生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202403033

Yali Qin, Liping Yao, Ling Yuan, Sheng Chen

{"title":"[Construction of a prediction model for induction of labor based on a small sample of clinical indicator data].","authors":"Yali Qin, Liping Yao, Ling Yuan, Sheng Chen","doi":"10.7507/1001-5515.202403033","DOIUrl":"10.7507/1001-5515.202403033","url":null,"abstract":"Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model's generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.","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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Heart sound classification algorithm based on bispectral feature extraction and convolutional neural networks]. [基于双谱特征提取和卷积神经网络的心音分类算法]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202310016

Liyong Peng, Haiyan Quan

{"title":"[Heart sound classification algorithm based on bispectral feature extraction and convolutional neural networks].","authors":"Liyong Peng, Haiyan Quan","doi":"10.7507/1001-5515.202310016","DOIUrl":"10.7507/1001-5515.202310016","url":null,"abstract":"Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Heart sound classification plays a key role in the early detection of CVD. The difference between normal and abnormal heart sounds is not obvious. In this paper, in order to improve the accuracy of the heart sound classification model, we propose a heart sound feature extraction method based on bispectral analysis and combine it with convolutional neural network (CNN) to classify heart sounds. The model can effectively suppress Gaussian noise by using bispectral analysis and can effectively extract the features of heart sound signals without relying on the accurate segmentation of heart sound signals. At the same time, the model combines with the strong classification performance of convolutional neural network and finally achieves the accurate classification of heart sound. According to the experimental results, the proposed algorithm achieves 0.910, 0.884 and 0.940 in terms of accuracy, sensitivity and specificity under the same data and experimental conditions, respectively. Compared with other heart sound classification algorithms, the proposed algorithm shows a significant improvement and strong robustness and generalization ability, so it is expected to be applied to the auxiliary detection of congenital heart disease.","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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Simulation study on parameter optimization of transcranial direct current stimulation based on rat brain slices]. [基于大鼠脑片的经颅直流电刺激参数优化模拟研究]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202402007

Shiji He, Guanghao Zhang, Changzhe Wu, Xiaolin Huo, Lijun Zhang, Jingxi Zhang, Cheng Zhang

{"title":"[Simulation study on parameter optimization of transcranial direct current stimulation based on rat brain slices].","authors":"Shiji He, Guanghao Zhang, Changzhe Wu, Xiaolin Huo, Lijun Zhang, Jingxi Zhang, Cheng Zhang","doi":"10.7507/1001-5515.202402007","DOIUrl":"10.7507/1001-5515.202402007","url":null,"abstract":"Transcranial direct current stimulation (tDCS) is an important method for treating mental illnesses and neurodegenerative diseases. This paper reconstructed two ex vivo brain slice models based on rat brain slice staining images and magnetic resonance imaging (MRI) data respectively, and the current densities of hippocampus after cortical tDCS were obtained through finite element calculation. Subsequently, a neuron model was used to calculate the response of rat hippocampal pyramidal neuron under these current densities, and the neuronal responses of the two models under different stimulation parameters were compared. The results show that a minimum stimulation voltage of 17 V can excite hippocampal pyramidal neuron in the model based on brain slice staining images, while 24 V is required in the MRI-based model. The results indicate that the model based on brain slice staining images has advantages in precision and electric field propagation simulation, and its results are closer to real measurements, which can provide guidance for the selection of tDCS parameters and scientific basis for precise stimulation.","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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Involvement of mitochondrial TRPV3 in cardiac hypertrophy induced by pressure overload in rats. 线粒体 TRPV3 参与了压力过载诱导的大鼠心肌肥大。

生理学报 Pub Date : 2024-10-25

Mei-Ping Zhu, Bing-Yi Zhang, Ting Lian, Yuan-Jia Tan, Lin-Lin Chang, Pan Xu, Jin-Yi Zhang, Yan-Huan Du, Zhen-Yu Xiong, Qiong Du, Shi-Zhong Zhang

