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BackgroundThe Ilizarov technique of distraction osteogenesis is an effective treatment for tibia defect. However, repeated attempts to reduce due to the complexity of the bone defect during the operation will increase the operation time and iatrogenic injury, and excessive radiation exposure. Three-dimensional (3D)-printed patient-specific instrument (PSI) for preoperative 3D planning and intraoperative navigation have the advantages of accuracy and visualization. The purpose of this study is to investigate whether 3D-printed PSI is helpful to correct tibial bone defects accurately and effectively.MethodFrom May 2019 to September 2022, 19 patients with tibial bone defects were treated, including 9 males and 10 females, aged 37 to 64 years. There were 4 cases in proximal tibia, 9 in midshaft tibia and 6 in distal tibia. All were treated with Ilizarov technique of distraction osteogenesis. 3D-printed PSI was used in 9 cases, while traditional surgery was used in 10 cases. All patients underwent computed tomography before surgery. Computer software was used to analyze the measurement results, design and print PSI. During the operation, PSI was used to assist in reduction of tibia. Operation times were recorded in all cases, the number of fluoroscopy during the operation, and the varus/valgus, anteversion/reversion angle after the operation were measured. All measurement data were expressed by means ± SD, and Student's t test was used to examine differences between groups. The chi square test or Fisher's precise test was used to compare the counting data of the two groups.ResultAll PSI matched well with the corresponding tibia bone defect, and were consistent with the preoperative plan and intraoperative operation. The affected limb had a good reduction effect. The operation time from the beginning of PSI installation to the completion of Ilizarov ring fixator installation was 31.33 ± 3.20 min, while that in the traditional operation group was 64.10 ± 6.14 min (p

Objective: Anlotinib is a novel tyrosine kinase inhibitor blocking angiogenesis. This study was performed to assess the efficacy and safety of anlotinib in patients with metastatic breast cancer. Methods: Patients with HER2-negative breast cancer, who were pre-treated with anthracycline or taxanes in a neoadjuvant, adjuvant, or metastatic setting, and had treatment failure after at least one prior chemotherapy regimen in the metastatic setting were enrolled. Anlotinib was administered at 12 mg daily for 14 days in a 21-day cycle until disease progression or unacceptable toxicity occurred. Simultaneously, 5–10 mL of venous blood was collected to perform circulating tumor DNA (ctDNA) testing every 2 treatment cycles. The primary endpoint was the objective response rate (ORR). Secondary endpoints included the disease control rate (DCR), progression-free survival (PFS), overall survival, safety, and biomarkers. Results: Twenty-six eligible patients were enrolled, with a median age of 56 (30–75) years. The median follow-up time was 10.5 months. The ORR was 15.4%, the DCR was 80.8%, and the median PFS was 5.22 months (95% confidence interval 2.86–6.24). Fourteen (53.8%) patients survived for more than 10 months. The changes in the detectable ctDNA variant allele frequency were consistent with the tumor response. The most common treatment-related adverse events were hypertension (57.7%), thyroid-stimulating hormone elevation (34.6%), and hand-foot syndrome (23.1%). Conclusion: Anlotinib showed objective efficacy with tolerable toxicity in heavily pre-treated, metastatic HER2-negative breast cancer. The dynamic changes in the ctDNA variant allele fraction may be predictive of the tumor response.

