Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines

Symptom-Specific Hospital Contacts in 12-18-Year-Olds Vaccinated against COVID-19: A Danish Register-Based Cohort Study

In this register-based real-life cohort study, changes in symptom-specific hospital contacts among 12-18-year-olds following two doses of the BNT162b2 COVID-19 vaccine compared to unvaccinated peers were investigated. Using national register data, vaccinated and unvaccinated adolescents were sex and age-matched each week during the inclusion period from May to September 2021. Symptom-specific hospital contacts covering ICD-10 R diagnoses were assessed before first the vaccine dose and after the second vaccine dose. Taking previous rates of symptom-specific hospital contacts into account, differences between vaccinated and unvaccinated adolescents were found. For some hospital contacts, higher rates were seen among the vaccinated, and for others, higher rates were seen among the unvaccinated. Unspecific cognition symptoms may be important to monitor in vaccinated girls, and likewise for throat and chest pain in vaccinated boys within the first months post-vaccination. In perspective, symptom-specific hospital contacts after vaccination against COVID-19 must be assessed by taking the risk of infection and symptoms following COVID-19 infection into account.

Progress of the "Molecular Informatics" Section in 2022

This is the first Editorial of the "Molecular Informatics" Section (MIS) of the International Journal of Molecular Sciences (IJMS), which was created towards the end of 2018 (the first article was submitted on 27 September 2018) and has experienced significant growth from 2018 to now [...].

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

PURPOSE OF REVIEW

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.

RECENT FINDINGS

In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.

Short- and Long-Term Self-Reported Symptoms in Adolescents Aged 12-19 Years after Vaccination against SARS-CoV-2 Compared to Adolescents Not Vaccinated-A Danish Retrospective Cohort Study

This study investigated self-reported short- and long-term symptoms among adolescents receiving the BNT162b2 (Pfizer/BioNTech) vaccine against SARS-CoV-2 and those who did not. A retrospective cohort study based on Danish national survey (collected between 20 July and 15 September 2021) and register data was conducted. Differences in short-term (<14 days) and long-term (>two months) symptoms were explored using logistic regression adjusted for confounders. A total of 747 vaccinated (first dose n = 326; second dose n = 421) and 6300 unvaccinated adolescents were included in analyses of short-term symptoms and 32 vaccinated and 704 unvaccinated adolescents in long-term symptom analyses. In the first 14 days after the first and second vaccine dose the most reported symptoms included headache and muscle or joint symptoms. In both vaccinated and unvaccinated adolescents, the 15−19-year-olds reported significantly higher proportions of all symptoms compared to the 12−14-year-olds. After the second vaccine dose vaccinated 12−14-year-olds reported significantly more headache in adjusted analyses (OR 2.20 (95% CI 1.24; 3.90)). Among the 15−19-year-olds, significantly more vaccinated adolescents reported gastrointestinal symptoms (1.38 (1.06; 1.81)), headache (1.66 (1.24; 2.22)), and tiredness (1.44 (1.08; 1.93)). No differences were found in long-term symptoms. Vaccinated adolescents reported significantly more short-term symptoms including headache, tiredness, and gastrointestinal symptoms after the second vaccine dose than unvaccinated adolescents. Long-term symptom results should be interpreted with caution due to limited sample size.