37 research outputs found

    Results of the standard set forpulmonary sarcoidosis: Feasibility and multicentre outcomes

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    Our study presents findings on a previously developed standard set of clinical outcome data for pulmonary sarcoidosis patients. We aimed to assess whether changes in outcome varied between the different centres and to evaluate the feasibility of collecting the standard set retrospectively. This retrospective observational comparative benchmark study included six interstitial lung disease expert centres based in the Netherlands, Belgium, the UK and the USA. The standard set of outcome measures included 1) mortality, 2) changes in pulmonary function (forced vital capacity (FVC), forced expiratory volume in 1 s, diffusing capacity of the lung for carbon monoxide), 3) soluble interleukin-2 receptor (sIL-2R) change, 4) weight changes, 5) quality-of-life (QoL) measures, 6) osteoporosis and 7) clinical outcome status (COS). Data collection was considered feasible if the data were collected in ⩾80% of all patients. 509 patients were included in the retrospective cohort. In total six patients died, with a mean survival of 38±23.4 months after the diagnosis. Centres varied in mean baseline FVC, ranging from 110 (95% CI 92–124)% predicted to 99 (95% CI 97–123)% pred. Mean baseline body mass index (BMI) of patients in the different centres varied between 27 (95% CI 23.6–29.4) kg·m−2 and 31.8 (95% CI 28.1–35.6) kg·m−2. 310 (60.9%) patients were still on systemic therapy 2 years after the diagnosis. It was feasible to measure mortality, changes in pulmonary function, weight changes and COS. It is not (yet) feasible to retrospectively collect sIL-2R, osteoporosis and QoL data internationally. This study shows that data collection for the standard set of outcome measures for pulmonary sarcoidosis was feasible for four out of seven outcome measures. Trends in pulmonary function and BMI were similar for different hospitals when comparing different practices

    Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

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    One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

    SARS-CoV-2-specific nasal IgA wanes 9 months after hospitalisation with COVID-19 and is not induced by subsequent vaccination

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    BACKGROUND: Most studies of immunity to SARS-CoV-2 focus on circulating antibody, giving limited insights into mucosal defences that prevent viral replication and onward transmission. We studied nasal and plasma antibody responses one year after hospitalisation for COVID-19, including a period when SARS-CoV-2 vaccination was introduced. METHODS: In this follow up study, plasma and nasosorption samples were prospectively collected from 446 adults hospitalised for COVID-19 between February 2020 and March 2021 via the ISARIC4C and PHOSP-COVID consortia. IgA and IgG responses to NP and S of ancestral SARS-CoV-2, Delta and Omicron (BA.1) variants were measured by electrochemiluminescence and compared with plasma neutralisation data. FINDINGS: Strong and consistent nasal anti-NP and anti-S IgA responses were demonstrated, which remained elevated for nine months (p < 0.0001). Nasal and plasma anti-S IgG remained elevated for at least 12 months (p < 0.0001) with plasma neutralising titres that were raised against all variants compared to controls (p < 0.0001). Of 323 with complete data, 307 were vaccinated between 6 and 12 months; coinciding with rises in nasal and plasma IgA and IgG anti-S titres for all SARS-CoV-2 variants, although the change in nasal IgA was minimal (1.46-fold change after 10 months, p = 0.011) and the median remained below the positive threshold determined by pre-pandemic controls. Samples 12 months after admission showed no association between nasal IgA and plasma IgG anti-S responses (R = 0.05, p = 0.18), indicating that nasal IgA responses are distinct from those in plasma and minimally boosted by vaccination. INTERPRETATION: The decline in nasal IgA responses 9 months after infection and minimal impact of subsequent vaccination may explain the lack of long-lasting nasal defence against reinfection and the limited effects of vaccination on transmission. These findings highlight the need to develop vaccines that enhance nasal immunity. FUNDING: This study has been supported by ISARIC4C and PHOSP-COVID consortia. ISARIC4C is supported by grants from the National Institute for Health and Care Research and the Medical Research Council. Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research. The PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research. The funders were not involved in the study design, interpretation of data or the writing of this manuscript

    Automatic Speech Recognition, Noise and Workload

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