The outbreak of coronavirus in 2020 will have a massive impact on the functioning of most companies in Poland. If the state of the epidemic in Poland continues until mid-April, the number of companies that will be forced to lay off all their employees as a result of the outbreak of coronavirus in 2020 will almost double.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Eighty percent of the aviation and manufacturing companies are expecting to dismiss employees as a result of the coronavirus outbreak in Poland in March 2020. In the trade and catering sector, the percentage companies in need of such measures amounted to 60 percent.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Due to the outbreak of coronavirus (COVID-19) in Poland in 2020, employees are concerned about their future. The majority of the respondents, regardless of the type of employment, are worried about a reduction in wages. However, employees with fixed-term contracts are most concerned about redundancy.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

According to a survey conducted during the Coronavirus outbreak, 52 percent of marketers in the United Kingdom (UK) said their organization had already applied for the Job Retention scheme in September 2020. 10 percent said they definitely would make permanent staff redundant.

BackgroundTwo years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited.ObjectivesTo measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively.Findings1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests.ConclusionsLaboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible.

BackgroundEarly stages of catastrophes like COVID-19 are often led by chaos and panic. To characterize the initial chaos phase of clinical research in such situations, we analyzed the first surge of more than 1000 clinical trials about the new disease at baseline and after two years follow-up. Our 3 main objectives were: (1) Assessment of spatial and temporal evolution of clinical research of COVID-19 across the globe, (2) Assessment of transparency and quality—trial registration, (3) Assessment of research waste and redundancies.MethodsBy entering the keyword “COVID-19” we screened the International Clinical Trials Registry Platform of the WHO and downloaded the search output when our goal of 1000 trials was reached on the 1st of April 2020. Additionally, we verified the integrity of the downloaded data from the meta registry by comparing the data with each individual registration record on their source register. Also, we conducted a follow-up after two years to track their progress.Results(1) The spatial evolution followed the geographical spread of the disease as expected, however, the temporal development suggested that panic was the main driver for clinical research activities. (2) Trial registrations and registers showed a huge lack of transparency by allowing retrospective registrations and not keeping their registration records up to date. Quality of trial registration seems to have improved over the last decade, yet crucial information still was missing. (3) Research waste and redundancies were present as suggested by discontinuation of trials, preventable flaws in study design, and similar but uncoordinated research topics operationally fragmented in isolated silo-structures.ConclusionThe scientific response mechanism across the globe was intact during the chaos phase. However, supervision, leadership, and accountability are urgently needed to prevent research waste, to ensure effective structure, quality, and validity to ultimately break the “panic-then-forget” cycle in future catastrophes.

A June 2021 study investigated the types of jobs lost within the museum industry of the United Kingdom following the coronavirus (COVID-19) pandemic. At least 25 museums reported redundancies in jobs related to learning and engagement, whereas 24 museums confirmed redundancies in the front of house and visitor operations department. In contrast, the least affected departments were digital & IT and finance, with only three museums declaring redundancies for these types of jobs.

Abstract copyright UK Data Service and data collection copyright owner.

The UCL COVID-19 Social Study at University College London (UCL) was launched on 21 March 2020. Led by Dr Daisy Fancourt and Professor Andrew Steptoe from the Department of Behavioural Science and Health, the team designed the study to track in real-time the psychological and social impact of the virus across the UK.

The study quickly became the largest in the country, growing to over 70,000 participants and providing rare and privileged insight into the effects of the pandemic on people’s daily lives. Through our participants’ remarkable two-year commitment to the study, 1.2 million surveys were collected over 105 weeks, and over 100 scientific papers and 44 public reports were published.

During COVID-19, population mental health has been affected both by the intensity of the pandemic (cases and death rates), but also by lockdowns and restrictions themselves. Worsening mental health coincided with higher rates of COVID-19, tighter restrictions, and the weeks leading up to lockdowns. Mental health then generally improved during lockdowns and most people were able to adapt and manage their well-being. However, a significant proportion of the population suffered disproportionately to the rest, and stay-at-home orders harmed those who were already financially, socially, or medically vulnerable. Socioeconomic factors, including low SEP, low income, and low educational attainment, continued to be associated with worse experiences of the pandemic. Outcomes for these groups were worse throughout many measures including mental health and wellbeing; financial struggles;self-harm and suicide risk; risk of contracting COVID-19 and developing long Covid; and vaccine resistance and hesitancy. These inequalities existed before the pandemic and were further exacerbated by COVID-19, and such groups remain particularly vulnerable to the future effects of the pandemic and other national crises.

