This project analyzed spatial correlation between zip codes, different types of grocery stores and positive covid rates via geoenrichment of data in sports events, runners, family lunches and dinners, fast food restaurant visits, average public transportation usage in the area, and population of the area. The project also trained a random forest regressor to predict covid cases. The most important features in the model were the amount of stores and transportation. Additional information in the Project PDFNotable Modules Used: Python: pandas, geopandas, numpy, matplotlib, sklearn, fiona ArcGIS: enrich, aggregate_points

Note: This dataset is no longer being updated due to the end of the COVID-19 Public Health Emergency. Note: On 2/16/22, 17,467 cases based on at-home positive test results were excluded from the probable case counts. Per national case classification guidelines, cases based on at-home positive results are now classified as “suspect” cases. The majority of these cases were identified between November 2021 and February 2022. CDPH tracks both probable and confirmed cases of COVID-19 to better understand how the virus is impacting our communities. Probable cases are defined as individuals with a positive antigen test that detects the presence of viral antigens. Antigen testing is useful when rapid results are needed, or in settings where laboratory resources may be limited. Confirmed cases are defined as individuals with a positive molecular test, which tests for viral genetic material, such as a PCR or polymerase chain reaction test. Results from both types of tests are reported to CDPH. Due to the expanded use of antigen testing, surveillance of probable cases is increasingly important. The proportion of probable cases among the total cases in California has increased. To provide a more complete picture of trends in case volume, it is now more important to provide probable case data in addition to confirmed case data. The Centers for Disease Control and Prevention (CDC) has begun publishing probable case data for states. Testing data is updated weekly. Due to small numbers, the percentage of probable cases in the first two weeks of the month may change. Probable case data from San Diego County is not included in the statewide table at this time. For more information, please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/Probable-Cases.aspx

Note: On 2/16/22, 17,467 cases based on at-home positive test results were excluded from the probable case counts. Per national case classification guidelines, cases based on at-home positive results are now classified as “suspect” cases. The majority of these cases were identified between November 2021 and February 2022.

CDPH tracks both probable and confirmed cases of COVID-19 to better understand how the virus is impacting our communities. Probable cases are defined as individuals with a positive antigen test that detects the presence of viral antigens. Antigen testing is useful when rapid results are needed, or in settings where laboratory resources may be limited. Confirmed cases are defined as individuals with a positive molecular test, which tests for viral genetic material, such as a PCR or polymerase chain reaction test. Results from both types of tests are reported to CDPH.

Due to the expanded use of antigen testing, surveillance of probable cases is increasingly important. The proportion of probable cases among the total cases in California has increased. To provide a more complete picture of trends in case volume, it is now more important to provide probable case data in addition to confirmed case data. The Centers for Disease Control and Prevention (CDC) has begun publishing probable case data for states.

Testing data is updated weekly. Due to small numbers, the percentage of probable cases in the first two weeks of the month may change. Probable case data from San Diego County is not included in the statewide table at this time.

For more information, please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/Probable-Cases.aspx

Dataset of Drug Use Positivity Rate by Gender Among San Diego County Arrestees.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Dataset of the Highest Criminal Charge by Drug Use among San Diego County Arrestees.

