Background:Prenatal evaluation of fetal lung maturity(FLM)is a challenge,and an effective non-invasive method for prenatal assessment of FLM is needed.The study aimed to establish a normal fetal lung gestational age(G...Background:Prenatal evaluation of fetal lung maturity(FLM)is a challenge,and an effective non-invasive method for prenatal assessment of FLM is needed.The study aimed to establish a normal fetal lung gestational age(GA)grading model based on deep learning(DL)algorithms,validate the effectiveness of the model,and explore the potential value of DL algorithms in assessing FLM.Methods:A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41+6 weeks were analyzed in this study.There were no pregnancy-related complications that affected fetal lung development,and all infants were born without neonatal respiratory diseases.The images were divided into three classes based on the gestational week:class I:20 to 29+6 weeks,class II:30 to 36+6 weeks,and class III:37 to 41+6 weeks.There were 3323,2142,and 1548 images in each class,respectively.First,we performed a pre-processing algorithm to remove irrelevant information from each image.Then,a convolutional neural network was designed to identify different categories of fetal lung ultrasound images.Finally,we used ten-fold cross-validation to validate the performance of our model.This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA.This was used to establish a grading model.The performance of the grading model was assessed using accuracy,sensitivity,specificity,and receiver operating characteristic curves.Results:A normal fetal lung GA grading model was established and validated.The sensitivity of each class in the independent test set was 91.7%,69.8%,and 86.4%,respectively.The specificity of each class in the independent test set was 76.8%,90.0%,and 83.1%,respectively.The total accuracy was 83.8%.The area under the curve(AUC)of each class was 0.982,0.907,and 0.960,respectively.The micro-average AUC was 0.957,and the macro-average AUC was 0.949.Conclusions:The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs,which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy.The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.展开更多
From the process of sedimentation the mathematical relationships among deposition volume and powder properties as well as sedimentation parameters were deduced. Based on the formula a mathematical model was set up and...From the process of sedimentation the mathematical relationships among deposition volume and powder properties as well as sedimentation parameters were deduced. Based on the formula a mathematical model was set up and simulated through the computer. At last the validity of mathematical model was supported by the representative experiment on Ti-Mo system FGM prepared by co-sedimentation.展开更多
BACKGROUND Gastrointestinal stromal tumors(GISTs)are the most common mesenchymal tumors of the gastrointestinal tract.Tyrosine kinase inhibitors,such as imatinib,have been used as first-line therapy for the treatment ...BACKGROUND Gastrointestinal stromal tumors(GISTs)are the most common mesenchymal tumors of the gastrointestinal tract.Tyrosine kinase inhibitors,such as imatinib,have been used as first-line therapy for the treatment of GISTs.Although these drugs have achieved considerable efficacy in some patients,reports of resistance and recurrence have emerged.Extracellular signal-regulated kinase 1/2(ERK1/2)protein,as a member of the mitogen-activated protein kinase(MAPK)family,is a core molecule of this signaling pathway.Nowadays,research reports on the important clinical and prognostic value of phosphorylated-ERK(P-ERK)and phosphorylated-MAPK/ERK kinase(P-MEK)proteins closely related to raf kinase inhibitor protein(RKIP)have gradually emerged in digestive tract tumors such as gastric cancer,colon cancer,and pancreatic cancer.However,literature on the expression of these downstream proteins combined with RKIP in GIST is scarce.This study will focus on this aspect and search for answers to the problem.AIM To detect the expression of RKIP,P-ERK,and P-MEK protein in GIST and to analyze their relationship with clinicopathological characteristics and prognosis of this disease.Try to establish a new prognosis evaluation model using RKIP and PERK in combination with analysis and its prognosis evaluation efficacy.METHODS The research object of our experiment was 66 pathologically diagnosed GIST patients with complete clinical and follow-up information.These patients received surgical treatment at China Medical University Affiliated Hospital from January 2015 to January 2020.Immunohistochemical method was used to detect the expression of RKIP,PERK,and P-MEK proteins in GIST tissue samples from these patients.Kaplan-Meier method was used to calculate the survival rate of 63 patients with complete follow-up data.A Nomogram was used to represent the new prognostic evaluation model.The Cox multivariate regression analysis was conducted separately for each set of risk evaluation factors,based on two risk classification systems[the new risk grade model vs the modified National Institutes of Health(NIH)2008 risk classification system].Receiver operating characteristic(ROC)curves were used for evaluating the accuracy and efficiency of the two prognostic evaluation systems.RESULTS In GIST tissues,RKIP protein showed positive expression in the cytoplasm and cell membrane,appearing as brownish-yellow or brown granules.The expression of RKIP was related to GIST tumor size,NIH grade,and mucosal invasion.P-ERK protein exhibited heterogeneous distribution in GIST cells,mainly in the cytoplasm,with occasional presence in the nucleus,and appeared as brownish-yellow granules,and the expression of P-ERK protein was associated with GIST tumor size,mitotic count,mucosal invasion,and NIH grade.Meanwhile,RKIP protein expression was negatively correlated with P-ERK expression.The results in COX multivariate regression analysis