NOTE: This dataset has been retired and marked as historical-only.

Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown".

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among cases based on the week of death.

For tests, each test is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts include multiple tests for the same person (a change made on 10/29/2020). PCR and antigen tests reported to Chicago Department of Public Health (CDPH) through electronic lab reporting are included. Electronic lab reporting has taken time to onboard and testing availability has shifted over time, so these counts are likely an underestimate of community infection.

The “Percent Tested Positive” columns are calculated by dividing the number of positive tests by the number of total tests . Because of the data limitations for the Tests columns, such as persons being tested multiple times as a requirement for employment, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code.

All data are provisional and subject to change. Information is updated as additional details are received.

To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records, American Community Survey (2018)

NOTE: This dataset has been retired and marked as historical-only. This dataset is a companion to the COVID-19 Daily Cases and Deaths dataset (https://data.cityofchicago.org/d/naz8-j4nc). The major difference in this dataset is that the case, death, and hospitalization corresponding rates per 100,000 population are not those for the single date indicated. They are rolling averages for the seven-day period ending on that date. This rolling average is used to account for fluctuations that may occur in the data, such as fewer cases being reported on weekends, and small numbers. The intent is to give a more representative view of the ongoing COVID-19 experience, less affected by what is essentially noise in the data. All rates are per 100,000 population in the indicated group, or Chicago, as a whole, for “Total” columns. Only Chicago residents are included based on the home address as provided by the medical provider. Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the date the test specimen was collected. Deaths among cases are aggregated by day of death. Hospitalizations are reported by date of first hospital admission. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation. Denominators are from the U.S. Census Bureau American Community Survey 1-year estimate for 2018 and can be seen in the Citywide, 2018 row of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa). All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects cases and deaths currently known to CDPH. Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases and deaths, sources used, how cases and deaths are associated to a specific date, and similar factors. Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, U.S. Census Bureau American Community Survey

ObjectivesOur objective was to determine the antibody and cytokine profiles in different COVID-19 patients.MethodsCOVID-19 patients with different clinical classifications were enrolled in this study. The level of IgG antibodies, IgA, IgM, IgE, and IgG subclasses targeting N and S proteins were tested using ELISA. Neutralizing antibody titers were determined by using a toxin neutralization assay (TNA) with live SARS-CoV-2. The concentrations of 8 cytokines, including IL-2, IL-4, IL-6, IL-10, CCL2, CXCL10, IFN-γ, and TNF-α, were measured using the Protein Sample Ella-Simple ELISA system. The differences in antibodies and cytokines between severe and moderate patients were compared by t-tests or Mann-Whitney tests.ResultsA total of 79 COVID-19 patients, including 49 moderate patients and 30 severe patients, were enrolled. Compared with those in moderate patients, neutralizing antibody and IgG-S antibody titers in severe patients were significantly higher. The concentration of IgG-N antibody was significantly higher than that of IgG-S antibody in COVID-19 patients. There was a significant difference in the distribution of IgG subclass antibodies between moderate patients and severe patients. The positive ratio of anti-S protein IgG3 is significantly more than anti-N protein IgG3, while the anti-S protein IgG4 positive rate is significantly less than the anti-N protein IgG4 positive rate. IL-2 was lower in COVID-19 patients than in healthy individuals, while IL-4, IL-6, CCL2, IFN-γ, and TNF-α were higher in COVID-19 patients than in healthy individuals. IL-6 was significantly higher in severe patients than in moderate patients. The antibody level of anti-S protein was positively correlated with the titer of neutralizing antibody, but there was no relationship between cytokines and neutralizing antibody.ConclusionsOur findings show the severe COVID-19 patients’ antibody levels were stronger than those of moderate patients, and a cytokine storm is associated with COVID-19 severity. There was a difference in immunoglobulin type between anti-S protein antibodies and anti-N protein antibodies in COVID-19 patients. And clarified the value of the profile in critical prevention.

NOTE: This dataset has been retired and marked as historical-only.

Weekly rates of COVID-19 cases, hospitalizations, and deaths among people living in Chicago by vaccination status and age.

