Ex vivo liver resection and auto-transplantation as an alternative for the treatment of liver malignancies: Progress and challenges.

Hepatectomy is still the major curative treatment for patients with liver malignancies. However, it is still a big challenge to remove the tumors in the central posterior area, especially if their location involves the retrohepatic inferior vena cava and hepatic veins. Ex vivo liver resection and auto-transplantation (ELRA), a hybrid technique of the traditional liver resection and transplantation, has brought new hope to these patients and therefore becomes a valid alternative to liver transplantation. Due to its technical difficulty, ELRA is still concentrated in a few hepatobiliary centers that have experienced surgeons in both liver resection and liver transplantation. The efficacy and safety of this technique has already been demonstrated in the treatment of benign liver diseases, especially in the advanced alveolar echinococcosis. Recently, the application of ELRA for liver malignances has gained more attention. However, standardization of clinical practice norms and international consensus are still lacking. The prognostic impact in these oncologic patients also needs further evaluation. In this review, we summarized the principles and recent progresses on ELRA.

Widespread extracellular electron transfer pathways for charging microbial cytochrome OmcS nanowires via periplasmic cytochromes PpcABCDE.

Extracellular electron transfer (EET) via microbial nanowires drives globally-important environmental processes and biotechnological applications for bioenergy, bioremediation, and bioelectronics. Due to highly-redundant and complex EET pathways, it is unclear how microbes wire electrons rapidly (>106 s-1) from the inner-membrane through outer-surface nanowires directly to an external environment despite a crowded periplasm and slow (<105 s-1) electron diffusion among periplasmic cytochromes. Here, we show that Geobacter sulfurreducens periplasmic cytochromes PpcABCDE inject electrons directly into OmcS nanowires by binding transiently with differing efficiencies, with the least-abundant cytochrome (PpcC) showing the highest efficiency. Remarkably, this defined nanowire-charging pathway is evolutionarily conserved in phylogenetically-diverse bacteria capable of EET. OmcS heme reduction potentials are within 200 mV of each other, with a midpoint 82 mV-higher than reported previously. This could explain efficient EET over micrometres at ultrafast (<200 fs) rates with negligible energy loss. Engineering this minimal nanowire-charging pathway may yield microbial chassis with improved performance.

All-Inorganic Perovskite Solar Cells: Defect Regulation and Emerging Applications in Extreme Environments.

All-inorganic perovskite solar cells (PSCs), such as CsPbX3 , have garnered considerable attention recently, as they exhibit superior thermodynamic and optoelectronic stabilities compared to the organic-inorganic hybrid PSCs. However, the power conversion efficiency (PCE) of CsPbX3 PSCs is generally lower than that of organic-inorganic hybrid PSCs, as they contain higher defect densities at the interface and within the perovskite light-absorbing layers, resulting in higher non-radiative recombination and voltage loss. Consequently, defect regulation has been adopted as an important strategy to improve device performance and stability. This review aims to comprehensively summarize recent progresses on the defect regulation in CsPbX3 PSCs, as well as their cutting-edge applications in extreme scenarios. The underlying fundamental mechanisms leading to the defect formation in the crystal structure of CsPbX3 PSCs are firstly discussed, and an overview of literature-adopted defect regulation strategies in the context of interface, internal, and surface engineering is provided. Cutting-edge applications of CsPbX3 PSCs in extreme environments such as outer space and underwater situations are highlighted. Finally, a summary and outlook are presented on future directions for achieving higher efficiencies and superior stability in CsPbX3 PSCs.

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features.

The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method.

Curvature-enhanced graph convolutional network for biomolecular interaction prediction.

Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further incorporated into the weight function for the feature aggregation in message-passing procedure. Our CGCN model is extensively validated on fourteen real-world bimolecular interaction networks and analyzed in details using a series of well-designed simulated data. It has been found that our CGCN can achieve the state-of-the-art results. It outperforms all existing models, as far as we know, in thirteen out of the fourteen real-world datasets and ranks as the second in the rest one. The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negative-curvature ratios, network densities, and network sizes (when larger than 500).

Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network.

BACKGROUND: Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. RESULTS: This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. CONCLUSION: The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.

Application of a novel polymer cross-linked with magnetite for efficient norfloxacin adsorption at a wide pH range.

Nowadays, NOR-containing wastewater has placed huge pressure on global ecology. In this study, a chemically-modified chitosan-based polymer was cross-linked with magnetite to prepare a novel magnetic composite adsorbent named Fe3O4/CS-P(AM-SSS) for norfloxacin (NOR) removal. The preparation conditions were optimized by single factor experiments and response surface methodology. A series of characterization analyses were carried out on the morphology, structure, and properties of Fe3O4/CS-P(AM-SSS), verifying that Fe3O4/CS-P(AM-SSS) was successfully prepared. Batch adsorption experiments showed that NOR was efficiently removed by Fe3O4/CS-P(AM-SSS), with a broad pH applicability of 3-10, short adsorption equilibrium time of 60 min, maximum adsorption capacity of 268.79 mg/g, and high regeneration rate of 86% after eight adsorption-desorption cycles. Due to the three-dimensional network structure and abundant functional groups provided by modified chitosan polymer, the superior adsorption capability of Fe3O4/CS-P(AM-SSS) was achieved through electrostatic interaction, π-π stacking, hydrophobic interaction, and hydrogen bonding. Adsorption process was exothermic and well fitted by the pseudo-second-order kinetic model and the Langmuir isothermal model. The presence of cations had a slight inhibitory effect on NOR adsorption, while humic acid nearly had no effect. In model swine wastewater, 90.3% NOR was removed by Fe3O4/CS-P(AM-SSS). Therefore, with these superior characteristics, Fe3O4/CS-P(AM-SSS) was expected to be an ideal material for treating NOR-containing wastewater in the future.

Degradation and mechanism of PFOA by peroxymonosulfate activated by nitrogen-doped carbon foam-anchored nZVI in aqueous solutions.

Perfluorooctanoic acid (PFOA) is an emerging pollutant that is non-biodegradable and presents severe environmental and human health risks. In this study, we present an effective and mild approach for PFOA degradation that involves the use of nitrogen-doped carbon foam anchored with nanoscale zero-valent iron (nZVI@NCF) to activate low concentration peroxymonosulfate (PMS) for the treatment. The nZVI@NCF/PMS system efficiently removed 84.4% of PFOA (2.4 µM). The active sites of nZVI@NCF including Fe0 (110) and graphitic nitrogen played crucial roles in the degradation. Electrochemical analyses and density functional theory calculations revealed that nZVI@NCF acted as an electronic donor, transferring electrons to both PMS and PFOA during the reaction. By further analyzing the electron paramagnetic resonance and byproducts, it was determined that electron transfer and singlet oxygen were responsible for PFOA degradation. Three degradation pathways involving decarboxylation and surface reduction of PFOA in the nZVI@NCF/PMS system were determined. Finding from this study indicate that nZVI@NCF/PMS systems are effective in degrading PFOA and thus present a promising persulfate-advanced oxidation process technology for PFAS treatment.

The small RNA PrrH aggravates Pseudomonas aeruginosa-induced acute lung injury by regulating the type III secretion system activator ExsA.

