Laparoscopic single-layer running "trapezoid-shaped" suture versus mechanical stapling for esophagojejunostomy after total gastrectomy for gastric cancer: cost-effect analysis of propensity score-matched study cohorts.

OBJECTIVES: Totally laparoscopic total gastrectomy has been developed with difficulty in intracorporeal esophagojejunostomy. Although mechanical stapling has been widely used for intracorporeal esophagojejunostomy, manual suture holds great promise with the emergence of high-resolution 3D vision and robotic surgery. After exploration of how to improve the safety and efficiency of intracorporeal suture for esophagojejunostomy, we recommended the technique of single-layer running "trapezoid-shaped" suture. The cost-effectiveness was analyzed by comparing with conventional mechanical stapling. METHODS: The study retrospectively reviewed the patients undergoing laparoscopic gastrectomy for gastric cancer from January 2010 to December 2021. The patients were divided into two cohorts based on the methods of intracorporeal esophagojejunostomy: manual suture versus stapling suture. Propensity score matching was performed to match patients from the two cohorts at a ratio of 1:1. Then group comparison was made to determine whether manual suture was non-inferior to stapling suture in terms of operation time, anastomotic complications, postoperative hospital stay, and surgical cost. RESULTS: The study included 582 patients with laparoscopic total gastrectomy. The manual and stapling suture for esophagojejunostomy were performed in 50 and 532 patients, respectively. In manual suture cohort, the median time for the whole operation and digestive tract reconstruction were 300 min and 110 min. There was no anastomotic bleeding and stenosis but two cases of anastomotic leak which occurred at 3 days after surgery. The median length of postoperative hospital stay was 11 days. After propensity score matching, group comparison yielded two variables with statistical significance: time for digestive tract reconstruction and surgery cost. The manual suture cohort spent less money but more time for esophagojejunostomy. Intriguingly, the learning curve of manual suture revealed that the time for digestive tract reconstruction was declined with accumulated number of operations. CONCLUSIONS: Laparoscopic single-layer running "trapezoid-shaped" suture appears safe and cost-effective for intracorporeal esophagojejunostomy after total gastrectomy. Although the concern remains about prolonged operation time for beginners of performing the suture method, adequate practice is expected to shorten the operation time based on our learning curve analysis.

Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images.

Liver cancer is a malignant tumor with high morbidity and mortality, which has a tremendous negative impact on human survival. However, it is a challenging task to recognize tens of thousands of histopathological images of liver cancer by naked eye, which poses numerous challenges to inexperienced clinicians. In addition, factors such as long time-consuming, tedious work and huge number of images impose a great burden on clinical diagnosis. Therefore, our study combines convolutional neural networks with histopathology images and adopts a feature fusion approach to help clinicians efficiently discriminate the differentiation types of primary hepatocellular carcinoma histopathology images, thus improving their diagnostic efficiency and relieving their work pressure. In this study, for the first time, 73 patients with different differentiation types of primary liver cancer tumors were classified. We performed an adequate classification evaluation of liver cancer differentiation types using four pre-trained deep convolutional neural networks and nine different machine learning (ML) classifiers on a dataset of liver cancer histopathology images with multiple differentiation types. And the test set accuracy, validation set accuracy, running time with different strategies, precision, recall and F1 value were used for adequate comparative evaluation. Proved by experimental results, fusion networks (FuNet) structure is a good choice, which covers both channel attention and spatial attention, and suppresses channel interference with less information. Meanwhile, it can clarify the importance of each spatial location by learning the weights of different locations in space, then apply it to the study of classification of multi-differentiated types of liver cancer. In addition, in most cases, the Stacking-based integrated learning classifier outperforms other ML classifiers in the classification task of multi-differentiation types of liver cancer with the FuNet fusion strategy after dimensionality reduction of the fused features by principle component analysis (PCA) features, and a satisfactory result of 72.46% is achieved in the test set, which has certain practicality.

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism.

PURPOSE: Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. METHODS: Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. RESULTS: Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. CONCLUSIONS: This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

Bayesian adaptive lasso for additive hazard regression with current status data.

