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Intervertebral disc degeneration (IVDD) stands as a prevalent chronic orthopedic ailment, profoundly impacting patients' well‐being due to incapacitating low back pain. Studies have highlighted a close correlation between IVDD and the programmed cell death of nucleus pulposus (NP) cells orchestrated by interleukin‐1 beta (IL‐1β), tumor necrosis factor‐alpha (TNF‐α), and caspase‐3 (CASP3). Puerarin, renowned for its anti‐inflammatory attributes and its influence on IL‐1β and TNF‐α, emerges as a promising candidate for IVDD treatment. However, the precise mechanism by which it regulates apoptosis via these pathways remains ambiguous. This investigation utilizes bioinformatics to unveil the molecular intricacies of puerarin‐mediated apoptosis regulation in IVDD, substantiated by preliminary in vitro experiments. Analysis exposes aberrant expression of pivotal apoptosis‐associated proteins (IL‐1β, TNF‐α, CASP3, CASP8, and BCL2) in IVDD patients, with network pharmacology indicating puerarin's potential efficacy in IVDD treatment by modulating apoptosis and cellular senescence pathways. Further experiments elucidate puerarin's capacity to stimulate NP cell proliferation while inhibiting apoptosis, potentially contributing to IVDD mitigation. Western blot and PCR outcomes reveal escalated expression of apoptosis‐related proteins (IL‐1β, TNF‐α, and CASP3) in lipopolysaccharide‐treated NPCs, ameliorated by puerarin intervention. Molecular docking simulations demonstrate favorable binding properties of puerarin with apoptotic proteins, while flow cytometry analysis indicates its ability to diminish NPC apoptosis. These discoveries imply that puerarin might alleviate NPC apoptosis by modulating key targets, thereby potentially ameliorating IVDD. In summary, this study unveils the intrinsic mechanism of puerarin in regulating NPC apoptosis to alleviate IVDD, underscoring its therapeutic promise. [ABSTRACT FROM AUTHOR]

Background: Intervertebral disc degeneration (IVDD) is an age‐related orthopedic degenerative disease characterized by recurrent episodes of lower back pain, the pathogenesis of which is not fully understood. This study aimed to identify key biomarkers of IVDD and its causes. Methods: We acquired three gene expression profiles from the Gene Expression Omnibus (GEO) database, GSE56081, GSE124272, and GSE153761, and used limma fast differential analysis to identify differentially expressed genes (DEGs) between normal and IVDD samples after removing batch effects. We applied weighted gene co‐expression network (WGCNA) to identify the key modular genes in GSE124272 and intersected these with DEGs. Next, A protein–protein interaction network (PPI) was constructed, and Cytoscape was used to identify the Top 10 hub genes. Functional enrichment analyses were performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Three key genes were validated using Western Blot (WB) and qRT‐PCR. Additionally, we predicted miRNAs involved in hub gene co‐regulation and analyzed miRNA microarray data from GSE116726 to identify four differentially expressed miRNAs. Results: We identified 10 hub genes using bioinformatics analysis, gene function enrichment analysis revealed that they were primarily enriched in pathways, such as the TNF signaling pathway. We chose JUNB, SOCS3, and CEBPB as hub genes and used WB and qRT‐PCR to confirm their expression. All three genes were overexpressed in the IVDD model group compared to the control group. Furthermore, we identified four miRNAs involved in the co‐regulation of the hub genes using miRNet prediction: mir‐191‐5p, mir‐20a‐5p, mir‐155‐5p, and mir‐124‐3p. Using limma difference analysis, we discovered that mir‐191‐5p, mir‐20a‐5p, and mir‐155‐5p were all down‐regulated and expressed in IVDD samples, but mir‐124‐3p showed no significant change. Conclusion: JUNB, SOCS3, and CEBPB were identified as key genes in IVDD, regulated by specific miRNAs, providing potential biomarkers for early diagnosis and therapeutic targets. [ABSTRACT FROM AUTHOR]

