Clinical recrudescence of chronic untreated P. malariae infection after BNT162b2 CoVID-19 vaccine

We described a case of clinical reactivation of chronic P. malariae infection following CoVID-19 vaccination with BNT162b2 (Pifzer-Biontech CoVID-19 vaccine) in a 48-year old Italian man.The patient came to our attention for fever of unknown origin show a quartan pattern (every third day) associated to splenomegaly, the onset of the fever occurred one month after CoVID-19 vaccination with BNT162b2. P. malariae was diagnosed using Carestart™ malaria rapid test and Polymerase-Chain Reaction. Post-vaccine transient reduction of immune reactivity is described in literature, although the mechanism is unknown

Chlorpromazine and amitriptyline are substrates and inhibitors of the acrb multidrug efflux pump

Efflux is an important mechanism in Gram-negative bacteria conferring multidrug resistance. Inhibition of efflux is an encouraging strategy to restore the antibacterial activity of antibiotics. Chlorpromazine and amitriptyline have been shown to behave as efflux inhibitors. However, their mode of action is poorly under-stood. Exposure of Salmonella enterica serovar Typhimurium and Escherichia coli to chlorpromazine selected for mutations within genes encoding RamR and MarR, regu-lators of the multidrug tripartite efflux pump AcrAB-TolC. Further experiments with S. Typhimurium containing AcrB D408A (a nonfunctional efflux pump) and chlor-promazine or amitriptyline resulted in the reversion of the mutant acrB allele to the wild type. Together, this suggests these drugs are AcrB efflux substrates. Subsequent docking studies with AcrB from S. Typhimurium and E. coli, followed by molecular dynamics simulations and free energy calculations showed that chlorpromazine and amitriptyline bind at the hydrophobic trap, a preferred binding site for substrates and inhibitors within the distal binding pocket of AcrB. Based on these simulations, we suggest that chlorpromazine and amitriptyline inhibit AcrB-mediated efflux by in-terfering with substrate binding. Our findings provide evidence that these drugs are substrates and inhibitors of AcrB, yielding molecular details of their mechanism of action and informing drug discovery of new efflux inhibitors. IMPORTANCE Efflux pumps of the resistance nodulation-cell division (RND) super-family are major contributors to multidrug resistance for most of the Gram-negative ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acineto-bacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) pathogens. The development of inhibitors of these pumps would be highly desirable; how-ever, several issues have thus far hindered all efforts at designing new efflux in-hibitory compounds devoid of adverse effects. An alternative route to de novo design relies on the use of marketed drugs, for which side effects on human health have been already assessed. In this work, we provide experimental evidence that the antipsychotic drugs chlorpromazine and amitriptyline are inhibi-tors of the AcrB transporter, the engine of the major RND efflux pumps in Escherichia coli and Salmonella enterica serovar Typhimurium. Furthermore, in silico calculations have provided a molecular-level picture of the inhibition mechanism, allowing rationalization of experimental data and paving the way for similar studies with other classes of marketed compounds

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergency.

Aims- The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. Methods- This was an observational study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients\u2019 medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO 2 /FiO 2 ratio <150 mmHg in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Shapley Additive exPlanations values were used to quantify the positive or negative impact of each variable included in each model on the predicted outcome. Results- A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth \u201cboosted mixed model\u201d included 20 variables was selected from the model 3, achieved the best predictive performance (AUC=0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example. Conclusion- This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels

Tackling antibiotic resistance: the environmental framework

Antibiotic resistance is a threat to human and animal health worldwide, and key measures are required to reduce the risks posed by antibiotic resistance genes that occur in the environment. These measures include the identification of critical points of control, the development of reliable surveillance and risk assessment procedures, and the implementation of technological solutions that can prevent environmental contamination with antibiotic resistant bacteria and genes. In this Opinion article, we discuss the main knowledge gaps, the future research needs and the policy and management options that should be prioritized to tackle antibiotic resistance in the environment