Rates for fully vaccinated and unvaccinated begin the week ending April 3, 2021 when COVID-19 vaccines became widely available in Chicago. Rates for boosted begin the week ending October 23, 2021 after booster shots were recommended by the Centers for Disease Control and Prevention (CDC) for adults 65+ years old and adults in certain populations and high risk occupational and institutional settings who received Pfizer or Moderna for their primary series or anyone who received the Johnson & Johnson vaccine.

Chicago residency is based on home address, as reported in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE) and Illinois National Electronic Disease Surveillance System (I-NEDSS).

Outcomes: • Cases: People with a positive molecular (PCR) or antigen COVID-19 test result from an FDA-authorized COVID-19 test that was reported into I-NEDSS. A person can become re-infected with SARS-CoV-2 over time and so may be counted more than once in this dataset. Cases are counted by week the test specimen was collected. • Hospitalizations: COVID-19 cases who are hospitalized due to a documented COVID-19 related illness or who are admitted for any reason within 14 days of a positive SARS-CoV-2 test. Hospitalizations are counted by week of hospital admission. • Deaths: COVID-19 cases who died from COVID-19-related health complications as determined by vital records or a public health investigation. Deaths are counted by week of death.

Vaccination status: • Fully vaccinated: Completion of primary series of a U.S. Food and Drug Administration (FDA)-authorized or approved COVID-19 vaccine at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Boosted: Fully vaccinated with an additional or booster dose of any FDA-authorized or approved COVID-19 vaccine received at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Unvaccinated: No evidence of having received a dose of an FDA-authorized or approved vaccine prior to a positive test.

CLARIFYING NOTE: Those who started but did not complete all recommended doses of an FDA-authorized or approved vaccine prior to a positive test (i.e., partially vaccinated) are excluded from this dataset.

Incidence rates for fully vaccinated but not boosted people (Vaccinated columns) are calculated as total fully vaccinated but not boosted with outcome divided by cumulative fully vaccinated but not boosted at the end of each week. Incidence rates for boosted (Boosted columns) are calculated as total boosted with outcome divided by cumulative boosted at the end of each week. Incidence rates for unvaccinated (Unvaccinated columns) are calculated as total unvaccinated with outcome divided by total population minus cumulative boosted, fully, and partially vaccinated at the end of each week. All rates are multiplied by 100,000.

Incidence rate ratios (IRRs) are calculated by dividing the weekly incidence rates among unvaccinated people by those among fully vaccinated but not boosted and boosted people.

Overall age-adjusted incidence rates and IRRs are standardized using the 2000 U.S. Census standard population.

Population totals are from U.S. Census Bureau American Community Survey 1-year estimates for 2019.

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. This dataset reflects data known to CDPH at the time when the dataset is updated each week.

Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

Data Source: Illinois' National Electronic Disease Surveillance System (I-NEDSS), Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE), U.S. Census Bureau American Community Survey

Association between cytokine levels and leucocyte parameters among the COVID-19 active cases, recovered patients and unexposed healthy controls.

This is the place to look for important information about how to use this dataset, so please expand this box and read on!

This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown".

Confirmed cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among confirmed cases based on the week of death.

For tests, each individual is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts do not include multiple tests for the same person or some negative tests not reported to CDPH.

The “Percent Tested Positive” columns are calculated by dividing the corresponding Cases and Tests columns. Because of the data limitations for the Tests columns, as well as strict criteria for performing COVID-19 tests, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code. Of particular note, these rates do not represent population-level disease surveillance.

Population counts are from the 2010 Decennial Census.

All data are provisional and subject to change. Information is updated as additional details are received.

