Artificial Intelligence Integration in Nanobionic Systems: New Approaches for Toxicology and Medical Applications
Yıl 2025,
Cilt: 1 Sayı: 4, 34 - 61, 28.12.2025
N. Abdullah Cengiz
,
Hamdi Kabasakal
,
Rümeysa Kurt
,
Melike Erik
,
Fatma Nur Maran
,
İrem Türk
Öz
This report addresses the systemic reshaping effects on modern medicine, ranging from the "Bio-Nano-Cyber Convergence" paradigm—which represents the convergence of common knowledge areas such as artificial intelligence (AI), nanotechnology, and biomedical sciences—to cyber-biological security risks posed by neural interfaces based on the "Bio-Nano-Nano Internet of Things" (IoBNT) architecture to cyber-biological security risks posed by neural interfaces. The study carefully examines the systemic reshaping effects on modern medicine, based on the latest data in the literature. The research highlights how deep machine learning approaches have radically improved drug discovery processes and molecular design studies, the transformation caused by systems such as AlphaFold in structural biology, the high-precision monitoring provided by nanobionic sensors in environmental and clinical toxicology, and the impact of "in silico" prediction models (QSAR/Nano-SAR) in identifying "silent toxicity" risks. It also examines how the therapeutic opportunities offered by wireless brain-computer interfaces such as "Neural Dust" can be made compatible with extraordinary ethical and security threats such as "single-pixel attacks," "brain hijacking," and the "nano-abyss" from a "One Health" perspective.
Kaynakça
-
E. Erken et al., “New Pt(0) Nanoparticles as Highly Active and Reusable Catalysts in the C1–C3 Alcohol Oxidation and the Room Temperature Dehydrocoupling of Dimethylamine-Borane (DMAB),” J. Clust. Sci. 2015 271, vol. 27, no. 1, pp. 9–23, Jun. 2015, doi: 10.1007/S10876-015-0892-8.
-
B. Sen, S. Kuzu, E. Demir, S. Akocak, and F. Sen, “Polymer-graphene hybride decorated Pt nanoparticles as highly efficient and reusable catalyst for the dehydrogenation of dimethylamine–borane at room temperature,” Int. J. Hydrogen Energy, vol. 42, no. 36, pp. 23284–23291, Sep. 2017, doi: 10.1016/J.IJHYDENE.2017.05.112.
-
A. Hojjati-Najafabadi et al., “Bacillus thuringiensis Based Ruthenium/Nickel Co-Doped Zinc as a Green Nanocatalyst: Enhanced Photocatalytic Activity, Mechanism, and Efficient H2 Production from Sodium Borohydride Methanolysis,” Ind. Eng. Chem. Res., vol. 62, no. 11, pp. 4655–4664, Mar. 2023, doi: 10.1021/ACS.IECR.2C03833.
-
E. Demir, A. Savk, B. Sen, and F. Sen, “A novel monodisperse metal nanoparticles anchored graphene oxide as Counter Electrode for Dye-Sensitized Solar Cells,” Nano-Structures & Nano-Objects, vol. 12, pp. 41–45, Oct. 2017, doi: 10.1016/J.NANOSO.2017.08.018.
-
Y. Yıldız, İ. Esirden, E. Erken, E. Demir, M. Kaya, and F. Şen, “Microwave (Mw)-assisted Synthesis of 5-Substituted 1H-Tetrazoles via [3+2] Cycloaddition Catalyzed by Mw-Pd/Co Nanoparticles Decorated on Multi-Walled Carbon Nanotubes,” ChemistrySelect, vol. 1, no. 8, pp. 1695–1701, Jun. 2016, doi: 10.1002/SLCT.201600265.
-
F. Şen and G. Gökağaç, “Pt nanoparticles synthesized with new surfactants: Improvement in C 1-C3 alcohol oxidation catalytic activity,” J. Appl. Electrochem., vol. 44, no. 1, pp. 199–207, Jan. 2014, doi: 10.1007/S10800-013-0631-5/FIGURES/7.
-
F. Sen, A. A. Boghossian, S. Sen, Z. W. Ulissi, J. Zhang, and M. S. Strano, “Observation of oscillatory surface reactions of riboflavin, trolox, and singlet oxygen using single carbon nanotube fluorescence spectroscopy,” ACS Nano, vol. 6, no. 12, pp. 10632–10645, Dec. 2012, doi: 10.1021/NN303716N/ASSET/IMAGES/MEDIUM/NN-2012-03716N_0011.GIF.