Background: Although inactivated COVID-19 vaccines are proven to be safe and effective in the general population, the dynamic response and duration of antibodies after vaccination in the real world should be further assessed. Methods: We enrolled 1067 volunteers who had been vaccinated with one or two doses of CoronaVac in Zhejiang Province, China. Another 90 healthy adults without previous vaccinations were recruited and vaccinated with three doses of CoronaVac, 28 days and 6 months apart. Serum samples were collected from multiple timepoints and analyzed for specific IgM/IgG and neutralizing antibodies (NAbs) for immunogenicity evaluation. Antibody responses to the Delta and Omicron variants were measured by pseudovirus-based neutralization tests. Results: Our results revealed that binding antibody IgM peaked 14–28 days after one dose of CoronaVac, while IgG and NAbs peaked approximately 1 month after the second dose and then declined slightly over time. Antibody responses had waned by month 6 after vaccination and became undetectable in the majority of individuals at 12 months. Levels of NAbs to live SARS-CoV-2 were correlated with anti-SARS-CoV-2 IgG and NAbs to pseudovirus, but not IgM. Homologous booster around 6 months after primary vaccination activated anamnestic immunity and raised NAbs 25.5-fold. The neutralized fraction subsequently rose to 36.0% for Delta (p=0.03) and 4.3% for Omicron (p=0.004), and the response rate for Omicron rose from 7.9% (7/89) to 17.8% (16/90). Conclusions: Two doses of CoronaVac vaccine resulted in limited protection over a short duration. The inactivated vaccine booster can reverse the decrease of antibody levels to prime strain, but it does not elicit potent neutralization against Omicron; therefore, the optimization of booster procedures is vital. Methods Study design and participants The cross-sectional investigation was conducted in five counties of Zhejiang Province, mainland China (Xihu, Yuecheng, Shangyu, Kaihua, and Longyou Districts), after nationwide COVID-19 vaccinations from May to October 2021. Potential participants aged 18–59 years who had no prior vaccinations or were vaccinated with one or two doses of CoronaVac (Sinovac Life Sciences, Beijing, China) were recruited from the community. Individuals with a history of infection with SARS-CoV-2 (based on epidemic surveillance system) or the use of blood products or immunosuppressive drugs were excluded. We randomly enrolled 1067 volunteers, including those on day 0 (V-0, no vaccination), day 14 ± 2 (V1-14d), and day 28 ± 3 (V1-28d) after the first vaccine dose, and day 30 ± 3 (V2-1m), day 90 ± 7 (V2-3m), day 180 ± 14 (V2-6m), day 270 ± 14 (V2-9m), and day 365 ± 30 (V2-12m) after the second dose and collected their venous blood samples (3–5 ml) to detect serum antibody levels (Figure 1A). This was not a longitudinal survey, as different subjects were enrolled at each point in time. We employed a questionnaire survey at blood drawing visits to gather demographic information. In the prospective cohort study, we recruited 90 healthy adults aged 18–80 years from Jiaxing city, Zhejiang, in June 2021. The main exclusion criteria included previous or later SARS-CoV-2 infection; allergy to any ingredient included in the vaccine; those who had received any blood products or any research medicines or vaccines in the past month; those who had uncontrolled epilepsy or other serious neurological diseases, acute febrile disease, acute onset of chronic diseases, or uncontrolled severe chronic diseases; and those who were unable to comply with the study schedule. Subjects were administered 4 µg/0.5 mL of CoronaVac following a 3-shot vaccine schedule 28 days and 6 months apart. Following that, venous blood (3–5 ml) was collected from recipients at five time points: day 0 (Pre-V, before vaccination), day 30 ± 3 (V2-1m), day 90 ± 7 (V2-3m), and day 180 ± 14 (V2-6m) after the second dose, and day 30 ± 3 (V3-1m) after the third dose (Figure 1B). SARS-CoV-2-specific IgG and IgM assay The commercial detection kit iFlash-2019-nCoV NAb assay (Shenzhen YHLO Biotech Co. Ltd., Shenzhen, China) was employed to measure the levels of IgG and IgM against SARS-CoV-2 spike glycoprotein (S) and nucleocapsid protein (N) by chemiluminescence immunoassay. Briefly, serum samples