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Inherited deficiency of the RNA lariat-debranching enzyme 1 (DBR1) is a rare etiology of brainstem viral encephalitis. The cellular basis of disease and the range of viral predisposition are unclear. We report inherited DBR1 deficiency in a 14-year-old boy who suffered from isolated SARS-CoV-2 brainstem encephalitis. The patient is homozygous for a previously reported hypomorphic and pathogenic DBR1 variant (I120T). Consistently, DBR1 I120T/I120T fibroblasts from affected individuals from this and another unrelated kindred have similarly low levels of DBR1 protein and high levels of RNA lariats. DBR1 I120T/I120T human pluripotent stem cell (hPSC)-derived hindbrain neurons are highly susceptible to SARS-CoV-2 infection. Exogenous WT DBR1 expression in DBR1 I120T/I120T fibroblasts and hindbrain neurons rescued the RNA lariat accumulation phenotype. Moreover, expression of exogenous RNA lariats, mimicking DBR1 deficiency, increased the susceptibility of WT hindbrain neurons to SARS-CoV-2 infection. Inborn errors of DBR1 impair hindbrain neuron-intrinsic antiviral immunity, predisposing to viral infections of the brainstem, including that by SARS-CoV-2.

Abstract Background Life-sustaining therapy, including heart and lung resuscitation and transfer to the intensive care department, is demanding for patients and relatives and utilizes large amounts of healthcare resources. For older patients acutely admitted to the hospital, very sparse data exist on decision making about life-sustaining therapy. Methods Retrospective data were extracted from patients ≥ 70 years old who were acutely admitted to the hospital. Age, sex, clinical frailty scale score and Charlson comorbidity index were manually extracted from patients’ files. Furthermore, data about life-sustaining treatment decisions were extracted. This was further divided into decisions documented within 24 h from admission or during the hospital stay. Results Data were extracted for 200 patients with a median age of 80 years. Patients had a Charlson Comorbidity Index of 6 (5–8 IQR) and a Clinical Frailty Scale (CFS) score of 5 (3–6 IQR). During the first 24 h, 61 patients (30.5%) had a written decision about heart and cardiopulmonary resuscitation (CPR), and 52 patients (26%) had written information about intensive care therapy. A total of 93 patients (46.5%) had a written decision about cardiopulmonary resuscitation (CPR), intensive care therapy or both during their hospital stay. With increasing Charlson Comorbidity Index and Clinical Frailty Scale scores, more patients had decisions about limitations in therapy documented in their files. Conclusions Within the first 24 h, 30.5% of the patients had a written decision about cardiopulmonary resuscitation (CPR), and 26% had written information about intensive care therapy. These numbers increased to 46.5% of patients who had a decision made during their hospital stay whether they were candidates for either cardiopulmonary resuscitation (CPR), intensive care therapy or both. These data suggest that further work should be done to determine the limitations of therapy early on the admission for all older frail acutely admitted patients.

Abstract Crystalline solids play a fundamental role in a host of materials and technologies, ranging from pharmaceuticals to renewable energy. The thermodynamic properties of these solids are crucial determinants of their stability and therefore their behavior. The advent of large density functional theory databases with properties of solids has stimulated research on predictive methods for their thermodynamic properties, especially for the enthalpy of formation Δf H. Increasingly sophisticated artificial intelligence and machine learning (ML) models have primarily driven development in this field in recent years. However, these models can suffer from lack of generalizability and poor interpretability. In this work, we explore a different route and develop and evaluate a framework for the application of reaction network (RN) theory to the prediction of Δf H of crystalline solids. For an experimental dataset of 1550 compounds we are able to obtain a mean absolute error w.r.t Δf H of 29.6 meV atom−1 using the RN approach. This performance is better than existing ML-based predictive methods and close to the experimental uncertainty. Moreover, we show that the RN framework allows for straightforward estimation of the uncertainty of the predictions.

Abstract Gestational diabetes mellitus (GDM) adversely affects offspring glucose homeostasis and risk of developing obesity. Here, we examined the association between glycemia in pregnant women with overweight or obesity without GDM and offspring metabolic health. Maternal fasting glucose concentrations and glucose 2-h after an oral glucose tolerance test (OGTT) were measured in 208 women with a pre-pregnancy body mass index (BMI) of 28–45 kg/m2 without GDM. Offspring outcomes were collected at birth, 3, and 5 years of age. Linear mixed models with time as fixed factor and subject ID as random effects were used for analysis. No associations were found between maternal fasting or 2-h glucose concentrations with offspring glucose and insulin concentrations from birth to 5 years of age. However, maternal fasting glucose in GW 28 and 36, and 2-h OGTT glucose in GW 28 were positively associated with C-peptide concentration at birth. Maternal fasting glucose concentrations in GW 28 and 36 were positively associated with weight-for-length, and maternal fasting glucose in GW 36 was associated with BMI z-score at birth. In summary, blood glucose in pregnant women with overweight or obesity is positively associated with offspring C-peptide concentration, weight-for-length, and BMI z-score at birth, even in the absence of GDM.

Abstract Background Without increasing radiation exposure, ultralow-dose computed tomography (CT) of the chest provides improved diagnostic accuracy of radiological pneumonia diagnosis compared to a chest radiograph. Yet, radiologist resources to rapidly report the chest CTs are limited. This study aimed to assess the diagnostic accuracy of emergency clinicians’ assessments of chest ultralow-dose CTs for community-acquired pneumonia using a radiologist’s assessments as reference standard. Methods This was a cross-sectional diagnostic accuracy study. Ten emergency department clinicians (five junior clinicians, five consultants) assessed chest ultralow-dose CTs from acutely hospitalised patients suspected of having community-acquired pneumonia. Before assessments, the clinicians attended a focused training course on assessing ultralow-dose CTs for pneumonia. The reference standard was the assessment by an experienced emergency department radiologist. Primary outcome was the presence or absence of pulmonary opacities consistent with community-acquired pneumonia. Sensitivity, specificity, and predictive values were calculated using generalised estimating equations. Results All clinicians assessed 128 ultralow-dose CTs. The prevalence of findings consistent with community-acquired pneumonia was 56%. Seventy-eight percent of the clinicians’ CT assessments matched the reference assessment. Diagnostic accuracy estimates were: sensitivity = 83% (95%CI: 77–88), specificity = 70% (95%CI: 59–81), positive predictive value = 80% (95%CI: 74–84), negative predictive value = 78% (95%CI: 73–82). Conclusion This study found that clinicians could assess chest ultralow-dose CTs for community-acquired pneumonia with high diagnostic accuracy. A higher level of clinical experience was not associated with better diagnostic accuracy.

Abstract Diagnosing patients in the medical emergency department is complex and this is expected to increase in many countries due to an ageing population. In this study we investigate the feasibility of training machine learning algorithms to assist physicians handling the complex situation in the medical emergency departments. This is expected to reduce diagnostic errors and improve patient logistics and outcome. We included a total of 9,190 consecutive patient admissions diagnosed and treated in two hospitals in this cohort study. Patients had a biochemical workup including blood and urine analyses on clinical decision totaling 260 analyses. After adding nurse-registered data we trained 19 machine learning algorithms on a random 80% sample of the patients and validated the results on the remaining 20%. We trained algorithms for 19 different patient outcomes including the main outcomes death in 7 (Area under the Curve (AUC) 91.4%) and 30 days (AUC 91.3%) and safe-discharge(AUC 87.3%). The various algorithms obtained areas under the Receiver Operating Characteristics -curves in the range of 71.8–96.3% in the holdout cohort (68.3–98.2% in the training cohort). Performing this list of biochemical analyses at admission also reduced the number of subsequent venipunctures within 24 h from patient admittance by 22%. We have shown that it is possible to develop a list of machine-learning algorithms with high AUC for use in medical emergency departments. Moreover, the study showed that it is possible to reduce the number of venipunctures in this cohort.

Background: The transition from school to adult life is challenging for young people with intellectual disability. The study aimed to explore how young people with intellectual disability themselves experience the transition from school to adult life. Methods: A co-designed, qualitative study. Thematic data analysis of qualitative survey responses, semi-structured interviews, and focus groups with 27 young people with intellectual disability in three Australian states. Results: Participants found transition planning at school inconsistent or lacking and felt excluded from decision-making about their lives. Accessing meaningful services, training, and employment beyond volunteering was challenging and enduring, leaving participants in perpetual state of transition, feeling lost, and missing out of post-school adult milestones. Conclusions: Policy, system, and service gaps must be addressed with a nationally consistent and accountable approach that truly supports choice and control for young people with intellectual disability in transitioning from school into meaningful adult lives.