Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage.

The current global pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has taken a substantial number of lives across the world. Although few vaccines have been rolled-out, a number of vaccine candidates are still under clinical trials at various pharmaceutical companies and laboratories around the world. Considering the intrinsic nature of viruses in mutating and evolving over time, persistent efforts are needed to develop better vaccine candidates. In this study, various immuno-informatics tools and bioinformatics databases were deployed to derive consensus B-cell and T-cell epitope sequences of SARS-CoV-2 spike glycoprotein. This approach has identified four potential epitopes which have the capability to initiate both antibody and cell-mediated immune responses, are non-allergenic and do not trigger autoimmunity. These peptide sequences were also evaluated to show 99.82% of global population coverage based on the genotypic frequencies of HLA binding alleles for both MHC class-I and class-II and are unique for SARS-CoV-2 isolated from human as a host species. Epitope number 2 alone had a global population coverage of 98.2%. Therefore, we further validated binding and interaction of its constituent T-cell epitopes with their corresponding HLA proteins using molecular docking and molecular dynamics simulation experiments, followed by binding free energy calculations with molecular mechanics Poisson-Boltzmann surface area, essential dynamics analysis and free energy landscape analysis. The immuno-informatics pipeline described and the candidate epitopes discovered herein could have significant impact upon efforts to develop globally effective SARS-CoV-2 vaccines.

A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.

BACKGROUND: Multicentre training could reduce biases in medical artificial intelligence (AI); however, ethical, legal, and technical considerations can constrain the ability of hospitals to share data. Federated learning enables institutions to participate in algorithm development while retaining custody of their data but uptake in hospitals has been limited, possibly as deployment requires specialist software and technical expertise at each site. We previously developed an artificial intelligence-driven screening test for COVID-19 in emergency departments, known as CURIAL-Lab, which uses vital signs and blood tests that are routinely available within 1 h of a patient's arrival. Here we aimed to federate our COVID-19 screening test by developing an easy-to-use embedded system-which we introduce as full-stack federated learning-to train and evaluate machine learning models across four UK hospital groups without centralising patient data. METHODS: We supplied a Raspberry Pi 4 Model B preloaded with our federated learning software pipeline to four National Health Service (NHS) hospital groups in the UK: Oxford University Hospitals NHS Foundation Trust (OUH; through the locally linked research University, University of Oxford), University Hospitals Birmingham NHS Foundation Trust (UHB), Bedfordshire Hospitals NHS Foundation Trust (BH), and Portsmouth Hospitals University NHS Trust (PUH). OUH, PUH, and UHB participated in federated training, training a deep neural network and logistic regressor over 150 rounds to form and calibrate a global model to predict COVID-19 status, using clinical data from patients admitted before the pandemic (COVID-19-negative) and testing positive for COVID-19 during the first wave of the pandemic. We conducted a federated evaluation of the global model for admissions during the second wave of the pandemic at OUH, PUH, and externally at BH. For OUH and PUH, we additionally performed local fine-tuning of the global model using the sites' individual training data, forming a site-tuned model, and evaluated the resultant model for admissions during the second wave of the pandemic. This study included data collected between Dec 1, 2018, and March 1, 2021; the exact date ranges used varied by site. The primary outcome was overall model performance, measured as the area under the receiver operating characteristic curve (AUROC). Removable micro secure digital (microSD) storage was destroyed on study completion. FINDINGS: Clinical data from 130 941 patients (1772 COVID-19-positive), routinely collected across three hospital groups (OUH, PUH, and UHB), were included in federated training. The evaluation step included data from 32 986 patients (3549 COVID-19-positive) attending OUH, PUH, or BH during the second wave of the pandemic. Federated training of a global deep neural network classifier improved upon performance of models trained locally in terms of AUROC by a mean of 27·6% (SD 2·2): AUROC increased from 0·574 (95% CI 0·560-0·589) at OUH and 0·622 (0·608-0·637) at PUH using the locally trained models to 0·872 (0·862-0·882) at OUH and 0·876 (0·865-0·886) at PUH using the federated global model. Performance improvement was smaller for a logistic regression model, with a mean increase in AUROC of 13·9% (0·5%). During federated external evaluation at BH, AUROC for the global deep neural network model was 0·917 (0·893-0·942), with 89·7% sensitivity (83·6-93·6) and 76·6% specificity (73·9-79·1). Site-specific tuning of the global model did not significantly improve performance (change in AUROC <0·01). INTERPRETATION: We developed an embedded system for federated learning, using microcomputing to optimise for ease of deployment. We deployed full-stack federated learning across four UK hospital groups to develop a COVID-19 screening test without centralising patient data. Federation improved model performance, and the resultant global models were generalisable. Full-stack federated learning could enable hospitals to contribute to AI development at low cost and without specialist technical expertise at each site. FUNDING: The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.

