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Quantitative knowledge presentation models of traditional Chinese medicine (TCM): A review

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  Meaning Quantitative Knowledge Presentation Models of Traditional Chinese Medicine (TCM) refer to computational and mathematical frameworks designed to transform traditional qualitative, experience-based TCM knowledge into measurable, structured, and interpretable representations . These models aim to encode complex TCM concepts—such as syndromes (Zheng), herbal properties, meridians, and therapeutic relationships—into numerical, graphical, or algorithmic formats that enable statistical analysis, machine learning, and data-driven reasoning. In essence, these models serve as a bridge between ancient medical wisdom and modern biomedical informatics, allowing TCM knowledge to be stored, analyzed, shared, and validated using quantitative scientific methods. Introduction Traditional Chinese Medicine has evolved over thousands of years through empirical observation, philosophical reasoning, and clinical practice. While TCM is rich in theoretical depth and holistic perspectives, muc...
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40th Edition of Best Paper Awards 27-28th February 2026 | Singapore

  40th Edition of Best Paper Awards 27-28th February 2026 | Singapore International Research Excellence and Best Paper Awards Website: bestpaperawards.com Nomination Link: https://bestpaperawards.com/award-nom... For Enquiries: contact@bestpaperawards.com #Scifax #Beststpaperawards #Researcherawards #Mostcitedarticleaward #Researcharticleaward #AcademicAwards #ResearchInnovation #ScientificAchievement #TopResearch #AIResearch #STEMExcellence #PublishingSuccess #AwardWinningResearch #GlobalRecognition #InnovationAward #Scholarship #AcademicSuccess #ScienceAwards #TechBreakthrough #PeerReviewed #ScientificImpact #ResearchFunding #ExcellenceInResearch Get Connected Here: ================== Youtube: / @researchpapertv5756 Twitter : twitter.com/bestpaperaward Pinterest : in.pinterest.com/bestpaperaward Linkedin : www.linkedin.com/in/esha-gupta-18b2b4245 Instagram: www.instagram.com/eshagupta__2022/ Blogger: bestpaperaward.blogspot.com/
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Physical exercise, cognition, and brain health in aging

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  Meaning Physical exercise, cognition, and brain health in aging refers to the interconnected relationship between regular bodily movement and its influence on mental processes—such as memory, attention, learning, and executive function—as well as the structural and functional integrity of the brain across the lifespan. As individuals age, natural biological changes occur in neural networks, neurotransmitter systems, and cerebral blood flow, which can lead to gradual cognitive decline. Physical exercise serves as a powerful, non-pharmacological intervention that can slow, prevent, or even partially reverse many of these age-related changes. This concept emphasizes that movement is not only beneficial for muscles and cardiovascular health but also plays a critical role in maintaining cognitive vitality and neurological resilience. Introduction Aging is accompanied by a progressive decline in many physiological systems, and the brain is no exception. Older adults frequently expe...
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Waste Clearance Shapes Aging Brain Health

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  Meaning Waste clearance in the brain refers to the biological processes responsible for removing metabolic by-products, misfolded proteins, damaged cellular components, and toxins that accumulate during normal neuronal activity. Unlike other organs, the brain lacks conventional lymphatic vessels within its parenchyma and instead relies on specialized systems such as the glymphatic pathway , cerebrospinal fluid (CSF) circulation, blood– brain barrier (BBB) transport mechanisms, and cellular degradation pathways like autophagy and proteasomal systems. These interconnected processes collectively maintain neural homeostasis. As the brain ages, efficiency of waste clearance declines, leading to accumulation of neurotoxic substances that contribute to cognitive decline and neurodegenerative diseases. Introduction Aging is accompanied by progressive changes in brain structure and function, including reduced synaptic plasticity, neuronal loss, and altered neurotransmission. A central ...
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Strategies for studying sex differences in brain aging

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  Meaning Strategies for studying sex differences in brain aging refer to the systematic methodological, analytical, and conceptual approaches used to investigate how biological sex influences structural, functional, molecular, and cognitive changes in the brain across the lifespan. These strategies integrate neuroscience, genetics, endocrinology, neuroimaging, epidemiology, and computational modeling to identify sex-specific aging trajectories, vulnerability patterns, and resilience mechanisms. Introduction Brain aging is a complex, heterogeneous process involving gradual alterations in neural structure, connectivity, metabolism, and cognitive performance. Increasing evidence demonstrates that aging does not affect male and female brains identically. Sex-related differences emerge in brain volume decline, white matter integrity, neurotransmitter dynamics, hormonal modulation, and risk for neurodegenerative disorders such as Alzheimer’s disease and Parkinson’s disease. Histori...
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Evaluating demographic bias in brain age prediction across multiple deep learning model paradigms now.

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  Meaning Assessing demographic bias in brain age prediction models refers to examining whether artificial intelligence (AI) systems estimate brain age differently across demographic groups such as sex, ethnicity, socioeconomic status, and age ranges. These models use neuroimaging data and deep learning techniques to predict a person’s “brain age,” which can indicate neurological health. When demographic bias exists, predictions may systematically favor certain groups, leading to inaccurate or unfair outcomes. Introduction Brain age prediction has become an important tool in neuroscience and clinical research for identifying early signs of neurological disorders and tracking brain health. With the rise of deep learning, various model architectures—such as convolutional neural networks (CNNs), transformers, and hybrid models—are increasingly used for this task. However, concerns about fairness and bias have grown, especially when models are trained on datasets that underrepresent...
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