Anzalone, A. V., Koblan, L. W. & Liu, D. R. Genome editing with CRISPR-Cas nucleases, base editors, transposases and prime editors. Nat. Biotechnol. 38, 824\u2013844 (2020).<\/p>\n
Article<\/a>\u00a0 Doudna, J. A. The promise and challenge of therapeutic genome editing. Nature 578, 229\u2013236 (2020).<\/p>\n Article<\/a>\u00a0 Romero, P. A. & Arnold, F. H. Exploring protein fitness landscapes by directed evolution. Nat. Rev. Mol. Cell Biol. 10, 866\u2013876 (2009).<\/p>\n Article<\/a>\u00a0 Packer, M. S. & Liu, D. R. Methods for the directed evolution of proteins. Nat. Rev. Genet. 16, 379\u2013394 (2015).<\/p>\n Article<\/a>\u00a0 Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493\u2013500 (2024).<\/p>\n Article<\/a>\u00a0 Gaudelli, N. M. et al. Programmable base editing of A*T to G*C in genomic DNA without DNA cleavage. Nature 551, 464\u2013471 (2017).<\/p>\n Article<\/a>\u00a0 Richter, M. F. et al. Phage-assisted evolution of an adenine base editor with improved Cas domain compatibility and activity. Nat. Biotechnol. 38, 883\u2013891 (2020).<\/p>\n Article<\/a>\u00a0 Lapinaite, A. et al. DNA capture by a CRISPR-Cas9-guided adenine base editor. Science 369, 566\u2013571 (2020).<\/p>\n Article<\/a>\u00a0 Jeong, Y. K. et al. Adenine base editor engineering reduces editing of bystander cytosines. Nat. Biotechnol. 39, 1426\u20131433 (2021).<\/p>\n Article<\/a>\u00a0 Chen, L. et al. Engineering a precise adenine base editor with minimal bystander editing. Nat. Chem. Biol. 19, 101\u2013110 (2023).<\/p>\n Article<\/a>\u00a0 Wu, Y., Xiao, Y. L. & Tang, W. High-precision cytosine base editors by evolving nucleic-acid-recognition hotspots in deaminase. Nat. Biotechnol. https:\/\/doi.org\/10.1038\/s41587-025-02678-w<\/a> (2025).<\/p>\n Article<\/a>\u00a0 Doman, J. L., Raguram, A., Newby, G. A. & Liu, D. R. Evaluation and minimization of Cas9-independent off-target DNA editing by cytosine base editors. Nat. Biotechnol. 38, 620\u2013628 (2020).<\/p>\n Article<\/a>\u00a0 Cho, S. I. et al. Engineering TALE-linked deaminases to facilitate precision adenine base editing in mitochondrial DNA. Cell 187, 95\u2013109 (2024).<\/p>\n Article<\/a>\u00a0 Kim, N. et al. Prediction of the sequence-specific cleavage activity of Cas9 variants. Nat. Biotechnol. 38, 1328\u20131336 (2020).<\/p>\n Article<\/a>\u00a0 Shi, H. et al. Rapid two-step target capture ensures efficient CRISPR-Cas9-guided genome editing. Mol. Cell 85, 1730\u20131742 (2025).<\/p>\n Article<\/a>\u00a0 Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687\u2013694 (2019).<\/p>\n Article<\/a>\u00a0 Hsu, C., Nisonoff, H., Fannjiang, C. & Listgarten, J. Learning protein fitness models from evolutionary and assay-labeled data. Nat. Biotechnol. 40, 1114\u20131122 (2022).<\/p>\n Article<\/a>\u00a0 Meier, J. et al. Language models enable zero-shot prediction of the effects of mutations on protein function. Adv. Neural Inform. Process. Syst. 34, 29287\u201329303 (2021).<\/p>\n Dauparas, J. et al. Robust deep learning-based protein sequence design using ProteinMPNN. Science 378, 49\u201356 (2022).<\/p>\n Article<\/a>\u00a0 Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123\u20131130 (2023).<\/p>\n Article<\/a>\u00a0 Shanker, V. R., Bruun, T. U. J., Hie, B. L. & Kim, P. S. Unsupervised evolution of protein and antibody complexes with a structure-informed language model. Science 385, 46\u201353 (2024).