Objective To investigate the effect of levosimendan on the cyclic guanosine monophosphate-adenosin monophosphate synthase-interferon gene stimulating factor(cGAS-STING)signaling pathway during suspension-reperfusion in isolated rat myocardium.Methods The rats were divided into four groups(n=12)using random number table:continuous perfusion group(CO group),suspension-reperfusion group(SR group),suspension-reper-fusion+levosimendan group(SR-L group),and suspension-reperfusion+levosimendan+STING activator:DMXAA group(SR-LD group).Heart rate(HR),left ventricular end-diastolic pressure(LVEDP),left ventricular develop-mental pressure(LVDP),maximum rate of increase in left ventricular pressure(+dp/dtmax)and maximum rate of decrease in left ventricular pressure(-dp/dtmax),and Reperfusion Arrhythmia scores were recorded at the end of equilibrium perfusion(T0),30 min of reperfusion(T1),and 60 min of reperfusion(T2)respectively.Western blot was used to detect cGAS-STING signaling pathway and autophagy-related protein expression.The size of myocardial infarction was measured by using triphenyl tetrazolium chloride(TTC).Results Compared with CO group,SR group had decreased HR,LVDP,+dp/dtmax,and-dp/dtmax at T1 and T2,increase of LVEDP,Reperfusion Arrhythmia score,percentage of myocardial infarcted area,expression of myocardial tissue cGAS and STING pro-teins and increased LC3 Ⅱ/Ⅰratio,while p62 decreased(P<0.05);compared with SR group,SR-L group car-diac function indexes improved,myocardial tissue cGAS,STING protein expression was down-regulated,LC3 Ⅱ/Ⅰ ratio was decreased,and p62 was elevated(P<0.05);SR-LD group reversed the improvement of myocardial injury by levosimendan compared with SR-L.Conclusions Levosimendan may protect myocardial tissue by inhibi-ting the cGAS-STING signaling pathway,down-regulating cardiomyocyte autophagy and reducing myocardial infarc-tion size,so to improve cardial function.

Objective To analyze the literatures about artificial intelligence in cancer research in Web of Science (WOS) core collection database in 2010-2019 and summarize research hot spots and development trends. Methods Through bibliometrics methods and CiteSpace information visualization software, we applied the visual analysis of relevant literature on artificial intelligence in the field of cancer research retrieved from the Web of Science core collection database from 2010 to 2019. Results The number of published articles about artificial intelligence in the field of cancer research had been increasing year by year. The United States ranked first in the number of published articles in this field, the number of citations and cooperation capabilities. Although the number of published articles in China ranked the second, the number of citations was low. The hot spots of artificial intelligence in cancer research were mainly breast cancer and lung cancer. Machine learning, neural network and other methods were used to build models, which were used in basic cancer research, clinical diagnosis, treatment and prognosis prediction. The research frontiers were the methodological research of artificial intelligence, the research on the occurrence and classification of cancer and the research of protein in this field. Conclusion It will effectively promote the development of artificial intelligence in cancer research in China by learning the hot spots and cutting-edge technologies of international research, focusing on international cooperation and cooperation among national institutions and strengthening cross-disciplinary research.

Objective:To systematically understand the global research progress in the construction and validation of lung cancer risk prediction models.Methods:"lung neoplasms" , "lung cancer" , "lung carcinoma" , "lung tumor" , "risk" , "malignancy" , "carcinogenesis" , "prediction" , "assessment" , "model" , "tool" , "score" , "paradigm" , and "algorithm" were used as search keywords. Original articles were systematically searched from Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, Cochrane, and Web of Science) published prior to December 2018. The language of studies was restricted to Chinese and English. The inclusion criteria were human oriented studies with complete information for model development, validation and evaluation. The exclusion criteria were informal publications such as conference abstracts, Chinese dissertation papers, and research materials such as reviews, letters, and news reports. A total of 33 papers involving 27 models were included. The population characteristics of all included studies, study design, predicting factors and the performance of models were analyzed and compared.Results:Among 27 models, the number of American-based, European-based and Asian-based model studies was 12, 6 and 9, respectively. In addition, there were 6 Chinese-based model studies. According to the factors fitted into the models, these studies could be divided into traditional epidemiological models (11 studies), clinical index models (6 studies), and genetic index models (10 studies). 15 models were not validated after construction or were cross-validated only in the internal population, and the extrapolation effect of models was not effectively evaluated; 8 models were validated in single external population; only 4 models were verified in multiple external populations (3-7); the area under the curve (AUC) of models ranged from 0.57 to 0.90.Conclusion:Research on risk prediction models for lung cancer is in development stage. In addition to the lack of external validation of existing models, the exploration of potential clinical indicators was also limited.

