Tick-Derived Peptide Blocks Potassium Channel TREK-1.

Ticks transmit a variety of pathogens, including rickettsia and viruses, when they feed on blood, afflicting humans and other animals. Bioactive components acting on inflammation, coagulation, and the immune system were reported to facilitate ticks' ability to suck blood and transmit tick-borne diseases. In this study, a novel peptide, IstTx, from an Ixodes scapularis cDNA library was analyzed. The peptide IstTx, obtained by recombinant expression and purification, selectively inhibited a potassium channel, TREK-1, in a dose-dependent manner, with an IC50 of 23.46 ± 0.22 µM. The peptide IstTx exhibited different characteristics from fluoxetine, and the possible interaction of the peptide IstTx binding to the channel was explored by molecular docking. Notably, extracellular acidification raised its inhibitory efficacy on the TREK-1 channel. Our results found that the tick-derived peptide IstTx blocked the TREK-1 channel and provided a novel tool acting on the potassium channel.

Coffee consumption during pregnancy increases the risk of preeclampsia in rats by inhibiting 2-methoxyestradiol production.

Preeclampsia (PE) is a pregnancy-specific disease that causes maternal symptoms such as high blood pressure and adverse pregnancy outcomes. 2-methoxyestradiol (2-MeO-E2), an endogenous metabolite of 17ß-estradiol (E2) formed by Catechol-O-Methyltransferase (COMT), plays an important role in pregnancy. Our earlier studies have shown that polyphenols present in coffee can inhibit COMT activity, which may inhibit the formation of 2-MeO-E2 and contribute to PE. Therefore, the current study aims to investigate the possible effect and mechanism of coffee intake during pregnancy on PE in SD rats. Coffee is administered with or without cotreatment of 2-MeO-E2 to pregnant rats from the10th to the18th day of pregnancy. The results show that pregnant rats with coffee intake had prominent fetal growth restriction, hypertension and proteinuria, which can be ameliorated by co-treatment of 2-MeO-E2. In addition, coffee treatment leads to significantly decreased serum 2-MeO-E2. Therefore, the PE symptoms induced by coffee treatment is probably mediated by decreased 2-MeO-E2. Our findings provide new mechanistic insight into how coffee intake could lead to increased risk of PE, and demonstrate the effectiveness of 2-MeO-E2 supplementation as a potential therapeutic agent for PE.

Chitosan-coated liposome with lysozyme-responsive properties for on-demand release of levofloxacin.

As an anti-infection antibiotic delivery route, a drug-controlled release system based on a specific condition stimulus response can enhance drug stability and bioavailability, reduce antibiotic resistance, achieve on-demand release and improve targeting and utilization efficiency. In this study, chitosan-coated liposomes containing levofloxacin (Lef@Lip@CS) were prepared with lysozyme in body fluids serving as an intelligent "switch" to enable accurate delivery of antibiotics through the catalytic degradation ability of chitosan. Good liposome encapsulation efficacy (64.89 ± 1.86 %) and loading capacity (5.28 ± 0.18 %) were achieved. The controlled-release behavior and morphological characterization before and after enzymatic hydrolysis confirmed that the levofloxacin release rate depended on the lysozyme concentration and the degrees of deacetylation of chitosan. In vitro bacteriostatic experiments showed significant differences in the effects of Lef@Lip@CS before and after enzyme addition, with 6-h inhibition rate of 72.46 % and 100 %, and biofilm removal rates of 51 % and 71 %, respectively. These findings show that chitosan-coated liposomes are a feasible drug delivery system responsive to lysozyme stimulation.

The effect of incentive policies on the diffusion of construction and demolition waste recycling: A government perspective.

Construction and demolition (C&D) waste recycling plays a significant role in waste reduction and carbon reduction, which is critical for sustainable development. However, due to various limitations such as financial problems, C&D waste recycling industry is not well developed in developing countries. To address this problem, this study combines complex network theory and evolutionary game theory to analyse the diffusion of C&D waste recycling behaviour among enterprises under governmental incentive policies within a complex network context. The results demonstrate that the size of the network has limited effects on behaviour diffusion in Watts-Strogatz small-world network. Additionally, the study highlights the clear impact of governmental incentive probability, initial rate and connection degree on the diffusion path. By quantitatively investigating the effects of incentive tools, this study contributes to the knowledge of C&D waste management and provides valuable implications for stakeholders seeking to promote the diffusion of C&D waste recycling.

