Variability in detection of SARS-CoV-2-specific antibody responses following mild infection: a prospective multicentre cross-sectional study, London, United Kingdom, 17 April to 17 July 2020.

IntroductionImmunoassays targeting different SARS-CoV-2-specific antibodies are employed for seroprevalence studies. The degree of variability between immunoassays targeting anti-nucleocapsid (anti-NP; the majority) vs the potentially neutralising anti-spike antibodies (including anti-receptor-binding domain; anti-RBD), particularly in mild or asymptomatic disease, remains unclear.AimsWe aimed to explore variability in anti-NP and anti-RBD antibody detectability following mild symptomatic or asymptomatic SARS-CoV-2 infection and analyse antibody response for correlation with symptomatology.MethodsA multicentre prospective cross-sectional study was undertaken (April-July 2020). Paired serum samples were tested for anti-NP and anti-RBD IgG antibodies and reactivity expressed as binding ratios (BR). Multivariate linear regression was performed analysing age, sex, time since onset, symptomatology, anti-NP and anti-RBD antibody BR.ResultsWe included 906 adults. Antibody results (793/906; 87.5%; 95% confidence interval: 85.2-89.6) and BR strongly correlated (ρ = 0.75). PCR-confirmed cases were more frequently identified by anti-RBD (129/130) than anti-NP (123/130). Anti-RBD testing identified 83 of 325 (25.5%) cases otherwise reported as negative for anti-NP. Anti-NP presence (+1.75/unit increase; p < 0.001), fever (≥ 38°C; +1.81; p < 0.001) or anosmia (+1.91; p < 0.001) were significantly associated with increased anti-RBD BR. Age (p = 0.85), sex (p = 0.28) and cough (p = 0.35) were not. When time since symptom onset was considered, we did not observe a significant change in anti-RBD BR (p = 0.95) but did note decreasing anti-NP BR (p < 0.001).ConclusionSARS-CoV-2 anti-RBD IgG showed significant correlation with anti-NP IgG for absolute seroconversion and BR. Higher BR were seen in symptomatic individuals, particularly those with fever. Inter-assay variability (12.5%) was evident and raises considerations for optimising seroprevalence testing strategies/studies.

Pooled sputum to optimise the efficiency and utility of rapid, point-of-care molecular SARS-CoV-2 testing.

BACKGROUND: As SARS-CoV-2 testing expands, particularly to widespread asymptomatic testing, high sensitivity point-of-care PCR platforms may optimise potential benefits from pooling multiple patients' samples. METHOD: We tested patients and asymptomatic citizens for SARS-CoV-2, exploring the efficiency and utility of CovidNudge (i) for detection in individuals' sputum (compared to nasopharyngeal swabs), (ii) for detection in pooled sputum samples, and (iii) by modelling roll out scenarios for pooled sputum testing. RESULTS: Across 295 paired samples, we find no difference (p = 0.1236) in signal strength for sputum (mean amplified replicates (MAR) 25.2, standard deviation (SD) 14.2, range 0-60) compared to nasopharyngeal swabs (MAR 27.8, SD 12.4, range 6-56). At 10-sample pool size we find some drop in absolute strength of signal (individual sputum MAR 42.1, SD 11.8, range 13-60 vs. pooled sputum MAR 25.3, SD 14.6, range 1-54; p < 0.0001), but only marginal drop in sensitivity (51/53,96%). We determine a limit of detection of 250 copies/ml for an individual test, rising only four-fold to 1000copies/ml for a 10-sample pool. We find optimal pooled testing efficiency to be a 12-3-1-sample model, yet as prevalence increases, pool size should decrease; at 5% prevalence to maintain a 75% probability of negative first test, 5-sample pools are optimal. CONCLUSION: We describe for the first time the use of sequentially dipped sputum samples for rapid pooled point of care SARS-CoV-2 PCR testing. The potential to screen asymptomatic cohorts rapidly, at the point-of-care, with PCR, offers the potential to quickly identify and isolate positive individuals within a population "bubble".