Mutation of distal-less homeobox 3 (DLX3) is responsible for human tricho-dento-osseous syndrome (TDO) with amelogenesis imperfecta, indicating a crucial role of DLX3 in amelogenesis. However, the expression pattern of DLX3 and its specific function in amelogenesis remain largely unknown. The aim of this study was to investigate the effects of DLX3 on enamel matrix protein (EMP) genes. By immunohistochemistry assays of mouse tooth germs, stronger immunostaining of DLX3 protein was identified in ameloblasts in the secretory stage than in the pre-secretory and maturation stages, and the same pattern was found for Dlx3 mRNA using Realtime PCR. In a mouse ameloblast cell lineage, forced expression of DLX3 up-regulated the expression of the EMP genes Amelx, Enam, Klk4, and Odam, whereas knockdown of DLX3 down-regulated these four EMP genes. Further, bioinformatics, chromatin immunoprecipitation, and luciferase assays revealed that DLX3 transactivated Enam, Amelx, and Odam through direct binding to their enhancer regions. Particularly, over-expression of mutant-DLX3 (c.571_574delGGGG, responsible for TDO) inhibited the activation function of DLX3 on expression levels and promoter activities of the Enam, Amelx, and Odam genes. Together, our data show that DLX3 promotes the expression of the EMP genes Amelx, Enam, Klk4, and Odam in amelogenesis, while mutant-DLX3 disrupts this regulatory function, thus providing insights into the molecular mechanisms underlying the enamel defects of TDO disease.

BACKGROUND: MiR-499 is a cardiac-abundant miRNA. However, the biological functions of miR-499 in differentiated cardiomyocytes or in the cardiomyocyte differentiation process is not very clear. Sox6 is believed to be one of its targets, and is also believed to play a role in cardiac differentiation. Therefore, our aim was to investigate the association between Sox6 and miR-499 during cardiac differentiation. METHODOLOGY/PRINCIPAL FINDINGS: Using a well-established in vitro cardiomyocyte differentiation system, mouse P19CL6 cells, we found that miR-499 was highly expressed in the late stage of cardiac differentiation. In cells stably transfected with miR-499 (P-499 cells), it was found that miR-499 could promote the differentiation into cardiomyocytes at the early stage of cardiac differentiation. Notably, cell viability assay, EdU incorporation assay, and cell cycle profile analysis all showed that the P-499 cells displayed the distinctive feature of hyperplastic growth. Further investigation confirmed that miR-499 could promote neonatal rat cardiomyocyte proliferation. MiR-499 knock-down enhanced apoptosis in the late differentiation stage in P19CL6 cells, but overexpression of miR-499 resulted in a decrease in the apoptosis rate. Sox6 was identified as a direct target of miR-499 and its expression was detected from day 8 or day 10 of cardiac differentiation of P19CL6 cells. Sox6 played a role in cell viability, inhibited cell proliferation and promoted cell apoptosis in P19CL6 cells and cardiomyocytes. The overexpression of Sox6 could reverse the proliferation and anti-apoptosis effects of miR-499. It was also found that miR-499 might exert its function by regulating cyclin D1 via its influence on Sox6. CONCLUSIONS/SIGNIFICANCE: miR-499 probably regulates the proliferation and apoptosis of P19CL6 cells in the late stage of cardiac differentiation via its effects on Sox6 and cyclin D1.

Recent findings suggest the feasibility of cardiac repair by transplantation of bone marrow mesenchymal stem cell (MSCs). However, it remains controversial regarding which cell type is the best source for transplanting into the ischemic heart because of lack of well-defined cell markers. In this study, we investigated the in vitro and in vivo effects of the novel multipotent marrow mesenchymal stem cells (MMSCs) from human bone marrow. Pluripotent markers (Oct4, Bmi1, and Abcg2) and vascular endothelial growth factor (VEGF) were detected by RT-PCR and immunofluorescence in MMSCs. Myocardial differentiation was induced in the expanded MMSC cultures by treatment with 5-azacyline. Expressions of VEGF in the animals transplanted with MMSCs were markedly increased in comparison with the animals injected with fibroblasts or saline at both mRNA and protein levels. VEGF expression was observed in both transplanted MMSCs and recipient cardiomyocytes by immunofluorescence. Confocal immunofluorescence microscopy revealed the specific markers for cardiomyocytes and endothelial cells in transplanted MMSCs 14 days after transplantation. Vessel count was increased and left ventricular function improved post-MMSC transplantation. These results indicate that transplantation of purified MMSCs from human bone marrow upregulated VEGF expression, enhanced angiogenesis, and improved the functional recovery following myocardial infarction in rats.

