Skip to content
2000
Volume 26, Issue 11
  • ISSN: 1389-2010
  • E-ISSN: 1873-4316

Abstract

Glucose monitoring is essential for managing diabetes, and continuous glucose monitoring biosensors can offer real-time monitoring with little invasiveness. However, challenges remain in improving sensor accuracy, selectivity, and overall performance. This article aims to review current trends and recent advancements in glucose-monitoring biosensors while evaluating their benefits and limitations for diabetes monitoring. An analysis of current literature on transdermal glucose sensors was conducted, focusing on detection techniques, novel nanomaterials, and integrated sensor systems. Recent research has led to advancements in electrochemical, optical, electromagnetic, and sonochemical sensors for transdermal glucose detection. The use of novel nanomaterials and integrated sensor designs has improved sensitivity, selectivity, and accuracy. However, issues like calibration requirements, motion artifacts, and skin irritation persist. Transdermal glucose sensors show promise for non-invasive, convenient diabetes monitoring but require further enhancements to address limitations in accuracy, reliability, and biocompatibility. Continued research and innovation focusing on sensor materials, designs, and surface chemistry is needed to optimize biosensor performance and utility. The study offers a comprehensive analysis of the present status of technological advancement and highlights areas that need more research.

Loading

Article metrics loading...

/content/journals/cpb/10.2174/0113892010305386240625072535
2024-07-12
2025-09-01
Loading full text...

Full text loading...

References

  1. Villena GonzalesW. MobashsherA. AbboshA. The progress of glucose monitoring—A review of invasive to minimally and non-invasive techniques, devices and sensors.Sensors201919480010.3390/s19040800 30781431
    [Google Scholar]
  2. WHOGlobal Report on Diabetes.World Health Organization2016
    [Google Scholar]
  3. CzupryniakL. BarkaiL. BolgarskaS. BroniszA. BrozJ. CyprykK. HonkaM. JanezA. KrnicM. LalicN. MartinkaE. RahelicD. RomanG. TankovaT. VárkonyiT. WolnikB. ZherdovaN. Self-monitoring of blood glucose in diabetes: From evidence to clinical reality in Central and Eastern Europe-recommendations from the international Central-Eastern European expert group.Diabetes Technol. Ther.201416746047510.1089/dia.2013.0302 24716890
    [Google Scholar]
  4. YaoJ. WangH. YanJ. ShaoD. SunQ. YinX. Understanding the profiles of blood glucose monitoring among patients with type 2 diabetes mellitus: A cross-sectional study in shandong, China.Patient Prefer. Adherence20211539940910.2147/PPA.S292086 33654385
    [Google Scholar]
  5. ZengY. WuJ. HanY. ChenF. ChenL. YangS. FangY. Educational disparities in the associations between self‐monitoring of blood glucose and glycemic control in type 2 diabetes patients in X iamen, C hina.J. Diabetes201810971572310.1111/1753‑0407.12651 29446529
    [Google Scholar]
  6. ChenC.C. ChenL.W. ChengS.H. Rural-urban differences in receiving guideline-recommended diabetes care and experiencing avoidable hospitalizations under a universal coverage health system: Evidence from the past decade.Public Health2017151132210.1016/j.puhe.2017.06.009 28697373
    [Google Scholar]
  7. KimJ. CampbellA.S. de ÁvilaB.E.F. WangJ. Wearable biosensors for healthcare monitoring.Nat. Biotechnol.201937438940610.1038/s41587‑019‑0045‑y 30804534
    [Google Scholar]
  8. XueP. ZhangL. XuZ. YanJ. GuZ. KangY. Blood sampling using microneedles as a minimally invasive platform for biomedical diagnostics.Appl. Mater. Today20181314415710.1016/j.apmt.2018.08.013
    [Google Scholar]
  9. TranB.Q. MillerP.R. TaylorR.M. BoydG. MachP.M. RosenzweigC.N. BacaJ.T. PolskyR. GlarosT. Proteomic characterization of dermal interstitial fluid extracted using a novel microneedle-assisted technique.J. Proteome Res.201817147948510.1021/acs.jproteome.7b00642 29172549
