Ke Han, Zi-Xia Ding, Xiao-Yu He, Tian-Yu Wu, Yu-Hang Meng, Bang Du, Xiao-Nan Zhang
The etiology of rheumatoid arthritis (RA), a chronic inflammatory systemic disease, remains unclear. It is characterized by symmetrical and invasive joint inflammation, primarily affecting distal small joints such as those in the hands and feet. This inflammation can lead to joint deformity and loss of function, and often accompanied by involvement of extra-articular organs like the lungs and heart. Currently, anti-rheumatic drugs only provide symptom improvement but have toxic side effects that require optimization. Therefore, it is crucial to thoroughly analyze the mechanisms underlying RA development for the identification of new drug targets. Programmed cell death (PCD) has been extensively studied in recent years and proved to be one of the key pathogenic factors in RA. Dysregulation of PCD is particularly evident in synoviocytes, immune cells, and osteocytes. This review summarizes various forms of PCD including apoptosis, NETosis, autophagy, pyroptosis, necroptosis, ferroptosis, cuproptosis, as well as their regulatory roles in fibroblast synoviocytes, immune cells and osteocytes. These findings hold significant theoretical implications for optimizing clinical treatment options for RA and developing new target drugs.
类风湿性关节炎(RA)是一种慢性全身性炎症性疾病,其病因至今仍不清楚。其特点是对称性和侵袭性关节炎症,主要影响远端小关节,如手部和足部关节。这种炎症可导致关节畸形和功能丧失,通常还伴有肺和心脏等关节外器官的受累。目前,抗风湿药物只能改善症状,但其毒副作用需要优化。因此,透彻分析 RA 的发病机制以确定新的药物靶点至关重要。近年来,人们对程序性细胞死亡(PCD)进行了广泛研究,并证明它是RA的关键致病因素之一。PCD失调在滑膜细胞、免疫细胞和骨细胞中尤为明显。本综述总结了PCD的各种形式,包括凋亡、NETosis、自噬、热凋亡、坏死凋亡、铁凋亡、杯状凋亡,以及它们在成纤维滑膜细胞、免疫细胞和骨细胞中的调控作用。这些发现对优化RA的临床治疗方案和开发新的靶向药物具有重要的理论意义。
{"title":"[The research development of programmed cell death in rheumatoid arthritis].","authors":"Ke Han, Zi-Xia Ding, Xiao-Yu He, Tian-Yu Wu, Yu-Hang Meng, Bang Du, Xiao-Nan Zhang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The etiology of rheumatoid arthritis (RA), a chronic inflammatory systemic disease, remains unclear. It is characterized by symmetrical and invasive joint inflammation, primarily affecting distal small joints such as those in the hands and feet. This inflammation can lead to joint deformity and loss of function, and often accompanied by involvement of extra-articular organs like the lungs and heart. Currently, anti-rheumatic drugs only provide symptom improvement but have toxic side effects that require optimization. Therefore, it is crucial to thoroughly analyze the mechanisms underlying RA development for the identification of new drug targets. Programmed cell death (PCD) has been extensively studied in recent years and proved to be one of the key pathogenic factors in RA. Dysregulation of PCD is particularly evident in synoviocytes, immune cells, and osteocytes. This review summarizes various forms of PCD including apoptosis, NETosis, autophagy, pyroptosis, necroptosis, ferroptosis, cuproptosis, as well as their regulatory roles in fibroblast synoviocytes, immune cells and osteocytes. These findings hold significant theoretical implications for optimizing clinical treatment options for RA and developing new target drugs.</p>","PeriodicalId":7134,"journal":{"name":"生理学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142520690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.7507/1001-5515.202311061
Liang Jiang, Cheng Zhang, Hui Cao, Baihao Jiang
Breast cancer is a malignancy caused by the abnormal proliferation of breast epithelial cells, predominantly affecting female patients, and it is commonly diagnosed using histopathological images. Currently, deep learning techniques have made significant breakthroughs in medical image processing, outperforming traditional detection methods in breast cancer pathology classification tasks. This paper first reviewed the advances in applying deep learning to breast pathology images, focusing on three key areas: multi-scale feature extraction, cellular feature analysis, and classification. Next, it summarized the advantages of multimodal data fusion methods for breast pathology images. Finally, the study discussed the challenges and future prospects of deep learning in breast cancer pathology image diagnosis, providing important guidance for advancing the use of deep learning in breast diagnosis.