Variable selection is a crucial issue in model building and it has received considerable attention in the literature of survival analysis. However, available approaches in this direction have mainly focused on time-to-event data with right censoring. Moreover, a majority of existing variable selection procedures for survival models are developed in a frequentist framework. In this article, we consider additive hazards model in the presence of current status data. We propose a Bayesian adaptive least absolute shrinkage and selection operator procedure to conduct a simultaneous variable selection and parameter estimation. Efficient Markov chain Monte Carlo methods are developed to implement posterior sampling and inference. The empirical performance of the proposed method is demonstrated by simulation studies. An application to a study on the risk factors of heart failure disease for type 2 diabetes patients is presented.

915-MHz microwave-assisted laparoscopic hepatectomy: a new technique for liver resection.

BACKGROUND: Hemorrhage during the liver transection is the major hazard for laparoscopic hepatectomy (LH). We aimed to evaluate the feasibility and safety of a 915-MHz microwave device used in LH. METHODS: Data were retrospectively analyzed regarding 60 patients who underwent LH with or without 915-MHz microwave coagulation at our center from January 2016 to June 2016. 30 patients underwent the 915-MHz microwave-assisted LH (MW group), and 30 patients otherwise were considered as control group. RESULTS: No perioperative mortality was observed. Intraoperative blood loss amounts in microwave group and control group were 26.83 ml and 186.33 ml, respectively (P < 0.001). The durations of parenchyma transaction (55.17 vs. 70.83 min, P < 0.001), blood occlusion (2.17 vs. 25.33 min, P < 0.001), and operation (120.67 vs. 148.00 min, P < 0.001) were much shorter in microwave group compared with control group. Lower incidence of postoperative complications (0.0 vs. 14.3%, P = 0.038) and shorter length of postoperative hospital stay (6.00 vs. 7.23 days, P = 0.027) were also noted in the microwave group, compared with the control group. CONCLUSION: 915-MHz microwave-assisted LH was found to be safe and efficient.

Modularized laparoscopic regional en bloc mesogastrium excision (rEME) based on membrane anatomy for distal gastric cancer.

BACKGROUND: The purpose of the study was to evaluate the safety and feasibility of a new surgical procedure named modularized laparoscopic regional En bloc mesogastrium excision (rEME) based on the membrane anatomy in distal laparoscopic radical gastrectomy for gastric cancer. METHODS: From January 2014 to June 2017, 92 consecutive cases of patients with stages I-III distal gastric cancer were divided into 2 groups: laparoscopic radical gastrectomy plus standard D2 lymph node dissection (SD group, n = 44) and modularized rEME (rEME group, n = 48). Evaluations were made in terms of the operative data, pathological results, recovery time of digestive tract functions, complications, and length of stay. RESULTS: 85 patients (SD group, n = 40 and rEME group, n = 45) were finally included for analysis. There were no significant differences in the median total numbers of dissected LNs (31.98 ± 10.48 vs. 34.93 ± 13.12, p = 0.261), LNs in the greater curvature (12.18 ± 6.55 vs. 13.62 ± 8.09, p = 0.444), LNs in the lesser curvature (19.55 ± 7.40 vs. 17.98 ± 8.31, p = 0.365) between the SD and rEME groups. The rEME group showed lower loss of blood volume (107.11 ± 60.13 ml vs. 146.25 ± 85.78 ml, p = 0.019). No significant differences were found in recovery time of digestive tract functions, postoperative complication rates and length of hospital stay between the two groups. CONCLUSION: Laparoscopic radical gastrectomy plus modularized rEME based on the membrane anatomy is a safe and feasible procedure for distal gastric cancer.

MiR-24-3p enhances cell growth in hepatocellular carcinoma by targeting metallothionein 1M.

