[1] BASANT N, GUPTA S. Multi-target QSTR modeling for simultaneous prediction of multiple toxicity endpoints of nano-metal oxides [J]. Nanotoxicology, 2017, 11(3): 339-350. doi: 10.1080/17435390.2017.1302612
[2] KIM T, HYEON T. Applications of inorganic nanoparticles as therapeutic agents [J]. Nanotechnology, 2014, 25(1): 012001. doi: 10.1088/0957-4484/25/1/012001
[3] LI Y, YU S L, YUAN T Z, et al. Rational design of metal oxide nanocomposite anodes for advanced lithium ion batteries [J]. Journal of Power Sources, 2015, 282: 1-8. doi: 10.1016/j.jpowsour.2015.02.016
[4] ZHAO Z H, TIAN J, SANG Y H, et al. Structure, synthesis, and applications of TiO2 nanobelts [J]. Advanced Materials, 2015, 27(16): 2557-2582. doi: 10.1002/adma.201405589
[5] KAPRALOV A A, FENG W H, AMOSCATO A A, et al. Adsorption of surfactant lipids by single-walled carbon nanotubes in mouse lung upon pharyngeal aspiration [J]. ACS Nano, 2012, 6(5): 4147-4156. doi: 10.1021/nn300626q
[6] ZHAO Q, LI Y J, CHAI X L, et al. Interaction of pulmonary surfactant with silica and polycyclic aromatic hydrocarbons: Implications for respiratory health [J]. Chemosphere, 2019, 222: 603-610. doi: 10.1016/j.chemosphere.2019.02.002
[7] KIM K H, KABIR E, KABIR S. A review on the human health impact of airborne particulate matter [J]. Environment International, 2015, 74: 136-143. doi: 10.1016/j.envint.2014.10.005
[8] RAESCH S S, TENZER S, STORCK W, et al. Proteomic and lipidomic analysis of nanoparticle Corona upon contact with lung surfactant reveals differences in protein, but not lipid composition [J]. ACS Nano, 2015, 9(12): 11872-11885. doi: 10.1021/acsnano.5b04215
[9] MONOPOLI M P, ÅBERG C, SALVATI A, et al. Biomolecular coronas provide the biological identity of nanosized materials [J]. Nature Nanotechnology, 2012, 7(12): 779-786. doi: 10.1038/nnano.2012.207
[10] PARRA E, PÉREZ-GIL J. Composition, structure and mechanical properties define performance of pulmonary surfactant membranes and films [J]. Chemistry and Physics of Lipids, 2015, 185: 153-175. doi: 10.1016/j.chemphyslip.2014.09.002
[11] GUAGLIARDO R, PÉREZ-GIL J, de SMEDT S, et al. Pulmonary surfactant and drug delivery: Focusing on the role of surfactant proteins [J]. Journal of Controlled Release, 2018, 291: 116-126. doi: 10.1016/j.jconrel.2018.10.012
[12] THORLEY A J, RUENRAROENGSAK P, POTTER T E, et al. Critical determinants of uptake and translocation of nanoparticles by the human pulmonary alveolar epithelium [J]. ACS Nano, 2014, 8(11): 11778-11789. doi: 10.1021/nn505399e
[13] YU Q L, WANG H G, PENG Q, et al. Different toxicity of anatase and rutile TiO2 nanoparticles on macrophages: Involvement of difference in affinity to proteins and phospholipids [J]. Journal of Hazardous Materials, 2017, 335: 125-134. doi: 10.1016/j.jhazmat.2017.04.026
[14] KONDURU N V, DAMIANI F, STOILOVA-MCPHIE S, et al. Nanoparticle wettability influences nanoparticle-phospholipid interactions [J]. Langmuir, 2018, 34(22): 6454-6461. doi: 10.1021/acs.langmuir.7b03741
[15] LUO Z, LI S X, XU Y, et al. Extracting pulmonary surfactants to form inverse micelles on suspended graphene nanosheets [J]. Environmental Science:Nano, 2018, 5(1): 130-140. doi: 10.1039/C7EN00843K
[16] LUO Z, LI S X, XU Y, et al. The role of nanoparticle shape in translocation across the pulmonary surfactant layer revealed by molecular dynamics simulations [J]. Environmental Science:Nano, 2018, 5(8): 1921-1932. doi: 10.1039/C8EN00521D
[17] CHHODEN T, CLAUSEN P A, LARSEN S T, et al. Interactions between nanoparticles and lung surfactant investigated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry [J]. Rapid Communications in Mass Spectrometry, 2015, 29(11): 1080-1086. doi: 10.1002/rcm.7199
[18] WANG F, LIU J W. Liposome supported metal oxide nanoparticles: Interaction mechanism, light controlled content release, and intracellular delivery [J]. Small, 2014, 10(19): 3927-3931. doi: 10.1002/smll.201400850
[19] ZHANG X, PANDIAKUMAR A K, HAMERS R J, et al. Quantification of lipid Corona formation on colloidal nanoparticles from lipid vesicles [J]. Analytical Chemistry, 2018, 90(24): 14387-14394. doi: 10.1021/acs.analchem.8b03911
[20] BYLESJÖ M, RANTALAINEN M, CLOAREC O, et al. OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification [J]. Journal of Chemometrics, 2006, 20(8/9/10): 341-351.
[21] SCHÜÜRMANN G, EBERT R U, CHEN J W, et al. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean [J]. Journal of Chemical Information and Modeling, 2008, 48(11): 2140-2145. doi: 10.1021/ci800253u
[22] FATEMI M, GHORBANNEZHAD Z. Estimation of the volume of distribution of some pharmacologically important compounds from their structural descriptors [J]. Journal of the Serbian Chemical Society, 2011, 76(7): 1003-1014. doi: 10.2298/JSC101104091F
[23] HUANG Y, LI X H, XU S J, et al. Quantitative structure-activity relationship models for predicting inflammatory potential of metal oxide nanoparticles [J]. Environmental Health Perspectives, 2020, 128(6): 67010. doi: 10.1289/EHP6508
[24] GRAMATICA P. Principles of QSAR models validation: Internal and external [J]. QSAR & Combinatorial Science, 2007, 26(5): 694-701.
[25] 祁晓娟, 李雪花, 黄杨, 等. 预测农药植物角质层-水分配系数的LSER模型 [J]. 农药学学报, 2020, 22(2): 249-255. doi: 10.16801/j.issn.1008-7303.2020.0053 QI X J, LI X H, HUANG Y, et al. LSER model for predicting cuticle-water partition coefficients of pesticides [J]. Chinese Journal of Pesticide Science, 2020, 22(2): 249-255(in Chinese). doi: 10.16801/j.issn.1008-7303.2020.0053