工业工程最新文献

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.

Amine oxidase copper-containing 1 (AOC1) is a key member of copper amine oxidase family, which is responsible for deamination oxidation of histamine and putrescine. In recent years, AOC1 has been reported to be associated with various cancers, with its expression levels significantly elevated in certain cancer cells, suggesting its potential role in cancer progression. However, its function in lipid metabolism still remains unclear. Through genetic analysis, we have discovered a potential relationship between AOC1 and lipid metabolism. To further investigate, we generated Aoc1 ^-/- mice and characterized their metabolic phenotypes on both chow diet and high-fat diet (HFD) feeding conditions. On HFD feeding conditions, Aoc1 ^-/- mice exhibited significantly higher fat mass and impaired glucose sensitivity, and lipid accumulation in white adipose tissue and liver was also increased. This study uncovers the potential role of AOC1 in lipid metabolism and its implications in metabolic disorders such as obesity and type 2 diabetes, providing new targets and research directions for treating metabolic diseases.

Currently, the development of deep learning-based multimodal learning is advancing rapidly, and is widely used in the field of artificial intelligence-generated content, such as image-text conversion and image-text generation. Electronic health records are digital information such as numbers, charts, and texts generated by medical staff using information systems in the process of medical activities. The multimodal fusion method of electronic health records based on deep learning can assist medical staff in the medical field to comprehensively analyze a large number of medical multimodal data generated in the process of diagnosis and treatment, thereby achieving accurate diagnosis and timely intervention for patients. In this article, we firstly introduce the methods and development trends of deep learning-based multimodal data fusion. Secondly, we summarize and compare the fusion of structured electronic medical records with other medical data such as images and texts, focusing on the clinical application types, sample sizes, and the fusion methods involved in the research. Through the analysis and summary of the literature, the deep learning methods for fusion of different medical modal data are as follows: first, selecting the appropriate pre-trained model according to the data modality for feature representation and post-fusion, and secondly, fusing based on the attention mechanism. Lastly, the difficulties encountered in multimodal medical data fusion and its developmental directions, including modeling methods, evaluation and application of models, are discussed. Through this review article, we expect to provide reference information for the establishment of models that can comprehensively utilize various modal medical data.

Glaucoma stands as the leading irreversible cause of blindness worldwide. Regular visual field examinations play a crucial role in both diagnosing and treating glaucoma. Predicting future visual field changes can assist clinicians in making timely interventions to manage the progression of this disease. To integrate temporal and spatial features from past visual field examination results and enhance visual field prediction, a convolutional long short-term memory (ConvLSTM) network was employed to construct a predictive model. The predictive performance of the ConvLSTM model was validated and compared with other methods using a dataset of perimetry tests from the Humphrey field analyzer at the University of Washington (UWHVF). Compared to traditional methods, the ConvLSTM model demonstrated higher prediction accuracy. Additionally, the relationship between visual field series length and prediction performance was investigated. In predicting the visual field using the previous three visual field results of past 1.5~6.0 years, it was found that the ConvLSTM model performed better, achieving a mean absolute error of 2.255 dB, a root mean squared error of 3.457 dB, and a coefficient of determination of 0.960. The experimental results show that the proposed method effectively utilizes existing visual field examination results to achieve more accurate visual field prediction for the next 0.5~2.0 years. This approach holds promise in assisting clinicians in diagnosing and treating visual field progression in glaucoma patients.

The hemodynamic parameters in arteries are difficult to measure non-invasively, and the analysis and prediction of hemodynamic parameters based on computational fluid dynamics (CFD) has become one of the important research hotspots in biomechanics. This article establishes 15 idealized left coronary artery bifurcation models with concomitant stenosis and aneurysm lesions, and uses CFD method to numerically simulate them, exploring the effects of left anterior descending branch (LAD) stenosis rate and curvature radius on the hemodynamics inside the aneurysm. This study compared models with different stenosis rates and curvature radii and found that as the stenosis rate increased, the oscillatory shear index (OSI) and relative residence time (RRT) showed a trend of increase; In addition, the decrease in curvature radius led to an increase in the degree of vascular curvature and an increased risk of vascular aneurysm rupture. Among them, when the stenosis rate was less than 60%, the impact of stenosis rate on aneurysm rupture was greater, and when the stenosis rate was greater than 60%, the impact of curvature radius was more significant. Based on the research results of this article, it can be concluded that by comprehensively considering the effects of stenosis rate and curvature radius on hemodynamic parameters, the risk of aneurysm rupture can be analyzed and predicted. This article uses CFD methods to deeply explore the effects of stenosis rate and curvature radius on the hemodynamics of aneurysms, providing new theoretical basis and prediction methods for the assessment of aneurysm rupture risk, which has important academic value and practical guidance significance.

Tumors pose a significant global health concern and have long been a challenging issue in the medical field. Given its treatment dilemma, it is urgent to explore novel prevention and treatment strategies. Bilirubin, as a natural endogenous antioxidant, has the ability to inhibit the production of free radicals in the body, thereby alleviating the damage caused by oxidative stress to the organism. In recent years, the therapeutic effects of bilirubin in diseases mediated by oxidative stress and metabolic disorders have gradually gained widespread attention, demonstrating its potential therapeutic value for a variety of diseases. With further research, significant progress has also been made in the study of bilirubin in the field of oncology, suggesting its potential important role in the occurrence, development, and treatment of tumors. This article aims to review and summarize the recent advances in the study of bilirubin in the field of oncology, in order to provide new insights and guidance for the future directions of tumor diagnosis, prevention, and treatment.