To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records

IntroductionSARS-CoV-2 elicits a hyper-inflammatory response that contributes to increased morbidity and mortality in patients with COVID-19. In the case of HIV infection, despite effective anti-retroviral therapy, people living with HIV (PLWH) experience chronic systemic immune activation, which renders them particularly vulnerable to the life-threatening pulmonary, cardiovascular and other complications of SARS-CoV-2 co-infection. The focus of the study was a comparison of the concentrations of systemic indicators o\f innate immune dysfunction in SARS-CoV-2-PCR-positive patients (n=174) admitted with COVID-19, 37 of whom were co-infected with HIV.MethodsParticipants were recruited from May 2020 to November 2021. Biomarkers included platelet-associated cytokines, chemokines, and growth factors (IL-1β, IL-6, IL-8, MIP-1α, RANTES, PDGF-BB, TGF-β1 and TNF-α) and endothelial associated markers (IL-1β, IL-1Ra, ICAM-1 and VEGF).ResultsPLWH were significantly younger (p=0.002) and more likely to be female (p=0.001); median CD4+ T-cell count was 256 (IQR 115 -388) cells/μL and the median HIV viral load (VL) was 20 (IQR 20 -12,980) copies/mL. Fractional inspired oxygen (FiO2) was high in both groups, but higher in patients without HIV infection (p=0.0165), reflecting a greater need for oxygen supplementation. With the exception of PDGF-BB, the levels of all the biomarkers of innate immune activation were increased in SARS-CoV-2/HIV-co-infected and SARS-CoV-2/HIV-uninfected sub-groups relative to those of a control group of healthy participants. The magnitudes of the increases in the levels of these biomarkers were comparable between the SARS-CoV-2 -infected sub-groups, the one exception being RANTES, which was significantly higher in the sub-group without HIV. After adjusting for age, sex, and diabetes in the multivariable model, only the association between HIV status and VEGF was statistically significant (p=0.034). VEGF was significantly higher in PLWH with a CD4+ T-cell count >200 cells/μL (p=0.040) and those with a suppressed VL (p=0.0077).DiscussionThese findings suggest that HIV co-infection is not associated with increased intensity of the systemic innate inflammatory response during SARS-CoV-2 co-infection, which may underpin the equivalent durations of hospital stay, outcome and mortality rates in the SARS-CoV-2/HIV-infected and -uninfected sub-groups investigated in the current study. The apparent association of increased levels of plasma VEGF with SARS-CoV-2/HIV co-infection does, however, merit further investigation.

The objective of this retrospective cohort study was to assess the relationship between Corona Disease 2019 (COVID-19) and Secukinumab treatment in patients with Spondylarthritis (SpA) in China during the omicron surge. Researchers retrieved 1018 medical records of Secukinumab-treated patients between January 2020 and January 2023 from the West China Hospital of Sichuan University. Out of these, 190 SpA patients from the rheumatology clinic were selected for the study. Guided phone questionnaires were administered by research staff to collect baseline characteristics, SpA disease status, and COVID-19 clinical outcomes. Cohabitants served as the control group and provided COVID-19 related data. Of the 190 potential SpA patients, 122 (66%) completed the questionnaire via phone, along with 259 cohabitants. 84.4% of SpA patients were diagnosed with Ankylosing Spondylitis (AS), and 15.6% were diagnosed with Psoriatic Arthritis (PsA). The rate of SARS-CoV-2 infection was 83.6% in the Secukinumab group and 88.8% in the cohabitants control group, with no significant difference (OR = 0.684, CI 0.366–1.275). One instance of severe COVID-19 was observed in the Secukinumab group, while two were identified in the cohabitants control group. Patients in the Secukinumab group had less time with fever caused by COVID-19 (p = 0.004). Discontinuing Secukinumab after SARS-CoV-2 infection did not significantly affect the course of COVID-19 or worsen SpA status according to our data. Our study suggests that administering Secukinumab to SpA patients does not increase their susceptibility to contracting SARS-CoV-2, and may have a positive effect on the course of SARS-CoV-2 infection.

