71 research outputs found

    重症CPVTを引き起こす新規カルモジュリン変異p.E46Kは、ヒトiPS細胞由来心筋細胞において重度な催不整脈性を示す

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    京都大学新制・課程博士博士(医学)甲第24878号医博第5012号京都大学大学院医学研究科医学専攻(主査)教授 萩原 正敏, 教授 湊谷 謙司, 教授 江藤 浩之学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDFA

    Icariside II, a Phosphodiesterase-5 Inhibitor, Attenuates Beta-Amyloid-Induced Cognitive Deficits via BDNF/TrkB/CREB Signaling

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    Background/Aims: Icariside II (ICS II) is an active component from Epimedium brevicornum, a Chinese medicine extensively used in China. Our previous study has proved that ICS II protects against learning and memory impairments and neuronal apoptosis in the hippocampus induced by beta-amyloid25-35 (Aβ25-35) in rats. However, its in-depth underlying mechanisms remain still unclear. Hence this study was designed to explore the potential underlying mechanisms of ICS II by experiments with an in vivo model of Aβ25-35-induced cognitive deficits in rats combined with a neuronal-like PC12 cells injury in vitro model. Methods: The cognitive deficits was measured using Morris water maze test, and apoptosis, intracellular reactive oxygen species (ROS) and mitochondrial ROS levels were detected by TUNEL, DCFH-DA and Mito-SOX staining, respectively. Expression of Bcl-2, Bax, brain derived neurotrophic factor (BDNF), tyrosine receptor kinase B (TrkB), and cAMP response element binding (p-CREB) and active-Caspase 3 levels were evaluated by Western blot. Results: It was found that ICS II, a phosphodiesterase-5 inhibitor, significantly attenuated cognitive deficits caused by Aβ25-35 injection in rats, and ICS II not only significantly enhanced the expression of BDNF and TrkB, but also activated CREB. Furthermore, ICS II also significantly abrogated Aβ25-35-induced PC12 cell injury, and inhibited Aβ25-35-induced intracellular reactive oxygen species (ROS) overproduction, as well as mitochondrial ROS levels. In addition, ICS II up-regulated the expressions of BDNF and TrkB consistent with the findings in vivo. ANA-12, a TrkB inhibitor, blocked the neuroprotective effect of ICS II on Aβ25-35-induced neuronal injury. Conclusion: ICS II mitigates Aβ25-35-induced cognitive deficits and neuronal cell injury by upregulating the BDNF/TrkB/CREB signaling, suggesting that ICS II can be used as a potential therapeutic agent for dementia, such as Alzheimer’s disease

    Non-missense variants of KCNH2 show better outcomes in type 2 long QT syndrome

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    AIMS: More than one-third of type 2 long QT syndrome (LQT2) patients carry KCNH2 non-missense variants that can result in haploinsufficiency (HI), leading to mechanistic loss-of-function. However, their clinical phenotypes have not been fully investigated. The remaining two-thirds of patients harbour missense variants, and past studies uncovered that most of these variants cause trafficking deficiency, resulting in different functional changes: either HI or dominant-negative (DN) effects. In this study, we examined the impact of altered molecular mechanisms on clinical outcomes in LQT2 patients. METHODS AND RESULTS: We included 429 LQT2 patients (234 probands) carrying a rare KCNH2 variant from our patient cohort undergoing genetic testing. Non-missense variants showed shorter corrected QT (QTc) and less arrhythmic events (AEs) than missense variants. We found that 40% of missense variants in this study were previously reported as HI or DN. Non-missense and HI-groups had similar phenotypes, while both exhibited shorter QTc and less AEs than the DN-group. Based on previous work, we predicted the functional change of the unreported variants-whether they cause HI or DN via altered functional domains-and stratified them as predicted HI (pHI)- or pDN-group. The pHI-group including non-missense variants exhibited milder phenotypes compared to the pDN-group. Multivariable Cox model showed that the functional change was an independent risk of AEs (P = 0.005). CONCLUSION: Stratification based on molecular biological studies enables us to better predict clinical outcomes in the patients with LQT2

    You may also like: Machine-learning algorithms for collection recommendations

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    Traditional collection development relies heavily on human input, with librarians relying on reviews and subject selection lists, and through user requests. With the development of machine learning, more and more businesses seek automated methods to deliver results relevant to users. The Recommender system, a subclass of information filtering that seeks to predict the "rating" or "preference" of a user, is among the most successful systems of machine learning in action. It has been adopted by many major e-commerce businesses such as Amazon, Netflix, and Expedia, and has been widely implemented to predict product and media recommendations, making it a key factor in increasing product average order value and the number of items per order. Drawing inspiration from the benefits of a recommender system to business and its success in heightening the reliability of recommendations, we attempted to build optimal collection recommendations with machine-learning algorithms using Python. The purpose of this project is to help librarians make collection decisions using the recommender system, and in this presentation we will illustrate several examples that will appeal to both public and academic librarians. One example involves the merging of popular titles with reader rating data. We found that while The New York Times publishes best seller titles based on the rates of sales, they do not have any connection to user ratings. By leveraging data from Goodreads, the world’s largest site for readers and book recommendations, we will build a simple recommender system that produces The New York Times best seller titles that have higher user rating using a matrix factorization based method. Librarians in turn can purchase best seller titles with good reader ratings. Moreover, the recommender system will also enable librarians to recommend books that are similar to a particular title based on pairwise similarity scores. Therefore, if a user enjoys reading a book, the recommender system will pull titles with similar features. The recommender system can be used in other settings. Drawing on bibliographic data from highly circulated items, or frequently requested interlibrary loan items, the recommender system will suggest items with similar features using similarity metrics. As a result, librarians can acquire relevant materials based on users’ previous reading patterns. The recommender system will use machine-learning algorithms not only to simplify collection development for librarians, but will also help end users discover more items relevant to their interests.Librarie