Dysregulation of microRNAs has been demonstrated to contribute to malignant progression of cancers, including hepatocellular carcinoma (HCC). MiR-24-3p was previously reported to be significantly upregulated in HCC. However, the potential role and mechanism of action of miR-24-3p in the initiation and progression of HCC remain largely unknown. Quantitative reverse transcription polymerase chain reaction demonstrated that miR-24-3p was significantly upregulated in HCC tumor tissues compared with nontumor tissues. The cell viability, colony formation assay, and tumorigenicity assays in nude mice showed that miR-24-3p could enhance HCC cell growth in vitro and in vivo. Metallothionein 1M was verified as an miR-24-3p target gene by using dual-luciferase reporter assays, quantitative reverse transcription polymerase chain reaction, and Western blotting, which was involved in miR-24-3p regulated HCC cell growth. These results indicated that miR-24-3p plays an important role in the initiation and progression of HCC by targeting metallothionein 1M, and the miR-24-3p/metallothionein 1M pathway may contribute to the development of novel therapeutic strategies for HCC in the future.

Inhibitions of epithelial to mesenchymal transition and cancer stem cells-like properties are involved in miR-148a-mediated anti-metastasis of hepatocellular carcinoma.

The epithelial-mesenchymal transition (EMT) and acquisition of cancer stem cells (CSCs)-like properties are essential steps in the metastasis and postsurgical recurrence of hepatocellular carcinomas (HCCs). The molecular mechanisms involved, however, remain obscure. As determined by an miRNA microarray analysis, there was lower expression of miR-148a in poorly differentiated HCC tissues relative to well-differentiated HCC tissues. MHCC97H and MHCC97L (HCC cells with migratory capacity) and HCC tissues with various differentiation status were selected for further investigation. The results showed that miR-148a levels inversely correlated with the differentiation status of HCC tissues. In MHCC97H and MHCC97L cells, over-expression of miR-148a blocked the EMT process, attenuated the expression of CD90 and CD44 (biomarkers for liver cancer stem cells), and inhibited their migratory capacity. Via TargetScan and microRNA.org algorithms, miR-148a was predicted to bind to the Wnt1 mRNA 3'-UTR. Wnt1 was confirmed as a target gene of miR-148a in HCC cells, and the Wnt signal pathway was determined to be involved in the miR-148a-mediated inhibition of EMT and CSCs-like properties of MHCC97H cells. Moreover, the expression of miR-148a in nonmetastatic HCC tissues was higher than that in metastatic HCC tissues. The results suggest that miR-148a inhibits the metastasis of HCCs by blocking EMT and CSCs-like properties through effects on the Wnt signaling pathway.

Variable selection for quantile autoregressive model: Bayesian methods versus classical methods.

In this article, we introduce three Bayesian variable selection methods for the quantile autoregressive model with explanatory variables. The Gibbs sampling algorithms are developed for each method by setting different priors. The numerical simulations suggest that the Gibbs sampling algorithms converge fast and Bayesian variable selection methods are reliable. A real example is given to analysis the relationship between the count of total rental bikes and five explanatory variables. Both simulations and data example indicate that the proposed methods are feasible, reliable, and appropriate for analyzing the Bike Sharing data set.

A hybrid ensemble forecasting model of passenger flow based on improved variational mode decomposition and boosting.

An accurate passenger flow forecast can provide key information for intelligent transportation and smart cities, and help promote the development of smart cities. In this paper, a mixed passenger flow forecasting model based on the golden jackal optimization algorithm (GJO), variational mode decomposition (VMD) and boosting algorithm was proposed. First, the data characteristics of the original passenger flow sequence were extended. Second, an improved variational modal decomposition method based on the Sobol sequence improved GJO algorithm was proposed. Next, according to the sample entropy of each intrinsic mode function (IMF), IMF with similar complexity is combined into a new subsequence. Finally, according to the determination rules of the sub-sequence prediction model, the boosting modeling and prediction of different sub-sequences were carried out, and the final passenger flow prediction result was obtained. Based on the experimental results of three scenic spots, the mean absolute percentage error (MAPE) of the mixed set model is 0.0797, 0.0424 and 0.0849, respectively. The fitting degree reached 95.33%, 95.63% and 95.97% simultaneously. The results show that the hybrid model proposed in this study has high prediction accuracy and can provide reliable information sources for relevant departments, scenic spot managers and tourists.

Expression of circular RNA has-circ-0009158 and identification of associated miRNA-mRNA network in hepatocellular carcinoma.

