BACKGROUND Liver cancer(LIHC)is a malignant tumor that occurs in the liver and has a high mortality in cancer.The ING family genes were identified as tumor suppressor genes.Dysregulated expression of these genes can l...BACKGROUND Liver cancer(LIHC)is a malignant tumor that occurs in the liver and has a high mortality in cancer.The ING family genes were identified as tumor suppressor genes.Dysregulated expression of these genes can lead to cell cycle arrest,senescence and/or apoptosis.ING family genes are promising targets for anticancer therapy.However,their role in LIHC is still not well understood.AIM To have a better understanding of the important roles of ING family members in LIHC.METHODS A series of bioinformatics approaches(including gene expression analysis,genetic alteration analysis,survival analysis,immune infiltration analysis,prediction of upstream microRNAs(miRNAs)and long noncoding RNAs(lncRNAs)of ING1,and ING1-related gene functional enrichment analysis)was applied to study the expression profile,clinical relationship,prognostic significance and immune infiltration of ING in LIHC.The relationship between ING family genes expression and tumor associated immune checkpoints was investigated in LIHC.The molecular mechanism of ING1 mediated hepatocarcinogenesis was preliminarily discussed.RESULTS mRNA/protein expression of different ING family genes in LIHC was analyzed in different databases,showing that ING family genes were highly expressed in LIHC.In 47 samples from 366 LIHC patients,the ING family genes were altered at a rate of 13%.By comprehensively analyzing the expression,clinical pathological parameters and prognostic value of ING family genes,ING1/5 was identified.ING1/5 was related to poor prognosis of LIHC,suggesting that they may play key roles in LIHC tumorigenesis and progression.One of the target miRNAs of ING1 was identified as hsa-miR-214-3p.Two upstream lncRNAs of hsa-miR-214-3p,U91328.1,and HCG17,were identified.At the same time,we found that the expression of ING family genes was correlated with immune cell infiltration and immune checkpoint genes.CONCLUSION This study lays a foundation for further research on the potential mechanism and clinical value of ING family genes in the treatment and prognosis of LIHC.展开更多
This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed a...This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.展开更多
近年来,子宫内膜癌患病率呈逐年升高的趋势^([1])。早期发现子宫内膜癌具有重要的意义^([2])。生长抑制因子(Inhibitor of growth family member,ING)蛋白家族中有一些因子能有效遏制肿瘤细胞的增殖与进展^([3])。其中,ING4在肿瘤细胞...近年来,子宫内膜癌患病率呈逐年升高的趋势^([1])。早期发现子宫内膜癌具有重要的意义^([2])。生长抑制因子(Inhibitor of growth family member,ING)蛋白家族中有一些因子能有效遏制肿瘤细胞的增殖与进展^([3])。其中,ING4在肿瘤细胞增殖过程中扮演着非常重要的角色^([4])。在乳腺癌^([5])、肺癌^([6])以及胶质瘤^([7])等恶性肿瘤中ING4能够促进细胞凋亡以及遏制肿瘤细胞增殖等。本研究探讨子宫内膜癌患者组织中ING4蛋白表达及与临床病理特征的关系,报道如下。展开更多
Objective:To explore the modeling of time series of animal bite occurrence in northwest Iran.Methods:In this study,we analyzed surveillance time series data for animal bite cases in the northwest Iran province of Iran...Objective:To explore the modeling of time series of animal bite occurrence in northwest Iran.Methods:In this study,we analyzed surveillance time series data for animal bite cases in the northwest Iran province of Iran from 2011 to 2017.We used decomposition methods to explore seasonality and long-term trends and applied the Autoregressive Integrated Moving Average(ARIMA)model to fit a univariate time series of animal bite incidence.The ARIMA modeling process involved selecting the time series,transforming the series,selecting the appropriate model,estimating parameters,and forecasting.Results:Our results using the Box Jenkins model showed a significant seasonal trend and an overall increase in animal bite incidents during the study period.The best-fitting model for the available data was a seasonal ARIMA model with drift in the form of ARIMA(2,0,0)(1,1,1).This model can be used to forecast the frequency of animal attacks in northwest Iran over the next two years,suggesting that the incidence of animal attacks in the region would continue to increase during this time frame(2018-2019).Conclusion:Our findings suggest that time series analysis is a useful method for investigating animal bite cases and predicting future occurrences.The existence of a seasonal trend in animal bites can also aid in planning healthcare services during different seasons of the year.Therefore,our study highlights the importance of implementing proactive measures to address the growing issue of animal bites in Iran.展开更多
The U.S.dollar has long stood tall as the pre-eminent global currency for international cross-border trade settlements and foreign exchange reserves ever since the launch of the Bretton Woods institutions-Internationa...The U.S.dollar has long stood tall as the pre-eminent global currency for international cross-border trade settlements and foreign exchange reserves ever since the launch of the Bretton Woods institutions-International Monetary Fund(IMF)and World Bank(WB).展开更多
基金Supported by Talent Scientific Research Start-up Foundation of Wannan Medical College,No.WYRCQD2023045.
