah University of Science and Technology, PMB, Kumasi, Ghana;
| | - Kofi Bonsu Amankwah
- Department of Biomedical Sciences, University of Cape Coast, PMB, Cape Coast, Ghana;
| | - Samuel Owusu Somuah
- Department of Pharmacy Practice, School of Pharmacy, University of Health and Allied Sciences, PMB, Ho, Ghana;
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16
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Esposito S, Argentiero A, Rebecchi F, Fainardi V, Pisi G, Principi N. The remaining unsolved problems for rational antibiotic therapy use in pediatric community-acquired pneumonia. Expert Opin Pharmacother 2022; 23:497-505. [PMID: 35094614 DOI: 10.1080/14656566.2022.2028773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Despite availability of several official guidelines, not all the problems related to the most effective and safe use of antibiotics in children with community-acquired pneumonia (CAP) have been solved. Presently, too many children receive unneeded antibiotics or, when antibiotics are mandatory, the choice of the drug is not appropriate. AREAS COVERED In this paper, the authors discuss the remaining unsolved problems for rational antibiotic therapy use in pediatric community-acquired pneumonia and provide their expert perspectives. EXPERT OPINION Further improvement in pediatric CAP management could be derived from physician education on antibiotic use and a larger use, particularly in office practice, of point of care testing or new technologies (i.e. artificial intelligence) to define etiology of a lower respiratory infection. However, recommendations regarding the duration of antibiotic therapy vary largely because of the absence of reliable data on the optimal CAP treatment according to the bacterial etiology of the disease, its severity, and child characteristics. Available evidence seems to confirm that a short course of antibiotics, approximately 5 days, can be effective and lead to results not substantially different from those obtained with prolonged-course antibiotic therapy, at least in patients with mild to moderate disease.
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Affiliation(s)
- Susanna Esposito
- Pediatric Clinic, Pietro Barilla Children's Hospital, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Alberto Argentiero
- Pediatric Clinic, Pietro Barilla Children's Hospital, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Francesca Rebecchi
- Pediatric Clinic, Pietro Barilla Children's Hospital, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Valentina Fainardi
- Pediatric Clinic, Pietro Barilla Children's Hospital, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Giovanna Pisi
- Pediatric Clinic, Pietro Barilla Children's Hospital, Department of Medicine and Surgery, University of Parma, Parma, Italy
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17
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Yang Y, Walker TM, Kouchaki S, Wang C, Peto TEA, Crook DW, Clifton DA. An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction. Brief Bioinform 2021; 22:6355133. [PMID: 34414415 PMCID: PMC8575050 DOI: 10.1093/bib/bbab299] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/28/2021] [Accepted: 07/16/2021] [Indexed: 11/23/2022] Open
Abstract
Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method—heterogeneous graph attention network (‘HGAT–AMR’)—to predict anti-tuberculosis (TB) drug resistance. The HGAT–AMR model is able to accommodate incomplete phenotypic profiles, as well as provide ‘attention scores’ of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is ‘paying attention to’ in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs (94.91% for isoniazid, 96.60% for ethambutol and 90.63% for pyrazinamide), and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.
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Affiliation(s)
- Yang Yang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK.,Oxford-Suzhou Centre for Advanced Research, Suzhou, 215123, China
| | - Timothy M Walker
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Samaneh Kouchaki
- Centre for vision, Speech, and Signal processing, University of Surrey, Guildford, UK
| | - Chenyang Wang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
| | - Timothy E A Peto
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital Headley Way, OX3 9DU, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital Headley Way, OX3 9DU, Oxford, UK.,NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way Headington, OX3 9DU, Oxford, UK
| | | | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK.,Oxford-Suzhou Centre for Advanced Research, Suzhou, 215123, China
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18
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Esposito S, Maglietta G, Di Costanzo M, Ceccoli M, Vergine G, La Scola C, Malaventura C, Falcioni A, Iacono A, Crisafi A, Iughetti L, Conte ML, Pierantoni L, Gatti C, Caminiti C, Biasucci G. Retrospective 8-Year Study on the Antibiotic Resistance of Uropathogens in Children Hospitalised for Urinary Tract Infection in the Emilia-Romagna Region, Italy. Antibiotics (Basel) 2021; 10:antibiotics10101207. [PMID: 34680787 PMCID: PMC8532648 DOI: 10.3390/antibiotics10101207] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 11/23/2022] Open
Abstract
