From entrepreneurial passion to business model innovation: The role of entrepreneurial learning and curiosity.

The impact of entrepreneurial passion on business model innovation has become a research focus in the field of entrepreneurship. This study aimed to examine the effect of entrepreneurial passion on entrepreneurial learning and business model innovation. The study used partial least square-structural equation modeling (PLS-SEM 4) to test the hypotheses on a sample of 389 manufacturing and technology enterprises in the South region of Henan province China. The results of this study revealed that entrepreneurial passion has a positive and significant influence on entrepreneurial learning and business model innovation. The findings of this study showed that entrepreneurial learning positively and significantly mediates the relationship between entrepreneurial passion and business model innovation. Furthermore, the results of this study indicated that curiosity positively and significantly moderates the relationship between entrepreneurial passion and entrepreneurial learning. Lastly, the results are discussed with implications and limitations, which open up future research perspectives.

Anatomic location of colorectal cancer presents a new paradigm for its prognosis in African American patients.

Among all racial groups in the U.S., African Americans (AA) have the highest incidence of and mortality from colorectal cancer (CRC). Although socioeconomic factors, as the major contributors to racial disparity of CRC, have been widely investigated, there is a dearth of information germane to understanding its biological basis. To better elucidate the clinicopathologic features we extracted demographic, clinical, pathologic and molecular features of 500 consecutive cases of CRC diagnosed at our institution which has an AA-predominant patient population (75% of all patients). We compared data from our AA patients with those of white patients both from our institution and from SEER and the published literature for meaningful comparison. AA patients were more likely to be at an advanced disease stage (25.9% vs. 20.8%, p = 0.041), have low grade tumors (89.2% vs. 77.5%, p<0.001) in cecum (18.7% vs. 16.2%, p<0.001) and <60-years-old than white patients (31.8% vs. 26.3%, p = 0.015). The frequency of KRAS mutation was higher in AA patients than in white patients (56.8% vs. 20.7%, p<0.001). Amongst subtypes of KRAS tested in CRC, codon 12 mutation is more common in AA than white patients (85.2% vs. 68.9%, p = 0.020). Compared with other racial groups, we found AA patients to have worse disease-free survival (HR = 3.682, p = 0.035). Also, AA patients with CRC in distal (sigmoid and rectum) or proximal (cecum) colon have worse overall survival than those with CRC in middle colon (HR = 2.926, p = 0.014), a finding not observed in white patients. In both racial groups, advanced stage, perforation, and hypertension were independent prognostic factors for overall survival (p<0.05). Similarly, low body-mass index at presentation, mucinous adenocarcinoma, lymphovascular invasion, perineural invasion and KRAS mutations were independent factors significantly associated with poor disease-free survival. Collectively, our data provide new insights into the roles of clinicopathologic features, especially anatomic distribution, in predicting outcomes of CRC in AA population.

iTRAQ-Based Proteome Profiling of Differentially Expressed Proteins in Insulin-Resistant Human Hepatocellular Carcinoma.

Recently, the incidences of insulin resistance (IR) and IR-related complications have increased throughout the world, which also associate with poor prognosis in hepatocellular carcinoma (HCC). Numerous studies had been focused on the role of IR in tumorigenesis and prognosis of HCC. The proteomic analysis of IR related hepatocellular carcinoma had not been reported by now. In the present study, 196 differentially expressed proteins (DEPs) were identified between insulin resistant HepG2 cells and their parental cells, of which 109 proteins were downregulated and 87 proteins were upregulated. Bioinformatics analysis indicated that these DEPs were highly enriched in process of tumorigenesis and tumor progression. PPI network analysis showed that SOX9, YAP1 and GSK3ß as the key nodes, were involved in Wnt and Hippo signaling pathways. Survival analysis revealed that high expression of SOX9 and PRKD3 were strongly associated with reduced patient survival rate. parallel reaction monitoring (PRM) and Western blot analysis were applied to verify the protein level of these four key nodes mentioned above, which showed the same trend as quantified by isobaric tags for relative and absolute quantitation (iTRAQ) and confirmed the reliability of our Proteome Profiling analysis. Our results indicated that IR related dysregulation of protein expression might participated in tumorigenesis and malignant phenotype of hepatocarcinoma cells.

