Zinc Status and Autism Spectrum Disorder in Children and Adolescents: A Systematic Review

do Nascimento, Priscila Kelly da Silva Bezerra; Oliveira Silva, David Franciole; de Morais, Tássia Louise Sousa Augusto; de Rezende, Adriana Augusto

doi:10.3390/nu15163663

Cited by 3 publications

(1 citation statement)

References 75 publications

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“…Feeding concerns can be a big issue for autistic children, with catastrophic consequences if the child suffers from nutritional deficits [6]. The concomitant lower concentrations of vitamins B6, B9, and B12 may cause methylation deficiency in autistic children [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Effects of Walnut and Pumpkin on Selective Neurophenotypes of Autism Spectrum Disorders: A Case Study

El-Ansary,

Al-Ayadhi

2023

Nutrients

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Special diets or nutritional supplements are regularly given to treat children with autism spectrum disorder (ASD). The increased consumption of particular foods has been demonstrated in numerous trials to lessen autism-related symptoms and comorbidities. A case study on a boy with moderate autism who significantly improved after three years of following a healthy diet consisting of pumpkin and walnuts was examined in this review in connection to a few different neurophenotypes of ASD. We are able to suggest that a diet high in pumpkin and walnuts was useful in improving the clinical presentation of the ASD case evaluated by reducing oxidative stress, neuroinflammation, glutamate excitotoxicity, mitochondrial dysfunction, and altered gut microbiota, all of which are etiological variables. Using illustrated figures, a full description of the ways by which a diet high in pumpkin and nuts could assist the included case is offered.

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Section: Introductionmentioning

confidence: 99%

Effects of Walnut and Pumpkin on Selective Neurophenotypes of Autism Spectrum Disorders: A Case Study

El-Ansary,

Al-Ayadhi

2023

Nutrients

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NMDA Receptors in Neurodevelopmental Disorders: Pathophysiology and Disease Models

Tumdam,

Hussein,

Garin-Shkolnik

et al. 2024

IJMS

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N-methyl-D-aspartate receptors (NMDARs) are critical components of the mammalian central nervous system, involved in synaptic transmission, plasticity, and neurodevelopment. This review focuses on the structural and functional characteristics of NMDARs, with a particular emphasis on the GRIN2 subunits (GluN2A-D). The diversity of GRIN2 subunits, driven by alternative splicing and genetic variants, significantly impacts receptor function, synaptic localization, and disease manifestation. The temporal and spatial expression of these subunits is essential for typical neural development, with each subunit supporting distinct phases of synaptic formation and plasticity. Disruptions in their developmental regulation are linked to neurodevelopmental disorders, underscoring the importance of understanding these dynamics in NDD pathophysiology. We explore the physiological properties and developmental regulation of these subunits, highlighting their roles in the pathophysiology of various NDDs, including ASD, epilepsy, and schizophrenia. By reviewing current knowledge and experimental models, including mouse models and human-induced pluripotent stem cells (hiPSCs), this article aims to elucidate different approaches through which the intricacies of NMDAR dysfunction in NDDs are currently being explored. The comprehensive understanding of NMDAR subunit composition and their mutations provides a foundation for developing targeted therapeutic strategies to address these complex disorders.

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A Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients

Orbatu,

Peker Bulğan,

Olmez

et al. 2024

Iran J Pediatr

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Background: Over the past three years, zinc deficiency among adolescents has varied based on region and access to healthcare. Globally, zinc deficiency affects approximately 2 billion people, leading to serious issues such as immune problems and growth delays, particularly in developing countries. In the U.S., around 10% of adolescents experienced zinc deficiency in 2021, with a higher prevalence among teenage girls. In Europe, deficiency rates are generally low but can be significant in Eastern Europe and Central Asia. In Asia, particularly in rural and low-income areas, deficiency rates range from 20 - 30%. In Turkey, the prevalence is high due to poor nutrition. Objectives: This study aimed to develop a machine learning-based decision support system to determine zinc deficiency in children and adolescents aged 10 - 18 years. Methods: This machine learning-based study was conducted with 370 adolescents aged 10 - 18 years to assess their zinc deficiency. The dataset consists of 8 feature vectors and an output vector. The machine learning methods used in the analysis include Logistic Regression, Naive Bayes, Decision Tree (CART), K-NN (K-Nearest Neighbors), SVM (Support Vector Machine), Gradient Boosting Classifier, AdaBoost Classifier, Bagging Classifier, Random Forest Classifier, MLP Classifier (Multilayer Perceptron), and XGB Classifier (XGBoost Classifier). Evaluation metrics such as accuracy, precision, recall, and F1 score were used to assess the performance of these methods. Including specific values for these metrics, such as "SVM achieved 94.6% accuracy," would allow readers to quickly compare the effectiveness of the models. Different metrics serve various purposes: Accuracy provides an overall view of performance, precision and recall highlight specific aspects, and the F1 score balances precision and recall. Results: The mean age of the patients in the dataset was 13.79 ± 1.18 years. Of the children, 64.32% (n = 238) were female and 35.68% (n = 132) were male. It was found that 62.7% (n = 232) of the children had low zinc levels, while 37.3% (n = 138) did not require zinc supplementation. Thirteen different machine learning methods were applied to a 70% training and 30% testing set. As a result, the SVM method provided the most successful outcome with 94.6% accuracy. Implementing the SVM-based system in pediatric clinics could improve efficiency and patient care by automatically detecting high-risk zinc deficiency patients based on lab results, providing early intervention alerts for faster treatment, and improving health outcomes. Highlighting these practical applications could increase the study’s appeal to healthcare professionals by demonstrating its real-world benefits. Providing detailed information on these applications would enhance the study’s clarity and practical value, making it more valuable for researchers and healthcare providers interested in AI tools for adolescent health. Conclusions: This study concluded that machine learning methods can effectively determine zinc deficiency in children. The SVM method demonstrated superior classification performance compared to the other methods. An SVM-based decision support system could be integrated into pediatric outpatient clinics to enhance diagnostic accuracy and patient care.

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Zinc Status and Autism Spectrum Disorder in Children and Adolescents: A Systematic Review

Cited by 3 publications

References 75 publications

Effects of Walnut and Pumpkin on Selective Neurophenotypes of Autism Spectrum Disorders: A Case Study

Effects of Walnut and Pumpkin on Selective Neurophenotypes of Autism Spectrum Disorders: A Case Study

NMDA Receptors in Neurodevelopmental Disorders: Pathophysiology and Disease Models

A Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients

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