Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk. Though generally considered representative of the actual ambient conditions and individual’s exposure, their broader application in environmental epidemiology is often constrained by inhomogeneous records and the sparse density of meteorological stations. In addition, the geographic proximity of the measurements to the population under study is not always guaranteed. These limitations are generally more evident in low- and middle-income countries where the network of ground stations is often sparse or non-existent, or the quality control protocols are usually not standardized2. Moreover, even in high-income regions where historical observations are better maintained, the stations are often located outside populated areas, e.g., at nearby airports or remote weather observatories, and thus not truly representative of the exposure of populations living in large urban centres4. This further limits the potential usage of single- or multiple-monitoring stations in capturing local phenomena, such as urban heat island (UHI) effects that can enhance heat stress especially during heat waves5.In situ measurements from weather stations are often regarded as the gold-standard in epidemiological studies7. Reanalysis data products are obtained by runs of global or regional weather forecasting models under observationally constrained scenarios via data assimilation9. These products offer an immediate advantage over in situ measurements by providing consistent historical records of numerous meteorological variables, spanning the whole globe at various spatial and temporal resolutions. A number of global forecasting and research centres make their quality-controlled reanalysis data products freely available. However, their usage in health impact assessment has been limited and often been restricted to regional scale studies11.In recent years, data from climate reanalysis are routinely being applied as pseudo-observations in sectoral impacts assessments13. These studies have generally found a good correlation between the two sources and similar estimates of health impact. However, these assessments were limited to single countries12 or regions13 with high-quality stations measurements, thus limiting the generalisability of the findings. More importantly, the evaluation has remained largely restricted to the inspection of temperature distributions and estimated exposure–response curves, without a comparative analysis of performance that can identify the preferable option and quantify potential biases.Only a few studies to date have compared the use of climate reanalysis data by comparing estimates of epidemiological associations versus station-based observations, in particular for quantifying mortality risks associated with non-optimal temperaturesHere, we perform a comprehensive assessment of temperature-related mortality risks using ground weather stations observations and state-of-the-art reanalysis data across 612 cities within 39 countries over the period 1985–2019. The analysis plan is illustrated in Fig. https://mccstudy.lshtm.ac.uk)  Copernicus programme that provides land-surface data at ~ 9 km resolution  from station records and all-mortality series for all or non-external causes for the 612 cities from the Multi-County Multi-City (MCC) Collaborative Research Network (uk) see “”, which ion see “”. As senr of 0.987, and 94% of cities with a score higher than 0.9. However, while the correlation is very high in high-income regions, it drops noticeably in few locations, especially in tropical and sub-tropical countries, such as the Philippines, Central America , Ecuador, Colombia, and parts of northern Brazil  versus 0.49% (0.43–0.53) for heat, and 6.02% (5.80–6.18) versus 6.25% (6.05–6.41) for cold, from ground stations and ERA5-Land data, respectively  as the difference in the quasi version of the Akaike information criterion (qAIC), which in turn is a statistic related to the goodness-of-fit of the data see “”. A negaFocusing on Fig. 2. A similar comparative analysis by estimating the exposure–response function at wider spatial scales  is left for future research. In spite of the large number of locations used in our study, our results for a few countries where the mortality data are restricted to limited locations  among other global reanalysis products, expanding our comparative analyses to a broader suite of datasets could enable a further systematic evaluation of the relative strengths and weaknesses of using reanalysis datasets in environmental epidemiology, an objective left for future studies. In addition, while the data gathered by the MCC network used in our study includes various types of geographical areas, we restricted our analysis to cities, as associating gridded data even at finer resolution risks of introducing aggregation biasns Table  could re11 (“Methods”). Noting other approaches such as the area- or the population-weighting of meteorological fields25 that are more commonly applied when air pollutants are the exposure variables13, our approach is unlikely to produce substantial variations in the temperature-mortality associations when using the high-resolution data from the recent iteration of ERA5 products, a finding highlighted in a recent study13.The primary motivation of our study was to examine the suitability of climate reanalysis for assessing temperature-related mortality, in a manner that would facilitate rapid application of gridded reanalysis products with location-specific counts of mortality in environmental epidemiology. We therefore followed the standard practice of utilising the location’s center point to extract the gridded ERA5-Land data29, our study focuses on temperature as the environmental exposure variable of interest. An added advantage of reanalysis data over ground station records is the wider availability of essential climate variables (ECV), such as wind, solar radiation, and relative humidity. ECV are commonly used to assemble thermal discomfort indices also referred to as measures of health stress, such as the apparent temperature (AT)31, the wet-bulb (WBT)32 and wet-bulb globe (WBGT)33 temperature, and the universal thermal comfort index (UTCI)34. However, these indices are less frequently employed to examine their relationship with mortality 11, and previous epidemiological analyses showed little evidence of a better predictive performance of such composite indices in predicting mortality36. Nonetheless, similar comparative analyses between reanalysis and station recorded ECV (where available) are also recommended.Moreover, following literatureFinally, several location-specific factors could explain the divergence in the reanalysis and station observations derived temperature-mortality relationship, especially in the low/high temperature extremes, across the 612 locations in our study. For instance, geographical and economic factors, such as proximity of the location to a coast or hilly terrains, investments in the upkeep and maintenance of the meteorological infrastructures; and other social demographics , could likely be some of the important determinants shaping the differences in exposure–response relationships between the two temperature sources. A detailed investigation into these falls outside the scope of the present study and is left as a topic for future research.27, or their statistically interpolated gridded counterparts37. The aim of our study was to demonstrate that climate reanalysis can potentially provide reliable surrogates of temperature for global scale epidemiological studies, thereby offering an alternative source of exposure variables. Here we evaluated the performance of temperature gathered from the ERA5-Land atmospheric reanalysis dataset, across multi-locations stratified by geographical regions, accounting for low-, middle- and high-income countries, with varying density of station records, thus providing a comprehensive and globally representative picture. To our knowledge, our study provides the first valuable insights on the suitability of current generation of quality-controlled global reanalysis datasets in a multi-location, multi-country framework, spanning five inhabited continents. By quantifying the mortality impacts from weather station and reanalysis data and performing a systematic analysis of their comparative performance, our study goes beyond published studies that limited their analyses to the exposure–response curves11, and for specific regions . Moreover, our study is the first to examine ERA5-Land for temperature-mortality associations at a global scale, the high spatial resolution of its gridded meteorological fields (~ 9 km) documented to represent land surface variables better14.Existing studies assessing the temperature-mortality associations in a large multi-location setting often employ in situ measurements10 and Europe11) that found largely comparable exposure–response curves from weather station and ERA5 data, our analyses using ERA5-Land revealed a similar pattern across 612 locations in 39 countries. Our results are also comparable to the earlier well-established global multi-location studies based on station records27 that document similar magnitude of excess mortality from heat and cold exposures across the full range of temperatures. However, it must be pointed out that while largely in agreement, the heat-related excess mortality estimated using ERA5-Land is marginally lower compared to those estimated using station observations. The magnitude of such differences is amplified in low- and middle-income countries generally in the tropics. Such differences, even in locations with relatively high correlations between the two temperature data sources, seems to suggest a sub-optimal performance of reanalysis data in identifying extremely high temperatures in specific regions, that in turn can lead to an underestimation of the health impacts of heat. While not investigated here and left as scope for further research, recent studies38 have documented improved ERA5-Land derived daily maximum temperatures compared to earlier reanalysis products, though marginal errors relative to the corresponding station observed daily maximum temperatures remain, which could explain the under-estimated excess mortality related to heat in our findings.Our analyses reveal that ambient temperature drawn from ERA5-Land is generally suitable to elucidate the effects of thermal stress on mortality. Importantly, in line with the previous two regional scale studies . The mortality series used in the study consist of data aggregated over large geographical areas, all ages, and all or non-external causes. The data was originally provided by statistical authorities in each country from separate data requests and were part of administrative databases including completely anonymised information for which so the informed consent is not required.All analyses in this study were done with R software (version 4.1.0)http://mccstudy.lshtm.ac.uk). MCC is a result of an international partnership between research teams producing epidemiological evidence on the association between weather and health across the globe. The MCC Network has been instrumental in developing state-of-the-art methods in environmental epidemiology, as well as in assembling the largest database on weather and health. The data, used in several earlier studies45, facilitates continent-wide analysis of environmental stressors and mortality. The data used in the present study consists of location-specific counts of daily mortality from all causes or non-external causes only  obtained from local authorities within each country or region, and the daily mean temperature (°C) gathered from the local weather stations 46 and the UK Met Office Hadley Centre Integrated Surface Database (HadISD)48 that often lack consistent spatio-temporal coverage to construct location-specific time series.Daily counts of mortality and station observed mean temperature for 612 sites across 39 countries Table  are drawns Table . For ourns Table . Though 24.Since the MCC data are available for different time intervals across countries, our multi-location daily time series span different time periods between 1985 and 2019, with the shortest being 4 years (2013–2016) for Panama, and the longest being 34 years (1985–2018) for Norway and Portugal. Further details on the individual location-specific sources of mortality and station data included in the MCC network are documented in Refs.49 made available by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S) Climate Data Store 50. ERA5-Land resolves meteorological fields at 0.09° (~ 9 km) gridded resolution, the finest spatial resolution across all global reanalysis data products that are currently available. As a secondary reanalysis data source, we utilised the hourly 2-m air temperature from ERA552 that is available at 0.25° (~ 31 km) gridded resolution (results are presented in the SI).We utilised hourly 2-m air temperature from ERA5-Land51 covering the land surface of the entire globe. It ingests more data sources along with the latest version of the Integrated Forecasting System, incorporates modern parameterizations techniques, and is till date the most advanced reanalysis53. It resolves many atmospheric and land-surface parameters in near real-time, thus offering a large number of meteorological parameters from 1950 to near-present day. In addition, the hourly time resolution enables an improved evolution of day-to-day weather systems9. The high spatial resolution in ERA5-Land is achieved by driving the model with statistically downscaled meteorological forcing51 and a lapse rate correction. ERA5-Land thus offers advantages with a better representation of the land surface processes compared to other current generation of reanalysis data products14.ERA5-Land is the most recent atmospheric reanalysis from the ECMWF family of reanalysis datasetsWe matched the reanalysis grid cell where each MCC location’s centre point was located and aggregated the hourly temperature fields to daily averages for years 1985–2019, taking care to recode the reanalysis daily values as missing where the corresponding station temperature was not available. Computation of the daily averages as 24-h average is also consistent with the daily average station recorded temperature across majority of the locations in the MCC network Table . The res43 to model the station observed and reanalysis temperature-mortality associations, across the 612 locations covering a wide range of climates and including low- and middle-income countries. Location specific temperature-mortality associations were estimated through time-series analyses with quasi-Poisson regression, with distributed lag non-linear models and multivariate meta-regression, using R packages dlnm40 and mixmeta41. Multivariate meta-analysis represents a useful analytical tool for pooling complex associations through a two-stage procedure22. The flexible modelling framework allows for non-linear/lagged responses, separation of effects due to cold/heat and moderate/extreme temperature, and heterogeneity of estimates at various geographical levels.41.We applied the well-established two-stage modelling framework54. DLNMs account for delayed effects of time-varying exposures and quantify overall effects over a predefined lag period. Following the DLNM methodology, we modelled the bidimensional exposure-lag-response association through the combination of two functions defined within a cross-basis term. Specifically, we selected a natural cubic spline function with three internal knots at the 10th, 75th and 90th percentile of the location-specific temperature distribution to model the exposure–response curve, and a natural cubic spline function with three internal knots at equally spaced values in the log scale over 21 days of lag for the lag-response dimension. Seasonality and long-term trends were modelled with a natural cubic spline with 8 degrees of freedom (df) of time, and the model included indicator variables for the day of the week to account for intra-weekly variations in mortality. These choices that specify the cross-basis and model terms used to control for long-term and seasonal trends were based on related studies from the MCC Collaborative Research Network20. The resulting bidimensional set of coefficients from each location was then reduced across the lag dimension into the overall cumulative exposure–response curve representing the association between temperature and mortality summed across the 21 days of lag21.To estimate location-specific temperature-mortality associations, we performed separate time series analyses with generalized linear models using observed- and reanalysis-temperature and mortality data over the entire year in each location. We applied a quasi-Poisson regression in which a quasi-likelihood was used to scale the standard deviation of the coefficients proportionally to the observed overdispersion. We modelled using distributed-lag non-linear models (DLNMs), a class of models that can describe the complex non-linear and lagged dependencies typically found in temperature-mortality studies41, with two nested levels of random effects defined a city and combinations of climate zones and country. This approach allows heterogeneous effects and provides improved estimates of temperature-mortality associations at city level, defined as best linear unbiased predictions (BLUPs). BLUPs borrow information across units within the same hierarchical level and can offer more accurate estimates, especially in locations with small daily mortality counts or short series. Put differently, BLUPs represent a trade-off between the location-specific association provided by the first-stage regression and the pooled association. This approach enables more robust estimates of risk ratios (RRs) in individual cities compared to location-specific models24. We also included, as fixed-effects meta-predictors, country-level gross domestic product, location-specific average temperature and interquartile range and indicators of climatic classification55.The location-specific set of reduced coefficients estimated in the first stage were then pooled in a multivariate multilevel meta-regression model2 statistic56. The city-specific associations defined by the BLUPs were used in the quantification of the cold- and heat-related mortality impacts.We tested and quantified the presence of heterogeneity using multilevel extensions of the Cochran Q test and I42. For each location-day combination, we computed the number of cold- and heat-related deaths on the basis of temperature series, daily baseline mortality and the estimated temperature-mortality association represented by the location-specific BLUPs23. Following earlier works43, we then estimated the total number of cold- and heat-related deaths in each location across the study period by summing the daily mortality contributions when the temperature on a specific day was lower (higher) than the location-specific reference temperature. This reference value corresponds to the minimum point of the BLUP curve and represents the optimal temperature value with the lowest mortality risk, often referred to as the minimum mortality temperature (MMT). We quantified the uncertainty of the estimates by generating 1,000 samples of the coefficients of the BLUPs (representing the association) through Monte Carlo simulations, assuming a multivariate normal distribution for the estimated spline model coefficients. We obtained empirical confidence intervals (eCI) corresponding to the 2.5th and 97.5th percentiles of the empirical distribution of the cold- and heat- related mortality impacts across coefficients. Finally, as elaborated in previous studies24, we computed the mortality fractions using the related total number of deaths as the denominator.Next, we quantified the station observed and reanalysis temperature-related mortality in each location during the study period of 1985–2019 following a method described in previous works36 to examine the ability of the two temperature series to predict all-cause mortality at each location. This approach provides a quantitative evaluation on the performance of each source  in modelling excess mortality risks associated with non-optimal temperature. As noted earlier, since the first-stage models employing station observed and reanalysis temperature were both fitted using the same data sample , the qAIC becomes comparable and can be used as a robust goodness of fit metric between the two sources. This statistic can be summed across countries or regions to facilitate comparisons at different geographical levels.We computed the quasi-Akaike information criterion (qAIC) used in earlier studiesTo facilitate an easier interpretation of the preferred model, we defined a new metric called relative fitting score (RFS), as a measure of relative fitting performance. The RFS is computed as the difference between the two qAICs:Supplementary Information 1.Supplementary Information 2.


