COVq dataset

This dataset was used in the paper GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning. Refer to https://arxiv.org/abs/2402.16829 for details. The code for generating the data is available at https://github.com/avsolatorio/GISTEmbed.

  Citation

@article{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}… See the full description on the dataset page: https://huggingface.co/datasets/avsolatorio/covid-bing-query-gpt4-avs_triplets.

Data set - responses of participants in a survey on well-being of students during self-isolation due to pandemic. Background: Covid-19 pandemic resulted with a lock-down measure imposed by the government of North Macedonia. Conditions of self-isolation during pandemic affect the mental health. We research the possible protective factors of psychological well-being. Method: A total of 510 college students from the biggest university in the country (70% females, M age = 21.12 years, SD = 1.58) responded to a structured online questionnaire, one month after the country's complete lock down. Results: The correlational analysis suggests that at this age, psychological well-being in conditions of isolation is higher when the perceived social support and adequacy of being informed about the virus, as well the self-engagement with physical activities are higher. Further, respondents who assessed and accept the official medical and restrictive measures higher, reported better overall well-being. Finally, those students who hold conspiratorial beliefs about the virus spread tend to feel more contented than those who do not. Conclusions: In the face of the possible second wave of pandemic, policy creators and scientific community should develop well-thought strategy, tailored to different groups, to support people to cope with pandemic, and to prevent fake news and conspiracy theories which undermine confidence in the health system.

A survey conducted to assess users understanding of four COVID-19 geovisualizations. Map 1: Bing covid tracker Map 2: ECDC covid map Map 3: Johns Hopkins CSSE covid dashboard Map 4: WHO covid dashboard

Publicly available geocoded social determinants of health and mobility datasets used in the analysis of "Chronic Acid Suppression and Social Determinants of COVID-19 Infection".These datasets are required for the analytical workflow shared on Github which demonstrates how the analysis in the manuscript was done using randomly generated samples to protect patient privacy.zcta_county_rel_10.txt - Population and housing density from the 2010 decennial census. Obtained from: https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txtcre-2018-a11.csv - Community Resilience Estimates which is is the capacity of individuals and households to absorb, endure, and recover from the health, social, and economic impacts of a disaster such as a hurricane or pandemic. Data obtained from: https://www.census.gov/data/experimental-data-products/community-resilience-estimates.htmlzcta_tract_rel_10.txt - Relationship between ZCTA and US Census tracts (used to map census tracts to ZCTA). Data obtained from: https://www.census.gov/geographies/reference-files/time-series/geo/relationship-files.html#par_textimage_674173622mask-use-by-county.txt - Mask Use By County comes from a large number of interviews conducted online by the global data and survey firm Dynata at the request of The New York Times. The firm asked a question about mask use to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. Data obtained from: https://github.com/nytimes/covid-19-data/tree/master/mask-usemobility_report_US.txt - Google mobility report which charts movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Data obtained from: https://github.com/ActiveConclusion/COVID19_mobility/blob/master/google_reports/mobility_report_US.csvACS2015_zctaallvars.csv - Social Deprivation Index is a composite measure of area level deprivation based on seven demographic characteristics collected in the American Community Survey (https://www.census.gov/programs-surveys/acs/) and used to quantify the socio-economic variation in health outcomes. Factors are: Income, Education, Employment, Housing, Household Characteristics, Transportation, Demographics. Data obtained from: https://www.graham-center.org/rgc/maps-data-tools/sdi/social-deprivation-index.html

COVID-19 Stats & Trends

Context

This dataset seeks to provide insights into what has changed due to policies aimed at combating COVID-19 and evaluate the changes in community activities and its relation to reduced confirmed cases of COVID-19. The reports chart movement trends, compared to an expected baseline, over time (from 2020/02/15 to 2020/02/05) by geography (across 133 countries), as well as some other stats about the country that might help explain the evolution of the disease.

