Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen-drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. MethodsWe estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen-drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drugresistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. FindingsOn the basis of our predictive statistical models, there were an estimated 4•95 million (3•62-6•57) deaths associated with bacterial AMR in 2019, including 1•27 million (95% UI 0•911-1•71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27•3 deaths per 100 000 (20•9-35•3), and lowest in Australasia, at 6•5 deaths (4•3-9•4) per 100 000. Lower respiratory infections accounted for more than 1•5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000-1 270 000) deaths attributable to AMR and 3•57 million (2•62-4•78) deaths associated with AMR in 2019. One pathogen-drug combination, meticillinresistant S aureus, caused more than 100 000 deaths attributa...

The protein kinase Pf CLK3 plays a critical role in the regulation of malarial parasite RNA splicing and is essential for the survival of blood stage Plasmodium falciparum . We recently validated Pf CLK3 as a drug target in malaria that offers prophylactic, transmission blocking, and curative potential. Herein, we describe the synthesis of our initial hit TCMDC-135051 (1) and efforts to establish a structure–activity relationship with a 7-azaindole-based series. A total of 14 analogues were assessed in a time-resolved fluorescence energy transfer assay against the full-length recombinant protein kinase Pf CLK3, and 11 analogues were further assessed in asexual 3D7 (chloroquine-sensitive) strains of P. falciparum parasites. SAR relating to rings A and B was established. These data together with analysis of activity against parasites collected from patients in the field suggest that TCMDC-135051 (1) is a promising lead compound for the development of new antimalarials with a novel mechanism of action targeting Pf CLK3.

B y the end of October 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic had spread to 6 continents and caused >45 million coronavirus disease (COVID-19) cases and 1.1 million deaths (1). Despite having 15.6% of the worldwide population (2), by October 31, 2020, Africa had only 3.9% (1.76 million) of the world's COVID-19 cases and 3.6% (42,233) of deaths during the pandemic (1). Data suggest that the pandemic is evolving differently in sub-Saharan Africa compared with the rest of the world and that the outbreak started later (3).Of note, severe COVID-19 cases seem to occur less frequently in Africa than in the rest of the world (4). Several factors have been proposed to explain this. Age is likely a major factor because older persons are at higher risk for severe disease, but Africa has an extremely young population; >60% of persons are <25 years of age (5). However, variation of CO-VID-19 severity with age alone does not fully explain the observed differences (4). Clinical cases and deaths in Africa likely are underreported because systematic surveillance is limited and no systematic death registration exists; thus, the true SARS-CoV-2 burden probably is underestimated (4). Nevertheless, local health systems in Africa, which have a lower capacity to deal with COVID-19 patients than healthcare systems in high-resource settings, were not overwhelmed, even at the peak of the epidemic (6). Although potential

The feasibility of using a sensitive polymerase chain reaction (PCR) to evaluate malaria vaccines in small group sizes was tested in 102 adult Gambian volunteers who received either the malaria vaccine regimen FP9 ME-TRAP/MVA ME-TRAP or rabies vaccine. All volunteers received the antimalarial drugs primaquine and Lapdap plus artesunate to eliminate malaria parasites. Volunteers in a further group received an additional single treatment with sulfadoxine-pyrimethamine (SP) to prevent new infections. There was substantially lower T-cell immunogenicity than in previous trials with this vaccine regimen and no protection against infection in the malaria vaccine group. Using the primary endpoint of 20 parasites per mL, no difference was found in the prevalence of low-level infections in volunteers who received SP compared with those who did not, indicating that SP did not reduce the incidence of very low-density infection. However, SP markedly reduced the incidence of higher density infections. These findings support the feasibility and potential of this approach to screen pre-erythrocytic vaccines for efficacy against infection in small numbers of vaccinees in endemic areas.

Summary During infection, increasing pathogen load stimulates both protective and harmful aspects of the host response. The dynamics of this interaction are hard to quantify in humans, but doing so could improve understanding of mechanisms of disease and protection. We sought to model the contributions of parasite multiplication rate and host response to observed parasite load in individual subjects with Plasmodium falciparum malaria, using only data obtained at the time of clinical presentation, and then to identify their mechanistic correlates. We predicted higher parasite multiplication rates and lower host responsiveness in severe malaria cases, with severe anemia being more insidious than cerebral malaria. We predicted that parasite growth-inhibition was associated with platelet consumption, lower expression of CXCL10 and type-1 interferon-associated genes, but increased cathepsin G and matrix metallopeptidase 9 expression. We found that cathepsin G and matrix metallopeptidase 9 directly inhibit parasite invasion into erythrocytes. Parasite multiplication rate was associated with host iron availability and higher complement factor H levels, lower expression of gametocyte-associated genes but higher expression of translation-associated genes in the parasite. Our findings demonstrate the potential of using explicit modelling of pathogen load dynamics to deepen understanding of host-pathogen interactions and identify mechanistic correlates of protection.