Objective The immature platelet fraction (IPF) could be detected quantificationally in Sysmex XE-2100 with the software of XE-pro and IPF master.The study aimed to perform the methodological evaluation of IPF detection and investigate the clinical significance for the monitoring for bone marrow hyperplasia in cancer chemotherapy patients.Methods The high-level, middle-level and low-level whole blood samples were randomly chosen for detection repeatly 20 times to obtain interrun coefficient of variation (CV) for evaluation of the precision and reproducibility. Integrated quality controls were determined for continuous 20 days to obtain intrarun CV, and the stability and carryover was investigated.Furthermore, the correlation between results from Sysmex XE-2100 and results from flow cytometry was assessed.182 healthy subjects and 130 cancer patients undergoing chemotherapy were selected and the latter were divided into two groups according to platelet counts after therapy, one was normal PLT group, the other was decreased PLT group.The IPF of either group was measured and was compared with each other.Results The precision of IPF for high-level, middle-level and low-level were 4.71%, 4.33% and 4.95%, respectively, they were less than 5%.The interrun CV of IPF detection for middle-level and low-level were less than 5%.The interrun CV of IPF detection for high-level were less than 10%.The carryover ranged from 0.6% to 2.7%,and the average rate was 1.2%.A good correlation for IPF detection was shown between results from Sysmex XE-2100 and flow cytometry(r = 0.880 9,P ＜ 0.01).Regarding clinical utility of IPF detection in treatment monitoring for chemotherapy effect, the median of IPF levels in decreased PLT group, normal PLT group,control group were 14.45% ,7.35% and 15.68%, respectively.There was significant difference among the three groups (H =49.032,P ＜0.01 ).The IPF level was higher in decreased PLT group than normal PLT group (t = -5.681, P ＜ 0.O1 ), and was lower in normal PLT group after chemotherapy than the control group (t = -6.662 ,P ＜0.01 ).Conclusions The determination of IPF by the Sysmex XE-2100 owns high precision and good stability. IPF is an effective marker for evaluation of thrombopoietic condition in the cancer chemotherapy patients.

Objective: To establish a method of restriction fragment length polymorphism (RFLP) to determine the origin of sika deer bones.Methods: The DNA in the bone samples was extracted after decalcification, and then amplified using polymerase chain reaction (PCR).The origin of the samples was further identified using RFLP analysis.Results: The bone samples of sika deer and red deer could be distinguished from those of pig, bovine and dog by PCR.And the samples of sika deer and red deer could be further distinguished by RFLP through the analysis of the length of restriction enzyme XbaI.Conclusion: A RFLP method is established to determine the origin of sika deer bones.

Objective To explore and organize the standards of rehabilitation big data. Methods The connotation and extension of rehabilitation big data were discussed based on International Classification of Functioning,Disability and Health(ICF)framework.Referring to the documents of Guidance on the analysis and use of routine health information systems rehabilitation module,Rehabilitation in health systems:guide for action,Rehabilitation indicator menu:a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation(FRAME),and Data quality assurance.Module 1.Framework and metrics,the sources,patterns,clas-sification systems and coding standards were discussed under the ICF theory,and the metadata standards were ex-plored.The application and management of rehabilitation big data standards were discussed according to Nation-al Health Medical Big Data Standards,Security and Service Management Measures(Trial). Results The rehabilitation big data included rehabilitation service data and personal health data,coming from population-based and institution-based data,covering macro,meso and micro levels.The pattern of rehabilitation data flow corresponded to the interaction and source of the entire process of rehabilitation service,to organize and manage rehabilitation big data.The classification system included object classes,object feature classes,participant role classes,relationship classes,and activity and event classes,each of which was further subdivided into subcatego-ries to cover the entities,features,roles,relationships and activities involved in the rehabilitation process.The metadata standards included three levels:core,general and specialized metadata,ensuring standardized manage-ment,sharing and interoperability of rehabilitation data. Conclusion This study delves into the standardization of rehabilitation big data based on the ICF framework,encompass-ing multiple dimensions such as the connotation and extension of rehabilitation big data,data sources,data mod-els,classification systems,coding standards,and metadata standards.The construction of a rehabilitation big data standard system involves standardization efforts in various aspects,including data content,data structure,data coding,and metadata.These standards not only adhere to the norms of data flow,but also take into account the complexity of data composition.This system aligns with health big data standards,ensuring data consistency,ac-curacy,and interoperability,thus providing a foundation for effective exchange and comparison between different data sources.The establishment of a rehabilitation big data standard system not only ensures the standardized pro-cessing of rehabilitation big data,but also lays a solid foundation for effective exchange between rehabilitation big data and other health data,as well as for the widespread application of rehabilitation big data.This provides crucial support for improving the quality and efficiency of rehabilitation services,ensuring that patients receive appropriate care,rehabilitation and support.It holds significant theoretical and practical implications for promot-ing the development of the rehabilitation field.