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Fatigue is a common and debilitating symptom among older adults, significantly impacting their quality of life and functional capacity. The relationship between serotonin and fatigue in aging is complex, involving multiple neurobiological pathways that influence sleep, mood, cognitive function, and energy metabolism. Understanding these mechanisms is crucial for developing effective management strategies. This review aims to elucidate the role of serotonin in the development of fatigue in elderly individuals and evaluate potential therapeutic strategies to mitigate this condition. A comprehensive review of recent literature was conducted, focusing on the neurobiological mechanisms linking serotonin to fatigue, the efficacy of pharmacological and non-pharmacological interventions, and future research directions. Studies were selected based on their relevance to serotonin’s role in aging and fatigue management. Serotonin plays a critical role in regulating various physiological and psychological functions that impact fatigue. Age-related declines in serotonin synthesis, receptor sensitivity, and transporter function contribute to increased fatigue through disrupted sleep, mood disorders, cognitive decline, and altered energy metabolism. Pharmacological treatments, including selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs), have shown efficacy in alleviating fatigue related to depression and chronic pain. Non-pharmacological interventions, such as cognitive behavioral therapy (CBT), exercise, sleep hygiene, and dietary modifications, are also effective in managing fatigue. Combining these approaches may offer the best outcomes for elderly patients. Addressing fatigue in older adults requires a multifaceted approach that integrates both pharmacological and non-pharmacological strategies. Personalized treatment plans considering individual patient profiles and preferences can enhance therapeutic efficacy. Future research should focus on exploring novel therapeutic targets within the serotonergic system, developing personalized medicine approaches, and evaluating the long-term impact of combined therapies.

Purpose: Osteoporosis is a multifactorial disease of the skeletal system and the main cause of fractures and death in elderly people, especially postmenopausal women. The purpose of this study was to investigate the effect of eight weeks of resistance training on some serum and metabolic markers of bone mass in postmenopausal women. Methods: 24 inactive postmenopausal women with an age range of 46-58 years participated in this study. Subjects were assigned to resistance training and control groups. Before and after the training period, weight, body mass index, alkaline phosphatase, calcium, serum phosphorus and bone density of lumbar vertebrae were measured (by Dexa method). The training protocol consisted of upper and lower body resistance training in 3 sets of 8 to 12 repetitions for 8 weeks and 3 sessions per week. Data were analyzed using SPSS software version 26 and independent t and paired t statistical tests. Results: The results of this research showed that the experimental group had a significant increase in the amount of alkaline phosphatase (p=0.041), serum calcium (p=0.035) and bone density of the lumbar vertebrae (p=0.11) and a significant decrease in weight compared to the control group. (p=0.035, and body mass index p=0.021). The results also showed no significant change in the amount of serum phosphorus (p=0.071). Conclusion: According to the findings of the present study, in order to maintain or prevent the decrease in bone density of postmenopausal women, resistance exercises can be recommended.

Over the past decade, there has been a rapid increase in the study of using artificial intelligence (AI) to improve the quality of life of individuals with disabilities. Therefore, the study aims to investigate the effect of a 6-week AI-generated core stability training program on balance and flatfoot in blind female students. This quasi-experimental study selected 30 female students aged 9-12 years with flatfoot in Tehran City, dividing them into two groups: one for experimental (N = 15) and another for control (N = 15). The experimental groups had six weeks of AI-based intervention with three sessions per week. During this period, the control group engaged in the routine activities of the physical education class. The navicular drop index and Y balance test were done as pre-posttest, respectively. The Covariance (ANCOVA) was used for inferential statistics. Data analysis was conducted at a significance level of 95% with an alpha level less than or equal to 0.05. The findings showed that there was a significant difference between the two groups in the scores of the Y balance test (p<0.035) and the navicular drop test (p<0.001), even when the pre-test effect was taken into account (covariate). By leveraging AI to design tailored exercise regimens, practitioners can enhance postural control and musculoskeletal health in visually impaired individuals. These results underscore the potential of AI-assisted rehabilitation strategies in special education settings, highlighting the need for further researc