Pseudomonas aeruginosa is an opportunistic pathogen that causes acute and chronic infections in immunocompromised individuals. Small regulatory RNAs (sRNAs) regulate multiple bacterial adaptations to environmental changes, especially virulence. Our previous study showed that sRNA PrrH negatively regulates the expression of a number of virulence factors, such as pyocyanin, rhamnolipid, biofilm, and elastase in the P. aeruginosa strain PAO1. However, previous studies have shown that the prrH-deficient mutant attenuates virulence in an acute murine lung infection model. All ΔprrH-infected mice survived the entire 28-day course of the experiment, whereas all mice inoculated with the wild-type or the complemented mutant succumbed to lung infection within 4 days of injection, but the specific mechanism is unclear. Herein, we explored how PrrH mediates severe lung injury by regulating the expression of virulence factors. In vivo mouse and in vitro cellular assays demonstrated that PrrH enhanced the pathogenicity of PAO1, causing severe lung injury. Mechanistically, PrrH binds to the coding sequence region of the mRNA of exsA, which encodes the type III secretion system master regulatory protein. We further demonstrated that PrrH mediates a severe inflammatory response and exacerbates the apoptosis of A549 cells. Overall, our results revealed that PrrH positively regulates ExsA, enhances the pathogenicity of P. aeruginosa, and causes severe lung injury. IMPORTANCE: Pseudomonas aeruginosa is a Gram-negative bacterium and the leading cause of nosocomial pneumonia. The pathogenicity of P. aeruginosa is due to the secretion of many virulence factors. Small regulatory RNAs (sRNAs) regulate various bacterial adaptations, especially virulence. Therefore, understanding the mechanism by which sRNAs regulate virulence is necessary for understanding the pathogenicity of P. aeruginosa and the treatment of the related disease. In this study, we demonstrated that PrrH enhances the pathogenicity of P. aeruginosa by binding to the coding sequence regions of the ExsA, the master regulatory protein of type III secretion system, causing severe lung injury and exacerbating the inflammatory response and apoptosis. These findings revealed that PrrH is a crucial molecule that positively regulates ExsA. Type III-positive strains are often associated with a high mortality rate in P. aeruginosa infections in clinical practice. Therefore, this discovery may provide a new target for treating P. aeruginosa infections, especially type III-positive strains.

Diagnostic value of 3D volume measurement of central pulmonary artery based on CTPA images in the pulmonary hypertension.

BACKGROUND: This retrospective study aims to evaluate the diagnostic value of volume measurement of central pulmonary arteries using computer tomography pulmonary angiography (CTPA) for predicting pulmonary hypertension (PH). METHODS: A total of 59 patients in our hospital from November 2013 to April 2021 who underwent both right cardiac catheterization (RHC) and CTPA examination were included. Systolic pulmonary artery pressure (SPAP), mean PAP (mPAP), and diastolic PAP (DPAP) were acquired from RHC testing. Patients were divided into the non-PH group (18 cases) and the PH group (41 cases). The diameters of the main pulmonary artery (DMPA), right pulmonary artery (DRPA), and left pulmonary artery (DLPA) were measured manually. A 3D model software was used for the segmentation of central pulmonary arteries. The cross-sectional areas (AMPA, ARPA, ALPA) and the volumes (VMPA, VRPA, VLPA) were calculated. Measurements of the pulmonary arteries derived from CTPA images were compared between the two groups, and correlated with the parameters of RHC testing. ROC curves and decision curve analysis (DCA) were used to evaluate the benefit of the three-dimensional CTPA parameters for predicting PH. A multiple linear regression model with a forward-step approach was adopted to integrate all statistically significant CTPA parameters for PH prediction. RESULTS: All parameters (DMPA, DRPA, DLPA, AMPA, ARPA, ALPA, VMPA, VRPA, and VLPA) of CTPA images exhibited significantly elevated in the PH group in contrast to the non-PH group (P < 0.05), and showed positive correlations with the parameters of RHC testing (mPAP, DPAP, SPAP) (r ranged 0.586~0.752 for MPA, 0.527~0.640 for RPA, and 0.302~0.495 for LPA, all with P < 0.05). For the MPA and RPA, 3D parameters showed higher correlation coefficients compared to their one-dimensional and two-dimensional counterparts. The ROC analysis indicated that the VMPA showed higher area under the curves (AUC) than the DMPA and AMPA without significance, and the VRPA showed higher AUC than the DRPA and ARPA significantly (DRPA vs. VRPA, Z = 2.029, P = 0.042; ARPA vs. VRPA, Z = 2.119, P = 0.034). The DCA demonstrated that the three-dimensional parameters could provide great net benefit for MPA and RPA. The predictive equations for mPAP, DPAP, and SPAP were formulated as [8.178 + 0.0006 * VMPA], [1.418 + 0.0005 * VMPA], and [-11.137 + 0.0006*VRPA + 1.259 * DMPA], respectively. CONCLUSION: The 3D volume measurement of the MPA and RPA based on CTPA images maybe more informative than the traditional diameter and cross-sectional area in predicting PH.

