工业工程最新文献

Accurately evaluating the local biomechanics of arterial wall is crucial for diagnosing and treating arterial diseases. Indentation measurement can be used to evaluate the local mechanical properties of the artery. However, the effects of the indenter's geometric structure and the analysis theory on measurement results remain uncertain. In this paper, four kinds of indenters were used to measure the pulmonary aorta, the proximal thoracic aorta and the distal thoracic aorta in pigs, and the arterial elastic modulus was calculated by Sneddon and Sirghi theory to explore the influence of the indenter geometry and analysis theory on the measured elastic modulus. The results showed that the arterial elastic modulus measured by cylindrical indenter was lower than that measured by spherical indenter. In addition, compared with the calculated results of Sirghi theory, the Sneddon theory, which does not take adhesion forces in account, resulted in slightly larger elastic modulus values. In conclusion, this study provides parametric support for effective measurement of arterial local mechanical properties by millimeter indentation technique.

Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5-9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.

The stiffness of an ideal fracture internal fixation implant should have a time-varying performance, so that the fracture can generate reasonable mechanical stimulation at different healing stages, and biodegradable materials meet this performance. A topology optimization design method for composite structures of fracture internal fixation implants with time-varying stiffness is proposed, considering the time-dependent degradation process of materials. Using relative density and degradation residual rate to describe the distribution and degradation state of two materials with different degradation rates and elastic modulus, a coupled mathematical model of degradation simulation mechanical analysis was established. Biomaterial composite structures were designed based on variable density method to exhibit time-varying stiffness characteristics. Taking the bone plate used for the treatment of tibial fractures as an example, a composite structure bone plate with time-varying stiffness characteristics was designed using the proposed method. The optimization results showed that material 1 with high stiffness formed a columnar support structure, while material 2 with low stiffness was distributed at the degradation boundary and inside. Using a bone remodeling simulation model, the optimized bone plates were evaluated. After 11 months of remodeling, the average elastic modulus of callus using degradable time-varying stiffness plates, titanium alloy plates, and stainless steel plates were 8 634 MPa, 8 521 MPa, and 8 412 MPa, respectively, indicating that the use of degradable time-varying stiffness plates would result in better remodeling effects on the callus.

The surface morphology of titanium metal is an important factor affecting its hydrophilicity and biocompatibility, and exploring the surface treatment strategy of titanium metal is an important way to improve its biocompatibility . In this study , titanium (TA4) was firstly treated by large particle sand blasting and acid etching (SLA) technology, and then the obtained SLA-TA4 was treated by single surface treatments such as alkali-heat, ultraviolet light and plasma bombardment. According to the experimental results, alkali-heat treatment is the best treatment method to improve and maintain surface hydrophilicity of titanium. Then, the nanowire network morphology of titanium surface and its biological property, formed by further surface treatments on the basis of alkali-heat treatment, were investigated. Through the cell adhesion experiment of mouse embryonic osteoblast cells (MC3T3-E1), the ability of titanium material to support cell adhesion and cell spreading was investigated after different surface treatments. The mechanism of biological activity difference of titanium surface formed by different surface treatments was investigated according to the contact angle, pit depth and roughness of the titanium sheet surface. The results showed that the SLA-TA4 titanium sheet after a treatment of alkali heat for 10 h and ultraviolet irradiation for 1 h has the best biological activity and stability. From the perspective of improving surface bioactivity of medical devices, this study has important reference value for relevant researches on surface treatment of titanium implantable medical devices.

In the extraction of fetal electrocardiogram (ECG) signal, due to the unicity of the scale of the U-Net same-level convolution encoder, the size and shape difference of the ECG characteristic wave between mother and fetus are ignored, and the time information of ECG signals is not used in the threshold learning process of the encoder's residual shrinkage module. In this paper, a method of extracting fetal ECG signal based on multi-scale residual shrinkage U-Net model is proposed. First, the Inception and time domain attention were introduced into the residual shrinkage module to enhance the multi-scale feature extraction ability of the same level convolution encoder and the utilization of the time domain information of fetal ECG signal. In order to maintain more local details of ECG waveform, the maximum pooling in U-Net was replaced by Softpool. Finally, the decoder composed of the residual module and up-sampling gradually generated fetal ECG signals. In this paper, clinical ECG signals were used for experiments. The final results showed that compared with other fetal ECG extraction algorithms, the method proposed in this paper could extract clearer fetal ECG signals. The sensitivity, positive predictive value, and F1 scores in the 2013 competition data set reached 93.33%, 99.36%, and 96.09%, respectively, indicating that this method can effectively extract fetal ECG signals and has certain application values for perinatal fetal health monitoring.

