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Abstract Orthorhombic Ca2CO4 is a recently discovered orthocarbonate whose high-pressure physical properties are critical for understanding the deep carbon cycle. Here, we study the structure, elastic and seismic properties of Ca2CO4-Pnma at 20–140 GPa using first-principles calculations, and compare them with the results of CaCO3 polymorphs. The results show that the structural parameters of Ca2CO4-Pnma are in good agreement with the experimental results. It could be the potential host of carbon in the Earth's mantle subduction slab, and its low wave velocity and small anisotropy may be the reason why it cannot be detected in seismic observation. The thermodynamic properties of Ca2CO4-Pnma at high temperature and high pressure are obtained using the quasi-harmonic approximation method. This study is helpful in understanding the behavior of Ca-carbonate in the Earth’s lower mantle conditions.

Abstract Background Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information. Methods Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics‐DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1‐score metrics. Results The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82–0.88), sensitivity of 0.85 (0.80–0.88), and specificity of 0.84 (0.83–0.87), performing better than the other models. Conclusion The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.

Abstract A two-dimensional phononic crystal sensor model with high-quality factor and excellent sensitivity for sensing acetone solutions and operating at 25–45 kHz is proposed. The model for filling solution cavities is based on reference designs of quasi-crystal and gradient cavity structures. The transmission spectrum of sensor is simulated by the finite element method. High-quality factor of 45,793.06 and sensitivity of 80,166.67 Hz are obtained for the acetone concentration with 1–9.1%, and quality factor of 61,438.09 and sensitivity of 24,400.00 Hz are obtained for the acetone concentration range of 10–100%, which indicated the sensor could still achieve high sensitivity and quality factor at operating frequencies from 25 to 45 kHz. To verify the application of the sensor to sensing other solutions, the sensitivity for sound velocity and density is calculated as 24.61 m-1 and 0.7764 m3/(kg × s), respectively. It indicates the sensor is sensitive to acoustic impedance changes of the solution and equally suitable for sensing other solutions. The simulation results reveal the phononic crystal sensor possessed high-performance in composition capture in pharmaceutical production and petrochemical industry, which can provide theoretical reference for the design of new biochemical sensors for reliable detection of solution concentration.

Objective To analyze the correlation between gastric mucosa serological biopsy ［serum pepsinogen Ⅰ（PGⅠ）, pepsinogen Ⅱ（PGⅡ）, ratio of PGⅠ/PGⅡ(PGR), gastrin-17 and helicobacter pyloriantibody（HP-IgG）］ and histological gastritis evaluation criteria （OLGA/OLGIM）, and to evaluate the efficacy of these indicators in the screening of gastric precancerous disease. Methods A total of 417 patients underwent gastroscopy for dyspepsia were selected in the 971st Hospital of the PLA Navy from January 2019 to December 2021 were selected. They were evaluated in groups according to the OLGA/OLGIM staging system, and the serum PGⅠ, PGⅡ, PGR, G-17 and HP-IgG were detected and analyzed. Results In OLGA/OLGIM group, PGⅠ and PGR decreased with the increase of group grade(P＜0.01), while the infection rates of G-17 and HP increased with the increase of group grade(P＜0.01). Kendall correlation analysis showed that there was a positive correlation between the combined scoring method and OLGA stage and OLGIM stage (r=0.44, 0.43,P

BackgroundThe incidence of prostate cancer (PCa) has continued to increase since the US Preventive Services Task Force (USPSTF) recommendations against prostate-specific antigen (PSA)-based screening for all men in 2012, approximately half of additional diagnosed cases are advanced-stage, including regional PCa and metastatic PCa (mPCa). It is very important to investigate the shift in mPCa incidence and mPCa-related mortality risk, as the survival of mPCa remains poor.ObjectiveTo investigate the incidence temporal trend of mPCa stratified by metastatic site, including bone and visceral metastatic involvement, and potential survival improvements.MaterialsBased on the recently released Surveillance, Epidemiology, and End Results (SEER) data (2010-2019), the age-adjusted incidence rates of mPCa with bone and visceral involvement with annual percentage changes (APCs) were assessed by a joinpoint regression model in men aged 45 years and older by age and race groups, and potential recent improvements in overall survival (OS) and cancer-specific survival (CSS) were estimated by the Kaplan−Meier method and Cox regression model.ResultsFrom 2010 to 2019, a total of 19081 (84.8%) and 3413 (15.2%) mPCa patients with bone and visceral involvement, respectively, were recorded in the SEER database. Considering all races and age groups, the incidence rate of mPCa with bone metastasis remained stable during 2017-2019 (APC, 0.9%; p=0.421) after increasing during 2010-2017 (APC, 5.8%; p