The factors that significantly affected the incidence of ventilator-associated pneumonia were ventilator-associated pneumonia care bundle, ventilator-days, and intensive care unit length of stay. A significant reduction in ventilator-associated pneumonia rate in the bundle group compared to the oral hygiene group  was observed, with 63.4% effectiveness. Application of an automatic medical information system to implement bundle care can significantly reduce the incidence of ventilator-associated pneumonia.Ventilator-associated pneumonia is a common hospital-acquired infection. It causes patients to stay longer in the hospital and increases medical costs. This study explores the effect of applying an automatic medical information system to implement five-item prevention care bundles on the prevention of ventilator-related pneumonia. This study was a retrospective cohort study. This study was conducted from October 2017 to February 2018 and collected data from the intensive care unit of a medical center in southern Taiwan from January 2013 to May 2016. The control group (enrolled from January 2013 to June 2014) received oral hygiene. The experimental group (enrolled from July 2014 to December 2015) received five-item ventilator-associated pneumonia prevention care bundles, which consisted of (1) elevation of the head of the bed to 30–45°; (2) daily oral care with 0.12−0.2% chlorhexidine twice daily; (3) daily assessment of readiness to extubate; (4) daily sedative interruption; and (5) emptying water from the respirator tube. Results showed the incidence of ventilator-associated pneumonia in the bundle group was significantly less than the oral hygiene group ( Ventilator-associated pneumonia is a common hospital acquired infection in patients in the intensive care unit ,2. The iOchoa-Hein et al. (2019)  studied This study was a retrospective cohort study. It was conducted from October 2017 to February 2018 and collected data from the intensive care unit of a medical center in southern Taiwan from January 2013 to May 2016. The purpose of this study was to conduct pre-intervention and post-intervention tests to explore the effects of ventilator-associated pneumonia care bundle intervention.This study was conducted in the adult intensive care unit of a medical center. All adult patients with mechanical ventilators admitted to the adult intensive care unit between 1 January 2013 and 31 December 2015, were enrolled in this study. The exclusion criteria were below 20 years of age, and patients who had been diagnosed with pneumonia before mechanical ventilation placement.Data were collected from the patients’ medical records. Data collection included patient age, gender, number of days on a ventilator, length of stay in the intensive care unit, total number of days admitted to the hospital, diagnosis, past disease history, and whether ventilator-associated pneumonia had occurred. The definition of respirator-associated pneumonia in Taiwan is implemented in accordance with the guidelines set by the National Nosocomial Infection Surveillance . The lisThe intervention measures used to prevent ventilator-associated pneumonia in ventilator patients in this study were divided into two groups. One was the oral hygiene group (enrolled in intensive care January 2013 to June 2014). The other was prevention ventilator-associated pneumonia care bundle group (enrolled in intensive care July 2014 to December 2015).For this group, only oral care was performed, including use of toothbrush with toothpaste, and mouth care with chlorhexidine for 30–60 s twice a day. 2/FiO2 > 200 mmHg, FiO2 < 45%, and PEEP ≤ 5 cm H2O. The automatic medical information system automatically judged the parameter conditions for the past 24 h at 7 o’clock every morning. When it was judged that the conditions for extubation were met, the system automatically displayed a reminder of “Please evaluate extubation”. Through the judgment and reminder of automatic medical information system, medical staff could allow patients to enter the process of preparing for extubation as soon as possible. (3) Established the standard procedure of oral care with 0.12–0.2% chlorhexidine for patients with indwelling endotracheal tube in the automatic medical information system, and provided staff with the procedures to perform oral care of patients.The group implemented five-item ventilator-associated pneumonia prevention care bundles, including (1) elevation of the head of the bed to 30–45°; (2) daily oral care with 0.12–0.2% chlorhexidine twice daily; (3) daily assessment of readiness to extubate; (4) daily sedative interruption; (5) emptying water from the respirator tube. This medical team innovated and developed automatic medical information system to ensure that the five-element measures to prevent ventilator-associated pneumonia were being implemented. The automatic medical information system had three main features: (1) five-element ventilator-associated pneumonia care bundle checklist: This medical team formulated a five-element ventilator-associated pneumonia care bundle checklist in automatic medical information system, and the nurse checked and recorded the results in this system every day. (2) The automatic medical information system automatically judged and reminded medical personnel when the respirator can stop being used: The medical team set the conditions for discontinuing respirator use in the automatic medical information system, including no fever within 24 h, PaOt-test or Mann–Whitney test for continuous variables. We verified that the data conformed to the assumptions and preconditions of the methods used to analyze them. The results showed that the age was normally distributed, but the ventilator days, intensive care unit days, and hospitalization days were not normally distributed. Therefore, Mann–Whitney U-test was used to evaluate the statistical significance between the two groups for ventilator days, intensive care unit days, and hospitalization days. Chi-square test was used for the two groups  and for the occurrence of ventilator-associated pneumonia to explore the influence of different interventions in the two groups on the occurrence of VAP. Next, we used Chi-square test or t-test or Mann–Whitney U test to explore the influencing factors of ventilator-associated pneumonia. Finally, logistic regression was used to predict the influencing factors and occurrence probability of ventilator-associated pneumonia. The occurrence of ventilator-associated pneumonia was a dependent variable. All related factors affecting the occurrence of ventilator-associated pneumonia, including group, ventilator days, intensive care unit days, hospitalization days, and past disease history, were included as independent variables. p-values < 0.05 were considered statistically significant. The data were managed in Excel  and analyzed using IBM SPSS Statistics for Windows, version 20.0 . Descriptive and inferential statistical methods were used. Mean values and standard deviations were presented for normally distributed continuous variables, median values and quartiles  for skewed continuous variables, while frequencies and percentages were used for categorical variables. We conducted a difference analysis on the personal characteristics of the two groups , using Chi-square test for categorical variables and This study was reviewed and approved by the Human Research Ethics Review Committee of the Medical Center (VGHKS16-CT10-02).A total of 1475 patients participated in this study. There were 779 in the oral hygiene group and 696 in the ventilator-associated pneumonia prevention bundles group. The basic information of the participants was shown in 2-test results showed a significant difference , indicating the occurrence of ventilator-associated pneumonia in the care bundle group was significantly less than oral hygiene group.As shown in p < 0.05). It showed the ventilator days, intensive care unit length of stay, and hospital day with ventilator-associated pneumonia cases were significantly higher than those without ventilator-associated pneumonia. There were significantly more ventilator-associated pneumonia cases in the oral hygiene group than in the care bundle group.As shown in As shown in After the implementation of the five-item ventilator-associated pneumonia prevention care bundles, this study found a significantly lower incidence of ventilator-associated pneumonia than for the oral hygiene group, which is consistent with the results of other studies showing that the implementation of ventilator-associated pneumonia care bundles can significantly reduce the incidence of ventilator-associated pneumonia ,9,10. CoThe results of this study showed that the OR of ventilator-associated pneumonia occurrence of bundles group was 0.366 times that of the oral hygiene group, reducing the OR of ventilator-associated pneumonia occurrence by 63.4%. For each additional ventilator-day, the OR of ventilator-associated pneumonia occurrence is 1.031 times, which is consistent with the results of the literature . It was This study was limited to results that were conducted only at a medical center and it only found results of the implementation of a five-item ventilator-associated pneumonia care bundle. To continuously improve the quality of care and promote better care results. In the future, it is recommended to explore the impact of reducing the number of ventilator days and intensive care unit stays on reducing the occurrence of ventilator-associated pneumonia, as well as the impact of other types of care bundles. This was a retrospective study, and it is necessary to conduct a better randomized controlled trial design to improve the reliability and validity of the research results.This study suggested that a five-element preventative ventilator-associated pneumonia care bundle can significantly reduce the incidence of ventilator-associated pneumonia. The significant factors affecting the occurrence of ventilator-associated pneumonia are the presence or absence of care bundles, ventilator days, and intensive care unit days. It is recommended that ventilator-associated pneumonia care bundles should be used, and further study is needed. Therefore, medical teams need to cooperate with each other. If the automated medical information system can provide busy medical staff with more accurate data to help patients in time to reduce the chance of ventilator-associated pneumonia.It is recommended to establish a complete simplified and automated reminder mechanism using technology and modern information systems to improve the quality of care. It is recommended to conduct a more comprehensive discussion to reduce the impact of ventilator days and length of stay in the intensive care unit on the incidence of ventilator-associated pneumonia, as well as the impact of different types of care bundles on reducing the incidence of ventilator-associated pneumonia to provide better quality of care and more good results.


