Low complexity domains of the nucleocapsid protein of SARS-CoV-2 form amyloid fibrils.

The self-assembly of the Nucleocapsid protein (NCAP) of SARS-CoV-2 is crucial for its function. Computational analysis of the amino acid sequence of NCAP reveals low-complexity domains (LCDs) akin to LCDs in other proteins known to self-assemble as phase separation droplets and amyloid fibrils. Previous reports have described NCAP's propensity to phase-separate. Here we show that the central LCD of NCAP is capable of both, phase separation and amyloid formation. Within this central LCD we identified three adhesive segments and determined the atomic structure of the fibrils formed by each. Those structures guided the design of G12, a peptide that interferes with the self-assembly of NCAP and demonstrates antiviral activity in SARS-CoV-2 infected cells. Our work, therefore, demonstrates the amyloid form of the central LCD of NCAP and suggests that amyloidogenic segments of NCAP could be targeted for drug development.

Improving the Efficiency of Geographic Target Regions for Healthcare Interventions.

Appropriate prioritisation of geographic target regions (TRs) for healthcare interventions is critical to ensure the efficient distribution of finite healthcare resources. In delineating TRs, both 'targeting efficiency', i.e., the return on intervention investment, and logistical factors, e.g., the number of TRs, are important. However, existing approaches to delineate TRs disproportionately prioritise targeting efficiency. To address this, we explored the utility of a method found within conservation planning: the software Marxan and an extension, MinPatch ('Marxan + MinPatch'), with comparison to a new method we introduce: the Spatial Targeting Algorithm (STA). Using both simulated and real-world data, we demonstrate superior performance of the STA over Marxan + MinPatch, both with respect to targeting efficiency and with respect to adequate consideration of logistical factors. For example, by design, and unlike Marxan + MinPatch, the STA allows for user-specification of a desired number of TRs. More broadly, we find that, while Marxan + MinPatch does consider logistical factors, it also suffers from several limitations, including, but not limited to, the requirement to apply two separate software tools, which is burdensome. Given these results, we suggest that the STA could reasonably be applied to help prevent inefficiencies arising due to targeting of interventions using currently available approaches.

Safety of Four COVID-19 Vaccines across Primary Doses 1, 2, 3 and Booster: A Prospective Cohort Study of Australian Community Pharmacy Vaccinations.

Four COVID-19 vaccines are approved for use in Australia: Pfizer-BioNTech BNT162b2 (Comirnaty), AstraZeneca ChAdOx1 (Vaxzevria), Moderna mRNA-1273 (Spikevax) and Novavax NVX-CoV2373 (Nuvaxovid). We sought to examine adverse events following immunisation (AEFI) at days 3 and 42 after primary doses 1, 2, 3 and booster. We conducted active vaccine safety surveillance from 130 community pharmacies in Australia integrated with AusVaxSafety, between August 2021-April 2022. Main outcomes: AEFI at 0-3 days post-vaccination; medical review/advice at 3 days and 42 days post-vaccination; SARS-CoV-2 breakthrough infection by day 42. Of 110,024 completed day 3 surveys (43.6% response rate), 50,367 (45.8%) reported any AEFI (highest proportions: Pfizer 42%, primary dose 3; AstraZeneca 58.3%, primary dose 1; Moderna 65.4% and Novavax 58.8%, both primary dose 2). The most common AEFI reported across all doses/vaccines were local reactions, systemic aches and fatigue/tiredness. Overall, 2172/110,024 (2.0%) and 1182/55,329 (2.1%) respondents sought medical review at days 3 and 42, respectively, and 931/42,318 (2.2%) reported breakthrough SARS-CoV-2 infection at day 42. We identified similar AEFI profiles but at lower proportions than previously reported for Pfizer, AstraZeneca, Moderna and Novavax COVID-19 vaccines. Moderna vaccine was the most reactogenic and associated with higher AEFI proportions across primary doses 2, 3, and booster.

Higher premorbid serum testosterone predicts COVID-19-related mortality risk in men.

