Preparation of Controlled Multicompartmental Gel Microcarriers Based on Aqueous Two-Phase Emulsions for 3D Partitioned Cell Coculture In Vitro.

A facile method was proposed for preparing controllable multicompartment gel microcarriers using an aqueous two-phase emulsion system. By leveraging the density difference between the upper polyethylene glycol solution and the lower dextran-calcium chloride (CaCl2) solution in the collection solution and the high viscosity of the lower solution, controllable fusion of core-shell droplets made by coextrusion devices was achieved at the water/water (w/w) interface to fabricate microcarriers with separated core compartments. By adjusting the sodium alginate concentration, collected solution composition, and number of fused liquid droplets, the pore size, shape, and number of compartments could be controlled. Caco-2 and HepG2 cells were encapsulated in different compartments to establish gut-liver coculture models, exhibiting higher viability and proliferation compared to monoculture models. Notably, significant differences in cytokine expression and functional proteins were observed between the coculture and monoculture models. This method provides new possibilities for preparing complex and functional three-dimensional coculture materials.

Effect of proprioceptive neuromuscular facilitation technique on the treatment of frozen shoulder: a pilot randomized controlled trial.

BACKGROUND AND OBJECTIVES: Frozen shoulder is a common painful disease of the shoulder joint characterized by structural changes in the shoulder joint, restricting both active and passive shoulder joint activities. Proprioceptive neuromuscular facilitation (PNF) effectively improved and maintained the range of motion; however, it is not clear whether it can improve the shoulder joint structure in patients with frozen shoulder. This pilot study used magnetic resonance imaging (MRI) observation to assess the improvement of the local structure of the shoulder joint upon PNF treatment to elucidate a target based on structure for the treatment of frozen shoulder. MATERIALS AND METHODS: Forty-eight patients with frozen shoulder were randomly divided into the traditional manual therapy group and the PNF technique group. Changes in the thicknesses of the coracohumeral ligament (CHL) and capsule in axillary recess (CAR) of the shoulder joint were observed via MRI upon admission and at 4 weeks after treatment. A visual analog scale (VAS) and passive shoulder range of motion (ROM) at abduction, anteflexion and external rotation position were used to evaluate the improvement of shoulder joint pain and function in the initial, mid-term, and discharge of the two groups of patients. RESULTS: The primary outcome results shown that the PNF joint mobilization significantly reduced the thickness of the CHL (p = 0.0217) and CAR (p = 0.0133). Compared with simple joint mobilization, The mid-term and discharge rehabilitation assessment results showed that PNF has a better effect on shoulder pain. At the mid-term evaluation, the ROM of the PNF group was significantly better than that of the Control group in the three directions (p < 0.05). CONCLUSION: As an adjunctive therapy, PNF can improve the shoulder joint structure of patients with frozen shoulder and is an effective treatment strategy for frozen shoulder.

Three Highly Sensitive and High-Throughput Serological Approaches for Detecting Dickeya dadantii in Sweet Potato.

Sweet potato stem and root rot is an important bacterial disease and often causes serious economic losses to sweet potato. Development of rapid and sensitive detection methods is crucial for diagnosis and management of this disease in field. Here, we report the production of four hybridoma cell lines (25C4, 16C10, 9B1, and 9H10) using Dickeya dadantii strain FY1710 as an immunogen. Monoclonal antibodies (MAbs) produced by these four hybridoma cell lines were highly specific and sensitive for D. dadantii detection. Indirect enzyme-linked immunosorbent assay (indirect-ELISA) results showed that the four MAbs 25C4, 16C10, 9B1, and 9H10 could detect D. dadantii in suspensions diluted to 4.89 × 104, 4.89 × 104, 9.78 × 104, and 9.78 × 104 CFU/ml, respectively. Furthermore, all four MAbs can react strongly and specifically with all four D. dadantii strains used in this study, not with the other seven tested bacterial strains. Using these four MAbs, three different serological approaches, triple-antibody sandwich enzyme-linked immunosorbent assay (TAS-ELISA), dot-ELISA, and tissue-print-ELISA, were developed for detection of D. dadantii in crude extracts prepared from field-collected sweet potato plants. Among these three methods, TAS-ELISA and dot-ELISA were used to detect D. dadantii in suspensions diluted up to 1.23 × 104 and 1.17 × 106 CFU/ml, respectively, or in sweet potato crude extracts diluted up to 1:3,840 and 1:1,920 (wt/vol, grams per milliliter), respectively. Surprisingly, both TAS-ELISA and dot-ELISA serological approaches were more sensitive than the conventional PCR. Analyses using field-collected sweet potato samples showed that the newly developed TAS-ELISA, dot-ELISA, or tissue-print-ELISA were reliable in detecting D. dadantii in sweet potato tissues. Thus, the three serological approaches were highly valuable for diagnosis of stem and root rot in sweet potato production.

LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products.

The remarkable success of convolutional neural networks (CNNs) in computer vision tasks is shown in large-scale datasets and high-performance computing platforms. However, it is infeasible to deploy large CNNs on resource constrained platforms, such as embedded devices, on account of the huge overhead. To recognize the label numbers of industrial black material product and deploy deep CNNs in real-world applications, this research uses an efficient method to simultaneously (a) reduce the network model size and (b) lower the amount of calculation without compromising accuracy. More specifically, the method is implemented by pruning channels and corresponding filters that are identified as having a trivial effect on the output accuracy. In this paper, we prune VGG-16 to obtain a compact network called LeanNet, which gives a 25× reduction in model size and a 4.5× reduction in float point operations (FLOPs), while the accuracy on our dataset is close to the original accuracy by retraining the network. Besides, we also find that LeanNet could achieve better performance on reductions in model size and computation compared to some lightweight networks like MobileNet and SqueezeNet, which are widely used in engineering applications. This research has good application value in the field of industrial production.

Revisiting the evolutionary events in Allium subgenus Cyathophora (Amaryllidaceae): Insights into the effect of the Hengduan Mountains Region (HMR) uplift and Quaternary climatic fluctuations to the environmental changes in the Qinghai-Tibet Plateau.

The respective roles that the Hengduan Mountains Region (HMR) uplift around 4-3 Ma and Quaternary climatic oscillations played in causing the environmental changes in the Qinghai-Tibet Plateau (QTP) remain unknown. Here, we reconstruct the evolutionary history of two varieties of Allium cyathophorum and A. spicatum of subgenus Cyathophora, restricted to the HMR and the western QTP, respectively. Forty-five populations were surveyed for chloroplast and nuclear sequence variation to evaluate phylogenetic relationships, dates of divergence and ancestral area/inflorescence reconstructions. In addition, analyses were conducted on discernable micromorphologies, cytotypes and seed size variation. Our results indicated that two varieties of A. cyathophorum are separate species, i.e. A. farreri and A. cyathophorum, and the initial split of Cyathophora was triggered by the HMR uplift around 4-3 Ma. Subsequently, A. spicatum originated through the strengthened aridification in the western QTP induced vicariance of the ancestral populations in the HMR during the early Pleistocene. A self-sustaining allotetraploid species from A. farreri and A. cyathophorum was established during an interglacial period of penultimate glaciation of the QTP. Seed size variation also supports these by the colonization-competition tradeoff among small and large seeds. Our findings appear to suggest that the HMR uplift could have strengthened the development of the Asian monsoon regimes in this region and aridification in the western QTP, while the Quaternary climatic oscillations spurred the allopatric species' range shifts and created new open microhabitat for the alloploid species.

Phylogenetic reappraisal of Allium subgenus Cyathophora (Amaryllidaceae) and related taxa, with a proposal of two new sections.

The phylogeny of subgenus Cyathophora and representatives of its closely related taxa within Allium were reconstructed based on nrDNA ITS and two plastid fragments (trnL-F and rpl32-trnL). The constructed phylogenies indicated that subgenus Cyathophora was not monophyletic and to be split in three parts positioned in different clusters. Allium kingdonii was unequivocally placed within subgenus Amerallium and formed an immediate sister relationship with New World Amerallium clade, suggesting an unexpected intercontinental disjunct distribution. For another, Allium trifurcatum was firmly nested within subgenus Butomissa next to A. tuberosum and A. ramosum, but it is distinctly different morphologically from the latter by thinly leathery bulb tunics, uniovulate locule and obviously 3-cleft stigma. Based on the geographic features, morphological and molecular evidences, two new sections, Kingdonia X.J.He et D.Q.Huang for A. kingdonii and Trifurcatum X.J.He et D.Q.Huang for A. trifurcatum, were proposed. The remaining three species of subgenus Cyathophora formed a well-defined clade, and the phylogenetic relationships among them recovered were consistent with previous findings. In addition, A. weschniakowii and A. subtilissimum were proven to be a member of subgenera Rhizirideum sensu stricto (s. str.) and Cepa, respectively, rather than subgenera Cepa and Polyprason previously proposed. Section Rhizomatosa represented by A. caespitosum should be subsumed within section Caespitosoprason of subgenus Rhizirideum s. str.