RNF187 governs the maintenance of mouse GC-2 cell development by facilitating histone H3 ubiquitination at K57/80.

RING finger 187 (RNF187), a ubiquitin-ligating (E3) enzyme, plays a crucial role in the proliferation of cancer cells. However, it remains unclear whether RNF187 exhibits comparable functionality in the development of germline cells. To investigate the potential involvement of RNF187 in germ cell development, we conducted interference and overexpression assays using GC-2 cells, a mouse spermatocyte-derived cell line. Our findings reveal that the interaction between RNF187 and histone H3 increases the viability, proliferation, and migratory capacity of GC-2 cells. Moreover, we provide evidence demonstrating that RNF187 interacts with H3 and mediates the ubiquitination of H3 at lysine 57 (K57) or lysine 80 (K80), directly or indirectly resulting in increased cellular transcription. This is a study to report the role of RNF187 in maintaining the development of GC-2 cells by mediating histone H3 ubiquitination, thus highlighting the involvement of the K57 and K80 residues of H3 in the epistatic regulation of gene transcription. These discoveries provide a new theoretical foundation for further comprehensive investigations into the function of RNF187 in the reproductive system.

Slc26a1 is not essential for spermatogenesis and male fertility in mice.

Thousands of genes are expressed in the testis of mice. However, the details about their roles during spermatogenesis have not been well-clarified for most genes. The purpose of this study was to examine the effect of Slc26a1 deficiency on mouse spermatogenesis and male fertility. Slc26a1-knockout (KO) mice were generated using CRISPR/Cas9 technology on C57BL/6J background. We found no obvious differences between Slc26a1-KO and Slc26a1-WT mice in fertility tests, testicular weight, sperm concentrations, or morphology. Histological analysis found that Slc26a1-KO mouse testes had normal germ cell types and mature sperm. These findings indicated that Slc26a1 was dispensable for male fertility in mice. Our results may save time and resources by allowing other researchers to focus on genes that are more meaningful for fertility studies. We also found that mRNAs of two Slc26a family members (Slc26a5 and Slc26a11) were expressed on higher mean levels in Slc26a1-KO total mouse testes, compared to Slc26a1-WT mice. This effect was not found in mouse GC-1 and GC-2 germ cell lines with the Slc26a1 gene transiently knocked down. This result may indicate that a gene compensation phenomenon was present in the testes of Slc26a1-KO mice.

Multitask joint learning with graph autoencoders for predicting potential MiRNA-drug associations.

The occurrence of many diseases is associated with miRNA abnormalities. Predicting potential drug-miRNA associations is of great importance for both disease treatment and new drug discovery. Most computation-based approaches learn one task at a time, ignoring the information contained in other tasks in the same domain. Multitask learning can effectively enhance the prediction performance of a single task by extending the valid information of related tasks. In this paper, we presented a multitask joint learning framework (MTJL) with a graph autoencoder for predicting the associations between drugs and miRNAs. First, we combined multiple pieces of information to construct a high-quality similarity network of both drugs and miRNAs and then used a graph autoencoder (GAE) to learn their embedding representations separately. Second, to further improve the embedding quality of drugs, we added an auxiliary task to classify drugs using the learned representations. Finally, the embedding representations of drugs and miRNAs were linearly transformed to obtain the predictive association scores between them. A comparison with other state-of-the-art models shows that MTJL has the best prediction performance, and ablation experiments show that the auxiliary task can enhance the embedding quality and improve the robustness of the model. In addition, we show that MTJL has high utility in predicting potential associations between drugs and miRNAs by conducting two case studies.

The Prevalence and Characteristics of Circulating IgA Anti-Glomerular Basement Membrane Autoantibodies in Anti-Glomerular Basement Membrane Disease.