were allowed to form a complex with SARS-CoV-2 S- and N-protein antigen-coated paramagnetic microparticles, then an acridinium-ester-labeled ACE2 conjugate was added to competitively combine with the particles, forming another reaction mixture. The analyzer converted relative light units (RLUs) into an antibody titer (AU/mL) through a two-point calibration curve. An inverse relationship existed between the amount of SARS-CoV-2 NAb in the sample and the RLUs detected by the iFlash optical system. According to the manufacturer, titers of ≥10.0 AU/mL and ≥1.0 AU/mL are considered positive (or reactive) for IgG and IgM, respectively. IgG and IgM against SARS-CoV-2 receptor binding domain (RBD) were detected using a commercial ELISA kit (Bioscience Biotech Co. Ltd., Chongqing, China). The positive cutoff values for RBD-specific IgG and IgM antibodies were defined as titers of ≥1.0 AU/mL. All tests were performed according to the manufacturer’s protocols (Chan et al. 2021; Li et al. 2021). Live virus neutralization antibody assays The levels of neutralizing antibodies to live SARS-CoV-2 were assessed by the reduction in the cytopathic effect (CPE) in Vero cells with infectious SARS-CoV-2 strain 19nCoVCDC-Tan-HB01 (HB01) in a BSL-3 laboratory(Zhang et al. 2021). Briefly, serum samples were heat-inactivated for 30 min at 56°C and successively diluted from 1:4 to the required concentration in a 2-fold series. An equal volume of challenge solution containing 100 TCID50 virus was added. After neutralization in a 37°C incubator for 2 h, a 1.5–2.5 × 105/ml cell suspension was added to the wells. The CPE (cytopathic effect) on VeroE6 cells was analyzed at 4-days post-infection. NT50 (50% neutralization titer, the reciprocal of the highest dilution protecting 50% of the cells from virus challenge) was used to show the neutralization titers. NT50 above 1:4 was defined as positive. Pseudovirus-based neutralization test Serum samples were also quantified for their content of SARS-CoV-2-neutralizing antibodies to wildtype (Wuhan), Delta (B.1.617.2), and Omicron (B.1.1.529) using the pseudovirus-based virus neutralization test (Nie et al. 2020). Briefly, serum samples and a positive or negative reference sample were each diluted 50 times with phosphate-buffered saline combined with 50 µl of pseudovirus diluent per well in a 96-well plate. The mixed sample/pseudovirus was incubated at 37°C and 5% CO2 for 1 h. A 2 × 105/ml BHK-21-ACE2 cell suspension was added to each well of the plate containing the sample/pseudovirus mixture, then the plate was incubated in a 37°C and 5% CO2 cell incubator for 48 h. Finally, the number of green-fluorescence-protein-positive cells per well was read with a porous plate imager (Tecan, Shanghai, SparkCyto). The results were determined by comparing the neutralized fraction using the following calculation: (1 – (fluorescence value of each well/average virus control value)) × 100%(Karaba et al. 2022). At least 4 wells were left blank for calibration to 0% inhibition. The conversion of neutralized fraction to serum titer was shown in Table S2. Statistical analysis Sex, age, BMI, and other clinical characteristics were collected for each vaccination recipient. We used the medians and interquartile ranges (IQR) for age, and numbers (percentages) for categorical variables. Specific binding antibodies against SARS-CoV-2 (IgG, IgM) and neutralized fraction of SARS-CoV-2-neutralizing antibodies are presented as mean ± standard error (SEM). Neutralizing antibodies are presented as geometric mean titers (GMT), and their 95% confidence interval (CI) was calculated with Student’s t distribution on log-transformed data and then back-transformed. Comparisons of titer-level differences between the two groups were performed using the paired Student's t-test. One-way analysis of variance (one-way ANOVA) was used to analyze the differences between the mean values at different timepoints. Correlations between NAb titers, neutralized fraction, and IgG/IgM levels were evaluated by Pearson’s correlation coefficient. Statistical tests were two-sided, and we considered p-values of less than 0.05 as statistically significant. All statistical analyses were conducted in SPSS 18.0 (IBM Corporation, Armonk, NY, USA) and GraphPad Prism 9 (San Diego CA, USA).