Intervertebral disc degeneration (IVDD) is a prevalent orthopedic condition with lower back pain as the predominant clinical presentation that challenges clinical treatment with few therapeutic options. Duhuo Jisheng Decoction (DHJSD) has been proven effective in the therapy of IVDD, but the precise underlying mechanisms remain not fully elucidated. The current study was designed to test our hypothesis that DHJSD may systematically correct the phenotypic disruption of the gut microbiota and changes in the serum metabolome linked to IVDD. Analysis of the active ingredients of DHJSD by ultra high performance liquid chromatography. An integrated metagenomic and metabonomic approach was used to analyze feces and blood samples from normal and IVDD rats. Compared to the control group, fiber ring pinning on the caudal 3 to caudal 5 segments of the rats caused IVDD and significantly altered the compositions of the intestinal microbiota and serum metabolites. Integrated analysis revealed commonly-altered metabolic pathways shared by both intestinal microbiota and serum metabolome of the IVDD rats. DHJSD inhibited the degenerative process and restored the compositions of the perturbed gut microbiota, particularly the relative abundance of commensal microbes of the Prevotellaceae family. DHJSD also corrected the altered metabolic pathways involved in the metabolism of glycine, serine, threonine, valine, the citric acid cycle, and biosynthesis of leucine and isoleucine. DHJSD inhibited the disc degeneration process by an integrated metagenomic and metabonomic mechanism to restore the microbiome profile and normalize the metabonomic pathways. [ABSTRACT FROM AUTHOR]

To tackle the clinical challenge of noninvasively assessing immunotherapy efficacy in patients, here we used positron emission tomography (PET) with 68Ga-grazytracer, which targets granzyme B, a crucial effector molecule secreted by activated CD8+ T cells. In this phase 1/2 clinical trial (NCT05000372) involving a diverse cohort of 24 patients with solid tumors and lymphomas who received immunotherapies, including immune checkpoint inhibitors (either alone or with chemotherapies) and chimeric antigen receptor-T cell therapy, we examined the in vivo behaviors of 68Ga-grazytracer. Primary endpoints were safety, biodistribution, granzyme B specificity, and the predictive utility of 68Ga-grazytracer, while secondary endpoint was the relationship between 68Ga-grazytracer uptake and tumor immune phenotype. 68Ga-grazytracer exhibited a safe profile and specifically targeted granzyme B in patients. 68Ga-grazytracer PET showed superior predictive value for short-term prognosis and progression-free survival than those of conventional assessment criteria, including RECIST 1.1 and PERCIST. Moreover, the uptake of 68Ga-grazytracer in tumors was significantly higher in those with a "non-desert" immune phenotype than those with an immune "desert" phenotype, thereby meeting the primary and secondary endpoints of this trial. Collectively, we successfully visualized CD8+ T cell effector function in humans using 68Ga-grazytracer PET, offering insights for enhancing immunotherapy assessment, patient stratification and treatment planning. Several approaches based on positron emission tomography (PET) are currently used to evaluate cancer response to immunotherapy. Here the authors report the results of a phase 1/2 trial evaluating a PET radiotracer, 68Ga-grazytracer, for the imaging of granzyme B expression in patients treated with immune checkpoint inhibitors or CAR-T cell therapy. [ABSTRACT FROM AUTHOR]

Intervertebral disc degeneration (IVDD) is a common chronic orthopaedic disease in orthopaedics that imposes a heavy economic burden on people and society. Although it is well established that IVDD is associated with genetic susceptibility, ageing and obesity, its pathogenesis remains incompletely understood. Previously, IVDD was thought to occur because of excessive mechanical loading leading to destruction of nucleus pulposus cells (NPCs), but studies have shown that IVDD is a much more complex process associated with inflammation, metabolic factors and NPCs death and can involve all parts of the disc, characterized by causing NPCs death and extracellular matrix (ECM) degradation. The damage pattern of NPCs in IVDD is like that of some programmed cell death, suggesting that IVDD is associated with programmed cell death. Although apoptosis and pyroptosis of NPCs have been studied in IVDD, the pathogenesis of intervertebral disc degeneration can still not be fully elucidated by using only traditional cell death modalities. With increasing research, some new modes of cell death, PANoptosis, ferroptosis and senescence have been found to be closely related to intervertebral disc degeneration. Among these, PANoptosis combines essential elements of pyroptosis, apoptosis and necroptosis to form a highly coordinated and dynamically balanced programmed inflammatory cell death process. Furthermore, we believe that PANoptosis may also crosstalk with pyroptosis and senescence. Therefore, we review the progress of research on multiple deaths of NPCs in IVDD to provide guidance for clinical treatment. [ABSTRACT FROM AUTHOR]