    [Google Scholar]
  10. PuZ. ZhangX. YuH. TuJ. ChenH. LiuY. SuX. WangR. ZhangL. LiD. A thermal activated and differential self-calibrated flexible epidermal biomicrofluidic device for wearable accurate blood glucose monitoring.Sci. Adv.202175eabd019910.1126/sciadv.abd0199 33571117
    [Google Scholar]
  11. CampbellA.S. KimJ. WangJ. Wearable electrochemical alcohol biosensors.Curr. Opin. Electrochem.20181012613510.1016/j.coelec.2018.05.014 30859141
    [Google Scholar]
  12. HeikenfeldJ. JajackA. FeldmanB. GrangerS.W. GaitondeS. BegtrupG. KatchmanB.A. Accessing analytes in biofluids for peripheral biochemical monitoring.Nat. Biotechnol.201937440741910.1038/s41587‑019‑0040‑3 30804536
    [Google Scholar]
  13. RibetF. StemmeG. RoxhedN. Real-time intradermal continuous glucose monitoring using a minimally invasive microneedle-based system.Biomed. Microdevices201820410110.1007/s10544‑018‑0349‑6 30523421
    [Google Scholar]
  14. TeymourianH. MoonlaC. TehraniF. VargasE. AghavaliR. BarfidokhtA. TangkuaramT. MercierP.P. DassauE. WangJ. Microneedle-based detection of ketone bodies along with glucose and lactate: Toward real-time continuous interstitial fluid monitoring of diabetic ketosis and ketoacidosis.Anal. Chem.20209222291230010.1021/acs.analchem.9b05109 31874029
    [Google Scholar]
  15. PengZ. XieX. TanQ. KangH. CuiJ. ZhangX. LiW. FengG. Blood glucose sensors and recent advances: A review.J. Innov. Opt. Health Sci.2022152223000310.1142/S1793545822300038
    [Google Scholar]
  16. BaileyT.S. WalshJ. StoneJ.Y. Emerging technologies for diabetes care.Diabetes Technol. Ther.201820S2S278S28410.1089/dia.2018.0115
    [Google Scholar]
  17. RahmanM.M. AhammadA.J.S. JinJ.H. AhnS.J. LeeJ.J. A comprehensive review of glucose biosensors based on nanostructured metal-oxides.Sensors20101054855488610.3390/s100504855 22399911
    [Google Scholar]
  18. KlonoffD.C. AhnD. DrincicA. Continuous glucose monitoring: A review of the technology and clinical use.Diabetes Res. Clin. Pract.201713317819210.1016/j.diabres.2017.08.005 28965029
    [Google Scholar]
  19. ChauhanN. SaxenaK. TikadarM. JainU. Recent advances in the design of biosensors based on novel nanomaterials: An insight.Nanotechnology and Precision Engineering20214404500310.1063/10.0006524
    [Google Scholar]
  20. GrayM. MeehanJ. WardC. LangdonS.P. KunklerI.H. MurrayA. ArgyleD. Implantable biosensors and their contribution to the future of precision medicine.Vet. J.2018239212910.1016/j.tvjl.2018.07.011 30197105
    [Google Scholar]
  21. RodriguesD. BarbosaA.I. RebeloR. KwonI.K. ReisR.L. CorreloV.M. Skin-integrated wearable systems and implantable biosensors: A comprehensive review.Biosensors20201077910.3390/bios10070079 32708103
    [Google Scholar]
  22. RayT.R. ChoiJ. BandodkarA.J. KrishnanS. GutrufP. TianL. GhaffariR. RogersJ.A. Bio-integrated wearable systems: A comprehensive review.Chem. Rev.201911985461553310.1021/acs.chemrev.8b00573 30689360
    [Google Scholar]
  23. ContrerasI. VehiJ. Artificial intelligence for diabetes management and decision support: Literature review.J. Med. Internet Res.2018205e1077510.2196/10775 29848472
    [Google Scholar]
  24. ZafarH. ChannaA. JeotiV. StojanovićG.M. Comprehensive review on wearable sweat-glucose sensors for continuous glucose monitoring.Sensors202222263810.3390/s22020638 35062598
    [Google Scholar]
  25. ZhangS. ZengJ. WangC. FengL. SongZ. ZhaoW. WangQ. LiuC. The application of wearable glucose sensors in point-of-care testing.Front. Bioeng. Biotechnol.2021977421010.3389/fbioe.2021.774210 34957071
    [Google Scholar]
  26. WangJ. Electrochemical glucose biosensors.Chem. Rev.2008108281482510.1021/cr068123a 18154363
    [Google Scholar]