In response to the issues of single-scale information loss and large model parameter size during the sampling process in U-Net and its variants for medical image segmentation, this paper proposes a multi-scale medical image segmentation method based on pixel encoding and spatial attention. Firstly, by redesigning the input strategy of the Transformer structure, a pixel encoding module is introduced to enable the model to extract global semantic information from multi-scale image features, obtaining richer feature information. Additionally, deformable convolutions are incorporated into the Transformer module to accelerate convergence speed and improve module performance. Secondly, a spatial attention module with residual connections is introduced to allow the model to focus on the foreground information of the fused feature maps. Finally, through ablation experiments, the network is lightweighted to enhance segmentation accuracy and accelerate model convergence. The proposed algorithm achieves satisfactory results on the Synapse dataset, an official public dataset for multi-organ segmentation provided by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), with Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) scores of 77.65 and 18.34, respectively. The experimental results demonstrate that the proposed algorithm can enhance multi-organ segmentation performance, potentially filling the gap in multi-scale medical image segmentation algorithms, and providing assistance for professional physicians in diagnosis.

The present study aimed to explore the effects of different exercise modes on neuromuscular junction (NMJ) and metabolism of skeletal muscle-related proteins in aging rats. Ten from 38 male Sprague-Dawley (SD) rats (3-month-old) were randomly selected into young (Y) group, while the rest were raised to 21 months old and randomly divided into elderly control (O), endurance exercise (EN) and resistance exercise (R) groups. After 8 weeks of corresponding exercises training, the gastrocnemius muscles of rats were collected, and the expression of S100B in Schwann cells was detected by immunofluorescence staining. Western blot was used to detect the protein expression levels of agglutinate protein (Agrin), low-density lipoprotein receptor-related protein 4 (Lrp4), muscle- specific kinase protein (MuSK), downstream tyrosine kinase 7 (Dok7), phosphorylated protein kinase B (p-Akt), phosphorylated mammalian target rapamycin (p-mTOR), and phosphorylated forkhead box O1 (p-FoxO1) in rat gastrocnemius muscles. The results showed that, endurance and resistance exercises increased the wet weight ratio of gastrocnemius muscle in the aging rats. The protein expression of S100B in the R group was significantly higher than those in the O and EN groups. Proteins related to NMJ function, including Agrin, Lrp4, MuSK, and Dok7 were significantly decreased in the O group compared with those in the Y group. Resistance exercise up-regulated these four proteins in the aging rats, whereas endurance exercise could not reverse the protein expression levels of Lrp4, MuSK and Dok7. Regarding skeletal muscle-related proteins, the O group showed down-regulated p-Akt, and p-mTOR protein expression levels and up-regulated p-FoxO1 protein expression level, compared to the Y group. Resistance and endurance exercises reversed the changes in p-mTOR and p-FoxO1 protein expression in the aging rats. These findings demonstrate that both exercise modes can enhance NMJ function, increase protein synthesis and reduce the catabolism of skeletal muscle-related proteins in aging rats, with resistance exercise showing a more pronounced effect.

Hemoglobinopathies are one of the most common single-gene genetic disorders globally, with approximately 1% to 5% of the global population carrying the mutated gene for thalassemia. Thalassemia are classified into transfusion-dependent thalassemia and non-transfusion-dependent thalassemia based on the need for blood transfusion. Traditional treatment modalities include blood transfusion, splenectomy, hydroxyurea therapy, and iron chelation therapy, which are now widely used for clinical treatment and constitute the main methods recommended in the β-thalassemia treatment guidelines. However, there are multiple barriers and limitations to the application of these approaches, and there is an urgent need to explore new therapeutic approaches. With the in-depth study of the pathophysiological process of β-thalassemia, a deeper understanding of the pathogenesis of the disease has been gained. It has been demonstrated that the pathogenesis of thalassemia is closely related to ineffective erythropoiesis (IE), imbalance in the ratio of α/β-globin protein chains and iron overload. New therapeutic approaches are emerging for different pathogenic mechanisms. Among them, new drugs for the treatment of IE mainly include activin receptor II trap ligands, Janus kinase 2 inhibitors, pyruvate kinase activators, and glycine transporter protein 1 inhibitors. Correcting the imbalance in the hemoglobin chain is mainly due to emerging technologies such as bone marrow transplantation and gene editing. Measures in reducing iron overload are associated with inhibiting the activity of transferrin and hepcidin. These new approaches provide new ideas and options for the treatment and management of β-thalassemia.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.

Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of ischemic stroke. Accurate segmentation of the infarct is of great significance for selecting intervention treatment methods and evaluating the prognosis of patients. To address the issue of poor segmentation accuracy of existing methods for multiscale stroke lesions, a novel encoder-decoder architecture network based on depthwise separable convolution is proposed. Firstly, this network replaces the convolutional layer modules of the U-Net with redesigned depthwise separable convolution modules. Secondly, an modified Atrous spatial pyramid pooling (MASPP) is introduced to enlarge the receptive field and enhance the extraction of multiscale features. Thirdly, an attention gate (AG) structure is incorporated at the skip connections of the network to further enhance the segmentation accuracy of multiscale targets. Finally, Experimental evaluations are conducted using the ischemic stroke lesion segmentation 2022 challenge (ISLES2022) dataset. The proposed algorithm in this paper achieves Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity (SEN), and precision (PRE) scores of 0.816 5, 3.668 1, 0.889 2, and 0.894 6, respectively, outperforming other mainstream segmentation algorithms. The experimental results demonstrate that the method in this paper effectively improves the segmentation of infarct lesions, and is expected to provide a reliable support for clinical diagnosis and treatment.