工业工程最新文献

Temporomandibular joint disorder (TMD) is a common oral and maxillofacial disease, which is difficult to detect due to its subtle early symptoms. In this study, a TMD intelligent diagnostic system implemented on edge computing devices was proposed, which can achieve rapid detection of TMD in clinical diagnosis and facilitate its early-stage clinical intervention. The proposed system first automatically segments the important components of the temporomandibular joint, followed by quantitative measurement of the joint gap area, and finally predicts the existence of TMD according to the measurements. In terms of segmentation, this study employs semi-supervised learning to achieve the accurate segmentation of temporomandibular joint, with an average Dice coefficient (DC) of 0.846. A 3D region extraction algorithm for the temporomandibular joint gap area is also developed, based on which an automatic TMD diagnosis model is proposed, with an accuracy of 83.87%. In summary, the intelligent TMD diagnosis system developed in this paper can be deployed at edge computing devices within a local area network, which is able to achieve rapid detecting and intelligent diagnosis of TMD with privacy guarantee.

Ultrasonic microfluidic technology is a technique that couples high-frequency ultrasonic excitation to microfluidic chips. To improve the issues of poor disturbance effects with flexible tip structures and the susceptibility of bubbles to thermal deformation, we propose an enhanced ultrasonic microchannel structure that couples flexible tips with bubbles aiming to improve the disturbance effects and the stability duration. Firstly, we used finite element analysis to simulate the flow field distribution characteristics of the flexible tip, the bubble, and the coupling structure and obtained the steady-state distribution characteristics of the velocity field. Next, we fabricated ultrasonic microfluidic chips based on these three structures, employing 2.8 μm polystyrene microspheres as tracers to analyze the disturbance characteristics of the flow field. Additionally, we analyzed the bubble size and growth rate within the adhering bubbles and coupling structures. Finally, we verified the applicability of the coupling structure for biological samples using human red blood cells (RBCs). Experimental results indicated that, compared to the flexible tip and adhering bubble structures, the flow field disturbance range of the coupling structure increased by 439.53% and 133.48%, respectively; the bubble growth rate reduced from 14.4% to 3.3%. The enhanced ultrasonic microfluidic structure proposed in this study shows great potential for widespread applications in micro-scale flow field disturbance and particle manipulation.

The regulation of adipose tissue homeostasis is essential for maintaining energy and metabolism balance in the body. The peripheral nervous system plays a crucial role in this process. Previous related research primarily focused on the sympathetic nervous system and its release of norepinephrine, while recent attention has shifted to the field of adipose sensory nerves. Studies demonstrate that external stimuli can activate adipose sensory nerves through pathways involving transient receptor potential vanilloid-1 (TRPV1), adipokines, and fatty acids, thereby transmitting signals to the brain. Emerging techniques, such as adipose nerve imaging and denervation of tissues, have revealed the critical role of sensory nerves in the glucose and lipid metabolism, thermogenic function, and vascular regulation of adipose tissue. This article comprehensively reviews the latest research on the regulation and function of sensory nerves in adipose tissue, with a focus on the impact of metabolic diseases on adipose sensory nerves. This review discusses current issues and prospects on the mechanisms behind neural regulation in adipose tissue, hoping to contribute to a comprehensive understanding and providing directions for future research.

Dilated cardiomyopathy (DCM) is a non-ischemic cardiomyopathy with abnormal myocardial structure and function. It is challenging to construct human primary cardiac myocytes from DCM patients due to ethical constraints. In addition, animal models failed to adequately replicate the complexity of the human disease. The mechanism of DCM remains unclear. The emergence of human induced pluripotent stem cells (hiPSCs) provides a new tool for basic research in DCM. Researchers have produced hiPSCs-derived cardiomyocytes (hiPSC-CMs) and applied them to drug screening, leading to new insight into the pathomechanism and treatment in DCM. This review summarizes the research progress in the establishment, drug screening and mechanism research of DCM patient-specific hiPSC-CMs (DCM-hiPSC-CMs) model.

