Symptoms before and after COVID-19: a population and case-control study using prospective data.

BACKGROUND: Some individuals experience prolonged illness after acute COVID-19. We assessed whether pre-infection symptoms affected post-COVID illness duration. METHODS: Survival analysis was performed in adults (n=23 452) with community-managed SARC-CoV-2 infection prospectively self-logging data through the ZOE COVID Symptom Study app, at least weekly, from 8 weeks before to 12 weeks after COVID-19 onset, conditioned on presence versus absence of baseline symptoms (4-8 weeks before COVID-19). A case-control study was performed in 1350 individuals with long illness (≥8 weeks, 906 [67.1%] with illness ≥12 weeks), matched 1:1 (for age, sex, body mass index, testing week, prior infection, vaccination, smoking, index of multiple deprivation) with 1350 individuals with short illness (<4 weeks). Baseline symptoms were compared between the two groups; and against post-COVID symptoms. RESULTS: Individuals reporting baseline symptoms had longer post-COVID symptom duration (from 10 to 15 days) with baseline fatigue nearly doubling duration. Two-thirds (910 of 1350 [67.4%]) of individuals with long illness were asymptomatic beforehand. However, 440 (32.6%) had baseline symptoms, versus 255 (18.9%) of 1350 individuals with short illness (p<0.0001). Baseline symptoms increased the odds ratio for long illness (2.14 [CI: 1.78; 2.57]). Prior comorbidities were more common in individuals with long versus short illness. In individuals with long illness, baseline symptomatic (versus asymptomatic) individuals were more likely to be female, younger, and have prior comorbidities; and baseline and post-acute symptoms and symptom burden correlated strongly. CONCLUSIONS: Individuals experiencing symptoms before COVID-19 have longer illness duration and increased odds of long illness. However, many individuals with long illness are well before SARS-CoV-2 infection.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Metrics reloaded: recommendations for image analysis validation.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

SARS-CoV-2 infection following booster vaccination: Illness and symptom profile in a prospective, observational community-based case-control study.

BACKGROUND: Booster COVID-19 vaccines have shown efficacy in clinical trials and effectiveness in real-world data against symptomatic and severe illness. However, some people still become infected with SARS-CoV-2 following a third (booster) vaccination. This study describes the characteristics of SARS-CoV-2 illness following a third vaccination and assesses the risk of progression to symptomatic disease in SARS-CoV-2 infected individuals with time since vaccination. METHODS: This prospective, community-based, case-control study used data from UK-based, adult (≥18 years) users of the COVID Symptom Study mobile application, self-reporting a first positive COVID-19 test between June 1, 2021 and April 1, 2022. To describe the characteristics of SARS-CoV-2 illness following a third vaccination, we selected cases and controls who had received a third and second dose of monovalent vaccination against COVID-19, respectively, and reported a first positive SARS-CoV-2 test at least 7 days after most recent vaccination. Cases and controls were matched (1:1) based on age, sex, BMI, time between first vaccination and infection, and week of testing. We used logistic regression models (adjusted for age, sex, BMI, level of social deprivation and frailty) to analyse associations of disease severity, overall disease duration, and individual symptoms with booster vaccination status. To assess for potential waning of vaccine effectiveness, we compared disease severity, duration, and symptom profiles of individuals testing positive within 3 months of most recent vaccination (reference group) to profiles of individuals infected between 3 and 4, 4-5, and 5-6 months, for both third and second dose. All analyses were stratified by time period, based on the predominant SARS-CoV-2 variant at time of infection (Delta: June 1, 2021-27 Nov, 2021; Omicron: 20 Dec, 2021-Apr 1, 2022). FINDINGS: During the study period, 50,162 (Delta period) and 162,041 (Omicron) participants reported a positive SARS-CoV-2 test. During the Delta period, infection following three vaccination doses was associated with lower odds of long COVID (symptoms≥ 4 weeks) (OR=0.83, CI[0.50-1.36], p < 0.0001), hospitalisation (OR=0.55, CI[0.39-0.75], p < 0.0001) and severe symptoms (OR=0.36, CI[0.27-0.49], p < 0.0001), and higher odds of asymptomatic infection (OR=3.45, CI[2.86-4.16], p < 0.0001), compared to infection following only two vaccination doses. During the Omicron period, infection following three vaccination doses was associated with lower odds of severe symptoms (OR=0.48, CI[0.42-0.55], p < 0.0001). During the Delta period, infected individuals were less likely to report almost all individual symptoms after a third vaccination. During the Omicron period, individuals were less likely to report most symptoms after a third vaccination, except for upper respiratory symptoms e.g. sneezing (OR=1.40, CI[1.18-1.35], p < 0.0001), runny nose (OR=1.26, CI[1.18-1.35], p < 0.0001), sore throat (OR=1.17, CI[1.10-1.25], p < 0.0001), and hoarse voice (OR=1.13, CI[1.06-1.21], p < 0.0001), which were more likely to be reported. There was evidence of reduced vaccine effectiveness during both Delta and Omicron periods in those infected more than 3 months after their most recent vaccination, with increased reporting of severe symptoms, long duration illness, and most individual symptoms. INTERPRETATION: This study suggests that a third dose of monovalent vaccine may reduce symptoms, severity and duration of SARS-CoV-2 infection following vaccination. For Omicron variants, the third vaccination appears to reduce overall symptom burden but may increase upper respiratory symptoms, potentially due to immunological priming. There is evidence of waning vaccine effectiveness against progression to symptomatic and severe disease and long COVID after three months. Our findings support ongoing booster vaccination promotion amongst individuals at high risk from COVID-19, to reduce severe symptoms and duration of illness, and health system burden. Disseminating knowledge on expected symptoms following booster vaccination may encourage vaccine uptake.

