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Knee joint stiffness occurs and severely limits its range of motion (ROM) after facture around the knee. During mobility training, knee joints need to be flexed to the maximum angle position (maxAP) that can induce pain at an appropriate level in order to pull apart intra-articular adhesive structures while avoiding secondary injuries. However, the maxAP varies with training and is mostly determined by the pain level of patients. In this study, the feasibility of utilizing electromyogram (EMG) activities to detect maxAP was investigated. Specifically, the maxAP detection was converted into a binary classification between pain level three of the numerical rating scales (pain) and below (painless) according to clinical requirements. Firstly, 12 post-fracture patients with knee joint stiffness participated in Experiment I, with a therapist performing routine mobility training and EMG signals being recorded from knee flexors and extensors. The results showed that the extracted EMG features were significantly different between the pain and painless states. Then, the maxAP estimation performance was tested on a knee rehabilitation robot in Experiment II, with another seven patients being involved. The support vector machine and random forest models were used to classify between pain and painless states and obtained a mean accuracy of 87.90% ± 4.55% and 89.10% ± 4.39%, respectively, leading to an average estimation bias of 6.5° ± 5.1° and 4.5° ± 3.5°. These results indicated that the pain-induced EMG can be used to accurately classify pain states for the maxAP estimation in post-fracture mobility training, which can potentially facilitate the application of robotic techniques in fracture rehabilitation.

Background Many observational studies have reported correlations between postoperative complications and prognosis after radical gastrectomy but the results are controversial. This meta-analysis was performed to investigate whether there is a correlation between postoperative complications and prognosis after radical gastrectomy. Methods Literature searches were performed in PubMed, EMBASE, and the Cochrane Library. Studies that investigated the correlations between any postoperative complications and prognosis after radical gastrectomy were included. The pooled hazard ratio (HR) with 95% confidence interval (CI) for postoperative complications regarding overall survival (OS) or recurrence-free survival (RFS) was calculated by using RevMan 5.3.5. Subgroup analyses were performed within pathological stages I, II, and III. Results Sixteen retrospective studies comprising 12,065 patients were included. The pooled HR (95% CI) for complications regarding OS was 1.79 (1.39, 2.30) and was 1.40 (1.06, 1.84) after excluding in-hospital mortality; the pooled HR (95% CI) for complications regarding RFS was 1.28 (1.10, 1.49). The pooled HR (95% CI) for infectious complications and leakage regarding OS was 1.86 (1.22, 2.83) and 2.02 (1.02, 4.00), respectively. The pooled HR (95% CI) for any reported postoperative complications regarding OS for stage I, II, and III diseases was 2.39 (0.77, 7.46), 4.35 (2.58, 7.35), and 2.84 (1.77, 4.56), respectively. Conclusions Postoperative complications correlate with poor prognosis after radical gastrectomy. Such correlations are found in stage II and III gastric cancer patients but remain to be determined in stage I gastric cancer patients.

Objective: To evaluate the accuracy of musculoskeletal ultrasound measurement of plantar fascia thickness and shear wave elastography (SWE) calculation of Young's modulus in the diagnosis of plantar fasciitis (PFis), and to explore whether it will have any impact on clinical decision-making, rather than just using established diagnostic methods of clinical symptoms and signs. Methods: A diagnostic single-center study was conducted in 60 patients with PFis and 64 patients with other types of heel pain. The sensitivity and specificity of musculoskeletal ultrasound in the diagnosis of PFis were calculated according to the diagnostic criteria for PFis recommended by the Clinical Practice Guide for PFis of the American Physical Therapy Association (APTA) Orthopaedic Branch. The analysis of the receiver operating characteristic curve of the subjects was used to study the accuracy of the diagnosis of PFis by two indicators alone and two indicators jointly. Results: The thickness of plantar fascia measured by musculoskeletal ultrasound and the area under the curve (AUCs) calculated by shear wave elastography for diagnosing PFis were 0.925 and 0.917, respectively, and the optimal cutoff values were 3.15 mm (sensitivity: 100.0%, specificity: 81.3%) and 63.24 kPa (sensitivity: 78.3%, specificity: 92.2%), respectively. The area under the curve (AUCs) for the combined diagnosis of the two is 0.973 (sensitivity: 93.3%, specificity: 93.8%). Conclusion: The thickness of plantar fascia measured by musculoskeletal ultrasound and Young's modulus calculated by SWE have high accuracy in diagnosing PFis, and the combined diagnosis of the two can improve the diagnostic accuracy of patients with PFis. Keywords: plantar fasciitis, shear wave elastography, musculoskeletal ultrasound, diagnosis, accuracy

