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Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning.

The article focuses on the importance of clinical informatics in neonatal care, highlighting its potential to provide critical resources for clinicians. Topics include the specialized data needed for neonatal care, the challenges in transitioning from paper to electronic health records, and the impact of informatics on real-time patient management and research.

In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly prominent. Therefore, the school has regarded students' mental health education as the top priority in teaching work. Effective expression classification can assist psychology researchers to study psychology and other disciplines and analyze children's psychological activities and mental states by classifying expressions, thereby reducing the occurrence of psychological behavior problems. Most of the current mainstream methods focus on the exploration of text explicit features and the optimization of representation models, and few works pay attention to deeper language expressions. Metaphors, as language expressions often used in daily life, are closely related to an individual's emotion, cognition, and psychological state. This paper studies children's smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students' online patterns from online behavior sequences, calculate abnormal scores based on students' consumption data in the cafeteria, and describe the dietary differences among students. At the same time, this paper uses the students' psychological state data provided by the psychological center as a label to improve the deficiencies caused by the questionnaire. This paper uses the training set to train five common classification algorithms, evaluates them through the validation set, and selects the best classifier as our algorithm and uses it to identify students with mental health problems in the test set. The experimental results show that precision reaches 0.68, recall reaches 0.56, and F 1 -measure reaches 0.67. [ABSTRACT FROM AUTHOR]

Idiopathic anaphylaxis (IA) remains a frustrating challenge for both patients and physicians. The aim of this paper is to focus on IA in pediatric ages and suggest possible diagnostic algorithms according to specific age ranges (infants, children, and adolescents). In fact, in a variable percentage of patients, despite extensive diagnostic tests, the cause of anaphylactic episodes cannot be identified. Moreover, the lack of a unanimous IA definition requires a careful and detailed diagnostic workup. Prompt recognition of signs and symptoms, especially in younger children, and an accurate clinical history often allow a choice of the most appropriate diagnostic tests and a correct differential diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, I ask how parents understand and make sense of their children's relationships with social media algorithms. Drawing upon 30 think-aloud interviews with parents raising children aged 0 to 18 in England, in this paper, I pay attention to parents' understandings of and consequent approaches to platform algorithms in relation to their children's lives. I locate this work within user-centric research on people's understandings of algorithms, and research about parents' perspectives on data and datafication in relation to sharenting. Through my data, I draw out four modes – misunderstandings, parked understandings, transactional understandings and pro-active understandings. I suggest that parents' often flawed understandings of their children's myriad interfaces with algorithms deserve scrutiny not through a lens of blame or individualised parental (ir) responsibility but within cross-cutting contexts of parenting cultures and families' diverse contextual resources and restraints. I conclude by highlighting attention to parents' approaches to algorithms in children's lives as critical to parents' data and algorithm literacies. Prior State of Knowledge: Parents in diverse contexts try to understand and support their children's digital lives, and also often share content about their children on a variety of platforms. Prior research has shed significant light on the datafication of childhood. Novel Contributions: This study investigates parents' diverse understandings of algorithms underlying social media platforms and the ways in which they approach algorithms in their children's lives. Practical Implications: Parents' knowledge about algorithms and datafication is uneven. Policymakers need to better support adult media literacies, including data and algorithm literacies. Schools' communication to families and carers could also become key vehicles to raise awareness about datafication. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) remains a global health problem and is the second leading cause of death from a single infectious agent, behind the novel coronavirus disease of 2019. Children are amongst the most vulnerable groups affected by TB, and imaging manifestations are different in children when compared to adults. TB primarily involves the lungs and mediastinal lymph nodes. Clinical history, physical examination, laboratory examinations and various medical imaging tools are combined to establish the diagnosis. Even though chest radiography is the accepted initial radiological imaging modality for the evaluation of children with TB, this paper, the first of two parts, aims to discuss the advantages and limitations of the various medical imaging modalities and to provide recommendations on which is most appropriate for the initial diagnosis and assessment of possible complications of pulmonary TB in children. Practical, evidence-based imaging algorithms are also presented. [ABSTRACT FROM AUTHOR]

The vulnerable children refer to the special group of children with deviation in the process of children's psychological development and personality formation due to growth dilemmas. This may incur a series of serious social and family problems. The vulnerable children mainly cover the children suffering from children's psychological problems, such as childhood autism, autism, social anxiety and hypersensitivity, fear, depression, and PTSD arising from other factors. At present, the research results at home and abroad mainly focus on the psychological dynamic correlation investigation and solution discussion of a certain kind of difficult factor in the children's psychological development based on statistical data by the experimental methods, such as scale and model, and there is a blind spot in the humanistic orientation theory construction of psychological treatment for vulnerable children, causing the social reflection on children's psychological predicament from the humanistic perspective cannot be performed in related researches and going against searching for universal and integral theoretical paradigm for solving related problems. Sophisticated technologies for the observations have emerged increasingly for enabling the psychological features of vulnerable children through developmental cognitive neuroscience experiments. This paper introduces humanistic art therapy theory, focuses on the construction of a theoretical paradigm, and verifies its effectiveness based on the experimental results on the psychological development of vulnerable children, with an efficient performance. This study mainly refers to children with difficulty in social inclusion and psychological development. The results showed that two kinds of art therapy can obviously improve the psychological disorders of vulnerable children. The goal was to enhance self-cognition, strengthen emotional interaction, and implement positive motivation. [ABSTRACT FROM AUTHOR]

The survival prediction of children undergoing hematopoietic stem-cell transplantation is essential for successful transplantation. However, the performance of current algorithms for predicting mortality in this patient group has not improved over recent decades. This paper proposes a new feature selection technique for survival prediction problems using the Mud Ring Algorithm (MRA). Experiments and tests were initially performed on 13 real datasets with varying occurrences to compare the suggested algorithm with other algorithms. After that, the constructed model classification performance was compared to other techniques using the bone marrow transplant children's dataset. Modern techniques were used to acquire their classification results, which were then compared to the suggested outcomes using a variety of well-known metrics, graphical tools, and diagnostic analysis. This investigation has demonstrated that our suggested approach is comparable and outperformed other methods in terms of results. In addition, the results showed that the constructed model enhanced prediction accuracy by up to 82.6% for test cases. [ABSTRACT FROM AUTHOR]

BACKGROUND: Active commuting to school can be a meaningful contributor to overall physical activity in children. To inform better micro-level urban design near schools that can support active commuting to school, there is a need for measures that capture these elements. This paper describes the adaptation of an observational instrument for use in assessing micro-scale environments around urban elementary schools in the United States. METHODS: The Micro-scale Audit of Pedestrian Streetscapes for Safe Routes to School (MAPS-SRTS) was developed from existing audit instruments not designed for school travel environments and modifications for the MAPS-SRTS instrument include the structure of the audit tool sections, the content, the observation route, and addition of new subscales. Subscales were analyzed for inter-rater reliability in a sample of 36 schools in Austin, TX. To assess reliability for each subscale, one-way random effects single-measure intraclass correlation coefficients (ICC) were used. RESULTS: Compared to the 30 original subscales, the adapted MAPS-SRTS included 26 (86.6%) subscales with revised scoring algorithms. Most MAPS-SRTS subscales had acceptable inter-rater reliability, with an ICC of 0.97 for the revised audit tool. CONCLUSIONS: The MAPS-SRTS audit tool is a reliable instrument for measuring the school travel environment for research and evaluation purposes, such as assessing human-scale determinants of active commuting to school behavior and documenting built environment changes from infrastructure interventions.

