Chronic diseases are persistent health conditions that affect our quality of life, increase morbidity and mortality, and are a global challenge. Further, the increasing prevalence of chronic diseases requires the development of new methods for the early detection of these disease-specific biomarkers. Here, we provide a concise review of the chronic disease biomarkers acquired by electrochemical sensors. Then, we discuss the potential of artificial neural networks on the sensed data for disease monitoring and management. Next, we describe risk factors, causes, pathophysiological processes, and severity of chronic diseases. This is followed with a careful review of how we can use the sensed chronic disease biomarkers and clinical symptoms as features for the machine learning algorithms. Finally, we discuss how uncovered patterns in the biosensors’ data using artificial neural networks can be used to predict and diagnose chronic diseases. We believe this review will help in developing artificial neural network-based innovative analytical tools for chronic diseases and other healthcare applications in future.

Malnutrition is widely known to affect growth in children. There are many studies focusing on malnutrition globally in relation to limited food access; however, there is only limited research observing disease-related malnutrition, especially in chronic conditions and particularly in developing countries. This study aims to review articles on measurement of malnutrition in pediatric chronic disease, especially in developing countries where there are resource limitations in identifying nutritional status in pediatric chronic disease with complex conditions. This state-of-the-art narrative review is conducted through search of literatures through two databases, and identified 31 eligible articles published from 1990 to 2021. This study found no uniformity in malnutrition definitions and no consensus regarding screening tools for the identification of malnutrition risk in these children. In developing countries where resources are limited, instead of focusing on finding the best tools by which to identify malnutrition risk, the approach should be directed toward developing systems that work best according to capacity and that allow a combination of anthropometry assessment, clinical evaluation, and observation of feeding access and tolerance on a regular basis.

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Sofia
Journal Co-ordinator
Journal of Rare Disorders: Diagnosis & Therapy