From Sarcopenia to Frailty: The Pathophysiological Basis and Potential Target Molecules of Intervention

Skeletal muscle is not only an endocrine organ but also one of core components of muscloskeletal system. Sarcopenia refers to a decline in the skeletal muscle mass and function. The former involves the size and number of changes in two types of myofibers, lower satellite cell density, and regeneration ability. The latter shows a loss of muscle strength. Frailty is a geriatric syndrome with multisystem impairment associated with increased vulnerability to stressors. Sarcopenia increases the risk of frailty and may be one of the major causes of physical frailty phenotype. Sarcopenia is also potentially associated with cognitive frailty phenotype. Aging might be the common underlying pathophysiology of sarcopenia and frailty. Therefore, there are some potential target molecules in aging-related signaling pathways that might be associated with sarcopenia and frailty. Nevertheless, sarcopenia can mediate metabolism and promote accelerate systemic aging, frailty, and age-related diseases by myokines in an endocrine manner. Lifestyle interventions (resistance exercise and dietary restriction) of gerontoscience are effective in the prevention of sarcopenia. Some pharmacological agents are registered in different phases of clinical trials for sarcopenia intervention. Phytochemicals, mTOR inhibitors, metformin and acarbose, NAD precursors, and sirtuin activators demonstrated that multiple target antiaging effects might also have preventive and therapeutic perspectives on sarcopenia and frailty

Deep learning time pattern attention mechanism-based short-term load forecasting method

Accurate load forecasting is crucial to improve the stability and cost-efficiency of smart grid operations. However, how to integrate multiple significant factors for enhancing load forecasting performance is insufficiently investigated in previous studies. To fill the gap, this study proposes a novel hybrid deep learning model for short-term load forecasting. First, the long short-term memory network is utilized to capture patterns from historical load data. Second, a time pattern attention (TPA) mechanism is incorporated to improve feature extraction and learning capabilities. By discerning valuable features and eliminating irrelevant ones, the TPA mechanism enhances the learning process. Third, fully-connected layers are employed to integrate external factors such as climatic conditions, economic indicators, and temporal aspects. This comprehensive approach facilitates a deeper understanding of the impact of these factors on load profiles, leading to the development of a highly accurate load forecasting model. Rigorous experimental evaluations demonstrate the superior performance of the proposed approach in comparison to existing state-of-the-art load forecasting methodologies

Phytochemicals in the Treatment of Alzheimer's Disease: A Systematic Review

N/

Effect of Adsorbed Alcohol Layers on the Behavior of Water Molecules Confined in a Graphene Nanoslit: A Molecular Dynamics Study

With the rapid development of a two-dimensional (2D) nanomaterial, the confined liquid binary mixture has attracted increasing attention, which has significant potential in membrane separation. Alcohol/water is one of the most common systems in liquid–liquid separation. As one of the most focused systems, recent studies have found that ethanol molecules were preferentially adsorbed on the inner surface of the pore wall and formed an adsorbed ethanol layer under 2D nanoconfinement. To evaluate the effect of the alcohol adsorption layer on the mobility of water molecules, molecular simulations were performed to investigate four types of alcohol/water binary mixtures confined under a 20 Å graphene slit. Residence times of the water molecules covering the alcohol layer were in the order of methanol/water < ethanol/water < 1-propanol/water < 1-butanol/water. Detailed microstructural analysis of the hydrogen bonding (H-bond) network elucidated the underlying mechanism on the molecular scale in which a small average number of H-bonds between the preferentially adsorbed alcohol molecules and the surrounding water molecules could induce a small degree of damage to the H-bond network of the water molecules covering the alcohol layer, resulting in the long residence time of the water molecules

Depressive and Biopsychosocial Frailty Phenotypes: Impact on Late-life Cognitive Disorders

: In older age, frailty is a detrimental transitional status of the aging process featuring an increased susceptibility to stressors defined by a clinical reduction of homoeostatic reserves. Multidimensional frailty phenotypes have been associated with all-cause dementia, mild cognitive impairment (MCI), Alzheimer's disease (AD), AD neuropathology, vascular dementia, and non-AD dementias. In the present article, we reviewed current evidence on the existing links among depressive and biopsychosocial frailty phenotypes and late-life cognitive disorders, also examining common pathways and mechanisms underlying these links. The depressive frailty phenotype suggested by the construct of late-life depression (LLD) plus physical frailty is poorly operationalized. The biopsychosocial frailty phenotype, with its coexistent biological/physical and psychosocial dimensions, defines a biological aging status and includes motivational, emotional, and socioeconomic domains. Shared biological pathways/substrates among depressive and biopsychosocial frailty phenotypes and late-life cognitive disorders are hypothesized to be inflammatory and cardiometabolic processes, together with multimorbidity, loneliness, mitochondrial dysfunction, dopaminergic neurotransmission, specific personality traits, lack of subjective/objective social support, and neuroendocrine dysregulation. The cognitive frailty phenotype, combining frailty and cognitive impairment, may be a risk factor for LLD and vice versa, and a construct of depressive frailty linking physical frailty and LLD may be a good dementia predictor. Frailty assessment may enable clinicians to better target the pharmacological and psychological treatment of LLD. Given the epidemiological links of biopsychosocial frailty with dementia and MCI, multidomain interventions might contribute to delay the onset of late-life cognitive disorders and other adverse health-related outcomes, such as institutionalization, more frequent hospitalization, disability, and mortality