工业工程最新文献

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.

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.

Obstructive sleep apnea (OSA) affects quality of life and health in nearly 1 billion patients all over the world. With aging society, OSA increases the risk of Alzheimer's disease and leads to severe cognitive impairment. Chronic intermittent hypoxia (CIH), the core pathological mechanism of OSA, may induce synaptic plasticity damage and cognitive impairment, and decrease learning and memory and attention ability. However, the molecular mechanism underlying OSA is still not fully understood. And, there is no targeted treatment strategy for cognitive impairment in patients with OSA. Firstly, the correlation between OSA and cognitive dysfunction was summarized in this review. Secondly, the molecular mechanism of CIH-induced cognitive impairment was elucidated from the perspectives of synaptic plasticity damage, oxidative stress, inflammation, endoplasmic reticulum stress, apoptosis, mitochondrial dysfunction and autophagy. Finally, the current treatment strategy for cognitive impairment in patients with OSA was summarized.

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.

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.

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.

Deep learning-based automatic classification of diabetic retinopathy (DR) helps to enhance the accuracy and efficiency of auxiliary diagnosis. This paper presents an improved residual network model for classifying DR into five different severity levels. First, the convolution in the first layer of the residual network was replaced with three smaller convolutions to reduce the computational load of the network. Second, to address the issue of inaccurate classification due to minimal differences between different severity levels, a mixed attention mechanism was introduced to make the model focus more on the crucial features of the lesions. Finally, to better extract the morphological features of the lesions in DR images, cross-layer fusion convolutions were used instead of the conventional residual structure. To validate the effectiveness of the improved model, it was applied to the Kaggle Blindness Detection competition dataset APTOS2019. The experimental results demonstrated that the proposed model achieved a classification accuracy of 97.75% and a Kappa value of 0.971 7 for the five DR severity levels. Compared to some existing models, this approach shows significant advantages in classification accuracy and performance.

Oligodendrocyte precursor cells (OPCs) are recognized as the progenitors responsible for the generation of oligodendrocytes, which play a critical role in myelination of central nervous system. In addition, in demyelinating diseases, such as brain trauma, ischemia, and multiple sclerosis, OPCs are also found in demyelinated regions, but fail to differentiate into mature oligodendrocytes and remyelinate. From traditional view, OPC is victim of immune response. However, recent studies have shed light on immune associated OPCs (imOPCs), which are induced by interferon γ (IFN-γ), and interleukin 17 (IL-17), and are involved in the innate and adaptive immune activation. By expressing multiple natural immune pattern recognition receptors, such as Toll-like receptors, imOPCs can phagocytose myelin debris for antigen presentation. Furthermore, imOPCs can also secrete various inflammatory and chemotactic factors to regulate the differentiation of Th0 cells and the recruitment of NK cells, granulocytes and macrophages. Thus, it is of great importance to explore the immunoregulatory function of OPCs to elucidate the mechanisms and treatments of demyelinating diseases.

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.

Transcranial magnetic stimulation (TMS) as a non-invasive neuroregulatory technique has been applied in the clinical treatment of neurological and psychiatric diseases. However, the stimulation effects and neural regulatory mechanisms of TMS with different frequencies and modes are not yet clear. This article explores the effects of different frequency repetitive transcranial magnetic stimulation (rTMS) and burst transcranial magnetic stimulation (bTMS) on memory function and neuronal excitability in mice from the perspective of neuroelectrophysiology. In this experiment, 42 Kunming mice aged 8 weeks were randomly divided into pseudo stimulation group and stimulation groups. The stimulation group included rTMS stimulation groups with different frequencies (1, 5, 10 Hz), and bTMS stimulation groups with different frequencies (1, 5, 10 Hz). Among them, the stimulation group received continuous stimulation for 14 days. After the stimulation, the mice underwent new object recognition and platform jumping experiment to test their memory ability. Subsequently, brain slice patch clamp experiment was conducted to analyze the excitability of granulosa cells in the dentate gyrus (DG) of mice. The results showed that compared with the pseudo stimulation group, high-frequency (5, 10 Hz) rTMS and bTMS could improve the memory ability and neuronal excitability of mice, while low-frequency (1 Hz) rTMS and bTMS have no significant effect. For the two stimulation modes at the same frequency, their effects on memory function and neuronal excitability of mice have no significant difference. The results of this study suggest that high-frequency TMS can improve memory function in mice by increasing the excitability of hippocampal DG granule neurons. This article provides experimental and theoretical basis for the mechanism research and clinical application of TMS in improving cognitive function.