In view of the difficulties about school-enterprise cooperation on food safety testing laboratory reform,the current training program,teaching methods and teaching forms are not suitable for the new platform.This pape...In view of the difficulties about school-enterprise cooperation on food safety testing laboratory reform,the current training program,teaching methods and teaching forms are not suitable for the new platform.This paper provides new ideas and modes to solve these problems,such as integrating the resource elements between school and enterprise,establishing new platforms with the help of external force and innovating the cooperation modes to improve the laboratory,which can fully serve teaching,scientific research and enterprise production.展开更多
The 14th Scientific Congress of the Chinese Association for Laboratory Animal Science was held on October 11‐12,2018 in Qingdao,China.During this congress,an international forum on the development of Laboratory Anima...The 14th Scientific Congress of the Chinese Association for Laboratory Animal Science was held on October 11‐12,2018 in Qingdao,China.During this congress,an international forum on the development of Laboratory Animal Sciences(LAS)worldwide was held in which participants learnt about the development of new LAS resources and technologies,as well as the progress of LAS in China.The main points that were discussed are as follows.After nearly a century of development,there are more than 200 species of experimental animals,and more than 20 000 inbred lines,outbred lines,genetic engineered animals,animal models of diseases,and other resources all over the world,which provide abundant experimental animal resources for scientific research.These resources are widely used in life science research.展开更多
目的:分析实体恶性肿瘤相关静脉血栓栓塞症(Ta-VTE)患者的临床特征和实验室指标,研究其危险因素。方法:收集2020年1月至12月在本院住院的静脉血栓患者,根据是否患有实体恶性肿瘤,分为Ta-VET组和单纯VTE组,分析两组患者间的临床资料和实...目的:分析实体恶性肿瘤相关静脉血栓栓塞症(Ta-VTE)患者的临床特征和实验室指标,研究其危险因素。方法:收集2020年1月至12月在本院住院的静脉血栓患者,根据是否患有实体恶性肿瘤,分为Ta-VET组和单纯VTE组,分析两组患者间的临床资料和实验室指标的差异,将有显著差异的指标纳入logistic回归,分析Ta-VTE的危险因素。结果:本研究共纳入288例静脉血栓患者,其中Ta-VTE组64例,单纯VTE组224例。两组患者间住院时长(14.20±15.29 d vs 10.05±6.90 d,t=3.112,P=0.002)、疼痛患者比例(35.94%vs 65.18%,χ^(2)=17.554,P=0.000)、近期手术史患者比例(75.00%vs 37.50%,χ^(2)=28.196,P=0.000)、D-D[2.8(0.92,7.55)μg/ml vs 5.69(2.25,13.91)μg/ml,Z=-2.710,P=0.007]、PLR[198.59(139.54,312.16)vs 149.76(114.08,233.66),Z=-2.924,P=0.003]、TBIL[10.90(7.63,15.68)μmol/L vs 12.90(9.33,18.28)μmol/L,Z=-2.066,P=0.039]具有显著统计学差异,其余各指标均无明显差异(P>0.05)。多因素logistic回归分析结果显示,PLR水平升高(OR=1.003,95%CI:1.000-1.006,P=0.027)、近期手术史(OR=4.312,95%CI:2.093-8.885,P=0.000)和住院时间延长(OR=1.037,95%CI:1.002-1.074,P=0.038)是恶性实体肿瘤相关静脉血栓栓塞症的独立危险因素;而疼痛(OR=0.274,95%CI:0.133-0.564,P=0.000)是一项保护性因素。结论:PLR水平升高、近期手术史和住院时间延长是Ta-VTE患者的独立危险因素,合理利用这些指标有助于Ta-VTE患者的临床诊疗。展开更多
Recent advancements in science and technology,coupled with the proliferation of data,have also urged laboratory medicine to integrate with the era of artificial intelligence(AI)and machine learning(ML).In the current ...Recent advancements in science and technology,coupled with the proliferation of data,have also urged laboratory medicine to integrate with the era of artificial intelligence(AI)and machine learning(ML).In the current practices of evidencebased medicine,the laboratory tests analysing disease patterns through the association rule mining(ARM)have emerged as a modern tool for the risk assessment and the disease stratification,with the potential to reduce cardiovascular disease(CVD)mortality.CVDs are the well recognised leading global cause of mortality with the higher fatality rates in the Indian population due to associated factors like hypertension,diabetes,and lifestyle choices.AI-driven algorithms have offered deep insights in this field while addressing various challenges such as healthcare systems grappling with the physician shortages.Personalized medicine,well driven by the big data necessitates the integration of ML techniques and high-quality electronic health records to direct the meaningful outcome.These technological advancements enhance the computational analyses for both research and clinical practice.ARM plays a pivotal role by uncovering meaningful relationships within databases,aiding in patient survival prediction and risk factor identification.AI potential in laboratory medicine is vast and it must be cautiously integrated while considering potential ethical,legal,and privacy concerns.Thus,an AI ethics framework is essential to guide its responsible use.Aligning AI algorithms with existing lab practices,promoting education among healthcare professionals,and fostering careful integration into clinical settings are imperative for harnessing the benefits of this transformative technology.展开更多
基金Supported by Natural Science Foundation of Shandong Province(ZR2018PC010)Teaching and Research Project of Binzhou University(BZXYSYXM201810).