  27. YooE.H. LeeS.Y. Glucose biosensors: An overview of use in clinical practice.Sensors20101054558457610.3390/s100504558 22399892
    [Google Scholar]
  28. BishnoiM. Deepika; Mody, N.; Jain, A. Biomedical applications of nano-biosensor.Nanotechnology for Biomedical Applications. GopiS. BalakrishnanP. MubarakN.M. SingaporeSpringer Singapore202221924610.1007/978‑981‑16‑7483‑9_10
    [Google Scholar]
  29. ShenF. ArshiS. MagnerE. UlstrupJ. XiaoX. One-step electrochemical approach of enzyme immobilization for bioelectrochemical applications.Synth. Met.202229111720510.1016/j.synthmet.2022.117205
    [Google Scholar]
  30. WangB. ShenJ. HuangY. LiuZ. ZhuangH. Graphene quantum dots and enzyme-coupled biosensor for highly sensitive determination of hydrogen peroxide and glucose.Int. J. Mol. Sci.2018196169610.3390/ijms19061696 29875333
    [Google Scholar]
  31. Yousef ElahiM. KhodadadiA.A. MortazaviY. A glucose biosensor based on glucose oxidase immobilized on ZnO/Cu 2 O graphene oxide nanocomposite electrode.J. Electrochem. Soc.20141615B81B8710.1149/2.020405jes
    [Google Scholar]
  32. SchellerF.W. SchubertF. NeumannB. PfeifferD. HintscheR. DransfeldI. WollenbergerU. RennebergR. WarsinkeA. JohanssonG. SkoogM. YangX. BogdanovskayaV. BückmannA. ZaitsevS.Y. Second generation biosensors.Biosens. Bioelectron.19916324525310.1016/0956‑5663(91)80010‑U 1652986
    [Google Scholar]
  33. FrewJ.E. HillH.A.O. Electrochemical biosensors.Anal. Chem.19875915933A944A10.1021/ac00142a720 3631522
    [Google Scholar]
  34. HilditchP.I. GreenM.J. Disposable electrochemical biosensors.Analyst1991116121217122010.1039/an9911601217 1816740
    [Google Scholar]
  35. MatthewsD.R. BownE. WatsonA. HolmanR.R. SteemsonJ. HughesS. ScottD. Pen-sized digital 30-second blood glucose meter.Lancet1987329853677877910.1016/S0140‑6736(87)92802‑9 2882186
    [Google Scholar]
  36. MurrayR.W. EwingA.G. DurstR.A. Chemically modified electrodes. Molecular design for electroanalysis.Anal. Chem.1987595379A390A 3565770
    [Google Scholar]
  37. PalmisanoF. ZamboninP.G. CentonzeD. QuintoM. A disposable, reagentless, third-generation glucose biosensor based on overoxidized poly(pyrrole)/tetrathiafulvalene-tetracyanoquino-dimethane composite.Anal. Chem.200274235913591810.1021/ac0258608 12498183
    [Google Scholar]
  38. KhanG.F. OhwaM. WernetW. Design of a stable charge transfer complex electrode for a third-generation amperometric glucose sensor.Anal. Chem.199668172939294510.1021/ac9510393 8794929
    [Google Scholar]
  39. ZhangW. LiG. Third-generation biosensors based on the direct electron transfer of proteins.Anal. Sci.200420460360910.2116/analsci.20.603 15116955
    [Google Scholar]
  40. NaikooG.A. AwanT. SalimH. ArshadF. HassanI.U. PedramM.Z. AhmedW. FaruckH.L. AljabaliA.A.A. MishraV. Serrano-ArocaÁ. GoyalR. NegiP. BirkettM. NasefM.M. CharbeN.B. BakshiH.A. TambuwalaM.M. Fourth-generation glucose sensors composed of copper nanostructures for diabetes management: A critical review.Bioeng. Transl. Med.202271e1024810.1002/btm2.10248 35111949
    [Google Scholar]
  41. ChristiansenM.P. GargS.K. BrazgR. BodeB.W. BaileyT.S. SloverR.H. SullivanA. HuangS. ShinJ. LeeS.W. KaufmanF.R. Accuracy of a fourth-generation subcutaneous continuous glucose sensor.Diabetes Technol. Ther.201719844645610.1089/dia.2017.0087 28700272
    [Google Scholar]
  42. HassanM.H. VyasC. GrieveB. BartoloP. Recent advances in enzymatic and non-enzymatic electrochemical glucose sensing.Sensors20212114467210.3390/s21144672 34300412
    [Google Scholar]
  43. ZhaoT. FuY. SunC. ZhaoX. JiaoC. DuA. WangQ. MaoY. LiuB. Wearable biosensors for real-time sweat analysis and body motion capture based on stretchable fiber-based triboelectric nanogenerators.Biosens. Bioelectron.202220511411510.1016/j.bios.2022.114115 35219020
    [Google Scholar]
