Highlights Hydroxychloroquine (HCQ) 2400 mg over 5 days was used in Belgium for COVID-19. Impact of HCQ on mortality among 8075 patients with COVID-19 was assessed. Lower mortality in HCQ-treated patients as compared to supportive care. Lower mortality was irrespective of symptom duration.

This retrospective multi-center matched cohort study assessed the risk for severe COVID-19 (combination of severity indicators), intensive care unit (ICU) admission, and in-hospital mortality in hospitalized patients when infected with the Omicron variant compared to when infected with the Delta variant. The study is based on a causal framework using individually-linked data from national COVID-19 registries. The study population consisted of 954 COVID-19 patients (of which, 445 were infected with Omicron) above 18 years old admitted to a Belgian hospital during the autumn and winter season 2021–2022, and with available viral genomic data. Patients were matched based on the hospital, whereas other possible confounders (demographics, comorbidities, vaccination status, socio-economic status, and ICU occupancy) were adjusted for by using a multivariable logistic regression analysis. The estimated standardized risk for severe COVID-19 and ICU admission in hospitalized patients was significantly lower (RR = 0.63; 95% CI (0.30; 0.97) and RR = 0.56; 95% CI (0.14; 0.99), respectively) when infected with the Omicron variant, whereas in-hospital mortality was not significantly different according to the SARS-CoV-2 variant (RR = 0.78, 95% CI (0.28–1.29)). This study demonstrates the added value of integrated genomic and clinical surveillance to recognize the multifactorial nature of COVID-19 pathogenesis.

In Belgium, high-risk contacts of an infected person were offered PCR-testing irrespective of their vaccination status. We estimated vaccine effectiveness (VE) against infection and onwards transmission, controlling for previous infections, household-exposure and temporal trends. We included 301,741 tests from 25 January to 24 June 2021. Full-schedule vaccination was associated with significant protection against infection. In addition, mRNA-vaccines reduced onward transmission: VE-estimates increased to >90% when index and contact were fully vaccinated. The small number of viral-vector vaccines included limited interpretability.

The objective of this study was to investigate the incidence and risk factors associated with COVID-19 vaccine breakthrough infections. We included all persons ≥18 years that had been fully vaccinated against COVID-19 for ≥14 days, between 1 February 2021 and 5 December 2021, in Belgium. The incidence of breakthrough infections (laboratory confirmed SARS-CoV-2-infections) was determined. Factors associated with breakthrough infections were analyzed using COX proportional hazard models. Among 8,062,600 fully vaccinated adults, we identified 373,070 breakthrough infections with an incidence of 11.2 (95%CI 11.2–11.3)/100 person years. Vaccination with Ad26.COV2.S (HR1.54, 95%CI 1.52–1.56) or ChAdOx1 (HR1.68, 95%CI 1.66–1.69) was associated with a higher risk of a breakthrough infection compared to BNT162b2, while mRNA-1273 was associated with a lower risk (HR0.68, 95%CI 0.67–0.69). A prior COVID-19-infection was protective against a breakthrough infection (HR0.23, 95%CI 0.23–0.24), as was an mRNA booster (HR0.44, 95%CI 0.43–0.45). During a breakthrough infection, those who had a prior COVID-19 infection were less likely to have COVID-19 symptoms of almost all types than naïve persons. We identified risk factors associated with breakthrough infections, such as vaccination with adenoviral-vector vaccines, which could help inform future decisions on booster vaccination strategies. A prior COVID-19 infection lowered the risk of breakthrough infections and of having symptoms, highlighting the protective effect of hybrid immunity.

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Combining antibiotics with resistance reversing agents is a key strategy to overcome bacterial resistance. Upon screening antimicrobial activities of plants used in traditional medicine, we found that a leaf dichloromethane extract from the shea butter tree (Vitellaria paradoxa) had antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA) with further evidence of synergy when combined with β-lactams. Using HPLC-MS, we identified ursolic (UA) and oleanolic acids (OA) in leaves and twigs of this species, and quantified them by HPLC-UV as the major constituents in leaf extracts (21% and 6% respectively). Both pure triterpenic acids showed antimicrobial activity against reference and clinical strains of MRSA, with MICs ranging from 8–16 mg/L for UA to 32–128 mg/L for OA. They were highly synergistic with β-lactams (ampicillin and oxacillin) at subMIC concentrations. Reversion of MRSA phenotype was attributed to their capacity to delocalize PBP2 from the septal division site, as observed by fluorescence microscopy, and to disturb thereby peptidoglycan synthesis. Moreover, both compounds also inhibited β-lactamases activity of living bacteria (as assessed by inhibition of nitrocefin hydrolysis), but not in bacterial lysates, suggesting an indirect mechanism for this inhibition. In a murine model of subcutaneous MRSA infection, local administration of UA was synergistic with nafcillin to reduce lesion size and inflammatory cytokine (IL-1β) production. Thus, these data highlight the potential interest of triterpenic acids as resistance reversing agents in combination with β-lactams against MRSA.

Background SARS-CoV-2 strains evolve continuously and accumulate mutations in their genomes over the course of the pandemic. The severity of a SARS-CoV-2 infection could partly depend on these viral genetic characteristics. Here, we present a general conceptual framework that allows to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients. Methods A causal model is defined and visualized using a Directed Acyclic Graph (DAG), in which assumptions on the relationship between (confounding) variables are made explicit. Various DAGs are presented to explore specific study design options and the risk for selection bias. Next, the data infrastructure specific to the COVID-19 surveillance in Belgium is described, along with its strengths and weaknesses for the study of clinical impact of variants. Discussion A well-established framework that provides a complete view on COVID-19 disease severity among hospitalized patients by combining information from different sources on host factors, viral factors, and healthcare-related factors, will enable to assess the clinical impact of emerging SARS-CoV-2 variants and answer questions that will be raised in the future. The framework shows the complexity related to causal research, the corresponding data requirements, and it underlines important limitations, such as unmeasured confounders or selection bias, inherent to repurposing existing routine COVID-19 data registries. Trial registration Each individual research project within the current conceptual framework will be prospectively registered in Open Science Framework (OSF identifier: 10.17605/OSF.IO/UEF29). OSF project created on 18 May 2021.

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