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The China Surgery and Anaesthesia Cohort (CSAC) study was launched in July 2020 and is an ongoing prospective cohort study recruiting patients aged 40-65 years who underwent elective surgeries with general anaesthesia across four medical centres in China. The general objective of the CSAC study is to improve our understanding of the complex interaction between environmental and genetic components as well as to determine their effects on a wide range of interested surgery/anaesthesia-related outcomes. To achieve this goal, we collected enriched phenotypic data, e.g., sociodemographic characteristics, lifestyle factors, perioperative neuropsychological changes, anaesthesia- and surgery-related complications, and medical conditions, at recruitment, as well as through both active (at 1, 3, 7 days and 1, 3, 6, 12 months after surgery) and passive (for more than 1 year after surgery) follow-up assessments. We also obtained omics data from blood samples. In addition, COVID-19-related information was collected from all participants since January 2023, immediately after COVID-19 restrictions were eased in China. As of July 18, 2023, 12,766 participants (mean age = 52.40 years, 57.93% were female) completed baseline data collection (response rate = 94.68%), among which approximately 70% donated blood and hair samples. The follow-up rates within 12 months after surgery were > 92%. Our initial analyses have demonstrated the incidence of and risk factors for chronic postsurgical pain (CPSP) and postoperative cognitive dysfunction (POCD) among middle-aged Chinese individuals, which may prompt further mechanistic exploration and facilitate the development of effective interventions for preventing those conditions. Additional studies, such as genome-wide association analyses for identifying the genetic determinants of CPSP and POCD, are ongoing, and their findings will be released in the future., (© 2023. The Author(s).)

Background: Whether a genetic predisposition to psychiatric disorders is associated with coronavirus disease 2019 (COVID-19) is unknown., Methods: Our analytic sample consisted of 287,123 white British participants in UK Biobank who were alive on 31 January 2020. We performed a genome-wide association study (GWAS) analysis for each psychiatric disorder (substance misuse, depression, anxiety, psychotic disorder, and stress-related disorders) in a randomly selected half of the study population ("base dataset"). For the other half ("target dataset"), the polygenic risk score (PRS) was calculated as a proxy of individuals' genetic predisposition to a given psychiatric phenotype using discovered genetic variants from the base dataset. Ascertainment of COVID-19 was based on the Public Health England dataset, inpatient hospital data, or death registers in UK Biobank. COVID-19 cases from hospitalization records or death records were considered "severe cases." The association between the PRS for psychiatric disorders and COVID-19 risk was examined using logistic regression. We also repeated PRS analyses based on publicly available GWAS summary statistics., Results: A total of 143,562 participants (including 10,868 COVID-19 cases) were used for PRS analyses. A higher genetic predisposition to psychiatric disorders was associated with an increased risk of any COVID-19 and severe COVID-19. The adjusted odds ratio (OR) for any COVID-19 was 1.07 (95% confidence interval [CI] 1.02-1.13) and 1.06 (95% CI 1.01-1.11) among individuals with a high genetic risk (above the upper tertile of the PRS) for substance misuse and depression, respectively, compared with individuals with a low genetic risk (below the lower tertile). Slightly higher ORs were noted for severe COVID-19, and similar result patterns were obtained in analyses based on publicly available GWAS summary statistics., Conclusions: Our findings suggest a potential role of genetic factors in the observed phenotypic association between psychiatric disorders and COVID-19. Our data underscore the need for increased medical surveillance for this vulnerable population during the COVID-19 pandemic., (© 2022. The Author(s).)

Background: An increased susceptibility to COVID-19 has been suggested for individuals with neurodegenerative diseases, but data are scarce from longitudinal studies., Methods: In this community-based cohort study, we included 96,275 participants of the UK Biobank who had available SARS-CoV-2 test results in Public Health England. Of these, 2617 had a clinical diagnosis of neurodegenerative diseases in the UK Biobank inpatient hospital data before the outbreak of COVID-19 (defined as January 31st, 2020), while the remaining participants constituted the reference group. We then followed both groups from January 31st, 2020 to June 14th, 2021 for ascertainment of COVID-19 outcomes, including any COVID-19, inpatient care for COVID-19, and COVID-19 related death. Logistic regression was applied to estimate the association between neurogenerative disease and risks of COVID-19 outcomes, adjusted for multiple confounders and somatic comorbidities., Results: We observed an elevated risk of COVID-19 outcomes among individuals with a neurodegenerative disease compared with the reference group, corresponding to a fully adjusted odds ratio of 2.47 (95%CI 2.25-2.71) for any COVID-19, 2.18 (95%CI 1.94-2.45) for inpatient COVID-19, and 3.67 (95%CI 3.11-4.34) for COVID-19 related death. Among individuals with a positive test result for SARS-CoV-2, individuals with neurodegenerative diseases had also a higher risk of COVID-19 related death than others (fully adjusted odds ratio 2.08; 95%CI 1.71-2.53)., Conclusion: Among UK Biobank participants who received at least one test for SARS-CoV-2, a pre-existing diagnosis of neurodegenerative disease was associated with a subsequently increased risk of COVID-19, especially COVID-19 related death., (© 2022. The Author(s).)

