Immune Reconstitution Inflammatory Syndrome (IRIS): Incidence, Characteristics and Predictive factors

OBJECTIVES OF THE STUDY : To study the incidence and predictive factors of immune reconstitution inflammatory syndrome (IRIS) among a cohort of patients initiated on HAART and describe this syndrome among them. METHODS : A cohort of patients with HIV infection initiated on HAART at Christian Medical College and Hospital and government ART centres was prospectively followed up for a minimum of six months to observe for the development of IRIS. Baseline CD4 cell counts, WHO staging of patients, BMI and presence of previous opportunistic infections were recorded in all patients. Overall incidence and opportunistic infection specific incidences of IRIS were calculated. Clinical presentations, investigations and outcome of patients with IRIS were recorded. Baseline characteristics were compared between patients who developed IRIS and those who did not, and predictors for IRIS were identified. RESULTS : 14 cases of IRIS were diagnosed in a cohort of 109 patients initiated on HAART with an incidence of 12.8% (CI- 7.2-20.6%). The incidence of individual IRIS syndromes were: TB IRIS 8% (CI 3.8%, 15.1%), CMV IRIS 1.8% (CI 0.2%, 6.5%), Cryptococcal IRIS 1% (CI 0.02%,6.4%), PMLE IRIS 1% (CI 0.02%,6.4%) and Herpes zoster IRIS 1% (CI 0.02%,6.4%). The time to development of IRIS following initiation of HAART varied from 4-120 days with a mean of 33 days. 85% of patients developed IRIS within two months of initiation of HAART. In this study IRIS was associated with good outcomes. Clinical stage, BMI, baseline CD4 count, prior opportunistic infection, type of ART, timing of HAART treatment were not found to be significant risk factors in the development of IRIS. CONCLUSIONS : IRIS is an important clinical problem in this region which occurred at an incidence of 12.8% in this study. TB was the most common opportunistic infection presenting as IRIS which may be a reflection of the high prevalence of manifest or subclinical tuberculosis co-infection in patients with HIV being initiated on HAART in our setting. No significant predictors for IRIS were identified in this study. Outcomes in patients with IRIS appear to be fair with treatment of underlying opportunistic infections

Modelling the impact of lockdown-easing measures on cumulative COVID-19 cases and deaths in England.

OBJECTIVES: To assess the potential impacts of successive lockdown-easing measures in England, at a point in the COVID-19 pandemic when community transmission levels were relatively high. DESIGN: We developed a Bayesian model to infer incident cases and reproduction number (R) in England, from incident death data. We then used this to forecast excess cases and deaths in multiple plausible scenarios in which R increases at one or more time points. SETTING: England. PARTICIPANTS: Publicly available national incident death data for COVID-19 were examined. PRIMARY OUTCOME: Excess cumulative cases and deaths forecast at 90 days, in simulated scenarios of plausible increases in R after successive easing of lockdown in England, compared with a baseline scenario where R remained constant. RESULTS: Our model inferred an R of 0.75 on 13 May when England first started easing lockdown. In the most conservative scenario modelled where R increased to 0.80 as lockdown was eased further on 1 June and then remained constant, the model predicted an excess 257 (95% CI 108 to 492) deaths and 26 447 (95% CI 11 105 to 50 549) cumulative cases over 90 days. In the scenario with maximal increases in R (but staying ≤1), the model predicts 3174 (95% CI 1334 to 6060) excess cumulative deaths and 421 310 (95% CI 177 012 to 804 811) cases. Observed data from the forecasting period aligned most closely to the scenario in which R increased to 0.85 on 1 June, and 0.9 on 4 July. CONCLUSIONS: When levels of transmission are high, even small changes in R with easing of lockdown can have significant impacts on expected cases and deaths, even if R remains ≤1. This will have a major impact on population health, tracing systems and healthcare services in England. Following an elimination strategy rather than one of maintenance of R ≤1 would substantially mitigate the impact of the COVID-19 epidemic within England

Covid-19 in the UK: policy on children and schools

Key messagesPandemic policy on children and schools reflected UK based scientific narratives that did not align with global scientific consensusGovernment relied on evidence that downplayed the seriousness of covid-19 in children, underestimated the benefits of precautionary measures, and overestimated the harms of vaccinationReturn to school in September 2020 with minimal emphasis on masking and air quality, and inadequate support for isolation may have accelerated community transmissionThe public inquiry should explore why the UK was an international outlier in its approach to protecting children and making schools and communities safe

Complimentary Methods for Multivariate Genome-Wide Association Study Identify New Susceptibility Genes for Blood Cell Traits.