Detailed Methods. This is a retrospective study using prospectively collected plasma samples and clinical data. For oxylipin analysis, heparinized plasma was collected from six patients with laboratory-confirmed SARS-CoV-2 infection and admitted to the University of California Davis Medical Center in Sacramento, CA and 44 samples from healthy controls carefully chosen from a recently completed clinical study. For comparison of cytokines, plasma from healthy volunteers was collected from the California Central Valley Delta Blood Bank (Stockton, CA) prior to the COVID-19 pandemic. The methods used for blood collection, plasma processing, use of anti-coagulants/antioxidant/preservatives, and flash-freeze protocol were well-matched between case and control groups. The UC Davis and UC San Diego Institutional Review Boards approved the use of anonymized biospecimens for this study. Lipid mediator Profiling Plasma (200 μL) samples were aliquoted to a cocktail solution including 600 μL of methanol with 10 μL of 500 nM of surrogate solution including 9 isotope-labeled oxylipins (d4 PGF1a, d4 PGE2, d4 TXB2, d4 LTB4, d6 20 HETE, d11 14,15 DiHETrE, d8 9 HODE, d8 5 HETE, d11 11,12 EpETrE). Before the extraction, the samples were vortexed and centrifuged at 3000 rpm in a biosafety hood. The supernatants were then loaded on prewashed SPE cartridges and washed with two column volumes of 5% MeOH solution before elution by 0.5 mL of MeOH and 1.5 mL of ethyl acetate. The eluents were dried under vacuum using the Nutec MaxiVac vacuum concentrator (Farmingdale, NY USA) before reconstitution with 50 μL of 100 nM CUDA solution in methanol. Then, the extracted samples were analyzed using the UPLC/MS/MS system (Waters Acquity UPLC (Milford, MA, USA) hyphenated to AB Sciex 6500+ QTrap system (Redwood City, CA USA). The detailed parameters for the UPLC/MS/MS method were described previously (13, 14). Statistical analysis To test for differences between the COVID-19 and the control group cytokine levels, cytokine levels were log10 transformed to fit a normal distribution and analyzed in Graphpad Prism (version 8.4.3) using the Wilcoxon rank-sum test with COVID positive and negative status as the main effect. Lipid mediator results were analyzed using MetaboAnalyst (https://www.metaboanalyst.ca/) and scaled using autoscaling before analysis. Multiple data sets described below were integrated to prioritize the oxylipins as possible biomarkers contributing to the severity of COVID. Oxylipins were analyzed by multiple independent t-tests using patient vs. control as the variable and the two-stage step-up method of Benjamini, Krieger and Yekutieli to determine a false discovery rate (15) to generate the volcano plot. The lipid mediators were then ranked by their effect sizes (i.e., the fold-difference between mean analyte concentration in each group). The analytes with the largest effect sizes were further evaluated by random effect ANOVA models. We minimized type 1 errors by testing for between-group differences among the analytes with the largest effect sizes and to improve the likelihood of identifying analytes that showed best potential to seve as biomarkers of disease severity. Each analyte with an effect size above 8 (i.e., analyte concentrations >8-fold different) was used as a response variable. Random effect ANOVAs were run with ‘patient’ as a random effect to account for the multiple measurements from the same patient, and the fixed effect was ‘group’ (i.e., COVID positive or control). The log10-transformation of the analyte concentrations was applied. The analysis was done in JMP Pro Version15. Polyunsaturated fatty acids are metabolized into regulatory lipids important for initiating inflammatory responses in the event of disease or injury and for signaling the resolution of inflammation and return to homeostasis. The epoxides of linoleic acid (leukotoxins) regulate skin barrier function, perivascular and alveolar permeability and have been associated with poor outcomes in burn patients and in sepsis. It was later reported that blocking metabolism of leukotoxins into the vicinal diols ameliorated the deleterious effects of leukotoxins, suggesting that the leukotoxin diols are contributing to the toxicity. During quantitative profiling of fatty acid chemical mediators (eicosanoids) in COVID-19 patients, we found increases in the regioisomeric leukotoxin diols in plasma samples of hospitalized patients suffering from severe pulmonary involvement. In rodents these leukotoxin diols cause dramatic vascular permeability and are associated with acute adult respiratory like symptoms. Thus, pathways involved in the biosynthesis and degradation of these regulatory lipids should be investigated in larger biomarker studies to determine their significance in COVID-19 disease. In addition, incorporating diols in plasma multi-omics of patients could illuminate the COVID-19 pathological signature along with...

