Hesitancy, reactogenicity and immunogenicity of the mRNA and whole-virus inactivated Covid-19 vaccines in pediatric neuromuscular diseases.

The mRNA-based BNT162b2 and inactivated whole-virus CoronaVac are two widely used COVID-19 vaccines that confer immune protection to healthy individuals. However, hesitancy toward COVID-19 vaccination appeared to be common for patients with neuromuscular diseases (NMDs) due to the paucity of data on the safety and efficacy in this high-risk patient population. Therefore, we examined the underlying factors associated with vaccine hesitancy across time for NMDs and assessed the reactogenicity and immunogenicity of these two vaccines. Patients aged 8-18 years with no cognitive delay were invited to complete surveys in January and April 2022. Patients aged 2-21 years were enrolled for COVID-19 vaccination between June 2021 and April 2022, and they recorded adverse reactions (ARs) for 7 days after vaccination. Peripheral blood was obtained before and within 49 days after vaccination to measure serological antibody responses compared to healthy children and adolescents. Forty-one patients completed vaccine hesitancy surveys for both timepoints, while 22 joined the reactogenicity and immunogenicity arm of the study. Two or more family members vaccinated against COVID-19 was positively associated with intention of vaccination (odds ratio 11.7, 95% CI 1.81-75.1, p = .010). Pain at the injection site, fatigue, and myalgia were the commonest ARs. Most ARs were mild (75.5%, n = 71/94). All 19 patients seroconverted against the wildtype SARS-CoV-2 after two doses of either vaccine, similar to 280 healthy counterparts. There was lower neutralization against the Omicron BA.1 variant. BNT162b2 and CoronaVac were safe and immunogenic for patients with NMDs, even in those on low-dose corticosteroids.

Ultrapotent neutralizing antibodies against SARS-CoV-2 with a high degree of mutation resistance.

Many SARS-CoV-2 neutralizing antibodies (nAbs) lose potency against variants of concern. In this study, we developed 2 strategies to produce mutation-resistant antibodies. First, a yeast library expressing mutant receptor binding domains (RBDs) of the spike protein was utilized to screen for potent nAbs that are least susceptible to viral escape. Among the candidate antibodies, P5-22 displayed ultrahigh potency for virus neutralization as well as an outstanding mutation resistance profile. Additionally, P14-44 and P15-16 were recognized as mutation-resistant antibodies with broad betacoronavirus neutralization properties. P15-16 has only 1 binding hotspot, which is K378 in the RBD of SARS-CoV-2. The crystal structure of the P5-22, P14-44, and RBD ternary complex clarified the unique mechanisms that underlie the excellent mutation resistance profiles of these antibodies. Secondly, polymeric IgG enhanced antibody avidity by eliminating P5-22's only hotspot, residue F486 in the RBD, thereby potently blocking cell entry by mutant viruses. Structural and functional analyses of antibodies screened using both potency assays and the yeast RBD library revealed rare, ultrapotent, mutation-resistant nAbs against SARS-CoV-2.

Using Machine Learning of Clinical Data to Diagnose COVID-19

The recent pandemic of Coronavirus Disease 2019 (COVID-19) has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aimed to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID -19 patients and influenza patients based on clinical variables alone. We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement University of California, Office of the President/Tobacco-Related Disease Research Program Emergency COVID-19 Research Seed Funding Grant (R00RG2369) to W.M.O. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: N/A All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The datasets during and/or analysed during the current study available from the corresponding author on reasonable request.

Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.

BACKGROUND: The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. METHODS: In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. RESULTS: We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. CONCLUSIONS: We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.