The effects of COVID-19 on cognitive performance in a community-based cohort: a COVID symptom study biobank prospective cohort study.

Background: Cognitive impairment has been reported after many types of infection, including SARS-CoV-2. Whether deficits following SARS-CoV-2 improve over time is unclear. Studies to date have focused on hospitalised individuals with up to a year follow-up. The presence, magnitude, persistence and correlations of effects in community-based cases remain relatively unexplored. Methods: Cognitive performance (working memory, attention, reasoning, motor control) was assessed in a prospective cohort study of participants from the United Kingdom COVID Symptom Study Biobank between July 12, 2021 and August 27, 2021 (Round 1), and between April 28, 2022 and June 21, 2022 (Round 2). Participants, recruited from the COVID Symptom Study smartphone app, comprised individuals with and without SARS-CoV-2 infection and varying symptom duration. Effects of COVID-19 exposures on cognitive accuracy and reaction time scores were estimated using multivariable ordinary least squares linear regression models weighted for inverse probability of participation, adjusting for potential confounders and mediators. The role of ongoing symptoms after COVID-19 infection was examined stratifying for self-perceived recovery. Longitudinal analysis assessed change in cognitive performance between rounds. Findings: 3335 individuals completed Round 1, of whom 1768 also completed Round 2. At Round 1, individuals with previous positive SARS-CoV-2 tests had lower cognitive accuracy (N = 1737, ß = -0.14 standard deviations, SDs, 95% confidence intervals, CI: -0.21, -0.07) than negative controls. Deficits were largest for positive individuals with ≥12 weeks of symptoms (N = 495, ß = -0.22 SDs, 95% CI: -0.35, -0.09). Effects were comparable to hospital presentation during illness (N = 281, ß = -0.31 SDs, 95% CI: -0.44, -0.18), and 10 years age difference (60-70 years vs. 50-60 years, ß = -0.21 SDs, 95% CI: -0.30, -0.13) in the whole study population. Stratification by self-reported recovery revealed that deficits were only detectable in SARS-CoV-2 positive individuals who did not feel recovered from COVID-19, whereas individuals who reported full recovery showed no deficits. Longitudinal analysis showed no evidence of cognitive change over time, suggesting that cognitive deficits for affected individuals persisted at almost 2 years since initial infection. Interpretation: Cognitive deficits following SARS-CoV-2 infection were detectable nearly two years post infection, and largest for individuals with longer symptom durations, ongoing symptoms, and/or more severe infection. However, no such deficits were detected in individuals who reported full recovery from COVID-19. Further work is needed to monitor and develop understanding of recovery mechanisms for those with ongoing symptoms. Funding: Chronic Disease Research Foundation, Wellcome Trust, National Institute for Health and Care Research, Medical Research Council, British Heart Foundation, Alzheimer's Society, European Union, COVID-19 Driver Relief Fund, French National Research Agency.