The outbreak of the COVID-19 epidemic continues to unfold globally, and its negative impact on the public's mental health is starting to reveal. Serving as reserve talents for the healthcare system, medical students are not yet professionally matured enough to face one of the worst global public health crises. This may exert increased mental stress and loneliness feelings, which in turn negatively influence medical students' future career choice. To address the issue, we conducted three online survey studies investigating how the epidemic affects the mental health as well as career attitude of medical students in China during the COVID-19 pandemic outbreak. The results revealed preliminary evidence showing that the perceived stress induced by the COVID-19 epidemic might negatively affect medical students' future career choice, and the feeling of loneliness may play a mediating role. This study invites more attention to medical students' mental health during severe public health crisis such as the COVID-19 pandemic. The outbreak of the COVID-19 epidemic has affected the lives of billions worldwide. In addition to causing economic upheaval and political unrest, the epidemic also poses a major challenge to public health—both physical and psychological . TherefoIn order to contain the coronavirus, governments across the world have enacted rigorous measures, such as the closure of borders, curfews, a general ban on assembly, and the closure of public places including parks, playgrounds, schools, universities, and shops. These control measures placed millions of people in isolation , 13, andThe outbreak of the COVID-19 pandemic has exerted severe pressure on global healthcare systems and stressed the already busy labor capacities within the systems, highlighting an urgent need for more medical and healthcare professions –9. AccorPrevious studies have demonstrated that the perceived mental stress may intensify feelings of loneliness –32. For Loneliness is a social construct, characterized by subjective feelings of social pain and/or isolation . The desThis study aimed to examine the negative impact of perceived mental stress during the COVID-19 pandemic on medical students' attitude toward their future career choice, and to further uncover the mediating role of loneliness. A cross-sectional survey was conducted in March 2020 (Study 1), when the confirmed cases of the COVID-19 reached peak level in China, and a new survey (Study 2) with different measurements of perceived influence on medical students' career choice was collected in June 2020 to confirm the findings of Study 1. Study 3 was finally conducted in June 2020 with non-medical students as a control. Based on previous findings, we hypothesized that, compared with the non-medical students, the perceived mental stress by medical students during the COVID-19 pandemic would negatively influence their career choice, which was mediated by their feelings of loneliness. The respondents in three studies all gave their consent before filling in the questionnaire, and all studies were approved by the Institutional Review Boards of Fujian Medical University and School of Management, Zhejiang University.We conducted a cross-sectional survey to 12 medical colleges and universities in China in March 2020, the worst period of the COVID-19 pandemic in China. We finally recruited 906 medical students .Respondents were asked to provide their demographic and academic information such as gender, age, origin, major and college year.Respondents were asked to directly rate the influence of the COVID-19 pandemic on their attitude toward future career choice as medical professions in a 5-point Likert scale .Respondents then scored their mental stress status using a 14-item Chinese Perceived Stress Scale (CPSS)  modifiedRespondents also rated their loneliness feelings using a 20-item UCLA Loneliness Scale (UCLA-LS), version 3 . Sample We firstly statistically assessed the common method bias using the Harman one-factor analysis. The first factor in our data explained only 26.27% of the variance, suggesting that common method bias was unlikely to confound the interpretations of our results.M = 38.66, SD = 6.55) and loneliness feelings . In addition, the Pearson correlation analysis revealed significant positive correlations between the score of perceived influence on career choice  and the respondents' age , college year , as well as their rating scores on perceived mental stress  and loneliness feelings . While controlling variables of age and college year, the partial correlation analysis still revealed significant positive correlations between the score of perceived influence on career choice and the mental stress score  as well as the loneliness score .The Pearson correlation analysis revealed significant positive relations between the scores on perceived mental stress (p = 0.001) as shown in Model 2, which was non-significant (p = 0.426) while taking loneliness feelings  into consideration, suggesting a potential mediating role of loneliness.To further confirm the impact of perceived mental stress on respondents' career attitude and the mediating role of loneliness as well, we conducted a step-wise linear regression model taking age and college year as covariates. The mediation test, using bootstrap (5000 times) by Model 4 in PROCESS for SPSS v3.5, confirmed that respondents' loneliness feelings fully mediated the relation between their perceived mental stress and career attitude . While tStudy 1 showed preliminary evidence that the perceived mental stress by medical students during the COVID-19 pandemic negatively influenced their future career choice, which was mediated by loneliness feelings . Study 2 was then designed to confirm the main findings revealed in Study 1 three months later in June 2020 with different measurements of perceived influence on future career choice. In addition, Study 2 could also address the issue of whether the negative stress-career relation was a short-term phenomenon or not, since the COVID-19 pandemic had been well-controlled in China in June.We recruited 354 medical students from Fujian Medical University . All respondents gave their consent and finished the same survey as used in Study 1, with different measurement of perceived influence of COVID-19 pandemic on their future career choice, and additional measurements of perceived risk during the COVID-19 pandemic.In Study 1, we asked the respondents to directly rate the extent to which the COVID-19 pandemic influenced their future career attitude on a 5-point Likert scale, ranging from strong positive to strong negative influence. The results indicated that many of students rated positive influence, suggesting that the COVID-19 pandemic may exert impacts on medical students' future career attitude both negatively and positively. To focus on the negative influence, in Study 2 the respondents were asked to score two separate items that to what extent the COVID-19 pandemic negatively/positively influenced their future career attitude using a 3-point scale .In addition, to examine whether the COVID-19 pandemic increased medical students' mental stress or not, we also asked the respondents to score the possibility that they could get infected by COVID-19 using a 4-point scale .The Harman's single-factor analysis showed that the first factor in our data explained only 27.03% of the variance, suggesting that common method bias was unlikely to confound the interpretations of our results.M = 2.06, SD = 0.77) positively correlated with respondents perceived mental stress , indicating that the COVID-19 pandemic indeed increased medical students' mental stress. Consistent with the findings revealed in Study 1, the Pearson correlation analysis also revealed a significantly positive correlation between the scores on perceived mental stress and loneliness feelings . Focusing on the perceived negative influenced of the COVID-19 pandemic on medical students' future career choice , the Pearson correlation analysis revealed significant positive correlations between the negative career influence and their age  and college year . More importantly, the negative career influence also positively correlated with the perceived mental stress  as well as the loneliness feelings . The partial correlation analysis, taking age and college year as control variables, revealed similar results .As expected, the self-reported possibility of infection (p =.040), which was non-significant (p = 0.879) while taking loneliness feelings  into consideration. Consistently, as The step-wise linear regression models also revealed significant positive relation between the perceived mental stress and the negative career influence  during the same period as the Study 2 (in June 2020) in Zhejiang University. All respondents gave their consent and finished the same survey as used in Study 1.M = 40.65, SD = 8.02) and loneliness feelings . Even though, the perceived influence of the COVID-19 pandemic on non-medical students' future career choice  showed no significant correlations with either mental stress  or loneliness feelings .The Harman's single-factor analysis showed that the first factor in our data explained only 32.27% of the