[Inpatient care capacities in child and adolescent psychiatry-increase in emergency admissions during the COVID-19 pandemic?] / Stationäre Versorgungskapazitäten in der Kinder- und Jugendpsychiatrie ­ Zunahme der Akutaufnahmen während der COVID-19 Pandemie?

The COVID-19 pandemic and the accompanying containment measures have had a fundamental impact on the mental health of children and adolescents. In the present study, the emergency admissions of the first two Corona years (2020/21) at the Department for Child and Adolescent Psychiatry Hall i. T./Innsbruck were retrospectively analysed and compared with the two years before the COVID-19 pandemic (2018/19). There was no change in the total number of emergency admissions in 2020 compared to 2019 before Corona, but in 2021 emergency admissions increased by 40.1%. The pre-Corona (2018/19) gender ratio of 65.4% girls to 34.6% boys did not change in 2020. In 2021, the proportion of girls increased to 74.4%. In the COVID-19 pandemic, acute suicidality increased (+48.3%) while aggression decreased (-51.0%). Acute intoxications increased in the first Corona year and decreased again in 2021. The present study results show that the mental health needs of children and adolescents increased significantly in the course of the COVID-19 pandemic and that this was also reflected in child and adolescent psychiatry. The increased demands must now be met with appropriate care and prevention measures as well as sufficient child and adolescent psychiatric bed capacities in order to mitigate the longer-term psychosocial effects of the COVID-19 pandemic as best as possible.

The tem-per-ature-dependent conformational ensemble of SARS-CoV-2 main protease (Mpro).

The COVID-19 pandemic, instigated by the SARS-CoV-2 coronavirus, continues to plague the globe. The SARS-CoV-2 main protease, or Mpro, is a promising target for the development of novel antiviral therapeutics. Previous X-ray crystal structures of Mpro were obtained at cryogenic tem-per-ature or room tem-per-ature only. Here we report a series of high-resolution crystal structures of unliganded Mpro across multiple tem-per-atures from cryogenic to physiological, and another at high humidity. We inter-rogate these data sets with parsimonious multiconformer models, multi-copy ensemble models, and isomorphous difference density maps. Our analysis reveals a perturbation-dependent conformational landscape for Mpro, including a mobile zinc ion inter-leaved between the catalytic dyad, mercurial conformational heterogeneity at various sites including a key substrate-binding loop, and a far-reaching intra-molecular network bridging the active site and dimer inter-face. Our results may inspire new strategies for antiviral drug development to aid preparation for future coronavirus pandemics.

The tem­per­ature-dependent conformational ensemble of SARS-CoV-2 main protease (Mpro)

X-ray crystallography at variable tem­per­ature for SARS-CoV-2 Mpro reveals a com­plex conformational landscape, including a mobile metal at the catalytic dyad, mercurial conformational heterogeneity at various sites, and an intra­molecular network bridging the active site and dimer inter­face. The COVID-19 pandemic, instigated by the SARS-CoV-2 coronavirus, continues to plague the globe. The SARS-CoV-2 main protease, or Mpro, is a promising target for the development of novel antiviral therapeutics. Previous X-ray crystal structures of Mpro were obtained at cryogenic tem­per­ature or room tem­per­ature only. Here we report a series of high-resolution crystal structures of unliganded Mpro across multiple tem­per­atures from cryogenic to physiological, and another at high humidity. We inter­rogate these data sets with parsimonious multiconformer models, multi-copy ensemble models, and isomorphous difference density maps. Our analysis reveals a perturbation-dependent conformational landscape for Mpro, including a mobile zinc ion inter­leaved between the catalytic dyad, mercurial conformational heterogeneity at various sites including a key substrate-binding loop, and a far-reaching intra­molecular network bridging the active site and dimer inter­face. Our results may inspire new strategies for antiviral drug development to aid preparation for future coronavirus pandemics.

Hepatitis C virus NS3/4A inhibitors and other drug-like compounds as covalent binders of SARS-CoV-2 main protease.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), threatens global public health. The world needs rapid development of new antivirals and vaccines to control the current pandemic and to control the spread of the variants. Among the proteins synthesized by the SARS-CoV-2 genome, main protease (Mpro also known as 3CLpro) is a primary drug target, due to its essential role in maturation of the viral polyproteins. In this study, we provide crystallographic evidence, along with some binding assay data, that three clinically approved anti hepatitis C virus drugs and two other drug-like compounds covalently bind to the Mpro Cys145 catalytic residue in the active site. Also, molecular docking studies can provide additional insight for the design of new antiviral inhibitors for SARS-CoV-2 using these drugs as lead compounds. One might consider derivatives of these lead compounds with higher affinity to the Mpro as potential COVID-19 therapeutics for further testing and possibly clinical trials.

Fragment binding to the Nsp3 macrodomain of SARS-CoV-2 identified through crystallographic screening and computational docking.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) macrodomain within the nonstructural protein 3 counteracts host-mediated antiviral adenosine diphosphate-ribosylation signaling. This enzyme is a promising antiviral target because catalytic mutations render viruses nonpathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of 2533 diverse fragments resulted in 214 unique macrodomain-binders. An additional 60 molecules were selected from docking more than 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several fragment hits were confirmed by solution binding using three biophysical techniques (differential scanning fluorimetry, homogeneous time-resolved fluorescence, and isothermal titration calorimetry). The 234 fragment structures explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany.

BACKGROUND: As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale. METHODS: By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations. RESULTS: A strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables' partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure. CONCLUSIONS: The multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.