Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 10 de 10
Filter
Add filters

Document Type
Year range
1.
arxiv; 2022.
Preprint in English | PREPRINT-ARXIV | ID: ppzbmed-2207.10998v1

ABSTRACT

Artificial intelligence-based analysis of lung ultrasound imaging has been demonstrated as an effective technique for rapid diagnostic decision support throughout the COVID-19 pandemic. However, such techniques can require days- or weeks-long training processes and hyper-parameter tuning to develop intelligent deep learning image analysis models. This work focuses on leveraging 'off-the-shelf' pre-trained models as deep feature extractors for scoring disease severity with minimal training time. We propose using pre-trained initializations of existing methods ahead of simple and compact neural networks to reduce reliance on computational capacity. This reduction of computational capacity is of critical importance in time-limited or resource-constrained circumstances, such as the early stages of a pandemic. On a dataset of 49 patients, comprising over 20,000 images, we demonstrate that the use of existing methods as feature extractors results in the effective classification of COVID-19-related pneumonia severity while requiring only minutes of training time. Our methods can achieve an accuracy of over 0.93 on a 4-level severity score scale and provides comparable per-patient region and global scores compared to expert annotated ground truths. These results demonstrate the capability for rapid deployment and use of such minimally-adapted methods for progress monitoring, patient stratification and management in clinical practice for COVID-19 patients, and potentially in other respiratory diseases.

2.
biorxiv; 2022.
Preprint in English | bioRxiv | ID: ppzbmed-10.1101.2022.06.07.495142

ABSTRACT

Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches to reconstruct outbreaks exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is stable among repeated serial samples from the same host, is transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.

3.
researchsquare; 2022.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-1286644.v1

ABSTRACT

Background: Serological testing is used to quantify SARS-CoV-2 seroprevalence, guide booster vaccination and select patients for anti-SARS-CoV-2 antibodies therapy. However, our understanding of how serological tests perform as time passes after infection is limited.Methods: Four assays were compared in parallel: 1) the multiplexed spike, nucleoprotein and receptor binding domain Meso Scale Discovery (MSD) assay 2) the Roche Elecsys-Nucleoprotein assay (Roche-N) 3) the Roche Spike assay (Roche-S) and 4) the Abbott Nucleoprotein assay (Abbott-N) on serial positive monthly samples from hospital staff up to 200 days following infection as part of the Co-Stars study.Results: We demonstrate that 50% of the Abbott-N assays give a negative result after 175 days (median survival time 95% CI 168-185 days) while the Roche-N assay (93% survival probability at 200 days, 95% CI 88-97%) maintained seropositivity. The MSD spike (97% survival probability at 200 days, 95% CI 95-99%) and the Roche-S assay (95% survival probability at 200 days, 95% CI 93-97%) also remained seropositive. The best performing quantitative Roche-S assay showed no evidence of waning Spike antibody titres over 200-days.Conclusions: The Abbott-N assay fails to detect SARS-CoV-2 antibodies as time passes since infection. In contrast the Roche and the MSD assays maintained high sensitivity. The limitations of the Abbott assay must be considered in clinical decision making. The long duration of detectable neutralizing spike antibody titres by the quantitative Roche-S assay provides further evidence in support of long-lasting SARS-CoV-2 protection to pre-existing strains of SARS-CoV-2 following natural infection.Trial registration: Co-STARs study was registered with ClinicalTrials.gov on May 8th, 2020, with trial number NCT04380896 (www.clinicaltrials.gov, NCT04380896)

