Early insulin therapy is capable to achieve glycemic control and restoreβ-cell function in newly diagnosed type 2 diabetes(T2D),but its effect on cardiovascular outcomes in these patients remains unclear.In this nati...Early insulin therapy is capable to achieve glycemic control and restoreβ-cell function in newly diagnosed type 2 diabetes(T2D),but its effect on cardiovascular outcomes in these patients remains unclear.In this nationwide real-world study,we analyzed electronic health record data from 19 medical centers across China between 1 January 2000,and 26 May 2022.We included 5424 eligible patients(mean age 56 years,2176 women/3248 men)who were diagnosed T2D within six months and did not have prior cardiovascular disease.Multivariable Cox regression models were used to estimate the associations of early insulin therapy(defined as the first-line therapy for at least two weeks in newly diagnosed T2D patients)with the incidence of major cardiovascular events including coronary heart disease(CHD),stroke,and hospitalization for heart failure(HF).During 17,158 persons years of observation,we documented 834 incident CHD cases,719 stroke cases,and 230 hospitalized cases for HF.Newly diagnosed T2D patients who received early insulin therapy,compared with those who did not receive such treatment,had 31%lower risk of incident stroke,and 28%lower risk of hospitalization for HF.No significant difference in the risk of CHD was observed.We found similar results when repeating the aforesaid analysis in a propensity-score matched population of 4578 patients and with inverse probability of treatment weighting models.These findings suggest that early insulin therapy in newly diagnosed T2D may have cardiovascular benefits by reducing the risk of incident stroke and hospitalization for HF.展开更多
With the progress and development of computer technology,applying machine learning methods to cancer research has become an important research field.To analyze the most recent research status and trends,main research ...With the progress and development of computer technology,applying machine learning methods to cancer research has become an important research field.To analyze the most recent research status and trends,main research topics,topic evolutions,research collaborations,and potential directions of this research field,this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods.Python is used as a tool for bibliometric analysis,Gephi is used for social network analysis,and the Latent Dirichlet Allocation model is used for topic modeling.The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts.In terms of journals,Nature Communications is the most influential journal and Scientific Reports is the most prolific one.The United States and Harvard University have contributed the most to cancer research using machine learning methods.As for the research topic,“Support Vector Machine,”“classification,”and“deep learning”have been the core focuses of the research field.Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods,as well as to have a deeper understanding of research hotspots.展开更多
To the Editor: Genetic diseases contribute to 35% of deaths during the first year of life and are a significant cause of intensive care.[1] A previous study based on the China Neonatal Genomes Project investigated the...To the Editor: Genetic diseases contribute to 35% of deaths during the first year of life and are a significant cause of intensive care.[1] A previous study based on the China Neonatal Genomes Project investigated the genetic causes of early infant deaths and found that >25% of deceased neonates with genetic diagnoses can be cured if diagnosed in time.[2] Therefore, it is crucial to target and diagnose neonates with genetic diseases as early as possible. According to our experience, the typical phenotypes, such as special facial features or multiple congenital anomalies (MCAs), indicate a high risk of genetic disease and lead physicians to perform genetic testing in neonates as early as possible. However, in practice, infants without typical phenotypes typically undergo a long and costly diagnostic process before genetic diagnoses are confirmed. Moreover, a recent survey by the American College of Medical Genetics and Genomics (ACMG) and other national professional organizations indicated that there are insufficient numbers of qualified geneticists to fulfil genetic service needs.[3] The ACMG published the general clinical features for genetic testing indications. For example, patients with phenotypes or family history data that strongly implicate a genetic cause may undergo genetic testing.[1] However, the study indicated that many genetic conditions arise de novo or are inherited with no family history.[1] A previous study attempted to apply the non-phenotype-driven panel approach in neonates admitted to the neonate intensive care unit (NICU).[4] However, at present, the diagnostic yield is only 3.45% (1/29).[4] In addition, the economic and ethical issues associated with genomic screening remain challenging. Therefore, the available indications for genetic testing may improve the management of genetic diseases.展开更多