This study aimed to systematically review the literature to synthesise and summarise the evidence surrounding the efficacy of artificial intelligence (AI) in classifying paediatric pneumonia on chest radiographs (CXRs). Following the initial search of studies that matched the pre-set criteria, their data were extracted using a data extraction tool, and the included studies were assessed via critical appraisal tools and risk of bias. Results were accumulated, and outcome measures analysed included sensitivity, specificity, accuracy, and area under the curve (AUC). Five studies met the inclusion criteria. The highest sensitivity was by an ensemble AI algorithm (96.3%). DenseNet201 obtained the highest level of specificity and accuracy (94%, 95%). The most outstanding AUC value was achieved by the VGG16 algorithm (96.2%). Some of the AI models achieved close to 100% diagnostic accuracy. To assess the efficacy of AI in a clinical setting, these AI models should be compared to that of radiologists. The included and evaluated AI algorithms showed promising results. These algorithms can potentially ease and speed up diagnosis once the studies are replicated and their performances are assessed in clinical settings, potentially saving millions of lives. [ABSTRACT FROM AUTHOR]

Background: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. Methods: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. Results: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. Conclusions: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options. [ABSTRACT FROM AUTHOR]

Background: Elevated rates of suicidal behavior were reported during the COVID-19 pandemic. However, information is scarce on patients' profiles during this period. Studies evoke the potential adverse effects of the mandatory lockdown, but they remain relatively speculative. Methods: We monitored fluctuations in suicide attempts (SA) in six European countries. We gathered data, retrospectively for under 18-year-old SA episodes (1 January 2018 to 31 December 2021), through records of psychiatric emergency services. We collected clinical profiles individually. We extracted environmental indicators by month, as provided by Oxford COVID-19 Government Response Tracker (OxCGRT). We used the Pruned Exact Linear Time (PELT) method to identify breakpoints in SA episodes reported for each country, and logistic regressions to estimate changes in patients' characteristics after the breakpoints. Finally, we used a univariate and multivariate negative binomial model to assess the link between SA and OxCGRT indicators, accounting for the delay (lag) between the interventions and their impact on SA. Results: The study comprised 2,833 children and adolescents (mean age = 15.1 years (SD 1.6); M: F sex-ratio = 1:5.4). A significant increase in SA was found either 6 or 10 months after the beginning of the pandemic, varying by country. Patients were more likely to be girls (aOR = 1.77 [1.34; 2.34]) and used SA methods "other than self-poisoning" (aOR = 1.34 [1.05; 1.7]). In the multivariate model, an association was found between SA and the contact tracing indicator with an 11 months delay, and the number of COVID-19 deaths with a 3-months delay. Conclusions: Findings confirmed a delayed increase in SA during the COVID-19 pandemic in children and adolescents as well as changes in patients' profiles. The duration and severity of the pandemic emerged as the strongest predictor in the rise of SA. If faced with a similar pandemic in the future, the gap between the onset of pandemic and the increase in suicide attempts presents an opportunity for prevention. [ABSTRACT FROM AUTHOR]

AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model's discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy. [ABSTRACT FROM AUTHOR]

Background: Food insecurity significantly impacts children's health, affecting their development across cognitive, physical, and socio-emotional dimensions. This study explores the impact of food insecurity among children aged 6 months to 5 years, focusing on nutrient intake and its relationship with various forms of malnutrition. Methods: Utilizing machine learning algorithms, this study analyzed data from 819 children in the West Bank to investigate sociodemographic and health factors associated with food insecurity and its effects on nutritional status. The average age of the children was 33 months, with 52% boys and 48% girls. Results: The analysis revealed that 18.1% of children faced food insecurity, with household education, family income, locality, district, and age emerging as significant determinants. Children from food-insecure environments exhibited lower average weight, height, and mid-upper arm circumference compared to their food-secure counterparts, indicating a direct correlation between food insecurity and reduced nutritional and growth metrics. Moreover, the machine learning models observed vitamin B1 as a key indicator of all forms of malnutrition, alongside vitamin K1, vitamin A, and zinc. Specific nutrients like choline in the "underweight" category and carbohydrates in the "wasting" category were identified as unique nutritional priorities. Conclusion: This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been applied to medical diagnosis and decision-making but it has not been used for classification of Class III malocclusions in children. Objective: This study aims to propose an innovative machine learning (ML)-based diagnostic model for automatically classifies dental, skeletal and functional Class III malocclusions. Methods: The collected data related to 46 cephalometric feature measurements from 4–14-year-old children (n = 666). The data set was divided into a training set and a test set in a 7:3 ratio. Initially, we employed the Recursive Feature Elimination (RFE) algorithm to filter the 46 input parameters, selecting 14 significant features. Subsequently, we constructed 10 ML models and trained these models using the 14 significant features from the training set through ten-fold cross-validation, and evaluated the models' average accuracy in test set. Finally, we conducted an interpretability analysis of the optimal model using the ML model interpretability tool SHapley Additive exPlanations (SHAP). Results: The top five models ranked by their area under the curve (AUC) values were: GPR (0.879), RBF SVM (0.876), QDA (0.876), Linear SVM (0.875) and L2 logistic (0.869). The DeLong test showed no statistical difference between GPR and the other models (p > 0.05). Therefore GPR was selected as the optimal model. The SHAP feature importance plot revealed that he top five features were SN-GoMe (the ratio of the length of the anterior skull base SN to that of the mandibular base GoMe), U1-NA (maxillary incisor angulation to NA plane), Overjet (the distance between two lines perpendicular to the functional occlusal plane from U1 and L), ANB (the difference between angles SNA and SNB), and AB-NPo (the angle between the AB and N-Pog line). Conclusions: Our findings suggest that ML models based on cephalometric data could effectively assist dentists to classify dental, functional and skeletal Class III malocclusions in children. In addition, features such as SN_GoMe, U1_NA and Overjet can as important indicators for predicting the severity of Class III malocclusions. [ABSTRACT FROM AUTHOR]