Abstract Background Information complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation. Purpose The purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method. Methods This paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image. Results Qualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics. Conclusion The proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

The inertial navigation system (INS), which is frequently used in emergency rescue operations and other situations, has the benefits of not relying on infrastructure, high positioning frequency, and strong real-time performance. However, the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time. This paper aims to enhance the accuracy of zero-velocity interval (ZVI) detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet. Aiming at the observational noise problem of low-cost inertial sensors, we utilize a denoising autoencoder to automatically eliminate the inherent noise. Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error, we propose a sample-level ZVI detection algorithm based on the U-Net neural network, which effectively solves the problem of mislabeling caused by sliding windows. Aiming at the problem that Zero-Velocity Update (ZUPT) cannot suppress heading and altitude error, we propose a bipedal INS method based on the equation constraint and ellipsoid constraint, which uses foot-to-foot distance as a new observation to correct heading and altitude error. We conduct extensive and well-designed experiments to evaluate the performance of the proposed method. The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

Objectives Part of the tumor localization methods in radiotherapy have poor real-time performance and may generate additional radiation. We propose a multimodal point cloud-based method for tumor localization in robotic ultrasound-guided radiotherapy, which only irradiates computed tomography (CT) during radiotherapy planning to avoid additional radiation. Methods The tumor position was determined using the CT point cloud, and the red green blue depth (RGBD) point cloud was used to determine body surface scanning location corresponding to the tumor location. The relationship between the CT point cloud and RGBD point cloud was established through multi-modal point cloud registration. The point cloud was then used for robot tumor localization through coordinate transformation between camera and robot. Results The maximum mean absolute error of the tumor location in the X, Y, and Z directions of the robot coordinate system were 0.781, 1.334, and 1.490 mm, respectively. The average point-to-point translation mean absolute error between the actual and predicted positions of the localization points was 1.847 mm. The maximum error in the random positioning experiment was 1.77 mm. Conclusion The proposed method is radiation free and has real-time performance, with tumor localization accuracy that meets the requirements of radiotherapy. The proposed method, which potentially reduces the risks associated with radiation exposure while ensuring efficient and accurate tumor localization, represents a promising advancement in the field of radiotherapy.

Abstract Purpose To assess changes in neurovascular coupling (NVC) by evaluating the relationship between cerebral perfusion and brain connectivity in patients with end‐stage renal disease (ESRD) undergoing hemodialysis versus in healthy control participants. And by exploring brain regions with abnormal NVC associated with cognitive deficits in patients, we aim to provide new insights into potential preventive and therapeutic interventions. Materials and methods A total of 45 patients and 40 matched healthy controls were prospectively enrolled in our study. Montreal Cognitive Assessment (MoCA) was used to assess cognitive function. Arterial spin labeling (ASL) was used to calculate cerebral blood flow (CBF), and graph theory–based analysis of results from resting‐state functional magnetic resonance imaging (rs‐fMRI) was used to calculate brain network topological parameters (node betweenness centrality [BC], node efficiency [Ne], and node degree centrality [DC]). Three NVC biomarkers (CBF‐BC, CBF‐Ne, and CBF‐DC coefficients) at the whole brain level and 3 NVC biomarkers (CBF/BC, CBF/Ne, and CBF/DC ratios) at the local brain region level were used to assess NVC. Mann–Whitney U tests were used to compare the intergroup differences in NVC parameters. Spearman's correlation analysis was used to evaluate the relationship among NVC dysfunctional pattern, cognitive impairment, and clinical characteristics multiple comparisons were corrected using a voxel‐wise false‐discovery rate (FDR) method (p

Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients.

