[Outpatient Psychological Therapies in Children and Adolescents in Germany: Status Quo and Time Trends, 2009-2018]. / Ambulante psychotherapeutische Versorgung von Kindern und Jugendlichen in Deutschland: Status quo und Trends im Zeitraum 2009­2018.

OBJECTIVE: To determine the prevalence of outpatient psychological therapies (PT) in youths with statutory health insurance in Germany. METHODS: Based on statutory health insurance funds data for 2009-2018, the prevalence of outpatient PT was assessed, stratified by sex, age, and federal state. Psychotherapeutic specialty, coded psychiatric diagnoses, and type of PT were also analysed. RESULTS: In 2018, 7.3 % received any form of PT (2009: 7.1 %). Of these, 18.4 % (2009: 12.8 %) received therapy according to the directives for psychotherapy (dPT), with CBT (since 2012) being most frequently used. PT prevalence was highest in 15- to 19-year olds, and only marginally differed by sex. Child psychiatrists delivered the majority of PTs. Main diagnoses were anxiety/emotional disorders, ADHD, and adjustment disorders. CONCLUSION: During the studied period, PT prevalence has not changed markedly. Yet, the share of dPT has increased, with CBT ranking top.

Interpretable detection of novel human viruses from genome sequencing data.

Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.

DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks.

MOTIVATION: We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencing and metagenomics. Moreover, recent developments in synthetic biology raise concerns that some strains of bacteria could be modified for malicious purposes. Traditional approaches to open-view pathogen detection depend on databases of known organisms, which limits their performance on unknown, unrecognized and unmapped sequences. In contrast, machine learning methods can infer pathogenic phenotypes from single NGS reads, even though the biological context is unavailable. RESULTS: We present DeePaC, a Deep Learning Approach to Pathogenicity Classification. It includes a flexible framework allowing easy evaluation of neural architectures with reverse-complement parameter sharing. We show that convolutional neural networks and LSTMs outperform the state-of-the-art based on both sequence homology and machine learning. Combining a deep learning approach with integrating the predictions for both mates in a read pair results in cutting the error rate almost in half in comparison to the previous state-of-the-art. AVAILABILITY AND IMPLEMENTATION: The code and the models are available at: https://gitlab.com/rki_bioinformatics/DeePaC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrated analysis of the immunological and genetic status in and across cancer types: impact of mutational signatures beyond tumor mutational burden.

Harnessing the immune system by checkpoint blockade has greatly expanded the therapeutic options for advanced cancer. Since the efficacy of immunotherapies is influenced by the molecular make-up of the tumor and its crosstalk with the immune system, comprehensive analysis of genetic and immunologic tumor characteristics is essential to gain insight into mechanisms of therapy response and resistance. We investigated the association of immune cell contexture and tumor genetics including tumor mutational burden (TMB), copy number alteration (CNA) load, mutant allele heterogeneity (MATH) and specific mutational signatures (MutSigs) using TCGA data of 5722 tumor samples from 21 cancer types. Among all genetic variables, MutSigs associated with DNA repair deficiency and AID/APOBEC gene activity showed the strongest positive correlations with immune parameters. For smoking-related and UV-light-exposure associated MutSigs a few positive correlations were identified, while MutSig 1 (clock-like process) correlated non-significantly or negatively with the major immune parameters in most cancer types. High TMB was associated with high immune cell infiltrates in some but not all cancer types, in contrast, high CNA load and high MATH were mostly associated with low immune cell infiltrates. While a bi- or multimodal distribution of TMB was observed in colorectal, stomach and endometrial cancer where its levels were associated with POLE/POLD1 mutations and MSI status, TMB was unimodal distributed in the most other cancer types including NSCLC and melanoma. In summary, this study uncovered specific genetic-immunology associations in major cancer types and suggests that mutational signatures should be further investigated as interesting candidates for response prediction beyond TMB.

Diffusive tail anchorage determines velocity and force produced by kinesin-14 between crosslinked microtubules.

Form and function of the mitotic spindle depend on motor proteins that crosslink microtubules and move them relative to each other. Among these are kinesin-14s, such as Ncd, which interact with one microtubule via their non-processive motor domains and with another via their diffusive tail domains, the latter allowing the protein to slip along the microtubule surface. Little is known about the influence of the tail domains on the protein's performance. Here, we show that diffusive anchorage of Ncd's tail domains impacts velocity and force considerably. Tail domain slippage reduced velocities from 270 nm s-1 to 60 nm s-1 and forces from several piconewtons to the sub-piconewton range. These findings challenge the notion that kinesin-14 may act as an antagonizer of other crosslinking motors, such as kinesin-5, during mitosis. It rather suggests a role of kinesin-14 as a flexible element, pliantly sliding and crosslinking microtubules to facilitate remodeling of the mitotic spindle.

Alterations of mast cell mediator production and release by gamma-linolenic and docosahexaenoic acid.

The purpose of our investigations was to evaluate the supposed beneficial effects of gamma-linolenic (GLA) and docosahexaenoic acid (DHA) in a canine mastocytoma cell line (C2) as a model for canine atopic dermatitis. Cells were cultured in a basic medium (DEH) and in DEH supplemented with 14.3 microM GLA (DEH-GLA) or 14.3 microM DHA (DEH-DHA) for 8 days. Chymase and tryptase activity, as well as histamine and prostaglandin (PG)E(2) release were measured. To stimulate histamine and PGE(2) release, cells were incubated with the wasp venom peptide mastoparan (50 microM) for 30 min. GLA increased tryptase activity and decreased histamine release after C2 stimulation. DHA diminished PGE(2) production in activated C2. These results support the prescription of GLA- and DHA-enriched diets to reduce inflammatory signs in canine atopic dermatitis.