文摘In view of the difficulties about school-enterprise cooperation on food safety testing laboratory reform,the current training program,teaching methods and teaching forms are not suitable for the new platform.This paper provides new ideas and modes to solve these problems,such as integrating the resource elements between school and enterprise,establishing new platforms with the help of external force and innovating the cooperation modes to improve the laboratory,which can fully serve teaching,scientific research and enterprise production.
文摘The 14th Scientific Congress of the Chinese Association for Laboratory Animal Science was held on October 11‐12,2018 in Qingdao,China.During this congress,an international forum on the development of Laboratory Animal Sciences(LAS)worldwide was held in which participants learnt about the development of new LAS resources and technologies,as well as the progress of LAS in China.The main points that were discussed are as follows.After nearly a century of development,there are more than 200 species of experimental animals,and more than 20 000 inbred lines,outbred lines,genetic engineered animals,animal models of diseases,and other resources all over the world,which provide abundant experimental animal resources for scientific research.These resources are widely used in life science research.
文摘目的:分析实体恶性肿瘤相关静脉血栓栓塞症(Ta-VTE)患者的临床特征和实验室指标,研究其危险因素。方法:收集2020年1月至12月在本院住院的静脉血栓患者,根据是否患有实体恶性肿瘤,分为Ta-VET组和单纯VTE组,分析两组患者间的临床资料和实验室指标的差异,将有显著差异的指标纳入logistic回归,分析Ta-VTE的危险因素。结果:本研究共纳入288例静脉血栓患者,其中Ta-VTE组64例,单纯VTE组224例。两组患者间住院时长(14.20±15.29 d vs 10.05±6.90 d,t=3.112,P=0.002)、疼痛患者比例(35.94%vs 65.18%,χ^(2)=17.554,P=0.000)、近期手术史患者比例(75.00%vs 37.50%,χ^(2)=28.196,P=0.000)、D-D[2.8(0.92,7.55)μg/ml vs 5.69(2.25,13.91)μg/ml,Z=-2.710,P=0.007]、PLR[198.59(139.54,312.16)vs 149.76(114.08,233.66),Z=-2.924,P=0.003]、TBIL[10.90(7.63,15.68)μmol/L vs 12.90(9.33,18.28)μmol/L,Z=-2.066,P=0.039]具有显著统计学差异,其余各指标均无明显差异(P>0.05)。多因素logistic回归分析结果显示,PLR水平升高(OR=1.003,95%CI:1.000-1.006,P=0.027)、近期手术史(OR=4.312,95%CI:2.093-8.885,P=0.000)和住院时间延长(OR=1.037,95%CI:1.002-1.074,P=0.038)是恶性实体肿瘤相关静脉血栓栓塞症的独立危险因素;而疼痛(OR=0.274,95%CI:0.133-0.564,P=0.000)是一项保护性因素。结论:PLR水平升高、近期手术史和住院时间延长是Ta-VTE患者的独立危险因素,合理利用这些指标有助于Ta-VTE患者的临床诊疗。
文摘Recent advancements in science and technology,coupled with the proliferation of data,have also urged laboratory medicine to integrate with the era of artificial intelligence(AI)and machine learning(ML).In the current practices of evidencebased medicine,the laboratory tests analysing disease patterns through the association rule mining(ARM)have emerged as a modern tool for the risk assessment and the disease stratification,with the potential to reduce cardiovascular disease(CVD)mortality.CVDs are the well recognised leading global cause of mortality with the higher fatality rates in the Indian population due to associated factors like hypertension,diabetes,and lifestyle choices.AI-driven algorithms have offered deep insights in this field while addressing various challenges such as healthcare systems grappling with the physician shortages.Personalized medicine,well driven by the big data necessitates the integration of ML techniques and high-quality electronic health records to direct the meaningful outcome.These technological advancements enhance the computational analyses for both research and clinical practice.ARM plays a pivotal role by uncovering meaningful relationships within databases,aiding in patient survival prediction and risk factor identification.AI potential in laboratory medicine is vast and it must be cautiously integrated while considering potential ethical,legal,and privacy concerns.Thus,an AI ethics framework is essential to guide its responsible use.Aligning AI algorithms with existing lab practices,promoting education among healthcare professionals,and fostering careful integration into clinical settings are imperative for harnessing the benefits of this transformative technology.