Adaptive neural fault-tolerant prescribed performance control of a rehabilitation exoskeleton for lower limb passive training.

This article studies the passive tracking problem of a wearable exoskeleton for lower limb rehabilitation therapy in the face of unmodeled dynamics, interactive friction, disturbance, prescribed performance constraints, and actuator faults. Adaptive neural networks and a smooth performance function are incorporated to establish a novel fault-tolerant tracking scheme, which can not only compensate for the nonlinear uncertainties and disturbance, but also handle the actuator fault with guaranteed tracking performance. A state feedback controller is presented by using the full state information and an output feedback controller is developed when the angular velocity is unavailable. The differential explosion issue of the backstepping technique is resolved by constructing a first-order filter and the unmeasurable velocity is estimated by a nonlinear observer. Semiglobal uniform boundedness stabilities of the exoskeleton system are proved via the Lyapunov direct method. The tracking performances of the designed control approaches are tested by comparative simulations.

DMGM: deformable-mechanism based cervical cancer staging via MRI multi-sequence.

Objective.This study aims to leverage a deep learning approach, specifically a deformable convolutional layer, for staging cervical cancer using multi-sequence MRI images. This is in response to the challenges doctors face in simultaneously identifying multiple sequences, a task that computer-aided diagnosis systems can potentially improve due to their vast information storage capabilities.Approach.To address the challenge of limited sample sizes, we introduce a sequence enhancement strategy to diversify samples and mitigate overfitting. We propose a novel deformable ConvLSTM module that integrates a deformable mechanism with ConvLSTM, enabling the model to adapt to data with varying structures. Furthermore, we introduce the deformable multi-sequence guidance model (DMGM) as an auxiliary diagnostic tool for cervical cancer staging.Main results.Through extensive testing, including comparative and ablation studies, we validate the effectiveness of the deformable ConvLSTM module and the DMGM. Our findings highlight the model's ability to adapt to the deformation mechanism and address the challenges in cervical cancer tumor staging, thereby overcoming the overfitting issue and ensuring the synchronization of asynchronous scan sequences. The research also utilized the multi-modal data from BraTS 2019 as an external test dataset to validate the effectiveness of the proposed methodology presented in this study.Significance.The DMGM represents the first deep learning model to analyze multiple MRI sequences for cervical cancer, demonstrating strong generalization capabilities and effective staging in small dataset scenarios. This has significant implications for both deep learning applications and medical diagnostics. The source code will be made available subsequently.

All-aqueous droplets-templated tailorable core-shell alginate microspheres for constructing vascularized intestinal mucosain vitromodels.

Recently,in vitromodels of intestinal mucosa have become important tools for drug screening and studying the physiology and pathology of the intestine. These models enable the examination of cellular behavior in diseased states or in reaction to alterations in the microenvironment, potentially serving as alternatives to animal models. One of the major challenges in constructing physiologically relevantin vitromodels of intestinal mucosa is the creation of three-dimensional microstructures that accurately mimic the integration of intestinal epithelium and vascularized stroma. Here, core-shell alginate (Alg) microspheres were generated to create the compartmentalized extracellular matrix microenvironment needed to simulate the epithelial and vascularized stromal compartments of the intestinal mucosa. We demonstrated that NIH-3T3 and human umbilical vein endothelial cells embedded in the core of the microspheres can proliferate and develop a vascular network, while human colorectal adenocarcinoma cells (Caco-2) can form an epithelial monolayer in the shell. Compared to Caco-2 monolayer encapsulated within the shell, the presence of the vascularized stroma enhances their proliferation and functionality. As such, our core-shell Alg microspheres provide a valuable method for generatingin vitromodels of vascularized intestinal mucosa with epithelial and vascularized stroma arranged in a spatially relevant manner and demonstrating near-physiological functionality.

Adaptive Iterative Learning Control for a Class of Nonlinear Strict-Feedback Systems With Unknown State Delays.