Further information, including reports and publications, can be found on the UCL COVID-19 Social Study website.

The study asked baseline questions on the following:

• Demographics, including year of birth, sex, ethnicity, relationship status, country of dwelling, urban/rural dwelling, type of accommodation, housing tenure, number of adults and children in the household, household income, education, employment status, pet ownership, and personality.
• Health and health behaviours, including pre-existing physical health conditions, diagnosed mental health conditions, pregnancy, smoking, alcohol consumption, physical activity, caring responsibilities, usual social behaviours, and social network size.

It also asked repeated questions at every wave on the following:

• COVID-19 status, including whether the respondent had had COVID-19, whether they had come into likely contact with COVID-19, current isolation status and motivations for isolation, length of isolation, length of time not leaving the home, length of time not contacting others, trust in government, trust in the health service, adherence to health advice, and experience of adverse events due to COVID-19 (including severe illness within the family, bereavement, redundancy, or financial difficulties).
• Mental health, including wellbeing, depression, anxiety, which factors were causing stress, sleep quality, loneliness, social isolation, and changes in health behaviours such as smoking, drinking and exercise.
• How people were spending their time whilst in isolation, including questions on working, functional household activities, care, and schooling of any children in the household, hobbies, and relaxation.

Certain waves of the study also included one-off modules on topics including volunteering behaviours, locus of control, frustrations and expectations, coping styles, fear of COVID-19, resilience, arts and creative engagement, life events, weight, gambling behaviours, mental health diagnosis, use of financial support, faith and religion, relationships, neighbourhood satisfaction, healthcare usage, discrimination experiences, life changes, optimism, long COVID and COVID-19 vaccination.

The global outbreak of the COVID-19 epidemic has become a major public health problem. COVID-19 virus infection triggers a complex immune response. CD8+ T cells, in particular, play an essential role in controlling the severity of the disease. However, the mechanism of the regulatory role of CD8+ T cells on COVID-19 remains poorly investigated. In this study, single-cell gene expression profiles from three CD8+ T cell subtypes (effector, memory, and naive T cells) were downloaded. Each cell subtype included three disease states, namely, acute COVID-19, convalescent COVID-19, and unexposed individuals. The profiles on each cell subtype were individually analyzed in the same way. Irrelevant features in the profiles were first excluded by the Boruta method. The remaining features for each CD8+ T cells subtype were further analyzed by Max-Relevance and Min-Redundancy, Monte Carlo feature selection, and light gradient boosting machine methods to obtain three feature lists. These lists were then brought into the incremental feature selection method to determine the optimal features for each cell subtype. Their corresponding genes may be latent biomarkers to determine COVID-19 severity. Genes, such as ZFP36, DUSP1, TCR, and IL7R, can be confirmed to play an immune regulatory role in COVID-19 infection and recovery. The results of functional enrichment analysis revealed that these important genes may be associated with immune functions, such as response to cAMP, response to virus, T cell receptor complex, T cell activation, and T cell differentiation. This study further set up different gene expression pattens, represented by classification rules, on three states of COVID-19 and constructed several efficient classifiers to distinguish COVID-19 severity. The findings of this study provided new insights into the biological processes of CD8+ T cells in regulating the immune response.

A June 2021 report investigated the extent of redundancies in the museum industry of the United Kingdom after a year of the coronavirus (COVID-19) pandemic. The study identified that as of April 2021 roughly 4,100 jobs were deemed as surplus since the beginning of the pandemic. Specifically, there were 1,850 proposed redundancies in addition to 2,250 confirmed redundancies as of the period considered.