4.
arxiv; 2021.
Preprint in English | PREPRINT-ARXIV | ID: ppzbmed-2108.03138v1

ABSTRACT

Lung ultrasound imaging has been shown effective in detecting typical patterns for interstitial pneumonia, as a point-of-care tool for both patients with COVID-19 and other community-acquired pneumonia (CAP). In this work, we focus on the hyperechoic B-line segmentation task. Using deep neural networks, we automatically outline the regions that are indicative of pathology-sensitive artifacts and their associated sonographic patterns. With a real-world data-scarce scenario, we investigate approaches to utilize both COVID-19 and CAP lung ultrasound data to train the networks; comparing fine-tuning and unsupervised domain adaptation. Segmenting either type of lung condition at inference may support a range of clinical applications during evolving epidemic stages, but also demonstrates value in resource-constrained clinical scenarios. Adapting real clinical data acquired from COVID-19 patients to those from CAP patients significantly improved Dice scores from 0.60 to 0.87 (p < 0.001) and from 0.43 to 0.71 (p < 0.001), on independent COVID-19 and CAP test cases, respectively. It is of practical value that the improvement was demonstrated with only a small amount of data in both training and adaptation data sets, a common constraint for deploying machine learning models in clinical practice. Interestingly, we also report that the inverse adaptation, from labelled CAP data to unlabeled COVID-19 data, did not demonstrate an improvement when tested on either condition. Furthermore, we offer a possible explanation that correlates the segmentation performance to label consistency and data domain diversity in this point-of-care lung ultrasound application.

5.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.04.12.21255098

ABSTRACT

While the majority of children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) display mild or no symptoms, rare individuals develop severe disease presenting with multisystem inflammatory syndrome (MIS-C). The reason for variable clinical manifestations is not understood. Here, we carried out TCR sequencing and conducted comparative analyses of TCR repertoires between children with severe (n=12) or mild (n=8) COVID-19. We compared these repertoires with unexposed individuals (samples collected pre-COVID-19 pandemic: n=8) and with the Adaptive Biotechnologies MIRA dataset, which includes over 135,000 high-confidence SARS-CoV-2-specific TCRs. We show that the repertoires of severely ill children are characterised by the expansion of TRBV11-2 chains with high junctional and CDR3 diversity. Moreover, the CDR3 sequences of TRBV11-2 clones shift away from SARS-CoV-2 specific T cell clones, resulting in distorted TCR repertoires. In conclusion, our study reports that CDR3-independent expansion of TRBV11-2+ cells, lacking SARS-CoV-2 specificity, defines severity of disease in children.

6.
ssrn; 2020.
Preprint in English | PREPRINT-SSRN | ID: ppzbmed-10.2139.ssrn.3739808

ABSTRACT

Background: Antibodies to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) have been shown to neutralize the virus in-vitro and prevent disease in animal challenge models upon re-exposure. However, current understanding of SARS-CoV-2 humoral dynamics and longevity is conflicting.Methods: The Co-Stars study prospectively enrolled 3679 healthcare workers to comprehensively characterize the kinetics of SARS-CoV-2 spike (S), receptor-binding-domain (RBD) and nucleoprotein (N) antibodies in parallel. Participants screening seropositive had serial monthly serological testing for maximum 7 months with the Mesoscale Discovery Assay. Survival analysis determined the proportion of sero-reversion while two hierarchical Gamma models predicted the upper- and lower-bounds of long-term antibody trajectory.Results: A total of 1163 monthly samples were provided from 349 seropositive participants. At 200 days post-symptoms, 99% of participants had detectable S-antibodies compared to 75% with detectable N-antibodies. S-antibody was predicted to remain detectable in 95% of participants until 465 days [95%CI 370-575] using a ‘continuous-decay’ model and indefinitely using a ‘decay-to-plateau’ model to account for antibody secretion by long-lived plasma cells. S-antibody titers correlated strongly with surrogate neutralization in-vitro (R2=0.72). N-antibodies, however, decayed rapidly with a half-life of 60 days [95%CI 52-68].Conclusions: The Co-STAR's study data presented here provides evidence for long-term persistence of neutralizing S-antibodies. This has important implications for the duration of functional immunity following SARS-CoV-2 infection. In contrast, the rapid decay of N-antibodies must be considered in future seroprevalence studies and public health decision-making. This is the first study to establish a mathematical framework capable of predicting long-term humoral dynamics following SARS-CoV-2 infection.Trial Registration: NCT04380896.Funding Statement: GOSH charity, Wellcome Trust (201470/Z/16/Z and 220565/Z/20/Z). GOSH NIHR Funded Biomedical Research Centre.Declaration of Interests: The authors have declared that no competing interests exist.Ethics Approval Statement: This study was approved by the UK Health Research Authority (www.hra.nhs.uk). Written informed consent was obtained from all participants before recruitment to the study.