Supplementary Tables Abstract: COVID-19 patients are generally asymptomatic during initial SARS-CoV-2 replication, but may suffer severe immunopathology after the virus has receded and blood monocytes have infiltrated the airways. In the bronchoalveolar lavage fluid from patients with severe COVID-19, lung-infiltrating monocytes express high mRNA levels encoding inflammatory mediators, including CXCL8 and IL-1, and contain SARS-CoV-2 transcripts. To study this process in more depth, we leverage a small airway model of infection and inflammation whereby primary human blood monocytes transmigrate across a differentiated human lung epithelium infected by SARS-CoV-2. Infiltrating monocytes acquire SARS-CoV-2 from the epithelium and upregulate expression and secretion of inflammatory mediators including CXCL8 and IL-1, mirroring in vivo data. The JAK1/2 inhibitor baricitinib gained emergency use authorization by the FDA for the treatment of COVID-19 originally in combination with the antiviral remdesivir, and recently as a stand-alone treatment. To explore the mechanisms by which baricitinib alone or in combination with remdesivir may result in more favorable disease outcomes, we leverage this model to characterize viral burden, gene expression and inflammatory mediator secretion by lung epithelial cells and infiltrating monocytes. As expected, remdesivir decreases viral burden in both the epithelium and monocytes, while baricitinib enhances antiviral signaling and decreases specific inflammatory mediators in monocytes. Combined use of baricitinib and remdesivir enhances the rate of virus clearance from SARS-CoV-2-positive monocytes. Taken together, baricitinib enhances the antiviral state of monocytes infiltrating the COVID-19 lung, while decreasing the expression of inflammatory mediators, thus limiting the likelihood of a cytokine storm and ensuing acute respiratory distress syndrome.

Since its emergence in 2019, coronavirus disease (COVID-19) has spread worldwide and consumed public health resources. However, the world still has to address the burdens of other infectious diseases that continue to thrive. Countries in the tropics and neotropics, including Brazil, are affected by annual, cyclic dengue epidemics. Little is known about the impact of subsequent infections between DENV and SARS-CoV-2. Our study was performed on 400 serum samples collected from laboratory-confirmed COVID-19 patients between January and June 2021, months historically known for DENV outbreaks in Brazil. The samples were tested by serology and molecular assays for the presence of DENV and other arboviruses. While no DENV PCR results were detected, 6% were DENV IgM-positive, and 0.25% were DENV NS1-positive according to ELISA. IgM antibodies were isolated by chromatography, and 62.5% of the samples were positive for neutralizing antibodies (FRNT80) against DENV IgM, suggesting a recent infection. We also observed increased IL-10, TNF-α, and IL-1β levels in patients with overlapping SARS-CoV-2/DENV infections. Intriguingly, diabetes was the only relevant comorbidity (p=0.046). High rates of hospitalization (94.9%) and mortality (50%) were found, with a significant increase in invasive mechanical ventilatory support (86.96%) in SARS-CoV-2/DENV- infected patients, suggesting an impact on patient clinical outcomes. When analyzing previous exposure to DENV, secondary dengue patients infected with SARS-CoV-2 more frequently presented with dyspnea and respiratory distress, longer hospital and intensive care unit (ICU) stays (4 and 20.29 days, respectively) and a higher mortality rate (60%). However, a greater proportion of patients with primary DENV infection had fever and cough than patients with secondary dengue (87.50% vs. 33.33%, p=0.027 for fever). Our data demonstrate that differentiating between the two diseases is a great concern for tropical countries and should be explored to improve patient management.