Objective:To investigate the association between metabolic syndrome (MS) components and renal cell cancer in Chinese males.Methods:All male employees and retirees of the Kailuan Group were recruited in the Chinese Kailuan Male Cohort Study. They had been experienced routine physical examinations ever two years since May 2006. A total of 104 274 males were prospectively observed by 31 December 2015. Information on demographics, height, weight, blood glucose, blood lipid, blood pressure, as well as the information of incident renal cell cancer cases were collected at the baseline investigation by questionnaire, physical measurement and laboratory test. Cox proportional hazards regression models were used to evaluate the association between baseline MS and MS components (body mass index, blood glucose, blood lipid, blood pressure) and the risk of renal cell cancer in males.Results:A total of 104 274 males were recruited in our study with a age of (51.21±13.46) years, with 823 892.96 person-years follow-up and the median follow-up time was 8.88 years. A total of 131 new renal cell cancer cases were identified in the Kailuan male cohort study, and the crude incidence density was 15.90 per 100,000 person-years. Compared with no MS, the hazard ratios ( HR) (95% CI) of MS was 1.97 (1.32-2.94).When compared with normal level, the HR (95% CI) of obesity or overweight, hypertension, and dyslipidemia was 1.49 (1.04-2.14), 1.56 (1.06-2.29), and 1.77(1.23-2.54), after adjusting for potential confounding factors (i.e., age, education, income, smoke, and alcohol drink), respectively. In addition, a statistically significant trend ( P for trend<0.001) of increased renal cell cancer risk with an increasing number of abnormal MS components was observed. Conclusion:Obesity or overweight, hypertension, dyslipidemia and MS may increase the risk of renal cell cancer for Chinese males.

Objective:To investigate the association between total cholesterol (TC) and primary liver cancer in Chinese males.Methods:Since May 2006, all the male workers, including the employees and the retirees in Kailuan Group were recruited in the Kailuan male dynamic cohort study. Information about demographics, medical history and TC levels was collected at the baseline interview, as well as information on newly-diagnosed primary liver cancer cases during the follow-up period. A total of 110 612 males were recruited in the cohort by 31 December 2015. TC levels were divided into four categories by quartile (<4.27, 4.27-4.90, 4.90-5.56 and ≥5.56 mmol/L), with the first quartile group serving as the referent category. Cox proportional hazards regression model was used to evaluate the association between TC levels and primary liver cancer risk.Results:By December 31, 2015, a follow-up of 861 711.45 person-years was made with a median follow-up period of 8.83 years. During the follow-up, 355 primary liver cancer cases were identified. Compared with the first quartile, the HR of incident primary liver cancer among participants with the second, third and highest quartile TC levels were 0.76 (95% CI: 0.58-1.01), 0.59 (95% CI: 0.43-0.79), and 0.36 (95% CI: 0.25-0.52), respectively after adjusting for age, educational level, income level, smoking status, drinking status, body mass index, and HBsAg status ( P for trend<0.001). Subgroup analyses found that the association between TC levels and primary liver cancer was robust (all P for trend<0.05). The results didn’t change significantly after exclusion of newly-diagnosed cases within the first 2 years, males with history of cirrhosis or subjects who took antihyperlipidemic drugs, participants with higher TC levels had a lower risk of primary liver cancer (all P for trend<0.05) and HR(95% CI) of incident primary liver cancer among participants with the highest quartile TC levels were 0.41 (0.28-0.61), 0.36 (0.25-0.53) and 0.38 (0.26-0.54), respectively. Conslusion:In this large prospective study, we found that baseline TC levels were inversely associated with primary liver cancer risk, and low TC level might increase the risk of primary liver cancer.