In this article, an adaptive iterative learning control scheme is presented for a class of nonlinear parametric strict-feedback systems with unknown state delays, aiming to achieve the point-wise tracking of desired trajectory in a finite interval. The appropriate Lyapunov-Krasovskii functions are established to compensate the influence of time-delay uncertainties on the control systems. As the main features, the proposed approach integrates the command filter into the backstepping procedure to avoid the differential explosion problem that may occur with the increase of system order, and introduces the hyperbolic tangent functions into the learning controller to handle the singularity problem thus maintaining the continuity of input signal. The results of theoretical analysis and numerical simulation demonstrate that the tracking errors at the entire period will converge to a compact set along the iteration axis. Compared with the existing works, the proposed control scheme is promising to manifest the better performance and practicability owing to the learning mechanism, the dynamic model, as well as the implementation of controller.

Phylogenomic analyses and chromosome ploidy identification reveal multiple cryptic species in Allium sikkimense complex (Amaryllidaceae).

Polyploidization is a process that typically leads to instantaneous reproductive isolation and has, therefore, been considered as one of the major evolutionary forces in the species-rich Hengduan Mountains (HM), yet this topic remains poorly studied in the region. Allium sikkimense and its relatives (about eight species) compose a natural diploid-polyploid complex with the highest diversity in the HM and adjacent areas. A combination of nuclear ribosomal DNA (nrDNA), plastome, transcriptome, and ploidy identification through chromosome counting and flow cytometry is employed to reconstruct the phylogenetic relationships in this complex and to investigate the frequency and the evolutionary significance of polyploidy in the complex. The plastome failed to resolve the phylogenetic relationships of the different species in the A. sikkimense complex, and the phylogenetic tree based on nrDNA also has limited resolution. However, our study reveals a well-resolved phylogenetic framework for species in the A. sikkimense complex using more than 1,000 orthologous genes from the transcriptome data. Previously recognized morphospecies A. sikkimense are non-monophyletic and comprise at least two independently evolved lineages (i.e., cryptic species), each forming a clade with different diploid species in this complex. The embedded pattern of octoploid A. jichouense and tetraploid A. sp. nov. within different polyploid samples of A. sikkimense supports a possible scenario of budding speciation (via niche divergence). Furthermore, our results reveal that co-occurring species in the A. sikkimense complex usually have different ploidy levels, suggesting that polyploidy is an important process for reproductive isolation of sympatric Allium species. Phylogenetic network analyses suggested that the phylogenetic relationships of the A. sikkimense complex, allowing for reticulation events, always fit the dataset better than a simple bifurcating tree. In addition, the included or exserted filaments, which have long been used to delimit species, are highly unreliable taxonomically due to their extensive parallel and convergent evolution.

An Efficient Federated Learning Framework for Machinery Fault Diagnosis With Improved Model Aggregation and Local Model Training.

Due to device operating environment limitations and data privacy protection, it is frequently difficult to obtain sufficient high-quality labeled data from devices, resulting in an insufficient generalization ability of fault diagnosis model. Therefore, a high-performance federated learning framework is proposed in this work, which makes improvements in the procedure of model aggregation and local model training. In the model aggregation of central server, an optimization aggregation strategy in which forgetting Kalman filter (FKF) is combined with cubic exponential smoothing (CES) is proposed to improve the efficiency of federated learning. In the local model training of multiclient, a deep learning network combined with multiscale convolution, attention mechanism, and multistage residual connection is proposed, which is able to fully extract multiclient data features simultaneously. Meanwhile, experiments on two machinery fault datasets show that the proposed framework is capable of achieving high accuracy and strong generalization of fault diagnosis on the premise of protecting data privacy in actual industrial situations.

Analysis of antiplatelet therapy adherence in patients with ischemic cerebral stroke.

