Publications

Microbial biofilms are complex three-dimensional structures where sessile microbes are embedded in a polymeric extracellular matrix. Their resistance toward the host immune system as well as to a diverse range of antimicrobial treatments poses a serious health and development threat, being in the top 10 global public health threats declared by the World Health Organization. In an effort to combat biofilm-related microbial infections, several strategies have been developed to independently eliminate biofilms or to complement conventional antibiotic therapies. However, their limitations leave room for other treatment alternatives, where the application of nanotechnology to biofilm eradication has gained significant relevance in recent years. Their small size, penetration efficiency, and the design flexibility that they present makes them a promising alternative for biofilm infection treatment, although they also present set-backs. This review aims to describe the main possibilities and limitations of nanomedicine against biofilms, while covering the main aspects of biofilm formation and study, and the current therapies for biofilm treatment. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.

JTD Keywords: Anti-bacterial agents, Anti-infective agents, Antiinfective agent, Antimicrobial, Antimicrobials, Antimicrobials,bacteria,biofilm,infectious diseases,microorganism, Bacteria, Biofilm, Biofilm infections, Biofilms, Complex three dimensional structures, Diseases, Diverse range, Drug-delivery systems,in-vitro,cellular toxicity,nanoparticles,penetration,model,biocompatibility,perspectives,hyperthermia,diagnosi, Extracellular matrices, Global public health, Health risks, Infectious disease, Infectious diseases, Medical nanotechnology, Microbial biofilm, Microorganisms, Nanomedicine, Polymer, Polymers, Regulatory issues, Roadmap

Background: Home hospitalization is widely accepted as a cost-effective alternative to conventional hospitalization for selected patients. A recent analysis of the home hospitalization and early discharge (HH/ED) program at Hospital Clínic de Barcelona over a 10-year period demonstrated high levels of acceptance by patients and professionals, as well as health value-based generation at the provider and health-system levels. However, health risk assessment was identified as an unmet need with the potential to enhance clinical decision making. Objective: The objective of this study is to generate and assess predictive models of mortality and in-hospital admission at entry and at HH/ED discharge. Methods: Predictive modeling of mortality and in-hospital admission was done in 2 different scenarios: at entry into the HH/ED program and at discharge, from January 2009 to December 2015. Multisource predictive variables, including standard clinical data, patients’ functional features, and population health risk assessment, were considered. Results: We studied 1925 HH/ED patients by applying a random forest classifier, as it showed the best performance. Average results of the area under the receiver operating characteristic curve (AUROC; sensitivity/specificity) for the prediction of mortality were 0.88 (0.81/0.76) and 0.89 (0.81/0.81) at entry and at home hospitalization discharge, respectively; the AUROC (sensitivity/specificity) values for in-hospital admission were 0.71 (0.67/0.64) and 0.70 (0.71/0.61) at entry and at home hospitalization discharge, respectively. Conclusions: The results showed potential for feeding clinical decision support systems aimed at supporting health professionals for inclusion of candidates into the HH/ED program, and have the capacity to guide transitions toward community-based care at HH discharge.

JTD Keywords: Home hospitalization, Health risk assessment, Predictive modeling, Chronic care, Integrated care, Modeling, Hospitalization, Health risk, Prediction, Mortality, Clinical decision support