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The present study aimed to investigate the effect of covalent coupling with the dietary polyphenol rosmarinic acid (RA) on the structure and properties of β-lactoglobulin (β-LG). β-LG-RA complexes were prepared using the free radical and alkaline methods. The structural changes of β-LG after coupling with RA were analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), Fourier transform infrared (FTIR) spectroscopy, ultraviolet (UV) absorption spectroscopy, fluorescence spectroscopy, and circular dichroism (CD) spectroscopy. The antioxidant activity of β-LG and its complexes with RA was assessed using the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical cation scavenging methods. Furthermore, their binding capacity to serum-specific immunoglobulin G (IgG) were evaluated using enzyme-linked immunosorbent assay (ELISA). The results revealed that the secondary structure of β-LG transformed from α-helix to β-sheet and random coil after RA covalent conjugation. The protein underwent changes in its secondary and tertiary structures. Additionally, compared to β-LG, the β-LG-RA covalent conjugates showed significantly enhanced antioxidant activity and reduced binding ability with serum specific IgG. In conclusion, covalent coupling with RA led to a significant improvement in the antioxidant activity of β-LG and a significant decrease in its binding capacity to serum-specific IgG by introducing phenolic hydroxyl groups.

Real‐time gait estimation is important for the synchrony control of robotic exoskeleton to provide walking assistance. However, for stroke patients with hemiplegic paralysis, the gait pattern is very complex. Accurate and timely gait intention recognition is therefore difficult. To achieve human–robot synchrony control for an unilateral knee exoskeleton, a gait intention recognizer coupling the adaptive frequency oscillator (AFO) and back propagation neural networks (BPNN) is proposed in this paper. The BPNN is trained with gait data of healthy subjects and stroke patients to improve the accuracy of recognized gait pattern, which is then imported into flexible interaction module to provide appropriate assistance. To evaluate the performance of gait intention recognition, three stroke patients were recruited to conduct level ground walking tests. The kinematic and biomechanical data were captured in each test and processed for the evaluation. Experimental results demonstrate the effectiveness of gait intention recognition and movement assistance. [ABSTRACT FROM AUTHOR]

Stroke is a group of diseases that damage brain tissue caused by sudden local cerebral blood circulation disorder. If not diagnosed and treated in time, the patient will lose a large number of neurons and even die. Among the examination methods, using magnetic resonance scans is more accurate than CT images, but it also has other risk factors. In response to these existing problems, this paper attempts to use a stroke classification model based on pre-trained CNN networks, which can help doctors detect MRI images of ischemic stroke and reduce time delays. This paper uses two real datasets. Dataset 1 includes 1112 cases of ischemic stroke and 1202 normal MRI images, while dataset 2 includes 1008 cases of ischemic stroke and 1002 normal MRI images. After data processing and noise reduction, two processed datasets, which contain 4448 cases, 4808 cases and 4032 cases, 4008 cases of ischemic stroke and normal MRI images, respectively, were obtained. Features are obtained by extracting the last pooling layer and the last fully connected layer of the pre-trained CNN network ResNet50 and DenseNet201. After combining the features, the iterative mRMR feature selection method is used to select image features. A support vector machine classifier with 10-fold cross-validation is used to classify the data. Finally, IMV is applied to the prediction vectors to get the voting results, and the best results are selected from the results. The evaluation indicators of the final experimental results in dataset 1 yielded 94.74%, 93.56%, 96.22%, and 94.87% results for accuracy, recall, precision, and F1 score, respectively. The evaluation indicators in dataset 2 yielded 92.24%, 92.21%, 92.26%, and 92.23% results for accuracy, recall, precision, and F1 score, respectively. These results clearly demonstrate that ischemic stroke can be successfully classified based on MRI and confirm the success of the hybrid feature engineering approach. [ABSTRACT FROM AUTHOR]

