工业工程最新文献

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.

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.

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.

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.

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.

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.

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.

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.