Background: The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment. Methods: We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA). Results: The generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. Conclusions: Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features. [ABSTRACT FROM AUTHOR]

Fractures and bone nonunion commonly require surgical intervention. Serious outcomes of non‐healing in the late stages of fracture place a significant financial burden on society and families. Bone nonunion occurs when a fracture stops healing, for many reasons, and leads to a variety of bad outcomes. Numerous factors, including biomechanics and immunology, are involved in the complicated mechanisms of bone nonunion. The immune‐inflammatory response plays a significant part in the emergence of bone nonunion, and the occurrence, control, and remission of inflammation in the bone healing process have a significant influence on the ultimate success of bone tissue repair. In the bone microenvironment, immune cells and associated cytokines control bone repair, which is significantly influenced by macrophages, T cells, and fibroblast growth factor. To limit acute inflammation and balance osteogenesis and osteoblastogenesis for tissue repair and regeneration, immune cells and various cytokines in the local microenvironment must be precisely regulated. As a bad complication of late‐stage fractures, bone nonunion has a significant effect on patients' quality of life and socioeconomic development. Therefore, in‐depth research on its pathogenesis and treatment methods has important clinical value. To provide more precise, focused therapeutic options for the treatment of bone nonunion, we discuss the regulatory roles of the key immune cells engaged in bone healing within the microenvironment during bone healing and their effect on osteogenesis. [ABSTRACT FROM AUTHOR]

The anode material Sn used in sodium-ion batteries displays high theoretical capacity, complex phase transformation, and significant volume change during the charging/discharging process. In particular, the effects of small scale on the mechanical behavior of Sn anode at the nanoscale are very active research fields. However, the majority of these results are based on nonlocal gradient formulations. In this study, we proposed and established a model that combines the electrochemical reaction with stress-driven nonlocal integral elasticity for the nanoelectrode to analyze the evolution of diffusion-induced deformation during the sodiation process. Several critical features, such as the small-scale parameter, two-phase reaction, and concentration-dependent elastic modulus, were incorporated into the established model. The model demonstrated that a small scale could significantly affect the deformation behavior. The results obtained using the finite element method showed that the mechanical reliability of the Sn anode could be significantly enhanced when the anode was sodiated with larger nonlocal parameters and smaller slenderness. In addition, the axial action force exhibited a strong size effect and was influenced by the nondimensional thickness parameter of the anode. This work provides a framework for multi-scale research on high-capacity sodium-ion battery electrodes. [ABSTRACT FROM AUTHOR]

This study presents a novel bridge anomaly detection approach that employs the reconstruction error and structural similarity of an unsupervised convolutional auto-encoder. The presence of structural damage in a bridge typically results in changes in its vibration signals, and thus, the use of these signals for structural damage detection (SDD) has been widely investigated, with many methods relying on supervised learning. However, such existing SDD methods based on the supervised learning require prior knowledge of the damage states and cannot process monitoring data in real-time, thereby limiting their application to in-service bridges. To address this challenge, the authors propose the use of a convolutional auto-encoder as the reconstruction algorithm for real-time vibration signals. The auto-encoder is trained using normal signals and then used to reconstruct new inputs (either normal or abnormal). Two damage indicators (reconstruction error and structural similarity) are then calculated based on the reconstruction results and clustered to detect abnormal signals. The proposed approach was applied to the detection of various abnormalities in the old ADA Bridge, the results were 100% accurate, and about a 10% increase in accuracy was observed when compared to other control experiments. These results demonstrate the effectiveness of the proposed approach, with the auto-encoder achieving excellent reconstruction results for normal signals and clear discrepancies for abnormal signals. The proposed method was also validated on a cable-stayed bridge and an arch bridge, demonstrating its wide applicability in bridge anomaly detection. [ABSTRACT FROM AUTHOR]