According to Cognitive Market Research, the global Emerging Cancer Vaccines market size is USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of 12.50% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.7% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD XX million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.5% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.9% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.2% from 2024 to 2031.
The Genetic vaccines held the highest Emerging Cancer Vaccines market revenue share in 2024

Market Dynamics of Emerging Cancer Vaccines Market

Key Drivers for Emerging Cancer Vaccines Market

Increasing Prevalence of Cancer to Propel the Market Revenue Growth

One of the major factor that propel the emerging cancer market is rising prevalence of cancer across the globe as the traditional therapy such as chemotherapy and radiation therapy often have a significant side-effets. This side effects turn medical personnel for the R&D of cancer vaccines, which anticipated to drive the penetration of market revenue growth. For instance, according to the estimates by American Cancer Society, around 20 million cancer cases were newly diagnosed and 9.7 million people died from the cancer globally in 2022. This number of cancer cases is projected to grow 35 million by 2050. Thus, the aforementioned stats support the market growth.

Rising Clinical Trials to Boost Market Growth

The growing clinical trials are expected to propel the market growth during the forecast period. For instance, in December 2023, nixa Biosciences, Inc., a biotechnology business that specializes in cancer therapy and prevention, recently released updated and fresh positive data from their breast cancer vaccine's Phase 1 clinical study. The trial is being carried out with financial support from a grant from the U.S. Department of Defense in association with Cleveland Clinic.G. Thomas Budd, M.D., a staff physician at Cleveland Clinic Cancer Institute and the study's principal investigator, presented the data at the 2023 San Antonio Breast Cancer Symposium in a poster titled "Phase I Trial of alpha-lactalbumin vaccine in high-risk operable triple negative breast cancer (TNBC) and patients at high genetic risk for TNBC.

Source: https://www.prnewswire.com/news-releases/anixa-biosciences-and-cleveland-clinic-present-positive-new-data-from-phase-1-study-of-breast-cancer-vaccine-302007568.html.

Restraint Factor for the Emerging Cancer Vaccines Market

Complexity of Cancer Immunology to Limit the Market Growth

The complexity of cancer immunology is expected to hamper the market growth during the forecast period. As cancer disease is complex and heterogeneous in nature, and immune systems response to these disease is also very complex in nature. The R&D of the effective cancer vaccines demanding a good knowledge of tumor biology, immune evasion mechanisms, and the interplay between cancer cells and the immune system. The complex nature cancer immunology is the major challenge in identifying suitable vaccine targets and optimizing vaccine design, which may impede the development of theraupeutic treatment of cancer such as vaccines.

Impact of Covid-19 on the Emerging Cancer Vaccines Market

The COVID-19 pandemic had a mix impact on the emerging cancer vaccines market. The mRNA based vaccines manufactured by the global company such as Pfizer-BioNTech and Moderna, has brought attention to the potential of mRNA technology in the production of vaccines. As the same technology is used to modified target cancer-specific antigens, which has increased demand and funding for mRNA-based cancer vaccines. Furthermore, the COVID-19 has raised awareness of the significance of immunotherapy and vaccinations in fight aginst various disease including cancer, in...

