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

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

This dataset contains counts of unique tests and results for COVID-19. This dataset differs from https://data.cityofchicago.org/d/t4hh-4ku9 in that each person is in that dataset only once, even if tested multiple times. In this dataset, each test is counted, even if multiple tests are performed on the same person, although a person should not appear in this dataset more than once on the same day unless he/she had both a positive and not-positive test.

The positivity rate displayed in this dataset uses the method most commonly used by other jurisdictions in the United States.

Only Chicago residents are included based on the home address as provided by the medical provider.

Molecular (PCR) and antigen tests received through electronic lab reporting are included. Individuals may be tested multiple times. Tests are counted on the day the specimen was collected. A small number of tests collected prior to 3/1/2020 are not included in the table.

Not-positive lab results include negative results, invalid results, and tests not performed due to improper collection. Chicago Department of Public Health (CDPH) does not receive all not-positive results.

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

Data Source: Illinois Department of Public Health Electronic Lab Reports

This dataset is historical only and ends at 5/7/2021. For more information, please see http://dev.cityofchicago.org/open%20data/data%20portal/2021/05/04/covid-19-testing-by-person.html. The recommended alternative dataset for similar data beyond that date is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Testing-By-Test/gkdw-2tgv. 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. This dataset contains counts of people tested for COVID-19 and their results. This dataset differs from https://data.cityofchicago.org/d/gkdw-2tgv in that each person is in this dataset only once, even if tested multiple times. In the other dataset, each test is counted, even if multiple tests are performed on the same person, although a person should not appear in that dataset more than once on the same day unless he/she had both a positive and not-positive test. Only Chicago residents are included based on the home address as provided by the medical provider. Molecular (PCR) and antigen tests are included, and only one test is counted for each individual. Tests are counted on the day the specimen was collected. A small number of tests collected prior to 3/1/2020 are not included in the table. Not-positive lab results include negative results, invalid results, and tests not performed due to improper collection. Chicago Department of Public Health (CDPH) does not receive all not-positive results. Demographic data are more complete for those who test positive; care should be taken when calculating percentage positivity among demographic groups. All data are provisional and subject to change. Information is updated as additional details are received. Data Source: Illinois National Electronic Disease Surveillance System

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

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.

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.

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

Interleukin 1 Il1 Market is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2026 to 2032.

Global Interleukin 1 Il1 Market Drivers

The market drivers for the Interleukin 1 Il1 Market can be influenced by various factors. These may include:

Growing Prevalence of Inflammatory Diseases: Across the world, the prevalence of inflammatory diseases is rising, including gout, rheumatoid arthritis, systemic juvenile idiopathic arthritis, and inflammatory bowel disease. The need for efficient treatments, such as IL-1 inhibitors, is fueled by the rising burden of chronic illnesses.

Unmet Medical Needs: Many patients with inflammatory illnesses suffer from poor symptom control, treatment-related adverse effects, and disease progression despite the availability of a variety of therapeutic alternatives. IL-1 inhibitors provide a focused method for treating unmet medical needs, controlling inflammation, and enhancing patient outcomes.

Increasing Awareness and Research: The field of biomedical research has made significant strides in understanding how IL-1 mediates inflammatory responses and the pathophysiology of disease. The demand for these medicines is driven by growing patient and healthcare provider knowledge of the potential benefits of IL-1 inhibition.

Positive Safety and Efficacy Profiles: Clinical research has shown that IL-1 inhibitors are safe and effective at lowering inflammation and enhancing symptoms of a variety of inflammatory disorders in patients. Because of their specific mode of action, as well as their good safety and effectiveness profiles, IL-1 inhibitors are considered valuable therapeutic choices.

Trend Towards Biologic medicines: The use of biologic medicines, such as IL-1 inhibitors, in the treatment of inflammatory illnesses is becoming more and more popular. When opposed to traditional medicines, biologics offer tailored treatment approaches and the possibility for improved outcomes through the modulation of particular immune pathways.

Pipeline Development: In order to give patients more options for treatment, pharmaceutical companies are still investing in the study and creation of new IL-1 inhibitors. Improved efficaciousness, safety, and convenience profiles of next-generation IL-1 inhibitors are a result of developments in biotechnology and drug discovery methodologies.

Regulatory Approvals and Market Expansion: IL-1 inhibitors have been granted regulatory approval in a number of international regions, which has facilitated market access and growth. The market for IL-1 inhibitors is anticipated to expand as more nations and indications have access to these treatments.

