Nutritional Management of Athletes with Type 1 Diabetes: A Narrative Review.

Type 1 diabetes mellitus (T1DM) represents a complex clinical challenge for health systems. The autoimmune destruction of pancreatic beta cells leads to a complete lack of insulin production, exposing people to a lifelong risk of acute (DKA, coma) and chronic complications (macro and microvascular). Physical activity (PA) has widely demonstrated its efficacy in helping diabetes treatment. Nutritional management of people living with T1DM is particularly difficult. Balancing macronutrients, their effects on glycemic control, and insulin treatment represents a complex clinical challenge for the diabetologist. The effects of PA on glycemic control are largely unpredictable depending on many individual factors, such as intensity, nutrient co-ingestion, and many others. Due to this clinical complexity, we have reviewed the actual scientific literature in depth to help diabetologists, sport medicine doctors, nutritionists, and all the health figures involved in diabetes care to ameliorate both glycemic control and the nutritional status of T1DM people engaging in PA. Two electronic databases (PubMed and Scopus) were searched from their inception to January 2024. The main recommendations for carbohydrate and protein ingestion before, during, and immediately after PA are explained. Glycemic management during such activity is widely reviewed. Micronutrient needs and nutritional supplement effects are also highlighted in this paper.

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives.

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.