-
H. Göksu, H. Burhan, S. D. Mustafov, and F. Şen, “Oxidation of Benzyl Alcohol Compounds in the Presence of Carbon Hybrid Supported Platinum Nanoparticles (Pt@CHs) in Oxygen Atmosphere,” Sci. Rep., vol. 10, no. 1, pp. 1–8, Dec. 2020, doi: 10.1038/S41598-020-62400-5;TECHMETA=131,140,145,146;SUBJMETA=45,638,639,77,884;KWRD=BIOCHEMISTRY,CATALYST+SYNTHESIS.
-
A. Cherif, R. Nebbali, J. W. Sheffield, N. Doner, and F. Sen, “Numerical investigation of hydrogen production via autothermal reforming of steam and methane over Ni/Al2O3 and Pt/Al2O3 patterned catalytic layers,” Int. J. Hydrogen Energy, vol. 46, no. 75, pp. 37521–37532, Oct. 2021, doi: 10.1016/J.IJHYDENE.2021.04.032.
-
B. Şen et al., “High-performance graphite-supported ruthenium nanocatalyst for hydrogen evolution reaction,” J. Mol. Liq., vol. 268, pp. 807–812, Oct. 2018, doi: 10.1016/J.MOLLIQ.2018.07.117.
-
R. Darabi et al., “Simultaneous determination of ascorbic acid, dopamine, and uric acid with a highly selective and sensitive reduced graphene oxide/polypyrrole-platinum nanocomposite modified electrochemical sensor,” Electrochim. Acta, vol. 457, p. 142402, Jul. 2023, doi: 10.1016/J.ELECTACTA.2023.142402.
-
B. Sen, B. Demirkan, B. Şimşek, A. Savk, and F. Sen, “Monodisperse palladium nanocatalysts for dehydrocoupling of dimethylamineborane,” Nano-Structures & Nano-Objects, vol. 16, pp. 209–214, Oct. 2018, doi: 10.1016/J.NANOSO.2018.07.008.
-
J. Lin et al., “Phyto-mediated synthesis of nanoparticles and their applications on hydrogen generation on NaBH4, biological activities and photodegradation on azo dyes: Development of machine learning model,” Food Chem. Toxicol., vol. 163, p. 112972, May 2022, doi: 10.1016/J.FCT.2022.112972.
-
B. Sen, E. Kuyuldar, A. Şavk, H. Calimli, S. Duman, and F. Sen, “Monodisperse rutheniumcopper alloy nanoparticles decorated on reduced graphene oxide for dehydrogenation of DMAB,” Int. J. Hydrogen Energy, vol. 44, no. 21, pp. 10744–10751, Apr. 2019, doi: 10.1016/J.IJHYDENE.2019.02.176.
-
A. Şavk et al., “Highly monodisperse Pd-Ni nanoparticles supported on rGO as a rapid, sensitive, reusable and selective enzyme-free glucose sensor,” Sci. Reports 2019 91, vol. 9, no. 1, pp. 1–9, Dec. 2019, doi: 10.1038/s41598-019-55746-y.
-
A. Hojjati-Najafabadi, S. Salmanpour, F. Sen, P. N. Asrami, M. Mahdavian, and M. A. Khalilzadeh, “A Tramadol Drug Electrochemical Sensor Amplified by Biosynthesized Au Nanoparticle Using Mentha aquatic Extract and Ionic Liquid,” Top. Catal., vol. 65, no. 5–6, pp. 587–594, Apr. 2022, doi: 10.1007/S11244-021-01498-X/TABLES/2.
-
S. Eris, Z. Daşdelen, and F. Sen, “Investigation of electrocatalytic activity and stability of Pt@f-VC catalyst prepared by in-situ synthesis for Methanol electrooxidation,” Int. J. Hydrogen Energy, vol. 43, no. 1, pp. 385–390, Jan. 2018, doi: 10.1016/J.IJHYDENE.2017.11.063.
-
H. Burhan et al., “Highly efficient carbon hybrid supported catalysts using nano-architecture as anode catalysts for direct methanol fuel cells,” Int. J. Hydrogen Energy, vol. 48, no. 17, pp. 6657–6665, Feb. 2023, doi: 10.1016/J.IJHYDENE.2021.12.141.
-
N. H. Khand et al., “A new electrochemical method for the detection of quercetin in onion, honey and green tea using Co3O4 modified GCE,” J. Food Meas. Charact. 2021 154, vol. 15, no. 4, pp. 3720–3730, May 2021, doi: 10.1007/S11694-021-00956-0.
-
F. Gulbagça et al., “Facile bio-fabrication of Pd-Ag bimetallic nanoparticles and its performance in catalytic and pharmaceutical applications: Hydrogen production and in-vitro antibacterial, anticancer activities, and model development,” Chem. Eng. Res. Des., vol. 180, pp. 254–264, Apr. 2022, doi: 10.1016/J.CHERD.2022.02.024.
