Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach

Circadian Variation in the Response to Vaccination: A Systematic Review and Evidence Appraisal

Molecular timing mechanisms known as circadian clocks drive endogenous 24-h rhythmicity in most physiological functions, including innate and adaptive immunity. Consequently, the response to immune challenge such as vaccination might depend on the time of day of exposure. This study assessed whether the time of day of vaccination (TODV) is associated with the subsequent immune and clinical response by conducting a systematic review of previous studies. The Cochrane Library, PubMed, Google, Medline, and Embase were searched for studies that reported TODV and immune and clinical outcomes, yielding 3114 studies, 23 of which met the inclusion criteria. The global severe acute respiratory syndrome coronavirus 2 vaccination program facilitated investigation of TODV and almost half of the studies included reported data collected during the COVID-19 pandemic. There was considerable heterogeneity in the demography of participants and type of vaccine, and most studies were biased by failure to account for immune status prior to vaccination, self-selection of vaccination time, or confounding factors such as sleep, chronotype, and shiftwork. The optimum TODV was concluded to be afternoon (5 studies), morning (5 studies), morning and afternoon (1 study), midday (1 study), and morning or late afternoon (1 study), with the remaining 10 studies reporting no effect. Further research is required to understand the relationship between TODV and subsequent immune outcome and whether any clinical benefit outweighs the potential effect of this intervention on vaccine uptake.

Daily variation in blood glucose levels during continuous enteral nutrition in patients on the intensive care unit: a retrospective observational study

BACKGROUND

The circadian timing system coordinates daily cycles in physiological functions, including glucose metabolism and insulin sensitivity. Here, the aim was to characterise the 24-h variation in glucose levels in critically ill patients during continuous enteral nutrition after controlling for potential sources of bias.

METHODS

Time-stamped clinical data from adult patients who stayed in the Intensive Care Unit (ICU) for at least 4 days and received enteral nutrition were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Linear mixed-effects and XGBoost modelling were used to determine the effect of time of day on blood glucose values.

FINDINGS

In total, 207,647 glucose measurements collected during enteral nutrition were available from 6,929 ICU patients (3,948 males and 2,981 females). Using linear mixed-effects modelling, time of day had a significant effect on blood glucose levels (p < 0.001), with a peak of 9.6 [9.5-9.6; estimated marginal means, 95% CI] mmol/L at 10:00 in the morning and a trough of 8.6 [8.5-8.6] mmol/L at 02:00 at night. A similar impact of time of day on glucose levels was found with the XGBoost regression model.

INTERPRETATION

These results revealed marked 24-h variation in glucose levels in ICU patients even during continuous enteral nutrition. This 24-h pattern persists after adjustment for potential sources of bias, suggesting it may be the result of endogenous biological rhythmicity.

FUNDING

This work was supported by a VENI grant from the Netherlands Organisation for Health Research and Development (ZonMw), an institutional project grant, and by the Dutch Research Council (NWO).

Exploring post-COVID-19 health effects and features with advanced machine learning techniques

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson's coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact.

Hot topics in allergen immunotherapy, 2023: Current status and future perspective

The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality-of-life improvements and cost-effectiveness in the long term. Key mechanisms of allergen tolerance induced by AIT include changes in memory type allergen-specific T- and B-cell responses towards a regulatory phenotype with decreased Type 2 responses, suppression of allergen-specific IgE and increased IgG1 and IgG4, decreased mast cell and eosinophil numbers in allergic tissues and increased activation thresholds. The potential of novel patient enrolment strategies for AIT is taking into account recent advances in biomarkers discoveries, molecular allergy diagnostics and mobile health applications contributing to a personalized approach enhancement that can increase AIT efficacy and compliance. Artificial intelligence can help manage and interpret complex and heterogeneous data, including big data from omics and non-omics research, potentially predict disease subtypes, identify biomarkers and monitor patient responses to AIT. Novel AIT preparations, such as synthetic compounds, innovative carrier systems and adjuvants, are also of great promise. Advances in clinical trial models, including adaptive, complex and hybrid designs as well as real-world evidence, allow more flexibility and cost reduction. The analyses of AIT cost-effectiveness show a clear long-term advantage compared to pharmacotherapy. Important research questions, such as defining clinical endpoints, biomarkers of patient selection and efficacy, mechanisms and the modulation of the placebo effect and alternatives to conventional field trials, including allergen exposure chamber studies are still to be elucidated. This review demonstrates that AIT is still in its growth phase and shows immense development prospects.

Changes in Local and Systemic Adverse Effects following Primary and Booster Immunisation against COVID-19 in an Observational Cohort of Dutch Healthcare Workers Vaccinated with BNT162b2 (Comirnaty®)

In healthcare workers (HCWs) and in the general population, fear of adverse effects is among the main reasons behind COVID-19 vaccine hesitancy. We present data on self-reported adverse effects from a large cohort of HCWs who underwent primary (N = 470) and booster (N = 990) mRNA vaccination against SARS-CoV-2. We described general patterns in, and predictors of self-reported adverse effect profiles. Adverse effects following immunisation (AEFI) were reported more often after the second dose of primary immunisation than after the first dose, but there was no further increase in adverse effects following the booster round. Self-reported severity of systemic adverse effects was less following booster immunisation. Prior infection with SARS-CoV-2 was found to be a significant predictor of AEFI following primary immunisation, but was no longer a predictor after booster vaccination. Compared to other studies reporting specifically on adverse effects of SARS-CoV-2 vaccination in healthcare workers, we have a relatively large cohort size, and are the first to compare adverse effects between different rounds of vaccination. Compared to studies in the general population, we have a considerably homogenous population. Insights in AEFI following primary and booster vaccinations may help in addressing vaccine hesitancy, both in HCWs and in the general population.

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.

Predicting the negative conversion time of nonsevere COVID-19 patients using machine learning methods

Based on the patient's clinical characteristics and laboratory indicators, different machine-learning methods were used to develop models for predicting the negative conversion time of nonsevere coronavirus disease 2019 (COVID-19) patients. A retrospective analysis was performed on 376 nonsevere COVID-19 patients admitted to Wuxi Fifth People's Hospital from May 2, 2022, to May 14, 2022. The patients were divided into training set (n = 309) and test set (n = 67). The clinical features and laboratory parameters of the patients were collected. In the training set, the least absolute shrinkage and selection operator (LASSO) was used to select predictive features and train six machine learning models: multiple linear regression (MLR), K-Nearest Neighbors Regression (KNNR), random forest regression (RFR), support vector machine regression (SVR), XGBoost regression (XGBR), and multilayer perceptron regression (MLPR). Seven best predictive features selected by LASSO included: age, gender, vaccination status, IgG, lymphocyte ratio, monocyte ratio, and lymphocyte count. The predictive performance of the models in the test set was MLPR > SVR > MLR > KNNR > XGBR > RFR, and MLPR had the strongest generalization performance, which is significantly better than SVR and MLR. In the MLPR model, vaccination status, IgG, lymphocyte count, and lymphocyte ratio were protective factors for negative conversion time; male gender, age, and monocyte ratio were risk factors. The top three features with the highest weights were vaccination status, gender, and IgG. Machine learning methods (especially MLPR) can effectively predict the negative conversion time of non-severe COVID-19 patients. It can help to rationally allocate limited medical resources and prevent disease transmission, especially during the Omicron pandemic.