Brain networks/graphs have been widely recognized as powerful and efficient tools for identifying neurological disorders. In recent years, various graph neural network models have been developed to automatically extract features from brain networks. However, a key limitation of these models is that the inputs, namely brain networks/graphs, are constructed using predefined statistical metrics (e.g., Pearson correlation) and are not learnable. The lack of learnability restricts the flexibility of these approaches. While statistically-specific brain networks can be highly effective in recognizing certain diseases, their performance may not exhibit robustness when applied to other types of brain disorders. To address this issue, we propose a novel module called Brain Structure Inference (termed BSI), which can be seamlessly integrated with multiple downstream tasks within a unified framework, enabling end-to-end training. It is highly flexible to learn the most beneficial underlying graph structures directly for specific downstream tasks. The proposed method achieves classification accuracies of 74.83% and 79.18% on two publicly available datasets, respectively. This suggests an improvement of at least 3% over the best-performing existing methods for both tasks. In addition to its excellent performance, the proposed method is highly interpretable, and the results are generally consistent with previous findings.

Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the Euclidean distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis.

AbstractObjective The brain neuromechanism in maintenance hemodialysis patients (MHD) with cognitive impairment (CI) remains unclear. The study aimed to probe the relationship between spontaneous brain activity and CI by using resting-state functional magnetic resonance imaging (rs-fMRI) data.Methods Here, 55 MHD patients with CI and 28 healthy controls were recruited. For baseline data, qualitative data were compared between groups using the χ2 test; quantitative data were compared between groups using the independent samples t-test, ANOVA test, Mann–Whitney U-test, or Kruskal–Wallis test. Comparisons of ALFF/fALFF/ReHo values among the three groups were calculated by using the DPABI toolbox, and then analyzing the correlation with clinical variables. p

Selenium (Se) is a micronutrient known for its essential role in human health and plant metabolism. Waxy maize (Zea mays L. sinensis kulesh)—known for its high nutritional quality and distinctive flavor—holds significant consumer appeal. Therefore, this study aims to assess the effects of foliar Se spraying on the nutritional quality of waxy maize grains, with a focus on identifying varietal differences and determining optimal Se dosage levels for maximizing nutritional benefits. We employed a two-factor split-plot design to assess the nutritional quality, trace elements, and pigment content of jinnuo20 (J20) and caitiannuo1965 (C1965) at the milk stage after being subjected to varying Se doses sprayed on five leaves. Our findings indicate superior nutrient content in J20 compared to C1965, with both varieties exhibiting optimal quality under Se3 treatment, falling within the safe range of Se-enriched agricultural products. JS3 (0.793) demonstrated the highest overall quality, followed by JS2 (0.606), JS4 (0.411), and JS1 (0.265), while CS0 had the lowest (−0.894). These results underscore the potential of foliar biofortification to enhance the functional component contents of waxy maize grains.

As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction. Therefore, this article is a systematic review of the latest research on DL in cancer prognosis prediction. First, the data type, construction process, and performance evaluation index of the DL model are introduced in detail. Then, the current mainstream baseline DL cancer prognosis prediction models, namely, deep neural networks, convolutional neural networks, deep belief networks, deep residual networks, and vision transformers, including network architectures, the latest application in cancer prognosis, and their respective characteristics, are discussed. Next, some key factors that affect the predictive performance of the model and common performance enhancement techniques are listed. Finally, the limitations of the DL cancer prognosis prediction model in clinical practice are summarized, and the future research direction is prospected. This article could provide relevant researchers with a comprehensive understanding of DL cancer prognostic models and is expected to promote the research progress of cancer prognosis prediction.

Purpose: During ultrasound (US)-guided radiotherapy, the tissue is deformed by probe pressure, and the US image is limited by changes in tissue and organ position and geometry when the US image is aligned with computed tomography (CT) image, leading to poor alignment. Accordingly, a pixel displacement-based nondeformed US image production method is proposed. Methods: The correction of US image deformation is achieved by calculating the pixel displacement of an image. The positioning CT image (CT std ) is used as the gold standard. The deformed US image (US def ) is inputted into the Harris algorithm to extract corner points for selecting feature points, and the displacement of adjacent pixels of feature points in the US video stream is calculated using the Lucas-Kanade optical flow algorithm. The moving least squares algorithm is used to correct US def globally and locally in accordance with image pixel displacement to generate a nondeformed US image (US rev ). In addition, US def and US rev were separately aligned with CT std to evaluate the improvement of alignment accuracy through deformation correction. Results: In the phantom experiment, the overall and local average correction errors of the US image under the optimal probe pressure were 1.0944 and 0.7388 mm, respectively, and the registration accuracy of US def and US rev with CT std was 0.6764 and 0.9016, respectively. During the volunteer experiment, the correction error of all 12 patients’ data ranged from −1.7525 to 1.5685 mm, with a mean absolute error of 0.8612 mm. The improvement range of US and CT registration accuracy, before and after image deformation correction in the 12 patients evaluated by a normalized correlation coefficient, was 0.1232 to 0.2476. Conclusion: The pixel displacement-based deformation correction method can solve the limitation imposed by image deformation on image alignment in US-guided radiotherapy. Compared with US def , the alignment results of US rev with CT were better.