A review of natural plant extracts in beverages: Extraction process, nutritional function, and safety evaluation.

The demand for foods and beverages with therapeutic and functional features has increased as a result of rising consumer awareness of health and wellness. In natural, plants are abundant, widespread, and inexpensive, in addition to being rich in bioactive components that are beneficial to health. The bioactive substances contained in plants include polyphenols, polysaccharides, flavonoids, aromatics, aliphatics, terpenoids, etc., which have rich active functions and application potential for plant-based beverages. In this review, various existing extraction processes and their advantages and disadvantages are introduced. The antioxidant, anti-inflammatory, intestinal flora regulation, metabolism regulation, and nerve protection effects of plant beverages are described. The biotoxicity and sensory properties of plant-based beverages are also summarized. With the diversification of the food industry and commerce, plant-based beverages may become a promising new category of health functional foods in our daily lives.

An 18-year evolution of congenital heart disease in China: An echocardiographic database-based study.

OBJECTIVE: Data on the evolution of congenital heart disease (CHD) in China remain scarce. Based on a Chinese echocardiography database, we analyzed the observed rate (OR) and spectrum changes of CHD over the past 18 years with a focus on the congenital aortic valve malformation (CAVM) and Adult CHD (ACHD). METHODS AND RESULTS: The transthoracic echocardiographic data of 682,565 records from 2003 to 2020 were retrospectively reviewed at Fujian Medical University Union Hospital, China. A total of 37,200 CHD cases were recruited in this study. Over the three periods (from 2003 to 2008, 2009-2014, to 2015-2020), the OR of Total CHD decreased (106.72, 90.64, and 67.43 per 1000 cases, respectively); the proportion of Simple CHD to Total CHD increased (80.96%, 83.41%, and 87.97%, respectively), with a decrease in the proportion of Complex CHD (18.11%, 15.51%, and 10.42%, respectively) (p < 0.05 for all). The proportion of ACHD increased in most types of CHD [Total CHD: 25.79%, 27.84%, and 31.43%; CAVM: 69.02%, 73.42%, and 78.16%; CAVM with aortic stenosis (AS): 67.42%, 70.73%, and 79.25%; respectively, p < 0.05 for all], with a much higher proportion in both CAVM and CAVM with AS than in the other CHD types. The proportion of CHD patients receiving intervention increased over the designated periods. CONCLUSIONS: This study depicts the longitudinal changes of CHD in the Chinese population with a single-center echocardiographic data, revealing an increased proportion of Simple CHD, ACHD (including CAVM and CAVM with AS), and a decreased OR of Total CHD and proportion of Complex CHD.

Underlying mechanisms and molecular targets of genistein in the management of type 2 diabetes mellitus and related complications.

Diabetes mellitus (DM) is a chronic metabolic disease caused by a complex interaction of genetic and environmental factors and is characterized by persistent hyperglycemia. Long-term hyperglycemia can cause macrovascular and microvascular damage, and compromise the heart, brain, kidney, peripheral nerves, eyes and other organs, leading to serious complications. Genistein, a phytoestrogen derived from soybean, is known for its various biological activities and therapeutic properties. Recent studies found that genistein not only has hypoglycemic activity but can also decrease insulin resistance. In addition, genistein has particular activity in the prevention and treatment of diabetic complications, such as nephropathy, cardiovascular disease, osteoarthrosis, encephalopathy and retinopathy. Therefore, the purpose of this review is to summarize the latest medical research and progress of genistein in DM and related complications and highlights its potential molecular mechanisms and therapeutic targets. Meanwhile, evidence is provided for the development and application of genistein as a potential drug or functional food in the prevention and treatment of diabetes and its related complications.

Technology adoption path of construction and demolition waste recycling under governmental subsidy: a theoretical model.