Primary hepatocellular carcinoma (HCC) affects people all over the world. Circular RNAs are involved in the growth and development of several malignancies and regulate a number of biological processes. However, the roles of has-circ-0009158 in HCC remain unknown. This study explored the expression and associated miRNA-mRNA network of has-circ-0009158 in HCC. Quantitative real-time polymerase chain reaction was used to measure the expression of hsa-circ-0009158 in the HCC tissues of 143 patients and four human HCC cell lines. Then, the potential relationship of hsa-circ-0009158 expression with clinical characteristics and prognosis of patients was analyzed using the GO and KEGG databases. Correlated miRNA-mRNA networks were forecasted using the TCGA database and Cytoscape software. The hsa-circ-0009158 expression was significantly upregulated in HCC tissues and cell lines (P<0.001). The multivariate Cox analysis revealed that HCC patients were associated with high hsa-circ-0009158 expression. The bioinformatics analysis screened 1 miRNA, and 248 mRNAs associated with the circRNA in HCC. A pathway analysis suggested that the differentially expressed genes (DEGs) may be linked to the development and growth of HCC tumors. Ten hub genes (MELK, NCAPG, BUB1B, BIRC5, CDCA8, CENPF, BUB1, CDK1, TTK, TPX2) were identified from the PPI network based on the 248 genes. Additionally, the 10 hub genes that were verified had an association between high expression levels and low overall survival rates. As a result, the high expression of hsa-circ-0009158 was found to be a separate risk factor for recurrence and a poor prognosis in HCC patients.

Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection.

Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum; the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend; and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems' dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization; for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features. Supplementary Information: The online version contains supplementary material available at 10.1007/s42235-022-00298-7.

Emotion recognition of EEG signals based on variational mode decomposition and weighted cascade forest.

Emotion recognition is of a great significance in intelligent medical treatment and intelligent transportation. With the development of human-computer interaction technology, emotion recognition based on Electroencephalogram (EEG) signals has been widely concerned by scholars. In this study, an EEG emotion recognition framework is proposed. Firstly, variational mode decomposition (VMD) is used to decompose the nonlinear and non-stationary EEG signals to obtain intrinsic mode functions (IMFs) at different frequencies. Then sliding window tactic is used to extract the characteristics of EEG signals under different frequency. Aiming at the issue of feature redundancy, a new variable selection method is proposed to improve the adaptive elastic net (AEN) by the minimum common redundancy maximum relevance criterion. Weighted cascade forest (CF) classifier is constructed for emotion recognition. The experimental results on the public dataset DEAP show that the valence classification accuracy of the proposed method reaches 80.94%, and the classification accuracy of arousal is 74.77%. Compared with some existing methods, it effectively improves the accuracy of EEG emotion recognition.

Least squares support vector regression for complex censored data.

Least squares support vector regression (LS-SVR) is a robust machine learning algorithm for small sample data. Its solution is derived from solving a set of linear equations, making the calculation process straightforward. In order to overcome the difficulties of the regression estimations when the responses are subject to interval censoring or left truncation and right censoring, two LS-SVR methods are proposed. For interval-censored data, one can easily estimate the regression functions by combining the imputation techniques and LS-SVR for right-censored data. For left-truncated and right-censored data, a weight is used to reduce the effects of truncation and censoring on the LS-SVR procedure. Simulation results show that the proposed methods can reduce regression error and yield high accuracy and stability.

China's carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression.

Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly.

Quantifying changes in soil organic carbon density from 1982 to 2020 in Chinese grasslands using a random forest model.