7.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.11.20.20235697

ABSTRACT

Background: Antibodies to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) have been shown to neutralize the virus in-vitro. Similarly, animal challenge models suggest that neutralizing antibodies isolated from SARS-CoV-2 infected individuals prevent against disease upon re-exposure to the virus. Understanding the nature and duration of the antibody response following SARS-CoV-2 infection is therefore critically important. Methods: Between April and October 2020 we undertook a prospective cohort study of 3555 healthcare workers in order to elucidate the duration and dynamics of antibody responses following infection with SARS-CoV-2. After a formal performance evaluation against 169 PCR confirmed cases and negative controls, the Meso-Scale Discovery assay was used to quantify in parallel, antibody titers to the SARS-CoV-2 nucleoprotein (N), spike (S) protein and the receptor-binding-domain (RBD) of the S-protein. All seropositive participants were followed up monthly for a maximum of 7 months; those participants that were symptomatic, with known dates of symptom-onset, seropositive by the MSD assay and who provided 2 or more monthly samples were included in the analysis. Survival analysis was used to determine the proportion of sero-reversion (switching from positive to negative) from the raw data. In order to predict long-term antibody dynamics, two hierarchical longitudinal Gamma models were implemented to provide predictions for the lower bound (continuous antibody decay to zero, 'Gamma-decay') and upper bound (decay-to-plateau due to long lived plasma cells, 'Gamma-plateau') long-term antibody titers. Results: A total of 1163 samples were provided from 349 of 3555 recruited participants who were symptomatic, seropositive by the MSD assay, and were followed up with 2 or more monthly samples. At 200 days post symptom onset, 99% of participants had detectable S-antibody whereas only 75% of participants had detectable N-antibody. Even under our most pessimistic assumption of persistent negative exponential decay, the S-antibody was predicted to remain detectable in 95% of participants until 465 days [95% CI 370-575] after symptom onset. Under the Gamma-plateau model, the entire posterior distribution of S-antibody titers at plateau remained above the threshold for detection indefinitely. Surrogate neutralization assays demonstrated a strong positive correlation between antibody titers to the S-protein and blocking of the ACE-2 receptor in-vitro [R2=0.72, p<0.001]. By contrast, the N-antibody waned rapidly with a half-life of 60 days [95% CI 52-68]. Discussion: This study has demonstrated persistence of the spike antibody in 99% of participants at 200 days following SARS-CoV-2 symptoms and rapid decay of the nucleoprotein antibody. Diagnostic tests or studies that rely on the N-antibody as a measure of seroprevalence must be interpreted with caution. Our lowest bound prediction for duration of the spike antibody was 465 days and our upper bound predicted spike antibody to remain indefinitely in line with the long-term seropositivity reported for SARS-CoV infection. The long-term persistence of the S-antibody, together with the strong positive correlation between the S-antibody and viral surrogate neutralization in-vitro, has important implications for the duration of functional immunity following SARS-CoV-2 infection.

8.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.11.18.20230599

ABSTRACT

While changes in SARS-CoV-2 viral load over time have been documented, detailed information on the impact of remdesivir and how it might alter intra-host viral evolution is limited. Sequential viral loads and deep sequencing of SARS-CoV-2 recovered from the upper respiratory tract of hospitalised children revealed that remdesivir treatment suppressed viral RNA levels in one patient but not in a second infected with an identical strain. Evidence of drug resistance to explain this difference was not found. Reduced levels of subgenomic (sg) RNA during treatment of the second patient, suggest an additional effect of remdesivir on viral replication that is independent of viral RNA levels. Haplotype reconstruction uncovered persistent SARS-CoV-2 variant genotypes in four patients. We conclude that these are likely to have arisen from within-host evolution, and not co-transmission, although superinfection cannot be excluded in one case. Sample-to-sample heterogeneity in the abundances of variant genotypes is best explained by the presence of discrete viral populations in the lung with incomplete population sampling in diagnostic swabs. Such compartmentalisation is well described in serious lung infections caused by influenza and Mycobacterium tuberculosis and has been associated with poor drug penetration, suboptimal treatment and drug resistance. Our data provide evidence that remdesivir is able to suppress SARS-CoV-2 replication in vivo but that its efficacy may be compromised by factors reducing penetration into the lung. Based on data from influenza and Mycobacterium tuberculosis lung infections we conclude that early use of remdesivir combined with other agents should now be evaluated. Summary SentenceDeep sequencing of longitudinal samples from SARS-CoV-2 infected paediatric patients identifies evidence of remdesivir-associated inhibition of viral replication in vivo and uncovers evidence of within host evolution of distinct viral genotypes.