To achieve sustainable development, waste recycling is regarded as an ideal method to dispose of construction and demolition (C&D) waste. The economy is seen as the priority factor influencing recycling technology adoption. Hence, the subsidy is generally used to cross the economic barrier. To illustrate the recycling technology adoption path under governmental subsidy, this paper constructs a non-cooperative game model to investigate the impact of governmental subsidy on the C&D waste recycling technology adoption. By taking adoption profit, opportunity cost, and initial adoption marginal cost into consideration, the best time to adopt recycling technology and adoption behavior is discussed in detail in four scenarios. Results show that the governmental subsidy has a positive impact on C&D waste recycling technology adoption, and the subsidy could advance the adoption time of recyclers. If the subsidy proportion can reach 70% of the cost, recyclers will adopt recycling technology at the initial time. The results could contribute to a deeper understanding of C&D waste management by promoting the development of C&D waste recycling projects and also provide references to governments.

Identification and Validation of Co-Expressed Immune-Related Gene Signature Affecting the Pattern of Immune Infiltrating in Esophageal Cancer.

OBJECTIVE: Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive tract, and its molecular mechanisms have not been fully clarified. This study aimed to evaluate the immune infiltration pattern of esophageal cancer through a gene co-expression network, and to provide biomarkers for immunotherapy of esophageal cancer. METHODS: We downloaded RNA-seq data of ESCC samples from GSE53625 and GSE66258 datasets, then assessed the immune score and tumor purity through the ESTIMATE algorithm. Next, a co-expression network was constructed by the weighted gene co-expression network analysis, and the key co-expressed immune- related genes were identified on the basis of existing human immune-related genes. Afterward, we utilized bioinformatics algorithms including GSVA, CIBERSORT, and ssGSEA to clarify the relationship between hub genes and immune infiltration patterns. Finally, these hub genes were used to evaluate the sensitivity to immunotherapy by the subclass mapping algorithm, which were further validated by digital pathology through the Hover- Net algorithm. RESULTS: Sixteen immune-related genes with robust expression characteristics were identified and used to build gene signatures. The expression of gene signature was significantly related to the immune infiltration pattern and immunotherapy sensitivity prediction in patients with esophageal cancer. Consistent with previous studies, genetic changes at the level of somatic mutations such as NFE2L2 were revealed. CONCLUSION: A total of 16 immune-related genes with the total expression gene signature can be used as biomarkers for immunotherapy of esophageal squamous cell carcinoma. Its molecular mechanisms deserve further study to guide clinical treatment in the future.

Hierarchical Structure Model of Safety Risk Factors in New Coastal Towns: A Systematic Analysis Using the DEMATEL-ISM-SNA Method.

When a coastal town transforms from a rural area to an emerging city, it faces many safety risks. Some are new risks from urban construction, while some are traditional risks that belong to this coastal area. The joint efforts of these risks may lead to new hazards, harming public health, but this problem has not been noticed in previous studies. Therefore, this study constructs the Triangular Framework for Safety Risk in New Towns to identify the risks and proposes strategies to reduce the risks. In this study, multiple methods are integrated, including Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Social Network Analysis (SNA). This study takes the Lin-gang Special Area in China as a case study to verify the framework's effectiveness. Sixteen disaster-causing factors are identified, and the internal linkages among these factors are clarified. Results show that the hybrid method performs well in quantitatively analyzing the risk factors of new coastal towns. A typhoon, public risk perception, and population migration are essential influencing factors. Disaster prevention capability of high-rise buildings, disaster prevention capacity of port facilities, and transportation are the most direct influencing factors. Environmental degradation is the most conductive among all elements. This study contributes to the theoretical theory by proposing an effective framework to analyze the safety risks in new coastal towns. In addition, it provides practical references for governments to make emergency plans in the city.

Pretreatment Inflammatory Markers Predict Outcomes and Prognosis in Colorectal Cancer Patients With Synchronous Liver Metastasis.

