Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-w (IFN-w) (13 patients), against the 13 types of IFN-a (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men

A draft human pangenome reference

Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock

Scores on trait and state anxiety of female karateka before the commencement of shotokan karate training

BACKGROUND: Most patients with advanced cancer, debilitating COPD or chronic heart failure (CHF) live at home. General practitioners (GPs) asked for guidance in how to recognize patients in need of palliative care in a timely way and to structure anticipatory care. For that reason, we developed a training for GPs in identifying patients in need of palliative care and in structuring anticipatory palliative care planning and studied its effect on out-of-hours contacts, contacts with their own GP, hospitalizations and place of death. METHODS: We performed a cluster randomised controlled trial. GPs in the intervention group were trained in identifying patients in need of palliative care and anticipatory care planning. Next, for each identified patient, they were offered a coaching session with a specialist in palliative care to fine-tune a structured care plan. The GPs in the control group did not receive training or coaching, and were asked to provide care as usual. After one year, characteristics of patients deceased of cancer, COPD or CHF in both study groups were compared with mixed effects models for out-of-hours contacts (primary outcome), contacts with their own GP, place of death and hospitalizations in the last months of their life (secondary outcomes). As a post-hoc analysis, of identified patients (of the intervention GPs) these figures were compared to all other deceased patients, who had not been identified as in need of palliative care. RESULTS: We did not find any differences between the intervention and control group. Yet, only half of the trained GPs (28) identified patients (52), which was only 24 % of the deceased patients. Those identified patients had significantly more contacts with their own GP (B 4.5218; p <0.0006), were less often hospitalized (OR 0.485; p 0.0437) more often died at home (OR 2.126; p 0.0572) and less often died in the hospital (OR 0.380; p 0.0449). CONCLUSIONS: Although we did not find differences between the intervention and control group, we found in a post-hoc analysis that those patients that had been identified as in need of palliative care had more contacts with their GP, less hospitalizations, and more often died at home. We recommend future controlled studies that try to further increase identification of patients eligible for anticipatory palliative care. The Netherlands National Trial Register: NTR2815 date 07-04-2010

Deriving animal behaviour from high-frequency GPS: tracking cows in open and forested habitat

The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data.We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level

Deriving animal behaviour from high-frequency GPS: tracking cows in open and forested habitat

The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data.We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level

Distinct defects in collagen microarchitecture underlie vessel-wall failure in advanced abdominal aneurysms and aneurysms in Marfan syndrome

An aneurysm of the aorta is a common pathology characterized by segmental weakening of the artery. Although it is generally accepted that the vessel-wall weakening is caused by an impaired collagen metabolism, a clear association has been demonstrated only for rare syndromes such as the vascular type Ehlers-Danlos syndrome. Here we show that vessel-wall failure in growing aneurysms of patients who have aortic abdominal aneurysm (AAA) or Marfan syndrome is not related to a collagen defect at the molecular level. On the contrary our findings indicate similar (Marfan) or even higher collagen concentrations (AAA) and increased collagen cross-linking in the aneurysms. Using 3D confocal imaging we show that the two conditions are associated with profound defects in collagen microarchitecture. Reconstructions of normal vessel wall show that adventitial collagen fibers are organized in a loose braiding of collagen ribbons. These ribbons encage the vessel, allowing the vessel to dilate easily but preventing overstretching. AAA and aneurysms in Marfan syndrome show dramatically altered collagen architectures with loss of the collagen knitting. Evaluations of the functional characteristics by atomic force microscopy showed that the wall has lost its ability to stretch easily and revealed a second defect: although vascular collagen in normal aortic wall behaves as a coherent network, in AAA and Marfan tissues it does not. As result, mechanical forces loaded on individual fibers are not distributed over the tissue. These studies demonstrate that the mechanical properties of tissue are strongly influenced by collagen microarchitecture and that perturbations in the collagen networks may lead to mechanical failure.Vascular Surger

DISTINCT DEFECTS IN COLLAGEN MICRO-ARCHITECTURE UNDERLIE VESSEL WALL FAILURE IN GROWING AORTIC ABDOMINAL ANEURYSMS AND ANEURYSMS IN MARFAN SYNDROME

Vascular Surger