Genome-wide association studies (GWAS) have found hundreds of novel loci associated with full blood count (FBC) phenotypes. However, most of these studies were performed in a single phenotype framework without putting into consideration the clinical relatedness among traits. In this work, in addition to the standard univariate GWAS, we also use two different multivariate methods to perform the first multiple traits GWAS of FBC traits in ∼7000 individuals from the Ugandan General Population Cohort (GPC). We started by performing the standard univariate GWAS approach. We then performed our first multivariate method, in this approach, we tested for marker associations with 15 FBC traits simultaneously in a multivariate mixed model implemented in GEMMA while accounting for the relatedness of individuals and pedigree structures, as well as population substructure. In this analysis, we provide a framework for the combination of multiple phenotypes in multivariate GWAS analysis and show evidence of multi-collinearity whenever the correlation between traits exceeds the correlation coefficient threshold of r 2 >=0.75. This approach identifies two known and one novel loci. In the second multivariate method, we applied principal component analysis (PCA) to the same 15 correlated FBC traits. We then tested for marker associations with each PC in univariate linear mixed models implemented in GEMMA. We show that the FBC composite phenotype as assessed by each PC expresses information that is not completely encapsulated by the individual FBC traits, as this approach identifies three known and five novel loci that were not identified using both the standard univariate and multivariate GWAS methods. Across both multivariate methods, we identified six novel loci. As a proof of concept, both multivariate methods also identified known loci, HBB and ITFG3. The two multivariate methods show that multivariate genotype-phenotype methods increase power and identify novel genotype-phenotype associations not found with the standard univariate GWAS in the same dataset

Long reads: their purpose and place.

In recent years long-read technologies have moved from being a niche and specialist field to a point of relative maturity likely to feature frequently in the genomic landscape. Analogous to next generation sequencing, the cost of sequencing using long-read technologies has materially dropped whilst the instrument throughput continues to increase. Together these changes present the prospect of sequencing large numbers of individuals with the aim of fully characterizing genomes at high resolution. In this article, we will endeavour to present an introduction to long-read technologies showing: what long reads are; how they are distinct from short reads; why long reads are useful and how they are being used. We will highlight the recent developments in this field, and the applications and potential of these technologies in medical research, and clinical diagnostics and therapeutics

Relation Extraction from News Articles (RENA): A Tool for Epidemic Surveillance

Relation Extraction from News Articles (RENA) is a browser-based tool designed to extract key entities and their semantic relationships in English language news articles related to infectious diseases. Constructed using the React framework, this system presents users with an elegant and user-friendly interface. It enables users to input a news article and select from a choice of two models to generate a comprehensive list of relations within the provided text. As a result, RENA allows real-time parsing of news articles to extract key information for epidemic surveillance, contributing to EPIWATCH, an open-source intelligence-based epidemic warning system.Comment: Under review in AAAI 202

Association between early life antibiotic use and childhood overweight and obesity: a narrative review.

Background: Recent research implicates antibiotic use as a potential contributor to child obesity risk. In this narrative review, we examine current observational evidence on the relation between antibiotic use in early childhood and subsequent measures of child body mass. Methods: We searched PubMed, Web of Science and the Cochrane Library to identify studies that assessed antibiotic exposure before 3 years of age and subsequent measures of body mass or risk of overweight or obesity in childhood. Results: We identified 13 studies published before October 2017, based on a total of 6 81 332 individuals, which examined the relation between early life antibiotic exposure and measures of child body mass. Most studies did not appropriately account for confounding by indication for antibiotic use. Overall, we found no consistent and conclusive evidence of associations between early life antibiotic use and later child body mass [minimum overall adjusted odds ratio (aOR) reported: 1.01, 95% confidence interval (95% CI) 0.98-1.04, N = 2 60 556; maximum overall aOR reported: 2.56, 95% CI 1.36-4.79, N = 616], with no clinically meaningful increases in weight reported (maximum increase: 1.50 kg at 15 years of age). Notable methodological differences between studies, including variable measures of association and inclusion of confounders, limited more comprehensive interpretations. Conclusions: Evidence to date is insufficient to indicate that antibiotic use is an important risk factor for child obesity, or leads to clinically important differences in weight. Further comparable studies using routine clinical data may help clarify this association.This work was supported by the Wellcome Trust (grant number 206194), the African Partnership for Chronic Disease Research (Medical Research Council UK partnership grant number MR/K013491/1) and the National Institute for Health Research Cambridge Biomedical Research Centre (UK). EP is supported by the Gates Cambridge Trust