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Atomically thin two-dimensional (2D) metals may be key ingredients in next-generation quantum and optoelectronic devices. However, 2D metals must be stabilized against environmental degradation and integrated into heterostructure devices at the wafer scale. The high-energy interface between silicon carbide and epitaxial graphene provides an intriguing framework for stabilizing a diverse range of 2D metals. Here we demonstrate large-area, environmentally stable, single-crystal 2D gallium, indium and tin that are stabilized at the interface of epitaxial graphene and silicon carbide. The 2D metals are covalently bonded to SiC below but present a non-bonded interface to the graphene overlayer; that is, they are ‘half van der Waals’ metals with strong internal gradients in bonding character. These non-centrosymmetric 2D metals offer compelling opportunities for superconducting devices, topological phenomena and advanced optoelectronic properties. For example, the reported 2D Ga is a superconductor that combines six strongly coupled Ga-derived electron pockets with a large nearly free-electron Fermi surface that closely approaches the Dirac points of the graphene overlayer. Single-crystal 2D metals are stabilized at the interface between epitaxial graphene and silicon carbide, with strong internal gradients in bonding character. The confined 2D metals demonstrate compelling superconducting properties.

Understanding the distribution of soil organic carbon and nitrogen (OC(N)) content, cation exchange capacity (CEC), and specific surface area (SSA) in different soil particle sizes is crucial for studying soil fertility and properties. In this study, we investigated the distribution characteristics of the OC(N), CECand SSA in different particles of yellow–brown soil under different methods. The result revealed that as the particle size decreased, the soil OC(N), SSA and CEC content gradually increase. The content of OC and ON different soil particles ranged from 1.50–28.16 g·kg −1 to 0.18–3.78 g·kg −1 , respectively, and exhibited significant differences between different particles. We observed good linear relationships between OC and ON in different particle sizes of yellow–brown soil under different utilization methods, with correlation coefficients ranging from 0.86 to 0.98, reaching a very significant level (n = 12, p  < 0.01). The ranges of SSA and CEC in different particles of the four soils were 0.30–94.70 m 2 ·g −1 and 0.70–62.91 cmol·kg −1 , respectively. Additionally, we found logarithmic relationships between SSA (CEC) and the equivalent diameter for the four soils, with correlation coefficients (r 2 ) higher than 0.91. Furthermore, there was an extremely significant linear relationship between CEC and SSA of the four soils, with correlation coefficients (r 2 ) of 0.92–0.97 (n = 12, p  < 0.01). These results highlight the close relationship between soil particle size and soil OC(N), SSA, and CEC. The conclusions drawn from this study provide valuable data support and a theoretical basis for further understanding soil properties.

Background Medical and clinical question answering (QA) is highly concerned by researchers recently. Though there are remarkable advances in this field, the development in Chinese medical domain is relatively backward. It can be attributed to the difficulty of Chinese text processing and the lack of large-scale datasets. To bridge the gap, this paper introduces a Chinese medical QA dataset and proposes effective methods for the task. Methods We first construct a large scale Chinese medical QA dataset. Then we leverage deep matching neural networks to capture semantic interaction between words in questions and answers. Considering that Chinese Word Segmentation (CWS) tools may fail to identify clinical terms, we design a module to merge the word segments and produce a new representation. It learns the common compositions of words or segments by using convolutional kernels and selects the strongest signals by windowed pooling. Results The best performer among popular CWS tools on our dataset is found. In our experiments, deep matching models substantially outperform existing methods. Results also show that our proposed semantic clustered representation module improves the performance of models by up to 5.5% Precision at 1 and 4.9% Mean Average Precision. Conclusions In this paper, we introduce a large scale Chinese medical QA dataset and cast the task into a semantic matching problem. We also compare different CWS tools and input units. Among the two state-of-the-art deep matching neural networks, MatchPyramid performs better. Results also show the effectiveness of the proposed semantic clustered representation module.