Patient-Centric Healthcare: Treatments that are in line with patients' preferences, objectives, and lifestyles are more likely to be adopted as a result of the increased emphasis on patient-centric healthcare and shared decision-making. With its tailored approach and capacity to modify disease, IL-1 inhibitors appeal to patients looking for safe, efficient treatments. Global Interleukin 1 Il1 Market Restraints

Several factors can act as restraints or challenges for the Interleukin 1 Il1 Market. These may include:

Restricted Patient Population: IL-1 inhibitors mainly target diseases like certain autoimmune disorders and autoinflammatory disorders that are characterised by excessive inflammation mediated by IL-1. The prospective patient population and market size for IL-1 inhibitors are restricted by the comparatively low prevalence of these illnesses.

High Treatment Costs: The development, production, and administration of biologic medicines, such as IL-1 inhibitors, are frequently associated with high costs. Patient access may be restricted by the high expense of therapy, especially in areas with limited insurance coverage or healthcare budgets.

Competition from Alternative Therapies: In order to treat inflammatory diseases, several groups of pharmaceuticals, including corticosteroids, NSAIDs, and disease-modifying antirheumatic drugs (DMARDs), are frequently prescribed. The market acceptance of IL-1 inhibitors may be hampered by competition from these well-established treatments.

Safety Concerns: Although IL-1 inhibitors have shown promise in lowering inflammation, there is a chance that they will also weaken the immune system and make infections more likely. Prescription decisions may be influenced by safety concerns, which can include the possibility of major adverse events like infections or cancers, which could prevent widespread use.

Obstacles related to regulations: IL-1 inhibitors must pass stringent clinical testing and comply with all rules, which include proving the drugs' efficacy and safety in studies. Development expenses might rise and the time to market can be extended by delays or setbacks in the regulatory approval process.

Complexity of Disease Pathogenesis: Many inflammatory disorders, including inflammatory bowel disease, systemic juvenile idiopathic arthritis, and rheumatoid arthritis, have complicated pathogenic processes involving multiple signalling pathways and cytokines. The effectiveness of IL-1 inhibitors in some patient populations may be limited by the possibility that targeting IL-1 alone may not address other facets of illness aetiology.

Access Challenges: Cost, infrastructure limitations, and regulatory restrictions are some of the reasons why access to biologic medicines, including IL-1 inhibitors, may be restricted in some areas, especially in poor nations. It will take coordinated efforts from pharmaceutical firms, lawmakers, and healthcare professionals to address these access issues.

Development of Biosimilars: The IL-1 inhibitors' original patents expiring may open the door for the creation and authorization of biosimilar variants. Prices and market share for original products may decline as a result of increased competition from biosimilars.

Figure 1. Design of two vaccine structures based on GEM particles: GEM -PA-VP1 (A) and GEM-Fc-VP1 (B) (A) The VP1-PA fusion protein was displayed on the surface of GEM particle via the mediation of PA. (B) The VP1-Fc fusion protein was displayed on the surface of GEM particles via the mediation of FcSP polypeptide.

Figure 2. The construction of recombinant plasmids and their subsequent transfection and expression validation in cells (A) shows the results of double digestion of the recombinant plasmids pcDNA3.1-PA-VP1 and pcDNA3.1-Fc-VP1 with KpnI and XhoI restriction enzymes. (B)Expression and localization of PA-VP1 and Fc-VP1 fusion proteins in CHO cells, as observed by immunofluorescence staining post-transfection. (C)Expression of fusion proteins in CHO cells confirmed using via immunofluorescence analysis.

Figure 3. The expression, purification, and display of recombinant proteins PA-VP1 and Fc-VP1 on GEM particles (A) SDS-PAGE analysis of the fusion proteins. (B)Western Blotting analysis results. (C) Transmission electron microscopy images of GEM, GEM-PA-VP1, and GEM-Fc-VP1 particle vaccines.

Figure 4. The effects of GEM-PA -VP1 and GEM-Fc -VP1 vaccines on IgG antibody levels, their subtypes, and neutralizing antibodies in mice Data are expressed as mean ± standard deviation and were analyzed using one-way ANOVA (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001). ELISA measured various indices in mouse serum on days 14, 28, and 42 post-initial, booster, and final immunizations, respectively. (A) Spleen index; (B) the distribution of IgG (C) and (D) the distribution of IgG2a and IgG1 (E) ratio of IgG2a to IgG1 (F) the levels of neutralizing antibodies in serum.

Figure 5. The impact of GEM-PA-VP1 and GEM-Fc-VP1 vaccines on cytokine secretion by mouse spleen cells Ten days post-immunization, spleens were harvested and stimulated in vitro with AP-1 specific antigen. Cytokine levels of IL-4 (A), IFN-γ (B), TNF-α (C), IL-2 (D), IL-6 (F), and IL-10 (E) were measured using ELISA. Data, derived from three parallel repeats per sample, are presented as mean ± SD. **P < 0.01; *P < 0.05.