The pilot, proof of concept REMOTE-COVID trial: remote monitoring use in suspected cases of COVID-19 (SARS-CoV 2).

BACKGROUND: SARS-CoV-2 has ever-increasing attributed deaths. Vital sign trends are routinely used to monitor patients with changes in these parameters preceding an adverse event. Wearable sensors can measure vital signs continuously and remotely, outside of hospital facilities, recognising early clinical deterioration. We aim to determine the feasibility & acceptability of remote monitoring systems for quarantined individuals in a hotel suspected of COVID-19. METHODS: A pilot, proof-of-concept, feasibility trial was conducted in engineered hotels near London airports (May-June 2020). Individuals arriving to London with mild suspected COVID-19 symptoms requiring quarantine, as recommended by Public Health England, or healthcare professionals with COVID-19 symptoms unable to isolate at home were eligible. The SensiumVitals™ patch, measuring temperature, heart & respiratory rates, was applied on arrival for the duration of their stay. Alerts were generated when pre-established thresholds were breeched; trained nursing staff could consequently intervene. RESULTS: Fourteen individuals (M = 7, F = 7) were recruited; the mean age was 34.9 (SD 11) years. Mean length of stay was 3 (SD 1.8) days. In total, 10 vital alerts were generated across 4 participants, resulting in telephone contact, reassurance, or adjustment of the sensor. No individuals required hospitalisation or virtual general practitioner review. DISCUSSION: This proof-of-concept trial demonstrated the feasibility of a rapidly implemented model of healthcare delivery through remote monitoring during a pandemic at a hotel, acting as an extension to a healthcare trust. Benefits included reduced viral exposure to healthcare staff, with recognition of clinical deterioration through ambulatory, continuous, remote monitoring using a discrete wearable sensor. CONCLUSION: Remote monitoring systems can be applied to hotels to deliver healthcare safely in individuals suspected of COVID-19. Further work is required to evaluate this model on a larger scale. TRIAL REGISTRATION: Clinical trials registration information: ClinicalTrials.gov Identifier: NCT04337489 (07/04/2020).

Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes.

BACKGROUND: Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2. METHOD: Between March 1 and April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: (1) a Cox regression model and (2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration. RESULTS: Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI) 73.8-91.1 and 90.0%, 95% CI 81.2-95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI 91.1-94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI 85.7-88.2), p = 0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. CONCLUSION: We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.

Are the Epworth Sleepiness Scale and Stop-Bang model effective at predicting the severity of obstructive sleep apnoea (OSA); in particular OSA requiring treatment?

Obstructive sleep apnoea (OSA) is a condition characterised by repetitive upper airway collapse during sleep. The condition carries a range of health sequelae that can prove fatal in cases with co-existing risk factors for the condition, such as obesity and hypertension. Utilisation of a high-performance screening tool for OSA is thus important. A retrospective audit using the ESS and Stop-Bang scores, alongside Apnoea-Hypopnea Index values, for patients who underwent polysomnography over 1 year. Multinomial logistic regression was used to compare the predictive abilities of ESS, SBM, and body mass index (BMI) for the patient outcome groups, "None" (No OSA), "Notreat" (OSA not requiring treatment) and "treat" (OSA requiring treatment). The influences of age, gender and BMI on outcome group were also assessed. 126 bariatric and 66 non-bariatric patients were included. Multinomial logistic regression failed to demonstrate predictive ability of ESS. A higher Stop-Bang score significantly increases the risk being in the "treat" group. In addition, male gender, greater age and a higher BMI each individually increase the risk of OSA requiring treatment. Stop-Bang failed to demonstrate predictive significance when age and gender were controlled for. ESS is not an appropriate screening tool for OSA. Stop-Bang, however, remains a useful screening tool, with the ability to detect patient with OSA in need of treatment. Further study may benefit the development and implementation of a concise and more specific screening tool that considers high evidence-based risk factors for OSA, including male gender, greater age and raised BMI.