    Connecting the dots: Reader ratings, bibliographic data, and machine-learning algorithms for monograph selection

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    Traditional collection development relies heavily on human input, with librarians relying on reviews and subject selection lists, and through user requests. With the development of machine learning, more and more businesses seek automated methods to deliver results relevant to users. The Recommender system, a subclass of information filtering that seeks to predict the "rating" or "preference" of a user, is among the most successful systems of machine learning in action. It has been adopted by many major e-commerce businesses such as Amazon, Netflix, and Expedia, and has been widely implemented to predict product and media recommendations, making it a key factor in increasing product average order value and the number of items per order. Drawing inspiration from the benefits of a recommender system to business and its success in heightening the reliability of recommendations, we attempted to build optimal collection recommendations with machine-learning algorithms using Python. The purpose of this project is to help librarians make collection decisions using the recommender system, and in this presentation we will illustrate several examples of building this system to aid in the selection of monographs. One example involves the merging of popular titles with reader rating data. We found that while The New York Times publishes best seller titles based on the rates of sales, they do not have any connection to user ratings. By leveraging data from Goodreads, the world’s largest site for readers and book recommendations, we will build a simple recommender system that produces The New York Times best seller titles that have higher user rating using a matrix factorization based method. Another example of using a recommender system is to have the ability to refer selectors to books that are similar to a particular title based on pairwise similarity scores. News services are already able to identify related articles of interest to readers based on the articles that they have read in the past, so applying this system to libraries is an exciting prospect. Drawing on bibliographic data from highly circulated items, the recommender system will suggest items with similar features using similarity metrics. The recommender system will use machine-learning algorithms not only to simplify collection development for librarians, but will also help end users discover more items relevant to their interests.Librarie

    Symbiosis-Michelson Interferometer-Based Detection Scheme for the Measurement of Dynamic Signals

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    Dihydrotanshinone I Attenuates Atherosclerosis in ApoE-deficient Mice: Role of NOX4/NF-κB Mediated Lectin-like Oxidized LDL Receptor-1 (LOX-1) of the Endothelium

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    Dihydrotanshinone I (DHT) is a natural compound extracted from Salvia miltiorrhiza Bunge which has been widely used for treating cardiovascular diseases. However, its role in atherosclerosis remains unclear. In this study, the effect of DHT on atherosclerosis were investigated using apolipoprotein E-deficient (ApoE-/-) mice and endothelial cells. In lipopolysaccharide (LPS)-stimulated human umbilical vein endothelial cells (HUVECs), DHT (10 nM) decreased lectin-like ox-LDL receptor-1 (LOX-1) and NADPH oxidase 4 (NOX4) expression, reactive oxygen species (ROS) production, NF-κB nuclear translocation, ox-LDL endocytosis and monocytes adhesion. Silence NOX4 inhibited LPS-induced LOX-1 expression, NF-κB nuclear translocation, ox-LDL endocytosis and monocytes adhesion. In ApoE-/- mice fed with an atherogenic diet, DHT (10 and 25 mg kg−1) significantly attenuated atherosclerotic plaque formation, altered serum lipid profile, decreased oxidative stress and shrunk necrotic core areas. The enhanced expression of LOX-1, NOX4, and NF-κB in aorta was also dramatically inhibited by DHT. In conclusion, these results suggested that DHT showed anti-atherosclerotic activity through inhibition of LOX-1 mediated by NOX4/NF-κB signaling pathways both in vitro and in vivo. This finding suggested that DHT might be used as a potential vascular protective candidate for the treatment of atherosclerosis

    Icariin induces synoviolin expression through NFE2L1 to protect neurons from ER stress-induced apoptosis.

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    By suppressing neuronal apoptosis, Icariin is a potential therapeutic drug for neuronal degenerative diseases. The molecular mechanisms of Icariin anti-apoptotic functions are still largely unclear. In this report, we found that Icariin induces the expression of Synoviolin, an endoplasmic reticulum (ER)-anchoring E3 ubiquitin ligase that functions as a suppressor of ER stress-induced apoptosis. The nuclear factor erythroid 2-related factor 1 (NFE2L1) is responsible for Icariin-mediated Synoviolin gene expression. Mutation of the NFE2L1-binding sites in a distal region of the Synoviolin promoter abolished Icariin-induced Synoviolin promoter activity, and knockdown of NFE2L1 expression prevented Icariin-stimulated Synoviolin expression. More importantly, Icariin protected ER stress-induced apoptosis of PC12 cells in a Synoviolin-dependent manner. Therefore, our study reveals Icariin-induced Synoviolin expression through NFE2L1 as a previously unappreciated molecular mechanism underlying the neuronal protective function of Icariin
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