Magnetic liquid metal (LM) soft robots attract considerable attentions because of distinctive immiscibility, deformability and maneuverability. However, conventional LM composites relying on alloying between LM and metallic magnetic powders suffer from diminished magnetism over time and potential safety risk upon leakage of metallic components. Herein, we report a strategy to composite inert and biocompatible iron oxide (Fe 3 O 4 ) magnetic nanoparticles into eutectic gallium indium LM via reactive wetting mechanism. To address the intrinsic interfacial non-wettability between Fe 3 O 4 and LM, a silver intermediate layer was introduced to fuse with indium component into Ag x In y intermetallic compounds, facilitating the anchoring of Fe 3 O 4 nanoparticles inside LM with improved magnetic stability. Subsequently, a miniature soft robot was constructed to perform various controllable deformation and locomotion behaviors under actuation of external magnetic field. Finally, practical feasibility of applying LM soft robot in an ex vivo porcine stomach was validated under in-situ monitoring by endoscope and X-ray imaging. Interfacial non-wettability between biocompatible iron oxide and liquid metal caused by the substantial mismatch in surface energy remains an issue. Here, the authors introduce a silver intermediate layer to reduce compositional mismatch and improve the wetting ability between iron oxide and liquid metal.

Background Hip fractures in the elderly are a common traumatic injury. Due to factors such as age and underlying diseases, these patients exhibit a high incidence of acute heart failure prior to surgery, severely impacting surgical outcomes and prognosis. Objective This study aims to explore the potential risk factors for acute heart failure before surgery in elderly patients with hip fractures and to establish an effective clinical prediction model. Methods This study employed a retrospective cohort study design and collected baseline and preoperative variables of elderly patients with hip fractures. Strict inclusion and exclusion criteria were adopted to ensure sample consistency. Statistical analyses were carried out using SPSS 24.0 and R software. A prediction model was developed using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. The accuracy of the model was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and a calibration curve was plotted to assess the model’s calibration. Results Between 2018 and 2019, 1962 elderly fracture patients were included in the study. After filtering, 1273 were analyzed. Approximately 25.7% of the patients experienced acute heart failure preoperatively. Through LASSO and logistic regression analyses, predictors for preoperative acute heart failure in elderly patients with hip fractures were identified as Gender was male (OR = 0.529, 95% CI: 0.381–0.734, P  < 0.001), Age (OR = 1.760, 95% CI: 1.251–2.479, P  = 0.001), Coronary Heart Disease (OR = 1.977, 95% CI: 1.454–2.687, P  < 0.001), Chronic Obstructive Pulmonary Disease (COPD) (OR = 2.484, 95% CI: 1.154–5.346, P  = 0.020), Complications (OR = 1.516, 95% CI: 1.033–2.226, P  = 0.033), Anemia (OR = 2.668, 95% CI: 1.850–3.847, P  < 0.001), and Hypoalbuminemia (OR 2.442, 95% CI: 1.682–3.544, P  < 0.001). The linear prediction model of acute heart failure was Logit(P) = -2.167–0.637×partial regression coefficient for Gender was male + 0.566×partial regression coefficient for Age + 0.682×partial regression coefficient for Coronary heart disease + 0.910×partial regression coefficient for COPD + 0.416×partial regression coefficient for Complications + 0.981×partial regression coefficient for Anemia + 0.893×partial regression coefficient for Hypoalbuminemia, and the nomogram prediction model was established. The AUC of the predictive model was 0.763, indicating good predictive performance. Decision curve analysis revealed that the prediction model offers the greatest net benefit when the threshold probability ranges from 4 to 62%. Conclusion The prediction model we developed exhibits excellent accuracy in predicting the onset of acute heart failure preoperatively in elderly patients with hip fractures. It could potentially serve as an effective and useful clinical tool for physicians in conducting clinical assessments and individualized treatments.

Background Elderly patients with hip fracture who develop perioperative acute heart failure (AHF) have a poor prognosis. The aim of the present study is to investigate the potential risks of AHF in elderly hip-fracture patients in the postoperative period and to evaluate the prognostic significance of AHF. Methods A retrospective analysis was conducted on hip fracture patients at the Third Hospital of Hebei Medical University, who were continuously in hospital from September 2018 to August 2020. To identify independent risk factors for AHF in elderly patients with hip fracture, univariate and multivariate Logistic regression analysis was employed. The Kaplan-Meier survival curve illustrated the relationship between all-cause mortality in the AHF and non-AHF groups. An assessment of the correlation between baseline factors and all-cause mortality was conducted by means of univariable and multivariable Cox proportional hazards analysis. Results We eventually recruited 492 patients,318 of whom were in the AHF group. Statistical significance was found between the two groups for age group, concomitant coronary heart disease, COPD, haemoglobin level below 100 g/L on admission, albumin level below 40 g/L on admission, and increased intraoperative blood loss. Age over 75, concomitant coronary artery disease, hemoglobin level below 100 g/L and albumin level below 40 g/L on admission were independent risk factors for AHF in older hip fracture patients. The AHF group exhibited a higher incidence of perioperative complications, such as anemia, cardiovascular issues, and stress hyperglycemia, as well as all-cause mortality. Based on our COX regression analysis, we have identified that the main risk factors for all-cause mortality in AHF patients are concomitant coronary heart disease, absence of pulmonary infection, absence of diabetes, absence of cancer, and absence of urinary tract infection. Conclusion Enhancing hip fracture prevention for AHF is particularly important. It is crucial to make informed decisions to avoid poor prognoses. Patients whose age over 75 years old, concomitant coronary heart disease, hemoglobin < 100 g/L and album< 40 g/L on admission are more likely to develop perioperative AHF. To avert complications and potential fatalities, patients with AHF must receive appropriate care during the perioperative period.