Understanding the psychological experiences of loneliness in later life: qualitative protocol to inform technology development.

OBJECTIVES: Loneliness is a public health issue impacting the health and well-being of older adults. This protocol focuses on understanding the psychological experiences of loneliness in later life to inform technology development as part of the 'Design for health ageing: a smart system to detect loneliness in older people' (DELONELINESS) study. METHODS AND ANALYSIS: Data will be collected from semi-structured interviews with up to 60 people over the age of 65 on their experiences of loneliness and preferences for sensor-based technologies. The interviews will be audio-recorded, transcribed and analysed using a thematic codebook approach on NVivo software. ETHICS AND DISSEMINATION: This study has received ethical approval by Research Ethics Committee's at King's College London (reference number: LRS/DP-21/22-33376) and the University of Sussex (reference number: ER/JH878/1). All participants will be required to provide informed consent. Results will be used to inform technology development within the DELONELINESS study and will be disseminated in peer-reviewed publications and conferences.

Metabolomic and gut microbiome profiles across the spectrum of community-based COVID and non-COVID disease.

Whilst most individuals with SARS-CoV-2 infection have relatively mild disease, managed in the community, it was noted early in the pandemic that individuals with cardiovascular risk factors were more likely to experience severe acute disease, requiring hospitalisation. As the pandemic has progressed, increasing concern has also developed over long symptom duration in many individuals after SARS-CoV-2 infection, including among the majority who are managed acutely in the community. Risk factors for long symptom duration, including biological variables, are still poorly defined. Here, we examine post-illness metabolomic profiles, using nuclear magnetic resonance (Nightingale Health Oyj), and gut-microbiome profiles, using shotgun metagenomic sequencing (Illumina Inc), in 2561 community-dwelling participants with SARS-CoV-2. Illness duration ranged from asymptomatic (n = 307) to Post-COVID Syndrome (n = 180), and included participants with prolonged non-COVID-19 illnesses (n = 287). We also assess a pre-established metabolomic biomarker score, previously associated with hospitalisation for both acute pneumonia and severe acute COVID-19 illness, for its association with illness duration. We found an atherogenic-dyslipidaemic metabolic profile, including biomarkers such as fatty acids and cholesterol, was associated with longer duration of illness, both in individuals with and without SARS-CoV-2 infection. Greater values of a pre-existing metabolomic biomarker score also associated with longer duration of illness, regardless of SARS-CoV-2 infection. We found no association between illness duration and gut microbiome profiles in convalescence. This highlights the potential role of cardiometabolic dysfunction in relation to the experience of long duration symptoms after symptoms of acute infection, both COVID-19 as well as other illnesses.

Profiling post-COVID-19 condition across different variants of SARS-CoV-2: a prospective longitudinal study in unvaccinated wild-type, unvaccinated alpha-variant, and vaccinated delta-variant populations.