BACKGROUND: The related factors affecting the adherence of ischemic cerebral stroke (ICS) patients to antiplatelet therapy have attracted much attention. METHODS: Patients with ICS (confirmed by CT or MRI) were enrolled from January 2020 to July 2021. The demographic data were retrospectively investigated and analyzed. The adherence calculation was as follows: Adherence = number of tablets taken/number of tablets needed to be taken. Adherence < 100% was defined as nonadherence. Severe nonadherence is defined as adherence ≤ 75%. RESULTS: A total of 229 patients with ICS were enrolled. We found no significant difference in the proportion of patients with nonadherence, while the proportion of severe nonadherence in the aspirin group was significantly higher (p < .001). Multivariable analysis indicated that medical insurance (odds ratio [OR] = 0.071, p < .001) and regular exercise (OR = 0.438, p = .015) were independent factors associated with adherence. In addition, only medical insurance (OR = 5.475, p < .001) and aspirin treatment (OR = 0.228, p < .001) were independent risk factors associated with severe nonadherence. We therefore constructed a nomogram plot and a model as follows: Adherence risk score = 3 × medical insurance + regular exercise. Patients were divided into low-risk and high-risk groups for adherence based on the median model score. A total of 13.3% of patients in the low-risk group were nonadherent patients compared with 53.4% in the high-risk group (p < .001). Similarly, 8.4% of patients in the low-risk group had severe nonadherence compared with 19.9% in the high-risk group (p = .022). Moreover, in low-risk patients, no significant difference was observed. In patients with high risk, aspirin-treated patients showed significantly decreased adherence compared with the other two groups. CONCLUSION: Medical insurance and regular exercise were independent factors for antiplatelet therapy adherence. For patients with high model scores, timely intervention is necessary.

Meta-analysis of randomized controlled trials on the efficacy of thoracic epidural anesthesia in preventing atrial fibrillation after coronary artery bypass grafting.

BACKGROUND: Postoperative atrial fibrillation (POAF) is one of the most common complications in patients undergoing coronary artery bypass grafting (CABG). The goal of this meta-analysis was to evaluate the efficacy of thoracic epidural anesthesia (TEA) in preventing POAF in adult patients undergoing CABG. METHODS: MEDLINE and EMBASE were searched to identify randomized controlled trails in adult patients undergoing CABG who were randomly assigned to receive general anesthesia plus thoracic epidural anesthesia (GA + TEA) or general anesthesia only (GA). Two authors independently extracted data using a standardized Excel file. The primary outcome measure was the incidence of POAF. We used DerSimonian-Laird random-effects models to compute summary risk ratios with 95% confidence intervals. RESULTS: Five studies involving 540 patients met our inclusion criteria. No significant difference in the incidence of POAF was observed between the two groups (risk ratio, 0.61; 95% confidence interval, 0.33 to 1.12; P = 0.11), with significant heterogeneity among the studies (I2 = 73%, P = 0.005). Sensitivity analyses by primary endpoint, methodological quality and surgical technique yielded similar results. CONCLUSIONS: The limited evidence suggests that TEA shows no beneficial efficacy in preventing POAF in adult patients undergoing CABG. However, the results of this meta-analysis should be interpreted with caution due to significant heterogeneity of the studies included. Thus, the potential infuence of TEA on the incidence of atrial fibrillation following CABG warrants further investigation.

Spatial Iterative Learning Control for Robotic Path Learning.

A spatial iterative learning control (sILC) method is proposed for a robot to learn a desired path in an unknown environment. When interacting with the environment, the robot initially starts with a predefined trajectory so an interaction force is generated. By assuming that the environment is subjected to fixed spatial constraints, a learning law is proposed to update the robot's reference trajectory so that a desired interaction force is achieved. Different from existing iterative learning control methods in the literature, this method does not require repeating the interaction with the environment in time, which relaxes the assumption of the environment and thus addresses the limits of the existing methods. With the rigorous convergence analysis, simulation and experimental results in two applications of surface exploration and teaching by demonstration illustrate the significance and feasibility of the proposed method.

Microstructure and Properties of Hollow Octet Nickel Lattice Materials.

In this study, electroless nickel plating and electrodeposition were used to deposit thin films on the polymer lattice template prepared by 3D printing, then seven Octet hollow nickel lattice materials with different structural parameters were synthesized by etching process at the expense of the polymer backbone. The microstructure and properties of the Octet structure nickel lattice were characterized by X-ray diffraction, Electron backscattering diffraction and transmission electron microscopy. According to the results, the average grain size of the electrodeposition Ni lattice material was 429 nm, and (001) weak texture was found along the direction of the film deposition. The lattice deformation mode changed with the increase of the lattice length-to-diameter ratio, and it shifted from the lattice deformation layer-by-layer and the overall deformation to the shear deformation in the 45° direction. The strength, modulus and energy absorption properties of the Octet lattice increased with the density, and they were exponentially related to density. In the relative density range of 0.7~5%, Octet hollow Ni lattices with the same density conditions but different structural parameters showed similar compressive strength and elasticity modulus; the energy absorption capacity, however, was weakened as the length-to-diameter ratio increased.

Effect of Aggregate Size on the Axial Compressive Behavior of FRP-Confined Coral Aggregate Concrete.