Background:Most patients with advanced non-small cell lung cancer(NSCLC)have a poor prognosis.Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum tr...Background:Most patients with advanced non-small cell lung cancer(NSCLC)have a poor prognosis.Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum treatment plan.We compared the performance of nomograms with machine-learning models at predicting the overall survival of NSCLC patients.This comparison benefits the development and selection of models during the clinical decision-making process for NSCLC patients.Methods:Multiple machine-learning models were used in a retrospective cohort of 6586 patients.First,we modeled and validated a nomogram to predict the overall survival of NSCLC patients.Subsequently,five machine-learning models(logistic regression,random forest,XGBoost,decision tree,and light gradient boosting machine)were used to predict survival status.Next,we evaluated the performance of the models.Finally,the machine-learning model with the highest accuracy was chosen for comparison with the nomogram at predicting survival status by observing a novel performance measure:time-dependent prediction accuracy.Results:Among the five machine-learning models,the accuracy of random forest model outperformed the others.Compared with the nomogram for time-dependent prediction accuracy with a follow-up time ranging from 12 to 60 months,the prediction accuracies of both the nomogram and machinelearning models changed as time varied.The nomogram reached a maximum prediction accuracy of 0.85 in the 60th month,and the random forest algorithm reached a maximum prediction accuracy of 0.74 in the 13th month.Conclusions:Overall,the nomogram provided more reliable prognostic assessments of NSCLC patients than machine-learning models over our observation period.Although machine-learning methods have been widely adopted for predicting clinical prognoses in recent studies,the conventional nomogram was competitive.In real clinical applications,a comprehensive model that combines these two methods may demonstrate superior capabilities.展开更多
Cancer informatics has significantly progressed in the big data era.We summarize the application of informatics approaches to the cancer domain from both the informatics perspective(e.g.,data management and data scien...Cancer informatics has significantly progressed in the big data era.We summarize the application of informatics approaches to the cancer domain from both the informatics perspective(e.g.,data management and data science)and the clinical perspective(e.g.,cancer screening,risk assessment,diagnosis,treatment,and prognosis).We discuss various informatics methods and tools that are widely applied in cancer research and practices,such as cancer databases,data standards,terminologies,high‐throughput omics data mining,machine‐learning algorithms,artificial intelligence imaging,and intelligent radiation.We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes,and focus on how informatics can provide opportunities for cancer research and practices.Finally,we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices.It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics‐specific insights.展开更多
基金the National Key R&D Program of China(grant No.2021YFC2500501)the Strategic Priority Research Program of Chinese Academy of Sciences(grant no.XDB38010100)+4 种基金the National Natural Science Foundation of China(grant no.82030022 and 82330020)Program for Innovative Research Team of The First Affiliated Hospital of USTC(grant no.CXGG02)the Program of Introducing Talents of Discipline to Universities,111 Plan(grant no.D18005)Guangdong Provincial Clinical Research Center for Kidney Disease(2020B1111170013)Key Technologies R&D Program of Guangdong Province(grant no.2023B1111030004).
文摘Early insulin therapy is capable to achieve glycemic control and restoreβ-cell function in newly diagnosed type 2 diabetes(T2D),but its effect on cardiovascular outcomes in these patients remains unclear.In this nationwide real-world study,we analyzed electronic health record data from 19 medical centers across China between 1 January 2000,and 26 May 2022.We included 5424 eligible patients(mean age 56 years,2176 women/3248 men)who were diagnosed T2D within six months and did not have prior cardiovascular disease.Multivariable Cox regression models were used to estimate the associations of early insulin therapy(defined as the first-line therapy for at least two weeks in newly diagnosed T2D patients)with the incidence of major cardiovascular events including coronary heart disease(CHD),stroke,and hospitalization for heart failure(HF).During 17,158 persons years of observation,we documented 834 incident CHD cases,719 stroke cases,and 230 hospitalized cases for HF.Newly diagnosed T2D patients who received early insulin therapy,compared with those who did not receive such treatment,had 31%lower risk of incident stroke,and 28%lower risk of hospitalization for HF.No significant difference in the risk of CHD was observed.We found similar results when repeating the aforesaid analysis in a propensity-score matched population of 4578 patients and with inverse probability of treatment weighting models.These findings suggest that early insulin therapy in newly diagnosed T2D may have cardiovascular benefits by reducing the risk of incident stroke and hospitalization for HF.
基金Natural Science Foundation of Guangdong Province,Grant/Award Number:2021A1515011339。
文摘With the progress and development of computer technology,applying machine learning methods to cancer research has become an important research field.To analyze the most recent research status and trends,main research topics,topic evolutions,research collaborations,and potential directions of this research field,this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods.Python is used as a tool for bibliometric analysis,Gephi is used for social network analysis,and the Latent Dirichlet Allocation model is used for topic modeling.The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts.In terms of journals,Nature Communications is the most influential journal and Scientific Reports is the most prolific one.The United States and Harvard University have contributed the most to cancer research using machine learning methods.As for the research topic,“Support Vector Machine,”“classification,”and“deep learning”have been the core focuses of the research field.Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods,as well as to have a deeper understanding of research hotspots.