Background: Femoral head varus triggers poor clinical prognosis in intertrochanteric fracture patients with proximal femoral nail antirotation (PFNA) fixation. Studies present that changes in nail position and screw insertion angles will affect fixation stability, but the biomechanical significance of these factors on the risk of femoral head varus has yet to be identified in PFNA fixed patients. Methods: Clinical data in PFNA fixed intertrochanteric fracture patients have been reviewed, the relative position of intermedullary nail has been judged in the instant postoperative lateral radiography. Regression analyses have been performed to identify the effect of this factor on femoral head varus. Corresponding biomechanical mechanism has been identified by numerical mechanical simulations. Results: A clinical review revealed that ventral side nail insertion can trigger higher risk of femoral head varus, corresponding numerical mechanical simulations also recorded poor fixation stability in models with ventral side nail insertion, and changes in the trajectory of anti-rotation blade will not obviously affect this tendency. Conclusions: Ventral side insertion of intramedullary nail can trigger higher risk of femoral head varus in PFNA fixed patients by deteriorating the instant postoperative biomechanical environment, and changes in blade trajectory cannot change this tendency biomechanically. Therefore, this nail position should be adjusted to optimize patients' prognosis. [ABSTRACT FROM AUTHOR]

Background: Intervertebral disc degeneration (IVDD) is a common chronic disease in orthopedics, and its molecular mechanisms are still not well explained. Aim: This study's objective was to bioinformatics‐based discovery of IVDD biomarkers and immune‐inflammatory infiltrates. Materials and Methods: The IVDD illness gene collection was gathered from GeneCards, DisGeNet, and gene expression profiles were chosen from the extensive Gene Expression Omnibus database (GSE124272, GSE150408, and GSE153761). The STRING database was used to create a network of protein–protein interactions, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases were used for functional enrichment analysis. Using hub genes, the immune cell infiltration between IVDD patient samples and control tissues was examined. Finally, quantitative polymerase chain reaction and Western blot experiments were used to verify the expression of hub genes. Results: A total of 27 differentially expressed hub genes were identified by bioinformatics. According to GO and KEGG analyses, hub genes were prominent in immunological responses, chemokine‐mediated signaling pathways, and inflammatory responses, with the key signaling pathways engaged in cellular senescence, apoptosis, Th1 and Th2 cell differentiation, and Th17 cell differentiation. Immune cell infiltration research revealed that T cells, lymphocytes, B cells, and NK cells were decreased in IVDD patients while monocytes, neutrophils, and CD8 T cells were increased. The expression levels of the senescence hub genes SP1, VEGFA, IL‐6, and the apoptosis key gene CASP3 were considerably greater in the IVDD model group than in the control group, according to in vitro validation. Conclusion: In conclusion, the cellular senescence signaling pathway, the apoptosis signaling pathway, and associated hub genes play significant roles in the development and progression of IVDD, this finding may help direct future research on the senescence signaling route in IVDD. [ABSTRACT FROM AUTHOR]

Objective: Intervertebral disc degeneration (IVDD) is a chronic degenerative orthopedic illness that causes lower back pain as a typical clinical symptom, severely reducing patients' quality of life and work efficiency, and imposing a significant economic burden on society. IVDD is defined by rapid extracellular matrix breakdown, nucleus pulposus cell loss, and an inflammatory response. It is intimately related to the malfunction or loss of myeloid cells among them. Many mechanisms have been implicated in the development of IVDD, including inflammatory factors, oxidative stress, apoptosis, cellular autophagy, and mitochondrial dysfunction. In recent years, mitochondrial dysfunction has become a hot research topic in age-related diseases. As the main source of adenosine triphosphate (ATP) in myeloid cells, mitochondria are essential for maintaining myeloid cell survival and physiological functions. Methods: We searched the PUBMED database with the search term "intervertebral disc degeneration and mitochondrial dysfunction" and obtained 82 articles, and after reading the abstracts and eliminating 30 irrelevant articles, we finally obtained 52 usable articles. Results: Through a review of the literature, it was discovered that IVDD and cellular mitochondrial dysfunction are also linked. Mitochondrial dysfunction contributes to the advancement of IVDD by influencing a number of pathophysiologic processes such as mitochondrial fission/fusion, mitochondrial autophagy, cellular senescence, and cell death. Conclusion: We examine the molecular mechanisms of IVDD-associated mitochondrial dysfunction and present novel directions for quality management of mitochondrial dysfunction as a treatment approach to IVDD. [ABSTRACT FROM AUTHOR]