{"title":"[Research progress of breast pathology image diagnosis based on deep learning].","authors":"Liang Jiang, Cheng Zhang, Hui Cao, Baihao Jiang","doi":"10.7507/1001-5515.202311061","DOIUrl":"https://doi.org/10.7507/1001-5515.202311061","url":null,"abstract":"<p><p>Breast cancer is a malignancy caused by the abnormal proliferation of breast epithelial cells, predominantly affecting female patients, and it is commonly diagnosed using histopathological images. Currently, deep learning techniques have made significant breakthroughs in medical image processing, outperforming traditional detection methods in breast cancer pathology classification tasks. This paper first reviewed the advances in applying deep learning to breast pathology images, focusing on three key areas: multi-scale feature extraction, cellular feature analysis, and classification. Next, it summarized the advantages of multimodal data fusion methods for breast pathology images. Finally, the study discussed the challenges and future prospects of deep learning in breast cancer pathology image diagnosis, providing important guidance for advancing the use of deep learning in breast diagnosis.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N6-methyladenosine (m6A) is the most common type of RNA modification in eukaryotes, which affects intracellular RNA metabolism and controls gene expression of related pathophysiological processes through dynamic reversible regulation of methyltransferases, demethylases and m6A-binding proteins. In recent years, the involvement of m6A methylation in the study of neuropathic pain has become a hot topic, some new understandings have been emerging, and m6A methylation has become a potential biological target for the treatment of neuropathic pain. Therefore, this article reviews the role and regulation of m6A methylation in neuropathic pain, in order to provide new enlightenment for the drug development and treatment of neuropathic pain.
{"title":"[m<sup>6</sup>A RNA methylation is a potential biological target for neuropathic pain].","authors":"Yu-Ting Zhang, Li-Cai Zhang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>N6-methyladenosine (m<sup>6</sup>A) is the most common type of RNA modification in eukaryotes, which affects intracellular RNA metabolism and controls gene expression of related pathophysiological processes through dynamic reversible regulation of methyltransferases, demethylases and m<sup>6</sup>A-binding proteins. In recent years, the involvement of m<sup>6</sup>A methylation in the study of neuropathic pain has become a hot topic, some new understandings have been emerging, and m<sup>6</sup>A methylation has become a potential biological target for the treatment of neuropathic pain. Therefore, this article reviews the role and regulation of m<sup>6</sup>A methylation in neuropathic pain, in order to provide new enlightenment for the drug development and treatment of neuropathic pain.</p>","PeriodicalId":7134,"journal":{"name":"生理学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142520680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adipose tissue holds a pivotal position in maintaining systemic energy homeostasis. Brown adipose tissue (BAT) expresses uncoupling protein 1 (UCP1), which is specialized in dissipating chemical energy as heat to maintain euthermia, a process called non-shivering thermogenesis. Conversely, white adipocyte (WAT) predominantly serves as the primary reservoir for energy storage, while also exhibiting endocrine activity by secreting various adipokines, thereby modulating systemic metabolism. Under the stimulation of cold exposure, physical activity and pharmacological intervention, WAT can occur as "browning" or "beiging", and transform into beige adipose tissue. The morphology and function of beige adipocyte are similar to brown adipocyte, both of which express higher levels of UCP1 and also have the function of thermogenesis. Thus, exploring methods to regulate the functional homeostasis of adipose tissue and its underlying molecular mechanisms hold promise for advancing preventative and therapeutic approaches against metabolic diseases. Exosomes, a subtype of extracellular vesicles (EVs) with a diameter of 40-100 nm, facilitate intercellular communication in obese individuals and exert significant influence on insulin resistance and impaired glucose tolerance within adipose tissue. These effects are primarily mediated by microRNA (miRNA) transported by exosomes. MiRNA, originating from various cellular sources, traverses between different cell types via EVs, thereby orchestrating reciprocal functional modulation among diverse tissues and organs. This review systematically summarized the research progress in exosomal miRNA-mediated regulation of adipose tissue functional homeostasis, with the aim of offering novel insights into the diagnosis and treatment of obesity and associated metabolic diseases.
