Screening for autism spectrum disorders with the brief infant-toddler social and emotional assessment

Objective: Using parent-completed questionnaires in (preventive) child health care can facilitate the early detection of psychosocial problems and psychopathology, including autism spectrum disorders (ASD). A promising questionnaire for this purpose is the Brief Infant-Toddler Social and Emotional Assessment (BITSEA). The screening accuracy with regard to ASD of the BITSEA Problem and Competence scales and a newly calculated Autism score were evaluated. Method: Data, that was collected between April 2010 and April 2011, from a community sample of 2-year-olds (N = 3127), was combined with a sample of preschool children diagnosed with ASD (N = 159). For the total population and for subgroups by child's gender, area under the Receiver Operating Characteristic (ROC) curve was examined, and across a range of BITSEA Problem, Competence and Autism scores, sensitivity, specificity, positive and negative likelihood ratio's, diagnostic odds ratio and Youden's index were reported. Results: The area under the ROC curve (95% confidence interval, [95%CI]) of the Problem scale was 0.90(0.87-0.92), of the Competence scale 0.93(0.91-0.95), and of the Autism score 0.95(0.93-0.97). For the total population, the screening accuracy of the Autism score was significantly better, compared to the Problem scale. The screening accuracy of the Competence scale was significantly better for girls (AUC = 0.97; 95%CI = 0.95-0.98) than for boys (AUC = 0.91; 95%CI = 0.88-0.94). Conclusion: The results indicate that the BITSEA scales and newly calculated Autism score have good discriminative power to differentiate children with and without ASD. Therefore, the BITSEA may be helpful in the early detection of ASD, which could have beneficial effects on the child's development

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

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization 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