Introduction: Pretreatment inflammatory markers were applied to predict the prognosis of colorectal cancer. However, the role of these markers in predicting survival in patients with synchronous colorectal liver metastasis (CLM) is rarely reported. Notably, lymphocyte-to-monocyte ratio (LMR) was mainly reported in hematologic malignancies and is worth to be further explored to predict the survival of synchronous CLM. Methods: Totally, 196 patients who were diagnosed with synchronous CLM were enrolled. Their clinical and laboratory data before treatment were collected, retrospectively. Univariate and multivariate analyses were performed to analyze the inflammatory biomarkers. Results: LMR (P = .002) and lactate dehydrogenase (LDH) (P = .017) were significantly related to the progression-free survival (PFS). More factors such as neutrophil-to-lymphocyte ratio (NLR) (P = .011), carbohydrate antigen 19-9 (CA19-9) (P = .001), number of metastatic foci (P = .006), and adjuvant chemotherapy (P = .027) were correlated with overall survival (OS). In multivariate analysis, LMR remained statistically associated with PFS (P = .003). Regarding OS, LMR (P = .016) and LDH (P = .013) were significantly independent predictive factors. Conclusions: The higher LMR and lower LDH were strongly correlated with better survival in synchronous CLM patients. In addition, the result also indicated that enhanced LMR was related to better PFS. The LMR and LDH can be used to predict prognosis of the synchronous CLM.

Promoting construction and demolition waste recycling by using incentive policies in China.

In the recent two decades, construction and demolition (C&D) waste is becoming a major source of municipal waste which causes severe damage to the environment. To solve the problem, waste recycling measures are gradually used to turn waste into treasures. Meanwhile, several kinds of policies such as waste disposal charging fees have been issued to stimulate stakeholders' behavior to take waste recycling measures to promote the C&D waste recycling industry. However, the C&D waste recycling rate is still too low in China. In order to promote C&D waste recycling industrial development, this paper aims at introducing subsidy and environmental tax policies to promote C&D waste recycling. Based on system dynamics method, this study establishes a model to determine the proper subsidy and environmental tax range. According to the simulation results, three kinds of incentive policies are obtained, namely, single subsidy policy, single environmental tax, and combined incentive policies. Optimal single subsidy and environmental tax are in the interval, [10, 30] and [20, 60], respectively. The best combination strategy is subsidy = 10 yuan/ton and environmental tax = 20 yuan/ton. The results from this paper could be a foundation for government to establish incentive policies to promote C&D waste recycling.

Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma.

Histopathological images and omics profiles play important roles in prognosis of cancer patients. Here, we extracted quantitative features from histopathological images to predict molecular characteristics and prognosis, and integrated image features with mutations, transcriptomics, and proteomics data for prognosis prediction in lung adenocarcinoma (LUAD). Patients obtained from The Cancer Genome Atlas (TCGA) were divided into training set (n = 235) and test set (n = 235). We developed machine learning models in training set and estimated their predictive performance in test set. In test set, the machine learning models could predict genetic aberrations: ALK (AUC = 0.879), BRAF (AUC = 0.847), EGFR (AUC = 0.855), ROS1 (AUC = 0.848), and transcriptional subtypes: proximal-inflammatory (AUC = 0.897), proximal-proliferative (AUC = 0.861), and terminal respiratory unit (AUC = 0.894) from histopathological images. Moreover, we obtained tissue microarrays from 316 LUAD patients, including four external validation sets. The prognostic model using image features was predictive of overall survival in test and four validation sets, with 5-year AUCs from 0.717 to 0.825. High-risk and low-risk groups stratified by the model showed different survival in test set (HR = 4.94, p < 0.0001) and three validation sets (HR = 1.64-2.20, p < 0.05). The combination of image features and single omics had greater prognostic power in test set, such as histopathology + transcriptomics model (5-year AUC = 0.840; HR = 7.34, p < 0.0001). Finally, the model integrating image features with multi-omics achieved the best performance (5-year AUC = 0.908; HR = 19.98, p < 0.0001). Our results indicated that the machine learning models based on histopathological image features could predict genetic aberrations, transcriptional subtypes, and survival outcomes of LUAD patients. The integration of histopathological images and multi-omics may provide better survival prediction for LUAD.

