Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots

References

• Hernando-Amado S, Coque TM, Baquero F, Martínez JL. Defining and combating antibiotic resistance from one health and global health perspectives. Nat Microbiol. 2019;4(9):1432–1442. doi:10.1038/s41564-019-0503-9
   PubMed Web of Science ®Google Scholar
• Kwon JH, Powderly WG. The post-antibiotic era is here. Science. 2021;373(6554):471. doi:10.1126/science.abl599
   PubMed Web of Science ®Google Scholar
• Nelson RE, Hatfield KM, Wolford H, et al. National estimates of healthcare costs associated with multidrug-resistant bacterial infections among hospitalized patients in the United States. Clin Infect Dis. 2021;72(Supplement_1):S17–S26. doi:10.1093/cid/ciaa1581
   PubMed Web of Science ®Google Scholar
• Baquero F, Levin BR. Proximate and ultimate causes of the bactericidal action of antibiotics. Nat Rev Microbiol. 2021;19(2):123–132. doi:10.1038/s41579-020-00443-1
   PubMed Web of Science ®Google Scholar
• Chu J, Koirala B, Forelli N, et al. Synthetic-bioinformatic natural product antibiotics with diverse modes of action. J Am Chem Soc. 2020;142(33):14158–14168. doi:10.1021/jacs.0c04376
   PubMed Web of Science ®Google Scholar
• Lai CKC, Ng RWY, Leung SSY, Hui M, Ip M. Overcoming the rising incidence and evolving mechanisms of antibiotic resistance by novel drug delivery approaches – an overview. Advanced Drug Delivery Reviews. 2022;181:114078. doi:10.1016/j.addr.2021.114078
   PubMed Web of Science ®Google Scholar
• Hochvaldová L, Večeřová R, Kolář M, et al. Antibacterial nanomaterials: upcoming hope to overcome antibiotic resistance crisis. Nanotechnol Rev. 2022;11:1115–1142. doi:10.1515/ntrev-2022-0059
   Web of Science ®Google Scholar
• Qin S, Xie M, Cao S, et al. Insight into the antibacterial resistance of graphdiyne functionalized by silver nanoparticles. Cell Prolif. 2022;55:13236. doi:10.1111/cpr.13236
   Web of Science ®Google Scholar
• Dat NM, Thinh DB, Huong LM, et al. Facile synthesis and antibacterial activity of silver nanoparticles-modified graphene oxide hybrid material: the assessment, utilization, and anti-virus potentiality. Mater Today Chem. 2022;23:100738. doi:10.1016/j.mtchem.2021.100738
   Web of Science ®Google Scholar
• Nguyen NTT, Nguyen LM, Nguyen TTT, Nguyen TT, Nguyen DTC, Van Tran T. Formation, antimicrobial activity, and biomedical performance of plant-based nanoparticles: a review. Environ Chem Lett. 2022;20:2531–2571. doi:10.1007/s10311-022-01425-w
   PubMed Web of Science ®Google Scholar
• Yu Z, Li Q, Wang J, et al. Reactive oxygen species-related nanoparticle toxicity in the biomedical field. Nanoscale Res Lett. 2020;15:115. doi:10.1186/s11671-020-03344-7
   PubMed Web of Science ®Google Scholar
• Wang Z, Wang X, Wang Y, et al. NanoZnO ‑ modified titanium implants for enhanced anti ‑ bacterial activity, osteogenesis and corrosion resistance. J Nanobiotechnology. 2021;19:353. doi:10.1186/s12951-021-01099-6
   PubMed Web of Science ®Google Scholar
• Ji Y, Han Z, Ding H, et al. Enhanced eradication of bacterial/fungi biofilms by glucose oxidase-modified magnetic nanoparticles as a potential treatment for persistent endodontic infections. ACS Appl Mater Interfaces. 2021;13:17289–17299. doi:10.1016/j.bioactmat.2021.04.033
   PubMed Web of Science ®Google Scholar
• Wang Z, Mei L, Liu X, Zhou Q. Hierarchically hybrid biocoatings on Ti implants for enhanced antibacterial activity and osteogenesis. Colloids Surf B Biointerfaces. 2021;204:111802. doi:10.1016/j.colsurfb.2021.111802
   PubMed Web of Science ®Google Scholar