We conducted anchoring performance, stress distribution, and full-scale indoor pulling tests on glass-fiber-reinforced polymer (GFRP) bolts. The tests were conducted using finite element software while considering the multi-interface contact and BK criterion by using the cohesive element to simulate the contact relations between the anchor rod body and concrete and building an axial symmetry calculation model of the GRFP bolt and concrete. The results indicated that the finite element model based on cohesive element accurately represents the load–displacement relationship of the GFRP bolt and the distribution law of axial stress along the anchoring length. In addition, the simulation outcomes of the load–displacement relationship were in good agreement with the measured test values. Under the same load, the axial-force-transferred depth of the bolt body was identical regardless of the anchorage length. As anchoring length increases, the pull load on the bolt and the decay rate of axial stress along the anchoring length rises gradually. There is a critical value for the anchorage length of the bolt.

An efficient topology optimization based on the adaptive auxiliary reduced model reanalysis (AARMR) method is proposed to improve computational efficiency and scale. In this method, a projection auxiliary reduced model (PARM) is integrated into the combined approximation reduced model (CARM) to reduce the dimension of the model in different aspects. First, the CARM restricts the solution space to avoid large matrix factorization. Second, the PARM is proposed to construct the CARM dynamically to save computational cost. Furthermore, the multi-grid conjugate gradient method is suggested to update PARM adaptively. Finally, several classic numerical examples are tested to show that the proposed method not only significantly improves computational efficiency, but also can solve large-scale problems that are difficult to solve by direct solvers due to the memory limitations.

Spinal cord injury (SCI) is a devastating condition affecting hundreds of thousands of people worldwide annually. SCI results in activation of the inflammatory response and apoptosis, and generates oxidative stress, which has deleterious effects on the recovery of motor function. Apocynin, an inhibitor of NADPH oxidase, has been demonstrated to improve neuronal functional recovery in rat models of SCI. However, the efficacy of apocynin treatment post-SCI has not been investigated. The aim of this study was to observe the effects of apocynin on the repair of acute spinal cord damage in rats and to examine the potential beneficial effects. A rat model of SCI was established, and apocynin (50 mg/kg) was administered intraperitoneally at 30 min after SCI and then every 12 h for 3 days. In order to examine oxidative tissue injury, the levels of malondialdehyde and glutathione and activities of myeloperoxidase and superoxide dismutase in the spinal cord tissues were measured. Histological evaluations were also conducted. NeuN labeling, TUNEL staining and caspase 3 immunohistochemical staining were performed to analyze neuronal damage and apoptosis around the lesion. Immunohistochemical analysis was also carried out to observe the expression of CD11b and glial fibrillary acidic protein. The expression levels of bax, bcl-2, tumor necrosis-α, interleukin (IL)-1β and IL-6 in the spinal cord tissue were assayed by western blotting. Finally, locomotor function was evaluated using the inclined plane test and Basso, Beattie and Bresnahan scores. The results showed that treatment with apocynin decreased oxidative damage, alleviated neuronal apoptosis, inhibited the inflammatory response and resulted in the promotion of locomotor function. Therefore, this study confirmed the therapeutic efficacy of apocynin in the repair of SCI, which was probably mediated via the inhibition of apoptosis and the inflammatory response, thus promoting the restoration of nerve function.