Multisystem inflammatory syndrome in children (MIS-C) is a severe, post-infectious sequela of SARS-CoV-2 infection, yet the pathophysiological mechanism connecting the infection to the broad inflammatory syndrome remains unknown. Here we leveraged a large set of MIS-C patient samples (n=199) to identify a distinct set of host proteins that are differentially targeted by patient autoantibodies relative to matched controls. We identified an autoreactive epitope within SNX8, a protein expressed primarily in immune cells that regulates an antiviral pathway associated with MIS-C pathogenesis. In parallel, we also probed the SARS-CoV-2 proteome-wide MIS-C patient antibody response and found it to be differentially reactive to a distinct domain of the SARS-CoV-2 nucleocapsid (N) protein relative to controls. This viral N region and the mapped SNX8 epitope bear remarkable biochemical similarity. Furthermore, we find that many children with anti-SNX8 autoantibodies also have T cells cross-reactive to both SNX8 and this distinct region of the SARS-CoV-2 N protein. Together, these findings suggest that MIS-C patients develop a characteristic immune response against the SARS-CoV-2 N protein that is associated with cross-reactivity to the self-protein SNX8, demonstrating a mechanistic link from the infection to the inflammatory syndrome with implications for better understanding a range of post-infectious autoinflammatory diseases. Methods Patients Patients were recruited through the prospectively enrolling multicenter Overcoming COVID-19 and Taking on COVID-19 Together study in the United States. The study was approved by the central Boston Children’s Hospital Institutional Review Board (IRB) and reviewed by IRBs of participating sites with CDC IRB reliance. A total of 292 patients were enrolled into 1 of the following independent cohorts between June 1, 2020 and September 9, 2021: 223 patients hospitalized with MIS-C (199 in the primary discovery cohort, 24 in a separate subsequent validation cohort), 29 patients hospitalized for COVID-19 in either an intensive care or step-down unit (referred to as severe acute COVID-19 in this study), and 45 outpatients (referred to as “at-risk controls” in this study) post-SARS-CoV-2 infections associated with mild or no symptoms. The demographic and clinical data are summarized in Table I, Extended Data Table 1, and Extended Data Table 2. The 2020 US Centers for Disease Control and Prevention case definition was used to define MIS-C(1). All patients with MIS-C had positive SARS-CoV-2 serology results and/or positive SARS-CoV-2 test results by reverse transcriptase quantitative PCR. All patients with severe COVID-19 or outpatient SARS-CoV-2 infections had a positive antigen test or nucleic acid amplification test for SARS-CoV-2. For outpatients, samples were collected from 36 to 190 days after the positive test (median, 70 days after the positive test; interquartile range, 56-81 days). For use as controls in the SARS-CoV-2 specific PhIP-Seq, plasma from 48 healthy, pre-COVID-19 controls was obtained as deidentified samples from the New York Blood Center. These samples were part of retention tubes collected at the time of blood donations from volunteer donors who provided informed consent for their samples to be used for research. Human proteome PhIP-Seq Human Proteome PhIP-Seq was performed following our previously published vacuum-based PhIP-Seq protocol (2) (https://www.protocols.io/view/scaled-high-throughput-vacuum-phip-protocol-ewov1459kvr2/v1). Our human peptidome library consists of a custom-designed phage library of 731,724 unique T7 bacteriophage each presenting a different 49 amino-acid peptide on its surface. Collectively these peptides tile the entire human proteome including all known isoforms (as of 2016) with 25 amino-acid overlaps. 1 milliliter of phage library was incubated with 1 microliter of human serum overnight at 4C and immunoprecipitated with 25 microliters of 1:1 mixed protein A and protein G magnetic beads (Thermo Fisher, Waltham, MA, #10008D and #10009D). These beads were then washed, and the remaining phage-antibody complexes were eluted in 1 milliliter of E.Coli (BLT5403, EMD Millipore, Burlington, MA) at 0.5-0.7 OD and amplified by growing in 37C incubator. This new phage library was then re-incubated with the same individual’s serum and the previously described protocol was repeated. DNA was then extracted from the final phage library, barcoded, and PCR-amplified, and Illumina adaptors were added. Next-generation sequencing was then performed using an Illumina sequencer (Illumina, San Diego, CA) to a read depth of approximately 1 million per sample. Human proteome PhIP-Seq analysis All human peptidome analysis (except when specifically stated otherwise) was performed at the gene level, in which all reads for all peptides mapping to the same gene were summed, and 0.5 reads were added to each gene to allow the inclusion of genes with zero reads in mathematical analyses. Within each individual sample, reads were normalized by converting to the percentage of total reads. To normalize each sample against background non-specific binding, a fold-change (FC) over mock-IP was calculated by dividing the sample read percentage for each gene by the mean read percentage of the same gene for the AG bead-only controls. This FC signal was then used for side-by-side comparison between samples and cohorts. FC values were also used to calculate z-scores for each MIS-C patient relative to controls and for each control sample by using all remaining controls. These z-scores were used for the logistic regression feature weighting. In instances of peptide-level analysis, raw reads were normalized by calculating the number of reads per 100,000 reads. SARS-CoV-2 proteome PhIP-Seq SARS-CoV-2 Proteome PhIP-Seq was performed as previously described(3). Briefly, 38 amino acid fragments tiling all open reading frames from SARS-CoV-2, SARS-CoV-1, and 7 other CoVs were expressed on T7 bacteriophage with 19 amino acid overlaps. 1 milliliter of phage library was incubated with 1 microliter of human serum overnight at 4C and immunoprecipitated with 25 microliters of 1:1 mixed protein A and protein G magnetic beads (Thermo Fisher, Waltham, MA, #10008D and #10009D). Beads were washed 5 times on a magnetic plate using a P1000 multichannel pipette. The remaining phage-antibody complexes were eluted in 1 milliliter of E.Coli (BLT5403, EMD Millipore, Burlington, MA) at 0.5-0.7 OD and amplified by growing in a 37°C incubator. This new phage library was then re-incubated with the same individual’s serum and the previously described protocol was repeated for a total of 3 rounds of immunoprecipitations. DNA was then extracted from the final phage library, barcoded, and PCR-amplified, and Illumina adaptors were added. Next-generation sequencing was then performed using an Illumina sequencer (Illumina, San Diego, CA) to a read depth of approximately 1 million per sample. Coronavirus proteome PhIP-Seq analysis To account for differing read depths between samples, the total number of reads for each peptide fragment was converted to the number of reads per 100k (RPK). To calculate normalized enrichment relative to pre-COVID controls (FC > Pre-COVID), the RPK for each peptide fragment within each sample was divided by the mean RPK of each peptide fragment among all pre-COVID controls. These FC > Pre-COVID values were used for all subsequent analyses as described in the text and figures.

