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Bacterial resistance to known and currently used antibiotics represents a growing issue worldwide. It poses a major problem in the treatment of infectious diseases in general and hospital-acquired infections in particular. This is in part due to the overuse and misuse of antibiotics in past decades, which led to the selection of highly resistant bacteria and even so-called superbugs – multidrug-resistant (MDR) bacteria. Nosocomial infections, particularly, are often caused by MDR bacterial pathogens and the treatment of such infections is very complicated and extensive, often leading to various side effects, including adverse effects on the natural human microbiome. At the same time, the development of novel antibiotics is lagging with very few new ones in the pipeline. Finding viable alternatives to treat such infections may help to overcome these therapeutic issues. This publication brings novel developments in the field of bacterial resistance, mainly in the hospital settings, adequate antibiotic therapy, and identification of compounds useful to battle this growing issue.

Medicine --- Epidemiology & medical statistics --- VRE --- GIT --- hemato-oncological patients --- clonality --- antibiotic stewardship --- resistance --- consumption of antibiotics --- clonal spread --- Enterococcus faecium --- Enterococcus faecalis --- linezolid resistance --- 23S rRNA --- optrA --- carbapenem-resistant Klebsiella pneumoniae --- carbapenem-resistant Acinetobacter baumannii --- N-acetylcysteine --- septic shock --- critically ill patients --- newborn --- infection --- bacteria --- antibiotic therapy --- hops --- C. difficile --- rat model --- Staphylococcus aureus --- MRSA --- spa typing --- MLST --- SCCmec typing --- clonal analysis --- epidemiology --- cancer patients --- duration of treatment --- colistin --- propensity score analysis --- multidrug-resistant Acinetobacter baumannii --- urinary tract infections --- UTIs --- MDR --- Escherichia coli --- Klebsiella --- uropathogens --- AMR --- antibiotic resistance --- ESBL-producing Klebsiella pneumoniae --- urinary tract infection --- clinical impact --- economic impact --- ventilator-associated pneumonia --- Klebsiella spp. --- Escherichia spp. --- pulsed-field gel electrophoresis (PFGE) --- endogenous infection --- methicillin-resistant --- porcine model --- methicillin-resistant Staphylococcus aureus (MRSA) --- long term care facilities (LTCF) --- multidrug resistance (MDR) --- enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR) --- ESBL --- PCR --- primer --- antimicrobial resistance --- infection prevention and control --- antimicrobial stewardship --- hospital --- cluster analysis --- principal component analysis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Bacterial resistance to known and currently used antibiotics represents a growing issue worldwide. It poses a major problem in the treatment of infectious diseases in general and hospital-acquired infections in particular. This is in part due to the overuse and misuse of antibiotics in past decades, which led to the selection of highly resistant bacteria and even so-called superbugs – multidrug-resistant (MDR) bacteria. Nosocomial infections, particularly, are often caused by MDR bacterial pathogens and the treatment of such infections is very complicated and extensive, often leading to various side effects, including adverse effects on the natural human microbiome. At the same time, the development of novel antibiotics is lagging with very few new ones in the pipeline. Finding viable alternatives to treat such infections may help to overcome these therapeutic issues. This publication brings novel developments in the field of bacterial resistance, mainly in the hospital settings, adequate antibiotic therapy, and identification of compounds useful to battle this growing issue.

VRE --- GIT --- hemato-oncological patients --- clonality --- antibiotic stewardship --- resistance --- consumption of antibiotics --- clonal spread --- Enterococcus faecium --- Enterococcus faecalis --- linezolid resistance --- 23S rRNA --- optrA --- carbapenem-resistant Klebsiella pneumoniae --- carbapenem-resistant Acinetobacter baumannii --- N-acetylcysteine --- septic shock --- critically ill patients --- newborn --- infection --- bacteria --- antibiotic therapy --- hops --- C. difficile --- rat model --- Staphylococcus aureus --- MRSA --- spa typing --- MLST --- SCCmec typing --- clonal analysis --- epidemiology --- cancer patients --- duration of treatment --- colistin --- propensity score analysis --- multidrug-resistant Acinetobacter baumannii --- urinary tract infections --- UTIs --- MDR --- Escherichia coli --- Klebsiella --- uropathogens --- AMR --- antibiotic resistance --- ESBL-producing Klebsiella pneumoniae --- urinary tract infection --- clinical impact --- economic impact --- ventilator-associated pneumonia --- Klebsiella spp. --- Escherichia spp. --- pulsed-field gel electrophoresis (PFGE) --- endogenous infection --- methicillin-resistant --- porcine model --- methicillin-resistant Staphylococcus aureus (MRSA) --- long term care facilities (LTCF) --- multidrug resistance (MDR) --- enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR) --- ESBL --- PCR --- primer --- antimicrobial resistance --- infection prevention and control --- antimicrobial stewardship --- hospital --- cluster analysis --- principal component analysis

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This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all.

Medicine --- machine learning-enabled decision support system --- improving diagnosis accuracy --- Bayesian network --- bariatric surgery --- health-related quality of life --- comorbidity --- voice change --- larynx cancer --- machine learning --- deep learning --- voice pathology classification --- imputation --- electronic health records --- EHR --- laboratory measures --- medical informatics --- inflammatory bowel disease --- C. difficile infection --- osteoarthritis --- complex diseases --- healthcare --- artificial intelligence --- interpretable machine learning --- explainable machine learning --- septic shock --- clinical decision support system --- electronic health record --- cerebrovascular disorders --- stroke --- SARS-CoV-2 --- COVID-19 --- cluster analysis --- risk factors --- ischemic stroke --- outcome prediction --- recurrent stroke --- cardiac ultrasound --- echocardiography --- portable ultrasound --- aneurysm surgery --- temporary artery occlusion --- clipping time --- artificial neural network --- digital imaging --- monocytes --- promonocytes and monoblasts --- chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia --- concordance between hematopathologists --- mechanical ventilation --- respiratory failure --- ADHD --- social media --- Twitter --- pharmacotherapy --- stimulants --- alpha-2-adrenergic agonists --- non-stimulants --- trust --- passive adherence --- human factors