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Tetrahedral framework nucleic acids promote the proliferation and differentiation potential of diabetic bone marrow mesenchymal stem cell
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作者 Yanjing Li Jiayin Li +2 位作者 Yuqi Chang Yunfeng Lin lei sui 《Chinese Chemical Letters》 SCIE CAS CSCD 2024年第9期284-288,共5页
Diabetes mellitus considerably affects bone marrow mesenchymal stem cells(BMSCs),for example,by inhibiting their proliferation and differentiation potential,which enhances the difficulty in endogenous bone regeneratio... Diabetes mellitus considerably affects bone marrow mesenchymal stem cells(BMSCs),for example,by inhibiting their proliferation and differentiation potential,which enhances the difficulty in endogenous bone regeneration.Hence,effective strategies for enhancing the functions of BMSCs in diabetes have farreaching consequences for bone healing and regeneration in diabetes patients.Tetrahedral framework nucleic acids(tFNAs)are nucleic acid nanomaterials that can autonomously enter cells and regulate their behaviors.In this study,we evaluated the effects of tFNAs on BMSCs from diabetic rats.We found that tFNAs could promote the proliferation,migration,and osteogenic differentiation of BMSCs from rats with type 2 diabetes mellitus,and inhibited cell senescence and apoptosis.Furthermore,tFNAs effectively scavenged the accumulated reactive oxygen species and activated the suppressed protein kinase B(Akt)signaling pathway.Overall,we show that tFNAs can recover the proliferation and osteogenic potential of diabetic BMSCs by alleviating oxidative stress and activating Akt signaling.The study provides a strategy for endogenous bone regeneration in diabetes and also paves the way for exploiting DNA-based nanomaterials in regenerative medicine. 展开更多
关键词 Bone marrow mesenchymal stem cell Diabetes mellitus DNA-based nanomaterial Tetrahedral framework nucleic acids Endogenous bone regeneration
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基于机器视觉的铁路车轴标记识别算法研究
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作者 雷随 曾爽爽 王诗堃 《中阿科技论坛(中英文)》 2025年第1期85-89,共5页
为了能够对铁路货车车轴标记进行自动识别录入,文章基于机器视觉技术提出了一种针对车轴标记的制造单位代号、锻造顺序号(轴号)、锻造年月、轴型等4种类型标记的整体自动识别算法。在对获取的轴端图像进行图像缩放、灰度处理及滤波处理... 为了能够对铁路货车车轴标记进行自动识别录入,文章基于机器视觉技术提出了一种针对车轴标记的制造单位代号、锻造顺序号(轴号)、锻造年月、轴型等4种类型标记的整体自动识别算法。在对获取的轴端图像进行图像缩放、灰度处理及滤波处理等操作的基础上,采用圆检测和字符检测相结合的方法,实现了车轴标记各角度的倾斜校正和目标区域提取,采用图像投影法对字符区域进行行分割,依据字符排列规则对字符行进行字符分割,最后采用自适应图像增强联合Transformer自注意力的字符识别算法对字符进行识别,并构建了识别系统。实验结果显示,该算法能有效针对车轴标记进行识别,整体字符识别准确率不低于97%,最大计算耗时可控制在2 s以内。 展开更多
关键词 铁路货车 机器视觉 车轴标记 识别算法
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