Selective Trapping of DNA Using Glass Microcapillaries.

We show experimentally that an inexpensive glass microcapillary can accumulate λ-phage DNA at its tip and deliver the DNA into the capillary using a combination of electro-osmotic flow, pressure-driven flow, and electrophoresis. We develop an efficient simulation model based on the electrokinetic equations and the finite-element method to explain this phenomenon. As a proof of concept for the generality of this trapping mechanism we use our numerical model to explore the effect of the salt concentration, the capillary surface charge, the applied voltage, the pressure difference, and the mobility of the analyte molecules. Our results indicate that the simple microcapillary system has the potential to capture a wide range of analyte molecules based on their electrophoretic mobility that extends well beyond our experimental example of λ-phage DNA. Our method for separation and preconcentration of analytes therefore has implications for the development of low-cost lab-on-a-chip devices.

Controlled capillary assembly of magnetic Janus particles at fluid-fluid interfaces.

Capillary interactions can be used to direct assembly of particles adsorbed at fluid-fluid interfaces. Precisely controlling the magnitude and direction of capillary interactions to assemble particles into favoured structures for materials science purposes is desirable but challenging. In this paper, we investigate capillary interactions between magnetic Janus particles adsorbed at fluid-fluid interfaces. We develop a pair-interaction model that predicts that these particles should arrange into a side-side configuration, and carry out simulations that confirm the predictions of our model. Finally, we investigate the monolayer structures that form when many magnetic Janus particles adsorb at the interface. We find that the particles arrange into long, straight chains exhibiting little curvature, in contrast with capillary interactions between ellipsoidal particles. We further find a regime in which highly ordered, lattice-like monolayer structures form, which can be tuned dynamically using an external magnetic field.

Reducing spurious flow in simulations of electrokinetic phenomena.

Electrokinetic transport phenomena can strongly influence the behaviour of macromolecules and colloidal particles in solution, with applications in, e.g., DNA translocation through nanopores, electro-osmotic flow in nanocapillaries, and electrophoresis of charged macromolecules. Numerical simulations are an important tool to investigate these electrokinetic phenomena, but are often plagued by spurious fluxes and spurious flows that can easily exceed physical fluxes and flows. Here, we present a method that reduces one of these spurious currents, spurious flow, by several orders of magnitude. We demonstrate the effectiveness and generality of our method for both the electrokinetic lattice-Boltzmann and finite-element-method based algorithms by simulating a charged sphere in an electrolyte solution and flow through a nanopore. We also show that previous attempts to suppress these spurious currents introduce other sources of error.

Dipolar capillary interactions between tilted ellipsoidal particles adsorbed at fluid-fluid interfaces.

Capillary interactions have emerged as a tool for the directed assembly of particles adsorbed at fluid-fluid interfaces, and play a role in controlling the mechanical properties of emulsions and foams. In this paper, following Davies et al. [Adv. Mater., 2014, 26, 6715] investigation into the assembly of ellipsoidal particles at interfaces interacting via dipolar capillary interactions, we numerically investigate the interaction between tilted ellipsoidal particles adsorbed at a fluid-fluid interface as their aspect ratio, tilt angle, bond angle, and separation vary. High-resolution Surface Evolver simulations of ellipsoidal particle pairs in contact reveal an energy barrier between a metastable tip-tip configuration and a stable side-side configuration. The side-side configuration is the global energy minimum for all parameters we investigated. Lattice Boltzmann simulations of clusters of up to 12 ellipsoidal particles show novel highly symmetric flower-like and ring-like arrangements.

Tunable dipolar capillary deformations for magnetic Janus particles at fluid-fluid interfaces.

