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Systemic sclerosis is a multisystemic autoimmune disease characterized by vasculopathy and fibrosis. Racial factors exert a significant influence on the epidemiology, clinical manifestations, antibody profile, mortality and genetic factors in systemic sclerosis. In this review, we examined Asian systemic sclerosis cohorts reported in Asia and multi-racial cohort studies to evaluate the disease characteristics and outcomes of systemic sclerosis in Asians. Asian patients have distinct genetic susceptibility to systemic sclerosis, younger age of systemic sclerosis onset, higher frequency of diffuse skin involvement, different autoantibody profiles such as higher frequency of anti-Scl70 and anti-U1-RNP antibodies, and more severe clinical phenotype. There was a suggestion of poorer survival among Asians that may be contributed by more severe disease, socioeconomic factors and differences in healthcare systems. Recognizing the influence of racial differences in systemic sclerosis disease course is important as it has implications for appropriate treatment, monitoring and prognostication.

ObjectiveNailfold capillaroscopy (NFC) is increasingly used in the early identification of systemic sclerosis (SSc)-related disorders. A consensus "Fast Track algorithm" was developed by the European Alliance of Associations for Rheumatology to aid differentiation of scleroderma from nonscleroderma pattern on NFC. Our objective was to evaluate the online training of NFC using the Fast Track algorithm in the assessment of scleroderma vs nonscleroderma NFC pattern.MethodsParticipants attended the NFC online training workshop and were taught the Fast Track algorithm. Following the training, participants independently evaluated 45 NFC images in the same session, and then 2 to 4 weeks later, through the online platform. Participants had to differentiate between scleroderma vs nonscleroderma pattern, and additionally nonscleroderma pattern (normal) vs nonscleroderma pattern (nonspecific). The inter- and intrarater Cohen κ agreement was calculated.ResultsNinety-eight participants took part in the baseline evaluation, and 61 in the reevaluation session. For identification of scleroderma vs nonscleroderma pattern, the mean (95% CI) inter- and intrarater κ were 0.86 (0.83-0.88) and 0.83 (0.79-0.87), respectively. The overall inter- and intrarater κ in the identification of scleroderma, nonscleroderma (normal), and nonscleroderma (nonspecific) patterns were 0.71 (0.69-0.74) and 0.71 (0.67-0.75), respectively. For nonscleroderma (normal) vs nonscleroderma (nonspecific) pattern, the inter- and intrarater κ were 0.59 (0.55-0.63) and 0.59 (0.54-0.65), respectively.ConclusionIn this first study evaluating NFC online training using the Fast Track algorithm, we showed very good inter- and intrarater agreement for the identification of scleroderma and nonscleroderma NFC pattern, supporting the feasibility of online NFC standardized training workshops.

There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of Influenza strains because of the need to sift through vast amounts of literature (including those of major viruses including HIV and Dengue, besides others). None have offered complete, strain specific protein-protein interaction records for the Influenza A group of viruses.In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between Influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information (lethal dose) into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI.The virulence network can be easily navigated via a web browser, with the associated virulence information (LD50values) prominently displayed. The network will aid Influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering Influenza infection mechanisms mediated through protein domain interactions between viral and host proteins.

Epitope residues located on viral surface proteins are of immense interest in immunology and related applications such as vaccine development, disease diagnosis and drug design. Most tools rely on sequence based statistical comparisons, such as information entropy of residue positions in aligned columns to infer location and properties of epitope sites. To facilitate cross-structural comparisons of epitopes on viral surface proteins, a python-based extraction tool implemented with Jupyter notebook is presented (Jupytope). Given a viral antigen structure of interest, a list of known epitope sites and a reference structure, the corresponding epitope structural properties can quickly be obtained. The tool integrates biopython modules for commonly used software such as NACCESS, DSSP as well as residue depth and outputs a list of structure derived properties such as dihedral angles, solvent accessibility, residue depth and secondary structure that can be saved in several convenient data formats. To ensure correct spatial alignment, Jupytope takes a list of given epitope sites and their corresponding reference structure and aligns them before extracting the desired properties. Examples are demonstrated for epitopes of Influenza and SARS-CoV2 viral strains. The extracted properties assist detection of two Influenza subtypes and show potential in distinguishing between four major clades of SARS-CoV2, as compared with randomized labels. The tool will facilitate analytical and predictive works on viral epitopes through the extracted structural information.Key MessagesJupytope combines existing 3D-structural software to extract the properties of viral epitopes into a convenient text or csv file formatThe structural properties serve as parameters or features that quantitatively capture viral epitopesAssociation of structural properties to viral subtypes (for Influenza) or clades (SARS-CoV2) is demonstrated with a simple XGBoost modelStructure datasets mapped to SARS-CoV2 WHO clades and Pango lineages, as well as chain annotations are available for download

Aim To investigate the efficacy of bisphosphonates and compare oral and IV formulations on bone mineral density (BMD) and fracture incidence in post-orthotopic liver transplant (OLT) patients. Methods Electronic databases were searched, and six RCTs and three cohort studies were included out of 711 articles. Main outcomes included post-OLT BMD changes, fracture incidence, and treatment adverse reactions. Pairwise meta-analysis was conducted for binary and continuous outcomes, while pooled fracture incidence utilized single-arm meta-analysis. Results Post-OLT fracture incidence was reported in nine studies (n=591). Total fracture incidence was 6.6% (CI: 3.4% to 12.4%) in bisphosphonate group and 19.1% (CI: 14.3% to 25.1%) in calcium and vitamin D group. Total fractures were significantly lower in patients on bisphosphonate, compared to calcium and vitamin D (n = 591; OR = 0.037; CI: 0.18 to 0.77; p=0.008) Overall fractures were significantly lower in the oral group (n= 263; OR = 0.26; CI: 0.08 to 0.85, p=0.02) but not in the IV group (n = 328; OR = 0.45; CI: 0.16 to 1.26, p=0.129). Conclusion Both oral and IV bisphosphonates are effective in reducing fracture incidence post-OLT compared to calcium and vitamin D. Oral formulations may also have an advantage over IV in reducing bone loss and fracture incidence post-OLT.

Modern sequencing technologies have revolutionized our detection of gene variants. However, in most genes, including KCNH2, the majority of missense variants are currently classified as variants of uncertain significance (VUSs). The aim of this study was to investigate the utility of an automated patch-clamp assay for aiding clinical variant classification in KCNH2. The assay was designed according to recommendations proposed by the Clinical Genome Sequence Variant Interpretation Working Group. Thirty-one variants (17 pathogenic/likely pathogenic, 14 benign/likely benign) were classified internally as variant controls. They were heterozygously expressed in Flp-In HEK293 cells for assessing the effects of variants on current density and channel gating in order to determine the sensitivity and specificity of the assay. All 17 pathogenic variant controls had reduced current density, and 13 of 14 benign variant controls had normal current density, which enabled determination of normal and abnormal ranges for applying evidence of moderate or supporting strength for VUS reclassification. Inclusion of functional assay evidence enabled us to reclassify 6 out of 44 KCNH2 VUSs as likely pathogenic. The high-throughput patch-clamp assay can provide moderate-strength evidence for clinical interpretation of clinical KCNH2 variants and demonstrates the value of developing automated patch-clamp assays for functional characterization of ion channel gene variants.