Content

1. Grocery & Pharmacy: Mobility trends for places like grocery markets, food warehouses, farmers' markets, specialty food shops, drug stores, and pharmacies.
2. Parks: Mobility trends for places like national parks, public beaches, marinas, dog parks, plazas, and public gardens.
3. Residential: Mobility trends for places of residence.
4. Retail & Recreation: Mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.
5. Transit stations: Mobility trends for places like public transport hubs such as subway, bus, and train stations.
6. Workplaces: Mobility trends for places of work.
7. Total Cases: Total number of people infected with the SARS-CoV-2.
8. Fatalities: Total number of deaths caused by CoV-19.
9. Government Response Stringency Index: Additive score of nine indicators of government response to CoV-19: School closures, workplace closures, cancellation of public events, public information campaigns, stay at home policies, restrictions on internal movement, international travel controls, testing policy, and contact tracing.
10. COVID-19 Testing: Total number of tests performed.
11. Total Vaccinations: Total number of shots given.
12. Total People Vaccinated: Total number of people given a shot.
13. Total People Fully Vaccinated: Total number of people fully vaccinated (might require two shots of some vaccines).
14. Population: Total number of inhabitants.
15. Population Density per km2: Number of human inhabitants per square kilometer.
16. Health System Index: Overall performance of the health system.
17. Human Development Index (HDI): Summary index based on life expectancy at birth, expected years of schooling for children and mean years of schooling for adults, and GNI per capita.
18. GDP (PPP) per capita: Gross Domestic Product (GDP) per capita based on Purchasing Power Parity (PPP), taking into account the relative cost of local goods, services and inflation rates of the country, rather than using international market exchange rates, which may distort the real differences in per capita income.
19. Elderly Population (percentage): Percentage of the population above the age of 65 years old.

References & Acknowledgements

Bing COVID-19 data. Available at: https://github.com/microsoft/Bing-COVID-19-Data COVID-19 Community Mobility Report. Available at: https://www.google.com/covid19/mobility/ COVID-19: Government Response Stringency Index. Available at: https://ourworldindata.org/grapher/covid-stringency-index Coronavirus (COVID-19) Testing. Available at: https://github.com/owid/covid-19-data/blob/master/public/data/testing/covid-testing-all-observations.csv Coronavirus (COVID-19) Vaccination. Available at: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv List of countries and dependencies by population. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries and dependencies by population density. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries by Human Development Index. Available at: http://hdr.undp.org/en/data Measuring Overall Health System Performance. Available at: https://www.who.int/healthinfo/paper30.pdf?ua=1 List of countries by GDP (PPP) per capita. Available at: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD List of countries by age structure (65+). Available at: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS

Authors

• Diogo Silva, up201706892@fe.up.pt

The dataset we provide is composed of a CSV file containing the answers of responders to our questionnaire conducted to explore perceptions and feelings on the COVID-19 pandemic. The survey was conducted from June 27 to July 2 2022 among university students and adult residents of Milan, Italy, and New York City, NY, U.S.A. The two target demographics for this study were adult residents of the two cities who were employed at the beginning of 2020 and students who attended university during 2020 or joined during the pandemic. The survey was accompanied by a promotional video and an introductory paragraph describing the objective of the study. It was shared through social media platforms, on specialized social media groups, and on university students’ mailing lists. The total number of questions asked is a maximum of 20, variable depending on answers given by a user since we employed branching based on previous answers. This feature was particularly useful in creating questions that were specific to a subset of the sample population The topics of questions cover the following broad areas: Relationships: Multiple Choice and sorting/ranking questions designed to understand who the respondents spent lockdown with, if they managed to keep in touch with those they could not meet, and to family, friends, and intimate relationships during the pandemic Policies: Likert scale questions measuring agreement with measures put in place in both Milan and New York Personal Life: questions about one’s priorities before and during the pandemic Occupation: Multiple Choice questions about one’s occupation during the pandemic and feelings towards work or university Post-pandemic: Likert scale questions about one's perception of contagion threats and feelings of normalcy at the time they responded to the survey Demographics: Multiple choice questions to describe the pool of respondents and control sample bias The types of questions are one of the following: Multiple choice (one or more selections or single selection); Ranking; and Numeric scale (1-5 or 1-10). The “ranking” question type allowed users to sort a list of items in descending order of importance. In the dataset the column name represents the ranking given to the item, e.g. 1. highest priority. (2023-02-03)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a lasting threat to public health. To minimize the viral spread, it is essential to develop more reliable approaches for early diagnosis of the infection and immediate suppression of the viral replication. Herein, through computational prediction of SARS-CoV-2 genome and screening analysis of specimens from covid-19 patients, we predicted 15 precursors for SARS-CoV-2-encoded miRNAs (CvmiRNAs) containing 20 mature CvmiRNAs, in which CvmiR-2 was successfully detected by quantitative analysis in both serum and nasal swab samples of patients. CvmiR-2 showed high specificity in distinguishing covid-19 patients from normal controls, and high conservation between SARS-CoV-2 and its mutants. A positive correlation was observed between the CvmiR-2 expression level and the severity of patients. The biogenesis and expression of CvmiR-2 were validated in the pre-CvmiR-2-transfected A549 cells, showing a dose-dependent pattern. The sequence of CvmiR-2 was validated by sequencing analysis of human cells infected by either SARS-CoV-2 or pre-CvmiR-2. Target gene prediction analysis suggested CvmiR-2 may be involved in the regulation of the immune response, muscle pain and/or neurological disorders in covid-19 patients. In conclusion, the current study identified a novel v-miRNA encoded by SARS-CoV-2 upon infection of human cells, which holds the potential to serve as a diagnostic biomarker or a therapeutic target in clinic.

These are output files of shared R scripts used in prediction of repurposed drugs for treating lung injury in COVID-19.

R scripts are available here: https://doi.org/10.5281/zenodo.3822923

Description of files:

HCC515_6_data_for_drug.csv #Differential expression of genes in HCC515 cell at 6 h after treatment of ACE2 inhibitor

HCC515_24_data_for_drug.csv #Differential expression of genes in HCC515 cell at 24 h after treatment of ACE2 inhibitor

COVID19-Lung_data_for_drug.csv #Differential expression of genes in lung tissues with COVID-19

HCC515_6_drug.csv #Drugs for HCC515 cell at 6 h after transfection of ACE2 inhibitor

HCC515_24_drug.csv #Drugs for HCC515 cell at 24 h after transfection of ACE2 inhibitor

COVID19-Lung_drug.csv #Drugs for lung tissuse from COVID-19 patients

COL-3_single_treatment_response_data.csv #Differential expression of genes in HCC515 cell at 24h after treatment of COL-3

CGP-60474_single_treatment_response_data.csv #Differential expression of genes in HCC515 cell at 24h after treatment of CGP-60474

To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources. 6,415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6,351 patients from another three hospitals in Wuhan, 2,169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses. Eight factors, namely, Oxygen saturation, blood Urea nitrogen, Respiratory rate, admission before the date the national Maximum number of daily new cases was reached, Age, Procalcitonin, C-reactive protein (CRP), and absolute Neutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90-0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score >11 compared with those with scores ≤11 was 18.18 (95% CI 13.93-23.71; P 

In 2022, the "right to be forgotten" or "right to erasure" requests issued to Google and Bing from 34 European countries decreased by 16.2 percent compared to the previous year. In 2020, requests surged by almost 30 percent after the COVID-19 outbreak, with an increase of nearly 30 percent.

