MDMs from healthy volunteers differentiated in the presence of DEP showed reduced proinflammatory responses to <i>E. coli</i>.

<p>MDMs from healthy volunteers, COPD and age matched controls were differentiated from purified monocytes in the presence or absence of varied concentrations of DEP as described in the methods. At day 14 MDMs were washed and stimulated with heat killed <i>E. coli</i> as shown. After 24 hours, levels of CXCL8 in the supernatant were determined by ELISA. Data shown are mean±SEM of n = 5, performed on different donors with significant differences denoted by *p<0.05, **p<0.01 and ***p<0.001, compared to MDMs differentiated in the absence of DEP, as measured by one way ANOVA and Dunnett’s post test.</p

MDMs differentiated in the presence of DEP showed loss of lysosomal acidification.

<p>MDMs were differentiated from purified monocytes in the presence or absence of varied concentrations of DEP as described in the methods. At day 14 cells from healthy volunteers were stained with 5 µM acridine orange (AO) for 30 minutes at 37°C. The cells were harvested and resuspended in ice cold PBS for analysis using a FACSCalibur flow cytometer. Loss of lysosomal acidification was measured in the FL-3 channel. Viable MDMs were identified based on size and granularity on the forward and side scatter plots and ten thousand events were recorded. Data were presented as percentage of cells showing loss of green staining compared to control cells (A) and geomean fluorescence (B) and data analysis was performed using FlowJo software. Figure C shows a representative histogram flow analysis plot from one donor. Data shown are mean±SEM of n = 4–5, performed on different donors with significant differences denoted by *p<0.05, **p<0.01 and ***p<0.001, compared to MDMs differentiated in the absence of DEP, as measured by one way ANOVA and Dunnett’s post test.</p

DEP induced cell loss of MDMs from COPD and age matched controls.

<p>MDMs were differentiated from purified monocytes obtained from stage II GOLD criteria COPD and age matched controls in the presence or absence of varied concentrations of DEP as described in the methods. A control well of MDMs was incubated overnight in 1 µM staurosporine (ST). MDMs were visualised by microscopy at the stated time points. A total of 4 randomly selected 40 magnification fields were visualised and cell counts were performed and denoted as total cell count. Data shown are mean±SEM of n = 4, performed on different donors with significant differences denoted by *p<0.05, **p<0.01 and ***p<0.001 compared to MDMs differentiated in the absence of DEP, as measured by one way ANOVA and Dunnett’s post test. To facilitate comparison the percentage cell loss was calculated compared to MDMs differentiated in the absence of DEP.</p

MDMs differentiated in the presence of DEP showed reduced CD14, CD11b and CD86 surface marker expression.

<p>MDMs from healthy volunteers were differentiated from purified monocytes in the presence or absence of varied concentrations of DEP as described in the methods. At day 14 MDMs were harvested and non specific binding was blocked by incubating at room temperature for 10 minutes with 1∶50 IgG from murine serum reagent grade in FACS buffer. Cells were then incubated with 1∶10 PE conjugated mouse anti-human CD14, CD11b, HLA-DR, TLR2, TLR4, CD80 or CD86 or the respective isotype controls. PE fluorescence was measured on the FL-2 channel of a FACSCalibur flow cytometer. Data are presented as a mean±SEM of n = 4 performed on different donors and representative histogram flow analysis plots from one donor. Significant differences between MDMs and DEP-MDMs cell surface expression are denoted by *p<0.05 and **p<0.01 as measured by one way ANOVA and Dunnett’s post test.</p

MDMs differentiated in the presence of DEP showed loss of mitochondrial membrane potential (ΔΨm).

<p>MDMs were differentiated from purified monocytes in the presence or absence of varied concentrations of DEP as described in the methods. At day 14 MDMs and DEP-MDMs from healthy volunteers were washed three times in PBS. Cells were stained with 10 µM JC-1 in serum free RPMI 1640 for 30 minutes at 37°C. The cells were harvested and resuspended in ice cold PBS for analysis by flow cytometry. Loss of Δψ_m was detected using a FACSCalibur flow cytometer and was indicated by a decrease in red fluorescence (FL-2). Viable MDMs were identified based on size and granularity on the forward and side scatter plots and ten thousand events were recorded. Data were presented as percentage of cells showing loss of red staining compared to control cells (A) and geomean fluorescence (B) and data analysis was performed using FlowJo software. Figure C shows a representative histogram flow analysis plot from one donor. Data shown are mean±SEM of n = 4–5, performed on different donors with significant differences denoted by *p<0.05, **p<0.01 and ***p<0.001, compared to MDMs differentiated in the absence of DEP, as measured by one way ANOVA and Dunnett’s post test.</p

DEP induced cell loss of MDMs from healthy volunteers.