-
R. Ulus, Y. Yıldız, S. Eriş, B. Aday, F. Şen, and M. Kaya, “Functionalized Multi-Walled Carbon Nanotubes (f-MWCNT) as Highly Efficient and Reusable Heterogeneous Catalysts for the Synthesis of Acridinedione Derivatives,” ChemistrySelect, vol. 1, no. 13, pp. 3861–3865, Aug. 2016, doi: 10.1002/SLCT.201600719.
-
M. S. Nas, M. H. Calimli, H. Burhan, M. Yılmaz, S. D. Mustafov, and F. Sen, “Corrigendum to ‘Synthesis, characterization, kinetics and adsorption properties of Pt-Co@GO nano-adsorbent for methylene blue removal in the aquatic mediums using ultrasonic process systems’. [J. Mol. Liquids 296 (2019) 112100],” J. Mol. Liq., vol. 340, p. 117289, Oct. 2021, doi: 10.1016/J.MOLLIQ.2021.117289.
-
F. Ameen et al., “Synthesis and characterization of activated carbon supported bimetallic Pd based nanoparticles and their sensor and antibacterial investigation,” Environ. Res., vol. 221, p. 115287, Mar. 2023, doi: 10.1016/J.ENVRES.2023.115287.
-
K. Arikan, H. Burhan, R. Bayat, and F. Sen, “Glucose nano biosensor with non-enzymatic excellent sensitivity prepared with nickel–cobalt nanocomposites on f-MWCNT,” Chemosphere, vol. 291, p. 132720, Mar. 2022, doi: 10.1016/J.CHEMOSPHERE.2021.132720.
-
H. Seckin, R. N. E. Tiri, I. Meydan, A. Aygun, M. K. Gunduz, and F. Sen, “An environmental approach for the photodegradation of toxic pollutants from wastewater using Pt–Pd nanoparticles: Antioxidant, antibacterial and lipid peroxidation inhibition applications,” Environ. Res., vol. 208, p. 112708, May 2022, doi: 10.1016/J.ENVRES.2022.112708.
-
I. Meydan, H. Burhan, T. Gür, H. Seçkin, B. Tanhaei, and F. Sen, “Characterization of Rheum ribes with ZnO nanoparticle and its antidiabetic, antibacterial, DNA damage prevention and lipid peroxidation prevention activity of in vitro,” Environ. Res., vol. 204, p. 112363, Mar. 2022, doi: 10.1016/J.ENVRES.2021.112363.
-
N. Korkmaz, Y. Ceylan, P. Taslimi, A. Karadağ, A. S. Bülbül, and F. Şen, “Biogenic nano silver: Synthesis, characterization, antibacterial, antibiofilms, and enzymatic activity,” Adv. Powder Technol., vol. 31, no. 7, pp. 2942–2950, Jul. 2020, doi: 10.1016/J.APT.2020.05.020.
-
F. Şen, G. Gökağaç, and S. Şen, “High performance Pt nanoparticles prepared by new surfactants for C 1 to C3 alcohol oxidation reactions,” J. Nanoparticle Res., vol. 15, no. 10, pp. 1–9, Oct. 2013, doi: 10.1007/S11051-013-1979-5/FIGURES/6.
-
Y. Yıldız, İ. Esirden, E. Erken, E. Demir, M. Kaya, and F. Şen, “Microwave (Mw)-assisted Synthesis of 5-Substituted 1H-Tetrazoles via [3+2] Cycloaddition Catalyzed by Mw-Pd/Co Nanoparticles Decorated on Multi-Walled Carbon Nanotubes,” ChemistrySelect, vol. 1, no. 8, pp. 1695–1701, Jun. 2016, doi: 10.1002/SLCT.201600265;WGROUP:STRING:PUBLICATION.
-
F. Sen, A. A. Boghossian, S. Sen, Z. W. Ulissi, J. Zhang, and M. S. Strano, “Observation of Oscillatory Surface Reactions of Riboflavin, Trolox, and Singlet Oxygen Using Single Carbon Nanotube Fluorescence Spectroscopy,” 2012, doi: 10.1021/NN303716N.
-
D. E. Mazouzi et al., “Auto-combustion designed BiFeO3/Bi2O3 photocatalyst for improved photodegradation of nitrobenzene under visible light and sunlight irradiation,” Surfaces and Interfaces, vol. 44, p. 103581, Jan. 2024, doi: 10.1016/J.SURFIN.2023.103581.
-
B. Çelik et al., “Retracted Article: Highly monodisperse Pt(0)@AC NPs as highly efficient and reusable catalysts: the effect of the surfactant on their catalytic activities in room temperature dehydrocoupling of DMAB,” Catal. Sci. Technol., vol. 6, no. 6, pp. 1685–1692, Mar. 2016, doi: 10.1039/C5CY01371B.
