Traditional polymeric photocatalysts are typically constructed using aromatic building blocks to enhanceπ-conjugation.However,their inherent hydrophobicity and rigid structure lead to poor dispersibility in aqueous s...Traditional polymeric photocatalysts are typically constructed using aromatic building blocks to enhanceπ-conjugation.However,their inherent hydrophobicity and rigid structure lead to poor dispersibility in aqueous solutions,resulting in significant optical losses and exciton recombination.In this study,two series of six novel polymer photocatalysts(FLUSO,FLUSO-PEG10,FLUSO-PEG30;CPDTSO,CPDTSO-PEG10,CPDTSO-PEG30)are designed and synthesized by incorporating the hydrophilic,non-conjugated polyethylene glycol(PEG)chain,into both the main and side chains of polymers.By precisely optimizing the ratio of hydrophilic PEG segments,the water dispersibility is significantly improved while the light absorption capability of the polymer photocatalysts is well maintained.The experimental results confirm that the optimized FLUSO-PEG10 exhibits excellent photocatalytic hydrogen evolution rate,reaching up to 33.9 mmol/(g·h),which is nearly three times higher than that of fullyπ-conjugated counterparts.Water contact angles and particle size analyses reveal that incorporating non-conjugated segments into the main chains enhances the capacitance of the polymer/water interface and reduces particle aggregation,leading to improved photocatalyst dispersion and enhanced charge generation.展开更多
目的:评价基于深度学习的继发性肺结核CT辅助诊断模型在临床应用中的价值。方法:回顾性收集2018年12月至2023年4月在重庆市公共卫生医疗救治中心接受胸部CT平扫的2004例患者的病例资料,分为肺部正常组(544例)、普通肺部感染组(526组)和...目的:评价基于深度学习的继发性肺结核CT辅助诊断模型在临床应用中的价值。方法:回顾性收集2018年12月至2023年4月在重庆市公共卫生医疗救治中心接受胸部CT平扫的2004例患者的病例资料,分为肺部正常组(544例)、普通肺部感染组(526组)和继发性肺结核组(934例)。按照随机分组(通过R语言的sample函数实现训练集和测试集的完全随机分组)的方式,将数据集划分为训练集(1402例,70.0%)和测试集(602例,30.0%)。所有图像采用肺野自动分割算法,获得肺野区域。进一步采用BasicNet和DenseNet算法进行三组间的分类研究。采用曲线下面积(area under curve,AUC)、敏感度、特异度和准确率评价模型的分类性能。最后,在测试数据中,将最优模型与3位不同年资的放射科医生的诊断结果进行比较。结果:602例独立测试集中,DenseNet模型的性能优于BasicNet模型,两种模型的平均AUC、敏感度、特异度和准确率分别为92.1%和89.4%、79.7%和74.0%、89.4%和86.6%、86.2%和83.3%。其中,DenseNet模型的诊断性能优于低年资医生(准确率分别为90.7%和89.1%,Kappa=0.677),与中年资和高年资医生的诊断水平(准确率分别为90.7%、92.2%和95.3%,Kappa值分别为0.746和0.819)保持高度一致性。结论:DenseNet模型能较准确地识别继发性肺结核,与放射科中年资医师的诊断水准相当,可以作为继发性肺结核的辅助诊断工具。展开更多
文摘Traditional polymeric photocatalysts are typically constructed using aromatic building blocks to enhanceπ-conjugation.However,their inherent hydrophobicity and rigid structure lead to poor dispersibility in aqueous solutions,resulting in significant optical losses and exciton recombination.In this study,two series of six novel polymer photocatalysts(FLUSO,FLUSO-PEG10,FLUSO-PEG30;CPDTSO,CPDTSO-PEG10,CPDTSO-PEG30)are designed and synthesized by incorporating the hydrophilic,non-conjugated polyethylene glycol(PEG)chain,into both the main and side chains of polymers.By precisely optimizing the ratio of hydrophilic PEG segments,the water dispersibility is significantly improved while the light absorption capability of the polymer photocatalysts is well maintained.The experimental results confirm that the optimized FLUSO-PEG10 exhibits excellent photocatalytic hydrogen evolution rate,reaching up to 33.9 mmol/(g·h),which is nearly three times higher than that of fullyπ-conjugated counterparts.Water contact angles and particle size analyses reveal that incorporating non-conjugated segments into the main chains enhances the capacitance of the polymer/water interface and reduces particle aggregation,leading to improved photocatalyst dispersion and enhanced charge generation.
文摘目的:评价基于深度学习的继发性肺结核CT辅助诊断模型在临床应用中的价值。方法:回顾性收集2018年12月至2023年4月在重庆市公共卫生医疗救治中心接受胸部CT平扫的2004例患者的病例资料,分为肺部正常组(544例)、普通肺部感染组(526组)和继发性肺结核组(934例)。按照随机分组(通过R语言的sample函数实现训练集和测试集的完全随机分组)的方式,将数据集划分为训练集(1402例,70.0%)和测试集(602例,30.0%)。所有图像采用肺野自动分割算法,获得肺野区域。进一步采用BasicNet和DenseNet算法进行三组间的分类研究。采用曲线下面积(area under curve,AUC)、敏感度、特异度和准确率评价模型的分类性能。最后,在测试数据中,将最优模型与3位不同年资的放射科医生的诊断结果进行比较。结果:602例独立测试集中,DenseNet模型的性能优于BasicNet模型,两种模型的平均AUC、敏感度、特异度和准确率分别为92.1%和89.4%、79.7%和74.0%、89.4%和86.6%、86.2%和83.3%。其中,DenseNet模型的诊断性能优于低年资医生(准确率分别为90.7%和89.1%,Kappa=0.677),与中年资和高年资医生的诊断水平(准确率分别为90.7%、92.2%和95.3%,Kappa值分别为0.746和0.819)保持高度一致性。结论:DenseNet模型能较准确地识别继发性肺结核,与放射科中年资医师的诊断水准相当,可以作为继发性肺结核的辅助诊断工具。