Development of Asthma-Like Symptoms After COVID-19: A Cross-Sectional Study in Dubai, United Arab Emirates.

Background Coronaviruses are viral agents that commonly infect animals, but have the ability to cause respiratory illness in humans, exemplified by the ongoing novel coronavirus outbreak (COVID-19). Due to the sparse literature on the effects of COVID-19 on the respiratory system, and the possible development of persistent asthma-like symptoms after infection, this cross-sectional analysis was performed in order to compare the clinical and investigative parameters between post-COVID patients and asthmatic patients. Methods A retrospective cross-sectional study was conducted on patients with prior history of COVID-19 infection that presented to the pulmonology or respiratory outpatient clinics with asthma-like symptoms and were subsequently compared to known asthmatic patients with absent history of prior COVID-19 infection, in order to evaluate the degree of similarity between both cohorts. In this study, asthma-like symptoms were defined as: (i) cough, (ii) wheezing, (iii) chest tightness, and (iv) shortness of breath. Moreover, comparisons of investigative parameters were also performed, including (i) fractional exhaled nitric oxide (FeNO), (ii) serum immunoglobulin E (IgE), (iii) absolute eosinophil counts, and (iv) qualitative spirometry results. All statistical analyses were conducted via chi-squared testing for categorical variables, and independent t-test for continuous variables. Results In this study, there were a total of 76 patients included that conformed to the eligibility criteria, including 39 patients with post-COVID symptoms with absent history of asthma or other respiratory illnesses, and 37 patients with known asthma with absent history of prior COVID-19 infection or other respiratory illnesses. Overall, this study revealed the similarities between both cohorts with respect to the incidence of cough, chest tightness, and shortness of breath. Moreover, there were similarities between the serum IgE and spirometry results. However, there were differences within the complaint of wheeze, FeNO values, and eosinophil counts between both cohorts. The placement of post-COVID patients on bronchodilator therapy involving inhaled corticosteroids and long-acting beta-agonists revealed improvement in all follow-up patients. Conclusion In conclusion, there was considerable similarity in the complaint of asthma-like symptoms after COVID-19 infection, associated with an improvement after the use of bronchodilator therapy, indicating the potential role of anti-asthma therapy (e.g., bronchodilator therapy) in managing post-COVID asthma-like symptoms. In order to validate our conclusion, further comprehensive studies with robust methodologies and larger sample populations are encouraged.

Clinical and Laboratory Associations with Methotrexate Metabolism Gene Polymorphisms in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that causes loss of joint function and significantly reduces quality of life. Plasma metabolite concentrations of disease-modifying anti-rheumatic drugs (DMARDs) can influence treatment efficacy and toxicity. This study explored the relationship between DMARD-metabolising gene variants and plasma metabolite levels in RA patients. DMARD metabolite concentrations were determined by tandem mass-spectrometry in plasma samples from 100 RA patients with actively flaring disease collected at two intervals. Taqman probes were used to discriminate single-nucleotide polymorphism (SNP) genotypes in cohort genomic DNA: rs246240 (ABCC1), rs1476413 (MTHFR), rs2231142 (ABCG2), rs3740065 (ABCC2), rs4149081 (SLCO1B1), rs4846051 (MTHFR), rs10280623 (ABCB1), rs16853826 (ATIC), rs17421511 (MTHFR) and rs717620 (ABCC2). Mean plasma concentrations of methotrexate (MTX) and MTX-7-OH metabolites were higher (p < 0.05) at baseline in rs4149081 GA genotype patients. Patients with rs1476413 SNP TT or CT alleles have significantly higher (p < 0.001) plasma poly-glutamate metabolites at both study time points and correspondingly elevated disease activity scores. Patients with the rs17421511 SNP AA allele reported significantly lower pain scores (p < 0.05) at both study intervals. Genotyping strategies could help prioritise treatments to RA patients most likely to gain clinical benefit whilst minimizing toxicity.

Assessment of a dried blood spot C-reactive protein method to identify disease flares in rheumatoid arthritis patients.