{"title":"[Research progress in the regulation of functional homeostasis of adipose tissue by exosomal miRNA].","authors":"Jun-Qing Xu, Meng-Xin Jiang, Ying-Jiang Xu, Sheng-Jun Dong","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Adipose tissue holds a pivotal position in maintaining systemic energy homeostasis. Brown adipose tissue (BAT) expresses uncoupling protein 1 (UCP1), which is specialized in dissipating chemical energy as heat to maintain euthermia, a process called non-shivering thermogenesis. Conversely, white adipocyte (WAT) predominantly serves as the primary reservoir for energy storage, while also exhibiting endocrine activity by secreting various adipokines, thereby modulating systemic metabolism. Under the stimulation of cold exposure, physical activity and pharmacological intervention, WAT can occur as \"browning\" or \"beiging\", and transform into beige adipose tissue. The morphology and function of beige adipocyte are similar to brown adipocyte, both of which express higher levels of UCP1 and also have the function of thermogenesis. Thus, exploring methods to regulate the functional homeostasis of adipose tissue and its underlying molecular mechanisms hold promise for advancing preventative and therapeutic approaches against metabolic diseases. Exosomes, a subtype of extracellular vesicles (EVs) with a diameter of 40-100 nm, facilitate intercellular communication in obese individuals and exert significant influence on insulin resistance and impaired glucose tolerance within adipose tissue. These effects are primarily mediated by microRNA (miRNA) transported by exosomes. MiRNA, originating from various cellular sources, traverses between different cell types via EVs, thereby orchestrating reciprocal functional modulation among diverse tissues and organs. This review systematically summarized the research progress in exosomal miRNA-mediated regulation of adipose tissue functional homeostasis, with the aim of offering novel insights into the diagnosis and treatment of obesity and associated metabolic diseases.</p>","PeriodicalId":7134,"journal":{"name":"生理学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142520681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.7507/1001-5515.202210059
Yao Xie, Dong Yang, Honglong Yu, Qilian Xie
Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.
{"title":"[Research on motion impedance cardiography de-noising method based on two-step spectral ensemble empirical mode decomposition and canonical correlation analysis].","authors":"Yao Xie, Dong Yang, Honglong Yu, Qilian Xie","doi":"10.7507/1001-5515.202210059","DOIUrl":"https://doi.org/10.7507/1001-5515.202210059","url":null,"abstract":"<p><p>Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.7507/1001-5515.202309047
An Zeng, Xianyang Lin, Jingliang Zhao, Dan Pan, Baoyao Yang, Xin Liu
In the segmentation of aortic dissection, there are issues such as low contrast between the aortic dissection and surrounding organs and vessels, significant differences in dissection morphology, and high background noise. To address these issues, this paper proposed a reinforcement learning-based method for type B aortic dissection localization. With the assistance of a two-stage segmentation model, the deep reinforcement learning was utilized to perform the first-stage aortic dissection localization task, ensuring the integrity of the localization target. In the second stage, the coarse segmentation results from the first stage were used as input to obtain refined segmentation results. To improve the recall rate of the first-stage segmentation results and include the segmentation target more completely in the localization results, this paper designed a reinforcement learning reward function based on the direction of recall changes. Additionally, the localization window was separated from the field of view window to reduce the occurrence of segmentation target loss. Unet, TransUnet, SwinUnet, and MT-Unet were selected as benchmark segmentation models. Through experiments, it was verified that the majority of the metrics in the two-stage segmentation process of this paper performed better than the benchmark results. Specifically, the Dice index improved by 1.34%, 0.89%, 27.66%, and 7.37% for each respective model. In conclusion, by incorporating the type B aortic dissection localization method proposed in this paper into the segmentation process, the overall segmentation accuracy is improved compared to the benchmark models. The improvement is particularly significant for models with poorer segmentation performance.