Figure 6. Two weeks post-final immunization, mice were challenged with CVB3 (104 TCID50). Seven days later, (A) weight loss, (B) and (C) myocardial enzyme activities CK and CK-MB, (D) survival rate 28 days post-CVB3 infection, and (E) myocardial tissue sections were assessed. Each group included 8 mice for weight and cardiac function evaluation, 6 mice for histological examination, and 15 mice for survival rate assessment. *P < 0.05; **P < 0.01; ***P < 0.001.

MIC: mononuclear inflammatory cells; *p

Bovine respiratory disease (BRD) is a leading cause of mortality and compromised welfare in bovines. It is a polymicrobial syndrome resulting from a complex interplay of viral and bacterial pathogens with environmental factors. Despite the availability of vaccines, incidence and severity in young calves remains unabated. A more precise analysis of host innate immune responses during infection will identify improved diagnostic and prognostic biomarkers for early intervention and targeted treatments to prevent severe disease and loss of production efficiency. Here, we investigate hematological and innate immune responses using standardized ex-vivo whole blood assays in calves diagnosed with BRD. A total of 65 calves were recruited for this study, all between 2–8 weeks of age with 28 diagnosed with BRD by a thoracic ultrasonography score (TUS) and 19 by Wisconsin health score (WHS) and all data compared to 22 healthy controls from the same 9 study farms. Haematology revealed circulating immune cell populations were similar in both TUS positive and WHS positive calves compared to healthy controls. Gene expression analysis of 48 innate immune signalling genes in whole blood stimulated with TLR ligands was completed in a subset of calves. TLR1/2 stimulation with Pam3CSK4 showed a decreased pattern of expression in IL-1 and inflammasome related genes in addition to chemokine genes in calves with BRD. In response to TLR ligands LPS, Pam3CSK4 and R848, protein analysis of supernatant collected from all calves with BRD revealed significantly increased IL-6, but not IL-1β or IL-8, compared to healthy controls. This hyper-induction of IL-6 was observed most significantly in response to TLR1/2 stimulation in TUS positive calves. ROC analysis identified this induced IL-6 response to TLR1/2 stimulation as a potential diagnostic for BRD with a 74% true positive and 5% false positive detection rate for an IL-6 concentration >1780pg/mL. Overall, these results show altered immune responses specifically upon TLR1/2 activation is associated with BRD pathology which may contribute to disease progression. We have also identified induced IL-6 as a potentially informative biomarker for improved early intervention strategies for BRD.

Groups 1–3 placebos combined* Group 1; HIVMAG prime, Ad35 GRIN/Env boost, Group 2; HIVMAG + 100ug IL-12 prime, Ad35 GRIN/Env boost, Group 3; HIVMAG + 1000ug IL-12 prime, Ad35 GRIN/Env boost***Each volunteer had results for 8 viruses and the median and maximum log inhibition is based on all 8 results and all volunteers per group.Distribution of Log Inhibition Responses and Percent Positive Responses in each Group to any Virus.

Population and COVID-19 mortality, Cook County, March 15-May 31, 2021.

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

Comparison of serum positive rate of CMV-IgG and CMV-IgM between PSS patients and controls.

Number of Volunteers positive over total tested (%)*Among positive vaccinee respondersAd35 Neutralization response rates and titers (EC90).

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

Viral reactivation was previously reported after severe acute respiratory syndrome coronavirus‐2 (SARS-CoV-2) infection but was seldom documented after SARS-CoV-2 vaccination, except varicella-zoster virus and cytomegalovirus. Here, we present a case of reactive Epstein–Barr virus (EBV)-associated hemophagocytic lymphohistiocytosis (HLH) and thrombosis with thrombocytopenia syndrome after receiving SARS-CoV-2 mRNA vaccination. Antiplatelet factor 4 antibody was detected, and the bone marrow study showed hemophagocytosis and was positive in the immunohistochemistry staining for EBV-encoded small nuclear RNAs and negative staining for CD3 and CD56 markers of small lymphocytes. The high percentage of CD38 high/HLA-DR+ cells among CD8+ T cells further confirmed HLH. After intravenous administration of immunoglobulin, the clinical symptoms, D-dimer level, fibrinogen, platelet count, EBV-DNA titer, and anti-PF4 level were all improved. Further investigation into the pathogenesis of vaccine-associated EBV reactivation, such as TNF-α, interleukin-1β (IL-1β), and interleukin-6 (IL-6), is warranted.