文摘BACKGROUND Liver cancer(LIHC)is a malignant tumor that occurs in the liver and has a high mortality in cancer.The ING family genes were identified as tumor suppressor genes.Dysregulated expression of these genes can lead to cell cycle arrest,senescence and/or apoptosis.ING family genes are promising targets for anticancer therapy.However,their role in LIHC is still not well understood.AIM To have a better understanding of the important roles of ING family members in LIHC.METHODS A series of bioinformatics approaches(including gene expression analysis,genetic alteration analysis,survival analysis,immune infiltration analysis,prediction of upstream microRNAs(miRNAs)and long noncoding RNAs(lncRNAs)of ING1,and ING1-related gene functional enrichment analysis)was applied to study the expression profile,clinical relationship,prognostic significance and immune infiltration of ING in LIHC.The relationship between ING family genes expression and tumor associated immune checkpoints was investigated in LIHC.The molecular mechanism of ING1 mediated hepatocarcinogenesis was preliminarily discussed.RESULTS mRNA/protein expression of different ING family genes in LIHC was analyzed in different databases,showing that ING family genes were highly expressed in LIHC.In 47 samples from 366 LIHC patients,the ING family genes were altered at a rate of 13%.By comprehensively analyzing the expression,clinical pathological parameters and prognostic value of ING family genes,ING1/5 was identified.ING1/5 was related to poor prognosis of LIHC,suggesting that they may play key roles in LIHC tumorigenesis and progression.One of the target miRNAs of ING1 was identified as hsa-miR-214-3p.Two upstream lncRNAs of hsa-miR-214-3p,U91328.1,and HCG17,were identified.At the same time,we found that the expression of ING family genes was correlated with immune cell infiltration and immune checkpoint genes.CONCLUSION This study lays a foundation for further research on the potential mechanism and clinical value of ING family genes in the treatment and prognosis of LIHC.
文摘This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.
文摘近年来,子宫内膜癌患病率呈逐年升高的趋势^([1])。早期发现子宫内膜癌具有重要的意义^([2])。生长抑制因子(Inhibitor of growth family member,ING)蛋白家族中有一些因子能有效遏制肿瘤细胞的增殖与进展^([3])。其中,ING4在肿瘤细胞增殖过程中扮演着非常重要的角色^([4])。在乳腺癌^([5])、肺癌^([6])以及胶质瘤^([7])等恶性肿瘤中ING4能够促进细胞凋亡以及遏制肿瘤细胞增殖等。本研究探讨子宫内膜癌患者组织中ING4蛋白表达及与临床病理特征的关系,报道如下。
文摘Objective:To explore the modeling of time series of animal bite occurrence in northwest Iran.Methods:In this study,we analyzed surveillance time series data for animal bite cases in the northwest Iran province of Iran from 2011 to 2017.We used decomposition methods to explore seasonality and long-term trends and applied the Autoregressive Integrated Moving Average(ARIMA)model to fit a univariate time series of animal bite incidence.The ARIMA modeling process involved selecting the time series,transforming the series,selecting the appropriate model,estimating parameters,and forecasting.Results:Our results using the Box Jenkins model showed a significant seasonal trend and an overall increase in animal bite incidents during the study period.The best-fitting model for the available data was a seasonal ARIMA model with drift in the form of ARIMA(2,0,0)(1,1,1).This model can be used to forecast the frequency of animal attacks in northwest Iran over the next two years,suggesting that the incidence of animal attacks in the region would continue to increase during this time frame(2018-2019).Conclusion:Our findings suggest that time series analysis is a useful method for investigating animal bite cases and predicting future occurrences.The existence of a seasonal trend in animal bites can also aid in planning healthcare services during different seasons of the year.Therefore,our study highlights the importance of implementing proactive measures to address the growing issue of animal bites in Iran.
文摘The U.S.dollar has long stood tall as the pre-eminent global currency for international cross-border trade settlements and foreign exchange reserves ever since the launch of the Bretton Woods institutions-International Monetary Fund(IMF)and World Bank(WB).
