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from: Kaohsiung
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from: yunlin
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from: taizhong
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from: Kaohsiung
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| 2024-03-28 13:10:08 | onclick： | Alzheimer's Disease Research Advances |
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Sheng Jinhua, a professor at Hangzhou University of Electronic Science and Technology, has published research on the development mechanism of Alzheimer's disease in two journals, "Cortex" and "Computers in Biology and Medicine".
Currently, about 50 million people worldwide suffer from Alzheimer's disease (AD), and it is expected that the number of people with Alzheimer's disease will increase to more than 150 million by 2050.However, there is still a lack of effective prevention and treatment methods, and early diagnosis and prevention of Alzheimer's disease have become a major scientific and social problem that needs to be solved urgently.
In the study published in Cerebral Cortex, Sheng and his collaborators found that neuroinflammatory-mediated neuronal dysfunction is closely related to activation of macrophage stimulating protein 1 (MST1).In 121 patients with cognitive impairment, patients with the MST1 rs3197999 risk allele had significantly reduced glucose and oxygen metabolic coupling in the prefrontal cortex.In addition, the risk allele RS3197999 was associated with a higher rate of increase in clinical dementia scores, mediated by a coupling of glucose and oxygen metabolism.From a clinical perspective, MST1 rs3197999 could become a target for drug development, providing new avenues for clinical treatment of AD or other neurodegenerative diseases.
In the study published in Computers in Biology and Medicine, Sheng Jinhua et al. proposed a multi-modal machine learning framework by combining the multimodal data of complementary biomarkers for AD diagnosis.The model incorporates magnetic resonance imaging, positron emission tomography and cerebrospinal fluid detection results to enhance the characterization of AD.A hybrid algorithm is proposed, which combines improved group intelligence optimization with kernel limit learning machine to achieve simultaneous feature selection and classification.The study demonstrates the advantages of using complementary multimodal data to improve AD diagnosis through advanced feature learning techniques.
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