ObjectivesCoronavirus disease-19 (COVID-19)/influenza poses unprecedented challenges to the global economy and healthcare services. Numerous studies have described alterations in the microbiome of COVID-19/influenza patients, but further investigation is needed to understand the relationship between the microbiome and these diseases. Herein, through systematic comparison between COVID-19 patients, long COVID-19 patients, influenza patients, no COVID-19/influenza controls and no COVID-19/influenza patients, we conducted a comprehensive review to describe the microbial change of respiratory tract/digestive tract in COVID-19/influenza patients.MethodsWe systematically reviewed relevant literature by searching the PubMed, Embase, and Cochrane Library databases from inception to August 12, 2023. We conducted a comprehensive review to explore microbial alterations in patients with COVID-19/influenza. In addition, the data on α-diversity were summarized and analyzed by meta-analysis.ResultsA total of 134 studies comparing COVID-19 patients with controls and 18 studies comparing influenza patients with controls were included. The Shannon indices of the gut and respiratory tract microbiome were slightly decreased in COVID-19/influenza patients compared to no COVID-19/influenza controls. Meanwhile, COVID-19 patients with more severe symptoms also exhibited a lower Shannon index versus COVID-19 patients with milder symptoms. The intestinal microbiome of COVID-19 patients was characterized by elevated opportunistic pathogens along with reduced short-chain fatty acid (SCFAs)-producing microbiota. Moreover, Enterobacteriaceae (including Escherichia and Enterococcus) and Lactococcus, were enriched in the gut and respiratory tract of COVID-19 patients. Conversely, Haemophilus and Neisseria showed reduced abundance in the respiratory tract of both COVID-19 and influenza patients.ConclusionIn this systematic review, we identified the microbiome in COVID-19/influenza patients in comparison with controls. The microbial changes in influenza and COVID-19 are partly similar.

As many general retailers or mass distribution channels experienced an exponential growth during the months of the COVID-19 induced lockdown in France, the source wanted to measure the share of organic traffic of the different retailers websites. Thus, we note that around ** percent of visits to Franprix.fr came from organic traffic, that is to say, visits coming from search engines such as Google or Bing. The “minor” competitors in the sector have clearly understood that the fight against the big names (and their direct traffic) requires an elaborate keyword strategy.

BackgroundMental health risks associated with the aftermath of the COVID-19 pandemic are often overlooked by the public. The aim of this study was to investigate the effects of the COVID-19 pandemic on depression and anxiety disorders in China.MethodsStudies were analyzed and extracted in accordance with the PRISMA 2020 flowchart. The studies were screened and extracted using electronic databases including PubMed, Web of Science, Embase, Cochrane Library, and ClinicalTrials.gov according to the predefined eligibility criteria. The Cochrane Review Manager software 5.3.1 was used for data analysis and the risk of bias assessment.ResultsAs of 2023, a total of 9,212,751 Chinese have been diagnosed with COVID-19 infection. A total of 913,036 participants in 44 studies were selected following the eligibility criteria, the statistical information of which was collected for meta-analysis. The pooled prevalence of depression and anxiety were 0.31 (95% CI: 0.28, 0.35; I2 = 100.0%, p 

Severe respiratory viral infections, including SARS-CoV-2, have resulted in high mortality rates despite corticosteroids and other immunomodulatory therapies. Despite recognition of the pathogenic role of neutrophils, in-depth analyses of this cell population have been limited, due to technical challenges of working with neutrophils. We undertook an unbiased, detailed analysis of neutrophil responses in adult patients with COVID-19 and healthy controls, to determine whether distinct neutrophil phenotypes could be identified during infections compared to the healthy state. Single-cell RNA sequencing analysis of peripheral blood neutrophils from hospitalized patients with mild or severe COVID-19 disease and healthy controls revealed distinct mature neutrophil subpopulations, with relative proportions linked to disease severity. Disruption of predicted cell-cell interactions, activated oxidative phosphorylation genes, and downregulated antiviral and host defense pathway genes were observed in neutrophils obtained during severe compared to mild infections. Our findings suggest that during severe infections, there is a loss of normal regulatory neutrophil phenotypes seen in healthy subjects, coupled with the dropout of appropriate cellular interactions. Given that neutrophils are the most abundant circulating leukocytes with highly pathogenic potential, current immunotherapies for severe infections may be optimized by determining whether they aid in restoring an appropriate balance of neutrophil subpopulations.

Summary table of the patient characteristics and the duration of hospitalization.