• Xu S, Zhou Q, Jiang Z, et al. The effect of doxycycline-containing chitosan/carboxymethyl chitosan nanoparticles on NLRP3 inflammasome in periodontal disease. Carbohydr Polym. 2020;237:116163. doi:10.1016/j.carbpol.2020.116163
   PubMed Web of Science ®Google Scholar
• Godoy-Gallardo M, Eckhard U, Delgado LM, et al. Antibacterial approaches in tissue engineering using metal ions and nanoparticles: from mechanisms to applications. Bioact Mater. 2021;6:4470–4490. doi:10.1016/j.bioactmat.2021.04.033
   PubMed Web of Science ®Google Scholar
• Huang S, Song Y, Zhang JR, Chen X, Zhu JJ. Antibacterial carbon dots-based composites. Small. 2023;2207385:1–28. doi:10.1002/smll.202207385
   Google Scholar
• Ezati P, Rhim JW, Molaei R, et al. Preparation and characterization of B, S, and N-doped glucose carbon dots: antibacterial, antifungal, and antioxidant activity, Sustain. Mater Technol. 2022;32:e00397. doi:10.1016/j.susmat.2022.e00397
   Google Scholar
• Liu Y, Xu B, Lu M, et al. Ultrasmall Fe-doped carbon dots nanozymes for photoenhanced antibacterial therapy and wound healing. Bioact Mater. 2022;12:246–256. doi:10.1016/j.bioactmat.2021.10.023
   PubMed Web of Science ®Google Scholar
• Wang Z, Sheng L, Yang X, et al. Natural biomass-derived carbon dots as potent antimicrobial agents against multidrug-resistant bacteria and their biofilms, Sustain. Mater Technol. 2023;36:e00584. doi:10.1016/j.susmat.2023.e00584
   Google Scholar
• Qiao G, Chen G, Wen Q, et al. Rapid conversion from common precursors to carbon dots in large scale: spectral controls, optical sensing, cellular imaging and LEDs application. J Colloid Interface Sci. 2020;580:88–98. doi:10.1016/j.jcis.2020.07.034
   PubMed Web of Science ®Google Scholar
• Yang J, Zhang X, Ma YH, et al. Carbon dot-based platform for simultaneous bacterial distinguishment and antibacterial applications. ACS Appl Mater Interfaces. 2016;8:32170–32181. doi:10.1021/acsami.8b08832
   PubMed Web of Science ®Google Scholar
• Wang X, Wang H, Zhou W, et al. Carbon dots with tunable third-order nonlinear coefficient instructed by machine learning. J Photochem Photobiol A. 2022;426:113729.
   Web of Science ®Google Scholar
• Gao C, Min X, Fang M, et al. Innovative materials science via machine learning. Adv Funct Mater. 2022;32:2108044. doi:10.1002/adfm.202108044
   Web of Science ®Google Scholar
• Choudhary K, DeCost B, Chen C, et al. Recent advances and applications of deep learning methods in materials science. npj Comput Mater. 2022;8:59. doi:10.1038/s41524-022-00734-6
   Web of Science ®Google Scholar
• Suwardi A, Wang FK, Xue K, et al. Machine learning-driven biomaterials evolution. Adv Mater. 2022;34:e2102703. doi:10.1002/adma.202102703
   PubMed Web of Science ®Google Scholar
• Furxhi I, Murphy F, Mullins M, et al. Nanotoxicology data for in silico tools: a literature review. Nanotoxicology. 2020;14:612–637. doi:10.1080/17435390.2020.1729439
   PubMed Web of Science ®Google Scholar
• Mirzaei M, Furxhi I, Murphy F, Mullins M. A machine learning tool to predict the antibacterial capacity of nanoparticles. Nanomaterials. 2021;11:1774. doi:10.1021/acsami.8b08832
   Web of Science ®Google Scholar
• Wang XY, Bin Chen B, Zhang J, et al. Exploiting deep learning for predictable carbon dot design. Chem Commun. 2021;57:532–535. doi:10.1039/d0cc07882d
   PubMed Web of Science ®Google Scholar
• Singh VK, Maurya NS, Mani A, Yadav RS. Machine learning method using position-specific mutation based classification outperforms one hot coding for disease severity prediction in haemophilia ‘A’. Genomics. 2020;112:5122–5128. doi:10.1039/d0cc07882d
   PubMed Web of Science ®Google Scholar
• Shlens J, A Tutorial on Principal Component Analysis; 2014. Available from: http://arxiv.org/abs/1404.1100. Accessed May 21, 2024.