ObjectiveThe world continues to face the COVID-19 crisis, and efforts are underway to integrate traditional medicine interventions for its effective management. The study aimed to determine the efficacy of the “AYURAKSHA” kit in terms of post-interventional percentage of COVID-19 IgG positivity, immunity levels, and quality of life (QoL) against COVID-19.MethodThis was a non-randomized controlled, prospective intervention trial, done after the distribution of 80,000 AYURAKSHA kits (constituent of Sanshamani Vati, AYUSH Kadha, and Anu Taila) among Delhi police participants in India. Among 47,827 participants, the trial group (n = 101) was evaluated with the positivity percentage of IgG COVID-19 and Immune Status Questionnaire (ISQ) scores as a primary outcome and the WHO Quality of Life Brief Version (QOL BREF) scores along with hematological parameters as a secondary outcome in comparison to the control group (n = 71).ResultsThe data showed that the percentage of COVID-19 IgG positivity was significantly lower in the trial group (17.5 %) as compared to the control group (39.4 %, p = 0.003), indicating the lower risk (55.6%) of COVID-19 infection in the trial group. The decreased incidence (5.05%) and reduced mortality percentage (0.44%) of COVID-19 among Delhi police officers during peak times of the pandemic also corroborate our findings. The ISQ score and WHO-QOL BREF tool analysis showed the improved scores in the trial group when compared with the controls. Furthermore, no dysregulated blood profile and no increase in inflammation markers like C-reactive protein, erythrocyte sedimentation rate, Interleukin-6 (IL-6) were observed in the trial group. However, significantly enhanced (p = 0.027) IL-6 levels and random blood sugar levels were found in the control group (p = 0.032), compared to a trial group (p = 0.165) post-intervention. Importantly, the control group showed more significant (p = 0.0001) decline in lymphocyte subsets CD3+ (% change = 21.04), CD4+ (% change = 20.34) and CD8+ (% change = 21.54) levels than in trial group, confirming more severity of COVID-19 infection in the control group.ConclusionThe AYURAKSHA kit is associated with reduced COVID-19 positivity and with a better quality of life among the trial group. Hence, the study encourages in-depth research and future integration of traditional medicines for the prevention of the COVID-19 pandemic.Clinical trial registrationhttp://ctri.nic.in/, identifier: CTRI/2020/05/025171.

Questo dataset contiene i prezzi previsti dell'asset COVID-19 per i prossimi 16 anni. Questi dati sono calcolati inizialmente con un tasso di crescita annuale predefinito del 5% e, dopo il caricamento della pagina, presentano una componente di scala mobile in cui l'utente può regolare ulteriormente il tasso di crescita in base alle proprie proiezioni positive o negative. Il tasso di crescita regolabile massimo positivo è del 100 percento, mentre il tasso di crescita regolabile minimo è del -100 percento.

Nombre quotidien de résidents d'Ottawa testés pour la COVID-19 et pourcentage de résidents testés chez qui la COVID-19 a été confirmée en laboratoire. Les données sont extraites du Système d’information de laboratoire de l’Ontario (SILO) les jeudis. Exactitude: Points à considérer pour l’interprétation des données Ce ne sont pas tous les laboratoires qui font rapport au SILO et seuls les patients détenteurs d’un numéro de carte Santé sont inclus dans le fichier de données du SILO.Lorsqu’une personne est confirmée positive, ses tests subséquents sont exclus des totaux quotidiens. Les tests en double ont été exclus du nombre total de cas positifs, y compris ceux qui découlent de plusieurs tests de cure.Les résultats pour les patients qui ont placé un blocage au consentement dans le SILO (~ 50 dossiers dans l’ensemble de la province) sont exclus.Le personnel travaillant dans des foyers de soins de longue durée n’est pas pris en compte dans le SILO.La détermination de l’unité de santé publique responsable pour chaque test dans le SILO est fondée sur l’adresse identifié sur la carte Santé d’un patient. Il est possible que l’adresse associée à la carte Santé des patients résidant dans des foyers de soins de longue durée ne soit pas exacte et par conséquent, il se peut que certains des tests effectués dans les foyers n’apparaissent pas dans le SILO. Les cas confirmés de la COVID-19 sont ceux qui ont obtenu des résultats positifs en laboratoire selon le document du ministère de la Santé de l’Ontario portant sur la prise en charge par la santé publique des cas de maladie à COVID-19 et des contacts qui y sont associés, 25 mars 2020, version 6.0.Les tests de surveillance pour la COVID-19 ont commencé dans les FSLD le 25 avril 2020.La province a dû limiter les tests aux groupes prioritaires au début de la pandémie. Puisque parmi toutes les personnes qui ont été infectées par la COVID-19, un faible pourcentage seulement a été testé, le nombre de cas confirmés dans la collectivité représente une sous-estimation du nombre réel d’infections. Les renseignements liés au taux global d’infection au Canada ne seront disponibles que lorsque des études de grande envergure auront été menées sur la présence d’anticorps dans le sérum sanguin des personnes touchées par la COVID-19. Si l’on se base sur les informations fournies, le nombre actuel d’infections pourrait être de 5 à 30 fois plus élevé que le nombre de cas signalés (1).Référence : Richterich P. Severe underestimation of COVID-19 case numbers: Effect of epidemic growth rate and test restrictions. medrxiv. Avril 2020 : 2020.04.13. doi.org/10.1101/2020.04.13.20064220Fréquence des mises à jour: Les jeudis.Attributs: Champs de données : Date - date du test (AAAA-MM-JJ).Nombre de tests – nombre de résidents d’Ottawa ayant subi un test de dépistage pour la COVID-19Pourcentage quotidien de positivité – le pourcentage de tests positifs parmi le nombre total de tests effectués à une date donnéeNombre de tests dans les foyers de soins de longue durée (FSLD)– nombre de résidents de FSLD à Ottawa ayant subi un test de dépistage pour la COVID-19Pourcentage quotidien de positivité dans les FSLD– le pourcentage de tests positifs dans les FSLD parmi le nombre total de tests effectués à une date donnée dans les FSLDCourriel de l'auteur: Équipe d’épidémiologie de SPO | Épidémiologie et données probantes, Santé publique Ottawa

