Betaine is a non-essential amino acid with proven functional properties and underutilized potential. The most common dietary sources of betaine are beets, spinach, and whole grains. Whole grains—such as quinoa, wheat and oat brans, brown rice, barley, etc.—are generally considered rich sources of betaine. This valuable compound has gained popularity as an ingredient in novel and functional foods due to the demonstrated health benefits that it may provide. This review study will provide an overview of the various natural sources of betaine, including different types of food products, and explore the potential of betaine as an innovative functional ingredient. It will thoroughly discuss its metabolic pathways and physiology, disease-preventing and health-promoting properties, and further highlight the extraction procedures and detection methods in different matrices. In addition, gaps in the existing scientific literature will be emphasized.

Objective: Decision trees are efficient and reliable decision‐making algorithms, and medicine has reached its peak of interest in these methods during the current pandemic. Herein, we reported several decision tree algorithms for a rapid discrimination between coronavirus disease (COVID‐19) and respiratory syncytial virus (RSV) infection in infants.Methods: A cross‐sectional study was conducted on 77 infants: 33 infants with novel betacoronavirus (SARS‐CoV‐2) infection and 44 infants with RSV infection. In total, 23 hemogram‐based instances were used to construct the decision tree models via 10‐fold cross‐validation method.Results: The Random forest model showed the highest accuracy (81.8%), while in terms of sensitivity (72.7%), specificity (88.6%), positive predictive value (82.8%), and negative predictive value (81.3%), the optimized forest model was the most superior one.Conclusion: Random forest and optimized forest models might have significant clinical applications, helping to speed up decision‐making when SARS‐CoV‐2 and RSV are suspected, prior to molecular genome sequencing and/or antigen testing.

Background and Objectives: Acute appendicitis in pediatric patients is one of the most common surgical emergencies, but the early diagnosis still remains challenging. The aim of this study was to determine the predictive value of Red blood cell distribution width (RDW), Mean platelet volume (MPV) and Platelet distribution width (PDW) in children with acute appendicitis. Materials and Methods: This study was a retrospective assessment of laboratory findings (RDW, MPV, PDW) of patients who underwent surgical treatment for acute appendicitis from January 2019 to December 2020. Result: During this period, 223 appendectomies were performed at our Institute. In 107 (43%) cases appendicitis was uncomplicated, while in 116 (46.6%) it was complicated. WBC and RDW/MPV ratio were significant parameters for the diagnosis of acute appendicitis with cut-off values of 12.86 (susceptibility: 66.3%; specificity: 73.2%) and 1.64 (susceptibility: 59.8%; specificity: 71.9%), respectively. WBC and RDW/RBC ratio were independent variables for the diagnosis of complicated appendicitis. The cut-off values were 15.05 for WBC (sensitivity: 60.5%; specificity: 70.7%) and 2.5 for RDW/RBC ratio (sensitivity: 72%; specificity: 52.8%). Conclusions: WBC is an important predictor of appendicitis and complicated appendicitis. RDW, MPV and PDW alone have no diagnostic value in pediatric acute appendicitis or predicting the degree of appendix inflammation. However, the RDW/MPV ratio can be an important predictor of appendix inflammation, with higher values in patients with more severe appendix inflammation. RDW/RBC ratio may be an important predictor of complicated appendicitis.

Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.

In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility.

BACKGROUND: Clinical judgment of initial baseline laboratory tests plays an important role in triage and preliminary diagnosis among coronavirus disease 2019 (COVID-19) patients. OBJECTIVES: To determine the differences in laboratory parameters between COVID-19 and COVID-like patients, and between COVID-19 and healthy children. Additionally, to ascertain whether healthy children or patients with COVID-like symptoms would form a better control group. DESIGN AND SETTING: Cross-sectional study at the Institute for Child and Youth Health Care of Vojvodina, Novi Sad, Serbia.METHODS: A retrospective study was conducted on 42 pediatric patients of both sexes with COVID-19. Hematological parameters (white blood cell count, absolute lymphocyte count and platelet count) and biochemical parameters (natremia, kalemia, chloremia, aspartate aminotransferase [AST], alanine amino-transferase [ALT], lactate dehydrogenase [LDH] and C-reactive protein [CRP]) were collected. The first con-trol group was formed by 80 healthy children and the second control group was formed by 55 pediatric patients with COVID-like symptoms.RESULTS: Leukocytosis, lymphopenia, thrombocytosis, elevated systemic inflammatory index and neutro-phil-lymphocyte ratio, hyponatremia, hypochloremia and elevated levels of AST, ALT, LDH and CRP were present in COVID patients, in comparison with healthy controls, while in comparison with COVID-like con-trols only lymphopenia was determined.CONCLUSIONS: The presence of leukocytosis, lymphopenia, thrombocytosis, elevated systemic inflam-matory index and neutrophil-lymphocyte ratio, hyponatremia, hypochloremia and elevated levels of AST, ALT, LDH and CRP may help healthcare providers in early identification of COVID-19 patients. Healthy con-trols were superior to COVID-like controls since they provided better insight into the laboratory character-istics of children with novel betacoronavirus (SARS-CoV-2) infection.