: Evidence suggests that reduced intake of fruit and vegetables may play a critical role in the development of asthma and allergies. The present review aimed to summarize the evidence for the association between fruit and vegetable intake, risk of asthma/wheeze and immune responses. Databases including PubMed, Cochrane, CINAHL and EMBASE were searched up to June 2016. Studies that investigated the effects of fruit and vegetable intake on risk of asthma/wheeze and immune responses were considered eligible (n = 58). Studies used cross-sectional (n = 30), cohort (n = 13), case-control (n = 8) and experimental (n = 7) designs. Most of the studies (n = 30) reported beneficial associations of fruit and vegetable consumption with risk of asthma and/or respiratory function, while eight studies found no significant relationship. Some studies (n = 20) reported mixed results, as they found a negative association between fruit only or vegetable only, and asthma. In addition, the meta-analyses in both adults and children showed inverse associations between fruit intake and risk of prevalent wheeze and asthma severity (p < 0.05). Likewise, vegetable intake was negatively associated with risk of prevalent asthma (p < 0.05). Seven studies examined immune responses in relation to fruit and vegetable intake in asthma, with n = 6 showing a protective effect against either systemic or airway inflammation. Fruit and vegetable consumption appears to be protective against asthma.

The COVID-19 outbreak has rapidly expanded to a global pandemic; however, our knowledge is limited with regards to the protective factors against this infection. The aim of this systematic literature review and meta-analysis was to evaluate the impact of vitamin D supplementation on COVID-19 related outcomes. A systematic search of relevant papers published until January 2022 was conducted to identify randomized controlled trials (RCTs) and non-randomized studies of intervention (NRISs). The primary outcomes included the risk of COVID-19 infection (primary prevention studies on uninfected individuals), hospital admission (secondary prevention studies on mild COVID-19 cases), and ICU admission and mortality rate (tertiary prevention studies on hospitalized COVID-19 patients). We identified five studies (one RCT, four NRISs) on primary prevention, with five (two RCTs, three NRISs) on secondary prevention, and 13 (six RCTs, seven NRISs) on tertiary prevention. Pooled analysis showed no significant effect on the risk of COVID-19 infection. No meta-analysis was possible on hospitalization risk due to paucity of data. Vitamin D supplementation was significantly associated with a reduced risk of ICU admission (RR = 0.35, 95% CI: 0.20, 0.62) and mortality (RR = 0.46, 95% CI: 0.30, 0.70). Vitamin D supplementation had no significant impact on the risk of COVID-19 infection, whereas it showed protective effects against mortality and ICU admission in COVID-19 patients.

Background: The integration of machine learning (ML) in predicting asthma-related outcomes in children presents a novel approach in pediatric health care. Objective: This scoping review aims to analyze studies published since 2019, focusing on ML algorithms, their applications, and predictive performances. Methods: We searched Ovid MEDLINE ALL and Embase on Ovid, the Cochrane Library (Wiley), CINAHL (EBSCO), and Web of Science (core collection). The search covered the period from January 1, 2019, to July 18, 2023. Studies applying ML models in predicting asthma-related outcomes in children aged <18 years were included. Covidence was used for citation management, and the risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Results: From 1231 initial articles, 15 met our inclusion criteria. The sample size ranged from 74 to 87,413 patients. Most studies used multiple ML techniques, with logistic regression (n=7, 47%) and random forests (n=6, 40%) being the most common. Key outcomes included predicting asthma exacerbations, classifying asthma phenotypes, predicting asthma diagnoses, and identifying potential risk factors. For predicting exacerbations, recurrent neural networks and XGBoost showed high performance, with XGBoost achieving an area under the receiver operating characteristic curve (AUROC) of 0.76. In classifying asthma phenotypes, support vector machines were highly effective, achieving an AUROC of 0.79. For diagnosis prediction, artificial neural networks outperformed logistic regression, with an AUROC of 0.63. To identify risk factors focused on symptom severity and lung function, random forests achieved an AUROC of 0.88. Sound-based studies distinguished wheezing from nonwheezing and asthmatic from normal coughs. The risk of bias assessment revealed that most studies (n=8, 53%) exhibited low to moderate risk, ensuring a reasonable level of confidence in the findings. Common limitations across studies included data quality issues, sample size constraints, and interpretability concerns. Conclusions: This review highlights the diverse application of ML in predicting pediatric asthma outcomes, with each model offering unique strengths and challenges. Future research should address data quality, increase sample sizes, and enhance model interpretability to optimize ML utility in clinical settings for pediatric asthma management.

