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Abstract Surgical site infection (SSI) following lumbar disc herniation (LDH) surgery leads to prolonged hospital stays, increased costs and reoperations. Therefore, we aim to develop and validate a nomogram to predict the risk of SSI following LDH surgery, thereby helping spine surgeons design personalized prevention strategies and promote early recovery. Data from 647 patients with SSI who underwent LDH surgery at the First Affiliated Hospital of Air Force Medical University (AFMU) from 2020 to 2023 were collected. Ultimately, 241 patients with SSI were selected based on inclusion and exclusion criteria. Patients were randomly divided into training and validation sets with a ratio of 7:3. LASSO regression, univariate, and multivariate logistic regression were utilized to identify target variables and establish the prediction model, which was subsequently validated. Six factors—Age, Body Mass Index (BMI), Postoperative Suction Drainage (PSD), Gelatin Sponge (GS), None-Preoperative Antibiotic (NPTA), and Thrombin Time (TT)—were selected to construct the nomogram model. In the training set, the area under the curve (AUC) for the nomogram was 0.818 (95% CI 0.779–0.857). In the validation set, the AUC was 0.782 (95% CI 0.717–0.846). Calibration curves for both sets showed satisfactory agreement between predicted and actual SSI probabilities. Decision curve analysis indicated that the nomogram is clinically useful with a threshold range of 1–90%. The Clinical Impact Curve (CIC) demonstrated an acceptable cost-benefit ratio. The developed nomogram model effectively predicts the risk of SSI following LDH surgery, enabling spine surgeons to formulate more professional and rational clinical prevention strategies.

Abstract Emulsified asphalt mixtures’ cold mix and paving features facilitate asphalt pavements in fulfilling dual-carbon and energy-saving demands. Anionic emulsifiers can enhance emulsion stability, ensure uniform dispersion of oil and water, possess good decompression viscosity, thickening, and lubricating properties, and maintain good stability under acidic conditions. Nevertheless, anionic emulsified asphalt is restricted in engineering applications due to problems like its storage stability. In this paper, eight anionic emulsifiers and two preparation procedures were chosen for stability tests. Through static tests, storage tests, sieve residue tests, and laser particle size tests, the impacts of emulsifiers on the storage stability and dispersion of asphalt were analyzed. Waterborne epoxy resin exhibits excellent adhesive properties, mechanical properties, chemical resistance, and heat stability. A fluorescence microscope test, static and storage test, laser particle size test, and cementation test were employed to explore the effects of different preparation processes and waterborne epoxy mixing ratios on emulsified asphalt’s storage stability, dispersion stability, and structural stability. The results showed that: (1) the emulsified asphalt prepared with the BWH-02 emulsifier exhibits the best storage stability, and blending with 20% of the waterborne epoxy modifier can notably enhance the bonding properties; (2) the shear strength of the BWH-02 waterborne epoxy emulsified asphalt prepared can reach 1.543 MPa, and the tensile viscosity can reach 0.848 MPa; (3), The emulsified asphalt prepared by the process of modification has better storage stability than that prepared by the side of the emulsification process. Moreover, the storage stability of emulsified asphalt prepared by emulsification and modification is superior to that of the emulsification and modification process. This research provides theoretical and technical support for popularizing and applying cold-mixed cold-paving asphalt mixtures.

Abstract The recently synthesized monolayer MoSi2N4 (Science 2020, 369, 367) exhibits exceptional environmental stability, a moderate band gap, and excellent mechanical properties, presenting exciting opportunities for the exploration of two-dimensional (2D) MX2Z4 materials. However, the low carrier mobility of α-phase MoSi2N4 significantly limits its potential applications in field-effect transistor (FET) devices. In this study, we systematically investigate the structural stability, elastic properties, and carrier mobility of a novel family of β-phase MX2N4 (M = Mo, W; X = Si, Ge) monolayers through first-principles calculations. Our findings reveal that these β-phase MX2N4 monolayers demonstrate remarkable dynamic, thermal, and mechanical stability. Specifically, we identify the MoSi2N4, MoGe2N4, WSi2N4, and WGe2N4 monolayers as semiconductors with band gaps of 2.70 eV, 1.57 eV, 3.12 eV, and 1.93 eV, respectively, as calculated using the HSE06 functional. Moreover, the MX2N4 monolayers exhibit significant elastic anisotropy, characterized by high ideal tensile strengths and a critical tensile strain exceeding 25%. Notably, the WGe2N4 monolayer displays exceptional anisotropic in-plane charge transport, achieving mobility levels of up to 104 cm2V− 1S− 1, surpassing those of the α-phase MX2N4 monolayers. These novel ternary monolayer structures have the potential to broaden the 2D MX2Z4 material family and emerge as promising candidates for applications in field-effect transistors.

Abstract To detect and analyze the changes of microorganisms in expressed prostatic secretion (EPS) of patients with IIIB prostatitis before and after low-intensity pulsed ultrasound (LIPUS) treatment, and to explore the mechanism of LIPUS in the treatment of chronic prostatitis (CP). 25 patients (study power was estimated using a Dirichlet-multinomial approach and reached 96.5% at α = 0.05 using a sample size of 25) with IIIB prostatitis who were effective in LIPUS treatment were divided into two groups before and after LIPUS treatment. High throughput second-generation sequencing technique was used to detect and analyze the relative abundance of bacterial 16 s ribosomal variable regions in EPS before and after treatment. The data were analyzed by bioinformatics software and database, and differences with P

Abstract In 1843, a hitherto unknown plant pathogen entered the US and spread to potato fields in the northeast. By 1845, the pathogen had reached Ireland leading to devastating famine. Questions arose immediately about the source of the outbreaks and how the disease should be managed. The pathogen, now known as Phytophthora infestans, still continues to threaten food security globally. A wealth of untapped knowledge exists in both archival and modern documents, but is not readily available because the details are hidden in descriptive text. In this work, we (1) used text analytics of unstructured historical reports (1843–1845) to map US late blight outbreaks; (2) characterized theories on the source of the pathogen and remedies for control; and (3) created modern late blight intensity maps using Twitter feeds. The disease spread from 5 to 17 states and provinces in the US and Canada between 1843 and 1845. Crop losses, Andean sources of the pathogen, possible causes and potential treatments were discussed. Modern disease discussion on Twitter included near-global coverage and local disease observations. Topic modeling revealed general disease information, published research, and outbreak locations. The tools described will help researchers explore and map unstructured text to track and visualize pandemics.

Abstract Renal cell carcinoma (RCC) is a kidney cancer that is originated from the lined proximal convoluted tubule, and its major histological subtype is clear cell RCC (ccRCC). This study aimed to retrospectively analyze single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, to explore the correlation among the evolution of tumor microenvironment (TME), clinical outcomes, and potential immunotherapeutic responses in combination with bulk RNA-seq data from The Cancer Genome Atlas (TCGA) database, and to construct a differentiation-related genes (DRG)-based prognostic risk signature (PRS) and a nomogram to predict the prognosis of ccRCC patients. First, scRNA-seq data of ccRCC samples were systematically analyzed, and three subsets with distinct differentiation trajectories were identified. Then, ccRCC samples from TCGA database were divided into four DRG-based molecular subtypes, and it was revealed that the molecular subtypes were significantly correlated with prognosis, clinicopathological features, TME, and the expression levels of immune checkpoint genes (ICGs). A DRG-based PRS was constructed, and it was an independent prognostic factor, which could well predict the prognosis of ccRCC patients. Finally, we constructed a prognostic nomogram based on the PRS and clinicopathological characteristics, which exhibited a high accuracy and a robust predictive performance. This study highlighted the significance of trajectory differentiation of ccRCC cells and TME evolution in predicting clinical outcomes and potential immunotherapeutic responses of ccRCC patients, and the nomogram provided an intuitive and accurate method for predicting the prognosis of such patients.

Abstract An interesting phenomenon that a layer of crystallized shell formed at the container wall during an orifice flow in a cylinder is observed experimentally and is investigated in DEM simulation. Different from shear or vibration driven granular crystallization, our simulation shows during the flow the shell layer is formed spontaneously from stagnant zone at the base and grows at a constant rate to the top with no external drive. Roughness of the shell surface is defined as a standard deviation of the surface height and its development is found to disobey existed growth models. The growth rate of the shell is found linearly proportional to the flow rate. This shell is static and served as a rough wall in an orifice flow with frictionless sidewall, which changes the flow profiles and its stress properties, and in turn guarantees a constant flow rate.

Abstract Breast cancer causes metabolic alteration, and volatile metabolites in the breath of patients may be used to diagnose breast cancer. The objective of this study was to develop a new breath test for breast cancer by analyzing volatile metabolites in the exhaled breath. We collected alveolar air from breast cancer patients and non-cancer controls and analyzed the volatile metabolites with an electronic nose composed of 32 carbon nanotubes sensors. We used machine learning techniques to build prediction models for breast cancer and its molecular phenotyping. Between July 2016 and June 2018, we enrolled a total of 899 subjects. Using the random forest model, the prediction accuracy of breast cancer in the test set was 91% (95% CI: 0.85–0.95), sensitivity was 86%, specificity was 97%, positive predictive value was 97%, negative predictive value was 97%, the area under the receiver operating curve was 0.99 (95% CI: 0.99–1.00), and the kappa value was 0.83. The leave-one-out cross-validated discrimination accuracy and reliability of molecular phenotyping of breast cancer were 88.5 ± 12.1% and 0.77 ± 0.23, respectively. Breath tests with electronic noses can be applied intraoperatively to discriminate breast cancer and molecular subtype and support the medical staff to choose the best therapeutic decision.

Smoking is a dominant risk factor for chronic obstructive pulmonary disease (COPD) and emphysema, but not every smoker develops emphysema. Immune responses in smokers vary. Some autoantibodies have been shown to contribute to the development of emphysema in smokers. β2-adrenergic receptors (β2-ARs) are important targets in COPD therapy. β2-adrenergic receptor autoantibodies (β2-AAbs), which may directly affect β2-ARs, were shown to be increased in rats with passive-smoking-induced emphysema in our current preliminary studies. Using cigarette-smoke exposure (CS-exposure) and active-immune (via injections of β2-AR second extracellular loop peptides) rat models, we found that CS-exposed rats showed higher serum β2-AAb levels than control rats before alveolar airspaces became enlarged. Active-immune rats showed increased serum β2-AAb levels, and exhibited alveolar airspace destruction. CS-exposed-active-immune treated rats showed more extensive alveolar airspace destruction than rats undergoing CS-exposure alone. In our current clinical studies, we showed that plasma β2-AAb levels were positively correlated with the RV/TLC (residual volume/total lung capacity) ratio (r = 0.455, p < 0.001) and RV%pred (residual volume/residual volume predicted percentage, r = 0.454, p < 0.001) in 50 smokers; smokers with higher plasma β2-AAb levels exhibited worse alveolar airspace destruction. We suggest that increased circulating β2-AAbs are associated with smoking-related emphysema. [ABSTRACT FROM AUTHOR]

Emissions of anthropogenic 129 I from human nuclear activities are now detected in the surface water of the Antarctic seas. Surface seawater samples from the Drake Passage, Bellingshausen, Amundsen, and Ross Seas were analyzed for total 129 I and 127 I, as well as for iodide and iodate of these two isotopes. The variability of 127 I and 129 I concentrations and their species ( 127 I - / 127 IO 3 - , 129 I - / 129 IO 3 - ) suggest limited environmental impact where ((1.15-3.15) × 10 6 atoms/L for 129 I concentration and (0.61-1.98) × 10 -11 for 129 I/ 127 I atomic ratios are the lowest ones compared to the other oceans. The iodine distribution patterns provide useful information on surface water transport and mixing that are vital for better understanding of the Southern Oceans effects on the global climate change. The results indicate multiple spatial interactions between the Antarctic Circumpolar Current (ACC) and Antarctic Peninsula Coastal Current (APCC). These interactions happen in restricted circulation pathways that may partly relate to glacial melting and icebergs transport. Biological activity during the warm season should be one of the key factors controlling the reduction of iodate in the coastal water in the Antarctic.