Abstract copyright UK Data Service and data collection copyright owner.BackgroundThe Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.Occupation data for 2021 and 2022 data filesThe ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2024 ReweightingIn February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).The Secure Access version contains more detailed variables relating to:age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent childfamily unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of familynationality and country of originfiner detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;health: including main health problem, and current and past health problemseducation and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeshipsindustry: including industry, industry class and industry group for main, second and last job, and industry made redundant fromoccupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant fromsystem variables: including week number when interview took place and number of households at addressother additional detailed variables may also be included.The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the second edition (June 2022), 2022 weighting variable PWT22 was added to the study, and the 2020 weight removed. Main Topics:The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter.The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details. Face-to-face interview Telephone interview The first interview is conducted face-to-face, and subsequent interviews by telephone where possible. 2021 ABSENTEEISM ACADEMIC ACHIEVEMENT ACCIDENTS AT WORK ADULT EDUCATION ADVANCED LEVEL EXAM... ADVANCED SUPPLEMENT... AGE ALLERGIES APPRENTICESHIP ATTITUDES BONUS PAYMENTS BUSINESS AND TECHNO... CARDIOVASCULAR DISE... CARE OF DEPENDANTS CERTIFICATE OF SECO... CERTIFICATE OF SIXT... CHILD BENEFITS CHILD CARE CHILDREN CHRONIC ILLNESS CITY AND GUILDS OF ... COHABITATION CONDITIONS OF EMPLO... COVID 19 DEBILITATIVE ILLNESS DEGREES DEPRESSION DIABETES DIGESTIVE SYSTEM DI... DISABILITIES DISABLED PERSONS DISMISSAL DISTANCE LEARNING DOMESTIC RESPONSIBI... EARLY RETIREMENT ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL CERTIFI... EDUCATIONAL COURSES EDUCATIONAL FEES EDUCATIONAL INSTITU... EDUCATIONAL LEVELS EDUCATIONAL OPPORTU... EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES EMPLOYMENT SERVICES ENDOCRINE DISORDERS EPILEPSY ETHNIC GROUPS FAMILIES FAMILY BENEFITS FAMILY MEMBERS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION GENERAL CERTIFICATE... GENERAL NATIONAL VO... GENERAL SCOTTISH VO... HEADS OF HOUSEHOLD HEALTH HEARING IMPAIRMENTS HIGHER EDUCATION HIGHER EDUCATION IN... HIGHER NATIONAL CER... HOLIDAY LEAVE HOME BASED WORK HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING BENEFITS HOUSING TENURE IMMIGRATION IN SERVICE TRAINING INCOME INDUSTRIES INVOLUNTARY SHORT T... JOB CHANGING JOB DESCRIPTION JOB HUNTING JOB SEEKER S ALLOWANCE LABOUR AND EMPLOYMENT LABOUR FORCE LANDLORDS LEARNING DISABILITIES LEAVE LONGTERM UNEMPLOYMENT Labour and employment MANAGERS MARITAL STATUS MATERNITY LEAVE MATERNITY PAY MENTAL DISORDERS MUSCULOSKELETAL DIS... NATIONAL IDENTITY NATIONAL VOCATIONAL... NATIONALITY NERVOUS SYSTEM DISE... OCCUPATIONAL QUALIF... OCCUPATIONAL SAFETY OCCUPATIONAL STATUS OCCUPATIONAL TRAINING OCCUPATIONS ORDINARY LEVEL EXAM... ORDINARY NATIONAL C... OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PATERNITY LEAVE PENSIONS PHOBIAS PLACE OF BIRTH PLACE OF RESIDENCE PRIVATE PENSIONS PRIVATE SECTOR PUBLIC HEALTH RISKS PUBLIC SECTOR QUALIFICATIONS REBATES RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY RESPIRATORY TRACT D... RETIREMENT ROYAL SOCIETY OF AR... SANDWICH COURSES SCOTTISH CERTIFICAT... SCOTTISH VOCATIONAL... SCOTTISH VOCATIONAL... SEASONAL EMPLOYMENT SELF EMPLOYED SEX SHARED HOME OWNERSHIP SHIFT WORK SICK LEAVE SICK PAY SICK PERSONS SICKNESS AND DISABI... SKIN DISEASES SMALL BUSINESSES SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPEECH IMPAIRMENTS

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of 167.6 thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of 263 thousand laid off employees in the global tech sector by trhe end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

The outbreak of coronavirus in 2020 will have a massive impact on the functioning of most companies in Poland. The survey shows that every second entrepreneur will not survive one month without the need to lay off employees.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

The global outbreak of the COVID-19 epidemic has become a major public health problem. COVID-19 virus infection triggers a complex immune response. CD8+ T cells, in particular, play an essential role in controlling the severity of the disease. However, the mechanism of the regulatory role of CD8+ T cells on COVID-19 remains poorly investigated. In this study, single-cell gene expression profiles from three CD8+ T cell subtypes (effector, memory, and naive T cells) were downloaded. Each cell subtype included three disease states, namely, acute COVID-19, convalescent COVID-19, and unexposed individuals. The profiles on each cell subtype were individually analyzed in the same way. Irrelevant features in the profiles were first excluded by the Boruta method. The remaining features for each CD8+ T cells subtype were further analyzed by Max-Relevance and Min-Redundancy, Monte Carlo feature selection, and light gradient boosting machine methods to obtain three feature lists. These lists were then brought into the incremental feature selection method to determine the optimal features for each cell subtype. Their corresponding genes may be latent biomarkers to determine COVID-19 severity. Genes, such as ZFP36, DUSP1, TCR, and IL7R, can be confirmed to play an immune regulatory role in COVID-19 infection and recovery. The results of functional enrichment analysis revealed that these important genes may be associated with immune functions, such as response to cAMP, response to virus, T cell receptor complex, T cell activation, and T cell differentiation. This study further set up different gene expression pattens, represented by classification rules, on three states of COVID-19 and constructed several efficient classifiers to distinguish COVID-19 severity. The findings of this study provided new insights into the biological processes of CD8+ T cells in regulating the immune response.