BACKGROUND: Self-reported symptom studies rapidly increased understanding of SARS-CoV-2 during the COVID-19 pandemic and enabled monitoring of long-term effects of COVID-19 outside hospital settings. Post-COVID-19 condition presents as heterogeneous profiles, which need characterisation to enable personalised patient care. We aimed to describe post-COVID-19 condition profiles by viral variant and vaccination status. METHODS: In this prospective longitudinal cohort study, we analysed data from UK-based adults (aged 18-100 years) who regularly provided health reports via the Covid Symptom Study smartphone app between March 24, 2020, and Dec 8, 2021. We included participants who reported feeling physically normal for at least 30 days before testing positive for SARS-CoV-2 who subsequently developed long COVID (ie, symptoms lasting longer than 28 days from the date of the initial positive test). We separately defined post-COVID-19 condition as symptoms that persisted for at least 84 days after the initial positive test. We did unsupervised clustering analysis of time-series data to identify distinct symptom profiles for vaccinated and unvaccinated people with post-COVID-19 condition after infection with the wild-type, alpha (B.1.1.7), or delta (B.1.617.2 and AY.x) variants of SARS-CoV-2. Clusters were then characterised on the basis of symptom prevalence, duration, demography, and previous comorbidities. We also used an additional testing sample with additional data from the Covid Symptom Study Biobank (collected between October, 2020, and April, 2021) to investigate the effects of the identified symptom clusters of post-COVID-19 condition on the lives of affected people. FINDINGS: We included 9804 people from the COVID Symptom Study with long COVID, 1513 (15%) of whom developed post-COVID-19 condition. Sample sizes were sufficient only for analyses of the unvaccinated wild-type, unvaccinated alpha variant, and vaccinated delta variant groups. We identified distinct profiles of symptoms for post-COVID-19 condition within and across variants: four endotypes were identified for infections due to the wild-type variant (in unvaccinated people), seven for the alpha variant (in unvaccinated people), and five for the delta variant (in vaccinated people). Across all variants, we identified a cardiorespiratory cluster of symptoms, a central neurological cluster, and a multi-organ systemic inflammatory cluster. These three main clusers were confirmed in a testing sample. Gastrointestinal symptoms clustered in no more than two specific phenotypes per viral variant. INTERPRETATION: Our unsupervised analysis identified different profiles of post-COVID-19 condition, characterised by differing symptom combinations, durations, and functional outcomes. Our classification could be useful for understanding the distinct mechanisms of post-COVID-19 condition, as well as for identification of subgroups of individuals who might be at risk of prolonged debilitation. FUNDING: UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation, UK Alzheimer's Society, and ZOE.

Robust and high-throughput lipidomic quantitation of human blood samples using flow injection analysis with tandem mass spectrometry for clinical use.

Direct infusion of lipid extracts into the ion source of a mass spectrometer is a well-established method for lipid analysis. In most cases, nanofluidic devices are used for sample introduction. However, flow injection analysis (FIA) based on sample infusion from a chromatographic pump can offer a simple alternative to shotgun-based approaches. Here, we describe important modification of a method based on FIA and tandem mass spectrometry (MS/MS). We focus on minimizing contamination of the FIA/MS both to render the lipidomic platform more robust and to increase its capacity and applicability for long-sequence measurements required in clinical applications. Robust validation of the developed method confirms its suitability for lipid quantitation in human plasma analysis. Measurements of standard human plasma reference material (NIST SRM 1950) and a set of plasma samples collected from kidney cancer patients and from healthy volunteers yielded highly similar results between FIA-MS/MS and ultra-high-performance supercritical fluid chromatography (UHPSFC)/MS, thereby demonstrating that all modifications have practically no effect on the statistical output. Newly modified FIA-MS/MS allows for the quantitation of 141 lipid species in plasma (11 major lipid classes) within 5.7 min. Finally, we tested the method in a clinical laboratory of the General University Hospital in Prague. In the clinical setting, the method capacity reached 257 samples/day. We also show similar performance of the classification models trained based on the results obtained in clinical settings and the analytical laboratory at the University of Pardubice. Together, these findings demonstrate the high potential of the modified FIA-MS/MS for application in clinical laboratories to measure plasma and serum lipid profiles.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

Frail older people and those in deprived areas remain at risk from covid-19, even after vaccination.

The studyAntonelli M, Penfold RS, Merino J, et al. Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study. Lancet Infect Dis 2022;22:1.To read the full NIHR Alert, go to: https://evidence.nihr.ac.uk/alert/older-people-at-risk-from-covid-even-after-vaccine/.