在主动脉夹层的分割中,存在主动脉夹层与周围器官和血管对比度低、夹层形态差异大、背景噪声高等问题。针对这些问题,本文提出了一种基于强化学习的 B 型主动脉夹层定位方法。在两阶段分割模型的辅助下,利用深度强化学习完成第一阶段主动脉夹层定位任务,确保定位目标的完整性。在第二阶段,将第一阶段的粗分割结果作为输入,获得精细分割结果。为了提高第一阶段分割结果的召回率,并将分割目标更完整地纳入定位结果,本文设计了基于召回率变化方向的强化学习奖励函数。此外,还将定位窗口与视场窗口分开,以减少分割目标丢失的发生。本文选择 Unet、TransUnet、SwinUnet 和 MTUnet 作为基准分割模型。通过实验验证,本文两阶段分割过程中的大多数指标都优于基准结果。具体来说,每个模型的 Dice 指数分别提高了 1.34%、0.89%、27.66% 和 7.37%。总之,通过将本文提出的 B 型主动脉夹层定位方法纳入分割过程,与基准模型相比,整体分割准确性得到了提高。对于分割性能较差的模型,这种改进尤为明显。
{"title":"[Reinforcement learning-based method for type B aortic dissection localization].","authors":"An Zeng, Xianyang Lin, Jingliang Zhao, Dan Pan, Baoyao Yang, Xin Liu","doi":"10.7507/1001-5515.202309047","DOIUrl":"https://doi.org/10.7507/1001-5515.202309047","url":null,"abstract":"<p><p>In the segmentation of aortic dissection, there are issues such as low contrast between the aortic dissection and surrounding organs and vessels, significant differences in dissection morphology, and high background noise. To address these issues, this paper proposed a reinforcement learning-based method for type B aortic dissection localization. With the assistance of a two-stage segmentation model, the deep reinforcement learning was utilized to perform the first-stage aortic dissection localization task, ensuring the integrity of the localization target. In the second stage, the coarse segmentation results from the first stage were used as input to obtain refined segmentation results. To improve the recall rate of the first-stage segmentation results and include the segmentation target more completely in the localization results, this paper designed a reinforcement learning reward function based on the direction of recall changes. Additionally, the localization window was separated from the field of view window to reduce the occurrence of segmentation target loss. Unet, TransUnet, SwinUnet, and MT-Unet were selected as benchmark segmentation models. Through experiments, it was verified that the majority of the metrics in the two-stage segmentation process of this paper performed better than the benchmark results. Specifically, the Dice index improved by 1.34%, 0.89%, 27.66%, and 7.37% for each respective model. In conclusion, by incorporating the type B aortic dissection localization method proposed in this paper into the segmentation process, the overall segmentation accuracy is improved compared to the benchmark models. The improvement is particularly significant for models with poorer segmentation performance.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.7507/1001-5515.202312023
Shijia Yan, Ye Yang, Peng Yi
This study aims to optimize surface electromyography-based gesture recognition technique, focusing on the impact of muscle fatigue on the recognition performance. An innovative real-time analysis algorithm is proposed in the paper, which can extract muscle fatigue features in real time and fuse them into the hand gesture recognition process. Based on self-collected data, this paper applies algorithms such as convolutional neural networks and long short-term memory networks to provide an in-depth analysis of the feature extraction method of muscle fatigue, and compares the impact of muscle fatigue features on the performance of surface electromyography-based gesture recognition tasks. The results show that by fusing the muscle fatigue features in real time, the algorithm proposed in this paper improves the accuracy of hand gesture recognition at different fatigue levels, and the average recognition accuracy for different subjects is also improved. In summary, the algorithm in this paper not only improves the adaptability and robustness of the hand gesture recognition system, but its research process can also provide new insights into the development of gesture recognition technology in the field of biomedical engineering.