Value of biomarkers in epithelial-mesenchymal transition models of liver cancer under different interventions: a meta-analysis.

Aim: A meta-analysis was conducted to evaluate the effectiveness of crucial biomarkers in HepG2 cells during epithelial-mesenchymal transformation induced by multiple interventions. Methods: PubMed, Web of Science, Embase, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, Wan Fang Data and VIP databases were systematically searched from inception to 14 June 2020, by two independent reviewers. Results: A total of 58 studies were included in the meta-analysis. E-cadherin, N-cadherin and vimentin performed well under medicinal interventions. E-cadherin worked well under genetic interventions. E-cadherin and N-cadherin also performed significantly well under tumor microenvironment interventions. Under ncRNA interventions, the expression of E-cadherin significantly changed. Conclusion: Different sets of biomarkers should be selected under various interventions based on their performance.

EIF5A expression and its role as a potential diagnostic biomarker in hepatocellular carcinoma.

Introduction and objectives: Eukaryotic translation initiation factor 5A (EIF5A) is a member of the identified eIF family and played an important role in cell proliferation. There are few studies about the correlation between EIF5A and hepatocellular carcinoma (HCC). Materials and methods: We evaluated the expression of the EIF5A in human HCC cell lines and tissues by western blot analysis. Immunohistochemistry analysis of EIF5A was performed on a tissue microarray including 10 normal liver samples and 90 pathological section of HCC. Receiver operating characteristic (ROC) was introduced to obtain an optimal cut-off score for EIF5A positive expression. Results: Western blot results showed that EIF5A was highly expressed in HCC cell lines and tissues. Based on ROC curve analysis, 1/10 (10.0%) of normal hepatic tissues and 67/90 (74.4%) of HCC tissues were tested positive for EIF5A expression, which indicated that EIF5A were significantly up-regulated in HCC tissues compared with normal liver tissues (χ2=17.177, P<0.001). Furthermore, expression of EIF5A was significantly correlated with histological grade (P=0.048), clinical stage (P=0.003) and pT stage (P=0.003) but not correlated with sex (P=0.617) and age (P=0.831). Conclusions: In our study, we demonstrated the expression of EIF5A is closely correlated with HCC. In consideration of its relationship with clinicopathological parameters including histological grade, clinical stage and pT stage of HCC, EIF5A could be a potential biomarker.

Feature extraction of EEG signals based on functional data analysis and its application to recognition of driver fatigue state.

OBJECTIVE: Our objective is to study how to obtain features which can reflect the continuity and internal dynamic changes of electroencephalography (EEG) signals and study an effective method for fatigued driving state recognition based on the obtained features. APPROACH: A method of EEG signalfeature extraction based on functional data analysis is proposed. Combined with kernel principal component analysis method, the obtained features are applied to the recognition of driver fatigue state, and a corresponding recognition model of fatigued driving state is constructed. MAIN RESULTS: The recognition model is tested on the real collected driver fatigue EEG signals by selecting a suitable classifier. The test results show that the proposed driver fatigue state recognition method has good recognition effect, especially on the classifier based on decision tree, with an average accuracy of 99.50%. SIGNIFICANCE: The extracted features well reflect the continuityand internal dynamic changes of the EEG signals, and it is of great significance and application value to study an effective method of fatigued driver state recognition based on the features.

Construction and Application of Functional Brain Network Based on Entropy.

Functional brain network (FBN) is an intuitive expression of the dynamic neural activity interaction between different neurons, neuron clusters, or cerebral cortex regions. It can characterize the brain network topology and dynamic properties. The method of building an FBN to characterize the features of the brain network accurately and effectively is a challenging subject. Entropy can effectively describe the complexity, non-linearity, and uncertainty of electroencephalogram (EEG) signals. As a relatively new research direction, the research of the FBN construction method based on EEG data of fatigue driving has broad prospects. Therefore, it is of great significance to study the entropy-based FBN construction. We focus on selecting appropriate entropy features to characterize EEG signals and construct an FBN. On the real data set of fatigue driving, FBN models based on different entropies are constructed to identify the state of fatigue driving. Through analyzing network measurement indicators, the experiment shows that the FBN model based on fuzzy entropy can achieve excellent classification recognition rate and good classification stability. In addition, when compared with the other model based on the same data set, our model could obtain a higher accuracy and more stable classification results even if the length of the intercepted EEG signal is different.