Objective:To develop a lung cancer risk prediction model for female non-smokers.Methods:Based on the Kailuan prospective dynamic cohort (2006.05-2015.12), a nested case-control study was conducted. Participants diagnosed with primary pathologically confirmed lung cancer during follow-up were identified as the case group, and others were identified as the control group. A total of 24 701 subjects were included in the study, including 86 lung cancer cases and 24 615 control population, respectively. Questionnaires, physical examinations, and laboratory tests were conducted to collect relevant information. Multivariable-adjusted logistic regressions were conducted to develop a lung cancer risk prediction model. Area Under the Curve (AUC) and Hosmer-Lemeshow tests were used to evaluate discrimination and calibration, respectively. Ten-fold cross-validation was used for internal validation.Results:Two sets of models were developed: the simple model (including age and monthly income) and the metabolic index model [including age, monthly income, fasting blood glucose (FBG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C)].The AUC (95%CI) [0.745 (0.719-0.771)] of the metabolic index model was higher than that of the simple prediction model [0.688 (0.660-0.716)] ( P=0.004). Both the simple model ( PHL=0.287) and the metabolic index model ( PHL=0.134) were well-calibrated. The results of ten-fold cross-validation indicated sufficient stability, with an average AUC of 0.699 and a standard error (SD) of 0.010. Conclusion:By incorporating metabolic markers, accurate and reliable lung cancer risk prediction model for female non smokers could be developed.

Objective:To systematically understand the global research progress in the construction and validation of lung cancer risk prediction models.Methods:"lung neoplasms" , "lung cancer" , "lung carcinoma" , "lung tumor" , "risk" , "malignancy" , "carcinogenesis" , "prediction" , "assessment" , "model" , "tool" , "score" , "paradigm" , and "algorithm" were used as search keywords. Original articles were systematically searched from Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, Cochrane, and Web of Science) published prior to December 2018. The language of studies was restricted to Chinese and English. The inclusion criteria were human oriented studies with complete information for model development, validation and evaluation. The exclusion criteria were informal publications such as conference abstracts, Chinese dissertation papers, and research materials such as reviews, letters, and news reports. A total of 33 papers involving 27 models were included. The population characteristics of all included studies, study design, predicting factors and the performance of models were analyzed and compared.Results:Among 27 models, the number of American-based, European-based and Asian-based model studies was 12, 6 and 9, respectively. In addition, there were 6 Chinese-based model studies. According to the factors fitted into the models, these studies could be divided into traditional epidemiological models (11 studies), clinical index models (6 studies), and genetic index models (10 studies). 15 models were not validated after construction or were cross-validated only in the internal population, and the extrapolation effect of models was not effectively evaluated; 8 models were validated in single external population; only 4 models were verified in multiple external populations (3-7); the area under the curve (AUC) of models ranged from 0.57 to 0.90.Conclusion:Research on risk prediction models for lung cancer is in development stage. In addition to the lack of external validation of existing models, the exploration of potential clinical indicators was also limited.

Objective:To investigate the association between metabolic syndrome (MS) components and renal cell cancer in Chinese males.Methods:All male employees and retirees of the Kailuan Group were recruited in the Chinese Kailuan Male Cohort Study. They had been experienced routine physical examinations ever two years since May 2006. A total of 104 274 males were prospectively observed by 31 December 2015. Information on demographics, height, weight, blood glucose, blood lipid, blood pressure, as well as the information of incident renal cell cancer cases were collected at the baseline investigation by questionnaire, physical measurement and laboratory test. Cox proportional hazards regression models were used to evaluate the association between baseline MS and MS components (body mass index, blood glucose, blood lipid, blood pressure) and the risk of renal cell cancer in males.Results:A total of 104 274 males were recruited in our study with a age of (51.21±13.46) years, with 823 892.96 person-years follow-up and the median follow-up time was 8.88 years. A total of 131 new renal cell cancer cases were identified in the Kailuan male cohort study, and the crude incidence density was 15.90 per 100,000 person-years. Compared with no MS, the hazard ratios ( HR) (95% CI) of MS was 1.97 (1.32-2.94).When compared with normal level, the HR (95% CI) of obesity or overweight, hypertension, and dyslipidemia was 1.49 (1.04-2.14), 1.56 (1.06-2.29), and 1.77(1.23-2.54), after adjusting for potential confounding factors (i.e., age, education, income, smoke, and alcohol drink), respectively. In addition, a statistically significant trend ( P for trend<0.001) of increased renal cell cancer risk with an increasing number of abnormal MS components was observed. Conclusion:Obesity or overweight, hypertension, dyslipidemia and MS may increase the risk of renal cell cancer for Chinese males.