γ-Aminobutyric acid (GABA) neurotransmission alterations have been implicated to play a role in depression pathogenesis. While GABA_A receptor positive allosteric modulators are emerging as promising in clinical practice, their precise antidepressant mechanism remains to be further elucidated. The aim of the present study was to investigate the effects of LY-02, a novel compound derived from the metabolite of timosaponin, on depression in animals and its mechanism. The results of behavioral tests showed that LY-02 exhibited better antidepressant effects in both male C57BL/6 mice and Sprague Dawley (SD) rats. The results of cellular voltage clamp experiments showed that LY-02 enhanced GABA-mediated currents in HEK293T cells expressing recombinant α6β3δ subunit-containing GABA_A receptors. Electrophysiological recording from brain slices showed that LY-02 decreased the amplitude of spontaneous inhibitory postsynaptic current (sIPSC) and increased action potentials of pyramidal neurons in the medial prefrontal cortex (mPFC) of C57BL/6 mice. Western blot results showed that LY-02 dose-dependently up-regulated the protein expression levels of brain-derived neurotrophic factor (BDNF), tropomyosin related kinase B (TrkB) and postsynaptic density protein 95 (PSD-95) in mPFC of mice. The above results suggest that LY-02, as a positive modulator of GABA_A receptors, reduces inhibitory neurotransmission in pyramidal neurons. It further activates the BDNF/TrkB signaling pathway, thus exerting antidepressant effects. It suggests that LY-02 is a potential novel therapeutic agent for depression treatment.

The issue of bacterial drug resistance has remained unresolved, and in recent years, biomimetic nanostructured surfaces inspired by nature have garnered significant attention due to their bactericidal properties demonstrated through mechanical mechanisms. This article reviewed the main research progress in the field of nanostructured mechanical bactericidal surfaces, including various preparation methods for nanostructured surfaces with mechanical bactericidal properties, as well as the basic mechanisms and related physical models of the interaction between bacteria and nanostructured surfaces. In addition, the application of nanostructured surfaces in biomedicine was introduced. Finally, the article proposed the major challenges faced by mechanical bactericidal research and the future development direction.

Image fusion currently plays an important role in the diagnosis of prostate cancer (PCa). Selecting and developing a good image fusion algorithm is the core task of achieving image fusion, which determines whether the fusion image obtained is of good quality and can meet the actual needs of clinical application. In recent years, it has become one of the research hotspots of medical image fusion. In order to make a comprehensive study on the methods of medical image fusion, this paper reviewed the relevant literature published at home and abroad in recent years. Image fusion technologies were classified, and image fusion algorithms were divided into traditional fusion algorithms and deep learning (DL) fusion algorithms. The principles and workflow of some algorithms were analyzed and compared, their advantages and disadvantages were summarized, and relevant medical image data sets were introduced. Finally, the future development trend of medical image fusion algorithm was prospected, and the development direction of medical image fusion technology for the diagnosis of prostate cancer and other major diseases was pointed out.

Ischemic stroke often leads to cognitive dysfunction, which delays the recovery process of patients. However, its pathogenesis is not yet clear. In this study, the cerebral ischemia-reperfusion model was built as the experimental object, and the hippocampal dentate gyrus (DG) was the target brain area. TTC staining was used to evaluate the degree of cerebral infarction, and nerve cell membrane potentials and local field potentials (LFPs) signals were collected to explore the mechanism of cognitive impairment in ischemia-reperfusion mice. The results showed that the infarcted area on the right side of the brain of the mice in the model group was white. The resting membrane potential, the number of action potential discharges, the post-hyperpolarization potential and the maximum ascending slope of the hippocampal DG nerve cells in the model mice were significantly lower than those in the control group ( P < 0.01); the peak time, half-wave width, threshold and maximum descending slope of the action potential were significantly higher than those in the control group ( P < 0.01). The time-frequency energy values of LFPs signals in the θ and γ bands of mice in the ischemia and reperfusion groups were significantly reduced ( P < 0.01), and the time-frequency energy values in the reperfusion group were increased compared with the ischemia group ( P < 0.01). The signal complexity of LFPs in the ischemia and reperfusion group was significantly reduced ( P < 0.05), and the signal complexity in the reperfusion group was increased compared with the ischemia group ( P < 0.05). In summary, cerebral ischemia-reperfusion reduced the excitability of nerve cells in the DG area of the mouse hippocampus; cerebral ischemia reduced the discharge activity and signal complexity of nerve cells, and the electrophysiological indicators recovered after reperfusion, but it failed to reach the healthy state during the experiment period.

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.

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.