  44. VermaD. SinghK.R.B. YadavA.K. NayakV. SinghJ. SolankiP.R. SinghR.P. Internet of things (IoT) in nano-integrated wearable biosensor devices for healthcare applications.Biosensors and Bioelectronics: X20221110015310.1016/j.biosx.2022.100153
    [Google Scholar]
  45. ForlenzaG.P. KushnerT. MesserL.H. WadwaR.P. SankaranarayananS. Factory-calibrated continuous glucose monitoring: How and why it works, and the dangers of reuse beyond approved duration of wear.Diabetes Technol. Ther.201921422222910.1089/dia.2018.0401 30817171
    [Google Scholar]
  46. SehitE. AltintasZ. Significance of nanomaterials in electrochemical glucose sensors: An updated review (2016-2020).Biosens. Bioelectron.202015911216510.1016/j.bios.2020.112165 32291248
    [Google Scholar]
  47. NishizawaM. Soft, wet and ionic microelectrode systems.Bull. Chem. Soc. Jpn.20189171141114910.1246/bcsj.20180064
    [Google Scholar]
  48. Okuda-ShimazakiJ. YoshidaH. SodeK. FAD dependent glucose dehydrogenases - Discovery and engineering of representative glucose sensing enzymes -.Bioelectrochemistry202013210741410.1016/j.bioelechem.2019.107414 31838457
    [Google Scholar]
  49. MugurumaH. HoshinoT. NowakiK. Electronically type-sorted carbon nanotube-based electrochemical biosensors with glucose oxidase and dehydrogenase.ACS Appl. Mater. Interfaces20157158459210.1021/am506758u 25522366
    [Google Scholar]
  50. BollellaP. SharmaS. CassA.E.G. TascaF. AntiochiaR. Minimally invasive glucose monitoring using a highly porous gold microneedles-based biosensor: Characterization and application in artificial interstitial fluid.Catalysts20199758010.3390/catal9070580
    [Google Scholar]
  51. KimS.K. JeonC. LeeG.H. KooJ. ChoS.H. HanS. ShinM.H. SimJ.Y. HahnS.K. Hyaluronate-gold nanoparticle/glucose oxidase complex for highly sensitive wireless noninvasive glucose sensors.ACS Appl. Mater. Interfaces20191140373473735610.1021/acsami.9b13874 31502433
    [Google Scholar]
  52. ChakrabortyP.P. PatraS. BhattacharjeeR. ChowdhuryS. Erroneously elevated glucose values due to maltose interference in mutant glucose dehydrogenase pyrroloquinolinequinone (Mutant GDH-PQQ) based glucometer.BMJ Case Rep.20172017bcr201721992810.1136/bcr‑2017‑219928
    [Google Scholar]
  53. Gnana kumar, G.; Amala, G.; Gowtham, S.M. Recent advancements, key challenges and solutions in non-enzymatic electrochemical glucose sensors based on graphene platforms.RSC Advances2017759369493697610.1039/C7RA02845H
    [Google Scholar]
  54. DervisevicM. AlbaM. YanL. SenelM. GengenbachT.R. Prieto-SimonB. VoelckerN.H. Transdermal electrochemical monitoring of glucose via high‐density silicon microneedle array patch.Adv. Funct. Mater.2022323200985010.1002/adfm.202009850
    [Google Scholar]
  55. PanovM.S. VereshchaginaO.A. ErmakovS.S. TumkinI.I. KhairullinaE.M. SkripkinM.Y. MereshchenkoA.S. RyazantsevM.N. KochemirovskyV.A. Non-enzymatic sensors based on in situ laser-induced synthesis of copper-gold and gold nano-sized microstructures.Talanta201716720120710.1016/j.talanta.2017.01.089 28340711
    [Google Scholar]
  56. CaiY. LiangB. ChenS. ZhuQ. TuT. WuK. CaoQ. FangL. LiangX. YeX. One-step modification of nano-polyaniline/glucose oxidase on double-side printed flexible electrode for continuous glucose monitoring: Characterization, cytotoxicity evaluation and in vivo experiment.Biosens. Bioelectron.202016511240810.1016/j.bios.2020.112408 32729528
    [Google Scholar]
  57. BollellaP. GortonL. Enzyme based amperometric biosensors.Curr. Opin. Electrochem.20181015717310.1016/j.coelec.2018.06.003
    [Google Scholar]
  58. SteinE.W. GrantP.S. ZhuH. McShaneM.J. Microscale enzymatic optical biosensors using mass transport limiting nanofilms. 1. Fabrication and characterization using glucose as a model analyte.Anal. Chem.20077941339134810.1021/ac061414z 17297932
    [Google Scholar]