Spinal cord injury (SCI) often leads to defecation dysfunction. Sacral nerve electrical stimulation (SNS) therapy could improve defecation function. The present study aimed to assess SNS therapy, with regard to the levels of serotonin (5-HT) and its receptors (5-HT3AR and 5-HT4R) in the colon and sacral cord, a rat model of acute severe SCI was used. This rat model was made using the New York University Impactor device. Model rats were randomized to the SCI and SNS (electrical stimulation on the S3 nerve) groups. After 14 days of treatment, enteric transmission function was assessed. 5-HT and 5-HT3AR/5-HT4R were measured by ELISA, quantitative PCR, immunohistochemistry and western blotting. In SCI rats, SNS significantly increased the quantity of feces, shortened the time to the first fecal passage, and improved fecal texture and colon histology. SNS elevated 5-HT contents in the colon and spinal cord, and enhanced 5-HT3AR/5-HT4R protein expression and distribution in the colonic myenteric plexus and mucosa, sacral intermediolateral nucleus and dorsal horn. SNS upregulated the relative expression levels of 5-HT3AR/5-HT4R mRNA and protein in the colon and spinal cord. SNS can improve defecation and accelerate the recovery of colonic transmission functions in rat models of acute SCI. These effects involved upregulation of the 5-HT/5-HT3AR/5-HT4R axes.

The aim of the present study was to observe the curative effect of traditional Chinese cajan leaves, combined with administration of bone marrow-derived mesenchymal stem cells (BMSCs), on osteonecrosis of the femoral head (ONFH) in rats and to investigate the underlying mechanisms. A total of 40 rat ONFH models were established through liquid nitrogen freezing and were subsequently divided into groups: A, control; B, treated with cajan leaf; C, treated with BMSCs and D, treated with cajan leaf combined with BMSCs. Samples were obtained 30 days following treatment, and immunohistochemical staining of vascular endothelial growth factor (VEGF) and image analysis were performed. Chondrocytes and vascular endothelial cells were stained as a result of immunohistochemical staining and group D exhibited markedly deeper staining, and a significantly larger number of stained cells, compared with group A. Thus, in the present study, cajan leaf combined with BMSCs was shown to promote VEGF expression and improve ONFH repair.

Modern reservoir management typically involves simulations of geological models to predict future recovery estimates, providing the economic assessment of different field development strategies. Integrating reservoir data is a vital step in developing reliable reservoir performance models. Currently, most effective strategies for traditional manual history matching commonly follow a structured approach with a sequence of adjustments from global to regional parameters, followed by local changes in model properties. In contrast, many of the recent automatic history matching methods utilize parameter sensitivities or gradients to directly update the fine-scale reservoir properties, often ignoring geological inconsistency. Therefore, there is need for combining elements of all of these scales in a seamless manner. We present a hierarchical streamline-assisted history matching, with a framework of global-local updates. A probabilistic approach, consisting of design of experiments, response surface methodology and the genetic algorithm, is used to understand the uncertainty in the large-scale static and dynamic parameters. This global update step is followed by a streamline-based model calibration for high resolution reservoir heterogeneity. This local update step assimilates dynamic production data. We apply the genetic global calibration to unconventional shale gas reservoir specifically we include stimulated reservoir volume as a constraint term in the data integration to improve history matching and reduce prediction uncertainty. We introduce a novel approach for efficiently computing well drainage volumes for shale gas wells with multistage fractures and fracture clusters, and we will filter stochastic shale gas reservoir models by comparing the computed drainage volume with the measured SRV within specified confidence limits. Finally, we demonstrate the value of integrating downhole temperature measurements as coarse-scale constraint during streamline-based history matching of dynamic production data. We first derive coarse-scale permeability trends in the reservoir from temperature data. The coarse information are then downscaled into fine scale permeability by sequential Gaussian simulation with block kriging, and updated by local-scale streamline-based history matching. The power and utility of our approaches have been demonstrated using both synthetic and field examples.

This study presents a discrete physics-informed neural network (dPINN) framework, enhanced with enforced interface constraints (EIC), for modeling physical systems using the domain decomposition method (DDM). Built upon finite element-style mesh discretization, the dPINN accurately evaluates system energy through Gaussian quadrature-based element-wise integration. To ensure physical field continuity across subdomain interfaces, the EIC mechanism enforces interfacial displacement constraints without requiring auxiliary sampling or loss penalties.This formulation supports independent meshing in each subdomain, simplifying preprocessing and improving computational flexibility. Additionally, by eliminating the influence of weak spatial constraints (WSC) commonly observed in traditional PINNs, the EIC-dPINN delivers more stable and physically consistent predictions.Extensive two- and three-dimensional numerical experiments validate the proposed framework's accuracy and demonstrate the computational efficiency gains achieved through parallel training. The results highlight the framework's scalability, robustness, and potential for solving large-scale, geometrically complex problems.