To explore the metabolic changes and immune profiles in patients with COVID-19, we analyzed the data of patients with mild and severe COVID-19 as well as young children with COVID-19. Of the leukocytes, 47% (IQR, 33–59) were lymphocytes [2.5 × 109/L (IQR, 2.2–3.3)], and monocytes were 0.51 × 109/L (IQR, 0.45–0.57) in young children with COVID-19. In 32 mild COVID-19 patients, circulating monocytes were 0.45 × 109/L (IQR, 0.36–0.64). Twenty-one severe patients had low PO2 [57 mmHg (IQR, 50–73)] and SO2 [90% (IQR, 86–93)] and high lactate dehydrogenase [580 U/L (IQR, 447–696)], cardiac troponin I [0.07 ng/mL (IQR, 0.02–0.30)], and pro-BNP [498 pg/mL (IQR, 241–1,726)]. Serum D-dimer and FDP were 9.89 mg/L (IQR, 3.62–22.85) and 32.7 mg/L (IQR, 12.8–81.9), and a large number of RBC (46/μL (IQR, 4–242) was presented in urine, a cue of disseminated intravascular coagulation (DIC) in severe patients. Three patients had comorbidity with diabetes, and 18 patients without diabetes also presented high blood glucose [7.4 mmol/L (IQR, 5.9–10.1)]. Fifteen of 21 (71%) severe cases had urine glucose +, and nine of 21 (43%) had urine ketone body +. The increased glucose was partially caused by reduced glucose consumption of cells. Severe cases had extraordinarily low serum uric acid [176 μmol/L (IQR, 131–256)]. In the late stage of COVID-19, severe cases had extremely low CD4+ T cells and CD8+ T cells, but unusually high neutrophils [6.5 × 109/L (IQR, 4.8–9.6)], procalcitonin [0.27 ng/mL (IQR, 0.14–1.94)], C-reactive protein [66 mg/L (IQR, 25–114)] and an extremely high level of interleukin-6. Four of 21 (19%) severe cases had co-infection with fungi, and two of 21 (9%) severe cases had bacterial infection. Our findings suggest that, severe cases had acute respiratory distress syndrome (ARDS) I–III, and metabolic disorders of glucose, lipid, uric acid, etc., even multiple organ dysfunction (MODS) and DIC. Increased neutrophils and severe inflammatory responses were involved in ARDS, MODS, and DIC. With the dramatical decrease of T-lymphocytes, severe cases were susceptible to co-infect with bacteria and fungi in the late stage of COVID-19. In young children, extremely high lymphocytes and monocytes might be associated with the low morbidity of COVID-19. The significantly increased monocytes might play an important role in the recovery of patients with mild COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global crisis; however, our current understanding of the host immune response to SARS-CoV-2 infection remains limited. Herein, we performed RNA sequencing using peripheral blood from acute and convalescent patients and interrogated the dynamic changes of adaptive immune response to SARS-CoV-2 infection over time. Our results revealed numerous alterations in these cohorts in terms of gene expression profiles and the features of immune repertoire. Moreover, a machine learning method was developed and resulted in the identification of five independent biomarkers and a collection of biomarkers that could accurately differentiate and predict the development of COVID-19. Interestingly, the increased expression of one of these biomarkers, UCHL1, a molecule related to nervous system damage, was associated with the clustering of severe symptoms. Importantly, analyses on immune repertoire metrics revealed the distinct kinetics of T-cell and B-cell responses to SARS-CoV-2 infection, with B-cell response plateaued in the acute phase and declined thereafter, whereas T-cell response can be maintained for up to 6 months post-infection onset and T-cell clonality was positively correlated with the serum level of anti-SARS-CoV-2 IgG. Together, the significantly altered genes or biomarkers, as well as the abnormally high levels of B-cell response in acute infection, may contribute to the pathogenesis of COVID-19 through mediating inflammation and immune responses, whereas prolonged T-cell response in the convalescents might help these patients in preventing reinfection. Thus, our findings could provide insight into the underlying molecular mechanism of host immune response to COVID-19 and facilitate the development of novel therapeutic strategies and effective vaccines.