China has the second-largest grassland area in the world. Soil organic carbon storage (SOCS) in grasslands plays a critical role in maintaining carbon balance and mitigating climate change, both nationally and globally. Soil organic carbon density (SOCD) is an important indicator of SOCS. Exploring the spatiotemporal dynamics of SOCD enables policymakers to develop strategies to reduce carbon emissions, thus meeting the goals of "emission peak" in 2030 and "carbon neutrality" in 2060 proposed by the Chinese government. The objective of this study was to quantify the dynamics of SOCD (0-100 cm) in Chinese grasslands from 1982 to 2020 and identify the dominant drivers of SOCD change using a random forest model. The results showed that the mean SOCD in Chinese grasslands was 7.791 kg C m-2 in 1982 and 8.525 kg C m-2 in 2020, with a net increase of 0.734 kg C m-2 across China. The areas with increased SOCD were mainly distributed in the southern (0.411 kg C m-2), northwestern (1.439 kg C m-2), and Qinghai-Tibetan (0.915 kg C m-2) regions, while those with decreased SOCD were mainly found in the northern (0.172 kg C m-2) region. Temperature, normalized difference vegetation index, elevation, and wind speed were the dominant factors driving grassland SOCD change, explaining 73.23% of total variation in SOCD. During the study period, grassland SOCS increased in the northwestern region but decreased in the other three regions. Overall, SOCS of Chinese grasslands in 2020 was 22.623 Pg, with a net decrease of 1.158 Pg since 1982. Over the past few decades, the reduction in SOCS caused by grassland degradation may have contributed to soil organic carbon loss and created a negative impact on climate. The results highlight the urgency of strengthening soil carbon management in these grasslands and improving SOCS towards a positive climate impact.

Expression of tumor-associated macrophages and PD-L1 in patients with hepatocellular carcinoma and construction of a prognostic model.

BACKGROUND: Hepatocellular carcinoma (HCC) is an inflammation-associated tumor involved in immune tolerance and evasion in the immune microenvironment. Immunotherapy can enhance the immune response of the body, break immune tolerance, and then recognize and kill tumor cells. The polarization homeostasis of M1 and M2 macrophages in tumor microenvironment (TME) is involved in the occurrence and development of tumors and has been considered a hot topic in tumor research. Programmed cell death ligand 1 (PD-L1) plays an important role in the polarity of TAM and affects the prognosis of HCC patients as a target of immunotherapy. To this end, efforts were hereby made to further explore the application value of PD-L1, M1 macrophages (CD86), and M2 macrophages (CD206) in the prognosis assessment of HCC, their correlation with immune cell infiltration in HCC tissues, and their bioenrichment function. METHODS: The gene expression omnibus (GEO) and the Cancer Genome Atlas (TCGA) database were used to analyze the expression of PD-L1, CD86, and CD206 in different tumor tissues. The correlation between the expression of PD-L1, CD86, and CD206 and the infiltration of immune cells was analyzed using the Tumor Immune Estimation Resource (TIMER). The tissue specimens and clinicopathological data of hepatocellular carcinoma patients having undergone surgical treatment in our hospital were collected. Immunohistochemistry was used to verify the expression of PD-L1, CD86, and CD206, and analyze the relationship with clinicopathological features and prognosis of patients. Besides, nomogram was constructed to predict the overall survival (OS) of patients at 3 and 5 years. Finally, the protein-protein interaction network information was analyzed using STRING database, and GO analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis were performed to study the biological functions of PD-L1, CD86, and CD206. RESULT: Bioinformatics analysis found that PD-L1, CD86, and CD206 were underexpressed in various tumor tissues including liver cancer, while the present immunohistochemical detection found that PD-L1, CD86, and CD206 were overexpressed in liver cancer tissues. Expressions of PD-L1, CD86, and CD206 were positively correlated with the infiltration level of immune cells in liver cancer, while the expression of PD-L1 was positively correlated with the degree of tumor differentiation. Meanwhile, the expression level of CD206 was positively correlated with gender and preoperative hepatitis, and patients with high expression of PD-L1 or low expression of CD86 had poor prognosis. AJCC stage, preoperative hepatitis, and the expression levels of PD-L1 and CD86 in cancer tissues were independent risk factors affecting survival of patients after radical hepatoma surgery. KEGG pathway enrichment analysis showed that PD-L1 was significantly enriched in T cell aggregation and lymphocyte aggregation, and might be involved in the formation of T cell antigen receptor CD3 complex and cell membrane. Besides, CD86 was significantly enriched in positive regulation of cell adhesion, regulation of mononuclear cell proliferation, regulation of leukocyte proliferation, and transduction of T cell receptor signaling pathway, while CD206 was significantly enriched in type 2 immune response, cellular response to LPS, cellular response to LPS, and involvement in cellular response to LPS. CONCLUSION: In conclusion, these results suggest that PD-L1, CD86, and CD206 may be involved not only in the occurrence and development of HCC, but also in immune regulation, indicating the potential role of PD-L1 and CD86 as potential biomarkers and new therapeutic targets for prognosis assessment of liver cancer.