Objective: Men are at greater risk from COVID-19 than women. Older, overweight men, and those with type 2 diabetes, have lower testosterone concentrations and poorer COVID-19-related outcomes. We analysed the associations of premorbid serum testosterone concentrations, not confounded by the effects of acute SARS-CoV-2 infection, with COVID-19-related mortality risk in men. Design: This study is a United Kingdom Biobank prospective cohort study of community-dwelling men aged 40-69 years. Methods: Serum total testosterone and sex hormone-binding globulin (SHBG) were measured at baseline (2006-2010). Free testosterone values were calculated (cFT). the incidence of SARS-CoV-2 infections and deaths related to COVID-19 were ascertained from 16 March 2020 to 31 January 2021 and modelled using time-stratified Cox regression. Results: In 159 964 men, there were 5558 SARS-CoV-2 infections and 438 COVID-19 deaths. Younger age, higher BMI, non-White ethnicity, lower educational attainment, and socioeconomic deprivation were associated with incidence of SARS-CoV-2 infections but total testosterone, SHBG, and cFT were not. Adjusting for potential confounders, higher total testosterone was associated with COVID-19-related mortality risk (overall trend P = 0.008; hazard ratios (95% CIs) quintile 1, Q1 vs Q5 (reference), 0.84 (0.65-1.12) Q2:Q5, 0.82 (0.63-1.10); Q3:Q5, 0.80 (0.66-1.00); Q4:Q5, 0.82 (0.75-0.93)). Higher SHBG was also associated with COVID-19 mortality risk (P = 0.008), but cFT was not (P = 0.248). Conclusions: Middle-aged to older men with the highest premorbid serum total testosterone and SHBG concentrations are at greater risk of COVID-19-related mortality. Men could be advised that having relatively high serum testosterone concentrations does not protect against future COVID-19-related mortality. Further investigation of causality and potential underlying mechanisms is warranted.

Active vaccine safety surveillance of seasonal influenza vaccination via a scalable, integrated system in Western Australian pharmacies: a prospective cohort study.

OBJECTIVES: We integrated an established participant-centred active vaccine safety surveillance system with a cloud-based pharmacy immunisation-recording program in order to measure adverse events following immunisation (AEFI) reported via the new surveillance system in pharmacies, compared with AEFI reported via an existing surveillance system in non-pharmacy sites (general practice and other clinics). DESIGN: A prospective cohort study. PARTICIPANTS AND SETTING: Individuals >10 years receiving influenza immunisations from 22 pharmacies and 90 non-pharmacy (general practice and other clinic) sites between March and October 2020 in Western Australia. Active vaccine safety surveillance was conducted using short message service and smartphone technology, via an opt-out system. OUTCOME MEASURES: Multivariable logistic regression was used to assess the primary outcome: differences in proportions of AEFI between participants immunised in pharmacies compared with non-pharmacy sites, adjusting for confounders of age, sex and influenza vaccine brand. A subgroup analysis of participants over 65 years was also performed. RESULTS: Of 101 440 participants (6992 from pharmacies; 94 448 from non-pharmacy sites), 77 498 (76.4%) responded; 96.1% (n=74 448) within 24 hours. Overall, 4.8% (n=247) pharmacy participants reported any AEFI, compared with 6% (n=4356) non-pharmacy participants (adjusted OR: 0.87; 95% CI: 0.76 to 0.99; p=0.039). Similar proportions of AEFIs were reported in pharmacy (5.8%; n=31) and non-pharmacy participants (6; n=1617) aged over 65 years (adjusted OR: 0.94; 95% CI: 0.65 to 1.35; p=0.725). The most common AEFIs in pharmacy were: pain (2%; n=104), tiredness (1.9%; n=95) and headache (1.7%; n=88); and in non-pharmacy sites: pain (2.3%; n=1660), tiredness (1.9%; n=1362) and swelling (1.5%; n=1121). CONCLUSIONS: High and rapid response rates demonstrate good participant engagement with active surveillance in both pharmacy and non-pharmacy participants. Significantly fewer AEFIs reported after pharmacist immunisations compared with non-pharmacy immunisations, with no difference in older adults, may suggest different cohorts attend pharmacy versus non-pharmacy immunisers. The integrated pharmacy system is rapidly scalable across Australia with global potential.