Utilizing deep learning for semantic segmentation of cropland from remote sensing imagery has become a crucial technique in land surveys. Cropland is highly heterogeneous and fragmented, and existing methods often suffer from inaccurate boundary segmentation. This paper introduces a UNet-like boundary-aware compensation model (BAFormer). Cropland boundaries typically exhibit rapid transformations in pixel values and texture features, often appearing as high-frequency features in remote sensing images. To enhance the recognition of these high-frequency features as represented by cropland boundaries, the proposed BAFormer integrates a Feature Adaptive Mixer (FAM) and develops a Depthwise Large Kernel Multi-Layer Perceptron model (DWLK-MLP) to enrich the global and local cropland boundaries features separately. Specifically, FAM enhances the boundary-aware method by adaptively acquiring high-frequency features through convolution and self-attention advantages, while DWLK-MLP further supplements boundary position information using a large receptive field. The efficacy of BAFormer has been evaluated on datasets including Vaihingen, Potsdam, LoveDA, and Mapcup. It demonstrates high performance, achieving mIoU scores of 84.5%, 87.3%, 53.5%, and 83.1% on these datasets, respectively. Notably, BAFormer-T (lightweight model) surpasses other lightweight models on the Vaihingen dataset with scores of 91.3% F1 and 84.1% mIoU. [ABSTRACT FROM AUTHOR]

Preventing and treating avascular necrosis at the distal end of the flaps are critical to surgery success, but current treatments are not ideal. A recent study shows that apoptotic bodies (ABs) generated near the site of apoptosis can be taken up and promote cell proliferation. The study reveals that ABs derived from fibroblast‐like cells in the subcutaneous connective tissue (FSCT cells) of skin flaps promoted ischaemic flap survival. It is also found that ABs inhibited cell death and oxidative stress and promoted M1‐to‐M2 polarization in macrophages. Transcriptome sequencing and protein level testing demonstrated that ABs promoted ischaemic flap survival in endothelial cells and macrophages by inhibiting ferroptosis via the KEAP1‐Nrf2 axis. Furthermore, microRNA (miR) sequencing data and in vitro and in vivo experiments demonstrated that ABs inhibited KEAP1 by delivering miR‐339‐5p to exert therapeutic effects. In conclusion, FSCT cell‐derived ABs inhibited ferroptosis, promoted the macrophage M1‐to‐M2 transition via the miR‐339‐5p/KEAP1/Nrf2 axis and promoted ischaemic flap survival. These results provide a potential therapeutic strategy to promote ischaemic flap survival by administering ABs. [ABSTRACT FROM AUTHOR]

Purpose: To explore 1) the level of shared decision-making (SDM) participation in intraocular lens (IOL) selection in cataract patients and the factors that influence this participation and 2) the relationships between preparation for decision-making (PrepDM)and the level of SDM participation and satisfaction with the decision (SWD). Provide guidance for improving SDM in ophthalmology. Patients and Methods: 176 cataract patients were asked to complete the PrepDM scale, the 9-item Shared Decision Making Questionnaire (SDM-Q-9) and the SWD instrument in IOL decision-making process. Multiple linear regression was used to analyze the influencing factors of the level of SDM. The Process program and bootstrap sampling method was used to test whether the level of participation in SDM was a mediating variable among the three. Results: The SDM-Q-9 median score was 77.78 (IQR 31.11– 88.89). Patients with a history of surgery in the operative eye (P=0.022) or PrepDM < 60 points (P< 0.001) had lower SDM-Q-9 scores than patients with no history of surgery in the operative eye or PrepDM ≥ 60 points. Patients with an education level lower than primary school had lower SDM-Q-9 scores than patients with other education levels (P< 0.05). The PrepDM of cataract patients was positively correlated with the level of SDM (r=0.768, P< 0.001) and with the SWD (r=0.727, P< 0.001), and the level of SDM was positively correlated with the SWD (r=0.856, P< 0.001). The level of SDM fully mediated PrepDM and SDW, with a mediating effect value of 0.128 and a mediating effect of 86.66% of the total effect. Conclusion: The SDM of cataract patients involved in IOL selection was in the upper middle range. Education, history of surgery in the operated eye, and PrepDM were factors that influenced the level of SDM. The level of participation in SDM fully mediated the relationship between PrepDM and SWD. [ABSTRACT FROM AUTHOR]