Timely defect detection plays a positive role in preventing the evaluation of steel derivative damage. As a state-of-the-art image recognition technology, pixel-level segmentation technology can obtain the pixel distribution and generate the shape of the objects accurately, which provides a potential detection method for steel surface defects. Therefore, this paper employed the well-known pixel-level segmentation CNN (DeepLab_v3+) to detect three defect categories (inclusions, patches and scratches) on the steel surface, and the 'regionprops' function was used to quantify the defect features (length, average width, maximum width, area and ratio). The results show that ResNet50, as the backbone network of DeepLab_v3+, has the highest detection precision for steel surface defects, and its accuracy is more competitive than that of other algorithms (FCN, SegNet, U-Net and PAG-Net). The proposed quantitative method also achieved encouraging results (the average relative error (ARE) of the evaluation indicator, 10%, 18%, 17%, 23% and 23%, respectively), and the precision was higher than that of the other methods. This demonstrated that the proposed method can greatly benefit steel surface defect detection and evaluation of defect levels. [ABSTRACT FROM AUTHOR]

Intervertebral disc degeneration (IVDD) has become a serious public health problem, placing a heavy burden on society and the healthcare system. Its pathogenesis is not completely clear and may be closely related to mechanical damage, inflammatory factors, oxidative stress and death of nucleus pulposus cells (NPCs). The treatment of IVDD mainly includes conservative treatment and surgery. Conservative treatment is based on hormonal and anti-inflammatory drugs and massage techniques, which can relieve the pain symptoms to a certain extent, but cannot solve the problem from the root cause. Surgical treatment is mainly by removing the herniated nucleus pulposus, but it is more traumatic for IVDD patients, expensive and not suitable for all patients. Therefore, it is extremely important to clarify the pathogenesis of IVDD, to find an effective and convenient treatment and to further elaborate its mechanism of action. The effectiveness of traditional Chinese medicine in the treatment of IVDD has been well demonstrated in clinical medical research. We have been working on the Chinese herbal formula Duhuo Jisheng Decoction, which is a common formula for the treatment of degenerative disc disease. Not only does it have significant clinical effects, but it also has few adverse effects. At present, we found that its mechanism of action mainly involves regulation of inflammatory factors, reduction of apoptosis and pyroptosis of NPCs, inhibition of extracellular matrix degradation, improvement of intestinal flora, etc. However, a few relevant articles have yet comprehensively and systematically summarized the mechanisms by which they exert their effect. Therefore, this paper will comprehensively and systematically explain on it. This is of great clinical significance and social value for elucidating the pathogenesis of IVDD and improving the symptoms of patients, and will provide a theoretical basis and scientific basis for the treatment of IVDD with traditional Chinese medicine. [ABSTRACT FROM AUTHOR]

This paper proposes a slope stability prediction model based on deep learning and digital twinning methods. To establish a reliable slope database, 30 actual slopes were collected, and 100 digital twin (DT) models were generated for each actual slope by fine-tuning the slope profiles. The safety factors of all slope samples were calculated using the Limit Equilibrium Methods (LEMs). A convolutional neural network (CNN) regression model was established, and the root mean square error (RMSE) was used as the evaluation indicator. In order to find an excellent CNN model, the K-fold (K = 10) cross-validation was used to compare the predictive effect of 1D CNN and 2D CNN on the slope safety factor. On this basis, CNN models with different network depths were compared. The results showed that the 2D CNN model with six convolutional layers had the best network prediction effect for the slope dataset. To validate the generalization ability of the model, an actual slope was input into the CNN model; its prediction result was 1.0229, and the absolute error with its real safety factor (1.0197) was 0.0032. With the slope stability prediction model proposed in this paper, the safety factor of slopes can be obtained from their geological and physical data, which greatly simplifies the calculation of the safety factor and has great engineering significance. [ABSTRACT FROM AUTHOR]