9.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.07.16.20155663

ABSTRACT

Introduction: Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) specific antibodies have been shown to neutralize the virus in-vitro. Understanding antibody dynamics following SARS-CoV-2 infection is therefore crucial. Sensitive measurement of SARS-CoV-2 antibodies is also vital for large seroprevalence surveys which inform government policies and public health interventions. However, rapidly waning antibodies following SARS-CoV-2 infection could jeopardize the sensitivity of serological testing on which these surveys depend. Methods: This prospective cohort study of SARS-CoV-2 humoral dynamics in a central London hospital analyzed 137 serial samples collected from 67 participants seropositive to SARS-CoV-2 by the Meso-Scale Discovery assay. Antibody titers were quantified to the SARS-CoV-2 nucleoprotein (N), spike (S-)protein and the receptor-binding-domain (RBD) of the S-protein. Titers were log-transformed and a multivariate log-linear model with time-since-infection and clinical variables was fitted by Bayesian methods. Results: The mean estimated half-life of the N-antibody was 52 days (95% CI 42-65). The S- and RBD-antibody had significantly longer mean half-lives of 81 days (95% CI 61-111) and 83 days (95% CI 55-137) respectively. An ACE-2-receptor competition assay demonstrated significant correlation between the S and RBD-antibody titers and ACE2-receptor blocking in-vitro. The time-to-a-negative N-antibody test for 50% of the seropositive population was predicted to be 195 days (95% CI 163-236). Discussion: After SARS-CoV-2 infection, the predicted half-life of N-antibody was 52 days with 50% of seropositive participants becoming seronegative to this antibody at 195 days. Widely used serological tests that depend on the N-antibody will therefore significantly underestimate the prevalence of infection following the majority of infections.

10.
biorxiv; 2020.
Preprint in English | bioRxiv | ID: ppzbmed-10.1101.2020.07.20.213249

ABSTRACT

BackgroundThe emergence of SARS-CoV-2 has led to the development of new serological assays that could aid in diagnosis and evaluation of seroprevalence to inform an understanding of the burden of COVID-19 disease. Many available tests lack rigorous evaluation and therefore results may be misleading. ObjectivesThe aim of this study was to assess the performance of a novel multiplexed immunoassay for the simultaneous detection of antibodies against SARS-CoV-2 trimeric spike (S), spike receptor binding domain (RBD), spike N terminal domain and nucleocapsid antigen and a novel pseudo-neutralisation assay. MethodsA multiplexed solid-phase chemiluminescence assay (Meso Scale Discovery) was evaluated for the simultaneous detection of IgG binding to four SARS-CoV-2 antigens and the quantification of antibody-induced ACE-2 binding inhibition (pseudo-neutralisation assay). Sensitivity was evaluated with a total of 196 COVID-19 serum samples (169 confirmed PCR positive and 27 anti-nucleocapsid IgG positive) from individuals with mild symptomatic or asymptomatic disease. Specificity was evaluated with 194 control serum samples collected from adults prior to December 2019. ResultsThe specificity and sensitivity of the binding IgG assay was highest for S protein with a specificity of 97.4% and sensitivity of 96.2% for samples taken 14 days and 97.9% for samples taken 21 days following the onset of symptoms. IgG concentration to S and RBD correlated strongly with percentage inhibition measured by the pseudo-neutralisation assay. ConclusionExcellent sensitivity for IgG detection was obtained over 14 days since onset of symptoms for three SARS-CoV-2 antigens (S, RBD and N) in this multiplexed assay which can also measure antibody functionality.

SELECTION OF CITATIONS
SEARCH DETAIL