Post-vaccination infection rates and modification of COVID-19 symptoms in vaccinated UK school-aged children and adolescents: A prospective longitudinal cohort study.

Background: We aimed to explore the effectiveness of one-dose BNT162b2 vaccination upon SARS-CoV-2 infection, its effect on COVID-19 presentation, and post-vaccination symptoms in children and adolescents (CA) in the UK during periods of Delta and Omicron variant predominance. Methods: In this prospective longitudinal cohort study, we analysed data from 115,775 CA aged 12-17 years, proxy-reported through the Covid Symptom Study (CSS) smartphone application. We calculated post-vaccination infection risk after one dose of BNT162b2, and described the illness profile of CA with post-vaccination SARS-CoV-2 infection, compared to unvaccinated CA, and post-vaccination side-effects. Findings: Between August 5, 2021 and February 14, 2022, 25,971 UK CA aged 12-17 years received one dose of BNT162b2 vaccine. The probability of testing positive for infection diverged soon after vaccination, and was lower in CA with prior SARS-CoV-2 infection. Vaccination reduced proxy-reported infection risk (-80·4% (95% CI -0·82 -0·78) and -53·7% (95% CI -0·62 -0·43) at 14-30 days with Delta and Omicron variants respectively, and -61·5% (95% CI -0·74 -0·44) and -63·7% (95% CI -0·68 -0.59) after 61-90 days). Vaccinated CA who contracted SARS-CoV-2 during the Delta period had milder disease than unvaccinated CA; during the Omicron period this was only evident in children aged 12-15 years. Overall disease profile was similar in both vaccinated and unvaccinated CA. Post-vaccination local side-effects were common, systemic side-effects were uncommon, and both resolved within few days (3 days in most cases). Interpretation: One dose of BNT162b2 vaccine reduced risk of SARS-CoV-2 infection for at least 90 days in CA aged 12-17 years. Vaccine protection varied for SARS-CoV-2 variant type (lower for Omicron than Delta variant), and was enhanced by pre-vaccination SARS-CoV-2 infection. Severity of COVID-19 presentation after vaccination was generally milder, although unvaccinated CA also had generally mild disease. Overall, vaccination was well-tolerated. Funding: UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation and Alzheimer's Society, and ZOE Limited.

The Medical Segmentation Decathlon.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

COVID-19 due to the B.1.617.2 (Delta) variant compared to B.1.1.7 (Alpha) variant of SARS-CoV-2: a prospective observational cohort study.

The Delta (B.1.617.2) variant was the predominant UK circulating SARS-CoV-2 strain between May and December 2021. How Delta infection compares with previous variants is unknown. This prospective observational cohort study assessed symptomatic adults participating in the app-based COVID Symptom Study who tested positive for SARS-CoV-2 from May 26 to July 1, 2021 (Delta overwhelmingly the predominant circulating UK variant), compared (1:1, age- and sex-matched) with individuals presenting from December 28, 2020 to May 6, 2021 (Alpha (B.1.1.7) the predominant variant). We assessed illness (symptoms, duration, presentation to hospital) during Alpha- and Delta-predominant timeframes; and transmission, reinfection, and vaccine effectiveness during the Delta-predominant period. 3581 individuals (aged 18 to 100 years) from each timeframe were assessed. The seven most frequent symptoms were common to both variants. Within the first 28 days of illness, some symptoms were more common with Delta versus Alpha infection (including fever, sore throat, and headache) and some vice versa (dyspnoea). Symptom burden in the first week was higher with Delta versus Alpha infection; however, the odds of any given symptom lasting ≥ 7 days was either lower or unchanged. Illness duration ≥ 28 days was lower with Delta versus Alpha infection, though unchanged in unvaccinated individuals. Hospitalisation for COVID-19 was unchanged. The Delta variant appeared more (1.49) transmissible than Alpha. Re-infections were low in all UK regions. Vaccination markedly reduced the risk of Delta infection (by 69-84%). We conclude that COVID-19 from Delta or Alpha infections is similar. The Delta variant is more transmissible than Alpha; however, current vaccines showed good efficacy against disease. This research framework can be useful for future comparisons with new emerging variants.

