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      Mechanism of Dopaminergic Nerve Transmission in Different Doses of Morphine Addiction and Stress-Induced Depression

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      1 , 2 ,
      Journal of Healthcare Engineering
      Hindawi

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          Abstract

          Depression not only threatens the health and quality of life of patients but also brings a huge mental and economic burden to the patients' families. This paper mainly studies the mechanism of dopaminergic neurotransmission in different doses of morphine addiction and stress-induced depression. In the experiment, 40 male SD rats were selected. The experiment established a rat model of chronic stress depression. The rats used in this model are all raised in a single cage, and there will be various stimuli every day for 21 days, but high-intensity continuous stimuli must be avoided, and the same stimuli will not appear continuously. The experiment established a depression animal model through chronic unpredictable mild stress (CUMS), combined with the conditioned position preference (CPP) model of morphine addiction to detect the establishment of CPP in such animals, so as to explore certain stress stimuli or depression, the influence on morphine addiction, and the relationship between them. The second or third branches of pyramidal neurons were selected to analyze the PL and CA3 regions. When analyzing the density of dendrites, each animal selected at least 8 dendrites in order to count the number of dendrites and selected a length of 20  μm on each branch to record the number of dendrites. All measured values are expressed as average ± standard deviation and analyzed by SPSS17.0 statistical software, and Levene test is used in the scattered consistency test. The average NIV of PEN before injection was 11.92 ± 2.90 Hz, and the average latency was 0.16 ± 0.03 s. The results indicate that CUMS may reduce the conditioned learning and memory ability by damaging the learning loop, rather than affecting the reward loop to weaken the establishment of morphine-dependent CPP.

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          Medical image fusion method by deep learning

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            Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization

            Deep Convolutional Neural Network (CNN) has achieved remarkable results in computer vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn robust features from raw Electroencephalogram (EEG) data to detect seizures. Seizures are hard to detect, as they vary both inter- and intra-patient. In this article, we use a deep CNN model for seizure detection task on an open-access EEG epilepsy dataset collected at the Boston Children's Hospital. Our deep learning model is able to extract spectral, temporal features from EEG epilepsy data and use them to learn the general structure of a seizure that is less sensitive to variations. For cross-patient EEG data, our method produced an overall sensitivity of 90.00%, specificity of 91.65%, and overall accuracy of 98.05% for the whole dataset of 23 patients. The system can detect seizures with an accuracy of 99.46%. Thus, it can be used as an excellent cross-patient seizure classifier. The results show that our model performs better than the previous state-of-the-art models for patient-specific and cross-patient seizure detection task. The method gave an overall accuracy of 99.65% for patient-specific data. The system can also visualize the special orientation of band power features. We use correlation maps to relate spectral amplitude features to the output in the form of images. By using the results from our deep learning model, this visualization method can be used as an effective multimedia tool for producing quick and relevant brain mapping images that can be used by medical experts for further investigation.
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              Efficient induction of dopaminergic neuron differentiation from induced pluripotent stem cells reveals impaired mitophagy in PARK2 neurons.

              Patient-specific induced pluripotent stem cells (iPSCs) show promise for use as tools for in vitro modeling of Parkinson's disease. We sought to improve the efficiency of dopaminergic (DA) neuron induction from iPSCs by the using surface markers expressed in DA progenitors to increase the significance of the phenotypic analysis. By sorting for a CD184high/CD44- fraction during neural differentiation, we obtained a population of cells that were enriched in DA neuron precursor cells and achieved higher differentiation efficiencies than those obtained through the same protocol without sorting. This high efficiency method of DA neuronal induction enabled reliable detection of reactive oxygen species (ROS) accumulation and vulnerable phenotypes in PARK2 iPSCs-derived DA neurons. We additionally established a quantitative system using the mt-mKeima reporter system to monitor mitophagy in which mitochondria fuse with lysosomes and, by combining this system with the method of DA neuronal induction described above, determined that mitophagy is impaired in PARK2 neurons. These findings suggest that the efficiency of DA neuron induction is important for the precise detection of cellular phenotypes in modeling Parkinson's disease.
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                Author and article information

                Contributors
                Journal
                J Healthc Eng
                J Healthc Eng
                JHE
                Journal of Healthcare Engineering
                Hindawi
                2040-2295
                2040-2309
                2021
                11 May 2021
                : 2021
                : 9987441
                Affiliations
                1Graduate School, Jiamusi University, Jiamusi 154000, Heilongjiang, China
                2Department of Neurology, The First Affiliated Hospital of Jiamusi University, Jiamusi 154000, Heilongjiang, China
                Author notes

                Academic Editor: Dilbag Singh

                Author information
                https://orcid.org/0000-0003-0043-0943
                Article
                10.1155/2021/9987441
                8131158
                34055279
                32c64b68-1c0f-4e07-9981-2f445dfc4267
                Copyright © 2021 Qing Ji and Xin Li.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 1 April 2021
                : 26 April 2021
                : 27 April 2021
                Funding
                Funded by: National Key R&D Program of China
                Award ID: 2018YFC1314400
                Categories
                Research Article

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