showed that RKIP protein expression was not an independent risk factor for tumor prognosis.However,RKIP combined with P-ERK protein expression were identified as independent risk factors for prognosis with statistical significance.Furthermore,we establish a new prognosis evaluation model using RKIP and P-ERK in combination and obtained the nomogram of the new prognosis evaluation model.ROC curve analysis also showed that the new evaluation model had better prognostic performance than the modified NIH 2008 risk classification system.CONCLUSION Our experimental results showed that the expression of RKIP and P-ERK proteins in GIST was associated with tumor size,NIH 2008 staging,and tumor invasion,and P-ERK expression was also related to mitotic count.The expression of the two proteins had a certain negative correlation.The combined expression of RKIP and P-ERK proteins can serve as an independent risk factor for predicting the prognosis of GIST patients.The new risk assessment model incorporating RKIP and P-ERK has superior evaluation efficacy and is worth further practical application to validate.展开更多
Future quantitative assessments will be expected to estimate quantities, values, and locations of undiscovered mineral resources in a form that conveys both economic viability and uncertainty associated with the resou...Future quantitative assessments will be expected to estimate quantities, values, and locations of undiscovered mineral resources in a form that conveys both economic viability and uncertainty associated with the resources. Historically, declining metal prices point to the need for larger deposits over time. Sensitivity analysis demonstrates that the greatest opportunity for reducing uncertainty in assessments lies in lowering uncertainty associated with tonnage estimates. Of all errors possible in assessments, these affecting tonnage estimates are by far the most important. Selecting the correct deposit model is the most important way of controlling errors because of the dominance of tonnage-deposit models are the best known predictors of tonnage. Much of the surface is covered with apparently barren rocks and sediments in many large regions. Because many exposed mineral deposits are believed to have been found, a prime concern is the presence of possible mineralized rock under cover. Assessments of areas with resources under cover must rely on extrapolation from surrounding areas, new geologic maps of rocks under cover, or analogy with other well-explored areas that can be considered training tracts. Cover has a profound effect on uncertainty and on methods and procedures of assessments because geology is seldom known and geophysical methods typically have attenuated responses. Many earlier assessment methods were based on relationships of geochemical and geophysical variables to deposits learned from deposits exposed on the surface-these will need to be relearned based on covered deposits. Mineral-deposit models are important in quantitative resource assessments for two reasons: (1) grades and tonnages of most deposit types are significantly different, and (2) deposit types are present in different geologic settings that can be identified from geologic maps. Mineral-deposit models are the keystone in combining the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Grade and tonnage models and development of quantitative descriptive, economic, and deposit density models will help reduce the uncertainty of these new assessments.展开更多
基金a grant from the National Key Research and Development Program of China(No.2016YFC1000104).
文摘Background:Prenatal evaluation of fetal lung maturity(FLM)is a challenge,and an effective non-invasive method for prenatal assessment of FLM is needed.The study aimed to establish a normal fetal lung gestational age(GA)grading model based on deep learning(DL)algorithms,validate the effectiveness of the model,and explore the potential value of DL algorithms in assessing FLM.Methods:A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41+6 weeks were analyzed in this study.There were no pregnancy-related complications that affected fetal lung development,and all infants were born without neonatal respiratory diseases.The images were divided into three classes based on the gestational week:class I:20 to 29+6 weeks,class II:30 to 36+6 weeks,and class III:37 to 41+6 weeks.There were 3323,2142,and 1548 images in each class,respectively.First,we performed a pre-processing algorithm to remove irrelevant information from each image.Then,a convolutional neural network was designed to identify different categories of fetal lung ultrasound images.Finally,we used ten-fold cross-validation to validate the performance of our model.This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA.This was used to establish a grading model.The performance of the grading model was assessed using accuracy,sensitivity,specificity,and receiver operating characteristic curves.Results:A normal fetal lung GA grading model was established and validated.The sensitivity of each class in the independent test set was 91.7%,69.8%,and 86.4%,respectively.The specificity of each class in the independent test set was 76.8%,90.0%,and 83.1%,respectively.The total accuracy was 83.8%.The area under the curve(AUC)of each class was 0.982,0.907,and 0.960,respectively.The micro-average AUC was 0.957,and the macro-average AUC was 0.949.Conclusions:The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs,which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy.The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.
文摘From the process of sedimentation the mathematical relationships among deposition volume and powder properties as well as sedimentation parameters were deduced. Based on the formula a mathematical model was set up and simulated through the computer. At last the validity of mathematical model was supported by the representative experiment on Ti-Mo system FGM prepared by co-sedimentation.
基金Natural Science Foundation of Liaoning Province,No.2020-MS-148。
文摘BACKGROUND Gastrointestinal stromal tumors(GISTs)are the most common mesenchymal tumors of the gastrointestinal tract.Tyrosine kinase inhibitors,such as imatinib,have been used as first-line therapy for the treatment of GISTs.Although these drugs have achieved considerable efficacy in some patients,reports of resistance and recurrence have emerged.Extracellular signal-regulated kinase 1/2(ERK1/2)protein,as a member of the mitogen-activated protein kinase(MAPK)family,is a core molecule of this signaling pathway.Nowadays,research reports on the important clinical and prognostic value of phosphorylated-ERK(P-ERK)and phosphorylated-MAPK/ERK kinase(P-MEK)proteins closely related to raf kinase inhibitor protein(RKIP)have gradually emerged in digestive tract tumors such as gastric cancer,colon cancer,and pancreatic cancer.However,literature on the expression of these downstream proteins combined with RKIP in GIST is scarce.This study will focus on this aspect and search for answers to the problem.AIM To detect the expression of RKIP,P-ERK,and P-MEK protein in GIST and to analyze their relationship with clinicopathological characteristics and prognosis of this disease.Try to establish a new prognosis evaluation model using RKIP and PERK in combination with analysis and its prognosis evaluation efficacy.METHODS The research object of our experiment was 66 pathologically diagnosed GIST patients with complete clinical and follow-up information.These patients received surgical treatment at China Medical University Affiliated Hospital from January 2015 to January 2020.Immunohistochemical method was used to detect the expression of RKIP,PERK,and P-MEK proteins in GIST tissue samples from these patients.Kaplan-Meier method was used to calculate the survival rate of 63 patients with complete follow-up data.A Nomogram was used to represent the new prognostic evaluation model.The Cox multivariate regression analysis was conducted separately for each set of risk evaluation factors,based on two risk classification systems[the new risk grade model vs the modified National Institutes of Health(NIH)2008 risk classification system].Receiver operating characteristic(ROC)curves were used for evaluating the accuracy and efficiency of the two prognostic evaluation systems.RESULTS In GIST tissues,RKIP protein showed positive expression in the cytoplasm and cell membrane,appearing as brownish-yellow or brown granules.The expression of RKIP was related to GIST tumor size,NIH grade,and mucosal invasion.P-ERK protein exhibited heterogeneous distribution in GIST cells,mainly in the cytoplasm,with occasional presence in the nucleus,and appeared as brownish-yellow granules,and the expression of P-ERK protein was associated with GIST tumor size,mitotic count,mucosal invasion,and NIH grade.Meanwhile,RKIP protein expression was negatively correlated with P-ERK expression.The results in COX multivariate regression analysis showed that RKIP protein expression was not an independent risk factor for tumor prognosis.However,RKIP combined with P-ERK protein expression were identified as independent risk factors for prognosis with statistical significance.Furthermore,we establish a new prognosis evaluation model using RKIP and P-ERK in combination and obtained the nomogram of the new prognosis evaluation model.ROC curve analysis also showed that the new evaluation model had better prognostic performance than the modified NIH 2008 risk classification system.CONCLUSION Our experimental results showed that the expression of RKIP and P-ERK proteins in GIST was associated with tumor size,NIH 2008 staging,and tumor invasion,and P-ERK expression was also related to mitotic count.The expression of the two proteins had a certain negative correlation.The combined expression of RKIP and P-ERK proteins can serve as an independent risk factor for predicting the prognosis of GIST patients.The new risk assessment model incorporating RKIP and P-ERK has superior evaluation efficacy and is worth further practical application to validate.
文摘Future quantitative assessments will be expected to estimate quantities, values, and locations of undiscovered mineral resources in a form that conveys both economic viability and uncertainty associated with the resources. Historically, declining metal prices point to the need for larger deposits over time. Sensitivity analysis demonstrates that the greatest opportunity for reducing uncertainty in assessments lies in lowering uncertainty associated with tonnage estimates. Of all errors possible in assessments, these affecting tonnage estimates are by far the most important. Selecting the correct deposit model is the most important way of controlling errors because of the dominance of tonnage-deposit models are the best known predictors of tonnage. Much of the surface is covered with apparently barren rocks and sediments in many large regions. Because many exposed mineral deposits are believed to have been found, a prime concern is the presence of possible mineralized rock under cover. Assessments of areas with resources under cover must rely on extrapolation from surrounding areas, new geologic maps of rocks under cover, or analogy with other well-explored areas that can be considered training tracts. Cover has a profound effect on uncertainty and on methods and procedures of assessments because geology is seldom known and geophysical methods typically have attenuated responses. Many earlier assessment methods were based on relationships of geochemical and geophysical variables to deposits learned from deposits exposed on the surface-these will need to be relearned based on covered deposits. Mineral-deposit models are important in quantitative resource assessments for two reasons: (1) grades and tonnages of most deposit types are significantly different, and (2) deposit types are present in different geologic settings that can be identified from geologic maps. Mineral-deposit models are the keystone in combining the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Grade and tonnage models and development of quantitative descriptive, economic, and deposit density models will help reduce the uncertainty of these new assessments.