Food insecurity is a public health concern that affects children worldwide, yet it represents a particular burden for low- and middle-income countries. This study aims to utilize machine learning to identify the associations between food insecurity and nutrient intake among children aged 5 to 18 years. The study's sample encompassed 1040 participants selected from a 2022 food insecurity household conducted in the West Bank, Palestine. The results indicated that food insecurity was significantly associated with dietary nutrient intake and sociodemographic factors, such as age, gender, income, and location. Indeed, 18.2% of the children were found to be food-insecure. A significant correlation was evidenced between inadequate consumption of various nutrients below the recommended dietary allowance and food insecurity. Specifically, insufficient protein, vitamin C, fiber, vitamin B12, vitamin B5, vitamin A, vitamin B1, manganese, and copper intake were found to have the highest rates of food insecurity. In addition, children residing in refugee camps experienced significantly higher rates of food insecurity. The findings emphasize the multilayered nature of food insecurity and its impact on children, emphasizing the need for personalized interventions addressing nutrient deficiencies and socioeconomic factors to improve children's health and well-being. [ABSTRACT FROM AUTHOR]

To investigate the association between birth weight to placental weight (BW/PW) ratio, and echocardiographic left ventricle (LV) morphology at birth, while accounting for other relevant perinatal factors. A prospective cohort study was conducted on neonates at NewYork-Presbyterian Brooklyn Methodist Hospital from 2014 to 2018, categorized by their BW/PW percentile. Missing data were imputed with principal component analysis. Chi-squared and one-way analysis of variance were used to compare BW/PW groups and the best regression model was selected using a genetic and backward stepwise algorithm. We analyzed 827 neonates in three BW/PW groups: small (n=16), normal (n=488), and large (n=323). Placental thickness and smallest diameter were positively correlated with several LV parameters, including inter-ventricular septal thickness during diastole (IVSd) (p=0.002, p<0.001) and systole (IVSs) (p=0.001, p<0.001), LV posterior wall thickness at end of diastole (LVPWd) (p=0.003, p<0.001) and systole (LVPWs) (p<0.001, p<0.001), LV mass (p=0.017, p<0.001), and LV mass/volume (p=0.011, p<0.001). The BW/PW ratio correlated with an increased shortening fraction (estimate=0.29, 95 % CI 0.03–0.55, p=0.027). PW correlated with IVSs (p=0.019), while the longest placental diameter was linked to a decrease in LV internal dimension during diastole (LVIDd) (estimate=−0.07, p=0.039), LV mass (estimate=−0.11, p=0.024), and LV mass/volume (estimate=−0.55, p=0.005). This study found that several placental factors, including the BW/PW ratio, can independently affect LV dimension and morphology, highlighting the importance of fetal growth and placental health in the physiological adaptation of the fetal heart. More research is needed to establish causation and inform newborn prevention strategies. [ABSTRACT FROM AUTHOR]

Childhood obesity might threaten children's current and adulthood health outcomes. Previous studies have illustrated the positive effects of physical activity on weight control; however, there is a lack of evidence on the optimal dose of physical activity. Therefore, we aimed to explore the relationship between physical activity and overweight and obesity, as well as the optimal threshold for physical activity. The median (interquartile range) and number (proportion) were used to describe the statistics. The Mann–Whitney U test and chi-square test were used for an univariable analysis. The generalized additive model with a smooth function was used to depict the preliminary relationship between physical activity and overweight and obesity. The cut-off level of physical activity was identified using AddFor algorithms, and a logistic regression model was applied to explore the multivariable relationship between physical activity and overweight and obesity after adjusting for control variables. According to the statistical analyses, 24.3% of 842 children and adolescents had overweight and obesity. The average number of days that the participants engaged in more than one hour of physical activity was three days a week. The optimal cut-off level of physical activity for the prevention of childhood overweight and obesity was 4 times a week. The participants who exercised more than four times a week (AOR(95% CI) = 0.56(0.38, 0.83), p = 0.004) were less likely to be overweight and obese. In the context of the general lack of physical activity among children and adolescents, we recommend that children and adolescents engage in 60 min of physical activity more than four times a week for a healthy weight. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop an accessible stepwise management algorithm for pediatric presentations of occipital condyle fractures (OCFs) based on a systematic review of the published literature regarding diagnostic evaluation, treatment, and outcomes. Methods: A systematic review of the literature was conducted on PubMed to locate English language studies reporting on the management of pediatric OCFs. Data extraction of clinical presentation, management strategies, imaging, and treatment outcome was performed. Results: A total of 15 studies reporting on 38 patients aged 18 years and younger presenting with OCFs were identified. Loss of consciousness (LOC), depressed level of consciousness, neck pain, decreased neck range of motion (ROM), and cranial nerve injury were the most common presenting symptoms. Diagnostic imaging included radiographs, computed tomography (CT) scans, magnetic resonance imaging (MRI), and functional radiographs to assess cervical stability. Treatment options varied and included soft collar, hard collar, and halo vest. All studies resulted in a complete healing of the OCF, with resolution of associated pain. Conclusion: The proposed treatment algorithm suggests a framework for the management of pediatric OCFs based on the available evidence (levels of evidence: 3, 4). This review of the literature indicated that a stepwise approach should be utilized in the management of isolated pediatric OCFs. [ABSTRACT FROM AUTHOR]

Children with developmental central hypotonia have reduced muscle tone secondary to non-progressive damage to the brain or brainstem. Children may have transient delays, mild or global functional impairments, and the lack of a clear understanding of this diagnosis makes evaluating appropriate interventions challenging. This overview aimed to systematically describe the best available evidence for tools to identify and evaluate children with developmental central hypotonia aged 2 months to 6 years. A systematic review of systematic reviews or syntheses was conducted with electronic searches in PubMed, Medline, CINAHL, Scopus, Cochrane Database of Systematic Reviews, Google Scholar, and PEDro and supplemented with hand-searching. Methodological quality and risk-of-bias were evaluated, and included reviews and tools were compared and contrasted. Three systematic reviews, an evidence-based clinical assessment algorithm, three measurement protocols, and two additional measurement tools were identified. For children aged 2 months to 2 years, the Hammersmith Infant Neurological Examination has the strongest measurement properties and contains a subset of items that may be useful for quantifying the severity of hypotonia. For children aged 2–6 years, a clinical algorithm and individual tools provide guidance. Further research is required to develop and validate all evaluative tools for children with developmental central hypotonia. [ABSTRACT FROM AUTHOR]

