The analytic network processes method & fuzzy cognitive map method in decision making: a comparative study

Decision making problems always puzzled managers and decision makers in enterprises or organizations. There are many useful soft operation research methods could solve the complicated problem. The paper raises two classic approaches ANP (Analytical network process) and FCM (Fuzzy cognitive map). Those two methods have some similarities and differences. In thus, according to different decision problems, people could compare the two approaches’ characteristics and choose the best tool to deal with the problems. At the end of the paper, a case study has been given for us

Cdc6 contributes to abrogating the G1 checkpoint under hypoxic conditions in HPV E7 expressing cells

The human papillomavirus (HPV) plays a central role in cervical carcinogenesis and its oncogene E7 is essential in this process. We showed here that E7 abrogated the G1 cell cycle checkpoint under hypoxia and analyzed key cell cycle related proteins for their potential role in this process. To further explore the mechanism by which E7 bypasses hypoxia-induced G1 arrest, we applied a proteomic approach and used mass spectrometry to search for proteins that are differentially expressed in E7 expressing cells under hypoxia. Among differentially expressed proteins identified, Cdc6 is a DNA replication initiation factor and exhibits oncogenic activities when overexpressed. We have recently demonstrated that Cdc6 was required for E7-induced re-replication. Significantly, here we showed that Cdc6 played a role in E7-mediated G1 checkpoint abrogation under hypoxic condition, and the function could possibly be independent from its role in DNA replication initiation. This study uncovered a new function of Cdc6 in regulating cell cycle progression and has important implications in HPV-associated cancers

CIP2A facilitates the G1/S cell cycle transition via B-Myb in human papillomavirus 16 oncoprotein E6-expressing cells

Infection with high-risk human papillomaviruses (HR-HPVs, including HPV-16, HPV-18, HPV-31) plays a central aetiologic role in the development of cervical carcinoma. The transforming properties of HR-HPVs mainly reside in viral oncoproteins E6 and E7. E6 protein degrades the tumour suppressor p53 and abrogates cell cycle checkpoints. Cancerous inhibitor of protein phosphatase 2A (CIP2A) is an oncoprotein that is involved in the carcinogenesis of many human malignancies. Our previous data showed that CIP2A was overexpressed in cervical cancer. However, the regulation of CIP2A by HPV-16E6 remains to be elucidated. In this study, we demonstrated that HPV-16E6 significantly up-regulated CIP2A mRNA and protein expression in a p53-degradation-dependent manner. Knockdown of CIP2A by siRNA inhibited viability and DNA synthesis and caused G1 cell cycle arrest of 16E6-expressing cells. Knockdown of CIP2A resulted in a significant reduction in the expression of cyclin-dependent kinase 1 (Cdk1) and Cdk2. Although CIP2A has been reported to stabilize c-Myc by inhibiting PP2A-mediated dephosphorylation of c-Myc, we have presented evidence that the regulation of Cdk1 and Cdk2 by CIP2A is dependent on transcription factor B-Myb rather than c-Myc. Taken together, our study reveals the role of CIP2A in abrogating the G1 checkpoint in HPV-16E6-expressing cells and helps in understanding the molecular basis of HPV-induced oncogenesis

Hypoxia associated multi-omics molecular landscape of tumor tissue in patients with hepatocellular carcinoma

The present study was designed to update the knowledge about hypoxia-related multi-omic molecular landscape in hepatocellular carcinoma (HCC) tissues. Large-size HCC datasets from multiple centers were collected. The hypoxia exposure of tumor tissue from patients in 10 HCC cohorts was estimated using a novel HCC-specific hypoxia score system constructed in our previous study. A comprehensive bioinformatical analysis was conducted to compare hypoxia-associated multi-omic molecular features in patients with a high hypoxia score to a low hypoxia score. We found that patients with different exposure to hypoxia differed significantly in transcriptomic, genomic, epigenomic, and proteomic alterations, including differences in mRNA, microRNA (miR), and long non-coding RNA (lncRNA) expression, differences in copy number alterations (CNAs), differences in DNA methylation levels, differences in RNA alternative splicing events, and differences in protein levels. HCC survival-associated molecular events were identified. The potential correlation between molecular features related to hypoxia has also been explored, and various networks have been constructed. We revealed a particularly comprehensive hypoxia-related molecular landscape in tumor tissues that provided novel evidence and perspectives to explain the role of hypoxia in HCC. Clinically, the data obtained from the present study may enable the development of individualized treatment or management strategies for HCC patients with different levels of hypoxia exposure.</p