Background: In mouse, it was discovered that resveratrol (Res) enhanced osteoporosis (OP) by boosting osteogenesis. Besides, Res can also have an impact on MC3T3-E1 cells, which are crucial for the control of osteogenesis and thus increase osteogenesis. Although some articles have discovered that Res enhanced autophagy to promote the value-added differentiation of MC3T3, it is unclear exactly how this affects the process of osteogenesis in mouse. Therefore, we will show that Res encourages MC3T3-E1 proliferation and differentiation in mouse pre-osteoblasts and further investigate the autophagy-related mechanism for this impact. Methods: (1) MC3T3-E1 cells were separated into blank control group and various concentrations (0.01, 0.1, 1, 10, 100µmol/L) of group in order to determine the ideal Res concentration. In the Res group, Cell Counting Kit-8 (CCK-8) was used to measure the proliferation activity of pre-osteoblasts in mice in each group after resveratrol intervention. Alkaline Phosphatase (ALP) and alizarin red staining were used to gauge the degree of osteogenic differentiation, and RT-qPCR was used to measure the expression levels of Runx2 and OCN in the osteogenic differentiation ability of the cells. (2) In the experiment, four groups were set up: the control group, 3MA group, Res group, and Res + 3MA group. To examine cell mineralization, ALP and alizarin red staining were utilized. RT-qPCR and Western blot detection of cell autophagy activity levels and osteogenic differentiation capacity in each group following intervention. Results: (1) Resveratrol might increase the number of mice pre-osteoblast, with the impact being most pronounced at 10µmol/L (P < 0.05). The nodules developed substantially more often than in the blank control group, and Runx2 and OCN expressions significantly increased (P < 0.05). (2) In contrast to the Res group, after 3MA purine blocked autophagy, the Res + 3MA group's alkaline phosphatase staining and the development of mineralized nodules were reduced. Runx2, OCN, LC3II / LC3I expression decreased, p62 expression increased (P < 0.05). Conclusion: The present study partially or indirectly demonstrated that Res may, through increased autophagy, induce osteogenic differentiation of MC3T3-E1 cells. [ABSTRACT FROM AUTHOR]

Stud connectors in steel‐concrete composite track beams of straddle‐type monorail transits experience longitudinal and transverse shear forces. Little is known about the effect of transverse shear forces on stud connectors; thus, further research is needed to evaluate the serviceability of steel‐concrete composite track beams. This study investigated the shear performance of stud connectors subjected to bidirectional shear force by means of modified push‐out tests and finite element (FE) analyses. The test results demonstrated that the presence of transverse shear forces significantly decreased the vertical shear strength and stiffness of the stud connectors. In addition, the group stud effect aggravated the reduction in shear strength and shear stiffness per stud under bidirectional shear forces. The transverse shear force also considerably affected the failure modes of the stud root and the damage range of the concrete. Furthermore, the numerical results were verified using the test results in terms of the load‐slip relationship and failure modes. Based on the verified FE model, the strength reduction factor of stud connectors was proposed incorporating the presence of transverse shear force, and the decreasing trend of stud shear strength under bidirectional shear force was presented. This study provides additional data for formulating design rules of stud connectors in monorail steel‐concrete composite track beams. [ABSTRACT FROM AUTHOR]

Intervertebral disc degeneration (IVDD) is the pathological basis of disc herniation, spinal stenosis, and other related diseases, and the lower back pain it produces lays a heavy financial burden on individuals and society. Thus, it is essential to comprehend IVDD's pathophysiology. Numerous factors, such as inflammatory factors, oxidative stress, apoptosis, matrix metalloproteinases, are linked to IVDD pathogenesis. Despite the fact that many researches has provided explanations for the pathophysiology of IVDD, these studies are typically singular, restricted, and isolated, expound only on one or two components, and do not systematically analyze and summarize the numerous influencing elements. In addition, we discovered that the incidence of many chronic diseases in the field of orthopedics may be thoroughly and systematically defined in terms of immunological systems. In order to provide a theoretical foundation for an in‐depth understanding of the pathological process of IVDD and the formulation of more effective prevention and treatment measures, this review provides a comprehensive and systematic account of the pathogenesis of IVDD from the physical to the molecular barriers of the intervertebral disc, from the nucleus pulposus tissue to the cellular to the immune‐molecular level. [ABSTRACT FROM AUTHOR]

Unsteady aerodynamic forces are significantly critical to the safety and stability of trains traveling in high winds. This paper describes a study into unsteady crosswind forces of a moving train subjected to a three-dimensional stochastic wind field with longitudinal, lateral, and vertical turbulences. Initially, a three-dimensional computational fluid dynamic (CFD) model is established to calculate the aerodynamic coefficient of a moving train, and then the wind velocity time histories at the position of the train are generated. Finally, the quasi-steady theory and weighting function method are used to model the unsteady crosswind forces of a moving train in a three-dimensional turbulence field. The results demonstrate that a generalized sine form is useful for predicting the aerodynamic coefficient that varies with the resultant wind yaw angle, and an adequate modeling of unsteady crosswind forces with complete wind turbulences can produce a greater force fluctuation and peak. Particularly when the flow direction of crosswind deviates from 90°, consideration of only a portion of the turbulence components may underestimate the dynamic response of trains. [ABSTRACT FROM AUTHOR]