Background: With the increasing number of people infected with and recovered from coronavirus disease 2019 (COVID-19), the extent of major health consequences of COVID-19 is unclear, including risks of severe secondary infections., Methods: Based on 445,845 UK Biobank participants registered in England, we conducted a matched cohort study where 5151 individuals with a positive test result or hospitalized with a diagnosis of COVID-19 were included in the exposed group. We then randomly selected up to 10 matched individuals without COVID-19 diagnosis for each exposed individual (n = 51,402). The life-threatening secondary infections were defined as diagnoses of severe secondary infections with high mortality rates (i.e., sepsis, endocarditis, and central nervous system infections) from the UK Biobank inpatient hospital data, or deaths from these infections from mortality data. The follow-up period was limited to 3 months after the initial COVID-19 diagnosis. Using a similar study design, we additionally constructed a matched cohort where exposed individuals were diagnosed with seasonal influenza from either inpatient hospital or primary care data between 2010 and 2019 (6169 exposed and 61,555 unexposed individuals). After controlling for multiple confounders, Cox models were used to estimate hazard ratios (HRs) of life-threatening secondary infections after COVID-19 or seasonal influenza., Results: In the matched cohort for COVID-19, 50.22% of participants were male, and the median age at the index date was 66 years. During a median follow-up of 12.71 weeks, the incidence rate of life-threatening secondary infections was 2.23 (123/55.15) and 0.25 (151/600.55) per 1000 person-weeks for all patients with COVID-19 and their matched individuals, respectively, which corresponded to a fully adjusted HR of 8.19 (95% confidence interval [CI] 6.33-10.59). The corresponding HR of life-threatening secondary infections among all patients with seasonal influenza diagnosis was 4.50, 95% CI 3.34-6.08 (p for difference < 0.01). Also, elevated HRs were observed among hospitalized individuals for life-threatening secondary infections following hospital discharge, both in the COVID-19 (HR = 6.28 [95% CI 4.05-9.75]) and seasonal influenza (6.01 [95% CI 3.53-10.26], p for difference = 0.902) cohorts., Conclusion: COVID-19 patients have increased subsequent risks of life-threatening secondary infections, to an equal extent or beyond risk elevations observed for patients with seasonal influenza., (© 2021. The Author(s).)

COVID-19, caused by SARS-CoV-2, is an acute and rapidly developing pandemic, which leads to a global health crisis. SARS-CoV-2 primarily attacks human alveoli and causes severe lung infection and damage. To better understand the molecular basis of this disease, we sought to characterize the responses of alveolar epithelium and its adjacent microvascular endothelium to viral infection under a co-culture system. SARS-CoV-2 infection caused massive virus replication and dramatic organelles remodeling in alveolar epithelial cells, alone. While, viral infection affected endothelial cells in an indirect manner, which was mediated by infected alveolar epithelium. Proteomics analysis and TEM examinations showed viral infection caused global proteomic modulations and marked ultrastructural changes in both epithelial cells and endothelial cells under the co-culture system. In particular, viral infection elicited global protein changes and structural reorganizations across many sub-cellular compartments in epithelial cells. Among the affected organelles, mitochondrion seems to be a primary target organelle. Besides, according to EM and proteomic results, we identified Daurisoline, a potent autophagy inhibitor, could inhibit virus replication effectively in host cells. Collectively, our study revealed an unrecognized cross-talk between epithelium and endothelium, which contributed to alveolar-capillary injury during SARS-CoV-2 infection. These new findings will expand our understanding of COVID-19 and may also be helpful for targeted drug development.