The study aimed to explore the effect and mechanism of resistance exercise (RE) on cognitive dysfunction in type 2 diabetes mellitus (T2DM) mice. Six 8-week-old male m/m mice were used as control (Con) group, and db/db mice of the matched age were randomly divided into model control (db/db) group and db+RE group, with 6 mice in each group. The db+RE group was given 8 weeks of resistance climbing ladder exercise intervention. The fasting blood glucose and body weight of the mice were measured weekly. After the intervention, the spatial learning and memory of the mice were detected by Morris water maze, and the neuronal damage in the hippocampus of the mice was detected by Nissl staining. The protein expression levels of PSD93, PSD95, BDNF, CREB, p-CREB, IL-6, IL-1β, TNF-α, Iba-1, iNOS, CD206, Arg1, triggering receptor expressed on myeloid cells 2 (TREM2), NF-κB, p-STAT3, and STAT3 were detected by Western blot. The mRNA expression levels of inflammatory factors and TREM2 in hippocampus were evaluated by qRT-PCR, and the expression and localization of Iba-1, CD206, CD86, and TREM2 were determined by immunofluorescence staining. The results showed that the spatial learning and memory of the db/db group were significantly declined, the neurons in the hippocampus were damaged, the protein levels of PSD93, PSD95, BDNF, CD206, Arg1, TREM2 and the ratio of p-CREB/CREB were significantly down-regulated, the mRNA and protein expression levels of IL-6, IL-1β and TNF-α were significantly up-regulated, and the protein levels of iNOS, Iba-1, NF-κB and the ratio of p-STAT3/STAT3 were significantly increased compared with the Con group. However, the 8-week RE improved the spatial learning and memory of db/db mice, alleviated the damage of hippocampal neurons, promoted the polarization of M2 microglia, and inhibited the neuroinflammation. The above results suggest that RE can improve cognitive dysfunction in T2DM mice, and its mechanism may be related to regulating microglia polarization via TREM2/NF-κB/STAT3 signaling pathway.

Fear emotion is a typical negative emotion that is commonly present in daily life and significantly influences human behavior. A deeper understanding of the mechanisms underlying negative emotions contributes to the improvement of diagnosing and treating disorders related to negative emotions. However, the neural mechanisms of the brain when faced with fearful emotional stimuli remain unclear. To this end, this study further combined electroencephalogram (EEG) source analysis and cortical brain network construction based on early posterior negativity (EPN) analysis to explore the differences in brain information processing mechanisms under fearful and neutral emotional picture stimuli from a spatiotemporal perspective. The results revealed that neutral emotional stimuli could elicit higher EPN amplitudes compared to fearful stimuli. Further source analysis of EEG data containing EPN components revealed significant differences in brain cortical activation areas between fearful and neutral emotional stimuli. Subsequently, more functional connections were observed in the brain network in the alpha frequency band for fearful emotions compared to neutral emotions. By quantifying brain network properties, we found that the average node degree and average clustering coefficient under fearful emotional stimuli were significantly larger compared to neutral emotions. These results indicate that combining EPN analysis with EEG source component and brain network analysis helps to explore brain functional modulation in the processing of fearful emotions with higher spatiotemporal resolution, providing a new perspective on the neural mechanisms of negative emotions.

Stent migration is one of the common complications after tracheal stent implantation. The causes of stent migration include size mismatch between the stent and the trachea, physiological movement of the trachea, and so on. In order to solve the above problems, this study designed a non-uniform Poisson ratio tracheal stent by combining the size and structure of the trachea and the physiological movement of the trachea to improve the migration of the stent, meanwhile ensuring the support of the stent. In this study, the stent corresponding to cartilage was constructed with negative Poisson's ratio, and the stent corresponding to the circular connective tissue and muscular membrane was constructed with positive Poisson's ratio. And four kinds of non-uniform Poisson's ratio tracheal stents with different link lengths and negative Poisson's ratio were designed. Then, this paper numerically simulated the expansion and rebound process of the stent after implantation to observe the support of the stent, and further simulated the stretch movement of the trachea to calculate the diameter changes of the stent corresponding to different negative Poisson's ratio structures. The axial migration of the stent was recorded by applying different respiratory pressure to the wall of the tracheal wall to evaluate whether the stent has anti-migration effect. The research results show that the non-uniform Poisson ratio stent with connecting rod length of 3 mm has the largest diameter expansion in the negative Poisson ratio section when the trachea was stretched. Compared with the positive Poisson's ratio structure, the axial migration during vigorous breathing was reduced from 0.024 mm to 0.012 mm. The negative Poisson's ratio structure of the non-uniform Poisson's ratio stent designed in this study did not fail in the tracheal expansion effect. Compared with the traditional stent, the non-uniform Poisson's ratio tracheal stent has an anti-migration effect under the normal movement of the trachea while ensuring the support force of the stent.

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.