Background Acute heart failure (AHF) has become a significant challenge in older people with hip fractures. Timely identification and assessment of preoperative AHF have become key factors in reducing surgical risks and improving outcomes. Objective This study aims to precisely predict the risk of AHF in older people with hip fractures before surgery through machine learning techniques and SHapley Additive exPlanations (SHAP), providing a scientific basis for clinicians to optimize patient management strategies and reduce adverse events. Methods A retrospective study design was employed, selecting patients admitted for hip surgery in the Department of Geriatric Orthopedics at the Third Hospital of Hebei Medical University from January 2018 to December 2022 as research subjects. Data were analyzed using logistic regression, random forests, support vector machines, AdaBoost, XGBoost, and GBM machine learning methods combined with SHAP analysis to interpret relevant factors and assess the risk of AHF. Results A total of 2,631 patients were included in the final cohort, with an average age of 79.3 ± 7.7. 33.7% of patients experienced AHF before surgery. A predictive model for preoperative AHF in older people hip fracture patients was established through multivariate logistics regression: Logit(P) = -2.262–0.315 × Sex + 0.673 × Age + 0.556 × Coronary heart disease + 0.908 × Pulmonary infection + 0.839 × Ventricular arrhythmia + 2.058 × Acute myocardial infarction + 0.442 × Anemia + 0.496 × Hypokalemia + 0.588 × Hypoalbuminemia, with a model nomogram established and an AUC of 0.767 (0.723–0.799). Predictive models were also established using five machine learning methods, with GBM performing optimally, achieving an AUC of 0.757 (0.721–0.792). SHAP analysis revealed the importance of all variables, identifying acute myocardial infarction as the most critical predictor and further explaining the interactions between significant variables. Conclusion This study successfully developed a predictive model based on machine learning that accurately predicts the risk of AHF in older people with hip fractures before surgery. The application of SHAP enhanced the model’s interpretability, providing a powerful tool for clinicians to identify high-risk patients and take appropriate preventive and therapeutic measures in preoperative management.

Background Hip fractures are a serious health concern among the elderly, particularly in patients with hypertension, where the incidence of acute heart failure preoperatively is high, significantly affecting surgical outcomes and prognosis. This study aims to assess the risk of preoperative acute heart failure in elderly patients with hypertension and hip fractures by constructing a predictive model using machine learning on potential risk factors. Methods A retrospective study design was employed, collecting preoperative data from January 2018 to December 2019 of elderly hypertensive patients with hip fractures at the Third Hospital of Hebei Medical University. Using SPSS 24.0 and R software, predictive models were established through LASSO regression and multivariable logistic regression analysis. The models' predictive performance was evaluated using metrics such as the concordance index (C-index), receiver operating characteristic curve (ROC curve), and decision curve analysis (DCA), providing insights into the nomogram's predictive accuracy and clinical utility. Results Out of 1038 patients screened, factors such as gender, age, history of stroke, arrhythmias, anemia, and complications were identified as independent risk factors for preoperative acute heart failure in the study population. Notable predictors included Sex (OR 0.463, 95% CI 0.299–0.7184, P  = 0.001), Age (OR 1.737, 95% CI 1.213–2.488, P  = 0.003), Stroke (OR 1.627, 95% CI 1.137–2.327, P  = 0.008), Arrhythmia (OR 2.727, 95% CI 1.490–4.990, P  = 0.001), Complications (OR 2.733, 95% CI 1.850–4.036, P  < 0.001), and Anemia (OR 3.258, 95% CI 2.180–4.867, P  < 0.001). The prediction model of acute heart failure was Logit(P) = -2.091–0.770 × Sex + 0.552 × Age + 0.487 × Stroke + 1.003 × Arrhythmia + 1.005 × Complications + 1.181 × Anemia, and the prediction model nomogram was established. The model's AUC was 0.785 (95% CI, 0.754–0.815), Decision curve analysis (DCA) further validated the nomogram's excellent performance, identifying an optimal cutoff value probability range of 3% to 58% for predicting preoperative acute heart failure in elderly patients with hypertension and hip fractures. Conclusion The predictive model developed in this study is highly accurate and serves as a powerful tool for the clinical assessment of the risk of preoperative acute heart failure in elderly hypertensive patients with hip fractures, aiding in the optimization of preoperative risk assessment and patient management.

