infections: presentation, diagnosis and treatment.

Listeriosis is an infective complication that primarily affects pregnant women, patients at extremes of age or those with weakened immune systems. Ingestion of food contaminated with is the most common source of infection, causing self-limiting illness in immunocompetent hosts but associated with invasive infection and high mortality in high-risk patient groups. Milder illness presents as gastroenteritis with fever, diarrhoea, nausea and vomiting common in the 7 days post exposure.

Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19.

Background: Bacterial infection has been challenging to diagnose in patients with COVID-19. We developed and evaluated supervised machine learning algorithms to support the diagnosis of secondary bacterial infection in hospitalized patients during the COVID-19 pandemic.

Methods: Inpatient data at three London hospitals for the first COVD-19 wave in March and April 2020 were extracted.

The general stress response of promotes tolerance of antibiotics and survival in whole human blood.

is a frequent cause of invasive human infections such as bacteraemia and infective endocarditis. These infections frequently relapse or become chronic, suggesting that the pathogen has mechanisms to tolerate the twin threats of therapeutic antibiotics and host immunity. The general stress response of is regulated by the alternative sigma factor B (σB) and provides protection from multiple stresses including oxidative, acidic and heat.

MALDI-TOF mass spectrometry on intact bacteria combined with a refined analysis framework allows accurate classification of MSSA and MRSA.

Fast and reliable detection coupled with accurate data-processing and analysis of antibiotic-resistant bacteria is essential in clinical settings. In this study, we use MALDI-TOF on intact cells combined with a refined analysis framework to demonstrate discrimination between methicillin-susceptible (MSSA) and methicillin-resistant (MRSA) Staphylococcus aureus. By combining supervised and unsupervised machine learning methods, we firstly show that the mass spectroscopy data contains strong signal for the clustering of MSSA and MRSA.

An inventory of supranational antimicrobial resistance surveillance networks involving low- and middle-income countries since 2000.

Low- and middle-income countries (LMICs) shoulder the bulk of the global burden of infectious diseases and drug resistance. We searched for supranational networks performing antimicrobial resistance (AMR) surveillance in LMICs and assessed their organization, methodology, impacts and challenges. Since 2000, 72 supranational networks for AMR surveillance in bacteria, fungi, HIV, TB and malaria have been created that have involved LMICs, of which 34 are ongoing.

International cooperation to improve access to and sustain effectiveness of antimicrobials.

Securing access to effective antimicrobials is one of the greatest challenges today. Until now, efforts to address this issue have been isolated and uncoordinated, with little focus on sustainable and international solutions. Global collective action is necessary to improve access to life-saving antimicrobials, conserving them, and ensuring continued innovation.