Progress in Understanding the Mechanism by Which Probiotics Alletivate Hyperuricemia

Hyperuricemia (HUA) is a disease caused by excessive production and/or inadequate excretion of uric acid (UA) in the body, and persistent HUA can lead to gout. Asymptomatic HUA is closely related to other metabolic diseases, such as chronic kidney disease, diabetes and cardiovascular disease. Probiotic intervention is a potentially safe, cost-effective treatment to improve HUA. This paper mainly describes the mechanisms of action of probiotics in reducing the level of uric acid, including inhibiting xanthine oxidase activity, absorbing and degrading purines in the intestine, regulating intestinal flora homeostasis and restoring intestinal barrier function. In addition, probiotics can promote uric acid excretion and consequently reduced uric acid levels by promoting the expression of urate transport proteins and inhibiting the expression of urate reabsorbing proteins. Probiotics have great potential in alleviating HUA. This review hopes to provide a theoretical basis for subsequent research

A novel hypoxia gene signature indicates prognosis and immune microenvironments characters in patients with hepatocellular carcinoma

Due to the lack of a suitable gene signature, it is difficult to assess the hypoxic exposure of HCC tissues. The clinical value of assessing hypoxia in HCC is short of tissue-level evidence. We tried to establish a robust and HCC-suitable hypoxia signature using microarray analysis and a robust rank aggregation algorithm. Based on the hypoxia signature, we obtained a hypoxia-associated HCC subtypes system using unsupervised hierarchical clustering and a hypoxia score system was provided using gene set variation analysis. A novel signature containing 21 stable hypoxia-related genes was constructed to effectively indicate the exposure of hypoxia in HCC tissues. The signature was validated by qRT-PCR and compared with other published hypoxia signatures in multiple large-size HCC cohorts. The subtype of HCC derived from this signature had different prognosis and other clinical characteristics. The hypoxia score obtained from the signature could be used to indicate clinical characteristics and predict prognoses of HCC patients. Moreover, we reveal a landscape of immune microenvironments in patients with different hypoxia score. In conclusion, we identified a novel HCC-suitable 21-gene hypoxia signature that could be used to estimate the hypoxia exposure in HCC tissues and indicated prognosis and a series of important clinical features in HCCs. It may enable the development of personalized counselling or treatment strategies for HCC patients with different levels of hypoxia exposure

Machine learning-assisted prediction of pneumonia based on non-invasive measures

BackgroundPneumonia is an infection of the lungs that is characterized by high morbidity and mortality. The use of machine learning systems to detect respiratory diseases via non-invasive measures such as physical and laboratory parameters is gaining momentum and has been proposed to decrease diagnostic uncertainty associated with bacterial pneumonia. Herein, this study conducted several experiments using eight machine learning models to predict pneumonia based on biomarkers, laboratory parameters, and physical features.MethodsWe perform machine-learning analysis on 535 different patients, each with 45 features. Data normalization to rescale all real-valued features was performed. Since it is a binary problem, we categorized each patient into one class at a time. We designed three experiments to evaluate the models: (1) feature selection techniques to select appropriate features for the models, (2) experiments on the imbalanced original dataset, and (3) experiments on the SMOTE data. We then compared eight machine learning models to evaluate their effectiveness in predicting pneumoniaResultsBiomarkers such as C-reactive protein and procalcitonin demonstrated the most significant discriminating power. Ensemble machine learning models such as RF (accuracy = 92.0%, precision = 91.3%, recall = 96.0%, f1-Score = 93.6%) and XGBoost (accuracy = 90.8%, precision = 92.6%, recall = 92.3%, f1-score = 92.4%) achieved the highest performance accuracy on the original dataset with AUCs of 0.96 and 0.97, respectively. On the SMOTE dataset, RF and XGBoost achieved the highest prediction results with f1-scores of 92.0 and 91.2%, respectively. Also, AUC of 0.97 was achieved for both RF and XGBoost models.ConclusionsOur models showed that in the diagnosis of pneumonia, individual clinical history, laboratory indicators, and symptoms do not have adequate discriminatory power. We can also conclude that the ensemble ML models performed better in this study