NVS-ZP7-4 inhibits hepatocellular carcinoma tumorigenesis and promotes apoptosis via PI3K/AKT signaling.

NVS-ZP7-4 was identified as a novel chemical reagent targeting the zinc input protein ZIP7, which accounts for the zinc surge from the apparatus to the cytoplasm. Since zinc dysregulation is related to multiple diseases, in this study, we aimed to identify the anti-tumor effects of NVS-ZP7-4 and explore the molecular mechanisms of NVS-ZP7-4 in hepatocellular carcinoma (HCC) progression. We found that NVS-ZP7-4 inhibited cell viability, caused cell cycle arrest, induced apoptosis, and inhibited the proliferation, migration, and invasion of HCCLM3 and Huh7 cells. We further investigated the inhibited activation of the phosphatidylinositol 3-kinase (PI3K)/Akt pathway was involved in the antitumor effect of NVS-ZP7-4 in HCC. Furthermore, NVS-ZP7-4 inhibited HCC tumor growth in vivo. The present study demonstrated that NVS-ZP7-4 is a promising therapeutic target for HCC by regulating PI3K/AKT signaling.

Survival prediction model for right-censored data based on improved composite quantile regression neural network.

With the development of the field of survival analysis, statistical inference of right-censored data is of great importance for the study of medical diagnosis. In this study, a right-censored data survival prediction model based on an improved composite quantile regression neural network framework, called rcICQRNN, is proposed. It incorporates composite quantile regression with the loss function of a multi-hidden layer feedforward neural network, combined with an inverse probability weighting method for survival prediction. Meanwhile, the hyperparameters involved in the neural network are adjusted using the WOA algorithm, integer encoding and One-Hot encoding are implemented to encode the classification features, and the BWOA variable selection method for high-dimensional data is proposed. The rcICQRNN algorithm was tested on a simulated dataset and two real breast cancer datasets, and the performance of the model was evaluated by three evaluation metrics. The results show that the rcICQRNN-5 model is more suitable for analyzing simulated datasets. The One-Hot encoding of the WOA-rcICQRNN-30 model is more applicable to the NKI70 data. The model results are optimal for k=15 after feature selection for the METABRIC dataset. Finally, we implemented the method for cross-dataset validation. On the whole, the Cindex results using One-Hot encoding data are more stable, making the proposed rcICQRNN prediction model flexible enough to assist in medical decision making. It has practical applications in areas such as biomedicine, insurance actuarial and financial economics.

Two-stage feature selection for classification of gene expression data based on an improved Salp Swarm Algorithm.

Microarray technology has developed rapidly in recent years, producing a large number of ultra-high dimensional gene expression data. However, due to the huge sample size and dimension proportion of gene expression data, it is very challenging work to screen important genes from gene expression data. For small samples of high-dimensional biomedical data, this paper proposes a two-stage feature selection framework combining Wrapper, embedding and filtering to avoid the curse of dimensionality. The proposed framework uses weighted gene co-expression network (WGCNA), random forest and minimal redundancy maximal relevance (mRMR) for first stage feature selection. In the second stage, a new gene selection method based on the improved binary Salp Swarm Algorithm is proposed, which combines machine learning methods to adaptively select feature subsets suitable for classification algorithms. Finally, the classification accuracy is evaluated using six methods: lightGBM, RF, SVM, XGBoost, MLP and KNN. To verify the performance of the framework and the effectiveness of the proposed algorithm, the number of genes selected and the classification accuracy was compared with the other five intelligent optimization algorithms. The results show that the proposed framework achieves an accuracy equal to or higher than other advanced intelligent algorithms on 10 datasets, and achieves an accuracy of over 97.6% on all 10 datasets. This shows that the method proposed in this paper can solve the feature selection problem related to high-dimensional data, and the proposed framework has no data set limitation, and it can be applied to other fields involving feature selection.