The most common acute infection and leading cause of death in children worldwide is pneumonia. Clinical and laboratory tests essentially diagnose community-acquired pneumonia (CAP). CAP can be caused by bacteria, viruses, or atypical microorganisms. Imaging is usually reserved for children who do not respond to treatment, need hospitalisation, or have hospital-acquired pneumonia. This review discusses the imaging findings for acute CAP complications and the diagnostic role of each imaging modality. Pleural effusion, empyema, necrotizing pneumonia, abscess, pneumatocele, pleural fistulas, and paediatric acute respiratory distress syndrome (PARDS) are acute CAP complications. When evaluating complicated CAP patients, chest radiography, lung ultrasonography, computed tomography (CT), and magnetic resonance imaging (MRI) can be used, with each having their own pros and cons. Imaging is usually not needed for CAP diagnosis, but it is essential for complicated cases and follow-ups. Lung ultrasound can supplement chest radiography (CR), which starts the diagnostic algorithm. Contrast-enhanced computed tomography (CECT) is used for complex cases. Advances in MRI protocols make it a viable alternative for diagnosing CAP and its complications. [ABSTRACT FROM AUTHOR]

In recent years, advances in ultrasound therapeutics have been implemented into treatment algorithms for the adult population; however, the use of therapeutic ultrasound in the pediatric population still needs to be further elucidated. In order to better characterize the utilization and practicality of sonothrombolysis in the juvenile population, the authors conducted a literature review of current pediatric research in therapeutic ultrasound. The PubMed database was used to search for all clinical and preclinical studies detailing the use and applications of sonothrombolysis, with a focus on the pediatric population. As illustrated by various review articles, case studies, and original research, sonothrombolysis demonstrates efficacy and safety in clot dissolution in vitro and in animal studies, particularly when combined with microbubbles, with potential applications in conditions such as deep venous thrombosis, peripheral vascular disease, ischemic stroke, myocardial infarction, and pulmonary embolism. Although there is limited literature on the use of therapeutic ultrasound in children, mainly due to the lower prevalence of thrombotic events, sonothrombolysis shows potential as a noninvasive thrombolytic treatment. However, more pediatric sonothrombolysis research needs to be conducted to quantify the safety and ethical considerations specific to this vulnerable population. [ABSTRACT FROM AUTHOR]

Preventing stunting is particularly important for healthy development across the life course. In Papua New Guinea (PNG), the prevalence of stunting in children under five years old has consistently not improved. Therefore, the primary objective of this study was to employ multiple machine learning algorithms to identify the most effective model and key predictors for stunting prediction in children in PNG. The study used data from the 2016–2018 Papua New Guinea Demographic Health Survey, including from 3380 children with complete height-for-age data. The least absolute shrinkage and selection operator (LASSO) and random-forest-recursive feature elimination were used for feature selection. Logistic regression, a conditional decision tree, a support vector machine with a radial basis function kernel, and an extreme gradient boosting machine (XGBoost) were employed to construct the prediction model. The performance of the final model was evaluated using accuracy, precision, recall, F1 score, and area under the curve (AUC). The results of the study showed that LASSO-XGBoost has the best performance for predicting stunting in PNG (AUC: 0.765; 95% CI: 0.714–0.819) with accuracy, precision, recall, and F1 scores of 0.728, 0.715, 0.628, and 0.669, respectively. Combined with the SHAP value method, the optimal prediction model identified living in the Highlands Region, the age of the child, being in the richest family, and having a larger or smaller birth size as the top five important characteristics for predicting stunting. Based on the model, the findings support the necessity of preventing stunting early in life. Emphasizing the nutritional status of vulnerable maternal and child populations in PNG is recommended to promote maternal and child health and overall well-being. [ABSTRACT FROM AUTHOR]

Ileocolic intussusception is one of the common acute abdomens in children and is first diagnosed urgently using ultrasound. Manual diagnosis requires extensive experience and skill, and identifying surgical indications in assessing the disease severity is more challenging. We aimed to develop a real-time lesion visualization deep-learning pipeline to solve this problem. This multicenter retrospective-prospective study used 14,085 images in 8736 consecutive patients (median age, eight months) with ileocolic intussusception who underwent ultrasound at six hospitals to train, validate, and test the deep-learning pipeline. Subsequently, the algorithm was validated in an internal image test set and an external video dataset. Furthermore, the performances of junior, intermediate, senior, and junior sonographers with AI-assistance were prospectively compared in 242 volunteers using the DeLong test. This tool recognized 1,086 images with three ileocolic intussusception signs with an average of the area under the receiver operating characteristic curve (average-AUC) of 0.972. It diagnosed 184 patients with no intussusception, nonsurgical intussusception, and surgical intussusception in 184 ultrasound videos with an average-AUC of 0.956. In the prospective pilot study using 242 volunteers, junior sonographers' performances were significantly improved with AI-assistance (average-AUC: 0.966 vs. 0.857, P < 0.001; median scanning-time: 9.46 min vs. 3.66 min, P < 0.001), which were comparable to those of senior sonographers (average-AUC: 0.966 vs. 0.973, P = 0.600). Thus, here, we report that the deep-learning pipeline that guides lesions in real-time and is interpretable during ultrasound scanning could assist sonographers in improving the accuracy and efficiency of diagnosing intussusception and identifying surgical indications. [ABSTRACT FROM AUTHOR]

Background. There is a narrow therapeutic window for sodium valproate, and the blood concentration is too low to control epilepsy, while it is easy to poison the body if the concentration is too high. It is therefore necessary to monitor drug concentration reasonably in order to control epilepsy. The purpose of this study was to establish a model for predicting concentrations of sodium valproate below 50 μg/mL in children with epilepsy. Methods. The clinical data and biochemical examination results of children with epilepsy treated in the pediatric outpatient department of our hospital from June 2019 to March 2022 were retrospectively collected and divided into a development group and a validation group according to a patient ratio of 8 to 2. Five machine learning algorithms were used to identify the key variable factors, and a risk prediction model for sodium valproate blood concentrations lower than the standard concentration was established. The area under the curve (AUC), calibration curve, GiViTi calibration band, and clinical influence curve were used to evaluate the diagnostic efficacy and clinical application value of the model. Results. A total of 525 children with epilepsy were enrolled. In the development group, the random forest algorithm performed best in predicting that the blood concentration of sodium valproate was lower than the standard concentration, showing the highest AUC (1.00). Six factors were determined as a nomogram to predict the incidence of low concentrations. In the validation group and the development group, the calibration curve, GiViTi calibration band, and clinical influence curve all performed well in the evaluation of the diagnostic efficacy and clinical application value of the model. Conclusions. This finding highlights the importance of examining biochemical indices in patients when data regarding the blood concentration of sodium valproate are lacking. [ABSTRACT FROM AUTHOR]

