Objective:The Delphi method was used to propose health effect evaluation indicators to assess foods for special medical purposes(FSMPs).This lays the foundation for the formation of a big data model for human health t...Objective:The Delphi method was used to propose health effect evaluation indicators to assess foods for special medical purposes(FSMPs).This lays the foundation for the formation of a big data model for human health testing,as well as a big data platform for the health and safety evaluation of special medical foods.Methods:The Delphi method was used to conduct two rounds of expert consultation on the constructed FSMP health effect evaluation indicators.Results:Ten major items were identified after two rounds of expert consultation.Among these,there were 10 primary entries,32 secondary entries,50 tertiary entries,and 28 quaternary entries.Conclusion:The complete list of evaluation indicators contains 10 entries,which can comprehensively and systematically monitor adverse reactions to the use of FSMPs.The present findings lay the foundation for a big data platform to evaluate the health and safety of special foods.展开更多
Background: Classification of the pulmonary neuroendocrine tumor (pNET) categories is a step-wise process identified by presence of necrosis and number of mitoses per 2 mm^2. In neuroendocrine tumor pathology, Ki-67 w...Background: Classification of the pulmonary neuroendocrine tumor (pNET) categories is a step-wise process identified by presence of necrosis and number of mitoses per 2 mm^2. In neuroendocrine tumor pathology, Ki-67 was first described as a prognostic factor in the pancreas and incorporated into the grading system of digestive tract neuroendocrine neoplasms in the 2010 WHO classification. However, the significance of Ki-67 in pNETs was still a controversial issue. This study was to investigate the potentially diagnostic value of Ki-67 in pNETs. Methods: We retrieved 159 surgical specimens of pNETs, including 35 typical carcinoids (TCs), 2 atypical carcinoid (ACs), 28 largecell neuroendocrine carcinomas (LCNECs), 94 small-cell lung cancers (SCLCs). Manual conventional method (MCM) and computer-assisted image analysis method (CIAM) were used to calculate the Ki-67 proliferative index. In CIAM, 6 equivalent fields lly annotated for digital image analysis. Results: The Ki-67 index among the 4 groups with ranges of 0.38% to 12.66% for TC, 4.34% to 29.48% for AC, 30.67% to 93.74% for LCNEC, and 40.71% to 96.87% for SCLC. The cutoff value of Ki-67 index to distinguish low grade with high grade was 30.07%. For the univariate survival analyses in pNETs, both the overall survival and progression-free survival correlated with Ki-67 index. In addition, the Ki-67 index performed by CIAM was proved to be of great positive correlation with MCM.(500 ×500 μm) at 10× magnification were manua Conclusions: Ki-67 index counted by CIAM is a reliable method and can be a useful adjunct to classify the low- and high-grade NETs.展开更多
基金This research was supported by the National Key Research and Development Program of China(2019YFC1606400).
文摘Objective:The Delphi method was used to propose health effect evaluation indicators to assess foods for special medical purposes(FSMPs).This lays the foundation for the formation of a big data model for human health testing,as well as a big data platform for the health and safety evaluation of special medical foods.Methods:The Delphi method was used to conduct two rounds of expert consultation on the constructed FSMP health effect evaluation indicators.Results:Ten major items were identified after two rounds of expert consultation.Among these,there were 10 primary entries,32 secondary entries,50 tertiary entries,and 28 quaternary entries.Conclusion:The complete list of evaluation indicators contains 10 entries,which can comprehensively and systematically monitor adverse reactions to the use of FSMPs.The present findings lay the foundation for a big data platform to evaluate the health and safety of special foods.
文摘Background: Classification of the pulmonary neuroendocrine tumor (pNET) categories is a step-wise process identified by presence of necrosis and number of mitoses per 2 mm^2. In neuroendocrine tumor pathology, Ki-67 was first described as a prognostic factor in the pancreas and incorporated into the grading system of digestive tract neuroendocrine neoplasms in the 2010 WHO classification. However, the significance of Ki-67 in pNETs was still a controversial issue. This study was to investigate the potentially diagnostic value of Ki-67 in pNETs. Methods: We retrieved 159 surgical specimens of pNETs, including 35 typical carcinoids (TCs), 2 atypical carcinoid (ACs), 28 largecell neuroendocrine carcinomas (LCNECs), 94 small-cell lung cancers (SCLCs). Manual conventional method (MCM) and computer-assisted image analysis method (CIAM) were used to calculate the Ki-67 proliferative index. In CIAM, 6 equivalent fields lly annotated for digital image analysis. Results: The Ki-67 index among the 4 groups with ranges of 0.38% to 12.66% for TC, 4.34% to 29.48% for AC, 30.67% to 93.74% for LCNEC, and 40.71% to 96.87% for SCLC. The cutoff value of Ki-67 index to distinguish low grade with high grade was 30.07%. For the univariate survival analyses in pNETs, both the overall survival and progression-free survival correlated with Ki-67 index. In addition, the Ki-67 index performed by CIAM was proved to be of great positive correlation with MCM.(500 ×500 μm) at 10× magnification were manua Conclusions: Ki-67 index counted by CIAM is a reliable method and can be a useful adjunct to classify the low- and high-grade NETs.