Recently accumulated evidence implicates a close association of vitamin D (VitD) insufficiency to the incidence and clinical manifestations of the COVID-19 caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2). Populations with insufficient VitD including patients with osteoporosis are more susceptible to SARS-COV-2 infection and patients with COVID-19 worsened or developed osteoporosis. It is currently unknown, however, whether osteoporosis and COVID-19 are linked by VitD insufficiency. In this study, 42 common targets for VitD on both COVID-19 and osteoporosis were identified among a total of 243 VitD targets. Further bioinformatic analysis revealed 8 core targets (EGFR, AR, ESR1, MAPK8, MDM2, EZH2, ERBB2 and MAPT) in the VitD-COVID-19-osteoporosis network. These targets are involved in the ErbB and MAPK signaling pathways critical for lung fibrosis, bone structural integrity, and cytokines through a crosstalk between COVID-19 and osteoporosis via the VitD-mediated conventional immune and osteoimmune mechanisms. Molecular docking confirmed that VitD binds tightly to the predicted targets. These findings support that VitD may target common signaling pathways in the integrated network of lung fibrosis and bone structural integrity as well as the immune systems. Therefore, VitD may serve as a preventive and therapeutic agent for both COVID-19 and osteoporosis. [ABSTRACT FROM AUTHOR]

Automatic bridge surface defect detection is of wide concern; it can save human resources and improve work efficiency. The object detection algorithm, especially the You Only Look Once (YOLO) series of networks, has important potential in real-time object detection because of its fast detection speed, and it provides an efficient and automatic detection method for bridge surface defect detection. Hence, this paper employs an improved YOLOv3 network for detecting bridge surface defects (cracks and exposed rebar) and compares the effects of the advanced YOLOv2, YOLOv3 and faster region-based convolutional neural network (Faster RCNN) in bridge surface defect detection, and then two approaches (transfer learning and data augmentation) are used to improve the YOLOv3. The results confirm that, by combining high- and low-resolution feature images, the YOLOv3 improves the detection effect of the YOLOv2 (using single-resolution feature images); the average precision (AP) value of the improved YOLOv3 (0.9–0.91) is 6–10% higher than that of the YOLOv2 (0.83–0.86). Then, the anti-noise abilities of the YOLOv2 and YOLOv3 are studied by introducing white Gaussian noise, and the YOLOv3 is better than the YOLOv2. Simultaneously, the YOLO series of detectors perform better in detection speed; the detection speed of the improved YOLOv3 (FPS (frames per second) = 23.8) is 103 times that of the Faster RCNN (FPS = 0.23) with comparable mAP values (improved YOLOv3 = 0.91; Faster RCNN = 0.9). It is demonstrated that, in consideration of detection precision and speed, the proposed improved YOLOv3 is a decent detector for fast and real-time bridge defect detection. [ABSTRACT FROM AUTHOR]

Timely crack detection plays an important role in building damage assessment. In this study, an automatic crack detection method based on image registration and pixel-level segmentation (improved DeepLab_v3+) is proposed. Firstly, the moving images are calibrated by image registration, and the similarity method is adopted to evaluate the calibrated results. Secondly, the DeepLab_v3+ is improved and used to segment the fixed images and the calibrated images. Finally, the difference of crack pixels between the fixed and calibrated images is estimated, and the key parameter is investigated to find the optimal optimizer and learning rate. The results illustrate that: (1) the image registration technology shows excellent calibration achievement and the average error is only 4%; (2) with the resnet50 being selected as the backbone network of improved Deeplab_v3+, the automatic detection method proposed in this study is more efficient in comparison with other common pixel-level segmentation algorithms; (3) the best network optimizer of improved Deeplab_v3+ and learning rate of crack segmentation task are sgdm and 0.001, respectively. The crack detection method proposed in this study can significantly improves the technical level of crack detection in practical projects. [ABSTRACT FROM AUTHOR]