   Google Scholar
• Juan Y, Dai Y, Yang Y, Zhang J. Accelerating materials discovery using machine learning. J Mater Sci Technol. 2021;79:178–190. doi:10.1016/j.jmst.2020.12.010
   Web of Science ®Google Scholar
• Katsikas G, Sarafidis C, Kioseoglou J. Machine learning in magnetic materials. Physica Status Solidi. 2021;258:2000600. doi:10.1002/pssb.202000600
   Web of Science ®Google Scholar
• Rickert CA, Hayta EN, Selle DM, et al. Machine learning approach to analyze the surface properties of biological materials. ACS Biomater Sci Eng. 2021;7:4614–4625. doi:10.1021/acsbiomaterials.1c00869
   PubMed Web of Science ®Google Scholar
• Duan J, Asteris PG, Nguyen H, Bui XN, Moayedi H. A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput. 2021;37:3329–3346. doi:10.1007/s00366-020-01003-0
   Web of Science ®Google Scholar
• Agrawal A, Deshpande PD, Cecen A, Basavarsu GP, Choudhary AN, Kalidindi SR. Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters, Integr. Mater Manuf Innov. 2014;3:8. doi:10.1186/2193-9772-3-8
   Google Scholar
• Prasad D, Sreekumar P, Karumbu P. Identification and classification of materials using machine vision and machine learning in the context of industry 4. 0. J Intell Manuf. 2020;31:1229–1241. doi:10.1007/s10845-019-01508-6
   Web of Science ®Google Scholar
• Chicco D, Tötsch N, Jurman G. The Matthews correlation coefficient (Mcc) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Min. 2021;14:612–637. doi:10.1186/s13040-021-00244-z
   Web of Science ®Google Scholar
• Omar L, Ivrissimtzis I. Using theoretical ROC curves for analysing machine learning binary classifiers. Pattern Recognit Lett. 2019;128:447–451. doi:10.1016/j.patrec.2019.10.004
   Web of Science ®Google Scholar
• Georgescu AB, Ren P, Toland AR, et al. Database, features, and machine learning model to identify thermally driven metal-insulator transition compounds. Chem Mater. 2021;33:5591–5605. doi:10.1021/acs.chemmater.1c00905
   Web of Science ®Google Scholar
• Lee HC, Yoon HK, Nam K, et al. Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J Clin Med. 2018;7:332. doi:10.3390/jcm7100322
   PubMed Web of Science ®Google Scholar
• Dou Q, Fang X, Jiang S, Chee PL, Lee T, Loh XJ. RSC Advances antibacterial and gene delivery properties. RSC Adv. 2015;5:46817–46822.