Gestational diabetes status and dietary intake modify maternal and cord blood allostatic load markers.

INTRODUCTION: Allostatic load (AL) defines cardiometabolic, inflammatory, and neuroendocrine changes in the body in response to internal and external stressors. It is largely unknown whether gestational diabetes mellitus (GDM) alters maternal and fetal AL, which in turn affects GDM outcomes. Whether dietary intakes and quality can modify AL and thus influence GDM progression is also unknown. RESEARCH DESIGN AND METHODS: In this study, we recruited 35 GDM and 30 non-GDM women in gestational week 25-33. Fasting blood samples were collected at enrollment, and cord venous blood samples were collected at delivery for the measurement of a series of AL biomarkers to calculate the composite AL index. Three-day dietary recalls were conducted at enrollment. RESULTS: Results suggest that GDM women had 60% higher composite AL index scores (p value=0.01). Maternal AL index was associated with shorter duration of gestation (ß=-0.33, p value=0.047) and higher fetal AL index (ß=0.47, p value=0.006) after adjusting for GDM status. Dietary intake of monounsaturated fatty acids was negatively associated with maternal AL index (ß=-0.20, p value=0.006). GDM women had lower total caloric intake and dietary glycemic load, yet their linolenic acid, vitamin C and E intakes were also decreased (all p value<0.05). These dietary differences were not related to birth outcomes measured. CONCLUSIONS: In this study, GDM status and dietary intakes modify AL in this population. AL may serve as an indicator of GDM control. Future research on dietary interventions that can improve maternal AL markers during GDM is warranted.

Constructing Multi-scale Entropy Based on the Empirical Mode Decomposition(EMD) and its Application in Recognizing Driving Fatigue.

BACKGROUND: Fatigue is one of the important factors in traffic accidents. Hence, it is necessary to devise methods to detect the fatigue and apply practical fatigue detection solutions for drivers. NEW METHOD: This paper presents a method based on the empirical mode decomposition(EMD) of multi-scale entropy on the recorded forehead Electroencephalogram(EEG) signals. These EEG signals are decomposed to extract intrinsic mode functions(IMFs) by using the EMD technique. Then, the IMFs components are selected out by using the Pearson correlation coefficient and the best scale features on each signal are determined in multiple experiments. RESULTS: Results indicate that the empirical mode decomposition multi-scale fuzzy entropy feature classification recognition rate is up to 87.50%, the highest is 88.74%, which is 23.88% higher than the single-scale fuzzy entropy and 5.56% higher than multi-scale fuzzy entropy. COMPARISON WITH EXISTING METHOD: Three types of entropies measures, permutation entropy(PE), sample entropy(SE), fuzzy entropy(FE), were applied for the analysis of signal and compared by seven classifiers in 10-fold and Leave-One-Out cross-validation experiments. CONCLUSIONS: The proposed method can be effectively applied to the detection of driving fatigue.

Effect of human epididymis protein 4 gene silencing on the malignant phenotype in ovarian cancer.

BACKGROUND: Human epididymis secretory protein 4 (HE4) has been proved to be a promising novel biomarker for the detection of epithelial ovarian carcinomas. Compared with CA125, HE4 assay demonstrated an improved ability to discriminate between pelvic mass with malignant and benign disease. Though it is well known that HE4 is overexpressed in ovarian cancer, however, the role of HE4 in the carcinogenesis and progression of ovarian cancer remains unkown. METHODS: In this study, we explored the role of HE4 in the carcinogenesis and progression of ovarian cancer. We screened nine ovarian cancer cell lines for HE4 expression, and using RNA interference (RNAi), we silenced HE4 gene expression in CaoV3 and SKOV3.ip1 ovarian cancer cell lines. We assessed the effect of HE4 gene silencing on the transformed phenotype by examining the cell cycle, apoptosis, proliferation and transwell migration/invasion in vitro. RESULTS: HE4 gene silencing induces G0/G1 arrest and blocks the progression from the G1 to S phase in CaoV3 and SKOV3.ip1 cells. HE4 knockdown also inhibited cell proliferation, migration and invasion in SKOV3.ip1 cells in vitro. CONCLUSION: HE4 may be involved in the regulation of the cell cycle and promote ovarian cancer migration and invasion.