The development trend of Fischer–Tropsch (F–T) technology is to develop high value-added products. The separation of linear α-olefins with low cost is an effective method. Nevertheless, the lack of thermodynamic data and the huge energy consumption are the two main problems restricting the development of the separation process. The thermodynamic data of the key components (1-dodecene and n-dodecane) in the F–T product were measured. The Wilson binary interaction parameters of the key components were obtained. Next, one traditional distillation column sequence and two dividing wall column (DWC) sequences were designed to separate the F–T heavy oil to obtain narrow fractions with different carbon numbers. Then, the obtained fractions of C10 and C12 were simulated to obtain 1-decene and 1-dodecene, respectively. There was a traditional distillation and a differential pressure thermal coupling distillation process. When separating 95.0% purity 1-decene and 1-octene, the direct DWC process and differential pressure thermal coupled distillation are an excellent combination, which can reduce the energy by 33.1% (i.e., 11,286 kW) and total annual cost by 15.9% (i.e., 3.96 × 106 $) compared with traditional distillation. [ABSTRACT FROM AUTHOR]

Background: We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature.Methods: This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score.Results: We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990-1.000] and 0.822 [95% CI 0.649-0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did.Conclusions: The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making. [ABSTRACT FROM AUTHOR]

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for 'resnet18', 'alexnet', and 'vgg16', respectively meanwhile, the 'googlenet' (AP = 0.84) and 'mobilenetv2' (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the 'alexnet' takes the least computational time, the 'squeezenet' and 'resnet18' are ranked second and third respectively; therefore, the 'resnet18' is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role. [ABSTRACT FROM AUTHOR]

Increasing evidence indicates that depression affects bone metabolism to some extent, but the specific mechanisms are still unclear. Numerous studies have confirmed that a variety of signaling molecules are involved in depression's impact on fracture healing, including serum monoamine neurotransmitters, cytokines, inflammatory markers, growth factors, and metabolites. This article comprehensively discusses the effects of depression-associated signaling molecules on bone metabolism and their underlying mechanisms to provide a basis for early preventive intervention for delayed fracture healing in patients with depression. [ABSTRACT FROM AUTHOR]

Measurements of various effective stiffnesses have been suggested as non-invasive biomechanical methods to assess healing status of a fractured long bone. This paper has compared the sensitivities of five assessment methods for fracture healing of long bones: torsion, compression, 3-point bending, 4-point bending and cantilever bending. A fractured human femur is modeled by an Euler beam and Castigliano's theorem is used to obtain the effective stiffnesses of the fractured bone. The variations of the effective stiffnesses of the fractured bone with the healing status of the callus have been investigated. The healing process of the callus is represented by gradual increases of the Young's modulus. The callus is divided into multiple regions. The narrowing process of the fracture gap is characterized by assigning different Young's modulus for different regions. Our findings showed that torsional, compressional, and bending stiffnesses all perform similarly with respect to the healing process. Before the rapid increase, all effective stiffness curves show a substantial creeping stage which corresponds to the narrowing process of the fracture gap. A higher value of them indicates bony bridging of healing callus. The effective stiffnesses for cantilever bending and 3-point bending are more sensitive to the fracture location, while torsional and compressional stiffness are independent from the fracture location. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to substantially improve the detection accuracy of structural damage via a one-dimensional convolutional neural network (1-D CNN) and a decision-level fusion strategy. As structural damage usually induces changes in the dynamic responses of a structure, a CNN can effectively extract structural damage information from the vibration signals and classify them into the corresponding damage categories. However, it is difficult to build a large-scale sensor system in practical engineering; the collected vibration signals are usually non-synchronous and contain incomplete structure information, resulting in some evident errors in the decision stage of the CNN. In this study, the acceleration signals of multiple acquisition points were obtained, and the signals of each acquisition point were used to train a 1-D CNN, and their performances were evaluated by using the corresponding testing samples. Subsequently, the prediction results of all CNNs were fused (decision-level fusion) to obtain the integrated detection results. This method was validated using both numerical and experimental models and compared with a control experiment (data-level fusion) in which all the acceleration signals were used to train a CNN. The results confirmed that: by fusing the prediction results of multiple CNN models, the detection accuracy was significantly improved; for the numerical and experimental models, the detection accuracy was 10% and 16–30%, respectively, higher than that of the control experiment. It was demonstrated that: training a CNN using the acceleration signals of each acquisition point and making its own decision (the CNN output) and then fusing these decisions could effectively improve the accuracy of damage detection of the CNN. [ABSTRACT FROM AUTHOR]