Dear Colleagues,

Antimicrobial resistance is considered a worldwide burden, affecting the patients of critical hospital wards, such as intensive care units (ICUs). Hospitalized patients have been demonstrated to have an increased risk of developing infections due to exposure to several invasive devices (mechanical ventilation, urinary tract catheters) and to other related conditions. Careful clinical surveillance, together with the monitoring of the well-known bacterial strains responsible for inducing HAI, may help clinicians to choose the appropriate antibiotic therapies.

Bacterial infections have impacted humans throughout the centuries, and were particularly harmful until the discovery of antibiotics, which revolutionized the treatment of infectious diseases. Because of their ability to survive in different environments, bacteria are increasingly able to face antibacterial treatments over time by means of different adaptative strategies. They can modify the quaternary structure of specific target proteins, substitute metabolic pathways by synthesizing alternative biomolecules, and produce enzymes able to inactivate antibiotics; this is also possible through the camouflage of their structure, for example, behind a proteoglycan capsule.

A common bacterial weapon against penicillin is the beta-lactamase enzyme, which alters the beta-lactamic structure, thus maintaining the building of the bacterial wall and creating the local conditions to promote several diseases. Bacteria are also able to synthesize effective isoforms of the beta-lactamase enzyme; the extended-spectrum beta lactamase (ESBL) and the ESBL carbapenemase give bacteria resistance to third-generation cephalosporins and carbapenamase-class antibiotics, respectively. These antibiotics are widely used in several nosocomial infections.

Recently, several strategies have been proposed to face this challenge, such as strong prevention or the support of computer-based analyses. The EU’s call for projects has also promoted the development of innovative artificial intelligence (AI) solutions to prevent infections inside clinical departments. In particular, an interesting ongoing project (LAOCOONTE, by Energent S.p.A.) has the objective of developing specific use cases, where data can be used by machine- and deep-learning models to evaluate the likelihood of infection in clinical departments, in an Italian clinical setting. This approach is very promising; in fact, AI currently plays an important role in different fields, from smart manufacturing to the Internet of things, human–computer interaction and medicine.

The attention paid by the scientific community and industry to the AI field is related to the excellent performance achieved in recent years by the so-called artificial neural networks, in particular the deep architectures, in various fields such as text, images and audio.

Nosocomial infections (NIs) are even more preventable, as they represent a biological and social cost for hospitalized patients. The growing availability of computerized patient records in hospitals allows for the improvement of data storage with traditional machine-learning methods, which have been shown to outperform deep learning’s performance when applied to tabular data.

Additionally, nanoparticles associated to antibiotics are used as an effective therapeutic method to combat multi-drug-resistant bacteria, and gold nanoparticles are used against viruses such as HIV and Ebola.

Prof. Dr. Danila De Vito
Dr. Antonio Parisi
Dr. Patrizia Nardulli
Dr. Maria Rita Laforgia
Guest Editors

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