The integration of clinical data in the assessment of multiple sclerosis - A review

Remyelinating Drugs at a Crossroad: How to Improve Clinical Efficacy and Drug Screenings

Axons wrapped around the myelin sheath enable fast transmission of neuronal signals in the Central Nervous System (CNS). Unfortunately, myelin can be damaged by injury, viral infection, and inflammatory and neurodegenerative diseases. Remyelination is a spontaneous process that can restore nerve conductivity and thus movement and cognition after a demyelination event. Cumulative evidence indicates that remyelination can be pharmacologically stimulated, either by targeting natural inhibitors of Oligodendrocyte Precursor Cells (OPCs) differentiation or by reactivating quiescent Neural Stem Cells (qNSCs) proliferation and differentiation in myelinating Oligodendrocytes (OLs). Although promising results were obtained in animal models for demyelination diseases, none of the compounds identified have passed all the clinical stages. The significant number of patients who could benefit from remyelination therapies reinforces the urgent need to reassess drug selection approaches and develop strategies that effectively promote remyelination. Integrating Artificial Intelligence (AI)-driven technologies with patient-derived cell-based assays and organoid models is expected to lead to novel strategies and drug screening pipelines to achieve this goal. In this review, we explore the current literature on these technologies and their potential to enhance the identification of more effective drugs for clinical use in CNS remyelination therapies.

Psychodynamic-based virtual reality cognitive training system with personalized emotional arousal elements for mild cognitive impairment patients

BACKGROUND AND OBJECTIVE

Mild cognitive impairment (MCI) is a serious threat to the physical health and quality of life of the elderly, as well as a heavy burden on families and society. The current computer-based rehabilitation training ignores the role of emotions in cognitive impairment rehabilitation, making it difficult to improve patient engagement and efficiency. To address this, a psychodynamics-based cognitive rehabilitation training method with personalized emotional arousal elements was proposed using virtual reality technology.

METHODS

Our proposed method contains four training tasks, which cover (audiovisual memory, attention & processing, working memory, abstract & Logic, spatial pathfinding) and six positive emotional arousal elements (sensory feedback, achievement system, multiplayer interaction, score comparison, relaxation scenarios, and peaceful videos) to motivate participants to persist during cognitive training continuously and maintain a positive mental attitude toward training. The six emotional arousal elements were divided into two personalized combinations-full combination and half combination-based on the results of the pre-assessment and were dynamically distributed throughout both the training tasks and post-training.

RESULTS

Fifteen participants with MCI were recruited to complete the proposed experiment and validate the effectiveness of the system. They were first asked to complete two assessments (e.g., the big five scale and the positive and negative affect scale) to investigate their personalities. Based on the results of the assessments, they were provided with a full or half combination of arousal elements in the training tasks and post-training. Finally, the acceptability of the system and task experience were assessed using questionnaires. Notably, there was a significant increase in training scores for participants who completed a six-week training period (66.7%, 33.4%, and 25.0% for attention and processing, working memory, and abstraction and logic, respectively). The results show that positive emotional arousal had a positive effect on the MCI participants. The training tasks and arousal elements can improve cognitive function and enhance the confidence and engagement of participants. There were no significant differences in cognitive domain training scores between the two groups.

CONCLUSIONS

This personalized cognitive training system has the potential to serve as a convenient solution for complementary treatment of MCI.

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

Effects of nutrition education given to persons with multiple sclerosis and their families on diet quality and anthropometric and biochemical measurements

ABSTRACT Objective: In this study, it was aimed to investigate the effects of nutrition education given to persons with multiple sclerosis and their families on anthropometric and biochemical measurements and diet quality. Methods: Data from 51 persons with multiple sclerosis were analysed in this intervention study. The study was conducted with 3 groups. The education group consisted of only persons with multiple sclerosis, the family education group consisted of persons with multiple sclerosis and a family member living with them, and the control group consisted of persons with multiple sclerosis who had not received an education. Anthropometric and biochemical measurements and dietary quality assessments were made before (T1) and immediately after education (T2) and also 3 months after education (follow-up, T3). Results: The distribution of family education group diet quality scores showed a significant increase from “poor” to “needs improvement” at T3 compared to T1. The upper middle arm circumference measurements of the female control group were decreased at T2 and T3 [from 29.0 (23-34) cm to 28.0 (22-31) cm and to 27.5 (22-31) cm]. Women in family education group, levels of serum haemoglobin and haematocrit were higher than in control group at T2 and T3. Also, men in family education group, levels of alanine aminotransferase were lower than those in education group at follow up. Levels of total cholesterol and low-density lipoprotein cholesterol in education group were higher than those control group at T1, T2, and T3. Conclusion: This study indicates that nutrition education affects some biochemical and anthropometric measurements in persons with multiple sclerosis. Diet quality improved when receiving education together with families.