Abstract A large number of epidemics, including COVID-19 and SARS, quickly swept the world and claimed the precious lives of large numbers of people. Due to the concealment and rapid spread of the virus, it is difficult to track down individuals with mild or asymptomatic symptoms with limited human resources. Building a low-cost and real-time epidemic early warning system to identify individuals who have been in contact with infected individuals and determine whether they need to be quarantined is an effective means to mitigate the spread of the epidemic. In this paper, we propose a smartphone-based zero-effort epidemic warning method for mitigating epidemic propagation. Firstly, we recognize epidemic-related voice activity relevant to epidemics spread by hierarchical attention mechanism and temporal convolutional network. Subsequently, we estimate the social distance between users through sensors built-in smartphone. Furthermore, we combine Wi-Fi network logs and social distance to comprehensively judge whether there is spatiotemporal contact between users and determine the duration of contact. Finally, we estimate infection risk based on epidemic-related vocal activity, social distance, and contact time. We conduct a large number of well-designed experiments in typical scenarios to fully verify the proposed method. The proposed method does not rely on any additional infrastructure and historical training data, which is conducive to integration with epidemic prevention and control systems and large-scale applications.

The structure and function of brain networks (BN) may be altered in patients with end-stage renal disease (ESRD). However, there are relatively few attentions on ESRD associated with mild cognitive impairment (ESRDaMCI). Most studies focus on the pairwise relationships between brain regions, without taking into account the complementary information of functional connectivity (FC) and structural connectivity (SC). To address the problem, a hypergraph representation method is proposed to construct a multimodal BN for ESRDaMCI. First, the activity of nodes is determined by connection features extracted from functional magnetic resonance imaging (fMRI) (i.e., FC), and the presence of edges is determined by physical connections of nerve fibers extracted from diffusion kurtosis imaging (DKI) (i.e., SC). Then, the connection features are generated through bilinear pooling and transformed into an optimization model. Next, a hypergraph is constructed according to the generated node representation and connection features, and the node degree and edge degree of the hypergraph are calculated to obtain the hypergraph manifold regularization (HMR) term. The HMR and L1 norm regularization terms are introduced into the optimization model to achieve the final hypergraph representation of multimodal BN (HRMBN). Experimental results show that the classification performance of HRMBN is significantly better than that of several state-of-the-art multimodal BN construction methods. Its best classification accuracy is 91.0891%, at least 4.3452% higher than that of other methods, verifying the effectiveness of our method. The HRMBN not only achieves better results in ESRDaMCI classification, but also identifies the discriminative brain regions of ESRDaMCI, which provides a reference for the auxiliary diagnosis of ESRD.

To investigate the influence of temperature and humidity variations on creep in high-performance concrete beams, beam tests were conducted in both natural and laboratory settings. The findings indicate that the variations in creep primarily stem from temperature changes, whereas humidity changes have little influence on fluctuations in both basic creep and total creep. The influence of humidity on creep is more strongly reflected in the magnitude of creep. Functions describing the influence of temperature and humidity on the creep behavior of high-performance concrete (HPC) subjected to fluctuating conditions are proposed. The findings were employed to examine creep deformation in engineering applications across four places. This study complements the correction method for the creep of members under fluctuating temperature and humidity. This research application can provide a basis for the calculation of the long-term deformation of HPC structures in natural environments.