To address the issues of poor strength and low efficiency in traditional clay-cement composite gas-sealing materials (CCC), a method was proposed to prepare a new type of sealing material by utilizing multi-walled carbon nanotubes (MWCNTs) along with xanthan gum (XG) and magnesium oxide (MgO) to modify CCC. Through controlled experiments of water extraction rate testing, the optimal water-to-solid ratio for the multi-component system material has been determined to be 0.6. Mechanical performance testing reveals that when 1.5% xanthan gum, 5% magnesium oxide, and 1.39% multi-walled carbon nanotubes are added, the compressive strength of the multi-walled carbon nanotube-xanthan gum-magnesium oxide-clay-cement composite (MXM-CCC) reaches 18.60 MPa, with a flexural strength of 3.89 MPa. Pore integration analysis reveals that MXM-CCC has a porosity of 17.29%, with pore sizes ranging from 2.00 nm to 50 nm accounting for 71.46% of the total. The proportion of larger pores has decreased, resulting in a more optimal distribution of pore sizes. The formation mechanism and sealing mechanism of MXM-CCC were explored using characterization techniques such as XRD, FTIR, SEM, and thermogravimetric analysis. The hydroxyl and carboxyl groups in konjac gum undergo chelation with Ca²⁺ in CCC, forming a chelate structure. This causes the hydration products of the clay and cement to adhere together, improving the pore structure and mechanical properties of MXM-CCC. The addition of multi-walled carbon nanotubes accelerates the hydration reaction, increasing the content of substances such as C-(A)-S-H gel, ettringite, and Mg(OH) 2 in the MXM-CCC. These chemicals act as a framework, providing support within the pores and inhibiting the shrinkage of MXM-CCC, and improving the adhesion between various hydration products. Additionally, multi-walled carbon nanotubes perform a nano-filling role, filling the pores and improving the density of the multi-component material, thereby enhancing its mechanical properties.

Background Patients with acute ischemic stroke (AIS) after hip fracture in the elderly have worse prognosis. We aimed to describe the characteristics and complications of hip fracture with AIS in the elderly. Methods This cross-sectional study selected patients with hip fracture (age ≥65 years) from January 2018 to September 2020. The collected data included age, sex, fracture types, comorbidities. In above screened patients, we further collected cerebral infarction related information of AIS patients. The least absolute shrinkage and selection operator (LASSO) logistic regression was performed to identify the strongest predictors of AIS after hip fracture. Multivariate logistic regression analysis was conducted to find independent risk factors for AIS after hip fracture. Results Sixty patients (mean age 79.7 years;female 56.7%) occurred AIS after hip fracture in 1577 cases. The most common infarction type was partial anterior circulation infarction (PACI) (70.0%). The majority of these infarction lesions were single (76.7%) and most infarction lesions(65.0%) were located in the left side. 81.7% of AIS patients had mild (Health stroke scale NIHSS <4) AIS. Older patients with AIS after hip fracture were more frequently complicated by hypertension(73.3%), prior stroke (46.7%), diabetes(35.0%) and were more likely to have hypoproteinemia(68.3%), electrolyte disorders ( 66.7%), anemia (65.0%), deep vein thrombosis (51.6%), pneumonia (46.6%),cardiac complications (45.0%). Combined with hypertension (OR 2.827, 95%CI 1.557-5.131) and male sex(OR 1.865, 95%CI 1.095-3.177) were associated with the increased risk of AIS after hip fracture. Conclusions Older patients combined with hypertension are more likely to have AIS after hip fracture. For these patients, early preventions should be administered. AIS patients after hip fracture are prone to have multiple complications under traumatic stress, and we should enhance the management of these patients to reduce the stress and avoid occurrence of complications.