  59. MelloG.P.C. SimõesE.F.C. CristaD.M.A. LeitãoJ.M.M. Pinto da SilvaL. Esteves da SilvaJ.C.G. Glucose Sensing by Fluorescent Nanomaterials.Crit. Rev. Anal. Chem.201949654255210.1080/10408347.2019.1565984 30739473
    [Google Scholar]
  60. HsiehH.V. PfeifferZ.A. AmissT.J. ShermanD.B. PitnerJ.B. Direct detection of glucose by surface plasmon resonance with bacterial glucose/galactose-binding protein.Biosens. Bioelectron.200419765366010.1016/S0956‑5663(03)00271‑9 14709382
    [Google Scholar]
  61. LiuB. MonshatH. GuZ. LuM. ZhaoX. Recent advances in merging photonic crystals and plasmonics for bioanalytical applications.Analyst2018143112448245810.1039/C8AN00144H 29748684
    [Google Scholar]
  62. MunirS. HussainS. ParkS.Y. Patterned photonic array based on an intertwined polymer network functionalized with a nonenzymatic moiety for the visual detection of glucose.ACS Appl. Mater. Interfaces20191141374343744110.1021/acsami.9b10316 31544450
    [Google Scholar]
  63. AbderrahmaneA. SenouciK. HachemiB. KoP.J. 2D gallium sulfide-based 1D photonic crystal biosensor for glucose concentration detection.Materials20231613462110.3390/ma16134621 37444934
    [Google Scholar]
  64. ConteducaD. Photonic biosensors: Detection, analysis and medical diagnostics.Biosensors202212423810.3390/bios12040238 35448298
    [Google Scholar]
  65. ThremD. NazirizadehY. GerkenM. Photonic crystal biosensors towards on-chip integration.J. Biophotonics201258-960161610.1002/jbio.201200039 22678992
    [Google Scholar]
  66. LinY. ZhaoM. GuoY. MaX. LuoF. GuoL. QiuB. ChenG. LinZ. Multicolor colormetric biosensor for the determination of glucose based on the etching of gold nanorods.Sci. Rep.2016613787910.1038/srep37879 27885274
    [Google Scholar]
  67. LiuX. HuangD. LaiC. QinL. ZengG. XuP. LiB. YiH. ZhangM. Peroxidase‐like activity of smart nanomaterials and their advanced application in colorimetric glucose biosensors.Small20191517190013310.1002/smll.201900133 30908899
    [Google Scholar]
  68. AnS. ShangN. ChenB. KangY. SuM. WangC. ZhangY. Co-Ni layered double hydroxides wrapped on leaf-shaped copper oxide hybrids for non-enzymatic detection of glucose.J. Colloid Interface Sci.202159220521410.1016/j.jcis.2021.02.046 33662825
    [Google Scholar]
  69. NicholasD. LoganK.A. ShengY. GaoJ. FarrellS. DixonD. CallanB. McHaleA.P. CallanJ.F. Rapid paper based colorimetric detection of glucose using a hollow microneedle device.Int. J. Pharm.20185471-224424910.1016/j.ijpharm.2018.06.002 29879505
    [Google Scholar]
  70. YetisenA.K. MoredduR. SeifiS. JiangN. VegaK. DongX. DongJ. ButtH. JakobiM. ElsnerM. KochA.W. Dermal tattoo biosensors for colorimetric metabolite detection.Angew. Chem. Int. Ed.20195831105061051310.1002/anie.201904416 31157485
    [Google Scholar]
  71. ZengY. WangJ. WangZ. ChenG. YuJ. LiS. LiQ. LiH. WenD. GuZ. GuZ. Colloidal crystal microneedle patch for glucose monitoring.Nano Today20203510098410.1016/j.nantod.2020.100984
    [Google Scholar]
  72. Khosravi ArdakaniH. GeramiM. ChashmpooshM. OmidifarN. GholamiA. Recent progress in nanobiosensors for precise detection of blood glucose level.Biochem. Res. Int.2022202211210.1155/2022/2964705 35083086
    [Google Scholar]
  73. FengX. ChengH. PanY. ZhengH. Development of glucose biosensors based on nanostructured graphene-conducting polyaniline composite.Biosens. Bioelectron.20157041141710.1016/j.bios.2015.03.046 25845333
    [Google Scholar]
  74. HuY. ChengH. ZhaoX. WuJ. MuhammadF. LinS. HeJ. ZhouL. ZhangC. DengY. WangP. ZhouZ. NieS. WeiH. Surface-enhanced raman scattering active gold nanoparticles with enzyme-mimicking activities for measuring glucose and lactate in living tissues.ACS Nano20171165558556610.1021/acsnano.7b00905 28549217
    [Google Scholar]
  75. ZhuL. ZhangX. YuanR. ChaiY. Ladder-like DNA nanostructure-mediated cascade catalytic nanomachine for construction of ultrasensitive biosensors.Anal. Chem.20229421264127010.1021/acs.analchem.1c04489 34962118
