| 1 | Black phosphorus quantum dots as multifunctional nanozymes for tumor photothermal/catalytic synergistic therapy显示文摘Nanozymes are nanomaterials with enzyme-like properties that have attracted significant interest owing to their high stability,easy preparation,and tunable catalytic properties,especially in the field of cancer therapy.However,the unfavorable catalytic effects of nanozymes in the acidic tumor microenvironment have limited their applications.Herein,we developed a biomimetic erythrocyte membrane-camouflaged ultrasmall black phosphorus quantum dots(BPQDs)nanozymes that simultaneously exhibited an exceptional near-infrared(NIR)photothermal property and dramatically photothermal-enhanced glucose oxidase(GOx)-like activity in the acidic tumor microenvironment.We demonstrated the engineered BPQDs gave a photothermal conversion efficiency of 28.9%that could rapidly heat the tumor up to 50℃ while effectively localized into tumors via homing peptide iRGD leading after intravenously injection.Meanwhile,the significantly enhanced GOx-like activity of BPQDs under NIR irradiation was capable of catalytical generating massive toxic reactive oxygen species via using cellular glucose.By combining the intrinsic photothermal property and the unique photothermal-enhanced GOx-like catalytic activity,the developed BPQDs were demonstrated to be an effective therapeutic strategy for inhibiting tumor growth in vivo.We believe that this work will provide a novel perspective for the development of nanozymes in tumor catalytic therapy. | Hui Ding Daji Wang Haibing Huang Xiaozhu Chen Jie Wang Jinjie Sun Jianlin Zhang Lu Lu Beiping Miao Yanjuan Cai Kelong Fan Yongtian Lu Hongsong Dong Xiyun Yan Guohui Nie Minmin Liang | 2022 | Nano Research2022，15，2： | 1 |
| 3 | Prediction of Outcomes in Mini-Basketball Training Program for Preschool Children with Autism Using Machine Learning Models显示文摘In recent years evidence has emerged suggesting that Mini-basketball training program(MBTP)can be an effec-tive intervention method to improve social communication(SC)impairments and restricted and repetitive beha-viors(RRBs)in preschool children suffering from autism spectrum disorder(ASD).However,there is a considerable degree if interindividual variability concerning these social outcomes and thus not all preschool chil-dren with ASD profit from a MBTP intervention to the same extent.In order to make more accurate predictions which preschool children with ASD can benefit from an MBTP intervention or which preschool children with ASD need additional interventions to achieve behavioral improvements,further research is required.This study aimed to investigate which individual factors of preschool children with ASD can predict MBTP intervention out-comes concerning SC impairments and RRBs.Then,test the performance of machine learning models in predict-ing intervention outcomes based on these factors.Participants were 26 preschool children with ASD who enrolled in a quasi-experiment and received MBTP intervention.Baseline demographic variables(e.g.,age,body,mass index[BMI]),indicators of physicalfitness(e.g.,handgrip strength,balance performance),performance in execu-tive function,severity of ASD symptoms,level of SC impairments,and severity of RRBs were obtained to predict treatment outcomes after MBTP intervention.Machine learning models were established based on support vector machine algorithm were implemented.For comparison,we also employed multiple linear regression models in statistics.Ourfindings suggest that in preschool children with ASD symptomatic severity(r=0.712,p<0.001)and baseline SC impairments(r=0.713,p<0.001)are predictors for intervention outcomes of SC impair-ments.Furthermore,BMI(r=-0.430,p=0.028),symptomatic severity(r=0.656,p<0.001),baseline SC impair-ments(r=0.504,p=0.009)and baseline RRBs(r=0.647,p<0.001)can predict intervention outcomes of RRBs.Statistical models predicted 59.6%of variance in post-treatment SC impairments(MSE=0.455,RMSE=0.675,R2=0.596)and 58.9%of variance in post-treatment RRBs(MSE=0.464,RMSE=0.681,R2=0.589).Machine learning models predicted 83%of variance in post-treatment SC impairments(MSE=0.188,RMSE=0.434,R2=0.83)and 85.9%of variance in post-treatment RRBs(MSE=0.051,RMSE=0.226,R2=0.859),which were better than statistical models.Ourfindings suggest that baseline characteristics such as symptomatic severity of 144 IJMHP,2022,vol.24,no.2 ASD symptoms and SC impairments are important predictors determining MBTP intervention-induced improvements concerning SC impairments and RBBs.Furthermore,the current study revealed that machine learning models can successfully be applied to predict the MBTP intervention-related outcomes in preschool chil-dren with ASD,and performed better than statistical models.Ourfindings can help to inform which preschool children with ASD are most likely to benefit from an MBTP intervention,and they might provide a reference for the development of personalized intervention programs for preschool children with ASD. | Zhiyuan Sun Fabian Herold Kelong Cai Qian Yu Xiaoxiao Dong Zhimei Liu Jinming Li Aiguo Chen Liye Zou | 2022 | International Journal of Mental Health Promotion2022，24，2： | 0 |
| 4 | Determining the contribution of Mo single atoms components in MoO_(2)nanocatalyst in transfer hydrogenation显示文摘Nanocatalysts are likely to contain undetected single-atom components,which may have been ignored but have significant effect in catalytic reactions.Herein,we report a catalyst composed of Mo single atoms(SAs)and MoO_(2)nanoparticles(NPs)(MoSAs-MoO_(2)@NC),which is an exact model to understand how the SAs contribute to the nanocatalyst.Both experimental results and the density functional theory calculations reveal that Mo SAs on nitrogen-doped carbon provides the reaction zone for nitro reduction,while MoO_(2)is the active site for decomposing hydrazine hydrate to produce H*.Thanks to the synergy between Mo SAs and MoO_(2)NPs,this catalyst exhibits noble metal-like catalytic activity(100%conversion at 4 min)for the dechlorination-proof transfer hydrogenation.Additionally,the hydrogen migration on the catalyst is verified by the electrochemical tests in the absence of a hydrogen source.This work provides a model for further study on the coexistence of single atoms in nanoparticle catalysts. | Ze-Nan Hu Yongjian Ai Yan Zhao Yiming Wang Kelong Ding Wenhui Zhang Rongxiu Guo Xinyue Zhang Xiangbin Cai Ning Wang Jianshe Hu Qionglin Liang Hongyang Liu Fei Huang Limin Wu Jiangwei Zhang Hong-bin Sun | 2023 | Nano Research2023，16，2： | 0 |
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