Janus particles have attracted significant interest as building blocks for complex materials in recent years. Furthermore, capillary interactions have been identified as a promising tool for directed self-assembly of particles at fluid-fluid interfaces. In this paper, we develop theoretical models describing the behaviour of magnetic Janus particles adsorbed at fluid-fluid interfaces interacting with an external magnetic field. Using numerical simulations, we test the models predictions and show that the magnetic Janus particles deform the interface in a dipolar manner. We suggest how to utilise the resulting dipolar capillary interactions to assemble particles at a fluid-fluid interface, and further demonstrate that the strength of these interactions can be tuned by altering the external field strength, opening up the possibility to create novel, reconfigurable materials.

Interface deformations affect the orientation transition of magnetic ellipsoidal particles adsorbed at fluid-fluid interfaces.

Manufacturing new soft materials with specific optical, mechanical and magnetic properties is a significant challenge. Assembling and manipulating colloidal particles at fluid interfaces is a promising way to make such materials. We use lattice-Boltzmann simulations to investigate the response of magnetic ellipsoidal particles adsorbed at liquid-liquid interfaces to external magnetic fields. We provide further evidence for the first-order orientation phase transition predicted by Bresme and Faraudo [Journal of Physics: Condensed Matter, 2007, 19, 375110]. We show that capillary interface deformations around the ellipsoidal particle significantly affect the tilt-angle of the particle for a given dipole-field strength, altering the properties of the orientation transition. We propose scaling laws governing this transition, and suggest how to use these deformations to facilitate particle assembly at fluid-fluid interfaces.

Detachment energies of spheroidal particles from fluid-fluid interfaces.

The energy required to detach a single particle from a fluid-fluid interface is an important parameter for designing certain soft materials, for example, emulsions stabilised by colloidal particles, colloidosomes designed for targeted drug delivery, and bio-sensors composed of magnetic particles adsorbed at interfaces. For a fixed particle volume, prolate and oblate spheroids attach more strongly to interfaces because they have larger particle-interface areas. Calculating the detachment energy of spheroids necessitates the difficult measurement of particle-liquid surface tensions, in contrast with spheres, where the contact angle suffices. We develop a simplified detachment energy model for spheroids which depends only on the particle aspect ratio and the height of the particle centre of mass above the fluid-fluid interface. We use lattice Boltzmann simulations to validate the model and provide quantitative evidence that the approach can be applied to simulate particle-stabilized emulsions, and highlight the experimental implications of this validation.

Hybrid immunity in older adults is associated with reduced SARS-CoV-2 infections following BNT162b2 COVID-19 immunisation.

BACKGROUND: Older adults, particularly in long-term care facilities (LTCF), remain at considerable risk from SARS-CoV-2. Data on the protective effect and mechanisms of hybrid immunity are skewed towards young adults precluding targeted vaccination strategies. METHODS: A single-centre longitudinal seroprevalence vaccine response study was conducted with 280 LCTF participants (median 82 yrs, IQR 76-88 yrs; 95.4% male). Screening by SARS-CoV-2 polymerase chain reaction with weekly asymptomatic/symptomatic testing (March 2020-October 2021) and serology pre-/post-two-dose Pfizer-BioNTech BNT162b2 vaccination for (i) anti-nucleocapsid, (ii) quantified anti-receptor binding domain (RBD) antibodies at three time-intervals, (iii) pseudovirus neutralisation, and (iv) inhibition by anti-RBD competitive ELISA were conducted. Neutralisation activity: antibody titre relationship was assessed via beta linear-log regression and RBD antibody-binding inhibition: post-vaccine infection relationship by Wilcoxon rank sum test. RESULTS: Here we show neutralising antibody titres are 9.2-fold (95% CI 5.8-14.5) higher associated with hybrid immunity (p < 0.00001); +7.5-fold (95% CI 4.6-12.1) with asymptomatic infection; +20.3-fold, 95% (CI 9.7-42.5) with symptomatic infection. A strong association is observed between antibody titre: neutralising activity (p < 0.00001) and rising anti-RBD antibody titre: RBD antibody-binding inhibition (p < 0.001), although 18/169 (10.7%) participants with high anti-RBD titre (>100BAU/ml), show inhibition <75%. Higher RBD antibody-binding inhibition values are associated with hybrid immunity and reduced likelihood of infection (p = 0.003). CONCLUSIONS: Hybrid immunity in older adults was associated with considerably higher antibody titres, neutralisation and inhibition capacity. Instances of high anti-RBD titre with lower inhibition suggests antibody quantity and quality as independent potential correlates of protection, highlighting added value of measuring inhibition over antibody titre alone to inform vaccine strategy.