The global outbreak of the COVID-19 epidemic has become a major public health problem. COVID-19 virus infection triggers a complex immune response. CD8+ T cells, in particular, play an essential role in controlling the severity of the disease. However, the mechanism of the regulatory role of CD8+ T cells on COVID-19 remains poorly investigated. In this study, single-cell gene expression profiles from three CD8+ T cell subtypes (effector, memory, and naive T cells) were downloaded. Each cell subtype included three disease states, namely, acute COVID-19, convalescent COVID-19, and unexposed individuals. The profiles on each cell subtype were individually analyzed in the same way. Irrelevant features in the profiles were first excluded by the Boruta method. The remaining features for each CD8+ T cells subtype were further analyzed by Max-Relevance and Min-Redundancy, Monte Carlo feature selection, and light gradient boosting machine methods to obtain three feature lists. These lists were then brought into the incremental feature selection method to determine the optimal features for each cell subtype. Their corresponding genes may be latent biomarkers to determine COVID-19 severity. Genes, such as ZFP36, DUSP1, TCR, and IL7R, can be confirmed to play an immune regulatory role in COVID-19 infection and recovery. The results of functional enrichment analysis revealed that these important genes may be associated with immune functions, such as response to cAMP, response to virus, T cell receptor complex, T cell activation, and T cell differentiation. This study further set up different gene expression pattens, represented by classification rules, on three states of COVID-19 and constructed several efficient classifiers to distinguish COVID-19 severity. The findings of this study provided new insights into the biological processes of CD8+ T cells in regulating the immune response.

The global outbreak of the COVID-19 epidemic has become a major public health problem. COVID-19 virus infection triggers a complex immune response. CD8+ T cells, in particular, play an essential role in controlling the severity of the disease. However, the mechanism of the regulatory role of CD8+ T cells on COVID-19 remains poorly investigated. In this study, single-cell gene expression profiles from three CD8+ T cell subtypes (effector, memory, and naive T cells) were downloaded. Each cell subtype included three disease states, namely, acute COVID-19, convalescent COVID-19, and unexposed individuals. The profiles on each cell subtype were individually analyzed in the same way. Irrelevant features in the profiles were first excluded by the Boruta method. The remaining features for each CD8+ T cells subtype were further analyzed by Max-Relevance and Min-Redundancy, Monte Carlo feature selection, and light gradient boosting machine methods to obtain three feature lists. These lists were then brought into the incremental feature selection method to determine the optimal features for each cell subtype. Their corresponding genes may be latent biomarkers to determine COVID-19 severity. Genes, such as ZFP36, DUSP1, TCR, and IL7R, can be confirmed to play an immune regulatory role in COVID-19 infection and recovery. The results of functional enrichment analysis revealed that these important genes may be associated with immune functions, such as response to cAMP, response to virus, T cell receptor complex, T cell activation, and T cell differentiation. This study further set up different gene expression pattens, represented by classification rules, on three states of COVID-19 and constructed several efficient classifiers to distinguish COVID-19 severity. The findings of this study provided new insights into the biological processes of CD8+ T cells in regulating the immune response.

Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia.Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 pneumonia and 90 patients with influenza virus pneumonia in this retrospective study. Radiomics features were extracted from CT images. The radiomics features were reduced by the Max-Relevance and Min-Redundancy algorithm and the least absolute shrinkage and selection operator method. The radiomics model was built using the multivariate backward stepwise logistic regression. A nomogram of the radiomics model was established, and the decision curve showed the clinical usefulness of the radiomics nomogram.Results: The radiomics features, consisting of nine selected features, were significantly different between COVID-19 pneumonia and influenza virus pneumonia in both training and validation data sets. The receiver operator characteristic curve of the radiomics model showed good discrimination in the training sample [area under the receiver operating characteristic curve (AUC), 0.909; 95% confidence interval (CI), 0.859–0.958] and in the validation sample (AUC, 0.911; 95% CI, 0.753–1.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful.Conclusions: The radiomics model has good performance for distinguishing COVID-19 pneumonia from influenza virus pneumonia and may aid in the diagnosis of COVID-19 pneumonia.