{"title":"Involvement of mitochondrial TRPV3 in cardiac hypertrophy induced by pressure overload in rats.","authors":"Mei-Ping Zhu, Bing-Yi Zhang, Ting Lian, Yuan-Jia Tan, Lin-Lin Chang, Pan Xu, Jin-Yi Zhang, Yan-Huan Du, Zhen-Yu Xiong, Qiong Du, Shi-Zhong Zhang","doi":"","DOIUrl":"","url":null,"abstract":"Mitochondria play an important role in pressure overload-induced cardiac hypertrophy. The present study aimed to investigate the role of mitochondrial transient receptor potential vanilloid 3 (TRPV3) in myocardial hypertrophy. A 0.7 mm diameter U-shaped silver clip was used to clamp the abdominal aorta of Sprague Dawley (SD) rats and establish an animal model of abdominal aortic constriction (AAC). Rat H9C2 myocardial cells were treated with angiotensin II (Ang II) to establish a hypertrophic myocardial cell model, and TRPV3 expression was knocked down using TRPV3 small interfering RNA (siRNA). JC-1 probe was used to detect mitochondrial membrane potential (MMP). DHE probe was used to detect ROS generation. Enzyme activities of mitochondrial respiratory chain complex I and III and ATP production were detected by assay kits. Immunofluorescence staining was used to detect TRPV3 expression in H9C2 cells. Western blot was used to detect the protein expression levels of β-myosin heavy chain (β-MHC), mitochondrial TRPV3 and mitochondrial NOX4. The results showed that, in the rat AAC model heart tissue and H9C2 cells treated with Ang II, the protein expression levels of β-MHC, mitochondrial TRPV3 and mitochondrial NOX4 were up-regulated, MMP was decreased, ROS generation was increased, mitochondrial respiratory chain complex I and III enzyme activities were decreased, and ATP production was reduced. After knocking down mitochondrial TRPV3 in H9C2 cells, the protein expression levels of β-MHC and mitochondrial NOX4 were down-regulated, MMP was increased, ROS generation was decreased, mitochondrial respiratory chain complex I and III enzyme activities were increased, and ATP production was increased. These results suggest that mitochondrial TRPV3 in cardiomyocytes exacerbates mitochondrial dysfunction by up-regulating NOX4, thereby participating in the process of pressure overload-induced myocardial hypertrophy.","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":"142520694","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}

引用次数: 0

[Research progress on chronic intermittent hypoxia and cognitive impairment]. [慢性间歇性缺氧与认知障碍的研究进展]。

生理学报 Pub Date : 2024-10-25

Ke-Rong Qi, Xue Chen, Jian-Chao Si, Sheng-Chang Yang

引用次数: 0

[The role of oligodendrocyte precursor cells in immunoregulation]. [少突胶质前体细胞在免疫调节中的作用]。

生理学报 Pub Date : 2024-10-25

Xiang Chen, Cheng He, Peng Liu

引用次数: 0

[A review on depth perception techniques in organoid images]. [类器官图像深度感知技术综述]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202404036

Yu Sun, Fengliang Huang, Hanwen Zhang, Hao Jiang, Gangyin Luo

{"title":"[A review on depth perception techniques in organoid images].","authors":"Yu Sun, Fengliang Huang, Hanwen Zhang, Hao Jiang, Gangyin Luo","doi":"10.7507/1001-5515.202404036","DOIUrl":"10.7507/1001-5515.202404036","url":null,"abstract":"Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.","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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Improving adaptive noise reduction performance of body sound auscultation through linear preprocessing]. [通过线性预处理提高体声听诊的自适应降噪性能]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202307058