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma.

BACKGROUND: Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD. METHODS: We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF). RESULTS: There were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group. CONCLUSIONS: These results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer.

OBJECTIVE: This study used histopathological image features to predict molecular features, and combined with multi-dimensional omics data to predict overall survival (OS) in high-grade serous ovarian cancer (HGSOC). METHODS: Patients from The Cancer Genome Atlas (TCGA) were distributed into training set (n = 115) and test set (n = 114). In addition, we collected tissue microarrays of 92 patients as an external validation set. Quantitative features were extracted from histopathological images using CellProfiler, and utilized to establish prediction models by machine learning methods in training set. The prediction performance was assessed in test set and validation set. RESULTS: The prediction models were able to identify BRCA1 mutation (AUC = 0.952), BRCA2 mutation (AUC = 0.912), microsatellite instability-high (AUC = 0.919), microsatellite stable (AUC = 0.924), and molecular subtypes: proliferative (AUC = 0.961), differentiated (AUC = 0.952), immunoreactive (AUC = 0.941), mesenchymal (AUC = 0.918) in test set. The prognostic model based on histopathological image features could predict OS in test set (5-year AUC = 0.825) and validation set (5-year AUC = 0.703). We next explored the integrative prognostic models of image features, genomics, transcriptomics and proteomics. In test set, the models combining two omics had higher prediction accuracy, such as image features and genomics (5-year AUC = 0.834). The multi-omics model including all features showed the best prediction performance (5-year AUC = 0.911). According to risk score of multi-omics model, the high-risk and low-risk groups had significant survival differences (HR = 18.23, p < 0.001). CONCLUSIONS: These results indicated the potential ability of histopathological image features to predict above molecular features and survival risk of HGSOC patients. The integration of image features and multi-omics data may improve prognosis prediction in HGSOC patients.

The complete chloroplast genome sequence of Bambusa vulgaris cv. Wamin.

Bambusa vulgaris cv. Wamin is an attractive ornamental bamboo species of southern China. It has large swollen internodes and weeping culms, and it has considerable economic importance. In the present study, we sequenced the complete chloroplast genome of B. vulgaris cv. Wamin and reported it for the first time. The genome was 139,528 bp in total length, including a large single-copy (LSC) region of 83,038 bp, a small single-copy (SSC) region of 12,893 bp, and a pair of invert repeats (IR) regions of 21,799 bp. Plastid genome contained 138 genes, 82 protein-coding genes, 38 tRNA genes, and 8 rRNA genes. The overall GC content of the genome was 38.9%. The phylogenetic analysis based on the complete chloroplast genome reveals that B. vulgaris cv. Wamin is closely related to Bambusa teres. This research strengthens the genetic information of both the B. vulgaris cv. Wamin and the phylogenetic analyses of Gramineae.

Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma.

BACKGROUND: Histopathological image features offer a quantitative measurement of cellular morphology, and probably help for better diagnosis and prognosis in head and neck squamous cell carcinoma (HNSCC). METHODS: We first used histopathological image features and machine-learning algorithms to predict molecular features of 212 HNSCC patients from The Cancer Genome Atlas (TCGA). Next, we divided TCGA-HNSCC cohort into training set (n = 149) and test set (n = 63), and obtained tissue microarrays as an external validation set (n = 126). We identified the gene expression profile correlated to image features by bioinformatics analysis. RESULTS: Histopathological image features combined with random forest may predict five somatic mutations, transcriptional subtypes, and methylation subtypes, with area under curve (AUC) ranging from 0.828 to 0.968. The prediction model based on image features could predict overall survival, with 5-year AUC of 0.831, 0.782, and 0.751 in training, test, and validation sets. We next established an integrative prognostic model of image features and gene expressions, which obtained better performance in training set (5-year AUC = 0.860) and test set (5-year AUC = 0.826). According to histopathological transcriptomics risk score (HTRS) generated by the model, high-risk and low-risk patients had different survival in training set (HR = 4.09, p < 0.001) and test set (HR=3.08, p = 0.019). Multivariate analysis suggested that HTRS was an independent predictor in training set (HR = 5.17, p < 0.001). The nomogram combining HTRS and clinical factors had higher net benefit than conventional clinical evaluation. CONCLUSIONS: Histopathological image features provided a promising approach to predict mutations, molecular subtypes, and prognosis of HNSCC. The integration of image features and gene expression data had potential for improving prognosis prediction in HNSCC.

Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma.

OBJECTIVES: To assess the feasibility of predicting molecular characteristics by computed tomography (CT) radiomics features, and predicting overall survival (OS) using combination of omics data in clear cell renal cell carcinoma (ccRCC). METHODS: Genetic data of 207 ccRCC patients was retrieved from The Cancer Genome Atlas (TCGA) and matched contrast-enhanced CT images were obtained from The Cancer Imaging Archive (TCIA). Another cohort of 175 ccRCC patients from West China Hospital was used as external validation. We first applied radiomics features and machine learning algorithms to predict genetic mutations and mRNA-based molecular subtypes. Next, we established predictive models for OS based on single omics, combined omics (radiomics+genomics, radiomics+transcriptomics, radiomics+proteomics) and all features (multi-omics). RESULTS: Using radiomics features, random forest algorithm showed good capacity to identify the mutations VHL (AUC=0.971), BAP1 (AUC=0.955), PBRM1 (AUC=0.972), SETD2 (AUC=0.949), and molecular subtypes m1 (AUC=0.973), m2 (AUC=0.968), m3 (AUC=0.961), m4 (AUC=0.953). The TCGA cohort was divided into training (n=104) and validation (n=103) sets. The radiomics model had promising prognostic value for OS in validation set (5-year AUC=0.775) and external validation set (5-year AUC=0.755). In the validation set, the radiomics+omics models enhanced predictive accuracy than single-omics models, and the multi-omics model made further improvement (5-year AUC=0.846). High-risk group of validation set predicted by multi-omics model showed significantly poorer OS (HR=6.20, 95%CI: 3.19-8.44, p<0.0001). CONCLUSIONS: CT radiomics might be a feasible approach to predict genetic mutations, molecular subtypes and OS in ccRCC patients. Integrative analysis of radiogenomics may improve the survival prediction of ccRCC patients.

Developing a Dynamic Supervision Mechanism to Improve Construction Safety Investment Supervision Efficiency in China: Theoretical Simulation of Evolutionary Game Process.

The construction industry suffers from poor safety performance caused by the joint effect of insufficient safety investment by contractors and inefficient safety supervision by the government because of the information gap between the two sides. The present study aims to put forward a new pathway to improve safety investment supervision efficiency and analyze the decision-making interactions of stakeholders under this new pathway. For this purpose, this study establishes a safety investment information system to eliminate the information gap between the government and contractors for construction projects in China and further develops a dynamic safety investment supervision mechanism based on this. Evolutionary game theory is used to describe the decision-making interactions among stakeholders under the current static supervision mechanism and the dynamic supervision mechanism proposed in this research. Moreover, system dynamics is adopted to simulate the evolutionary game process and analyze the supervision effect and equilibrium state of different supervision mechanisms. The results reveal that the proposed safety investment information system could facilitate the transition of the supervision mode from static to dynamic; the evolutionarily stable strategy does not exist in the current static penalty scenario; and the dynamic supervision mechanism that correlates penalties with contractors' unlawful behavior probability can restrain the fluctuation of the evolutionary game model effectively and the players' strategy choices gradually stabilize in the equilibrium state. The results validate the effectiveness of the proposed dynamic supervision mechanism in improving supervision efficiency. This study not only contributes to the literature on safety supervision policy-making but also helps to improve supervision efficiency in practice.

Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients. METHOD: We extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients. RESULTS: A total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814 vs. 0.837, 0.74 vs. 0.806, and 0.689 vs. 0.751 in test set). Clinical characteristics including gender, TNM stage and IGPF were independent risk factors. The nomogram integrating clinical predictors and IGPF provided the best net benefit among the three models. CONCLUSION: In this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients.