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In resource-limited or point-of-care settings, rapid diagnostic tests (RDTs), that aim to simultaneously detect HIV antibodies and p24 capsid (p24CA) antigen with high sensitivity, can pose important alternatives to screen for early infections. We evaluated the performance of the antibody and antigen components of the old and novel version of the Determine™ HIV-1/2 Ag/Ab Combo RDTs in parallel to quantifications in a fourth-generation antigen/antibody immunoassay (4G-EIA), p24CA antigen immunoassay (p24CA-EIA), immunoblots, and nucleic acid quantification. We included plasma samples of acute, treatment-naïve HIV-1 infections (Fiebig stages I–VI, subtypes A1, B, C, F, CRF02_AG, CRF02_AE, URF) or chronic HIV-1 and HIV-2 infections. The tests’ antigen component was evaluated also for a panel of subtype B HIV-1 transmitted/founder (T/F) viruses, HIV-2 strains and HIV-2 primary isolates. Furthermore, we assessed the analytical sensitivity of the RDTs to detect p24CA using a highly purified HIV-1NL4-3 p24CA standard. We found that 77% of plasma samples from acutely infected, immunoblot-negative HIV-1 patients in Fiebig stages II–III were identified by the new RDT, while only 25% scored positive in the old RDT. Both RDTs reacted to all samples from chronically HIV-1-infected and acutely HIV-1-infected patients with positive immunoblots. All specimens from chronically infected HIV-2 patients scored positive in the new RDT. Of note, the sensitivity of the RDTs to detect recombinant p24CA from a subtype B virus ranged between 50 and 200 pg/mL, mirrored also by the detection of HIV-1 T/F viruses only at antigen concentrations tenfold higher than suggested by the manufacturer. The RTD failed to recognize any of the HIV-2 viruses tested. Our results indicate that the new version of the Determine™ HIV-1/2 Ag/Ab Combo displays an increased sensitivity to detect HIV-1 p24CA-positive, immunoblot-negative plasma samples compared to the precursor version. The sensitivity of 4G-EIA and p24CA-EIA to detect the major structural HIV antigen, and thus to diagnose acute infections prior to seroconversion, is still superior.
The COVID-19 pandemic has caused strains on health systems worldwide disrupting routine hospital services for all non-COVID patients. Within this retrospective study, we analyzed inpatient hospital admissions across 18 German university hospitals during the 2020 lockdown period compared to 2018. Patients admitted to hospital between January 1 and May 31, 2020 and the corresponding periods in 2018 and 2019 were included in this study. Data derived from electronic health records were collected and analyzed using the data integration center infrastructure implemented in the university hospitals that are part of the four consortia funded by the German Medical Informatics Initiative. Admissions were grouped and counted by ICD 10 chapters and specific reasons for treatment at each site. Pooled aggregated data were centrally analyzed with descriptive statistics to compare absolute and relative differences between time periods of different years. The results illustrate how care process adoptions depended on the COVID-19 epidemiological situation and the criticality of the disease. Overall inpatient hospital admissions decreased by 35% in weeks 1 to 4 and by 30.3% in weeks 5 to 8 after the lockdown announcement compared to 2018. Even hospital admissions for critical care conditions such as malignant cancer treatments were reduced. We also noted a high reduction of emergency admissions such as myocardial infarction (38.7%), whereas the reduction in stroke admissions was smaller (19.6%). In contrast, we observed a considerable reduction in admissions for non-critical clinical situations, such as hysterectomies for benign tumors (78.8%) and hip replacements due to arthrosis (82.4%). In summary, our study shows that the university hospital admission rates in Germany were substantially reduced following the national COVID-19 lockdown. These included critical care or emergency conditions in which deferral is expected to impair clinical outcomes. Future studies are needed to delineate how appropriate medical care of critically ill patients can be maintained during a pandemic.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Drebrin (DBN) regulates cytoskeletal functions during neuronal development, and is thought to contribute to structural and functional synaptic changes associated with aging and Alzheimer’s disease. Here we show that DBN coordinates stress signalling with cytoskeletal dynamics, via a mechanism involving kinase ataxia-telangiectasia mutated (ATM). An excess of reactive oxygen species (ROS) stimulates ATM-dependent phosphorylation of DBN at serine-647, which enhances protein stability and accounts for improved stress resilience in dendritic spines. We generated a humanized DBN Caenorhabditis elegans model and show that a phospho-DBN mutant disrupts the protective ATM effect on lifespan under sustained oxidative stress. Our data indicate a master regulatory function of ATM-DBN in integrating cytosolic stress-induced signalling with the dynamics of actin remodelling to provide protection from synapse dysfunction and ROS-triggered reduced lifespan. They further suggest that DBN protein abundance governs actin filament stability to contribute to the consequences of oxidative stress in physiological and pathological conditions.