    [Google Scholar]
  76. BiR. MaX. MiaoK. MaP. WangQ. Enzymatic biosensor based on dendritic gold nanostructure and enzyme precipitation coating for glucose sensing and detection.Enzyme Microb. Technol.202316211013210.1016/j.enzmictec.2022.110132 36152594
    [Google Scholar]
  77. RogersK.R. Principles of affinity-based biosensors.Mol. Biotechnol.200014210913010.1385/MB:14:2:109 10872504
    [Google Scholar]
  78. SunY. LinY. SunW. HanR. LuoC. WangX. WeiQ. A highly selective and sensitive detection of insulin with chemiluminescence biosensor based on aptamer and oligonucleotide-AuNPs functionalized nanosilica @ graphene oxide aerogel.Anal. Chim. Acta2019108915216410.1016/j.aca.2019.09.004 31627812
    [Google Scholar]
  79. KajisaT. SakataT. Molecularly imprinted artificial biointerface for an enzyme-free glucose transistor.ACS Appl. Mater. Interfaces20181041349833499010.1021/acsami.8b13317 30234958
    [Google Scholar]
  80. PsomaS.D. KanthouC. Wearable insulin biosensors for diabetes management: Advances and challenges.Biosensors202313771910.3390/bios13070719 37504117
    [Google Scholar]
  81. ChengZ. WangE. YangX. Capacitive detection of glucose using molecularly imprinted polymers.Biosens. Bioelectron.200116317918510.1016/S0956‑5663(01)00137‑3 11339996
    [Google Scholar]
  82. ChenG. QiuJ. FangX. XuJ. CaiS. ChenQ. LiuY. ZhuF. OuyangG. Boronate affinity-molecularly imprinted biocompatible probe: An alternative for specific glucose monitoring.Chem. Asian J.201611162240224510.1002/asia.201600797 27411946
    [Google Scholar]
  83. OmidvarA.H. Amanati ShahriA. SerranoA.L.C. GruberJ. Pamplona RehderG. A highly sensitive molecularly imprinted polymer (MIP)-coated microwave glucose sensor.Sensors20222222864810.3390/s22228648 36433245
    [Google Scholar]
  84. SunX. Glucose detection through surface-enhanced Raman spectroscopy: A review.Anal. Chim. Acta2022120633922610.1016/j.aca.2021.339226 35473867
    [Google Scholar]
  85. LiuQ. LiuY. WuF. CaoX. LiZ. AlharbiM. AbbasA.N. AmerM.R. ZhouC. Highly sensitive and wearable in 2 O 3 nanoribbon transistor biosensors with integrated on-chip gate for glucose monitoring in body fluids.ACS Nano20181221170117810.1021/acsnano.7b06823 29338249
    [Google Scholar]
  86. LeeH.W. KangD.H. ChoJ.H. LeeS. JunD.H. ParkJ.H. Highly sensitive and reusable membraneless field-effect transistor (FET)-type tungsten diselenide (WSe 2) biosensors.ACS Appl. Mater. Interfaces20181021176391764510.1021/acsami.8b03432 29767497
    [Google Scholar]
  87. VieiraN.C.S. FigueiredoA. de QueirozA.A.A. ZucolottoV. GuimarãesF.E.G. Self-assembled films of dendrimers and metallophthalocyanines as FET-based glucose biosensors.Sensors201111109442944910.3390/s111009442 22163704
    [Google Scholar]
  88. WangB. LuoY. GaoL. LiuB. DuanG. High-performance field-effect transistor glucose biosensors based on bimetallic Ni/Cu metal-organic frameworks.Biosens. Bioelectron.202117111273610.1016/j.bios.2020.112736 33080461
    [Google Scholar]
  89. WangF. ShiF. ChenC. HuangK. ChenN. XuZ. Electrochemical fabrication of Co(OH)2 nanoparticles decorated carbon cloth for non-enzymatic glucose and uric acid detection.Mikrochim. Acta20221891038510.1007/s00604‑022‑05437‑9 36125554
    [Google Scholar]
  90. SiP. KannanP. GuoL. SonH. KimD.H. Highly stable and sensitive glucose biosensor based on covalently assembled high density Au nanostructures.Biosens. Bioelectron.20112693845385110.1016/j.bios.2011.02.044 21454070
    [Google Scholar]
  91. TarasovS.E. EmetsV.V. KluyevA.L. AndreevV.N. ReshetilovA.N. Impedancemetric detection of glucose using a biosensor based on screen-printed electrodes.Prot. Met. Phys. Chem. Surf.20185461217122010.1134/S2070205118060242
    [Google Scholar]