Aims: To describe type 1 diabetes incidence in Scotland between 2006 and 2019. Methods: Repeated annual cross‐sectional studies of type 1 diabetes incidence were conducted. Incident cases were identified from the Scottish Care Information—Diabetes Collaboration (SCI‐DC), a population‐based register of people with diagnosed diabetes derived from primary and secondary care data. Mid‐year population estimates for Scotland were used as the denominator to calculate annual incidence with stratification by age and sex. Joinpoint regression was used to investigate whether incidence changed during the study period. Age and sex‐specific type 1 diabetes incidence over the whole time period was estimated by quintile of the Scottish Index of Multiple Deprivation (SIMD), an area‐based measure, in which Q1 and Q5 denote the most and least deprived fifths of the population, respectively, with quasi‐Poisson regression used to compare incidence for Q5 compared to Q1. Results: The median (IQR) age of the study population of 14,564 individuals with incident type 1 diabetes was 24.1 (12.3–42.4) years, 56% were men, 23% were in Q1 and 16% were in Q5. Incidence of T1DM was higher in men than women overall (at around 22 and 17 per 100,000, respectively) and in under 15 year olds (approximately 40 per 100,000 in both sexes) than other age groups and was similar across the study period in all strata. There was an inverse association between socio‐economic status and type 1 diabetes incidence for 15–29, 30–49 and 50+ year olds [incidence rate ratio (IRR) for Q5 compared to Q1; IRR (95% CI) 0.52 (0.47–0.58), 0.68 (0.61–0.76) and 0.53(0.46–0.61), respectively] but not for under 15 year olds [1.02 (0.92–1.12)]. Conclusion: Incidence of type 1 diabetes varies by age, sex and socio‐economic status and has remained approximately stable from 2006 to 2019 in Scotland. [ABSTRACT FROM AUTHOR]

Chest radiography is the modality of choice for the identification of rib fractures in young children and there is value for the development of computer-aided rib fracture detection in this age group. However, the automated identification of rib fractures on chest radiographs can be challenging due to the need for high spatial resolution in deep learning frameworks. A patch-based deep learning algorithm was developed to automatically detect rib fractures on frontal chest radiographs in children under 2 years old. A total of 845 chest radiographs of children 0–2 years old (median: 4 months old) were manually segmented for rib fractures by radiologists and served as the ground-truth labels. Image analysis utilized a patch-based sliding-window technique, to meet the high-resolution requirements for fracture detection. Standard transfer learning techniques used ResNet-50 and ResNet-18 architectures. Area-under-curve for precision-recall (AUC-PR) and receiver-operating-characteristic (AUC-ROC), along with patch and whole-image classification metrics, were reported. On the test patches, the ResNet-50 model showed AUC-PR and AUC-ROC of 0.25 and 0.77, respectively, and the ResNet-18 showed an AUC-PR of 0.32 and AUC-ROC of 0.76. On the whole-radiograph level, the ResNet-50 had an AUC-ROC of 0.74 with 88% sensitivity and 43% specificity in identifying rib fractures, and the ResNet-18 had an AUC-ROC of 0.75 with 75% sensitivity and 60% specificity in identifying rib fractures. This work demonstrates the utility of patch-based analysis for detection of rib fractures in children under 2 years old. Future work with large cohorts of multi-institutional data will improve the generalizability of these findings to patients with suspicion of child abuse. [ABSTRACT FROM AUTHOR]

Background Low- and middle-income countries face significant challenges in differentiating bacterial from viral causes of febrile illnesses, leading to inappropriate use of antibiotics. This trial aimed to evaluate the impact of an intervention package comprising diagnostic tests, a diagnostic algorithm, and a training-and-communication package on antibiotic prescriptions and clinical outcomes. Methods Patients aged 6 months to 18 years with fever or history of fever within the past 7 days with no focus, or a suspected respiratory tract infection, arriving at 2 health facilities were randomized to either the intervention package or standard practice. The primary outcomes were the proportions of patients who recovered at day 7 (D7) and patients prescribed antibiotics at day 0. Results Of 1718 patients randomized, 1681 (97.8%; intervention: 844; control: 837) completed follow-up: 99.5% recovered at D7 in the intervention arm versus 100% in standard practice (P =.135). Antibiotics were prescribed to 40.6% of patients in the intervention group versus 57.5% in the control arm (risk ratio: 29.3%; 95% CI: 21.8–36.0%; risk difference [RD]: −16.8%; 95% CI: −21.7% to −12.0%; P <.001), which translates to 1 additional antibiotic prescription saved every 6 (95% CI: 5–8) consultations. This reduction was significant regardless of test results for malaria, but was greater in patients without malaria (RD: −46.0%; −54.7% to −37.4%; P <.001), those with a respiratory diagnosis (RD: −38.2%; −43.8% to −32.6%; P <.001), and in children 6–59 months old (RD: −20.4%; −26.0% to −14.9%; P <.001). Except for the period July–September, the reduction was consistent across the other quarters (P <.001). Conclusions The implementation of the package can reduce inappropriate antibiotic prescription without compromising clinical outcomes. Clinical Trials Registration clinicaltrials.gov; NCT04081051. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: To examine group differences in self‐reported migraine days among youth who completed the Childhood and Adolescent Migraine Prevention (CHAMP) trial prior to its closure and explore the relationship between self‐reported and "nosology‐derived" (i.e., International Classification of Headache Disorders, 3rd edition [ICHD‐3]) migraine days. Background: The CHAMP trial compared amitriptyline and topiramate to placebo for migraine prevention in youth and proposed to analyze change in migraine days as a secondary outcome. There is considerable variability in the field regarding what constitutes a "migraine day," how this is determined and reported in trials, and how consistent these measures are with diagnostic nosology. Methods: CHAMP trial completers (N = 175) were randomized to receive amitriptyline (n = 77), topiramate (n = 63), or placebo (n = 35). Participants maintained daily headache diaries where they reported each day with headache and if they considered that headache to be a migraine. For each headache day, participants completed a symptom record and reported about symptoms such as pain location(s) and presence of nausea/vomiting or photophobia and phonophobia. We examined group differences in self‐reported migraine days at trial completion (summed from trial weeks 20–24) compared to baseline. We also used an algorithm to determine whether participants' symptom reports met ICHD‐3 criteria for migraine without aura, and examined the association between self‐reported and "nosology‐derived" migraine days. Results: Results showed no significant differences between groups in self‐reported migraine days over the course of the trial. Self‐reported and "nosology‐derived" migraine days during the baseline and treatment phases were strongly associated (r's = 0.73 and 0.83, respectively; p's < 0.001). Conclusion: Regardless of treatment, CHAMP trial completers showed clinically important reductions in self‐reported migraine days over the course of the trial (about 3.8 days less). The strong association between self‐reported and "nosology‐derived" migraine days suggests youth with migraine can recognize a day with migraine and reliably report their headache features and symptoms. Greater rigor and transparency in the calculation and reporting of migraine days in trials is needed. [ABSTRACT FROM AUTHOR]