   Web of Science ®Google Scholar
• Hou P, Yang T, Liu H, Li YF, Huang CZ. An active structure preservation method for developing functional graphitic carbon dots as an effective antibacterial agent and a sensitive pH and Al(III) nanosensor. Nanoscale. 2017;9:17334–17341. doi:10.1039/c7nr05539k
   PubMed Web of Science ®Google Scholar
• Ju B, Nie H, Zhang XG, et al. Inorganic salt incorporated solvothermal synthesis of multicolor carbon dots, emission mechanism, and antibacterial study. ACS Appl Nano Mater. 2018;1:6131–6138. doi:10.1021/acsanm.8b01355
   Web of Science ®Google Scholar
• Sun B, Wu F, Zhang Q, et al. Journal of colloid and interface science insight into the effect of particle size distribution differences on the antibacterial activity of carbon dots. J Colloid Interface Sci. 2021;584:505–519. doi:10.1016/j.jcis.2020.10.015
   PubMed Web of Science ®Google Scholar
• Serafim MSM, Kronenberger T, Oliveira PR, et al. The application of machine learning techniques to innovative antibacterial discovery and development. Expert Opin Drug Discov. 2020;15:1165–1180. doi:10.3390/jcm7100322
   PubMed Web of Science ®Google Scholar
• He M, Zhang L. Machine learning and symbolic regression investigation on stability of MXene materials. Comput. Mater. Sci. 2021;196:110578. doi:10.1016/j.commatsci.2021.110578
   Web of Science ®Google Scholar
• Nembrini S, Ko IR, Wright MN, Lu C. Data and text mining The revival of the Gini importance ? Bioinformatics 2018;34:3711–3718. doi:10.1093/bioinformatics/bty373
   PubMed Web of Science ®Google Scholar
• Jin J, Dong P, Lai L. One-step synthesis of silver nanoparticles using carbon dots as reducing and stabilizing agents and their antibacterial mechanisms. Carbon N Y. 2015;94:129–141. doi:10.1016/j.carbon.2015.05.084
   Web of Science ®Google Scholar
• Dong X, Liang W, Meziani MJ, Sun YP, Yang L. Carbon dots as potent antimicrobial agents. Theranostics. 2020;10:671–686. doi:10.7150/thno.39863
   PubMed Web of Science ®Google Scholar
• Jian HJ, Wu RS, Lin TY, et al. Super-cationic carbon quantum dots synthesized from spermidine as an eye drop formulation for topical treatment of bacterial keratitis. ACS Nano. 2017;11:6703–6716. doi:10.1016/j.commatsci.2021.110578
   PubMed Web of Science ®Google Scholar
• Anand A, Unnikrishnan B, Wei SC, Chou CP, Zhang LZ, Huang CC. Graphene oxide and carbon dots as broad-spectrum antimicrobial agents-a minireview. Nano Horiz. 2019;4:117–137. doi:10.1039/c8nh00174j
   PubMed Web of Science ®Google Scholar
• Bing W, Sun H, Yan Z, Ren J, Qu X. Programmed bacteria death induced by carbon dots with different surface charge. Small. 2016;12:4713–4718. doi:10.1002/smll.201600294
   PubMed Web of Science ®Google Scholar
• Ghirardello M, Ramos-Soriano J, Galan MC. Carbon dots as an emergent class of antimicrobial agents. Nanomaterials. 2021;11:1877. doi:10.3390/nano11081877
   Web of Science ®Google Scholar
• Jones DE, Ghandehari H, Facelli JC. A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles, Comput. Methods Programs Biomed. 2016;132:93–103. doi:10.1016/j.cmpb.2016.04.025
   PubMed Web of Science ®Google Scholar
• Yu Y, Tan X, Ning S, Wu Y. Machine learning for understanding compatibility of organic-inorganic hybrid perovskites with post-treatment amines. ACS Energy Lett. 2019;4:397–404. doi:10.1021/acsenergylett.8b02451
   Web of Science ®Google Scholar
• Sun B, Wu F, Zhang Q, et al. Insight into the effect of particle size distribution differences on the antibacterial activity of carbon dots. J Colloid Interface Sci. 2021;584:505–519. doi:10.1016/j.jcis.2020.10.015
   PubMed Web of Science ®Google Scholar
• Jin JC, Xu ZQ, Dong P, et al. One-step synthesis of silver nanoparticles using carbon dots as reducing and stabilizing agents and their antibacterial mechanisms. Carbon N Y. 2015;94:129–141. doi:10.1016/j.carbon.2015.05.084
   Web of Science ®Google Scholar
• Verma A, Arshad F, Ahmad K, et al. Role of surface charge in enhancing antibacterial activity of fluorescent carbon dots. Nanotechnology. 2020;31:095101. doi:10.1088/1361-6528/ab55b8
   PubMed Web of Science ®Google Scholar
• Peng J, Muhammad R, Wang SL, Zhong HZ. How machine learning accelerates the development of quantum dots? Chinese J Chem. 2021;39:181–188. doi:10.1002/cjoc.202000393
   Web of Science ®Google Scholar