Construction and validation of a machine learning-based nomogram to predict the prognosis of HBV associated hepatocellular carcinoma patients with high levels of hepatitis B surface antigen in primary local treatment: a multicenter study

BackgroundHepatitis B surface antigen (HBsAg) clearance is associated with improved long-term outcomes and reduced risk of complications. The aim of our study was to identify the effects of levels of HBsAg in HCC patients undergoing TACE and sequential ablation. In addition, we created a nomogram to predict the prognosis of HCC patients with high levels of HBsAg (≥1000U/L) after local treatment.MethodThis study retrospectively evaluated 1008 HBV-HCC patients who underwent TACE combined with ablation at Beijing Youan Hospital and Beijing Ditan Hospital from January 2014 to December 2021, including 334 patients with low HBsAg levels and 674 patients with high HBsAg levels. The high HBsAg group was divided into the training cohort (N=385), internal validation cohort (N=168), and external validation cohort (N=121). The clinical and pathological features of patients were collected, and independent risk factors were identified using Lasso-Cox regression analysis for developing a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the training and validation cohorts. Patients were classified into high-risk and low-risk groups based on the risk scores of the nomogram.ResultAfter PSM, mRFS was 28.4 months (22.1-34.7 months) and 21.9 months (18.5-25.4 months) in the low HBsAg level and high HBsAg level groups (P<0.001). The content of the nomogram includes age, BCLC stage, tumor size, globulin, GGT, and bile acids. The C-index (0.682, 0.666, and 0.740) and 1-, 3-, and 5-year AUCs of the training, internal validation, and external validation cohorts proved good discrimination of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classification of patients with high HBsAg levels into low-risk and high-risk groups according to the risk of recurrence. There was a statistically significant difference in RFS between the two groups in the training, internal validation, and external validation cohorts (P<0.001).ConclusionHigh levels of HBsAg were associated with tumor progression. The nomogram developed and validated in the study had good predictive ability for patients with high HBsAg levels

Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score

IntroductionThis study aimed to develop a prognostic nomogram for predicting the recurrence-free survival (RFS) of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) scores after transarterial chemoembolization (TACE) combined with local ablation treatment.MethodsWe gathered clinical data from 632 HBV-related HCC patients who received the combination treatment at Beijing You’an Hospital, affiliated with Capital Medical University, from January 2014 to January 2020. The patients were divided into two groups based on their PALBI scores: low PALBI group (n=247) and high PALBI group (n=385). The low PALBI group was then divided into two cohorts: training cohort (n=172) and validation cohort (n=75). We utilized eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox analysis to pinpoint the risk factors for RFS. Then, we developed a nomogram based on the screened factors and assessed its risk stratification capabilities and predictive performance.ResultsThe study finally identified age, aspartate aminotransferase (AST), and prothrombin time activity (PTA) as key predictors. The three variables were included to develop the nomogram for predicting the 1-, 3-, and 5-year RFS of HCC patients. We confirmed the nomogram’s ability to effectively discern high and low risk patients, as evidenced by Kaplan-Meier curves. We further corroborated the excellent discrimination, consistency, and clinical utility of the nomogram through assessments using the C-index, area under the curve (AUC), calibration curve, and decision curve analysis (DCA).ConclusionOur study successfully constructed a robust nomogram, effectively predicting 1-, 3-, and 5-year RFS for HBV-related HCC patients with low preoperative PALBI scores after TACE combined with local ablation therapy

Influence of the Interaction between Sphalerite and Pyrite on the Copper Activation of Sphalerite

In this paper, the effect of pyrite on the activation of sphalerite was investigated by micro-flotation, copper adsorption experiments, X-ray photoelectron spectroscopy (XPS), and electrochemical measurement. The micro-flotation test results showed that the recovery and flotation rate of sphalerite with copper sulphate as activator and butyl xanthate as collector were significantly decreased with the increasing content of pyrite in pulp. Cu2+ adsorption results indicated that the adsorption of Cu2+ on the sphalerite surface were decreased when pyrite was present in the pulp. XPS surface analysis demonstrated that the proportion of Cu+ species increased in the activation products on the sphalerite surface, but the total atomic concentration of Cu atom was decreased. Linear voltammetry measurement suggested that the current density of Cu+ species oxidizing to Cu2+ species was increased when sphalerite was electrically contacted with pyrite, which confirmed the increased proportion of Cu+ species on Cu-activation sphalerite surface when contacting with pyrite. These results indicated that there is not only a competitive adsorption for cupric ions (Cu2+), but the galvanic interaction between sphalerite and pyrite also has a significant influence on the copper activation of sphalerite

Application of Minnan Folk Light and Shadow Animation in Built Environment in Object Detection Algorithm

To resolve the problems of deep convolutional neural network models with many parameters and high memory resource consumption, a lightweight network-based algorithm for building detection of Minnan folk light synthetic aperture radar (SAR) images is proposed. Firstly, based on the rotating target detection algorithm R-centernet, the Ghost ResNet network is constructed to reduce the number of model parameters by replacing the traditional convolution in the backbone network with Ghost convolution. Secondly, a channel attention module integrating width and height information is proposed to enhance the network’s ability to accurately locate salient regions in folk light images. Content-aware reassembly of features (CARAFE) up-sampling is used to replace the deconvolution module in the network to fully incorporate feature map information during up-sampling to improve target detection. Finally, the constructed dataset of rotated and annotated light and shadow SAR images is trained and tested using the improved R-centernet algorithm. The experimental results show that the improved algorithm improves the accuracy by 3.8%, the recall by 1.2% and the detection speed by 12 frames/second compared with the original R-centernet algorithm