The development and spread of antibiotic resistance is an increasingly important global public health problem, even in paediatric urinary tract infection (UTI). In light of the variability in the data, it is necessary to conduct surveillance studies to determine the prevalence of antibiotic resistance in specific geographical areas to optimize therapeutic management. In this observational, retrospective, multicentre study, the medical records of 1801 paediatric patients who were hospitalised for UTI between 1 January 2012, and 30 June 2020, in Emilia-Romagna, Italy, were analysed. Escherichia coli was the most frequently detected pathogen (75.6%), followed by Klebsiella pneumoniae (6.9%) and Pseudomonas aeruginosa (2.5%). Overall, 840 cases (46.7%) were due to antimicrobial-resistant uropathogens: 83 (4.7%) extended spectrum beta-lactamase (ESBL)-producing, 119 (6.7%) multidrug resistant (MDR) and 4 (0.2%) extensively drug resistant (XDR) bacteria. Empirical antibiotic therapy failed in 172 cases (9.6%). Having ESBL or MDR/XDR uropathogens, a history of recurrent UTI, antibiotic therapy in the preceding 30 days, and empirical treatment with amoxicillin or amoxicillin/clavulanate were significantly associated with treatment failure, whereas first-line therapy with third-generation cephalosporins was associated with protection against negative outcomes. In conclusion, the increase in the resistance of uropathogens to commonly used antibiotics requires continuous monitoring, and recommendations for antibiotic choice need updating. In our epidemiological context, amoxicillin/clavulanate no longer seems to be the appropriate first-line therapy for children hospitalised for UTI, whereas third-generation cephalosporins continue to be useful. To further limit the emergence of resistance, every effort to reduce and rationalise antibiotic consumption must be implemented.
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Affiliation(s)
- Susanna Esposito
- Pediatric Clinic, University Hospital, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
- Correspondence: ; Tel.: +39-0521-903524
| | - Giuseppe Maglietta
- Research and Innovation Unit, University Hospital, 43126 Parma, Italy; (G.M.); (C.C.)
| | - Margherita Di Costanzo
- Paediatrics and Neonatology Unit, Guglielmo da Saliceto Hospital, 29122 Piacenza, Italy; (M.D.C.); (G.B.)
| | - Martina Ceccoli
- Paediatrics Unit, Department of Medical and Surgical Sciences of Mothers, Children and Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy; (M.C.); (L.I.)
| | - Gianluca Vergine
- Paediatrics Unit, Rimini Hospital, AUSL Romagna, 47921 Rimini, Italy; (G.V.); (M.L.C.)
| | - Claudio La Scola
- Paediatric Clinic, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | | | - Alice Falcioni
- Paediatric Unit, Forlì Hospital, AUSL Romagna, 47121 Forlì, Italy;
| | - Alessandra Iacono
- Paediatrics and Neonatology Unit, Ravenna Hospital, AUSL Romagna, 48121 Ravenna, Italy;
| | - Antonella Crisafi
- Paediatrics Unit, Santa Maria Nuova Hospital, AUSL-IRCCS of Reggio Emilia, 42123 Reggio Emilia, Italy;
| | - Lorenzo Iughetti
- Paediatrics Unit, Department of Medical and Surgical Sciences of Mothers, Children and Adults, University of Modena and Reggio Emilia, 41125 Modena, Italy; (M.C.); (L.I.)
| | - Maria Luisa Conte
- Paediatrics Unit, Rimini Hospital, AUSL Romagna, 47921 Rimini, Italy; (G.V.); (M.L.C.)
| | - Luca Pierantoni
- Paediatric Emergency Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Claudia Gatti
- Paediatric Surgery, University Hospital, 43126 Parma, Italy;
| | - Caterina Caminiti
- Research and Innovation Unit, University Hospital, 43126 Parma, Italy; (G.M.); (C.C.)
| | - Giacomo Biasucci
- Paediatrics and Neonatology Unit, Guglielmo da Saliceto Hospital, 29122 Piacenza, Italy; (M.D.C.); (G.B.)
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Petraroli M, Castellone E, Patianna V, Esposito S. Gut Microbiota and Obesity in Adults and Children: The State of the Art. Front Pediatr 2021; 9:657020. [PMID: 33816411 PMCID: PMC8017119 DOI: 10.3389/fped.2021.657020] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 02/22/2021] [Indexed: 12/12/2022] Open
Abstract
In recent decades, obesity has become a serious public health problem affecting both children and adults. Considering the multifactorial origin of obesity, including modifiable factors, childhood was identified as the golden age for investing in obesity prevention by both promoting proper lifestyles and actively intervening in possible triggers. The gut microbiota is at the center of the most recent scientific studies and plays a key role in obesity development because it is intimately linked to energetic-humoral variations in the host: its alterations can promote a state of excessive energy storage, and it can be manipulated to maintain energy homoeostasis. This review aims to offer a panoramic understanding of the interplay between obesity and the gut microbiota, focusing on the contribution that the gut microbiota could have to the prevention of childhood obesity and its complications in adulthood. Currently, the use of some specific probiotic strains has been shown to be able to act on some secondary metabolic consequences of obesity (such as liver steatosis and insulin resistance) without any effect on weight loss. Although definitive conclusions cannot be drawn on the real impact of probiotics and prebiotics, there is no doubt that they represent an exciting new frontier in the treatment of obesity and associated metabolic dysfunctions. Targeted studies randomized on specific populations and homogeneous for ethnicity, sex, and age are urgently needed to reach definitive conclusions about the influence of microbiota on weight. In particular, we still need more studies in the pediatric population to better understand when the switch to an obese-like gut microbiota takes place and to better comprehend the right timing of each intervention, including the use of pre/probiotics, to improve it.