An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high complexity of detection results, low computational efficiency, and high load in traditional obstacle detection methods. Firstly, an NDT registration method which uses the likelihood function as the optimal value of the registration score function to calculate the registration parameters is designed to match the scanning point cloud and the target point cloud. Secondly, a target reduction method combined with threshold judgment and the binary tree search algorithm is designed to filter the point cloud of non-road obstacles to improve the processing speed of the computing platform. Meanwhile, KD-tree is used to speed up the clustering process. Finally, a vehicle remote control simulation platform with the combination of a cloud platform and mobile terminal is designed to verify the effectiveness of the strategy in practical application. The results prove that the proposed obstacle detection method can improve the efficiency and accuracy of detection. [ABSTRACT FROM AUTHOR]

The ability of Staphylococcus aureus (S. aureus) to survive within macrophages is a critical strategy for immune evasion, contributing to the pathogenesis and progression of osteomyelitis. However, the underlying mechanisms remain poorly characterized. This study discovered that inhibiting the MEK1/2 pathway reduced bacterial load and mitigated bone destruction in a mouse model of S. aureus osteomyelitis. Histological staining revealed increased phosphorylated MEK1/2 levels in bone marrow macrophages surrounding abscess in the mouse model of S. aureus osteomyelitis. Activation of MEK1/2 pathway and its roles in impairing macrophage bactericidal function were confirmed in primary mouse bone marrow-derived macrophages (BMDMs). Transcriptome analysis and in vitro experiments demonstrated that S. aureus activates the MEK1/2 pathway through EGFR signaling. Moreover, we found that excessive activation of EGFR-MEK1/2 cascade downregulates mitochondrial reactive oxygen species (mtROS) levels by suppressing Chek2 expression, thereby impairing macrophage bactericidal function. Furthermore, pharmacological inhibition of EGFR signaling prevented upregulation of phosphorylated MEK1/2 and restored Chek2 expression in macrophages, significantly enhancing S. aureus clearance and improving bone microstructure in vivo. These findings highlight the critical role of the EGFR-MEK1/2 cascade in host immune defense against S. aureus, suggesting that S. aureus may reduce mtROS levels by overactivating the EGFR-MEK1/2 cascade, thereby suppressing macrophage bactericidal function. Therefore, combining EGFR-MEK1/2 pathway blockade with antibiotics could represent an effective therapeutic approach for the treatment of S. aureus osteomyelitis. Author summary: Osteomyelitis, characterized by progressive inflammation and tissue destruction in bone, is predominantly caused by Staphylococcus aureus (S. aureus). However, treating S. aureus osteomyelitis remains a major clinical challenge due to its ability to survive in phagocytes like macrophages, evading host immune surveillance and antibiotic treatments, leading to recurrent and persistent infections. This study aimed to elucidate a potential molecular mechanism underlying the S. aureus survival in macrophages. Our findings demonstrate that S. aureus induces phosphorylation of MEK1/2 in macrophages localized at infection sites. Inhibiting either the MEK1/2 pathway or EGFR signaling enhances bacterial clearance both in vivo and in vitro. Persistent S. aureus infection was found to suppress Chek2 expression through the activation of the EGFR-MEK1/2 pathway, leading to decreased production of mitochondrial ROS and impaired macrophage bactericidal activity. This study highlights the critical role of EGFR-MEK1/2 pathway in macrophage antimicrobial dysfunction and the pathogenesis of S. aureus osteomyelitis. Targeting this pathway in conjunction with antibiotics may offer a promising therapeutic strategy in S. aureus osteomyelitis. [ABSTRACT FROM AUTHOR]