variance, suggesting that common method bias was unlikely to confound interpretations of our results. Consistent with the findings of Study 1 and Study 2, a positive correlation was revealed between the scores of mental stress , while rating the item of “how much confidence do you have in the government policy supporting medical and healthcare systems” on a 4-point scale . Even though, our studies with 1260 medical students consistently showed that the COVID-19 pandemic increased medical students' mental stress, which in turn aggravated their feelings of loneliness and may further negatively impact their future career choice. Future studies are needed to confirm the negative career influence from the severe public health crises.Previous studies focusing on medical students' career choice have mainly emphasized intrinsic and extrinsic factors such as early specialty interest, self-competency, clinical exposure, and patient-doctor relationship , but negIt is noteworthy that Study 3 with non-medical students did not show the same stress-loneliness-career relation as revealed in medical students. Although the sample size of Study 3 was relatively small, even a clear tendency was not found. One possible explanation is that the COVID-19 pandemic not only poses a major challenge to public health, but also causes massive economic and political unrest, which further negatively impacted non-medical students' work situation. In order to contain the virus, governments across the world have enacted measures such as the closure of borders, curfews, a general ban on assembly, placing millions of people in isolation and subsequently damaging their own commercial system in the process. In this case, non-medical students may perceive feelings of uncertainty when it comes to job hunting. Thus, the perceived mental stress and feelings of loneliness may contribute little to their career choice. Future study is needed to address the issue of how the COVID-19 pandemic influences non-medical students' mental health and career situation.The present research has several limitations as well. First, this research was a cross-sectional design, and hence cannot conclude a causal relationship between variables. Second, the samples were mainly recruited from the East coast of China. More studies should be done to examine the influence of the COVID-19 epidemic on medical students' mental health and career choice globally. More importantly, researchers should pay more attention to other critical factors such as poverty and lack of resources and examine how such factors influence the stress-loneliness-career relation in medical students. Third, the measurement of career influence had only one item, the robustness of the results should be confirmed in future studies. Even though, the respondents were asked to directly rate the scores of how the COVID-19 epidemic impacts their future career choice as medical and healthcare professions, providing preliminary evidence emphasizing the importance of the potential negative influence of the COVID-19 epidemic on medical students' mental health and career choice. Fourth, this research did not collect comparable samples from both medical and non-medical students in one study, limiting our conclusion on the uniqueness of the stress-loneliness-career relation induced by the COVID-19 epidemic in medical students, which should be confirmed in the future study.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The present studies were approved by the Ethical Review Boards of Fujian Medical University and School of Management, Zhejiang University. The patients/participants provided their written informed consent to participate in this study.TL designed the theoretical framework and analyzed the data. QZ, XL, and LH collected the data. All authors wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The pharmacokinetic profiling study was conducted according to the four principles of the Lipinski rules and excluded derivative 31. Furthermore, ADMET and toxicity studies showed that derivatives 28, 34, and 47 have the potential to be drugs and have been demonstrated as safe when assessed via seven toxicity models. Finally, comparing the molecular orbital energies and the molecular electrostatic potential maps of 28, 34, and 47 against the co-crystallized ligand in a DFT study indicated that 28 is the most promising candidate to interact with the target receptor (PLpro).Papain-like protease is an essential enzyme in the proteolytic processing required for the replication of SARS-CoV-2. Accordingly, such an enzyme is an important target for the development of anti-SARS-CoV-2 agents which may reduce the mortality associated with outbreaks of SARS-CoV-2. A set of 69 semi-synthesized molecules that exhibited the structural features of SARS-CoV-2 papain-like protease inhibitors (PLPI) were docked against the coronavirus papain-like protease (PLpro) enzyme (PDB ID: (4OW0). Docking studies showed that derivatives  The docking investigation was accomplished using MOE2014 software. At first, the crystal structure of SARS-CoV-2 helicase was prepared by removing water molecules. Only one chain was retained beside the co-crystallized ligand (S88). Then, the selected chain was protonated and subjected to minimization of energy process. Next, the active site of the target protein was defined.Structures of the tested compounds and the co-crystallized ligand were drawn using ChemBioDraw Ultra 14.0 and saved as MDL-SD format. Such file was opened using MOE to display the 3D structures which were protonated and subjected to energy minimization. Formerly, validation of the docking process was performed by docking the co-crystallized ligand against the isolated pocket of active site. The produced RMSD value indicated the validity of process. Finally, docking of the tested compounds was done through the dock option inserted in computer window. For each docked molecule, 30 docked poses were produced using ASE for scoring function and force field for refinement. The retain was kept at 30. The crystal parameters were adjusted at default values ). The results of the docking process were then visualized using Discovery Studio 4.0 software . Pharmacokinetic profile of the compounds was determined using Discovery studio 4.0. .ADMET descriptors  of the compounds were determined using Discovery studio 4.0. At first, the CHARMM force field was applied, then the tested compounds were prepared and minimized according to the preparation of small molecule protocol. Then ADMET descriptors protocol was applied to carry out these studies .The toxicity parameters of the tested compounds were calculated using Discovery studio 4.0. Indinavir was used as a reference drug. At first, the CHARMM force field was applied then the compounds were prepared and minimized according to the preparation of small molecule protocol. Then different parameters were calculated from the toxicity prediction (extensible) protocol ,74,75. The DFT parameters  were calculated using Discovery studio software. The tested compounds were prepared using prepare ligand protocol. Then, the prepared compounds were subjected to DFT calculation protocol using the default option .


Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest.The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults.In total, 59 community-dwelling older adults  were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals.The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN.The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a ﬂexible solution to relieve the community’s burden of continuous health monitoring. Falls prevail among the aging population, and led to more than $30 billion in direct medical costs in 2015 . It is cFall risk factors can be grouped into extrinsic  factors and intrinsic factors . Outpatients and community dwellers usually suffer less from illnesses; consequently, intrinsic mobility and balance are the most discriminative intrinsic indicators of falls . MobilitNevertheless, such assessment tools carry disadvantages that prevent older adults from undergoing frequent fall assessments . First, Thanks to the rapid development of information technology, the sensor provides a practical solution to this predicament nowadays. Commercial sensors  are affordable to most community service sites or health care agents ,16. ThesAlthough there are advantages to the use of sensor technology, it is also important to acknowledge the residing problems. Ideally, individuals can perform these sensor-based assessments independently. In practice, irrelevant signals that come before and after performing the testing motion might be recorded. Therefore, an additional process to remove those irrelevant signals becomes necessary. Existing studies have relied on an additional research assistant’s manual observations ,31-33 orHuman activity recognition (HAR) is a practical remedy to relieve this burden. The majority of developed methods adopt machine learning models -39 or deThis study illustrates an automatic sensor-based framework  of motioIn total, 59 community-dwelling elders  participated in our study from September 2019 to December 2019. Participants with behavioral problems , unstable mental status , communication problems , or severe hearing impairment were excluded from the study. Written informed consent was obtained from all participants before performing data collection. The imbalanced gender distribution was a direct reflection of the outnumbered male participants enrolling in community services . Among tt test, P<.001). Age also showed a significant difference between the stand-to-sit score=2 and <2 groups . There was no gender difference in all of these tasks The score followed the Tinetti POMA grading guideline . The TinIn this step  was attached on an elastic belt and placed on the L4 vertebra of each participant prior to each task. The inertial signal was transmitted via Bluetooth to the dedicated mobile application. The participants would then listen to the instruction and perform it accordingly. As soon as a participant initiated the task, a research assistant would press the button  neural network was built for detecting the starting and ending times for each task. The motion detection model takes the raw acceleration and angular displacement at three axes in the 0.75 sliding windows as input. The convolutional layers and LSTM layers can extract the temporal human dynamic inertial signals to construct the classification. The overall network structure is shown in The convolution layer took the preprocessed sliding window segment as input and performed a convolution operation with 32 filters, kernel size 3. The LSTM layer is a specific implementation of the recurrent neural network that takes the previous state information into the current calculation. Finally, the densely connected layer operates by applying the activation function of the dot operation of weights. The first two dense layers did not apply any activation function, and the SoftMax function activated the last dense layer to calculate the percentage of labels containing and not containing the task.The direct prediction of the convolutional LSTM neural network appeared to be noisy, as shown in the top image of A mean filter was used to filter the convolutional LSTM neural network output with window sizes of 1, 1.25, and 1.5 s. These mean filter sizes used in this section are examined and discussed in the Results and Discussion sections. The output is shown in the middle image of Beginning of the interval: The closest peak of nontask probability before the point where task probability starts to be greater than 0.5End of the interval: The first peak of nontask probability after the point where task probability starts to be less than 0.5.Peak detection was conducted using the SciPy package  with 0.5Typically, only an interval would be identified. If more than one interval was identified, the following rules were applied accordingly to the different tasks. For sit-to-stand and stand-to-sit tasks, the time interval that contained the greatest anterior-posterior (AP) acceleration range was assigned to when the participant performed the motion. For the turning 360° task, intervals with cumulative angular movement from a starting time less than 360° along the turning axis  were selected. The final result is illustrated in the bottom image of These rules were inspired from the biomechanical point of view for human motion , where AAfter the signal was automatically annotated, sensor-based mobility and balance assessment was performed to evaluate the corresponding motion . The sensor features introduced above were used to predict the corresponding Tinetti POMA-B grading items. A detailed description is presented in A one-class support vector machine (SVM) , linear Accuracy and the area under the curve (AUC) were used to evaluate the performance of this work. Accuracy was defined as the percentage of observations classified into the correct class, as (TP + TN)/(TP + TN + FP + FN), where TP is the true-positive class being classified as positive, TN is the true-negative class being classified as negative, FP is the false-negative class being classified as positive, and FN is the false-positive class being classified as negative. The AUC was obtained from the area under the receiver operating characteristic (ROC) curve. The ROC curve plots the sensitivity [TP/(TP + FN)]) against the false-positive rate [1 – (TN/{TN + FP})] at different levels of thresholds.2 = (n12 – n21)2/(n12 + n21), where n12 is the prediction result of when the manual label sensor feature shows positive but the motion detection label sensor feature shows negative. In contrast, n21 is the prediction result where the manual label sensor feature shows negative but the motion detection label sensor feature shows positive. The test statistic followed a chi-square distribution with 1 degree of freedom.The agreement between the mobility and balance assessment results using the human-annotated label and the motion detection method was evaluated using the McNemar agreement test. The null hypothesis states that the two results show agreement in classifying the mobility and balance assessment evaluation results. The test statistic was calculated by zLeave-one-subject-out (LOSO) cross-validation (CV) was used for training and testing. Each time, all the participants’ inertial measurements except the i-th participant were used for training the motion detection and sensor-based balance assessment, and the inertial measurements from the i-th individual were used for testing. The convolutional LSTM neural network was built and trained using Keras  with TenThe computer used for training was an Intel E5-2670 CPU with Nvidia Tesla K20 and CUDA version 10.2 on a Linux system. The batch size was set at 200, and the stopping rule was set for no improvement after 20 epochs. The rest of the computation was conducted using SciPy  and NumPOur motion detection methods yielded moderate accuracy in detecting sit-to-stand, turning 360°, and stand-to-sit tasks from 85% to 88% at different levels see . The difThe sit-to-stand task motion was well detected and classified using the developed method. The detector had 85%-87% accuracy in detecting sit-to-stand motion that was not previously observed in the training set. The extracted feature, the peak count along the AP axis, exhibited a strong ability to discriminate between normal and abnormal motion in the sit-to-stand task through the k-NN method at 90% accuracy. The high accuracy could be attributed to the fact that multiple attempts to rise generate multiple peaks in the signals , which aThe turning 360° task motion was detected using the developed method at an accuracy ranging from 85% to 86%. The classification showed 92% agreement with the professional opinion using k-NN in terms of the Tinetti POMA turning motion outcome. These results indicate that Tinetti grading items may correlate with the turning speed of the participants. Previous studies ,67 have The stand-to-sit task motion was detected at 88%-89% accuracy by the developed method. Compared with the other two tasks, however, sensor-based prediction showed the least accuracy in predicting the functional assessment result, at 86% accuracy using the one-class SVM. This may be ascribed to Tinetti POMA’s grading to deduct a mark for participants who completed this task with the assistance of their arm, which occurred in the majority of the cases according to the report. This kind of information may not be revealed from the sensor signal, as the location of the sensor is at the participant’s lower back. Consequently, it could result in weaker performance in classifying the category of older adults.P=.42 and .45, respectively). The average turn speed showed discrepancy (P=.03) with a mean difference of 5.68°/s. In summary, the motion detection label features showed statistically no difference in the sit-to-stand and stand-to-sit motions but a slight difference in turning.The sensor features from the manual label signal and the motion detection method yielded little difference, which barely affects the sensor-based balance assessment results. Both sit-to-stand AP peak counts and stand-to-sit peak counts showed no statistical difference (Wilcoxon signed-rank test Using the manual label motion signal features or the detected motion signal features showed no statistical difference in classifying the Tinetti POMA outcomes in all circumstances . The McNPrevious research  has repoThere are four limitations of our study. First, we only analyzed sit-to-stand, turning 306°, and stand-to-sit motions rather than the complete mobility and balance assessment that covers comprehensive mobility and balance aspects. Nonetheless, motions used in this study have been frequently analyzed in several mobility and balance assessments to assess lower-body strength  and dynaWe presented human motion detection in a large-scale health care assessment. Existing works ,73 on HAThis research illustrated the idea that functional assessment motions can be detected through HAR models. Therefore, the sensor data collection process can be conducted without additional labor if there is a sufficiently pretrained model in the future. Without the additional labor, the cost of sensor-based functional assessment can be reduced, providing more incentive to conduct large-scale implementation in identifying potential fallers in the community.


