Anecdotal evidence suggests that Coronavirus disease 2019 (COVID-19), caused by the coronavirus SARS-CoV-2, exhibits differences in morbidity and mortality between sexes. Here, we present a meta-analysis of 3,111,714 reported global cases to demonstrate that, whilst there is no difference in the proportion of males and females with confirmed COVID-19, male patients have almost three times the odds of requiring intensive treatment unit (ITU) admission (OR = 2.84; 95% CI = 2.06, 3.92) and higher odds of death (OR = 1.39; 95% CI = 1.31, 1.47) compared to females. With few exceptions, the sex bias observed in COVID-19 is a worldwide phenomenon. An appreciation of how sex is influencing COVID-19 outcomes will have important implications for clinical management and mitigation strategies for this disease.

Antibodies predating infection: Immunological memory after infection with seasonal human coronaviruses (hCoVs) may potentially contribute to cross-protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Ng et al. report that in a cohort of 350 SARS-CoV-2–uninfected individuals, a small proportion had circulating immunoglobulin G (IgG) antibodies that could cross-react with the S2 subunit of the SARS-CoV-2 spike protein (see the Perspective by Guthmiller and Wilson). By contrast, COVID-19 patients generated IgA, IgG, and IgM antibodies that recognized both the S1 and S2 subunits. The anti-S2 antibodies from SARS-CoV-2–uninfected patients showed specific neutralizing activity against both SARS-CoV-2 and SARS-CoV-2 S pseudotypes. A much higher percentage of SARS-CoV-2–uninfected children and adolescents were positive for these antibodies compared with adults. This pattern may be due to the fact that children and adolescents generally have higher hCoV infection rates and a more diverse antibody repertoire, which may explain the age distribution of COVID-19 susceptibility. Science , this issue p. 1339 ; see also p. 1272

In the past decade, the emergence of machine learning (ML) applications has led to significant advances towards implementation of personalised medicine approaches for improved health care, due to the exceptional performance of ML models when utilising complex big data. The immune-mediated chronic inflammatory diseases are a group of complex disorders associated with dysregulated immune responses resulting in inflammation affecting various organs and systems. The heterogeneous nature of these diseases poses great challenges for tailored disease management and addressing unmet patient needs. Applying novel ML techniques to the clinical study of chronic inflammatory diseases shows promising results and great potential for precision medicine applications in clinical research and practice. In this review, we highlight the clinical applications of various ML techniques for prediction, diagnosis and prognosis of autoimmune rheumatic diseases, inflammatory bowel disease, autoimmune chronic kidney disease, and multiple sclerosis, as well as ML applications for patient stratification and treatment selection. We highlight the use of ML in drug development, including target identification, validation and drug repurposing, as well as challenges related to data interpretation and validation, and ethical concerns related to the use of artificial intelligence in clinical research.

The aetiology of primary chronic pain syndromes (CPS) is highly disputed. We performed a systematic review and meta-analysis aiming to assess differences in circulating cytokine levels in patients with diffuse CPS (fibromyalgia) vs healthy controls (HC). Human studies published in English from the PubMed, MEDLINE/Scopus and Cochrane databases were systematically searched from inception up to January 2020. We included full text cross-sectional or longitudinal studies with baseline cytokine measurements, reporting differences in circulating cytokine levels between fibromyalgia patients and HC. Random-effects meta-analysis models were used to report pooled effects and 95% CIs. This study is registered with PROSPERO (CRD42020193774). Our initial search yielded 324 papers and identified 29 studies (2458 participants) eligible for systematic review and 22 studies (1772 participants) suitable for meta-analysis. The systematic analysis revealed reproducible findings supporting different trends of cytokine levels when fibromyalgia patients were compared with HC, while the chemokine eotaxin, was consistently raised in fibromyalgia. Meta-analysis showed significantly increased TNF-α [standardized mean difference (SMD) = 0.36, 95% CI: 0.12, 0.60, P = 0.0034; I² = 71%, Q²P = 0.0002], IL-6 (SMD = 0.15, 95% CI: 0.003, 0.29, P = 0.045; I² = 39%, Q²P = 0.059), IL-8 (SMD = 0.26, 95% CI: 0.05, 0.47, P = 0.01; I² = 61%, Q²P = 0.005) and IL-10 (SMD = 0.61, 95% CI: 0.34, 0.89, P < 0.001; I² = 10%, Q²P = 0.34) in fibromyalgia patients compared with HC. We found evidence of significant differences in the peripheral blood cytokine profiles of fibromyalgia patients compared with HC. However, the distinctive profile associated with fibromyalgia includes both pro-inflammatory (TNF-α, IL-6, IL-8) and anti-inflammatory (IL-10) cytokines in pooled analysis, as well as chemokine (eotaxin) signatures. Further research is required to elucidate the role of cytokines in fibromyalgia.

Type 1 interferons (IFN) are an antiviral cytokine family, important in juvenile onset systemic lupus erythematosus (jSLE) which is more common in females, around puberty. We report that plasmacytoid dendritic cells (pDC) from healthy females produced more type 1 IFN after toll like receptor (TLR) 7 signaling than males, even before puberty, but that puberty itself associated with increased production of type 1 IFN. A unique human model allows us to show that this was related to X chromosome number, and serum testosterone concentration, in a manner which differed depending on the number of X chromosomes present. In addition, we have showed that pDC were more activated in females overall, and immune cell TLR7 gene expression was higher in females after puberty. Therefore, sex hormones and X chromosome number were associated individually and interactively with the type 1 IFN response, which contributes to our understanding of why females are more likely to develop an IFN mediated disease like jSLE after puberty.