HAN Archive - 00432 (2021). https://emergency.cdc.gov/han/2020/han00432.asp. S. E. Vazquez, S. A. Mann, A. Bodansky, A. F. Kung, Z. Quandt, E. M. N. Ferré, N. Landegren, D. Eriksson, P. Bastard, S.-Y. Zhang, J. Liu, A. Mitchell, I. Proekt, D. Yu, C. Mandel-Brehm, C.-Y. Wang, B. Miao, G. Sowa, K. Zorn, A. Y. Chan, V. M. Tagi, C. Shimizu, A. Tremoulet, K. Lynch, M. R. Wilson, O. Kämpe, K. Dobbs, O. M. Delmonte, R. Bacchetta, L. D. Notarangelo, J. C. Burns, J.-L. Casanova, M. S. Lionakis, T. R. Torgerson, M. S. Anderson, J. L. DeRisi, Autoantibody discovery across monogenic, acquired, and COVID-19-associated autoimmunity with scalable PhIP-seq. Elife 11 (2022). C. R. Zamecnik, J. V. Rajan, K. A. Yamauchi, S. A. Mann, R. P. Loudermilk, G. M. Sowa, K. C. Zorn, B. D. Alvarenga, C. Gaebler, M. Caskey, M. Stone, P. J. Norris, W. Gu, C. Y. Chiu, D. Ng, J. R. Byrnes, X. X. Zhou, J. A. Wells, D. F. Robbiani, M. C. Nussenzweig, J. L. DeRisi, M. R. Wilson, ReScan, a Multiplex Diagnostic Pipeline, Pans Human Sera for SARS-CoV-2 Antigens. Cell Rep Med 1, 100123 (2020).