{"title":"[Enhancement algorithm for surface electromyographic-based gesture recognition based on real-time fusion of muscle fatigue features].","authors":"Shijia Yan, Ye Yang, Peng Yi","doi":"10.7507/1001-5515.202312023","DOIUrl":"https://doi.org/10.7507/1001-5515.202312023","url":null,"abstract":"<p><p>This study aims to optimize surface electromyography-based gesture recognition technique, focusing on the impact of muscle fatigue on the recognition performance. An innovative real-time analysis algorithm is proposed in the paper, which can extract muscle fatigue features in real time and fuse them into the hand gesture recognition process. Based on self-collected data, this paper applies algorithms such as convolutional neural networks and long short-term memory networks to provide an in-depth analysis of the feature extraction method of muscle fatigue, and compares the impact of muscle fatigue features on the performance of surface electromyography-based gesture recognition tasks. The results show that by fusing the muscle fatigue features in real time, the algorithm proposed in this paper improves the accuracy of hand gesture recognition at different fatigue levels, and the average recognition accuracy for different subjects is also improved. In summary, the algorithm in this paper not only improves the adaptability and robustness of the hand gesture recognition system, but its research process can also provide new insights into the development of gesture recognition technology in the field of biomedical engineering.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.
{"title":"[Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network].","authors":"Yiyang Li, Jiayi Zhao, Ruoyi Yu, Huixiang Liu, Shuang Liang, Yu Gu","doi":"10.7507/1001-5515.202312014","DOIUrl":"https://doi.org/10.7507/1001-5515.202312014","url":null,"abstract":"<p><p>Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cerebral ischemia/reperfusion injury (CIRI) refers to secondary damage caused by reperfusion of blood flow following ischemic stroke. Its mechanism is complex, involving mitochondrial energy metabolism disorders, Ca2+ overload, oxidative stress, apoptosis, inflammatory responses, excitatory amino acid toxicity, blood-brain barrier disruption, excessive NO synthesis, and cell necrosis etc. Mitochondrial-associated endoplasmic reticulum membranes (MAMs) are specialized regions of the endoplasmic reticulum that play crucial roles in various cellular processes, including regulation of mitochondrial morphology and activity, lipid metabolism, Ca2+ homeostasis, and cell viability. Existing research has confirmed that mitochondrial homeostasis, cell apoptosis, and endoplasmic reticulum stress are closely related to MAMs. This article summarizes the research progress on MAMs in recent years, reviews the biological functions of MAMs and the localization of tethering proteins, analyzes the signaling between mitochondria and the endoplasmic reticulum, explores the impact of MAMs tethering proteins interaction on Ca2+ signaling and cell viability during the pathophysiological process of CIRI, aiming to provide a theoretical basis for the treatment of CIRI.
{"title":"[Research progress on the effects of mitochondrial-associated endoplasmic reticulum membranes tethering proteins interaction on cerebral ischemia/reperfusion].","authors":"Meng-Ling Huang, Li-Hong Zhang, Chang-Yu Gu, Jing-Jing Li, Rui-Qing Li, Xiao-Dong Feng, Jing Gao, Jian Guo","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Cerebral ischemia/reperfusion injury (CIRI) refers to secondary damage caused by reperfusion of blood flow following ischemic stroke. Its mechanism is complex, involving mitochondrial energy metabolism disorders, Ca<sup>2+</sup> overload, oxidative stress, apoptosis, inflammatory responses, excitatory amino acid toxicity, blood-brain barrier disruption, excessive NO synthesis, and cell necrosis etc. Mitochondrial-associated endoplasmic reticulum membranes (MAMs) are specialized regions of the endoplasmic reticulum that play crucial roles in various cellular processes, including regulation of mitochondrial morphology and activity, lipid metabolism, Ca<sup>2+</sup> homeostasis, and cell viability. Existing research has confirmed that mitochondrial homeostasis, cell apoptosis, and endoplasmic reticulum stress are closely related to MAMs. This article summarizes the research progress on MAMs in recent years, reviews the biological functions of MAMs and the localization of tethering proteins, analyzes the signaling between mitochondria and the endoplasmic reticulum, explores the impact of MAMs tethering proteins interaction on Ca<sup>2+</sup> signaling and cell viability during the pathophysiological process of CIRI, aiming to provide a theoretical basis for the treatment of CIRI.</p>","PeriodicalId":7134,"journal":{"name":"生理学报","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142520685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}