Rheumatoid arthritis (RA) is characterised by painful, stiff and swollen joints. RA features sporadic 'flares' or inflammatory episodes-mostly occurring outside clinics-where symptoms worsen and plasma C-reactive protein (CRP) becomes elevated. Poor control of inflammation results in higher rates of irreversible joint damage, increased disability, and poorer quality of life. Flares need to be accurately identified and managed. A method comparison study was designed to assess agreement between CRP values obtained by dried blood spot (DBS) versus conventional venepuncture sampling. The ability of a weekly DBS sampling and CRP test regime to detect flare outside the clinic was also assessed. Matched venepuncture and finger lancet DBS samples were collected from n = 100 RA patients with active disease at baseline and 6 weeks (NCT02809547). A subset of n = 30 RA patients submitted weekly DBS samples over the study period. Patient demographics, including self-reported flares were recorded. DBS sample CRP measurements were made by enzyme-linked immunosorbent assay, and venepuncture samples by a reference immunoturbometric assay. Data was compared between sample types by Bland-Altman and weighted Deming regression analyses. Flare detection sensitivity and specificity were compared between 'minimal' baseline and 6 week sample CRP data and the 'continuous' weekly CRP data. Baseline DBS ELISA assay CRP measures yielded a mean positive bias of 2.693 ± 8.640 (95% limits of agreement - 14.24 to 19.63%), when compared to reference assay data. Deming regression revealed good agreement between the DBS ELISA method and reference assay data, with baseline data slope of 0.978 and intercept -0.153. The specificity of 'continuous' area under the curve (AUC) CRP data (72.7%) to identify flares, was greater than 'minimal' AUC CRP data (54.5%). This study indicates reasonable agreement between DBS and the reference method, especially at low to mid-range CRP values. Importantly, longitudinal CRP measurement in RA patients helps to clearly identify flare and thus could assist in remote monitoring strategies and may facilitate timely therapeutic intervention.Trial registration: The Remote Arthritis Disease Activity MonitoR (RADAR) study was registered on 22/06/2016 at ClinicalTrials.gov Identifier: NCT02809547. https://clinicaltrials.gov/ct2/show/NCT02809547 .

Favourable Prognosis when Lung-Cancer Patients with Superior Vena Cava Obstruction (SVCO) are Referred Promptly to EBUS-TBNA Prior to Medical or Surgical Management.

BACKGROUND: Primary care patients with superior vena cava obstruction (SVCO) syndrome are usually referred to emergency departments for urgent medical management (high-dose corticosteroids to reduce inflammation), pre-biopsy radiotherapy and/or stent placements to restore patency to the vessel. Biopsy, diagnosis and staging of the mediastinal mass is often postponed until resolution of SVCO symptoms. However, lung cancers metastasise rapidly and delays can influence the eventual outcome of patients. An additional merit in treating SVCO symptoms post-biopsy is that high-dose corticosteroids and pre-biopsy radiotherapy will degrade the quality of biopsy specimens, complicating diagnosis and subsequent management. AIMS: To determine if direct referrals of SVCO patients from primary care to the respiratory department for Endobronchial ultrasound (EBUS)-transbronchial needle-aspiration (TBNA) resulted in better outcomes. METHODS: Direct referrals to the respiratory department from primary care physicians were sought. A total of 8 patients with symptoms of SVCO were rapidly diagnosed via EBUS-TBNA and ROSE, radiotherapy and specific chemotherapy was initiated following communication with oncology colleagues. High-dose corticosteroids were administered post-EBUS. RESULTS: Rapid resolution of symptoms for SVCO were noted, without need for surgical intervention. In particular, one patient with small-cell lung cancer (the most aggressive type of lung cancer) remains well and cancer-free 14 months from diagnosis. DISCUSSION: EBUS-TBNA is a safe modality for biopsy in SVCO as there is no risk of further compression of the vessel. We need a paradigm shift in referral and a guideline of SVCO patients in primary care, an urgent biopsy is important in mediastinal cancers which have high metastatic potentials.

Ryanodine receptor arrays: not just a pretty pattern?

Ryanodine receptors (RyRs) are colossal membrane protein complexes that reside in the endoplasmic reticulum of skeletal and cardiac muscle myocytes and neurons, in addition to many non-excitable cells. They comprise high-conductance ion channels that mediate the massive release of Ca2+ ions from the endoplasmic reticulum into the cytoplasm. This is the trigger for contraction during each muscle excitation-contraction coupling cycle. Individual RyRs are believed to network with other RyRs indirectly, through diffusion of released Ca2+ ions, namely the Ca2+-induced Ca2+ release phenomenon. However, RyRs can intrinsically organize into a regular array resembling a distinctive checkerboard pattern, with each square-shaped receptor appearing to abut four neighbours at each corner. In this opinion article, we describe recent data showing structural interactions between RyR oligomers in reconstituted arrays, and we suggest that this provides strong evidence for direct inter-RyR communication through a novel, allosteric regulatory mechanism.