  92. WangJ. CarmonK.S. LuckL.A. SuniI.I. Electrochemical impedance biosensor for glucose detection utilizing a periplasmic E. coli receptor protein.Electrochem. Solid-State Lett.200588H6110.1149/1.1943549
    [Google Scholar]
  93. ZhangX. YonzonC.R. YoungM.A. StuartD.A. Van DuyneR.P. Surface-enhanced Raman spectroscopy biosensors: Excitation spectroscopy for optimisation of substrates fabricated by nanosphere lithography.IEE Proc., Nanobiotechnol.2005152619520610.1049/ip‑nbt:20050009 16441180
    [Google Scholar]
  94. Shafer-PeltierK.E. HaynesC.L. GlucksbergM.R. Van DuyneR.P. Toward a glucose biosensor based on surface-enhanced Raman scattering.J. Am. Chem. Soc.2003125258859310.1021/ja028255v 12517176
    [Google Scholar]
  95. JuJ. HsiehC.M. TianY. KangJ. ChiaR. ChangH. BaiY. XuC. WangX. LiuQ. Surface enhanced raman spectroscopy based biosensor with a microneedle array for minimally invasive in vivo glucose measurements.ACS Sens.2020561777178510.1021/acssensors.0c00444 32426978
    [Google Scholar]
  96. MaK. YuenJ.M. ShahN.C. WalshJ.T.Jr GlucksbergM.R. Van DuyneR.P. In vivo, transcutaneous glucose sensing using surface-enhanced spatially offset Raman spectroscopy: Multiple rats, improved hypoglycemic accuracy, low incident power, and continuous monitoring for greater than 17 days.Anal. Chem.201183239146915210.1021/ac202343e 22007689
    [Google Scholar]
  97. (a GuoH. BaiM. WenC. LiuM. TianS. XuS. LiuX. MaY. ChenP. LiQ. ZhangX. YangJ. ZhangL. A zwitterionicaromatic motif-based ionic skin for highly biocompatible and glucose-responsive sensor.J. Colloid Interface Sci.202160056157110.1016/j.jcis.2021.05.012 34030011
    [Google Scholar]
  98. (b LiK. DanielsJ. LiuC. HerreroP. GeorgiouP. Convolutional recurrent neural networks for glucose prediction.IEEE J. Biomed. Health Inform.2020242603613
    [Google Scholar]
  99. ChenC. ZhaoX.L. LiZ.H. ZhuZ.G. QianS.H. FlewittA. Current and emerging technology for continuous glucose monitoring.Sensors2017171218210.3390/s17010182 28106820
    [Google Scholar]
  100. AbeY. NishizawaM. Electrical aspects of skin as a pathway to engineering skin devices.APL Bioeng.20215404150910.1063/5.0064529 34849444
    [Google Scholar]
  101. NiitsuK. KobayashiA. NishioY. HayashiK. IkedaK. AndoT. OgawaY. KaiH. NishizawaM. NakazatoK. A self-powered supply-sensing biosensor platform using bio fuel cell and low-voltage, low-cost CMOS supply-controlled ring oscillator with inductive-coupling transmitter for healthcare IoT.IEEE Trans. Circuits Syst. I Regul. Pap.20186592784279610.1109/TCSI.2018.2791516
    [Google Scholar]
  102. KusamaS. SatoK. MatsuiY. KimuraN. AbeH. YoshidaS. NishizawaM. Transdermal electroosmotic flow generated by a porous microneedle array patch.Nat. Commun.202112165810.1038/s41467‑021‑20948‑4 33510169
    [Google Scholar]
  103. PapanikolaouE. SimosY.V. SpyrouK. TzianniE.I. VezyrakiP. TsamisK. PatilaM. TigasS. ProdromidisM.I. GournisD.P. StamatisH. PeschosD. DounousiE. Is graphene the rock upon which new era continuous glucose monitors could be built?Exp. Biol. Med.20232481142510.1177/15353702221134105 36408556
    [Google Scholar]
  104. LahaS. RajputA. LahaS.S. JadhavR. A concise and systematic review on non-invasive glucose monitoring for potential diabetes management.Biosensors2022121196510.3390/bios12110965 36354474
    [Google Scholar]
  105. (a VettorettiM. CapponG. FacchinettiA. SparacinoG. Advanced diabetes management using artificial intelligence and continuous glucose monitoring sensors.Sensors20202014387010.3390/s20143870 32664432
    [Google Scholar]
  106. (b PlisK. BunescuR.C. MarlingC.R. ShubrookJ.H. SchwartzF. A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management. In: AAAI Workshop: Modern Artificial Intelligence for Health Analytics [Internet].2014Available from: https://api.semanticscholar.org/CorpusID:17274044 3. McCafferty EH, Scott LJ. Migalastat: A review in fabry disease. Drugs, 2019, 79(5), 543-54.