[Expressions of SHP and CYP7A1 in pregnant rats with intrahepatic cholestasis].

OBJECTIVE: To investigate the expressions of small heterodimer partner (SHP) and target gene cholesterol-7-hydroxylase (CYP7A1) in livers of rats with intrahepatic cholestasis of pregnancy (ICP), and to study the mechanism of ICP. METHODS: Thirty SD rats (pregnant for 15 days) were equally and randomly divided into two groups: an estradiol benzoate (EB) group and a normal saline (NS) group. Two ml blood was drawn from each rat before and on the 5th day after medicine administration to measure the levels of ALT, AST, ALP, TBA, TBIL, and DBIL. After delivery, the histopathological changes of the mother rat livers were studied. The mRNA and protein expressions of SHP and CYP7A1 in the livers were determined by RT-PCR and Western blot. RESULTS: (1) In the EB group, the serum levels of ALT, AST, ALP, TBA, TBil, and DBil after EB administration increased significantly (P less than 0.01), but there were no significant changes in the NS group (P more than 0.05); (2) Intrahepatic cholestasis appeared in the EB group, but not in the NS group; (3) The mRNA expressions of SHP and CYP7A1 were significantly higher in the EB group than in the NS group [(SHPmRNA: NS 0.365+/-0.0317 vs EB 0.4865+/-0.0237, P less than 0.01), (CYP7A1 mRNA: NS 0.3570+/-0.0175 vs EB 0.4802+/-0.0217, P less than 0.01)]; (4) The protein expressions of SHP and CYP7A1 were also higher in the EB group than that in the NS group [(SHP: NS 0.3762+/-0.0284 vs EB 0.5033+/-0.0274, P less than 0.01), (CYP7A1: NS 0.3570+/-0.0175 vs EB 0.4802+/-0.0217, P less than 0.01)]. CONCLUSION: Estrogen induces ICP in rats. The mRNA and protein expressions of SHP and CYP7A1 in livers of the ICP rats were increased, which causes more bile acids to be synthesized. This may be one of the mechanisms of ICP.

[Effects of farnesoid X receptor ligand on the metabolism of bile acids in rats with estrogen-induced intrahepatic cholestasis of pregnancy].

OBJECTIVE: To investigate the effects and mechanism of farnesoid X receptor (FXR) and its ligands on the metabolism of bile acids in rats with estrogen-induced intrahepatic cholestasis of pregnancy (ICP). METHODS: An ICP rat model was established with estradiol benzoate (EB) injections. Then FXR ligand chenodeoxycholic acid (CDCA) was administrated (100 mg/kg daily) to ICP rats for 5 days. The serum TBA and expression of FXR and bile salt export pump (BSEP) in the rat livers were examined by immunohistochemistry and reverse transcription PCR. RESULTS: The levels of TBA in the CDCA group rats were significantly lower than the untreated rats [(17.2+/-4.1)micromol/L vs (29.3+/-6.4)micromol/L], and the expressions of mRNA and protein of FXR were significantly higher [(0.76+/-0.09 vs 0.53+/-0.06, P<0.05 and 2.35+/-0.06 vs 1.83+/-0.05, P<0.017, respectively)], and the expressions of BSEP were also higher [(0.99+/-0.21 vs 0.76+/-0.07, P<0.017 and 1.88+/-0.03 vs 1.46+/-0.06, P<0.017, respectively)]. CONCLUSIONS: FXR plays an important role in modulating the metabolism of bile acids. CDCA can lower the levels of serum TBA by upregulating the expression of FXR and BSEP and then increasing the transport of the bile acids. These facts might present a new idea and target for the treatment of ICP.