Chromodomain Helicase DNA-Binding Protein 5 Inhibits Renal Cell Carcinoma Tumorigenesis by Activation of the p53 and RB Pathways

Chromodomain helicase DNA-binding protein 5 (CHD5) plays a crucial tumor suppressor role in multiple types of tumors. For this study, we investigated its clinical significance and the molecular mechanism(s) underlying tumorigenesis in renal cell carcinoma (RCC). Initially, CHD5 expression was assessed in primary tumor tissue and in tissue array. Correlations among CHD5 expression and clinicopathological characteristics were analyzed. Next, lentivirus-mediated CHD5 overexpression in the ACHN and 769-P cells was used to assess effects on proliferation, migration, invasion ability, and the regulation of the p14ARF/p53 and p16INK4a/RB signaling pathways. Finally, a xenograft mouse model was used to verify its impact on tumor growth in vivo. Results demonstrated that CHD5 was downregulated in tumor tissues and that low CHD5 expression was correlated with advanced TNM stage, high Fuhrman grade, lymph node metastasis, and poor survival. Overexpression of CHD5 inhibited proliferation, migration, and invasion in vitro; prompted cell cycle G1 phase arrest; induced apoptosis; and suppressed tumor growth in vivo. Furthermore, we confirmed that CHD5 activates the p53 and RB pathways to inhibit tumorigenesis in RCC. In summary, CHD5 is involved in the initiation and progression of RCC and may serve as a diagnostic biomarker and a potential therapeutic target for RCC

Cyanide Depression Mechanism for Sphalerite Flotation Separation Based on Density Functional Theory Calculations and Coordination Chemistry

In this paper, the adsorption of cyanide and its combination with zinc sulfate on the surface of sphalerite (110) was studied by density functional theory (DFT), and its configurational relationship was analyzed by coordination chemistry. The calculation results show that the adsorption configuration stability of CN− is stronger than that of Zn(CN)2; the chemical bond of Zn(OH)2 is less covalent. The three adsorption modes all make the surface of sphalerite lose more electrons, thereby weakening the reactivity of S and Zn atoms on the sphalerite surface. During the CN− adsorption process, both the 3d and 4p orbital peak energy levels of Zn decrease, indicating the strong inhibitory effect of CN−. Coordination chemistry also shows that CN− matches the Zn ion orbital in sphalerite and the π electron pair on the Zn ion can easily interact with the empty π orbital on CN− to form π-backbonding

A comprehensive analysis of ncRNA-mediated interactions reveals potential prognostic biomarkers in prostate adenocarcinoma

As one of common malignancies, prostate adenocarcinoma (PRAD) has been a growing health problem and a leading cause of cancer-related death. To obtain expression and functional relevant RNAs, we firstly screened candidate hub mRNAs and characterized their associations with cancer. Eight deregulated genes were identified and used to build a risk model (AUC was 0.972 at 10 years) that may be a specific biomarker for cancer prognosis. Then, relevant miRNAs and lncRNAs were screened, and the constructed primarily interaction networks showed the potential cross-talks among diverse RNAs. IsomiR landscapes were surveyed to understand the detailed isomiRs in relevant homologous miRNA loci, which largely enriched RNA interaction network due to diversities of sequence and expression. We finally characterized TK1, miR-222-3p and SNHG3 as crucial RNAs, and the abnormal expression patterns of them were correlated with poor survival outcomes. TK1 was found synthetic lethal interactions with other genes, implicating potential therapeutic target in precision medicine. LncRNA SNHG3 can sponge miR-222-3p to perturb RNA regulatory network and TK1 expression. These results demonstrate that TK1:miR-222-3p:SNHG3 axis may be a potential prognostic biomarker, which will contribute to further understanding cancer pathophysiology and providing potential therapeutic targets in precision medicine

Microbial functional genes commonly respond to elevated carbon dioxide

Atmospheric CO2 concentration is increasing, largely due to anthropogenic activities. Previous studies of individual free-air CO2 enrichment (FACE) experimental sites have shown significant impacts of elevated CO2 (eCO2) on soil microbial communities; however, no common microbial response patterns have yet emerged, challenging our ability to predict ecosystem functioning and sustainability in the future eCO2 environment. Here we analyzed 66 soil microbial communities from five FACE sites, and showed common microbial response patterns to eCO2, especially for key functional genes involved in carbon and nitrogen fixation (e.g., pcc/acc for carbon fixation, nifH for nitrogen fixation), carbon decomposition (e.g., amyA and pulA for labile carbon decomposition, mnp and lcc for recalcitrant carbon decomposition), and greenhouse gas emissions (e.g., mcrA for methane production, norB for nitrous oxide production) across five FACE sites. Also, the relative abundance of those key genes was generally increased and directionally associated with increased biomass, soil carbon decomposition, and soil moisture. In addition, a further literature survey of more disparate FACE experimental sites indicated increased biomass, soil carbon decay, nitrogen fixation, methane and nitrous oxide emissions, plant and soil carbon and nitrogen under eCO2. A conceptual framework was developed to link commonly responsive functional genes with ecosystem processes, such as pcc/acc vs. soil carbon storage, amyA/pulA/mnp/lcc vs. soil carbon decomposition, and nifH vs. nitrogen availability, suggesting that such common responses of microbial functional genes may have the potential to predict ecosystem functioning and sustainability in the future eCO2 environment