Demographic characteristics among the study participants.

Haematological parameters among the study participants.

BackgroundHemostasis and inflammation are both dysregulated in patients with moderate-to-severe coronavirus disease 2019 (COVID-19). Yet, both processes can also be disturbed in patients with other respiratory diseases, and the interactions between coagulation, inflammation, and disease severity specific to COVID-19 are still vague.MethodsHospitalized patients with acute respiratory symptoms and with severe acute respiratory syndrome coronavirus 2 (SARS-CoV2)-positive (COVpos) and SARS-CoV2-negative (COVneg) status were included. We assessed adenosine diphosphate (ADP)-, thrombin receptor activator peptide 6 (TRAP)-, and arachidonic acid (AA)-induced platelet reactivity by impedance aggregometry, as well as leukocyte subtype spectrum and platelet-leukocyte aggregates by flow cytometry and inflammatory cytokines by cytometric bead array.ResultsADP-, TRAP-, and AA-induced platelet reactivity was significantly higher in COVpos than in COVneg patients. Disease severity, assessed by sequential organ failure assessment (SOFA) score, was higher in COVpos than in COVneg patients and again higher in deceased COVpos patients than in surviving COVpos. The SOFA score correlated significantly with the mean platelet volume and TRAP-induced platelet aggregability. A larger percentage of classical and intermediate monocytes, and of CD4pos T cells (TH) aggregated with platelets in COVpos than in COVneg patients. Interleukin (IL)-1 receptor antagonist (RA) and IL-6 levels were higher in COVpos than in COVneg patients and again higher in deceased COVpos patients than in surviving COVpos. IL-1RA and IL-6 levels correlated with the SOFA score in COVpos but not in COVneg patients. In both respiratory disease groups, absolute levels of B-cell-platelet aggregates and NK-cell-platelet aggregates were correlated with ex vivo platelet aggegation upon stimulation with AA and ADP, respectively, indicating a universal, but not a COVID-19-specific mechanism.ConclusionIn moderate-to-severe COVID-19, but not in other respiratory diseases, disease severity was associated with platelet hyperreactivity and a typical inflammatory signature. In addition to a severe inflammatory response, platelet hyperreactivity associated to a worse clinical outcome in patients with COVID-19, pointing to the importance of antithrombotic therapy for reducing disease severity.

Univariable analyses of predictive variables and root-transformed COVID-19 incidence rates.