Introduction: In some cases, immunoglobulin (IgA)-mediated antiglomerular basement membrane (anti-GBM) disease has been reported. Whether circulating IgA anti-GBM antibodies affect the clinico-pathologic characteristics and outcome of typical anti-GBM disease deserves further study. Methods: Circulating IgA anti-α3(IV)NC1 antibodies were examined by enzyme-linked immunosorbent assay (ELISA) using recombinant human α3(IV)NC1 as solid phase antigens in 107 patients with anti-GBM disease and 115 controls. Clinical, pathological, and follow-up data of patients were retrospectively analyzed. Results: Circulating IgA anti-α3(IV)NC1 antibodies were found in 18.7% (20/107) of patients with anti-GBM disease but were not detected in healthy controls or in patients with other glomerular diseases. The positivity of circulating IgA anti-α3(IV)NC1 antibodies was not associated with whether the patient was with combined IgA nephropathy or other glomerulonephritis. Kidney immunofluorescence showed no statistical difference in IgA deposition between patients with circulating IgA anti-α3(IV)NC1 antibodies and patients without (30.0% vs. 40.4%, P = 0.725). The titers of circulating immunoglobulin G (IgG) anti-α3(IV)NC1 antibodies in patients with circulating IgA anti-α3(IV)NC1 antibodies were significantly higher than those without (200 [183.3, 200] vs. 161 [85.5, 200] U/ml, P = 0.005). There were no significant differences in kidney outcome and mortality between the 2 groups. Conclusion: Circulating IgA anti-α3(IV)NC1 antibodies occurred in 18.7% (20/107) of patients with anti-GBM in our center and were specific to anti-GBM disease. Patients with circulating IgA anti-α3(IV)NC1 antibodies showed a higher levels of serum IgG anti-α3(IV)NC1 antibodies than those without.

Multi-task learning for predicting synergistic drug combinations based on auto-encoding multi-relational graphs.

Combinatorial drug therapy is a promising approach for treating complex diseases by combining drugs with synergistic effects. However, predicting effective drug combinations is challenging due to the complexity of biological systems and the limited understanding of pathophysiological mechanisms and drug targets. In this paper, we proposed a computational framework called VGAETF (Variational Graph Autoencoder Tensor Decomposition), which leveraged multi-relational graph to model complex relationships between entities in biological systems and predicted disease-related synergistic drug combinations in an end-to-end manner. In the computational experiments, VGAETF achieved high performances (AUROC [the area under receiver operating characteristic] = 0.9767, AUPR [the area under precision-recall] = 0.9660), outperforming other compared methods. Moreover, case studies further demonstrated the effectiveness of VGAETF in identifying potential disease-related synergistic drug combinations.

Molecular geometric deep learning.

Molecular representation learning plays an important role in molecular property prediction. Existing molecular property prediction models rely on the de facto standard of covalent-bond-based molecular graphs for representing molecular topology at the atomic level and totally ignore the non-covalent interactions within the molecule. In this study, we propose a molecular geometric deep learning model to predict the properties of molecules that aims to comprehensively consider the information of covalent and non-covalent interactions of molecules. The essential idea is to incorporate a more general molecular representation into geometric deep learning (GDL) models. We systematically test molecular GDL (Mol-GDL) on fourteen commonly used benchmark datasets. The results show that Mol-GDL can achieve a better performance than state-of-the-art (SOTA) methods. Extensive tests have demonstrated the important role of non-covalent interactions in molecular property prediction and the effectiveness of Mol-GDL models.

Long non-coding RNA NRSN2-AS1 promotes ovarian cancer progression through targeting PTK2/ß-catenin pathway.

As a common malignant tumor among women, ovarian cancer poses a serious threat to their health. This study demonstrates that long non-coding RNA NRSN2-AS1 is over-expressed in ovarian cancer tissues using patient sample and tissue microarrays. In addition, NRSN2-AS1 is shown to promote ovarian cancer cell proliferation and metastasis both in vitro and in vivo. Mechanistically, NRSN2-AS1 stabilizes protein tyrosine kinase 2 (PTK2) to activate the ß-catenin pathway via repressing MG-53-mediated ubiquitinated degradation of PTK2, thereby facilitating ovarian cancer progression. Rescue experiments verify the function of the NRSN2-AS1/PTK2/ß-catenin axis and the effects of MG53 on this axis in ovarian cancer cells. In conclusion, this study demonstrates the key role of the NRSN2-AS1/PTK2/ß-catenin axis for the first time and explores its potential clinical applications in ovarian cancer.

Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study.

PURPOSE: The accurate assessment of axillary lymph node metastasis (LNM) in early-stage breast cancer (BC) is of great importance. This study aimed to construct an integrated model based on clinicopathology, ultrasound, PET/CT, and PET radiomics for predicting axillary LNM in early stage of BC. MATERIALS AND METHODS: 124 BC patients who underwent 18 F-fluorodeoxyglucose (18 F-FDG) PET/CT and whose diagnosis were confirmed by surgical pathology were retrospectively analyzed and included in this study. Ultrasound, PET and clinicopathological features of all patients were analyzed, and PET radiomics features were extracted to establish an ultrasound model (clinicopathology and ultrasound; model 1), a PET model (clinicopathology, ultrasound, and PET; model 2), and a comprehensive model (clinicopathology, ultrasound, PET, and radiomics; model 3), and the diagnostic efficacy of each model was evaluated and compared. RESULTS: The T stage, US_BIRADS, US_LNM, and PET_LNM in the positive axillary LNM group was significantly higher than that of in the negative LNM group (P = 0.013, P = 0.049, P < 0.001, P < 0.001, respectively). Radiomics score for predicting LNM (RS_LNM) for the negative LNM and positive LNM were statistically significant difference (-1.090 ± 0.448 vs. -0.693 ± 0.344, t = -4.720, P < 0.001), and the AUC was 0.767 (95% CI: 0.674-0.861). The ROC curves showed that model 3 outperformed model 1 for the sensitivity (model 3 vs. model 1, 82.86% vs. 48.57%), and outperformed model 2 for the specificity (model 3 vs. model 2, 82.02% vs. 68.54%) in the prediction of LNM. The AUC of mode 1, model 2 and model 3 was 0.687, 0.826 and 0.874, and the Delong test showed the AUC of model 3 was significantly higher than that of model 1 and model 2 (P < 0.05). Decision curve analysis showed that model 3 resulted in a higher degree of net benefit for all the patients than model 1 and model 2. CONCLUSION: The use of a comprehensive model based on clinicopathology, ultrasound, PET/CT, and PET radiomics can effectively improve the diagnostic efficacy of axillary LNM in BC. TRIAL REGISTRATION: This study was registered at ClinicalTrials Gov (number NCT05826197) on 7th, May 2023.

Electrohydraulic proportional position and pressure loading control utilizing a state perception and processing method.

A new multifunctional proportional control triaxial stress loading apparatus is presented in this study, which can apply an output force on coal rock to simulate the conditions of triaxial stress loading, aiming at solving the problems of excessive consideration of static indices in the design and the incompleteness of the simulation and test verification system for the test parts at the performance analysis stage. This apparatus mainly combines the configuration of a triaxial stress loading system which can meet the stress loading requirements under different operating conditions, with the effective integration of multiple pressure loading operations based on the electrohydraulic proportional control method. In this context, a pressure and position combined control strategy based on the sliding mode is proposed to control the vertical and longitudinal loading hydraulic cylinders. Then, a co-simulation mode including the triaxial stress loading hydraulic system is established to verify the control strategy, system response characteristics and selection of the controller parameters. Furthermore, a multifunctional stress loading experimental platform is developed, and the stress loading characteristics with the proposed strategy are tested and analyzed. The results show that the triaxial stress loading hydraulic system can meet the response characteristics, the fluctuating deviation of the constant loading test can be restricted to 8.5%, the tracking error of the variable loading test is small, the minimum response time of instantaneous loading can reach 2.8 s, and the stress loading effect is noticeable. The experimental platform fully indicates that the stress loading system with the state perception and processing method as the core can meet a variety of verification indices of constant, variable and instantaneous loading tests. This research provides technical support for the smooth, synchronous control and intelligent operation of various types of hydraulic actuator machines.