Illness Characteristics of COVID-19 in Children Infected with the SARS-CoV-2 Delta Variant.

BACKGROUND: The Delta (B.1.617.2) SARS-CoV-2 variant was the predominant UK circulating strain between May and November 2021. We investigated whether COVID-19 from Delta infection differed from infection with previous variants in children. METHODS: Through the prospective COVID Symptom Study, 109,626 UK school-aged children were proxy-reported between 28 December 2020 and 8 July 2021. We selected all symptomatic children who tested positive for SARS-CoV-2 and were proxy-reported at least weekly, within two timeframes: 28 December 2020 to 6 May 2021 (Alpha (B.1.1.7), the main UK circulating variant) and 26 May to 8 July 2021 (Delta, the main UK circulating variant), with all children unvaccinated (as per national policy at the time). We assessed illness profiles (symptom prevalence, duration, and burden), hospital presentation, and presence of long (≥28 day) illness, and calculated odds ratios for symptoms presenting within the first 28 days of illness. RESULTS: 694 (276 younger (5-11 years), 418 older (12-17 years)) symptomatic children tested positive for SARS-CoV-2 with Alpha infection and 706 (227 younger and 479 older) children with Delta infection. Median illness duration was short with either variant (overall cohort: 5 days (IQR 2-9.75) with Alpha, 5 days (IQR 2-9) with Delta). The seven most prevalent symptoms were common to both variants. Symptom burden over the first 28 days was slightly greater with Delta compared with Alpha infection (in younger children, 3 (IQR 2-5) symptoms with Alpha, 4 (IQR 2-7) with Delta; in older children, 5 (IQR 3-8) symptoms with Alpha, 6 (IQR 3-9) with Delta infection ). The odds of presenting several symptoms were higher with Delta than Alpha infection, including headache and fever. Few children presented to hospital, and long illness duration was uncommon, with either variant. CONCLUSIONS: COVID-19 in UK school-aged children due to SARS-CoV-2 Delta strain B.1.617.2 resembles illness due to the Alpha variant B.1.1.7., with short duration and similar symptom burden.

Symptom prevalence, duration, and risk of hospital admission in individuals infected with SARS-CoV-2 during periods of omicron and delta variant dominance: a prospective observational study from the ZOE COVID Study.

BACKGROUND: The SARS-CoV-2 variant of concern, omicron, appears to be less severe than delta. We aim to quantify the differences in symptom prevalence, risk of hospital admission, and symptom duration among the vaccinated population. METHODS: In this prospective longitudinal observational study, we collected data from participants who were self-reporting test results and symptoms in the ZOE COVID app (previously known as the COVID Symptoms Study App). Eligible participants were aged 16-99 years, based in the UK, with a body-mass index between 15 and 55 kg/m2, had received at least two doses of any SARS-CoV-2 vaccine, were symptomatic, and logged a positive symptomatic PCR or lateral flow result for SARS-CoV-2 during the study period. The primary outcome was the likelihood of developing a given symptom (of the 32 monitored in the app) or hospital admission within 7 days before or after the positive test in participants infected during omicron prevalence compared with those infected during delta prevalence. FINDINGS: Between June 1, 2021, and Jan 17, 2022, we identified 63 002 participants who tested positive for SARS-CoV-2 and reported symptoms in the ZOE app. These patients were matched 1:1 for age, sex, and vaccination dose, across two periods (June 1 to Nov 27, 2021, delta prevalent at >70%; n=4990, and Dec 20, 2021, to Jan 17, 2022, omicron prevalent at >70%; n=4990). Loss of smell was less common in participants infected during omicron prevalence than during delta prevalence (16·7% vs 52·7%, odds ratio [OR] 0·17; 95% CI 0·16-0·19, p<0·001). Sore throat was more common during omicron prevalence than during delta prevalence (70·5% vs 60·8%, 1·55; 1·43-1·69, p<0·001). There was a lower rate of hospital admission during omicron prevalence than during delta prevalence (1·9% vs 2·6%, OR 0·75; 95% CI 0·57-0·98, p=0·03). INTERPRETATION: The prevalence of symptoms that characterise an omicron infection differs from those of the delta SARS-CoV-2 variant, apparently with less involvement of the lower respiratory tract and reduced probability of hospital admission. Our data indicate a shorter period of illness and potentially of infectiousness which should impact work-health policies and public health advice. FUNDING: Wellcome Trust, ZOE, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, and Medical Research Council.