  107. ManciniG. BerioliM. SantiE. RogariF. ToniG. TasciniG. CrispoldiR. CeccariniG. EspositoS. Flash glucose monitoring: A review of the literature with a special focus on type 1 diabetes.Nutrients201810899210.3390/nu10080992 30060632
    [Google Scholar]
  108. JinX. LiuC. XuT. SuL. ZhangX. Artificial intelligence biosensors: Challenges and prospects.Biosens. Bioelectron.202016511241210.1016/j.bios.2020.112412 32729531
    [Google Scholar]
  109. MoonS.J. JungI. ParkC.Y. Current advances of artificial pancreas systems: A comprehensive review of the clinical evidence.Diabetes Metab. J.202145681383910.4093/dmj.2021.0177 34847641
    [Google Scholar]
  110. BakhtianiP.A. ZhaoL.M. El YoussefJ. CastleJ.R. WardW.K. A review of artificial pancreas technologies with an emphasis on bi‐hormonal therapy.Diabetes Obes. Metab.201315121065107010.1111/dom.12107 23602044
    [Google Scholar]
  111. PeyserT. DassauE. BretonM. SkylerJ.S. The artificial pancreas: Current status and future prospects in the management of diabetes.Ann. N. Y. Acad. Sci.20141311110212310.1111/nyas.12431 24725149
    [Google Scholar]
  112. NishizawaM. Electrochemistry-Based Smart Biodevices.Intelligent Nanosystems for Energy, Information and Biological Technologies SoneJ. TsujiS. Springer Japan: Tokyo201630332410.1007/978‑4‑431‑56429‑4_15
    [Google Scholar]
  113. ChiuI.M. ChengC.Y. ChangP.K. LiC.J. ChengF.J. LinC.H.R. Utilization of personalized machine-learning to screen for dysglycemia from ambulatory ECG, toward noninvasive blood glucose monitoring.Biosensors20221312310.3390/bios13010023 36671857
    [Google Scholar]
  114. Gonzalez-NavarroF. Stilianova-StoytchevaM. Renteria-GutierrezL. Belanche-MuñozL. Flores-RiosB. Ibarra-EsquerJ. Glucose oxidase biosensor modeling and predictors optimization by machine learning methods.Sensors20161611148310.3390/s16111483 27792165
    [Google Scholar]
  115. (a Ben AliJ HamdiT FnaiechN Di CostanzoV FnaiechF GinouxJM Continuous blood glucose level prediction of Type 1 diabetes based on artificial neural network.Biocybern Biomed Eng201838482884010.1016/j.bbe.2018.06.005
    [Google Scholar]
  116. (b KriventsovS LindseyA HayeriA The Diabits app for smartphone-assisted predictive monitoring of glycemia in patients with diabetes: Retrospective observational study.JMIR Diabetes202053e18660
    [Google Scholar]
  117. ZhouZ. WangL. WangJ. LiuC. XuT. ZhangX. Machine learning with neural networks to enhance selectivity of nonenzymatic electrochemical biosensors in multianalyte mixtures.ACS Appl. Mater. Interfaces20221447526845269010.1021/acsami.2c17593 36397204
    [Google Scholar]
  118. NimriR. BattelinoT. LaffelL.M. SloverR.H. SchatzD. WeinzimerS.A. DovcK. DanneT. PhillipM. PhillipM. NimriR. ShalitinS. BelloR. Nevo-ShenkerM. Fisch-ShvalbN. Shiovitch-MantzuriG. ChoreshO. DrutzI. NavaY. HemoA. HermonO. NaveR. BattelinoT. DovcK. BratinaN. Smigoe-SchweigerD. MaliB. GianiniA. SeverU. BerkopecB.M. LaffelL.M. KatzM. IsganaitisE. MehtaS. QuinnH. NaikN. GuoZ. VolkeningL. SloverR.H. ForlenzaG. WadwaR.P. AlonsoG.T. MesserL. TowersL. ThivenerK. BergetC. LangeS. JostE. Rossick-SolisM. SchatzD. HallerM. HiersP. JacobsenL. SmithM. O’NeillA. HosfordJ. PerryA. WeinzimerS.A. CengizE. SherrJ. GibbonsK. CarriaL. ZgorskiM. DanneT. BiesterT. KordonouriO. von dem BergeT. BiesterS. RemusK. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes.Nat. Med.20202691380138410.1038/s41591‑020‑1045‑7 32908282
    [Google Scholar]
  119. (a EllahhamS. Artificial intelligence: The future for diabetes care.Am. J. Med.2020133889590010.1016/j.amjmed.2020.03.033 32325045
    [Google Scholar]
  120. (b ZhuT. LiK. ChenJ. HerreroP. GeorgiouP. Dilated recurrent neural networks for glucose forecasting in type 1 diabetes.J. Healthc. Inform. Res.20204330832410.1016/j.amjmed.2020.03.033 32325045
    [Google Scholar]
  121. ZhangY. WuM. TanD. LiuQ. XiaR. ChenM. LiuY. XueL. LeiY. A dissolving and glucose-responsive insulin-releasing microneedle patch for type 1 diabetes therapy.J. Mater. Chem. B Mater. Biol. Med.20219364865710.1039/D0TB02133D 33306077
    [Google Scholar]
/content/journals/cpb/10.2174/0113892010305386240625072535
Loading
/content/journals/cpb/10.2174/0113892010305386240625072535
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test