Evidence from previous pandemics, and the current COVID-19 pandemic, has found that risk of infection/severity of disease is disproportionately higher for ethnic minority groups, and those in lower socioeconomic positions. It is imperative that interventions to prevent the spread of COVID-19 are targeted towards high-risk populations. We will investigate the associations between social characteristics  and COVID-19 outcomes and the extent to which characteristics/risk factors might explain observed relationships in Scotland.The primary objective of this study is to describe the epidemiology of COVID-19 by social factors. Secondary objectives are to (1) examine receipt of treatment and prevention of COVID-19 by social factors; (2) quantify ethnic/social differences in adverse COVID-19 outcomes; (3) explore potential mediators of relationships between social factors and SARS-CoV-2 infection/COVID-19 prognosis; (4) examine whether occupational COVID-19 differences differ by other social factors and (5) assess quality of ethnicity coding within National Health Service datasets.We will use a national cohort comprising the adult population of Scotland who completed the 2011 Census and were living in Scotland on 31 March 2020 (~4.3 million people). Census data will be linked to the Early Assessment of Vaccine and Anti-Viral Effectiveness II cohort consisting of primary/secondary care, laboratory data and death records. Sensitivity/specificity and positive/negative predictive values will be used to assess coding quality of ethnicity. Descriptive statistics will be used to examine differences in treatment and prevention of COVID-19. Poisson/Cox regression analyses and mediation techniques will examine ethnic and social differences, and drivers of inequalities in COVID-19. Effect modification  between key variables (such as ethnicity and occupation) will be assessed.Ethical approval was obtained from the National Research Ethics Committee, South East Scotland 02. We will present findings of this study at international conferences, in peer-reviewed journals and to policy-makers. This study uses a near-complete cohort of the Scottish population, providing considerable statistical power to investigate social inequalities in COVID-19.Linkage of the Scottish Census to administrative data allows for evaluation of differences in COVID-19 across specific occupational classes, which is not available in any other dataset in Scotland.Given the Census data are from 2011 and are being linked to data from 2020 to 2021, some occupational and social data may be out of date.Individuals who moved to Scotland post-Census will not be included in all analyses, for the same reasons as above.Historically, pandemics have disproportionately affected specific groups within the population. For example, during the Spanish influenza pandemic of 1918, individuals from poorer socioeconomic positions (SEP) experienced higher incidence of influenza in the USA.The SARS-CoV-2 and its resulting disease (COVID-19) is spreading rapidly worldwide, causing a high burden of mortality and morbidity.12 14Emerging evidence suggests that marked socio-economic inequalities in risk and outcome of COVID-19 exist, yet empirical evidence describing the role of socio-economic factors in Scotland is limited.The Early Assessment of Vaccine and Anti-Viral Effectiveness II (EAVE II) studyThis EAVE II substudy aims to quantify social inequalities in COVID-19 related outcomes in Scotland using linked census and administrative data, as well as examining the mechanisms through which inequalities in COVID-19 arise.Our primary objective is to describe the epidemiology of COVID-19 by: (1) ethnicity, (2) SEP, (3) occupation and (4) residential circumstances.In addition, we have five secondary objectives:To monitor receipt of COVID-related treatment and preventive interventions across social and ethnic groups.To quantify ethnic and social differences in the risk of adverse outcomes  from COVID-19.To explore the potential contribution of pre-existing health conditions and risk factors to ethnic and social inequalities in COVID-19 and prognosis.To investigate if occupational  differences in COVID-19 risks are modified by other social factors.To assess the quality of ethnicity coding within National Health Service (NHS) administrative datasets.Secondary objective b will be examined for three groups defined by experiencing: (1) laboratory confirmed SARS-CoV-2 infection, (2) clinically diagnosed SARS-CoV-2 infection and (3) serological evidence of previous infection with SARS-CoV-2.We will perform analyses on a national prospective cohort of the Scottish population, including all individuals who meet the following criteria: (1) alive on the 1 March 2020; (2) resident in Scotland on that date and (3) present in the 2011 Census in Scotland. A start date of 1 March 2020 has been selected as the first case of laboratory confirmed SARS-CoV-2 in Scotland was identified on 2 March 2020.The Community Health Index (CHI) is a unique numerical identifier, assigned to every individual registered to receive healthcare in Scotland, which can be used to identify and link all healthcare interactions and records for an individual.23In Scotland, the national Census is conducted every 10 years by means of a mandatory survey sent to every household in the country.25To identify individuals with confirmed SARS-CoV-2 infection, positive viral reverse transcriptase PCR (RT-PCR) results will be confirmed using Electronic Communication of Surveillance in Scotland (ECOSS) data, a national repository of diagnostic and reference laboratory data on testing for viral infections including COVID-19.27The Scottish Morbidity Record (SMR), maintained by PHS, is the primary repository for data describing hospital admissions in Scotland.The prescribing information system (PIS) is the repository for all prescriptions requested for individuals by general practices,Just under 100% of the population of Scotland is registered with a GP, which provides free-at-point-of-delivery primary healthcare services.National Records for Scotland (NRS) provides data about deaths attributed to COVID-19, which will be included in our dataset.We will examine four categories of social factors in relation to the study’s primary outcomes: ethnicity, SEP, occupation and residential circumstances. Ethnicity will be categorised based on standard Census classifications,The following additional characteristics will be used to address confounding, explore mediation and for assessing effect modification. Cultural factors will be investigated, including religion, and English language ability via a four-point Likert scale . Geographical variables will include 32 local authorities (LA) comprising local government in Scotland. While these LA have some devolved powers, primarily in service provision,The primary outcomes of this study will be laboratory confirmed SARS-CoV-2 infection, clinically diagnosed COVID-19 and serologically confirmed evidence of previous SARS-CoV-2 infection. Laboratory confirmed SARS-CoV-2 infection will be defined as positive RT-PCR via the ECOSS dataset. Serologically confirmed SARS-CoV-2 infection will be defined as positive antibodies on serology testing as identified through ESoCiS data, reflecting immunological evidence of previous infection. Clinically diagnosed COVID-19 disease will be defined as the requisite ICD-10 code being recorded on discharge from hospital via SMR, intensive care via SICSAG, or on NRS death records. Clinically suspected COVID-19 disease will be used as a secondary outcome in the absence of laboratory confirmed COVID-19 and recorded via SAS or A&E attendance records, GP consultation or out-of-hours records, COVID-19 clinical assessment centre data, or NHS 24 logs. Given testing practices are changing over time, we will conduct sensitivity analyses by stratifying analyses by time period and consider weighting analyses to account for differential testing practices.Secondary outcomes include uptake of vaccination for COVID-19, as recorded in GP records or the vaccine delivery app, and being prescribed treatment for COVID-19 either via the PIS or HEPMA. We will also examine adverse outcomes following COVID-19 infection: admission to hospital, critical care admission or death. Hospital admission or death will be defined on the basis of either COVID-19  recorded on a hospital admission in SMR01 or death record  or positive test, or admission in RAPID within 14 days of a SARS-CoV-2 RT-PCR positive test.We will describe the epidemiology of COVID-19 by ethnic and social factors, specifically in relation to: (1) incidence of confirmed SARS-CoV-2 infection; (2) incidence of clinically diagnosed disease and (3) evidence of previous infection. We will use summary statistics to describe the cohort by presence and absence of confirmed, diagnosed and suspected COVID-19. We will perform a descriptive analysis of the number of cases by exposure groups in the first instance, to determine the level of disaggregation feasible. In addition to analysing inequalities by ethnicity and social factors, we will also investigate the inequalities by country of birth .Poisson regression with robust standard errors will be used to estimate risk ratios for the risk of infection across exposure groups. We will consider age and sex to be key potential confounders for all analyses, with adjustment for LA also planned for most analyses . If case numbers allow, we will examine the incidence of confirmed and suspected COVID-19 by sex . We can assess whether findings are robust to more stringent definitions of confirmed and suspected infection, for example, by restricting the definition of a confirmed case to one with positive viral RT-PCR, and restricting the definition of a suspected case to exclude people with a negative viral RT-PCR result.We will describe treatment for and prevention of COVID-19 by ethnicity and other social factors , especially in relation to vaccination uptake. We will tabulate those who have been vaccinated as a proportion of the total eligible in different social subgroups, from when the vaccine is available and delivered nationally. This will be monitored over time to investigate whether there is a change in vaccination rates for different subgroups. If we have sufficient numbers of people vaccinated, we will also stratify these analyses by demographic factors. Similarly, we will also investigate the potential ethnic and other social differences in treatment for COVID-19 during acute infection and post-COVID via GP prescribing or hospital-administered medical care.We will quantify ethnic and social inequalities in the risk of adverse outcomes  from COVID-19. We will perform a descriptive analysis of the number of cases by exposure group in the first instance, to help determine the level of disaggregation feasible. We will follow a previously published approachWe will explore the contribution of several potential mediators of ethnic and social inequalities in COVID-19. The extent of these analyses will be determined by the magnitude of any observed inequalities in COVID-19 outcomes. Potential mediators to be investigated for ethnic inequalities include occupation, SEP, cultural factors (such as language ability and country of birth) and prepandemic health . We will conduct cross-tabulations of the relationships between exposures, potential mediators and outcomes, followed by a set of nested regression models for the same purpose. Following this, we will pursue more formal mediation analyses. We expect to use G methods,We will investigate whether interactions exist for key combinations of exposure variables, such as ethnicity, SEP and occupation. We will study whether occupational risks of COVID-19 differ by ethnic group. Occupation will be assessed on the basis of Standard Occupational Classification 2000 codes, aggregated from three-digit codes to a level that meets disclosure control requirements. We will perform descriptive analysis of the number of cases by occupation initially, followed by cross-tabulation by ethnicity. We will then estimate relative risks for outcomes of interest and check for evidence of effect modification on an additive and multiplicative scale. Should we observe evidence that occupational risk differs by ethnicity, we will calculate relative excess risks due to interaction.We will assess the quality of ethnicity coding within NHS administrative datasets, in order to assess whether, in the absence of Census data, administrative data can be reliably used to study ethnic inequalities. We will start by assessing the proportion of information on ethnicity missing within each dataset. Following this, we will consider the 2011 Census to be our ‘gold-standard’ dataset for comparison purposes. We will quantify the prevalence of different ethnic groups within each dataset and assess the sensitivity, specificity, positive predictive value and negative predictive value, following the methods used in previous validation studies.Accounting for non-linkage and non-participation in the Census (via migration or birth after the Census or otherwise), we estimate that the size of the cohort will be 4.3 million people. We are confident based on research from previous pandemics in similar-sized populationsAn EAVE II Patient and public involvement (PPI) group has been established, which includes diverse ethnic and socioeconomic representation. Study plans have been discussed with the group and this has confirmed the importance of investigating a broad range of social factors . The study design was initially developed by the study investigators without PPI due to delays in being able to establish the PPI group, but more substantive input into the creation of detailed analysis plans is now ongoing. Findings will be discussed regularly with the group and revised on the basis of their input accordingly. The PPI group will also inform the development of dissemination plans.This study, as part of the wider EAVE II project, has been approved by the National Research Ethics Committee, South East Scotland 02. The study has also received approval from the Public Benefit and Privacy Panel for Health and Social Care (2021–0015), and approval from the Statistics Public Benefit and Privacy Panel (2021–0115). Findings from this study will be published in peer-reviewed journals and presented at international conferences. Analyses will be presented to policy-makers within UK and Scottish governments and to relevant public health practitioners in Scotland throughout the course of the study. Strengthening the Reporting of Observational Studies in Epidemiology and Reporting of studies Conducted using Observational Routinely-collected Data (RECORD) guidelines will be used in reporting the findings of the study.


