Of 6812 callers, 492 (7.2%) were acutely hospitalised. Most callers rated their health as being excellent to good (65.3%) and 61% rated their worry to be low (DOW 1-3). Both the association between DOW and acute hospitalisation and SRH and acute hospitalisation indicated a dose-response relationship DOW 1=ref, 3=1.8 (1.1;3.1), 5=3.5 (2.0;5.9) and SRH 1=ref, 3=0.8 (0.6;1.4), 5=1.6 (1.1;2.4). The association between DOW and acute hospitalisation decreased slightly, when further adjusting for SRH, whereas the estimates for SRH weakened markedly when including DOW. DOW and poor SRH were associated with acute hospitalisation. However, DOW had a stronger association with hospitalisation than SRH. This suggests that DOW may capture acutely ill patients' perception of urgency better than SRH in relation to acute hospitalisation after calling a medical helpline. NCT02979457.NCT02979457. Combinations of unhealthy lifestyle factors are strongly associated with mortality, cardiovascular disease (CVD) and cancer. It is unclear how socioeconomic status (SES) affects those associations. Lower SES groups may be disproportionately vulnerable to the effects of unhealthy lifestyle factors compared with higher SES groups via interactions with other factors associated with low SES (eg, stress) or via accelerated biological ageing. This systematic review aims to synthesise studies that examine how SES moderates the association between lifestyle factor combinations and adverse health outcomes. Greater understanding of how lifestyle risk varies across socioeconomic spectra could reduce adverse health by (1) identifying novel high-risk groups or targets for future interventions and (2) informing research, policy and interventions that aim to support healthy lifestyles in socioeconomically deprived communities. Three databases will be searched (PubMed, EMBASE, CINAHL) from inception to March 2020. Referewed publication, professional networks, social media and conference presentations. CRD42020172588.CRD42020172588.Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring "virtual FC" from empirical SC or "virtual SC" from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.Dravet syndrome (DS) is a developmental and epileptic encephalopathy with an increased incidence of sudden death. Evidence of interictal breathing deficits in DS suggests that alterations in subcortical projections to brainstem nuclei may exist, which might be driving comorbidities in DS. The aim of this study was to determine whether a subcortical structure, the bed nucleus of the stria terminalis (BNST) in the extended amygdala, is activated by seizures, exhibits changes in excitability, and expresses any alterations in neurons projecting to a brainstem nucleus associated with respiration, stress response, and homeostasis. Experiments were conducted using F1 mice generated by breeding 129.Scn1a+/- mice with wild-type C57BL/6J mice. Immunohistochemistry was performed to quantify neuronal c-fos activation in DS mice after observed spontaneous seizures. Whole-cell patch-clamp and current-clamp electrophysiology recordings were conducted to evaluate changes in intrinsic and synaptic excitability in the BNST. Spontaneous seizures in DS mice significantly enhanced neuronal c-fos expression in the BNST. Further, the BNST had altered AMPA/NMDA postsynaptic receptor composition and showed changes in spontaneous neurotransmission, with greater excitation and decreased inhibition. Reversine BNST to parabrachial nucleus (PBN) projection neurons exhibited intrinsic excitability in wild-type mice, while these projection neurons were hypoexcitable in DS mice. The findings suggest that there is altered excitability in neurons of the BNST, including BNST-to-PBN projection neurons, in DS mice. These alterations could potentially be driving comorbid aspects of DS outside of seizures, including respiratory dysfunction and sudden death. Viral infections may trigger type 1 diabetes (T1D), and recent reports suggest an increased incidence of paediatric T1D and/or diabetic ketoacidosis (DKA) during the COVID-19 pandemic. To study whether the number of children admitted to the paediatric intensive care unit (PICU) for DKA due to new-onset T1D increased during the COVID-19 pandemic, and whether SARS-CoV-2 infection plays a role. This retrospective cohort study comprises two datasets (1) children admitted to PICU due to new-onset T1D and (2) children diagnosed with new-onset T1D and registered to the Finnish Pediatric Diabetes Registry in the Helsinki University Hospital from 1 April to 31 October in 2016-2020. We compared the incidence, number and characteristics of children with newly diagnosed T1D between the prepandemic and pandemic periods. The number of children admitted to PICU due to new-onset T1D increased from an average of 6.25 admissions in 2016-2019 to 20 admissions in 2020 (incidence rate ratio [IRR] 3.24 [95% CI 1.80 to 5.83]; p=0.