<\/p>\n Article<\/a>\u00a0 Fei, H. et al. Advancing protein evolution with inverse folding models integrating structural and evolutionary constraints. Cell 188, 4674\u20134692 (2025).<\/p>\n Article<\/a>\u00a0 Jiang, K. et al. Rapid in silico directed evolution by a protein language model with EVOLVEpro. Science 387, eadr6006 (2025).<\/p>\n Article<\/a>\u00a0 Bravo, J. P. K. et al. Structural basis for mismatch surveillance by CRISPR-Cas9. Nature 603, 343\u2013347 (2022).<\/p>\n Article<\/a>\u00a0 Pacesa, M. et al. Structural basis for Cas9 off-target activity. Cell 185, 4067\u20134081 (2022).<\/p>\n Article<\/a>\u00a0 Lei, Z., Meng, H., Zhuang, Y., Zhu, Q. & Yi, C. Chemical and biological approaches to interrogate off-target effects of genome editing tools. ACS Chem. Biol. 18, 205\u2013217 (2023).<\/p>\n Article<\/a>\u00a0 Tao, J., Bauer, D. E. & Chiarle, R. Assessing and advancing the safety of CRISPR-Cas tools: from DNA to RNA editing. Nat. Commun. 14, 212 (2023).<\/p>\n Article<\/a>\u00a0 Lopes, R. & Prasad, M. K. Beyond the promise: evaluating and mitigating off-target effects in CRISPR gene editing for safer therapeutics. Front. Bioeng. Biotechnol. 11, 1339189 (2024).<\/p>\n Article<\/a>\u00a0 Lei, Z. et al. Detect-seq reveals out-of-protospacer editing and target-strand editing by cytosine base editors. Nat. Methods 18, 643\u2013651 (2021).<\/p>\n Article<\/a>\u00a0 Lei, Z. et al. Mitochondrial base editor induces substantial nuclear off-target mutations. Nature 606, 804\u2013811 (2022).<\/p>\n Article<\/a>\u00a0 Tsai, S. Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat. Biotechnol. 33, 187\u2013197 (2015).<\/p>\n Article<\/a>\u00a0 Liao, J. et al. Therapeutic adenine base editing of human hematopoietic stem cells. Nat. Commun. 14, 207 (2023).<\/p>\n Article<\/a>\u00a0 Mayuranathan, T. et al. Potent and uniform fetal hemoglobin induction via base editing. Nat. Genet. 55, 1210\u20131220 (2023).<\/p>\n Article<\/a>\u00a0 United States Patent and Trademark Office, US Patent No. 11,752,202 B2 Compositions and Methods for Treating Hemoglobinopathies. Beam Therapeutics Inc.<\/p>\n Tian, R. et al. Massively parallel CRISPR off-target detection enables rapid off-target prediction model building. Med 4, 478\u2013492 (2023).<\/p>\n Article<\/a>\u00a0 Tsai, S. Q. et al. CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets. Nat. Methods 14, 607\u2013614 (2017).<\/p>\n Article<\/a>\u00a0 Yuan, K. et al. Selict-seq profiles genome-wide off-target effects in adenosine base editing. Nucleic Acids Res. 53, gkaf281 (2025).<\/p>\n Article<\/a>\u00a0 Zhu, M. et al. Tracking-seq reveals the heterogeneity of off-target effects in CRISPR-Cas9-mediated genome editing. Nat. Biotechnol. 43, 799\u2013810 (2025).<\/p>\n Article<\/a>\u00a0 Losey, H. C., Ruthenburg, A. J. & Verdine, G. L. Crystal structure of Staphylococcus aureus tRNA adenosine deaminase TadA in complex with RNA. Nat. Struct. Mol. Biol. 13, 153\u2013159 (2006).<\/p>\n Article<\/a>\u00a0 Pacesa, M. et al. R-loop formation and conformational activation mechanisms of Cas9. Nature 609, 191\u2013196 (2022).<\/p>\n Article<\/a>\u00a0 Chen, J. S. et al. Enhanced proofreading governs CRISPR-Cas9 targeting accuracy. Nature 550, 407\u2013410 (2017).<\/p>\n Article<\/a>\u00a0 Grunewald, J. et al. CRISPR DNA base editors with reduced RNA off-target and self-editing activities. Nat. Biotechnol. 37, 1041\u20131048 (2019).<\/p>\n Article<\/a>\u00a0 Zhou, C. et al. Off-target RNA mutation induced by DNA base editing and its elimination by mutagenesis. Nature 571, 275\u2013278 (2019).<\/p>\n Article<\/a>\u00a0 Rees, H. A., Wilson, C., Doman, J. L. & Liu, D. R. Analysis and minimization of cellular RNA editing by DNA adenine base editors. Sci. Adv. 5, eaax5717 (2019).<\/p>\n Article<\/a>\u00a0 Perrotta, R. M. et al. Engineered base editors with reduced bystander editing through directed evolution. Nat. Biotechnol. https:\/\/doi.org\/10.1038\/s41587-025-02937-w<\/a> (2025).<\/p>\n Article<\/a>\u00a0 Zetsche, B. et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell 163, 759\u2013771 (2015).<\/p>\n Article<\/a>\u00a0 Yamano, T. et al. Crystal structure of Cpf1 in complex with guide RNA and target DNA. Cell 165, 949\u2013962 (2016).<\/p>\n Article<\/a>\u00a0 Yang, L. et al. Engineering APOBEC3A deaminase for highly accurate and efficient base editing. Nat. Chem. Biol. 20, 1176\u20131187 (2024).<\/p>\n Article<\/a>\u00a0 Wang, X. et al. Cas12a base editors induce efficient and specific editing with low DNA damage Response. Cell Rep. 31, 107723 (2020).<\/p>\n Article<\/a>\u00a0 Wang, J. Y., Pausch, P. & Doudna, J. A. Structural biology of CRISPR-Cas immunity and genome editing enzymes. Nat. Rev. Microbiol. 20, 641\u2013656 (2022).<\/p>\n Article<\/a>\u00a0 Kryshtafovych, A. et al. Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15. Proteins 91, 1903\u20131911 (2023).<\/p>\n Article<\/a>\u00a0 Lu, D., Yu, S., Huang, Y. & Gong, X. Multimeric protein interaction and complex prediction: Structure, dynamics and function. Comput. Struct. Biotechnol. J. 27, 1975\u20131997 (2025).<\/p>\n Article<\/a>\u00a0 Masters, M. R., Mahmoud, A. H. & Lill, M. A. Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions. Nat. Commun. 16, 8854 (2025).<\/p>\n Article<\/a>\u00a0 Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871\u2013876 (2021).<\/p>\n Article<\/a>\u00a0 Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).<\/p>\n Article<\/a>\u00a0 Zou, S., Sun, Y. & Tang, W. Charting the development and engineering of CRISPR base editors: lessons and inspirations. Cell Chem. Biol. 32, 789\u2013808 (2025).<\/p>\n Article<\/a>\u00a0 Song, J., Zhuang, Y. & Yi, C. Programmable RNA base editing via targeted modifications. Nat. Chem. Biol. 20, 277\u2013290 (2024).<\/p>\n Article<\/a>\u00a0 Lei, Z., Meng, H., Rao, X., Zhao, H. & Yi, C. Detect-seq, a chemical labeling and biotin pull-down approach for the unbiased and genome-wide off-target evaluation of programmable cytosine base editors. Nat. Protoc. 18, 2221\u20132255 (2023).<\/p>\n Article<\/a>\u00a0 Zhuang, Y. et al. Increasing the efficiency and precision of prime editing with guide RNA pairs. Nat. Chem. Biol. 18, 29\u201337 (2022).<\/p>\n Article<\/a>\u00a0 Arbab, M. et al. Determinants of base editing outcomes from target library analysis and machine learning. Cell 182, 463\u2013480 (2020).<\/p>\n Article<\/a>\u00a0 Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10\u201312 (2011).<\/p>\n Article<\/a>\u00a0 Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754\u20131760 (2009).<\/p>\n Article<\/a>\u00a0 Li, H. et al. The Sequence Alignment\/Map format and SAMtools. Bioinformatics 25, 2078\u20132079 (2009).<\/p>\n Article<\/a>\u00a0 Meng, H., Zhang, S. & Yi, C. Customized code for dI-profiling analysis (v.1.0.0). Zenodo https:\/\/doi.org\/10.5281\/zenodo.20487721<\/a> (2026).<\/p>\n Zhang, Y., Park, C., Bennett, C., Thornton, M. & Kim, D. Rapid and accurate alignment of nucleotide conversion sequencing reads with HISAT-3N. Genome Res. 31, 1290\u20131295 (2021).<\/p>\n Article<\/a>\u00a0 Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15\u201321 (2013).<\/p>\n Article<\/a>\u00a0 Eastman, P. et al. OpenMM 8: molecular dynamics simulation with machine learning potentials. J. Phys. Chem. B 128, 109\u2013116 (2024).<\/p>\n Article<\/a>\u00a0 Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696\u20133713 (2015).<\/p>\n Article<\/a>\u00a0 Zgarbova, M. et al. Refinement of the sugar-phosphate backbone torsion beta for AMBER force fields improves the description of Z- and B-DNA. J. Chem. Theory Comput. 11, 5723\u20135736 (2015).<\/p>\n Article<\/a>\u00a0 Zgarbova, M. et al. Refinement of the Cornell et al. nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles. J. Chem. Theory Comput. 7, 2886\u20132902 (2011).<\/p>\n Article<\/a>\u00a0 Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926\u2013935 (1983).<\/p>\n Article<\/a>\u00a0 Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: an N\u22c5log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089\u201310092 (1993).<\/p>\n Article<\/a>\u00a0 Rallapalli, K. L. & Komor, A. C. The design and application of DNA-editing enzymes as base editors. Annu. Rev. Biochem. 92, 43\u201379 (2023).<\/p>\n Article<\/a>\u00a0 Xiao, Y. L., Wu, Y. & Tang, W. An adenine base editor variant expands context compatibility. Nat. Biotechnol. 42, 1442\u20131453 (2024).<\/p>\n Article<\/a>\u00a0 Ranzau, B. L., Rallapalli, K. L., Evanoff, M., Paesani, F. & Komor, A. C. The wild-type tRNA adenosine deaminase enzyme TadA is capable of sequence-specific DNA base editing. ChemBioChem 24, e202200788 (2023).<\/p>\n Article<\/a>\u00a0 Shi, K. et al. Structural basis for targeted DNA cytosine deamination and mutagenesis by APOBEC3A and APOBEC3B. Nat. Struct. Mol. Biol. 24, 131\u2013139 (2017).<\/p>\n Article<\/a>\u00a0 Glasscock, C. J. et al. Computational design of sequence-specific DNA-binding proteins. Nat. Struct. Mol. Biol. 32, 2252\u20132261 (2025).<\/p>\n Article<\/a>\u00a0 Sugiki, S., Niide, T., Toya, Y. & Shimizu, H. Logistic regression-guided identification of cofactor specificity-contributing residues in enzyme with sequence datasets partitioned by catalytic properties. ACS Synth. Biol. 11, 3973\u20133985 (2022).<\/p>\n Article<\/a>\u00a0 Meng, H. et al. Dataset: AF3 prediction for DNA-RNA-Cas protein ternary complex. Zenodo https:\/\/doi.org\/10.5281\/zenodo.17197986<\/a> (2026).<\/p>\n Meng, H. et al. Dataset: AF3 prediction for protein-NA and protein-ligand complex. Zenodo https:\/\/doi.org\/10.5281\/zenodo.19555204<\/a> (2026).<\/p>\n Meng, H. et al. Dataset: targeted amplicon sequencing (Target-seq) dataset for on- and off-target site analysis. Zenodo https:\/\/doi.org\/10.5281\/zenodo.17243125<\/a> (2026).<\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n