首页|Shengjing Hospital of China Medical University Reports Findings in Artificial In telligence (A prospective cohort-based artificial intelligence evaluation system for the protective efficacy and immune response of SARS-CoV-2 inactivated vacci nes)
Shengjing Hospital of China Medical University Reports Findings in Artificial In telligence (A prospective cohort-based artificial intelligence evaluation system for the protective efficacy and immune response of SARS-CoV-2 inactivated vacci nes)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Liaon ing, People's Republic of China, by NewsRx correspondents, research stated, "Nov el coronaviruses constitute a significant health threat, prompting the adoption of vaccination as the primary preventive measure. However, current evaluations o f immune response and vaccine efficacy are deemed inadequate." Our news editors obtained a quote from the research from the Shengjing Hospital of China Medical University, "The study sought to explore the evolving dynamics of immune response at various vaccination time points and during breakthrough in fections. It aimed to elucidate the synergistic effects of epidemiological facto rs, humoral immunity, and cellular immunity. Additionally, regression curves wer e used to determine the correlation between the protective efficacy of the vacci ne and the stimulated immune response. Employing LASSO for high-dimensional data analysis, the study utilised four machine learning algorithms-logistical regres sion, random forest, LGBM classifier, and AdaBoost classifier-to comprehensively assess the immune response following booster vaccination. Neutralising antibody levels exhibited a rapid surge post-booster, escalating to 102.38 AU/mL at one week and peaking at 298.02 AU/mL at two weeks. Influential factors such as sex, age, disease history, and smoking status significantly impacted post-booster ant ibody levels. The study further constructed regression curves for neutralising a ntibodies, non-switched memory B cells, CD4T cells, and CD8T cells using LASSO c ombined with the random forest algorithm. The establishment of an artificial int elligence evaluation system emerges as pivotal for predicting breakthrough infec tion prognosis after the COVID-19 booster vaccination. This research underscores the intricate interplay between various components of immunity and external fac tors, elucidating key insights to enhance vaccine effectiveness. 3D modelling di scerned distinctive interactions between humoral and cellular immunity within pr ognostic groups (Class 0-2)."
LiaoningPeople's Republic of ChinaAs iaArtificial IntelligenceBiological ProductsCOVID-19Communicable Disease ControlCoronavirusEmerging TechnologiesEnvironment and Public HealthEpi demiologyHealth and MedicineImmunizationImmunization and Public HealthIm munologyInactivated VaccinesMachine LearningPublic HealthPublic Health P racticeRNA VirusesRisk and PreventionSARS-CoV-2Severe Acute Respiratory Syndrome Coronavirus 2VaccinationVaccinesViralVirology
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