目的研究新佐剂-Montanide ISA 50V2在马免疫中的应用,制定合理的免疫程序,提高马免疫血清效价。方法用Montanide ISA 50V2乳化A型和F型肉毒类毒素制备成油乳抗原,免疫家兔和马,并与福氏不完全佐剂进行对比。结果在马免疫程序中,Montani...目的研究新佐剂-Montanide ISA 50V2在马免疫中的应用,制定合理的免疫程序,提高马免疫血清效价。方法用Montanide ISA 50V2乳化A型和F型肉毒类毒素制备成油乳抗原,免疫家兔和马,并与福氏不完全佐剂进行对比。结果在马免疫程序中,Montanide ISA 50V2与福氏不完全佐剂交替使用效果更好,免疫效价较高。结论在进一步试验确证后,将Montanide ISA 50V2作为佐剂应用于马免疫中,以获得更高的免疫血清的效价。展开更多
在戴尔(Dell)于今年6月发布了X3的升级版X30之后，我们就在期盼X5继任者的到来。近日，借助微软(Microsoft)和英特尔(Intel)发布Windows Media Player 10 Mobile以及2700G多媒体图形加速器的东风，戴尔乘势推出了搭载最新技术成就的顶...在戴尔(Dell)于今年6月发布了X3的升级版X30之后，我们就在期盼X5继任者的到来。近日，借助微软(Microsoft)和英特尔(Intel)发布Windows Media Player 10 Mobile以及2700G多媒体图形加速器的东风，戴尔乘势推出了搭载最新技术成就的顶级机型X50系列(本文以最高端型号X50v为例)，而价格定位却依然保持戴尔一贯的平民风格。展开更多
Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal...Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.展开更多
文摘目的研究新佐剂-Montanide ISA 50V2在马免疫中的应用,制定合理的免疫程序,提高马免疫血清效价。方法用Montanide ISA 50V2乳化A型和F型肉毒类毒素制备成油乳抗原,免疫家兔和马,并与福氏不完全佐剂进行对比。结果在马免疫程序中,Montanide ISA 50V2与福氏不完全佐剂交替使用效果更好,免疫效价较高。结论在进一步试验确证后,将Montanide ISA 50V2作为佐剂应用于马免疫中,以获得更高的免疫血清的效价。
文摘在戴尔(Dell)于今年6月发布了X3的升级版X30之后，我们就在期盼X5继任者的到来。近日，借助微软(Microsoft)和英特尔(Intel)发布Windows Media Player 10 Mobile以及2700G多媒体图形加速器的东风，戴尔乘势推出了搭载最新技术成就的顶级机型X50系列(本文以最高端型号X50v为例)，而价格定位却依然保持戴尔一贯的平民风格。
文摘Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.