<p>MDMs were differentiated from purified monocytes obtained from healthy volunteers in the presence or absence of varied concentrations of DEP as described in the methods. A control well of MDMs were incubated overnight in 1 µM staurosporine (ST). MDMs were visualised by microscopy at the stated time points. A total of 4 randomly selected 10×magnification fields were visualised and cell counts were performed and denoted as total cell count. Data shown are mean±SEM of n = 6 for panels A and B and n = 4 for C and D, performed on different donors. Significant differences in A and B are denoted by *p<0.05, **p<0.01 and ***p<0.001, compared to MDMs differentiated in the absence of DEP, as measured by two way ANOVA and Bonferroni’s post test. To facilitate comparison the percentage cell loss was calculated compared to MDMs differentiated in the absence of DEP (C and D).</p

Effects of sleep disturbance on dyspnoea and impaired lung function following hospital admission due to COVID-19 in the UK: a prospective multicentre cohort study.

BackgroundSleep disturbance is common following hospital admission both for COVID-19 and other causes. The clinical associations of this for recovery after hospital admission are poorly understood despite sleep disturbance contributing to morbidity in other scenarios. We aimed to investigate the prevalence and nature of sleep disturbance after discharge following hospital admission for COVID-19 and to assess whether this was associated with dyspnoea.MethodsCircCOVID was a prospective multicentre cohort substudy designed to investigate the effects of circadian disruption and sleep disturbance on recovery after COVID-19 in a cohort of participants aged 18 years or older, admitted to hospital for COVID-19 in the UK, and discharged between March, 2020, and October, 2021. Participants were recruited from the Post-hospitalisation COVID-19 study (PHOSP-COVID). Follow-up data were collected at two timepoints: an early time point 2-7 months after hospital discharge and a later time point 10-14 months after hospital discharge. Sleep quality was assessed subjectively using the Pittsburgh Sleep Quality Index questionnaire and a numerical rating scale. Sleep quality was also assessed with an accelerometer worn on the wrist (actigraphy) for 14 days. Participants were also clinically phenotyped, including assessment of symptoms (ie, anxiety [Generalised Anxiety Disorder 7-item scale questionnaire], muscle function [SARC-F questionnaire], dyspnoea [Dyspnoea-12 questionnaire] and measurement of lung function), at the early timepoint after discharge. Actigraphy results were also compared to a matched UK Biobank cohort (non-hospitalised individuals and recently hospitalised individuals). Multivariable linear regression was used to define associations of sleep disturbance with the primary outcome of breathlessness and the other clinical symptoms. PHOSP-COVID is registered on the ISRCTN Registry (ISRCTN10980107).Findings2320 of 2468 participants in the PHOSP-COVID study attended an early timepoint research visit a median of 5 months (IQR 4-6) following discharge from 83 hospitals in the UK. Data for sleep quality were assessed by subjective measures (the Pittsburgh Sleep Quality Index questionnaire and the numerical rating scale) for 638 participants at the early time point. Sleep quality was also assessed using device-based measures (actigraphy) a median of 7 months (IQR 5-8 months) after discharge from hospital for 729 participants. After discharge from hospital, the majority (396 [62%] of 638) of participants who had been admitted to hospital for COVID-19 reported poor sleep quality in response to the Pittsburgh Sleep Quality Index questionnaire. A comparable proportion (338 [53%] of 638) of participants felt their sleep quality had deteriorated following discharge after COVID-19 admission, as assessed by the numerical rating scale. Device-based measurements were compared to an age-matched, sex-matched, BMI-matched, and time from discharge-matched UK Biobank cohort who had recently been admitted to hospital. Compared to the recently hospitalised matched UK Biobank cohort, participants in our study slept on average 65 min (95% CI 59 to 71) longer, had a lower sleep regularity index (-19%; 95% CI -20 to -16), and a lower sleep efficiency (3·83 percentage points; 95% CI 3·40 to 4·26). Similar results were obtained when comparisons were made with the non-hospitalised UK Biobank cohort. Overall sleep quality (unadjusted effect estimate 3·94; 95% CI 2·78 to 5·10), deterioration in sleep quality following hospital admission (3·00; 1·82 to 4·28), and sleep regularity (4·38; 2·10 to 6·65) were associated with higher dyspnoea scores. Poor sleep quality, deterioration in sleep quality, and sleep regularity were also associated with impaired lung function, as assessed by forced vital capacity. Depending on the sleep metric, anxiety mediated 18-39% of the effect of sleep disturbance on dyspnoea, while muscle weakness mediated 27-41% of this effect.InterpretationSleep disturbance following hospital admission for COVID-19 is associated with dyspnoea, anxiety, and muscle weakness. Due to the association with multiple symptoms, targeting sleep disturbance might be beneficial in treating the post-COVID-19 condition.FundingUK Research and Innovation, National Institute for Health Research, and Engineering and Physical Sciences Research Council