Older adults continue to be at risk of COVID-19, particularly in residential care home settings. We investigated the effect of infection and vaccination on antibody development and subsequent SARS-CoV-2 infection in older adults. Antibodies are proteins that the immune system produces on infection or vaccination that can help respond to subsequent infection with SARS-CoV-2. We found that older adults produce antibodies to SARS-CoV-2 after 2-doses of Pfizer BioNTech BNT162b2 vaccine. The strongest immune responses were seen among those older adults who also had prior history of infection. The results highlight the importance of both antibody quality and quantity when considering possible indicators of protection against COVID-19 and supports the need for a third, booster, vaccination in this age group..

Early identification of high-risk individuals for monoclonal antibody therapy and prophylaxis is feasible by SARS-CoV-2 anti-spike antibody specific lateral flow assay.

Monoclonal antibody therapy has been approved for prophylaxis and treatment of severe COVID-19 infection. Greatest benefit appears limited to those yet to mount an effective immune response from natural infection or vaccination, but concern exists around ability to make timely assessment of immune status of community-based patients where laboratory-based serodiagnostics predominate. Participants were invited to undergo paired laboratory-based (Abbott Architect SARS-CoV-2 IgG Quant II chemiluminescent microparticle immunoassay) and lateral flow assays (LFA; a split SARS-CoV-2 IgM/IgG and total antibody test) able to detect SARS-CoV-2 anti-spike antibodies. LFA band strength was compared with CMIA titer by log-linear regression. Two hundred individuals (median age 43.5 years, IQR 30-59; 60.5% female) underwent testing, with a further 100 control sera tested. Both LFA band strengths correlated strongly with CMIA antibody titers (P < 0.001). LFAs have the potential to assist in early identification of seronegative patients who may demonstrate the greatest benefit from monoclonal antibody treatment.

Demographic and clinical characteristics of confirmed human monkeypox virus cases in individuals attending a sexual health centre in London, UK: an observational analysis.

BACKGROUND: Historically, human monkeypox virus cases in the UK have been limited to imported infections from west Africa. Currently, the UK and several other countries are reporting a rapid increase in monkeypox cases among individuals attending sexual health clinics, with no apparent epidemiological links to endemic areas. We describe demographic and clinical characteristics of patients diagnosed with human monkeypox virus attending a sexual health centre. METHODS: In this observational analysis, we considered patients with confirmed monkeypox virus infection via PCR detection attending open-access sexual health clinics in London, UK, between May 14 and May 25, 2022. We report hospital admissions and concurrent sexually transmitted infection (STI) proportions, and describe our local response within the first 2 weeks of the outbreak. FINDINGS: Monkeypox virus infection was confirmed in 54 individuals, all identifying as men who have sex with men (MSM), with a median age of 41 years (IQR 34-45). 38 (70%) of 54 individuals were White, 26 (48%) were born in the UK, and 13 (24%) were living with HIV. 36 (67%) of 54 individuals reported fatigue or lethargy, 31 (57%) reported fever, and ten (18%) had no prodromal symptoms. All patients presented with skin lesions, of which 51 (94%) were anogenital. 37 (89%) of 54 individuals had skin lesions affecting more than one anatomical site and four (7%) had oropharyngeal lesions. 30 (55%) of 54 individuals had lymphadenopathy. One in four patients had a concurrent STI. Five (9%) of 54 individuals required admission to hospital, mainly due to pain or localised bacterial cellulitis requiring antibiotic intervention or analgesia. We recorded no fatal outcomes. INTERPRETATION: Autochthonous community monkeypox virus transmission is currently observed among MSM in the UK. We found a high proportion of concomitant STIs and frequent anogenital symptoms, suggesting transmissibility through local inoculation during close skin-to-skin or mucosal contact, during sexual activity. Additional resources are required to support sexual health and other specialist services in managing this condition. A review of the case definition and better understanding of viral transmission routes are needed to shape infection control policies, education and prevention strategies, and contact tracing. FUNDING: None.