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Affiliation(s)
| | | | | | - Susanna Esposito
- Paediatric Clinic, Department of Medicine and Surgery, University Hospital, University of Parma, Parma, Italy
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Maassen O, Fritsch S, Palm J, Deffge S, Kunze J, Marx G, Riedel M, Schuppert A, Bickenbach J. Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey. J Med Internet Res 2021; 23:e26646. [PMID: 33666563 PMCID: PMC7980122 DOI: 10.2196/26646] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/29/2021] [Accepted: 02/15/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.
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Affiliation(s)
- Oliver Maassen
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Sebastian Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
| | - Julia Palm
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
| | - Saskia Deffge
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Julian Kunze
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Gernot Marx
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Morris Riedel
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
- School of Natural Sciences and Engineering, University of Iceland, Reykjavik, Iceland
| | - Andreas Schuppert
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute for Computational Biomedicine II, University Hospital RWTH Aachen, Aachen, Germany
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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Esposito S, Jones MH, Feleszko W, Martell JAO, Falup-Pecurariu O, Geppe N, Martinón-Torres F, Shen KL, Roth M, Principi N. Prevention of New Respiratory Episodes in Children with Recurrent Respiratory Infections: An Expert Consensus Statement. Microorganisms 2020; 8:E1810. [PMID: 33213053 PMCID: PMC7698530 DOI: 10.3390/microorganisms8111810] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/01/2020] [Accepted: 11/13/2020] [Indexed: 02/06/2023] Open
Abstract
In healthy infants and young children, the development of respiratory tract infections (RTIs) is extremely common. In this paper, we present an international consensus of the available approaches for the prevention of recurrent RTIs in children, including the atopic/allergic ones as well as those with asthma. Few convincing measures for reducing the frequency and clinical relevance of recurrent respiratory episodes in RTI-prone children have been developed until now. Among the most recently suggested measures, immunotherapy is attractive, but only for OM-85 is there a sufficient number of well-conducted clinical trials confirming efficacy in RTIs prevention with an adequate safety profile. In the case of probiotics, it is not clear which bacteria can offer the best results and which dosage and schedule of administration are the most effective. The problems of dosage and the schedule of administration are not solved also for vitamin D, despite some promising efficacy results. While we wait for new knowledge, the elimination or reduction as much as possible of the environmental factors that favor RTIs, vaccination when available and/or indicated, and the systematic application of the traditional methods for infection prevention, such as hand washing, remain the best measures to prevent recurrent infections in RTI-prone children.
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Affiliation(s)
- Susanna Esposito
- Pediatric Clinic, Pietro Barilla Children’s Hospital, University of Parma, 43126 Parma, Italy
| | - Marcus Herbert Jones
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre (RS) 90619-900, Brazil;
| | - Wojciech Feleszko
- Department of Pediatric Pneumonology and Allergy, The Medical University of Warsaw, 00-927 Warsaw, Poland;
| | - José A. Ortega Martell
- Department of Immunology, Universidad Autónoma del Estado de Hidalgo, Pachuca, Hidalgo 42082, Mexico;
| | - Oana Falup-Pecurariu
- Faculty of Medicine, Transilvania University, Children’s Clinic Hospital, 500036 Brasov, Romania;
| | - Natalia Geppe
- Department of Paediatrics, Sechenov First Moscow State Medical University, 115093 Moscow, Russia;
| | - Federico Martinón-Torres
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, 15706 Santiago de Compostela, Spain;
| | - Kun-Ling Shen
- China National Clinical Research Center for Respiratory Diseases, Department of Respiratory Medicine, Capital Medical University, National Center for Children’s Health, Beijing 100045, China;
| | - Michael Roth
- Pulmonary Cell Research and Pneumology, Department of Biomedicine and Internal Medicine, University Hospital Basel, 4002 Basel, Switzerland;
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