Dear Colleagues,

Inflammatory neurotoxins from pathogens contribute to the development of neurodegenerative disorders through the induction of neural dysfunctions in the pathogenesis process. In the early stages of neurodegenerative diseases, significant challenges arise in daily clinical practice. Data-driven analysis fosters a profound comprehension of these diseases and provides pathways toward overcoming these clinical challenges.

Moreover, a crucial understanding of the interplay among disease progression, free radical production, neuroinflammation, and neurotoxicity is imperative to elucidate disease mechanisms and formulate therapeutic strategies aimed at tackling disease progression. The prevalence and incidence of age-related neurodegenerative disorders are on the rise at a time when populations are living longer; as a result, there is an urgent need for new therapeutic approaches that reduce neuroinflammation and safeguard neurons against oxidative stress.

Diagnosis and treatment approaches for neurodegenerative diseases predominantly rely on clinical features, encompassing neuropsychological assessments and serological tests. These include the detection of medical images, blood examinations, as well as measurements of vitamin B-12 and folic acid. Furthermore, molecular characteristics linked to neurodegenerative diseases, such as Amyloid Beta and Phosphorylated Tau, are specifically relevant to Alzheimer's disease.

Cutting-edge data-driven construction initiatives aim to establish a reliable and robust empirical causality to behavior by means of thorough analysis grounded in comprehensive features. Due to the basic scientific mechanistic elements underlying neurodegeneration, we have insights into the roles of free radicals, mitochondrial dysfunction, excitotoxicity, neurotrophic factors, nitric oxide, metal ions, and neuroinflammation in the pathophysiology of apoptotic neuronal death. A common underlying theme within neurodegenerative disorders, pivotal for addressing clinical requirements, is protein misfolding. Therefore, meeting this pressing demand involves the advancement of techniques, modeling tools, and software aimed at enhancing our understanding of biological or clinical data. This entails the integration of artificial intelligence or deep learning methodologies, incorporating a robust, multi-dimensional dataset from thoroughly annotated and validated population-based cohorts, aligning with existing literature, and delivering superior explanatory accuracy in the realm of neurodegenerative diseases.

This special issue aims to explore reliable clinical features and basic (or reproducible) mechanisms for diagnosing, treating, and possibly prognosticating neurodegenerative diseases. It will achieve this through advanced data-oriented (cell or animal) modeling, spanning both wet and dry laboratory techniques, including in vivo in vitro, large data sets, data mining, brain imaging, exploring inhibitors, alternative medicine, nutrients, dietary factors, lifestyle influences, exercise effects, and fixed mechanisms. Advanced and basic mechanism-related studies are also welcome in this special issue.

Dr. Woon-Man Kung and Dr. I-Shiang Tzeng
Guest Editors