Design of the pilot, proof of concept REMOTE-COVID trial: remote monitoring use in suspected cases of COVID-19 (SARS-CoV-2).

BACKGROUND: The outbreak of SARS-CoV-2 (coronavirus, COVID-19), declared a pandemic by the World Health Organization (WHO), is a global health problem with ever-increasing attributed deaths. Vital sign trends are routinely used to monitor patients with changes in these parameters often preceding an adverse event. Wearable sensors can measure vital signs continuously (e.g. heart rate, respiratory rate, temperature) remotely and can be utilised to recognise early clinical deterioration. METHODS: We describe the protocol for a pilot, proof-of-concept, observational study to be conducted in an engineered hotel near London airports, UK. The study is set to continue for the duration of the pandemic. Individuals arriving to London with mild symptoms suggestive of COVID-19 or returning from high-risk areas requiring quarantine, as recommended by the Public Health England, or healthcare professionals with symptoms suggestive of COVID-19 unable to isolate at home will be eligible for a wearable patch to be applied for the duration of their stay. Notifications will be generated should deterioration be detected through the sensor and displayed on a central monitoring hub viewed by nursing staff, allowing for trend deterioration to be noted. The primary objective is to determine the feasibility of remote monitoring systems in detecting clinical deterioration for quarantined individuals in a hotel. DISCUSSION: This trial should prove the feasibility of a rapidly implemented model of healthcare delivery through remote monitoring during a global pandemic at a hotel, acting as an extension to a healthcare trust. Potential benefits would include reducing infection risk of COVID-19 to healthcare staff, with earlier recognition of clinical deterioration through ambulatory, continuous, remote monitoring using a discrete wearable sensor. We hope our results can power future, robust randomised trials. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04337489 .

Clinical Utility and Functionality of an Artificial Intelligence-Based App to Predict Mortality in COVID-19: Mixed Methods Analysis.

BACKGROUND: The artificial neural network (ANN) is an increasingly important tool in the context of solving complex medical classification problems. However, one of the principal challenges in leveraging artificial intelligence technology in the health care setting has been the relative inability to translate models into clinician workflow. OBJECTIVE: Here we demonstrate the development of a COVID-19 outcome prediction app that utilizes an ANN and assesses its usability in the clinical setting. METHODS: Usability assessment was conducted using the app, followed by a semistructured end-user interview. Usability was specified by effectiveness, efficiency, and satisfaction measures. These data were reported with descriptive statistics. The end-user interview data were analyzed using the thematic framework method, which allowed for the development of themes from the interview narratives. In total, 31 National Health Service physicians at a West London teaching hospital, including foundation physicians, senior house officers, registrars, and consultants, were included in this study. RESULTS: All participants were able to complete the assessment, with a mean time to complete separate patient vignettes of 59.35 (SD 10.35) seconds. The mean system usability scale score was 91.94 (SD 8.54), which corresponds to a qualitative rating of "excellent." The clinicians found the app intuitive and easy to use, with the majority describing its predictions as a useful adjunct to their clinical practice. The main concern was related to the use of the app in isolation rather than in conjunction with other clinical parameters. However, most clinicians speculated that the app could positively reinforce or validate their clinical decision-making. CONCLUSIONS: Translating artificial intelligence technologies into the clinical setting remains an important but challenging task. We demonstrate the effectiveness, efficiency, and system usability of a web-based app designed to predict the outcomes of patients with COVID-19 from an ANN.