Hongqiang Mo, Xiang Tian, Bin Li, Junzhang Tian

{"title":"[Improving adaptive noise reduction performance of body sound auscultation through linear preprocessing].","authors":"Hongqiang Mo, Xiang Tian, Bin Li, Junzhang Tian","doi":"10.7507/1001-5515.202307058","DOIUrl":"10.7507/1001-5515.202307058","url":null,"abstract":"Adaptive filtering methods based on least-mean-square (LMS) error criterion have been commonly used in auscultation to reduce ambient noise. For non-Gaussian signals containing pulse components, such methods are prone to weights misalignment. Unlike the commonly used variable step-size methods, this paper introduced linear preprocessing to address this issue. The role of linear preprocessing in improving the denoising performance of the normalized least-mean-square (NLMS) adaptive filtering algorithm was analyzed. It was shown that, the steady-state mean square weight deviation of the NLMS adaptive filter was proportional to the variance of the body sounds and inversely proportional to the variance of the ambient noise signals in the secondary channel. Preprocessing with properly set parameters could suppress the spikes of body sounds, and decrease the variance and the power spectral density of the body sounds, without significantly reducing or even with increasing the variance and the power spectral density of the ambient noise signals in the secondary channel. As a result, the preprocessing could reduce weights misalignment, and correspondingly, significantly improve the performance of ambient-noise reduction. Finally, a case of heart-sound auscultation was given to demonstrate how to design the preprocessing and how the preprocessing improved the ambient-noise reduction performance. The results can guide the design of adaptive denoising algorithms for body sound auscultation.","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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Recurrence prediction of gastric cancer based on multi-resolution feature fusion and context information]. [基于多分辨率特征融合和上下文信息的胃癌复发预测]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202403014

Hongyu Zhou, Haibo Tao, Feiyue Xue, Bin Wang, Huaiping Jin, Zhenhui Li

{"title":"[Recurrence prediction of gastric cancer based on multi-resolution feature fusion and context information].","authors":"Hongyu Zhou, Haibo Tao, Feiyue Xue, Bin Wang, Huaiping Jin, Zhenhui Li","doi":"10.7507/1001-5515.202403014","DOIUrl":"10.7507/1001-5515.202403014","url":null,"abstract":"Pathological images of gastric cancer serve as the gold standard for diagnosing this malignancy. However, the recurrence prediction task often encounters challenges such as insignificant morphological features of the lesions, insufficient fusion of multi-resolution features, and inability to leverage contextual information effectively. To address these issues, a three-stage recurrence prediction method based on pathological images of gastric cancer is proposed. In the first stage, the self-supervised learning framework SimCLR was adopted to train low-resolution patch images, aiming to diminish the interdependence among diverse tissue images and yield decoupled enhanced features. In the second stage, the obtained low-resolution enhanced features were fused with the corresponding high-resolution unenhanced features to achieve feature complementation across multiple resolutions. In the third stage, to address the position encoding difficulty caused by the large difference in the number of patch images, we performed position encoding based on multi-scale local neighborhoods and employed self-attention mechanism to obtain features with contextual information. The resulting contextual features were further combined with the local features extracted by the convolutional neural network. The evaluation results on clinically collected data showed that, compared with the best performance of traditional methods, the proposed network provided the best accuracy and area under curve (AUC), which were improved by 7.63% and 4.51%, respectively. These results have effectively validated the usefulness of this method in predicting gastric cancer recurrence.","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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning]. [基于机器学习的体内电子顺磁共振波谱分类和辐射剂量预测研究]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202302015

Guangwei Xiong, Bo Chen, Lei Ma, Longpeng Jia, Shunian Chen, Ke Wu, Jing Ning, Bin Zhu, Junwang Guo

{"title":"[Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning].","authors":"Guangwei Xiong, Bo Chen, Lei Ma, Longpeng Jia, Shunian Chen, Ke Wu, Jing Ning, Bin Zhu, Junwang Guo","doi":"10.7507/1001-5515.202302015","DOIUrl":"10.7507/1001-5515.202302015","url":null,"abstract":"The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.","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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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