Objective:To investigate the association between total cholesterol (TC) and primary liver cancer in Chinese males.Methods:Since May 2006, all the male workers, including the employees and the retirees in Kailuan Group were recruited in the Kailuan male dynamic cohort study. Information about demographics, medical history and TC levels was collected at the baseline interview, as well as information on newly-diagnosed primary liver cancer cases during the follow-up period. A total of 110 612 males were recruited in the cohort by 31 December 2015. TC levels were divided into four categories by quartile (<4.27, 4.27-4.90, 4.90-5.56 and ≥5.56 mmol/L), with the first quartile group serving as the referent category. Cox proportional hazards regression model was used to evaluate the association between TC levels and primary liver cancer risk.Results:By December 31, 2015, a follow-up of 861 711.45 person-years was made with a median follow-up period of 8.83 years. During the follow-up, 355 primary liver cancer cases were identified. Compared with the first quartile, the HR of incident primary liver cancer among participants with the second, third and highest quartile TC levels were 0.76 (95% CI: 0.58-1.01), 0.59 (95% CI: 0.43-0.79), and 0.36 (95% CI: 0.25-0.52), respectively after adjusting for age, educational level, income level, smoking status, drinking status, body mass index, and HBsAg status ( P for trend<0.001). Subgroup analyses found that the association between TC levels and primary liver cancer was robust (all P for trend<0.05). The results didn’t change significantly after exclusion of newly-diagnosed cases within the first 2 years, males with history of cirrhosis or subjects who took antihyperlipidemic drugs, participants with higher TC levels had a lower risk of primary liver cancer (all P for trend<0.05) and HR(95% CI) of incident primary liver cancer among participants with the highest quartile TC levels were 0.41 (0.28-0.61), 0.36 (0.25-0.53) and 0.38 (0.26-0.54), respectively. Conslusion:In this large prospective study, we found that baseline TC levels were inversely associated with primary liver cancer risk, and low TC level might increase the risk of primary liver cancer.

Objective:To develop a lung cancer risk prediction model for female non-smokers.Methods:Based on the Kailuan prospective dynamic cohort (2006.05-2015.12), a nested case-control study was conducted. Participants diagnosed with primary pathologically confirmed lung cancer during follow-up were identified as the case group, and others were identified as the control group. A total of 24 701 subjects were included in the study, including 86 lung cancer cases and 24 615 control population, respectively. Questionnaires, physical examinations, and laboratory tests were conducted to collect relevant information. Multivariable-adjusted logistic regressions were conducted to develop a lung cancer risk prediction model. Area Under the Curve (AUC) and Hosmer-Lemeshow tests were used to evaluate discrimination and calibration, respectively. Ten-fold cross-validation was used for internal validation.Results:Two sets of models were developed: the simple model (including age and monthly income) and the metabolic index model [including age, monthly income, fasting blood glucose (FBG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C)].The AUC (95%CI) [0.745 (0.719-0.771)] of the metabolic index model was higher than that of the simple prediction model [0.688 (0.660-0.716)] ( P=0.004). Both the simple model ( PHL=0.287) and the metabolic index model ( PHL=0.134) were well-calibrated. The results of ten-fold cross-validation indicated sufficient stability, with an average AUC of 0.699 and a standard error (SD) of 0.010. Conclusion:By incorporating metabolic markers, accurate and reliable lung cancer risk prediction model for female non smokers could be developed.