Pediatric surgical oncology is a technically challenging field that relies on CT and MRI as the primary imaging tools for surgical planning. However, recent advances in 3D reconstructions, including Cinematic Rendering, Volume Rendering, 3D modeling, Virtual Reality, Augmented Reality, and 3D printing, are increasingly being used to plan complex cases bringing new insights into pediatric tumors to guide therapeutic decisions and prognosis in different pediatric surgical oncology areas and locations including thoracic, brain, urology, and abdominal surgery. Despite this, challenges to their adoption remain, especially in soft tissue-based specialties such as pediatric surgical oncology. This work explores the main innovative imaging reconstruction techniques, 3D modeling technologies (CAD, VR, AR), and 3D printing applications through the analysis of three real cases of the most common and surgically challenging pediatric tumors: abdominal neuroblastoma, thoracic inlet neuroblastoma, and a bilateral Wilms tumor candidate for nephron-sparing surgery. The results demonstrate that these new imaging and modeling techniques offer a promising alternative for planning complex pediatric oncological cases. A comprehensive analysis of the advantages and limitations of each technique has been carried out to assist in choosing the optimal approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reports of acute hepatitis with unknown origin in children have been published worldwide since April 2022. Due to the unfamiliar nature of the disease and the alarming trend of increasing reports worldwide, health workers must become aware of its diagnosis and treatment. Accordingly, the committee for developing guidelines for the Department of Pediatrics, Tehran University of Medical Sciences, prepared a clinical guideline for more efficient management of these patients. Due to the limited information about this disease, it has been tried to categorize and specify the required diagnostic and treatment measures based on the latest published data. Herein we express this algorithmic approach and diagnostic and therapeutic guidelines for acute hepatitis with unknown origin in children. [ABSTRACT FROM AUTHOR]

To establish a diagnostic algorithm for predicting complicated appendicitis in children based on CT and clinical features. Methods: This retrospective study included 315 children (<18 years old) who were diagnosed with acute appendicitis and underwent appendectomy between January 2014 and December 2018. A decision tree algorithm was used to identify important features associated with the condition and to develop a diagnostic algorithm for predicting complicated appendicitis, including CT and clinical findings in the development cohort (n = 198). Complicated appendicitis was defined as gangrenous or perforated appendicitis. The diagnostic algorithm was validated using a temporal cohort (n = 117). The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) from the receiver operating characteristic curve analysis were calculated to evaluate the diagnostic performance of the algorithm. Results: All patients with periappendiceal abscesses, periappendiceal inflammatory masses, and free air on CT were diagnosed with complicated appendicitis. In addition, intraluminal air, transverse diameter of the appendix, and ascites were identified as important CT findings for predicting complicated appendicitis. C-reactive protein (CRP) level, white blood cell (WBC) count, erythrocyte sedimentation rate (ESR), and body temperature also showed important associations with complicated appendicitis. The AUC, sensitivity, and specificity of the diagnostic algorithm comprising features were 0.91 (95% CI, 0.86–0.95), 91.8% (84.5–96.4), and 90.0% (82.4–95.1) in the development cohort, and 0.7 (0.63–0.84), 85.9% (75.0–93.4), and 58.5% (44.1–71.9) in test cohort, respectively. Conclusion: We propose a diagnostic algorithm based on a decision tree model using CT and clinical findings. This algorithm can be used to differentiate between complicated and noncomplicated appendicitis and to provide an appropriate treatment plan for children with acute appendicitis. [ABSTRACT FROM AUTHOR]

Background and objective: The chest-related electronic patient reported outcome (ePRO) diary was recently developed to assess chest-related symptoms experienced by pediatric and adolescent populations during upper respiratory tract infections (URTI). The objective of this research was the psychometric evaluation of the chest-related ePRO diary in pediatric, adolescent and adult participants. Methods: This non-interventional, psychometric validation study involved participants (N = 195; n = 42 6–8 years; n = 47 9–11 years; n = 55 12–17 years, n = 51 18+ years) completing the chest-related ePRO diary twice daily for 10 days while experiencing an acute URTI. Preliminary item-level performance and dimensionality results, along with consideration of previous qualitative findings, were used to inform item reduction decisions, the structure of the measure and scoring algorithm development. Subsequent analyses on the finalized measure included assessments of reliability (internal consistency and test-retest reliability), construct validity (convergent validity and known groups validity) and ability to detect change. Comparisons of findings were made between the different age groups as part of the analyses to assess the psychometric properties of the chest-related ePRO diary and to characterize potential differences in the symptom experience of children, adolescents, and adults. Results: The measure demonstrated strong quality of completion and showed relatively similar trajectories of symptom scores over time within different age subgroups and good item response distribution properties. Exploratory factor analysis supported a one-factor solution in the total population and within age subgroups, and test-retest reliability of the measure was strong (Intra-class correlation: 0.843–0.894 between Visit 1 and Day 1). The measure also demonstrated strong construct validity through high correlations with relevant items on the Child Cold Symptom Questionnaire (CCSQ), strong known groups validity (with statistically significant differences between severity groups) and was responsive to change over time with change groups defined based on change on global items. Conclusion: The findings demonstrate that the chest-related ePRO diary provides a valid, reliable, responsive measure of chest congestion symptoms experienced with the common cold in pediatric and adolescent populations, and that only minor differences are present in the disease trajectory when comparing adults to younger participants, supporting the use of the measure in interventional studies. [ABSTRACT FROM AUTHOR]

Video recording and editing of general movements (GMs) takes time. We devised an algorithm to automatically extract the period of GMs emergence to assist in the assessment of GMs. The algorithm consisted of δHR: subtracting the moving average heart rate (HR) for the past 60 s from the average instantaneous HR; and %δHR: the percentage of the instantaneous HR to the moving average HR. Ten-second sections in which δHR was positive for three consecutive sections and contained at least one section with %δHR > 105% were extracted. Extracted periods are called automated extraction sections (AESs). We evaluated the concordance rate between AESs and GMs in three periods (gestational age 24–32, 33–34, and 35–36 weeks). The records of 84 very low birth weight infants were evaluated. Approximately 90% of AESs were accompanied by GMs at any period in both the supine and prone positions. The proportion of full-course (beginning to end) GMs among GMs in the AES was 80–85% in the supine position and 90% in the prone position in all periods. We could extract a sufficient number of assessable GMs with this algorithm, which is expected to be widely used for assisting in the assessment of GMs. [ABSTRACT FROM AUTHOR]

Early childhood is an important development period for establishing healthy physical activity (PA) habits. The objective of this study was to evaluate PA levels in a representative sample of U.S. preschool-aged children. The study sample included 301 participants (149 girls, 3–5 years of age) in the 2012 U.S. National Health and Examination Survey National Youth Fitness Survey. Participants were asked to wear an ActiGraph accelerometer on their wrist for 7 days. A machine learning random forest classification algorithm was applied to accelerometer data to estimate daily time spent in moderate- and vigorous-intensity PA (MVPA; the sum of minutes spent in running, walking, and other moderate- and vigorous-intensity PA) and total PA (the sum of MVPA and light-intensity PA). We estimated that U.S. preschool-aged children engaged in 28 min/day of MVPA and 361 min/day of total PA, on average. MVPA and total PA levels were not significantly different between males and females. This study revealed that U.S. preschool-aged children engage in lower levels of MVPA and higher levels of total PA than the minimum recommended by the World Health Organization. [ABSTRACT FROM AUTHOR]

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Objective: High‐fat high‐protein (HFHP) meals are associated with post‐prandial hyperglycemia in type 1 diabetes (T1D), administration of additional insulin for such meals is recommended in order to optimize glucose levels. Optimal timing of additional insulin for HFHP meals in children and young people receiving multiple daily injections (MDI) remains unclear. Aim: To investigate the glycemic impact of additional insulin doses given before or after eating a HFHP meal in children with T1D using MDI. Research Design and Methods: A randomized, controlled three period crossover trial of 27 participants aged 13 years (6.1–17.7) at two Pediatric Diabetes centers was conducted. Additional rapid‐acting insulin for the fat–protein content of a standardized HFHP meal was given at three time points +0+1+2hofusualpre‐prandial carbohydrateinsulin; calculated using an algorithm extrapolated from current evidence base and clinical recommendations. Post‐prandial glucose (PPG) parameters were calculated for 420 minutes using continuous glucose monitoring. The primary outcome was mean PPG excursion. Secondary outcomes included peak glucose, time to peak and hypoglycemia incidence. Results: There was no difference in post‐prandial glucose parameters when additional HFHP insulin was administered at +0,+1,or+2h: mean glucose excursion (mmol/L) (SE): 1.9(0.7), 1.2(0.7), 2.5(0.7); p = 0.5); mean peak glucose (mmol/L)(SE): 10.9(0.9), 11.5(0.8), 11.5(0.9); p = 0.9; time to peak glucose (mins)(SE): 82.3(35.4), 113.6(30.9), 95.1(32.1); p = 0.8. Mild hypoglycemia was common (55%) in all groups (p = 0.97). Conclusion: We found no benefit in giving additional insulin as a split dose for HFHP meals in children using MDI, mild hypoglycemia was common. Future studies would benefit from refinement of the insulin dose algorithm. [ABSTRACT FROM AUTHOR]

Simple Summary: Here we provide an overview of several genetically determined conditions that predispose to the development of solid and hematologic malignancies in children. Diagnosing these conditions, whose prevalence is estimated around 10% in children with cancer, is useful to warrant personalized oncologic treatment and follow-up, as well as psychological and genetic counseling to these children and their families. We reviewed the most recent studies focusing on the prevalence of cancer predisposition syndromes in cancer-bearing children and the most-used clinical screening tools. Our work highlighted the value of clinical screening tools in the management of young cancer patients, especially in settings where genetic testing is not promptly accessible. In the past recent years, the expanding use of next-generation sequencing has led to the discovery of new cancer predisposition syndromes (CPSs), which are now known to be responsible for up to 10% of childhood cancers. As knowledge in the field is in constant evolution, except for a few "classic" CPSs, there is no consensus about when and how to perform germline genetic diagnostic studies in cancer-bearing children. Several clinical screening tools have been proposed to help identify the patients who carry higher risk, with heterogeneous strategies and results. After introducing the main clinical and molecular features of several CPSs predisposing to solid and hematological malignancies, we compare the available clinical evidence on CPS prevalence in pediatric cancer patients and on the most used decision-support tools in identifying the patients who could benefit from genetic counseling and/or direct genetic testing. This analysis highlighted that a personalized stepwise approach employing clinical screening tools followed by sequencing in high-risk patients might be a reasonable and cost-effective strategy in the care of children with cancer. [ABSTRACT FROM AUTHOR]

Neurological vertigo is a common symptom in children and adults presenting to the emergency department (ED) and its evaluation may be challenging, requiring often the intervention of different medical specialties. When vertigo is associated with other specific symptoms or signs, a differential diagnosis may be easier. Conversely, if the patient exhibits isolated vertigo, the diagnostic approach becomes complex and only through a detailed history, a complete physical examination and specific tests the clinician can reach the correct diagnosis. Approach to vertigo in ED is considerably different in children and adults due to the differences in incidence and prevalence of the various causes. The aim of this systematic review is to describe the etiopathologies of neurological vertigo in childhood and adulthood, highlighting the characteristics and the investigations that may lead clinicians to a proper diagnosis. Finally, this review aims to develop an algorithm that could represent a valid diagnostic support for emergency physicians in approaching patients with isolated vertigo, both in pediatric and adult age. [ABSTRACT FROM AUTHOR]

Background: The bacterial load of the human lower respiratory tract is at least several times lower than that of the other parts of the body. This study aimed to identify the bacterial composition and colony structure of the lower respiratory tract in infants and children with recurrent wheezing compared with those of children with a bronchial foreign body and clarify whether the length of wheezing in infants can contribute to differences in the lower respiratory tract's bacterial colony structure. Methods: We collected specimens of alveolar lavage fluid from 48 infants and children who underwent fiberoptic bronchoscopy and were divided into groups: A1 (multiple wheezing: wheezing more than three times in < 1 month), A2 (persistent wheezing: > 1 month), and B (bronchial foreign body; control group). We analyzed the bacterial community structure of alveolar lavage fluid using high-throughput sequencing. The richness and diversity of the microbial communities were assessed by α and β diversity analyses. Results: A total of 6,644 operational taxonomic units (OTUs) were obtained based on the Illumina Nova sequencing platform and clustered according to those that met the 97% identity threshold, followed by species annotation of the OTU sequences. In the annotation results, 2,608 (39.25%) OTUs were annotated at the genus level. At the genus level, Sphingomonas and Phyllobacterium were significantly higher in group A1 than in group B. There were significantly more Phyllobacterium in group A2 than in group B. Prevotella, Neisseria, and Haemophilus were higher in group B than in groups A1 and A2. The differences in the between-group α and β diversity analyses were statistically significant. The microbial diversity in groups A1 and A2 was significantly less than that in group B, but there was no statistical difference in bacterial community diversity between groups A1 and A2. Conclusion: Recurrent wheezing in infants and children is more likely due to alterations in the overall bacterial microecology and disruption of host respiration and immune homeostasis than the effects of a single bacterium. [ABSTRACT FROM AUTHOR]