Using locally available raw materials for preparing concrete, such as coral reefs, seawater, and sea sand, is conducive to compensating for the shortage of construction materials used on remote islands. Jacketing fiber-reinforced polymer (FRP), as passive confinement, is a practical approach to enhance the strength, ductility, and durability of such coral aggregate concrete (CAC). Rational and economical CAC structural design requires understanding the interactions between the CAC fracture process and FRP confinement. The coral aggregate size is the critical parameter of their interaction since it affects the crack propagation of CAC and FRP confinement efficiency. This study conducted axial compression tests on FRP-confined CAC cylinders with varying coral aggregate sizes and FRP confinement levels. The test results indicate that the coral aggregate sizes affected the unconfined CAC strength. In addition, the dilation behavior of FRP-confined CAC varied with aggregate sizes, showing that CAC with smaller coral aggregate featured a more uniform hoop strain distribution and larger FRP rupture strain. These coupling effects are epitomized by the variation in the transition stress on the stress-strain curve, which makes the existing stress-strain models not applicable for FRP-confined CAC. A modified stress-strain model is subsequently proposed. Finally, the practical and environmental implications of the present study are discussed.

Efficient Axillary Lymph Node Detection Via Two-stage Spatial-information-fusion-based CNN.

BACKGROUND AND OBJECTIVE: Preoperative imaging diagnosis of axillary lymph node (ALN) metastasis is particularly important for breast cancer patients. This paper focuses on developing non-invasive and automatic schemes for accurate localization and classification (metastasis prediction) of ALN via contrast-enhanced computed tomography (CECT) image and deep learning models. METHODS: Based on a two-stage strategy, a novel detection neural network is proposed, where the convolutional block attention module is utilized to extract spacial information and the bottleneck feature fusion module is designed for feature fusion in different scales. RESULTS: Owing to the two embedded modules, the proposed convolutional neural network (CNN) model outperforms Faster R-CNN, YOLOv3, and EfficientDet in the sense that the achieved mAP is 0.454, higher than 0.247, 0.335, and 0.329, respectively. In particular, considering the function of classification only, the proposed model reaches the best performance on most indices (accuracy of 0.968, positive predictive value of 0.972, negative predictive value of 0.966, specificity of 0.983), compared with the methods that have been frequently adopted to predict ALN. In addition, the proposed CNN model has the function of locating ALN, which is lacking in existing models. CONCLUSIONS: In this paper, a supervised deep learning method is proposed to detect ALN in CECT images. The positive effect of new added modules are verified, and the benefits of spatial information in ALN detection are confirmed. Further, the two subtasks called localization and classification are evaluated separately, where the proposed model achieves the best performance on most indices. The source code mentioned in this article will be released later.

Shenfu Injection Protects Brain Injury in Rats with Cardiac Arrest through Nogo/NgR Pathway.

The effect of Shenfu injection on brain injury after cardiac arrest (CA) and cardiopulmonary resuscitation (CPR) along with the underlying mechanism of axonal regeneration was explored. CA/CPR model in rats was established for subsequent experiments. A total of 160 rats were randomly divided into sham group, model group, conventional western medicine (CWM) group, Shenfu group, and antagonist group (n = 32 per group). After 3 hours, 24 hours, 3 days, and 7 days of drug administration, the modified Neurological Severity Score tests were performed. The ultrastructure of the brain and hippocampus was observed by electron microscopy. Real-time quantitative polymerase chain reaction (PCR), western blotting, and immunohistochemistry were used to detect Nogo receptor (NgR) expression in the hippocampus and cerebral cortex, and Nogo-NgR expression in CA/CPR model. Neurological deficits in the model group were severe at 3 hours, 24 hours, 3 days, and 7 days after the recovery of natural circulation, whereas the neurological deficits in CWM, antagonist, and Shenfu group were relatively mild. The ultrastructure of neuronal cells in Shenfu group had relatively complete cell membranes and more vesicles than those in the model group. The results of PCR and western blotting showed lower messenger ribonucleic acid and protein expression of NgR in Shenfu group than the model group and CWM group. Immunohistochemical examination indicated a reduction of Nogo-NgR expression in Shenfu group and antagonist group. Our results suggested that Shenfu injection reduced brain injury by attenuating Nogo-NgR signaling pathway and promoting axonal regeneration.

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography.

BACKGROUND: The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately. PURPOSE: The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer. METHODS: We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC). RESULTS: The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer. CONCLUSION: The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.