Introduction: Argentinean quarantine during the COVID-19 pandemic is one of the most long-lasting worldwide. We focused on the first 80-days of this quarantine on Argentinean women. Our aims were to analyze differences in general mental health state (MHS) indicators, by the (1) sites of residence with different prevalence of COVID-19 cases, and (2) quarantine duration; (3) to assess multiple relationships between each general MHS indicator and potentially affecting factors.Methods: We used a cross-sectional design with convenience successive sampling (N = 5,013). The online survey included a socio-demographic questionnaire (elaborated ad hoc) with standardized and validated self-reported questionnaires (General Health Questionnaire, Kessler Psychological Distress Scale) measuring the MHS indicators: self-perceived health, psychological discomfort, social functioning and coping, and psychological distress.Results: Worse self-perceived health and higher psychological discomfort affected significantly more women residing in sites with high prevalence of COVID-19 cases, compared to those residing in sites with intermediate prevalence, but effect sizes were small. Mean scores of all general MHS indicators were significantly worse for longer quarantine sub-periods (up to 53, 68, and 80-day duration) than for shorter sub-periods (up to seven, 13, and 25-day duration). Being a younger age, having mental disorder history, and longer quarantine durations were associated to worsening MHS, while the lack of previous suicide attempt has a protective effect.Discussion: Our findings show that a worse MHS during quarantine may not be attributed to the objective risk of contagion (measured greater or less), and under quarantine, women MHS—as indicated by group central tendency measures—got worse as time went by. This strongly suggests that special attention needs to be paid to younger women and to women with history of mental disorder. Along with physical health, mental health must be a priority for the Government during and after quarantine and the COVID-19 pandemic.

Recurring coronavirus outbreaks, such as the current COVID-19 pandemic, establish a necessity to develop direct-acting antivirals that can be readily administered and are active against a broad spectrum of coronaviruses. Described in this Article are novel α-acyloxymethylketone warhead peptidomimetic compounds with a six-membered lactam glutamine mimic in P1. Compounds with potent SARS-CoV-2 3CL protease and in vitro viral replication inhibition were identified with low cytotoxicity and good plasma and glutathione stability. Compounds 15e, 15h, and 15l displayed selectivity for SARS-CoV-2 3CL protease over CatB and CatS and superior in vitro SARS-CoV-2 antiviral replication inhibition compared with the reported peptidomimetic inhibitors with other warheads. The cocrystallization of 15l with SARS-CoV-2 3CL protease confirmed the formation of a covalent adduct. α-Acyloxymethylketone compounds also exhibited antiviral activity against an alphacoronavirus and non-SARS betacoronavirus strains with similar potency and a better selectivity index than remdesivir. These findings demonstrate the potential of the substituted heteroaromatic and aliphatic α-acyloxymethylketone warheads as coronavirus inhibitors, and the described results provide a basis for further optimization.

Long COVID is an often-debilitating condition with severe, multisystem symptoms that can persist for weeks or months and increase the risk of various diseases. Currently, there is a lack of diagnostic tools for Long COVID in clinical practice. Therefore, this study utilizes plasma proteomics and metabolomics technologies to understand the molecular profile and pathophysiological mechanisms of Long COVID, providing clinical evidence for the development of potential biomarkers. This study included three age- and gender-matched cohorts: healthy controls (n = 18), COVID-19 recovered patients (n = 17), and Long COVID patients (n = 15). The proteomics results revealed significant differences in proteins between Long COVID-19 patients and COVID-19 recovered patients, with dysregulation mainly focused on pathways such as coagulation, platelets, complement cascade reactions, GPCR cell signal transduction, and substance transport, which can participate in regulating immune responses, inflammation, and tissue vascular repair. Metabolomics results showed that Long COVID patients and COVID-19 recovered patients have similar metabolic disorders, mainly involving dysregulation in lipid metabolites and fatty acid metabolism, such as glycerophospholipids, sphingolipid metabolism, and arachidonic acid metabolism processes. In summary, our study results indicate significant protein dysregulation and metabolic abnormalities in the plasma of Long COVID patients, leading to coagulation dysfunction, impaired energy metabolism, and chronic immune dysregulation, which are more pronounced than in COVID-19 recovered patients.