Summary: Background: A standardized approach for identifying and treating hypothalamic obesity (HO) in children with hypothalamic tumours is lacking. Objectives: To describe children with hypothalamic tumours at risk for obesity, assess outcomes of a novel HO clinical algorithm, and identify factors associated with weight gain. Methods: Retrospective analysis of youth with hypothalamic and suprasellar tumours, seen at a paediatric tertiary care centre from 2010 to 2020. Results: The study cohort (n = 130, 50% female, median age at diagnosis 5 [range 0–17]y) had a median duration of follow up of 5 (0.03–17)y. At last recorded body mass index (BMI) measurement, 34% had obesity, including 17% with severe obesity. Median onset of overweight and obesity after diagnosis was 6.2 (0.3–134) and 8.9 (0.7–65) months, respectively. After algorithm implementation (n = 13), the proportion that had an early dietitian visit (within 6 months) increased from 36% to 54%, (p = 0.498) and weight management referrals increased from 51% to 83% (p = 0.286). Higher BMI z‐score at diagnosis was associated with overweight and obesity development (p < 0.001). Conclusion: Patients with hypothalamic tumours commonly develop obesity. Use of a clinical algorithm may expedite recognition of HO. Further research is needed to identify predictors of weight gain and to develop effective treatment. [ABSTRACT FROM AUTHOR]

Hyponatremia, especially if acute and severe, can be a life-threatening condition. Several conditions can trigger hyponatremia. In this review, we will discuss two conditions that can determine euvolemic hyponatremia: the cerebral/renal salt wasting (CRSW) syndrome and the syndrome of inappropriate secretion of antidiuretic hormone (SIADH), including the two subtypes: reset osmostat (RO) and nephrogenic syndrome of inappropriate antidiuresis (NSIAD) and their differential diagnoses. Despite the passage of over 70 years since its first description, to date, the true etiopathogenesis of CRSW syndrome, a rare cause of hypovolemic/euvolemic hyponatremia, is almost unknown. SIADH, including RO and NSIAD, is sometimes difficult to differentiate from CRSW syndrome; in its differential diagnosis, the clinical approach based on the evaluation of the extracellular volume (ECV) was proven insufficient. We therefore suggest a simple diagnostic algorithm based on the assessment of the degree of hyponatremia, urinary osmolality, and the assessment of the fraction of urate excretion (FEUa) in conditions of hyponatremia and after serum sodium correction, to be applied in children over 1 year of life. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance. [ABSTRACT FROM AUTHOR]

Primary health care facilities offer an entry point to the health care system in Malawi. Challenges experienced by these facilities include limited resources (both material and human), poor or inadequate knowledge, skills and attitudes of health care workers in emergency management, and delay in referral from primary care level to other levels of care. These contribute to poor outcomes including children dying within the first 24 hours of hospital admission. Training of health care workers and support staff in Emergency Triage Assessment and Treatment (ETAT) at primary care levels can help improve care of children with acute and severe illnesses. Health care workers and support staff in the primary care settings were trained in pediatric ETAT. The training package for health care workers was adapted from the Ministry of Health ETAT training for district and tertiary health care. Content for support staff focused on non-technical responsibility for lifesaving in emergency situations. The primary health care facilities were provided with a minimum treatment package comprising emergency equipment, supplies and drugs. Supportive supervisory visits were conducted quarterly. The training manual for health care workers was adapted from the Ministry of Health package and the support staff training manual was developed from the adapted package. Eight hundred and seventy-seven participants were trained (336 health care workers and 541 support staff). Following the training, triaging of patients improved and patients were managed as emergency, priority or non-urgent. This reduced the number of referral cases and children were stabilized before referral. Capacity building of health care workers and support staff in pediatric ETAT and the provision of a basic health center package improved practice at the primary care level. The practice was sustained through institutional mentorship and pre-service and in-service training. The practice of triage and treatment including stabilization of children with dangerous signs at the primary health care facility improves emergency care of patients, reduces the burden of patients on referral hospitals and increases the number of successful referrals. [ABSTRACT FROM AUTHOR]

Background: Precise risk prediction of type 1 diabetes (T1D) facilitates early intervention and identification of risk factors prior to irreversible beta‐islet cell destruction, and can significantly improve T1D prevention and clinical care. Sharp et al. developed a genetic risk scoring (GRS) system for T1D (T1D‐GRS2) capable of predicting T1D risk in children of European ancestry. The T1D‐GRS2 was developed on the basis of causal genetic variants, thus may be applicable to minor populations, while a trans‐ethnic GRS for T1D may avoid the exacerbation of health disparities due to the lack of genomic information in minorities. Methods: Here, we describe a T1D‐GRS2 calculator validated in two independent cohorts, including African American children and European American children. Participants were recruited by the Center for Applied Genomics at the Children's Hospital of Philadelphia. Results: It demonstrates that GRS2 is applicable to the T1D risk prediction in the AA cohort, while population‐specific thresholds are needed for different populations. Conclusions: The study highlights the potential to further improve T1D‐GRS2 performance with the inclusion of additional genetic markers. [ABSTRACT FROM AUTHOR]

This study examines the psychometric properties of the Autism Diagnostic Interview-Revised (ADI-R) in the context of DSM-5 in a sample of Chinese children. Using re-mapped ADI-R items and algorithms matched to DSM-5 criteria, and administering to children with autism spectrum disorder (ASD) with and without intellectual disability, attention-deficit hyperactivity disorder, and typically developing, it evidenced high sensitivity and specificity. However, similar to DSM-IV algorithm, the DSM-5 algorithms were better at classifying ASD among children with intellectual disability than among those without intellectual disability. With the DSM-5's recognition of the spectrum nature of ASD, the performance of the ADI-R can be improved by having finer gradations in the ADI-R scoring and adding more items on the restricted and repetitve behavior domain. [ABSTRACT FROM AUTHOR]