COVID-19 pneumonia requires effective medical therapies. However, it is a challenge to find therapeutic drugs that not only inhibit viral replication, but also inhibit the accompanying cytokine storm and maintain an appropriate immune response. In this study, the effects of SARS-CoV-2 on gene expression in lung epithelial cells from patients with COVID-19 were systematically evaluated with bioinformatics analysis methods. Transcriptome expression specific to bystander (exposed but uninfected) and infected cells were found, and the vital pathways were identified by conducting differentially expressed gene analysis regarding the relationship between gene signatures of COVID-19 infection and disease severity. We found that a high viral load did not necessarily imply a low response of epithelial cells or a poor disease convalescence. The ability to distinguish the role of virus-correlated genes facilitates the development of potential new medicines and therapies for COVID-19 infection. Coronavirus disease 2019 (COVID-19) is confirmed as an acute respiratory infectious disease that has been attributed to infection with the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . Only a When coronaviruses enter lung epithelial cells, type I interferons in these cells are activated, thereby preventing the virus from replicating within the cell. How to target viral replication without affecting the interferon pathway turns out to be the toughest problem in drug therapy. Given that the production of type I interferon in the lung epithelial cells is relatively low, the replication and expansion of the virus is not limited, thereby allowing the release of progeny viruses, causing cell anxiety and stress, and then activating the innate and adaptive immune systems in the body . On thatThis study aimed to systematically assess the effects of SARS-CoV-2 on gene expression in the epithelial as well as various immune cells in the lung tissue of COVID-19 patients. The differences seen between COVID-19 pneumonia patients were compared with those with bacterial pneumonia and healthy subjects. This allowed the identification of the transcriptome expression specific to bystander (exposed but uninfected) and infected cells as well as important genes related to ARDS severity by conducting differentially expressed gene (DEG) analysis. It is envisaged that this will provide insights into the balance between anti-infection and the prevention of excessive immune responses in patients with COVID-19.in vivo study of COVID-19–related genes and the subsequent gene signature. GSE157526 was used for the in vitro study of COVID-19–related genes and the subsequent gene signature. Metadata from GSE167118 and Lambrechts  originated from the gene expression omnibus database and were accessed through NCBI GSE155249 , GSE1671mbrechts  were usembrechts  see text1. The PCA analysis was further conducted for the integrated data sets, and cluster analysis was performed with uniform manifold approximation and projection (UMAP). The cluster analysis of single-cell data was performed with Seurat’s graph-based clustering method. The resolution of the FindClusters feature was set to 0.1. Subsequently, the clusters were visualized with the UMAP version 0.2.6.0 graph. The R software package Seurat (version 2.3.4) was used for the data analysis. During quality control, unique molecular modifier (UMI) counts of less than 500 and those with double multiples were removed. Furthermore, cells with >5% of mitochondrial genes and >50% of ribosomal genes were filtered out.Specific to the integrated analysis of single-cell data, the data from infected new coronavirus and bacterial pneumonia samples as well as non-pneumonia samples were normalized using the SCTransform method . These wThe cells were first sorted into 10 cell types by complying with the single-cell data, and they were classified as samples infected with the new coronavirus and bacterial pneumonia as well as non-pneumonia samples. They were then divided into either a virus-infected lung-infection cell group or a virus-infected group and were further subdivided into high- and low-infection groups. The corresponding transcriptome analysis data were compared to screen DEGs, and to increase the efficiency of the study, among the mentioned candidate DEGs and the parameters used were min.pct > 0.25 and | Log2 (FC) | > 0.5. The DEGs underwent pathway enrichment analysis, and their gene expression profiles were analyzed using the Gene Set Enrichment Analysis (GSEA) method together with data sets from the MSigDB database .The gene features were processed and then analyzed with the method at WebGestalt webserver for the annotation of involved GO ontology and KEGG pathway .2. The differential output and comparison of the relevant genes of the mentioned intersection elements were also exploited to map with the corrplot software package3 or output for signaling pathway enrichment analysis based on the use of GSEA  network. Cytoscape was used to demonstrate the node and edge of the PPI network. CytoHubba was used to screen the hub genes, and the top 20 genes that were ranked by degree are shown.The STRING databasein vivo to study the transcriptome of different cells and compare non-COVID-19 patients with bacterial pneumonia or healthy subjects. In addition, the DEG transcriptomes of bystander (exposed but uninfected) and infected cells (COVID-19 gene-expressing cells) (BALF single-cell RNA sequencing data was used to study SARS-CoV-2 infection responses g cells)  were alsg cells) . Using tg cells) . As indig cells) .Although the proportion of SARS-CoV-2-positive cells in BALF was relatively similar , we founIn a study of SARS-CoV-2-infected cells, it was found that, although the viral infection rate of each cell was relatively consistent, the amount of infection could be very different . All celTo more effectively study the relationship between the degree of viral infection and the gene signatures, a series of comprehensive correlation studies were performed. In monocytes, macrophages and neutrophils, viral expressed genes showed high correlation only with virally expressed genes (VEGs), but significantly low correlation with other genes . In CD8 5, we were able to classify the upregulation of CXCL8 in the progression group from the patients with severe ARDS  cells infected with SARS-CoV-2 to investigate the self-response of epithelial cells  such as ISG15, IFIT3, and MX1, compared to non-infected (mock) epithelial cells  can be found in the article/LM, HQ, and JZ performed partial analysis and composed the manuscript. JSh, SH, QW, and ZW collected datasets and performed filtering for downstream analysis. SS edited the manuscript and brought strategic suggestions. JSo performed partial analysis, designed this study, supervised the whole study, and revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a shared affiliation with one of the authors JSo at time of review.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.