Children with asthma are at risk of acute exacerbations triggered mainly by viral infections. A diet high in fruit and vegetables (F&V), a rich source of carotenoids, may improve innate immune responses in children with asthma. Children with asthma (3–11 years) with a history of exacerbations and low F&V intake (≤3 serves/d) were randomly assigned to a high F&V diet or control (usual diet) for 6 months. Outcomes included respiratory-related adverse events and in-vitro cytokine production in peripheral blood mononuclear cells (PBMCs), treated with rhinovirus-1B (RV1B), house dust mite (HDM) and lipopolysaccharide (LPS). During the trial, there were fewer subjects with ≥2 asthma exacerbations in the high F&V diet group (n = 22) compared to the control group (n = 25) (63.6% vs. 88.0%, p = 0.049). Duration and severity of exacerbations were similar between groups. LPS-induced interferon (IFN)-γ and IFN-λ production showed a small but significant increase in the high F&V group after 3 months compared to baseline (p < 0.05). Additionally, RV1B-induced IFN-λ production in PBMCs was positively associated with the change in plasma lycopene at 6 months (r_s = 0.35, p = 0.015). A high F&V diet reduced asthma-related illness and modulated in vitro PBMC cytokine production in young children with asthma. Improving diet quality by increasing F&V intake could be an effective non-pharmacological strategy for preventing asthma-related illness by enhancing children’s innate immune responses.

Background: Early warning systems (EWSs) are tools that integrate clinical observations to identify patterns indicating increased risks of clinical deterioration, thus facilitating timely and appropriate interventions. EWSs can mitigate the impact of global infectious diseases by enhancing information exchange, monitoring, and early detection.Objective: We aimed to evaluate the effectiveness of EWSs in acute respiratory infections (ARIs) through a scoping review of EWSs developed, described, and implemented for detecting novel, exotic, and re-emerging ARIs.Methods: We searched Ovid MEDLINE ALL, Embase, Cochrane Library (Wiley), and CINAHL (Ebsco). The search was conducted on October 03, 2023. Studies that implemented EWSs for the detection of acute respiratory illnesses were included. Covidence was used for citation management, and a modified Critical Appraisal Skills Programme (CASP) checklist was used for quality assessment.Results: From 5838 initial articles, 29 met the inclusion criteria for this review. Twelve studies evaluated the use of EWSs within community settings, ranging from rural community reporting networks to urban online participatory surveillance platforms. Five studies focused on EWSs that used data from hospitalization and emergency department visits. These systems leveraged clinical and admission data to effectively detect and manage local outbreaks of respiratory infections. Two studies focused on the effectiveness of existing surveillance systems, assessing their adaptability and responsiveness to emerging threats and how they could be improved based on past performance. Four studies highlighted the integration of machine learning models to improve the predictive accuracy of EWSs. Three studies explored the applications of national EWSs in different health care settings and emphasized their potential in predicting clinical deterioration and facilitating early intervention. Lastly, 3 studies addressed the use of surveillance systems in aged-care facilities, highlighting the unique challenges and needs of monitoring and responding to health threats in environments housing vulnerable populations. The CASP tool revealed that most studies were relevant, reliable, and of high value (score 6: 11/29, 38%; score 5: 9/29, 31%). The common limitations included result generalizability, selection bias, and small sample size for model validation.Conclusions: This scoping review confirms the critical role of EWSs in enhancing public health responses to respiratory infections. Although the effectiveness of these systems is evident, challenges related to generalizability and varying methodologies suggest a need for continued innovation and standardization in EWS development.