Background: Psychiatric morbidities have been associated with a risk of severe infections through compromised immunity, health behaviours, or both. However, data are scarce on the association between multiple types of pre-pandemic psychiatric disorders and COVID-19. We aimed to assess the association between pre-pandemic psychiatric disorders and the subsequent risk of COVID-19 using UK Biobank., Methods: For this cohort analysis, we included participants from UK Biobank who were registered in England and excluded individuals who died before Jan 31, 2020, (the start of the COVID-19 outbreak in the UK) or had withdrawn from UK Biobank. Participants diagnosed with a psychiatric disorder before Jan 31 were included in the group of individuals with pre-pandemic psychiatric disorders, whereas participants without a diagnosis before the outbreak were included in the group of individuals without pre-pandemic psychiatric disorders. We used the Public Health England dataset, UK Biobank hospital data, and death registers to collect data on COVID-19 cases. To examine the relationship between pre-pandemic psychiatric disorders and susceptibility to COVID-19, we used logistic regression models to estimate odds ratios (ORs), controlling for multiple confounders and somatic comorbidities. Key outcomes were all COVID-19, COVID-19 specifically diagnosed in inpatient care, and COVID-19-related deaths. ORs were also estimated separately for each psychiatric disorder and on the basis of the number of pre-pandemic psychiatric disorders. As a positive disease control, we repeated analyses for hospitalisation for other infections., Findings: We included 421 014 UK Biobank participants in our study and assessed their COVID-19 status between Jan 31 and July 26, 2020. 50 809 participants were diagnosed with psychiatric disorders before the outbreak, while 370 205 participants had no psychiatric disorders. The mean age at outbreak was 67·80 years (SD 8·12). We observed an elevated risk of COVID-19 among individuals with pre-pandemic psychiatric disorders compared with that of individuals without such conditions. The fully adjusted ORs were 1·44 (95% CI 1·28-1·62) for All COVID-19 cases, 1·55 (1·34-1·78) for Inpatient COVID-19 cases, and 2·03 (1·59-2·59) for COVID-19-related deaths. We observed excess risk, defined as risk that increased with the number of pre-pandemic psychiatric disorders, across all diagnostic categories of pre-pandemic psychiatric disorders. We also observed an association between psychiatric disorders and elevated risk of hospitalisation due to other infections (OR 1·74, 95% CI 1·58-1·93)., Interpretation: Our findings suggest that pre-existing psychiatric disorders are associated with an increased risk of COVID-19. These findings underscore the need for surveillance of and care for populations with pre-existing psychiatric disorders during the COVID-19 pandemic., Funding: National Natural Science Foundation of China., (© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license.)

Coronavirus disease 2019 (COVID‐19) is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). The models that can accurately resemble human‐relevant responses to viral infection are lacking. Here, a biomimetic human disease model on chip that allows to recapitulate lung injury and immune responses induced by SARS‐CoV‐2 in vitro at organ level is created. This human alveolar chip reproduce the key features of alveolar‐capillary barrier by coculture of human alveolar epithelium, microvascular endothelium, and circulating immune cells under fluidic flow in normal and disease. Upon SARS‐CoV‐2 infection, the epithelium exhibits higher susceptibility to virus than endothelium. Transcriptional analyses show activated innate immune responses in epithelium and cytokine‐dependent pathways in endothelium at day 3 post‐infection, revealing the distinctive responses in different cell types. Notably, viral infection causes the immune cell recruitment, endothelium detachment, and increased inflammatory cytokines release, suggesting the crucial role of immune cells involved in alveolar barrier injury and exacerbated inflammation. Treatment with remdesivir can inhibit viral replication and alleviate barrier disruption on chip. This organ chip model can closely mirror human‐relevant responses to SARS‐CoV‐2 infection, which is difficult to be achieved by in vitro models, providing a unique platform for COVID‐19 research and drug development. [ABSTRACT FROM AUTHOR]

[Display omitted] Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic. Clinical evidence suggests that the intestine is another high-risk organ for SARS-CoV-2 infection besides the lungs. However, a model that can accurately reflect the response of the human intestine to the virus is still lacking. Here, we created an intestinal infection model on a chip that allows the recapitulation of human relevant intestinal pathophysiology induced by SARS-CoV-2 at organ level. This microengineered gut-on-chip reconstitutes the key features of the intestinal epithelium-vascular endothelium barrier through the three-dimensional (3D) co-culture of human intestinal epithelial, mucin-secreting, and vascular endothelial cells under physiological fluid flow. The intestinal epithelium showed permissiveness for viral infection and obvious morphological changes with injury of intestinal villi, dispersed distribution of mucus-secreting cells, and reduced expression of tight junction (E-cadherin), indicating the destruction of the intestinal barrier integrity caused by virus. Moreover, the vascular endothelium exhibited abnormal cell morphology, with disrupted adherent junctions. Transcriptional analysis revealed abnormal RNA and protein metabolism, as well as activated immune responses in both epithelial and endothelial cells after viral infection (e.g., upregulated cytokine genes), which may contribute to the injury of the intestinal barrier associated with gastrointestinal symptoms. This human organ system can partially mirror intestinal barrier injury and the human response to viral infection, which is not possible in existing in vitro culture models. It provides a unique and rapid platform to accelerate COVID-19 research and develop novel therapies. [ABSTRACT FROM AUTHOR]