IntroductionIn China, the long-term immunogenicity and adverse effects of inactivated vaccines produced by different or the same manufacturer remain unclear. Therefore, the objective of this study was to evaluate the cellular immune responses and neutralizing antibody kinetics of homologous and heterologous administrations of an inactivated coronavirus disease 2019 (COVID-19) vaccine 240 days after the second vaccination.MethodsThis prospective, multicenter, observational, longitudinal study involved 595 participants with a negative SARS-CoV-2 polymerase chain reaction result who were serologically tested and followed for 8 months after vaccination. Neutralizing antibodies, interferon-gamma (IFN-γ), interleukin (IL)-6, CD4+ T-lymphocyte, and B-lymphocyte counts were evaluated in serum samples after stimulation with 2 μg/mL SARS-CoV-2 spike protein for 16 h at follow-up intervals of 2 months.ResultsMost participants [582/595; 146 male participants, 449 female participants; mean age 35 (26–50 years)] rapidly developed neutralizing antibodies after two doses of the vaccine administered 3-weeks apart. The positive rate of neutralizing antibodies peaked at 97.7% at 60–90 days, decreased, and stabilized at 82.9% at 181–240 days post-vaccination. Lower antibody concentrations were correlated with older age, longer duration after vaccination, non-health care workers, mixed-manufacturer vaccinations, and intervals of less than 40 days between two doses of vaccination, whereas lower IFN-γ levels and B-lymphocyte counts were associated with older age, blood type A, and non-health care workers. A higher IL-6 level was associated with older age, mixed-manufacturer vaccinations, intervals of less than 40 days between two doses of vaccination, and medical staff. Adverse reactions were mild or moderate and self-limited, with no serious events reported.DiscussionTwo doses of the Chinese inactivated vaccine induced robust and rapid antibody expression and cellular immune responses. Boosting vaccination is considered important, as antibodies and cellular immune responses were reduced in susceptible populations.

BackgroundThis meta-analysis aimed to investigate the efficacy and safety of flavonoids in treating viral acute respiratory tract infections (ARTIs).MethodsRandomized controlled trials (RCTs) were entered into meta-analyses performed separately for each indication. Efficacy analyses were based on changes in disease-specific symptom scores. Safety was analyzed based on the pooled data from all eligible trials, by comparing the incidence of adverse events between flavonoids and the control.ResultsIn this study, thirty RCTs (n = 5,166) were included. In common cold, results showed that the flavonoids group decreased total cold intensity score (CIS), the sum of sum of symptom intensity differences (SSID) of CIS, and duration of inability to work vs. the control group. In influenza, the flavonoids group improved the visual analog scores for symptoms. In COVID−19, the flavonoids group decreased the time taken for alleviation of symptoms, time taken for SARS-CoV−2 RT-PCR clearance, the RT-PCR positive subjects at day 7, time to achievement of the normal status of symptoms, patients needed oxygen, patients hospitalized and requiring mechanical ventilation, patients in ICU, days of hospitalization, and mortality vs. the control group. In acute non-streptococcal tonsillopharyngitis, the flavonoids group decreased the tonsillitis severity score (TSS) on day 7. In acute rhinosinusitis, the flavonoids group decreased the sinusitis severity score (SSS) on day 7, days off work, and duration of illness. In acute bronchitis, the flavonoids group decreased the bronchitis severity score (BSS) on day 7, days off work, and duration of illness. In bronchial pneumonia, the flavonoids group decreased the time to symptoms disappearance, the level of interleukin−6 (IL−6), interleukin−8 (IL−8), and tumor necrosis factor-α (TNF-α). In upper respiratory tract infections, the flavonoids group decreased total CIS on day 7 and increased the improvement rate of symptoms. Furthermore, the results of the incidence of adverse reactions did not differ between the flavonoids and the control group.ConclusionResults from this systematic review and meta-analysis suggested that flavonoids were efficacious and safe in treating viral ARTIs including the common cold, influenza, COVID−19, acute non-streptococcal tonsillopharyngitis, acute rhinosinusitis, acute bronchitis, bronchial pneumonia, and upper respiratory tract infections. However, uncertainty remains because there were few RCTs per type of ARTI and many of the RCTs were small and of low quality with a substantial risk of bias. Given the limitations, we suggest that the conclusions need to be confirmed on a larger scale with more detailed instructions in future studies.Systematic Review Registration:inplasy.com/inplasy-2021-8-0107/, identifier: INPLASY20218010