Correlation between whole skeleton dual energy CT calcium-subtracted attenuation and bone marrow infiltration in multiple myeloma.

OBJECTIVES: Objective evaluation of the extent of skeletal marrow involvement in multiple myeloma remains a clinical gap for CT. We aimed to develop a quantitative segmentation pipeline for dual energy CT and to assess whether quantified whole skeleton calcium-subtracted attenuation values correlate with biopsy-derived bone marrow infiltration in multiple myeloma. METHODS: Consecutive prospective patients with suspected/established myeloma underwent dual source CT from the skull vertex to proximal tibia. Whole skeleton segmentation was performed for 120 kVp-equivalent images as follows: following Hounsfield unit (HU) thresholding, a Chan-Vese morphological operation was implemented to generate a whole skeleton segmentation mask. This mask was then applied to corresponding whole skeleton material decomposition calcium-subtracted maps, generating whole skeleton HU values. Associations with biopsy-derived bone marrow plasma cell infiltration percentage were assessed with Spearman's rank correlation; significance was at 5%. RESULTS: 21 patients (12 females; median (IQR) 67 (61, 73) years) were included; 16 patients had osteolytic bone lesions; 15 patients underwent bone marrow biopsy. Segmentation and quantification were feasible in all patients. Median (IQR) of the average skeletal calcium-subtracted attenuation was -59.9 HU (-66.3, -51.8HU). There was a positive correlation with bone marrow plasma cell infiltration percentage (Spearman's rho: + 0.79, p < 0.001). CONCLUSION: Whole skeleton calcium-subtracted attenuation is associated with the degree of bone marrow infiltration by plasma cells, providing an objective measure of marrow involvement with the potential to allow earlier detection of disease.

Ultrahigh-performance supercritical fluid chromatography - mass spectrometry for the qualitative analysis of metabolites covering a large polarity range.

The applicability of ultrahigh-performance supercritical fluid chromatography coupled with mass spectrometry (UHPSFC/MS) for the qualitative analysis of metabolites with a wide polarity range (log P: -3.89-18.95) was evaluated using a representative set of 78 standards belonging to nucleosides, biogenic amines, carbohydrates, amino acids, and lipids. The effects of the gradient shape and the percentage of water (1, 2, and 5%) were investigated on the Viridis BEH column. The screening of eight stationary phases was performed for columns with different interaction sites, such as hydrogen bonding, hydrophobic, π-π, or anionic exchange type interactions. The highest number of compounds (67) of the set studied was detected on the Torus Diol column, which provided a resolution parameter of 39. The DEA column had the second best performance with 58 detected standards and the resolution parameter of 54. The overall performance of other parameters, such as selectivity, peak height, peak area, retention time stability, asymmetry factor, and mass accuracy, led to the selection of the Diol column for the final method. The comparison of additives showed that ammonium acetate gave a superior sensitivity over ammonium formate. Moreover, the influence of the ion source on the ionization efficiency was studied by employing atmospheric pressure chemical ionization (APCI) and electrospray ionization (ESI). The results proved the complementarity of both ionization techniques, but also the superior ionization capacity of the ESI source in the negative ion mode, for which 53% of the analytes were detected compared to only 7% for the APCI source. Finally, optimized analytical conditions were applied to the analysis of a pooled human plasma sample. 44 compounds from the preselected set were detected in human plasma using ESI-UHPSFC/MS in MSE mode considering both ionization modes.