A circle swimmer at low Reynolds number

Swimming in circles occurs in a variety of situations at low Reynolds number. Here we propose a simple model for a swimmer that undergoes circular motion, generalising the model of a linear swimmer proposed by Najafi and Golestanian (Phys. Rev. E 69, 062901 (2004)). Our model consists of three solid spheres arranged in a triangular configuration, joined by two links of time-dependent length. For small strokes, we discuss the motion of the swimmer as a function of the separation angle between its links. We find that swimmers describe either clockwise or anticlockwise circular motion depending on the tilting angle in a non-trivial manner. The symmetry of the swimmer leads to a quadrupolar decay of the far flow field. We discuss the potential extensions and experimental realisation of our model.Comment: 9 pages, 9 Figure

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Cancer, immunodeficiency and antiretroviral treatment: Results from the Australian HIV Observational Database (AHOD)

The objective of the study was to conduct a within-cohort assessment of risk factors for incident AIDS-defining cancers (ADCs) and non-ADCs (NADCs) within the Australian HIV Observational Database (AHOD).A total of 2181 AHOD registrants were linked to the National AIDS Registry/National HIV Database (NAR/NHD) and the Australian Cancer Registry to identify those with a notified cancer diagnosis. Included in the current analyses were cancers diagnosed after HIV infection. Risk factors for cancers were also assessed using logistic regression methods.One hundred and thirty-nine cancer cases were diagnosed after HIV infection among 129 patients. More than half the diagnoses (n = 68; 60%) were ADCs, of which 69% were Kaposi's sarcoma and 31% non-Hodgkin's lymphoma. Among the NADCs, the most common cancers were melanoma (n = 10), lung cancer (n = 6), Hodgkin's lymphoma (n = 5) and anal cancer (n = 5). Over a total of 21021 person-years (PY) of follow-up since HIV diagnosis, the overall crude cancer incidence rate for any cancer was 5.09/1000 PY. The overall rate of cancers decreased from 15.9/1000 PY [95% confidence interval (CI) 9.25-25.40/1000 PY] for CD4 counts 350 cells/μL. Lower CD4 cell count and prior AIDS diagnoses were significant predictors for both ADCs and NADCs.ADCs remain the predominant cancers in this population, although NADC rates have increased in the more recent time period. Immune deficiency is a risk factor for both ADCs and NADCs

Cardiovascular disease and diabetes in HIV-positive and HIV-negative gay and bisexual men over the age of 55 years in Australia: insights from the Australian Positive & Peers Longevity Evaluation Study

Objectives: As HIV-positive people age, diagnosis and management of comorbidities associated with ageing are of increasing concern. In this study, we aimed to compare the self-reported prevalences of heart disease, stroke, thrombosis and diabetes in older Australian HIV-positive and HIV-negative gay and bisexual men (GBM). Methods: We analysed data from the Australian Positive & Peers Longevity Evaluation Study (APPLES), a study of a prospectively recruited cross-sectional sample of 228 (51.1%) HIV-positive and 218 (48.9%) HIV-negative GBM, aged >= 55 years. Regression methods were used to assess the association of HIV status with self-reported comorbidities. Results: Of 446 patients, 389 [200 (51.4%) HIV-positive] reported their disease history. The reported prevalence of comorbidities was higher in the HIV-positive group than in the HIV-negative group: heart disease, 19.5 versus 12.2%; stroke, 7.5 versus 4.2%; thrombosis, 10.5 versus 4.2%; and diabetes, 15.0 versus 9.0%, respectively. In adjusted analyses, HIV-positive GBM had significantly increased odds of reporting heart disease [adjusted odds ratio (aOR) 1.99; P = 0.03] and thrombosis (aOR 2.87; P = 0.01). In our analysis, HIV status was not significantly associated with either age at diagnosis of heart disease (median 53 years for HIV-positive GBM versus 55 years for HIV-negative GBM; P = 0.64) or 5-year cardiovascular disease (CVD) risk estimated using the Framingham risk score. Conclusions: HIV-positive GBM more commonly reported heart disease and thrombosis compared with their HIV-negative peers. These results further highlight the need to understand the impact of HIV on age-related comorbidities in GBM, to guide optimal screening and treatment strategies to reduce the risk of these comorbidities among the HIV-positive population

Comment on the article by J. Elgeti, U. B. Kaupp, and G. Gompper : hydrodynamics of sperm cells near surfaces

A recent study by Elgeti et al. used multiparticle collision dynamics to simulate a long-standing problem: the approach of sperm to surfaces, and subsequent accumulation. The authors highlight differences in their predictions with those of the earlier Stokes flow simulations of Smith et al. attributing the differences to methodological flaws in the earlier article. In this Comment, we discuss the criticisms leveled in detail, and review some recently published work that shows how species-specific details of cell morphology provides a more likely explanation for the differing predictions of the two studies. We also highlight experimental work that supports the study of Smith et al

Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction

Aims: The lack of effective therapies for patients with heart failure with preserved ejection fraction (HFpEF) is often ascribed to the heterogeneity of patients with HFpEF. We aimed to identify distinct pathophysiologic clusters of HFpEF based on circulating biomarkers. Methods and results: We performed an unsupervised cluster analysis using 363 biomarkers from 429 patients with HFpEF. Relative differences in expression profiles of the biomarkers between clusters were assessed and used for pathway over-representation analyses. We identified four distinct patient subgroups based on their biomarker profiles: cluster 1 with the highest prevalence of diabetes mellitus and renal disease; cluster 2 with oldest age and frequent age-related comorbidities; cluster 3 with youngest age, largest body size, least symptoms and lowest N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels; and cluster 4 with highest prevalence of ischaemic aetiology, smoking and chronic lung disease, most symptoms, as well as highest NT-proBNP and troponin levels. Over a median follow-up of 21 months, the occurrence of death or heart failure hospitalization was highest in clusters 1 and 4 (62.1% and 62.8%, respectively) and lowest in cluster 3 (25.6%). Pathway over-representation analyses revealed that the biomarker profile of patients in cluster 1 was associated with activation of inflammatory pathways while the biomarker profile of patients in cluster 4 was specifically associated with pathways implicated in cell proliferation regulation and cell survival. Conclusion: Unsupervised cluster analysis based on biomarker profiles identified mutually exclusive subgroups of patients with HFpEF with distinct biomarker profiles, clinical characteristics and outcomes, suggesting different underlying pathophysiological pathways. © 2021 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology