Designing nanotheranostics with machine studying

Designing nanotheranostics with machine studying


  • Chen, H., Zhang, W., Zhu, G., Xie, J. & Chen, X. Rethinking most cancers nanotheranostics. Nat. Rev. Mater. 2, 17024 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shi, J., Kantoff, P. W., Wooster, R. & Farokhzad, O. C. Most cancers nanomedicine: progress, challenges and alternatives. Nat. Rev. Most cancers 17, 20–37 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • AbdElFatah, T. et al. Nanoplasmonic amplification in microfluidics allows accelerated colorimetric quantification of nucleic acid biomarkers from pathogens. Nat. Nanotechnol. 18, 922–932 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mitchell, M. J. et al. Engineering precision nanoparticles for drug supply. Nat. Rev. Drug Discov. 20, 101–124 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hou, X., Zaks, T., Langer, R. & Dong, Y. Lipid nanoparticles for mRNA supply. Nat. Rev. Mater. 6, 1078–1094 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, M. et al. Detection of ovarian most cancers by way of the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine studying. Nat. Biomed. Eng. 6, 267–275 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, J., Zhao, T., Jakobsson, V. & Chen, X. Medical translation of radiotheranostics for precision oncology. Nat. Rev. Bioeng. 1, 612–614 (2023).

    Article 

    Google Scholar
     

  • Fang, R. H., Gao, W. & Zhang, L. Focusing on medicine to tumours utilizing cell membrane-coated nanoparticles. Nat. Rev. Clin. Oncol. 20, 33–48 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Li, X., Lovell, J. F., Yoon, J. & Chen, X. Medical improvement and potential of photothermal and photodynamic therapies for most cancers. Nat. Rev. Clin. Oncol. 17, 657–674 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Raguram, A., Banskota, S. & Liu, D. R. Therapeutic in vivo supply of gene modifying brokers. Cell 185, 2806–2827 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nam, J. et al. Most cancers nanomedicine for mixture most cancers immunotherapy. Nat. Rev. Mater. 4, 398–414 (2019).

    Article 

    Google Scholar
     

  • Zhao, H. et al. A robotic platform for the synthesis of colloidal nanocrystals. Nat. Synth. 2, 505–514 (2023).

    Article 

    Google Scholar
     

  • Huang, X. et al. Nanotechnology-based methods towards SARS-CoV-2 variants. Nat. Nanotechnol. 17, 1027–1037 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Park, J. et al. An built-in magneto-electrochemical system for the fast profiling of tumour extracellular vesicles from blood plasma. Nat. Biomed. Eng. 5, 678–689 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rao, L. et al. Hybrid mobile membrane nanovesicles amplify macrophage immune responses towards most cancers recurrence and metastasis. Nat. Commun. 11, 4909 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Butler, Ok. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine studying for molecular and supplies science. Nature 559, 547–555 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • LeCun, Y., Bengio, Y. & Hinton, G. Deep studying. Nature 521, 436–444 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Shen, D., Wu, G. & Suk, H.-I. Deep studying in medical picture evaluation. Annu. Rev. Biomed. Eng. 19, 221–248 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Camacho, D. M., Collins, Ok. M., Powers, R. Ok., Costello, J. C. & Collins, J. J. Subsequent-generation machine studying for organic networks. Cell 173, 1581–1592 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yu, Ok.-H., Beam, A. L. & Kohane, I. S. Synthetic intelligence in healthcare. Nat. Biomed. Eng. 2, 719–731 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Jumper, J. et al. Extremely correct protein construction prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ziatdinov, M., Ghosh, A., Wong, C. Y. & Kalinin, S. V. AtomAI framework for deep studying evaluation of picture and spectroscopy information in electron and scanning probe microscopy. Nat. Mach. Intell. 4, 1101–1112 (2022).

    Article 

    Google Scholar
     

  • Heinzmann, Ok., Carter, L. M., Lewis, J. S. & Aboagye, E. O. Multiplexed imaging for analysis and remedy. Nat. Biomed. Eng. 1, 697–713 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Multimodal biomedical AI. Nat. Med. 28, 1773–1784 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wong, F., de la Fuente-Nunez, C. & Collins, J. J. Leveraging synthetic intelligence within the combat towards infectious illnesses. Science 381, 164–170 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Breiman, L. Random forests. Mach. Study. 45, 5–32 (2001).

    Article 

    Google Scholar
     

  • Chih-Wei, H. & Chih-Jen, L. A comparability of strategies for multiclass help vector machines. IEEE Trans. Neural Netw. 13, 415–425 (2002).

    Article 

    Google Scholar
     

  • Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: a fundamental device of chemometrics. Chemometr. Intell. Lab. Syst. 58, 109–130 (2001).

    Article 
    CAS 

    Google Scholar
     

  • Masson, J.-F., Biggins, J. S. & Ringe, E. Machine studying for nanoplasmonics. Nat. Nanotechnol. 18, 111–123 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wan, F., Wong, F., Collins, J. J. & de la Fuente-Nunez, C. Machine studying for antimicrobial peptide identification and design. Nat. Rev. Bioeng. 2, 392–407 (2024).

    Article 

    Google Scholar
     

  • Mahmoudi, M., Landry, M. P., Moore, A. & Coreas, R. The protein corona from nanomedicine to environmental science. Nat. Rev. Mater. 8, 422–438 (2023).

    Article 

    Google Scholar
     

  • Tao, H. et al. Nanoparticle synthesis assisted by machine studying. Nat. Rev. Mater. 6, 701–716 (2021).

    Article 

    Google Scholar
     

  • Dai, X. & Chen, Y. Computational biomaterials: computational simulations for biomedicine. Adv. Mater. 35, 2204798 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Service provider, A. et al. Scaling deep studying for supplies discovery. Nature 624, 80–85 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Batra, R. et al. Machine studying overcomes human bias within the discovery of self-assembling peptides. Nat. Chem. 14, 1427–1435 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhu, M. et al. Machine-learning-assisted single-vessel evaluation of nanoparticle permeability in tumour vasculatures. Nat. Nanotechnol. 18, 657–666 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Boehnke, N. et al. Massively parallel pooled screening reveals genomic determinants of nanoparticle supply. Science 377, eabm5551 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yamankurt, G. et al. Exploration of the nanomedicine-design house with high-throughput screening and machine studying. Nat. Biomed. Eng. 3, 318–327 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shamay, Y. et al. Quantitative self-assembly prediction yields focused nanomedicines. Nat. Mater. 17, 361–368 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stater, E. P., Sonay, A. Y., Hart, C. & Grimm, J. The ancillary results of nanoparticles and their implications for nanomedicine. Nat. Nanotechnol. 16, 1180–1194 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lu, Y., Aimetti, A. A., Langer, R. & Gu, Z. Bioresponsive supplies. Nat. Rev. Mater. 1, 16075 (2016).

    Article 

    Google Scholar
     

  • Hong, G., Diao, S., Antaris, A. L. & Dai, H. Carbon nanomaterials for organic imaging and nanomedicinal remedy. Chem. Rev. 115, 10816–10906 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Suwardi, A. et al. Machine learning-driven biomaterials evolution. Adv. Mater. 34, 2102703 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Rycenga, M. et al. Controlling the synthesis and meeting of silver nanostructures for plasmonic functions. Chem. Rev. 111, 3669–3712 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, X., Yang, M., Pang, B., Vara, M. & Xia, Y. Gold nanomaterials at work in biomedicine. Chem. Rev. 115, 10410–10488 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Michalet, X. et al. Quantum dots for dwell cells, in vivo imaging, and diagnostics. Science 307, 538–544 (2005).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, P. et al. Quantifying the efficacy of magnetic nanoparticles for MRI and hyperthermia functions by way of machine studying strategies. Small 19, 2303522 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Serov, N. & Vinogradov, V. Synthetic intelligence to convey nanomedicine to life. Adv. Drug Deliv. Rev. 184, 114194 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Grand, J., Auguié, B. & Le Ru, E. C. Mixed extinction and absorption UV–seen spectroscopy as a way for revealing form imperfections of metallic nanoparticles. Anal. Chem. 91, 14639–14648 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gherman, A. M. M. et al. Synthetic neural networks modeling of the parameterized gold nanoparticles era by means of photo-induced course of. Mater. Res. Specific 5, 085011 (2018).

    Article 

    Google Scholar
     

  • Shafaei, A. & Khayati, G. R. A predictive mannequin on dimension of silver nanoparticles ready by inexperienced synthesis methodology utilizing hybrid synthetic neural community–particle swarm optimization algorithm. Measurement 151, 107199 (2020).

    Article 

    Google Scholar
     

  • Orimoto, Y. et al. Software of synthetic neural networks to fast information evaluation in combinatorial nanoparticle syntheses. J. Phys. Chem. C 116, 17885–17896 (2012).

    Article 
    CAS 

    Google Scholar
     

  • Salley, D. et al. A nanomaterials discovery robotic for the Darwinian evolution of form programmable gold nanoparticles. Nat. Commun. 11, 2771 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cheng, Q. et al. Selective organ concentrating on (SORT) nanoparticles for tissue-specific mRNA supply and CRISPR–Cas gene modifying. Nat. Nanotechnol. 15, 313–320 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ng, Ok. Ok. & Zheng, G. Molecular interactions in natural nanoparticles for phototheranostic functions. Chem. Rev. 115, 11012–11042 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Andrews, N. et al. COVID-19 vaccine effectiveness towards the Omicron (B.1.1.529) variant. N. Engl. J. Med. 386, 1532–1546 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Li, B. et al. Combinatorial design of nanoparticles for pulmonary mRNA supply and genome modifying. Nat. Biotechnol. 41, 1410–1415 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, W. et al. Prediction of lipid nanoparticles for mRNA vaccines by the machine studying algorithm. Acta Pharm. Sin. B 12, 2950–2962 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Walkey, C. D. & Chan, W. C. W. Understanding and controlling the interplay of nanomaterials with proteins in a physiological surroundings. Chem. Soc. Rev. 41, 2780–2799 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Youshia, J., Ali, M. E. & Lamprecht, A. Synthetic neural community primarily based particle dimension prediction of polymeric nanoparticles. Eur. J. Pharm. Biopharm. 119, 333–342 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Shalaby, Ok. S. et al. Dedication of things controlling the particle dimension and entrapment effectivity of noscapine in PEG/PLA nanoparticles utilizing synthetic neural networks. Int. J. Nanomed. 9, 4953–4964 (2014).

    CAS 

    Google Scholar
     

  • Ogden, P. J., Kelsic, E. D., Sinai, S. & Church, G. M. Complete AAV capsid health panorama reveals a viral gene and allows machine-guided design. Science 366, 1139–1143 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Meng, Q.-F. et al. Inhalation supply of dexamethasone with iSEND nanoparticles attenuates the COVID-19 cytokine storm in mice and nonhuman primates. Sci. Adv. 9, eadg3277 (2023).

  • Wilhelm, S. et al. Evaluation of nanoparticle supply to tumours. Nat. Rev. Mater. 1, 16014 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Herrmann, I. Ok., Wooden, M. J. A. & Fuhrmann, G. Extracellular vesicles as a next-generation drug supply platform. Nat. Nanotechnol. 16, 748–759 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Madigan, V., Zhang, F. & Dahlman, J. E. Drug supply techniques for CRISPR-based genome editors. Nat. Rev. Drug Discov. 22, 875–894 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kalluri, R. & LeBleu, V. S. The biology, operate, and biomedical functions of exosomes. Science 367, eaau6977 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zengel, J. et al. Hardwiring tissue-specific AAV transduction in mice by means of engineered receptor expression. Nat. Strategies 20, 1070–1081 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bryant, D. H. et al. Deep diversification of an AAV capsid protein by machine studying. Nat. Biotechnol. 39, 691–696 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • El Andaloussi, S., Mäger, I., Breakefield, X. O. & Wooden, M. J. A. Extracellular vesicles: biology and rising therapeutic alternatives. Nat. Rev. Drug Discov. 12, 347–357 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zheng, W. et al. Prognosis of paediatric tuberculosis by optically detecting two virulence components on extracellular vesicles in blood samples. Nat. Biomed. Eng. 6, 979–991 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kuypers, S. et al. Unsupervised machine learning-based clustering of nanosized fluorescent extracellular vesicles. Small 17, 2006786 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Mahmoudi, M. et al. Protein−nanoparticle interactions: alternatives and challenges. Chem. Rev. 111, 5610–5637 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Salvati, A. et al. Transferrin-functionalized nanoparticles lose their concentrating on capabilities when a biomolecule corona adsorbs on the floor. Nat. Nanotechnol. 8, 137–143 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nel, A. E. et al. Understanding biophysicochemical interactions on the nano–bio interface. Nat. Mater. 8, 543–557 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kingston, B. R., Syed, A. M., Ngai, J., Sindhwani, S. & Chan, W. C. W. Assessing micrometastases as a goal for nanoparticles utilizing 3D microscopy and machine studying. Proc. Natl Acad. Sci. USA 116, 14937–14946 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ferdosi, S. et al. Engineered nanoparticles allow deep proteomics research at scale by leveraging tunable nano–bio interactions. Proc. Natl Acad. Sci. USA 119, e2106053119 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cha, M. et al. Unifying structural descriptors for organic and bioinspired nanoscale complexes. Nat. Comput. Sci. 2, 243–252 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Ban, Z. et al. Machine studying predicts the purposeful composition of the protein corona and the mobile recognition of nanoparticles. Proc. Natl Acad. Sci. USA 117, 10492–10499 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ouassil, N., Pinals, R. L., Del Bonis-O’Donnell, J. T., Wang, J. W. & Landry, M. P. Supervised studying mannequin predicts protein adsorption to carbon nanotubes. Sci. Adv. 8, eabm0898 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Saldinger, J. C., Raymond, M., Elvati, P. & Violi, A. Area-agnostic predictions of nanoscale interactions in proteins and nanoparticles. Nat. Comput. Sci. 3, 393–402 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Liu, R., Jiang, W., Walkey, C. D., Chan, W. C. W. & Cohen, Y. Prediction of nanoparticles–cell affiliation primarily based on corona proteins and physicochemical properties. Nanoscale 7, 9664–9675 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lazarovits, J. et al. Supervised studying and mass spectrometry predicts the in vivo destiny of nanomaterials. ACS Nano 13, 8023–8034 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Fourches, D. et al. Quantitative nanostructure−exercise relationship modeling. ACS Nano 4, 5703–5712 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Behzadi, S. et al. Mobile uptake of nanoparticles: journey contained in the cell. Chem. Soc. Rev. 46, 4218–4244 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Walkey, C. D. et al. Protein corona fingerprinting predicts the mobile interplay of gold and silver nanoparticles. ACS Nano 8, 2439–2455 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Loecher, A., Bruyns-Haylett, M., Ballester, P. J., Borros, S. & Oliva, N. A machine studying method to foretell mobile uptake of pBAE polyplexes. Biomater. Sci. 11, 5797–5808 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Shirokii, N. et al. Quantitative prediction of inorganic nanomaterial mobile toxicity by way of machine studying. Small 19, 2207106 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Martin et al. Proof-based prediction of mobile toxicity for amorphous silica nanoparticles. ACS Nano 17, 9987–9999 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jyakhwo, S., Serov, N., Dmitrenko, A. & Vinogradov, V. V. Machine studying bolstered genetic algorithm for enormous focused discovery of selectively cytotoxic inorganic nanoparticles. Small 20, 2305375 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Puzyn, T. et al. Utilizing nano-QSAR to foretell the cytotoxicity of steel oxide nanoparticles. Nat. Nanotechnol. 6, 175–178 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sealfon, R. S. G., Wong, A. Ok. & Troyanskaya, O. G. Machine studying strategies to mannequin multicellular complexity and tissue specificity. Nat. Rev. Mater. 6, 717–729 (2021).

    Article 

    Google Scholar
     

  • Chen, Q. et al. Meta-analysis of nanoparticle distribution in tumors and main organs in tumor-bearing mice. ACS Nano 17, 19810–19831 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • MacMillan, P. et al. Towards predicting nanoparticle distribution in heterogeneous tumor tissues. Nano Lett. 23, 7197–7205 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Liu, X. et al. Predictive modeling of nanomaterial publicity results in organic techniques. Int. J. Nanomed. 8, 31–43 (2023).


    Google Scholar
     

  • Gilbertson, L. M. et al. Towards safer multi-walled carbon nanotube design: establishing a statistical mannequin that relates floor cost and embryonic zebrafish mortality. Nanotoxicology 10, 10–19 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • Music, Y. et al. 3D-printed epifluidic digital pores and skin for machine learning-powered multimodal well being surveillance. Sci. Adv. 9, eadi6492 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lin, A. A., Nimgaonkar, V., Issadore, D. & Carpenter, E. L. Extracellular vesicle-based multianalyte liquid biopsy as a diagnostic for most cancers. Annu. Rev. Biomed. Information Sci. 5, 269–292 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Xu, C., Solomon, S. A. & Gao, W. Synthetic intelligence-powered digital pores and skin. Nat. Mach. Intell. 5, 1344–1355 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Altug, H., Oh, S.-H., Maier, S. A. & Homola, J. Advances and functions of nanophotonic biosensors. Nat. Nanotechnol. 17, 5–16 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Safir, F. et al. Combining acoustic bioprinting with AI-assisted raman spectroscopy for high-throughput identification of micro organism in blood. Nano Lett. 23, 2065–2073 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shin, H. et al. Single test-based analysis of a number of most cancers sorts utilizing exosome-SERS-AI for early stage cancers. Nat. Commun. 14, 1644 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kavungal, D. et al. Synthetic intelligence-coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative illnesses. Sci. Adv. 9, eadg9644 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gao, Z. et al. Machine-learning-assisted microfluidic nanoplasmonic digital immunoassay for cytokine storm profiling in COVID-19 sufferers. ACS Nano 15, 18023–18036 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thrift, W. J. et al. Deep studying evaluation of vibrational spectra of bacterial lysate for fast antimicrobial susceptibility testing. ACS Nano 14, 15336–15348 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, Y., Zhao, Y., Bollas, A., Wang, Y. & Au, Ok. F. Nanopore sequencing expertise, bioinformatics and functions. Nat. Biotechnol. 39, 1348–1365 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, M. et al. Actual-time detection of 20 amino acids and discrimination of pathologically related peptides with functionalized nanopore. Nat. Strategies 21, 609–618 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ying, Y.-L. et al. Nanopore-based applied sciences past DNA sequencing. Nat. Nanotechnol. 17, 1136–1146 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jena, M. Ok. & Pathak, B. Improvement of an artificially clever nanopore for high-throughput DNA sequencing with a machine-learning-aided quantum-tunneling method. Nano Lett. 23, 2511–2521 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Taniguchi, M. et al. Combining machine studying and nanopore development creates a man-made intelligence nanopore for coronavirus detection. Nat. Commun. 12, 3726 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xia, Ok. et al. Artificial heparan sulfate requirements and machine studying facilitate the event of solid-state nanopore evaluation. Proc. Natl Acad. Sci. USA 118, e2022806118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, M. et al. Identification of tagged glycans with a protein nanopore. Nat. Commun. 14, 1737 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, Y. et al. Identification of nucleoside monophosphates and their epigenetic modifications utilizing an engineered nanopore. Nat. Nanotechnol. 17, 976–983 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Greive, S. J., Bacri, L., Cressiot, B. & Pelta, J. Identification of conformational variants for bradykinin biomarker peptides from a biofluid utilizing a nanopore and machine studying. ACS Nano 18, 539–550 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sajda, P. Machine studying for detection and analysis of illness. Annu. Rev. Biomed. Eng. 8, 537–565 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Tian, F. et al. Protein evaluation of extracellular vesicles to observe and predict therapeutic response in metastatic breast most cancers. Nat. Commun. 12, 2536 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sahu, A. et al. Regulation of aged skeletal muscle regeneration by circulating extracellular vesicles. Nat. Growing old 1, 1148–1161 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mangalwedhekar, R. et al. Attaining nanoscale precision utilizing neuromorphic localization microscopy. Nat. Nanotechnol. 18, 380–389 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Reis, M. et al. Machine-learning-guided discovery of 19F MRI brokers enabled by automated copolymer synthesis. J. Am. Chem. Soc. 143, 17677–17689 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ma, Z., Wang, F., Wang, W., Zhong, Y. & Dai, H. Deep studying for in vivo near-infrared imaging. Proc. Natl Acad. Sci. USA 118, e2021446118 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Moen, E. et al. Deep studying for mobile picture evaluation. Nat. Strategies 16, 1233–1246 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bouchard, C. et al. Decision enhancement with a task-assisted GAN to information optical nanoscopy picture evaluation and acquisition. Nat. Mach. Intell. 5, 830–844 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Park, J. et al. Synthetic intelligence-enabled quantitative part imaging strategies for all times sciences. Nat. Strategies 20, 1645–1660 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Shmatko, A., Ghaffari Laleh, N., Gerstung, M. & Kather, J. N. Synthetic intelligence in histopathology: enhancing most cancers analysis and scientific oncology. Nat. Most cancers 3, 1026–1038 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Hong, G. et al. By-skull fluorescence imaging of the mind in a brand new near-infrared window. Nat. Photon. 8, 723–730 (2014).

    Article 
    CAS 

    Google Scholar
     

  • Chen, X. et al. Synthetic confocal microscopy for deep label-free imaging. Nat. Photon. 17, 250–258 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Ham, D., Park, H., Hwang, S. & Kim, Ok. Neuromorphic electronics primarily based on copying and pasting the mind. Nat. Electron. 4, 635–644 (2021).

    Article 

    Google Scholar
     

  • Oumano, M. & Yu, H. A deep studying method to gold nanoparticle quantification in computed tomography. Phys. Med. 87, 83–89 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Reker, D. et al. Computationally guided high-throughput design of self-assembling drug nanoparticles. Nat. Nanotechnol. 16, 725–733 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hsueh, H. T. et al. Machine learning-driven multifunctional peptide engineering for sustained ocular drug supply. Nat. Commun. 14, 2509 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Castillo-Hair, S. M. & Seelig, G. Machine studying for designing next-generation mRNA therapeutics. Acc. Chem. Res. 55, 24–34 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, H. et al. Algorithm for optimized mRNA design improves stability and immunogenicity. Nature 621, 396–403 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ebrahimi, S. B., Samanta, D., Kusmierz, C. D. & Mirkin, C. A. Protein transfection by way of spherical nucleic acids. Nat. Protoc. 17, 327–357 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Huang, J. et al. Identification of potent antimicrobial peptides by way of a machine-learning pipeline that mines the complete house of peptide sequences. Nat. Biomed. Eng. 7, 797–810 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • O’Callaghan, J. How OpenAI’s text-to-video device Sora may change science—and society. Nature 627, 475–476 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Thorp, H. H. ChatGPT is enjoyable, however not an creator. Science 379, 313 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Tropsha, A., Mills, Ok. C. & Hickey, A. J. Reproducibility, sharing and progress in nanomaterial databases. Nat. Nanotechnol. 12, 1111–1114 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • de la Iglesia, D. et al. A machine studying method to determine scientific trials involving nanodrugs and nanodevices from ClinicalTrials.gov. PLoS ONE 9, e110331 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wyrzykowska, E. et al. Representing and describing nanomaterials in predictive nanoinformatics. Nat. Nanotechnol. 17, 924–932 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ekins, S. et al. Exploiting machine studying for end-to-end drug discovery and improvement. Nat. Mater. 18, 435–441 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Erion, G. et al. A value-aware framework for the event of AI fashions for healthcare functions. Nat. Biomed. Eng. 6, 1384–1398 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yan, X., Sedykh, A., Wang, W., Yan, B. & Zhu, H. Development of a web-based nanomaterial database by large information curation and modeling pleasant nanostructure annotations. Nat. Commun. 11, 2519 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, Y. & Kohane, D. S. Exterior triggering and triggered concentrating on methods for drug supply. Nat. Rev. Mater. 2, 17020 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Ling, Q., Herstine, J. A., Bradbury, A. & Grey, S. J. AAV-based in vivo gene remedy for neurological issues. Nat. Rev. Drug Discov. 22, 789–806 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hu, S. et al. A mussel-inspired movie for adhesion to moist buccal tissue and environment friendly buccal drug supply. Nat. Commun. 12, 1689 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leave a Reply

    Your email address will not be published. Required fields are marked *