基金Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01)。
文摘To the Editor: Genetic diseases contribute to 35% of deaths during the first year of life and are a significant cause of intensive care.[1] A previous study based on the China Neonatal Genomes Project investigated the genetic causes of early infant deaths and found that >25% of deceased neonates with genetic diagnoses can be cured if diagnosed in time.[2] Therefore, it is crucial to target and diagnose neonates with genetic diseases as early as possible. According to our experience, the typical phenotypes, such as special facial features or multiple congenital anomalies (MCAs), indicate a high risk of genetic disease and lead physicians to perform genetic testing in neonates as early as possible. However, in practice, infants without typical phenotypes typically undergo a long and costly diagnostic process before genetic diagnoses are confirmed. Moreover, a recent survey by the American College of Medical Genetics and Genomics (ACMG) and other national professional organizations indicated that there are insufficient numbers of qualified geneticists to fulfil genetic service needs.[3] The ACMG published the general clinical features for genetic testing indications. For example, patients with phenotypes or family history data that strongly implicate a genetic cause may undergo genetic testing.[1] However, the study indicated that many genetic conditions arise de novo or are inherited with no family history.[1] A previous study attempted to apply the non-phenotype-driven panel approach in neonates admitted to the neonate intensive care unit (NICU).[4] However, at present, the diagnostic yield is only 3.45% (1/29).[4] In addition, the economic and ethical issues associated with genomic screening remain challenging. Therefore, the available indications for genetic testing may improve the management of genetic diseases.
基金Novel Coronavirus Infection and Prevention Emergency Scientific Research Special Project of the Chongqing Municipal Education Commission,China,Grant/Award Number:CQEO[2020]no.13Chongqing Performance Incentive and Guidance Project for Scientific Research Institutions,Grant/Award Number:cstc2020jxjl130016+1 种基金Chongqing Key Disease Prevention and Control Technology Project,Grant/Award Number:2019ZX002Chongqing Technology Innovation and Application Development Project,Grant/Award Number:cstc2019jscxfxydX0008。
文摘Background:Most patients with advanced non-small cell lung cancer(NSCLC)have a poor prognosis.Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum treatment plan.We compared the performance of nomograms with machine-learning models at predicting the overall survival of NSCLC patients.This comparison benefits the development and selection of models during the clinical decision-making process for NSCLC patients.Methods:Multiple machine-learning models were used in a retrospective cohort of 6586 patients.First,we modeled and validated a nomogram to predict the overall survival of NSCLC patients.Subsequently,five machine-learning models(logistic regression,random forest,XGBoost,decision tree,and light gradient boosting machine)were used to predict survival status.Next,we evaluated the performance of the models.Finally,the machine-learning model with the highest accuracy was chosen for comparison with the nomogram at predicting survival status by observing a novel performance measure:time-dependent prediction accuracy.Results:Among the five machine-learning models,the accuracy of random forest model outperformed the others.Compared with the nomogram for time-dependent prediction accuracy with a follow-up time ranging from 12 to 60 months,the prediction accuracies of both the nomogram and machinelearning models changed as time varied.The nomogram reached a maximum prediction accuracy of 0.85 in the 60th month,and the random forest algorithm reached a maximum prediction accuracy of 0.74 in the 13th month.Conclusions:Overall,the nomogram provided more reliable prognostic assessments of NSCLC patients than machine-learning models over our observation period.Although machine-learning methods have been widely adopted for predicting clinical prognoses in recent studies,the conventional nomogram was competitive.In real clinical applications,a comprehensive model that combines these two methods may demonstrate superior capabilities.
基金National Key Research&Development Program of China。
文摘Cancer informatics has significantly progressed in the big data era.We summarize the application of informatics approaches to the cancer domain from both the informatics perspective(e.g.,data management and data science)and the clinical perspective(e.g.,cancer screening,risk assessment,diagnosis,treatment,and prognosis).We discuss various informatics methods and tools that are widely applied in cancer research and practices,such as cancer databases,data standards,terminologies,high‐throughput omics data mining,machine‐learning algorithms,artificial intelligence imaging,and intelligent radiation.We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes,and focus on how informatics can provide opportunities for cancer research and practices.Finally,we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices.It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics‐specific insights.