-
Y. Liang et al., “Facile synthesis of biogenic palladium nanoparticles using biomass strategy and application as photocatalyst degradation for textile dye pollutants and their in-vitro antimicrobial activity,” Chemosphere, vol. 306, p. 135518, Nov. 2022, doi: 10.1016/J.CHEMOSPHERE.2022.135518.
-
Y. Wu et al., “Hydrogen generation from methanolysis of sodium borohydride using waste coffee oil modified zinc oxide nanoparticles and their photocatalytic activities,” Int. J. Hydrogen Energy, vol. 48, no. 17, pp. 6613–6623, Feb. 2023, doi: 10.1016/J.IJHYDENE.2022.04.177.
-
B. Sen, B. Demirkan, A. Şavk, S. Karahan Gülbay, and F. Sen, “Trimetallic PdRuNi nanocomposites decorated on graphene oxide: A superior catalyst for the hydrogen evolution reaction,” Int. J. Hydrogen Energy, vol. 43, no. 38, pp. 17984–17992, Sep. 2018, doi: 10.1016/J.IJHYDENE.2018.07.122.
-
K. Nesrin et al., “Biogenic silver nanoparticles synthesized from Rhododendron ponticum and their antibacterial, antibiofilm and cytotoxic activities,” J. Pharm. Biomed. Anal., vol. 179, p. 112993, Feb. 2020, doi: 10.1016/J.JPBA.2019.112993.
-
H. Goksu, Y. Yildiz, B. Çelik, M. Yazici, B. Kilbas, and F. Sen, “Eco-friendly hydrogenation of aromatic aldehyde compounds by tandem dehydrogenation of dimethylamine-borane in the presence of a reduced graphene oxide furnished platinum nanocatalyst,” Catal. Sci. Technol., vol. 6, no. 7, pp. 2318–2324, Apr. 2016, doi: 10.1039/C5CY01462J.
-
N. Lolak, E. Kuyuldar, H. Burhan, H. Goksu, S. Akocak, and F. Sen, “Composites of Palladium-Nickel Alloy Nanoparticles and Graphene Oxide for the Knoevenagel Condensation of Aldehydes with Malononitrile,” ACS Omega, vol. 4, no. 4, pp. 6848–6853, Apr. 2019, doi: 10.1021/ACSOMEGA.9B00485/SUPPL_FILE/AO9B00485_SI_001.PDF.
-
S. Ertan, F. Şen, S. Şen, and G. Gökağaç, “Platinum nanocatalysts prepared with different surfactants for C1-C3 alcohol oxidations and their surface morphologies by AFM,” J. Nanoparticle Res., vol. 14, no. 6, pp. 1–12, Jun. 2012, doi: 10.1007/S11051-012-0922-5/FIGURES/8.
-
H. Göksu, Y. Yıldız, B. Çelik, M. Yazıcı, B. Kılbaş, and F. Şen, “Highly Efficient and Monodisperse Graphene Oxide Furnished Ru/Pd Nanoparticles for the Dehalogenation of Aryl Halides via Ammonia Borane,” ChemistrySelect, vol. 1, no. 5, pp. 953–958, Apr. 2016, doi: 10.1002/SLCT.201600207.
-
S. Günbatar, A. Aygun, Y. Karataş, M. Gülcan, and F. Şen, “Carbon-nanotube-based rhodium nanoparticles as highly-active catalyst for hydrolytic dehydrogenation of dimethylamineborane at room temperature,” J. Colloid Interface Sci., vol. 530, pp. 321–327, Nov. 2018, doi: 10.1016/J.JCIS.2018.06.100.
-
A. Aygun et al., “Highly active PdPt bimetallic nanoparticles synthesized by one-step bioreduction method: Characterizations, anticancer, antibacterial activities and evaluation of their catalytic effect for hydrogen generation,” Int. J. Hydrogen Energy, vol. 48, no. 17, pp. 6666–6679, Feb. 2023, doi: 10.1016/J.IJHYDENE.2021.12.144.
-
B. Şen, A. Aygün, T. O. Okyay, A. Şavk, R. Kartop, and F. Şen, “Monodisperse palladium nanoparticles assembled on graphene oxide with the high catalytic activity and reusability in the dehydrogenation of dimethylamine-borane,” Int. J. Hydrogen Energy, vol. 43, no. 44, pp. 20176–20182, Nov. 2018, doi: 10.1016/J.IJHYDENE.2018.03.175.
-
J. T. Abrahamson et al., “Excess Thermopower and the Theory of Thermopower Waves,” ACS Nano, vol. 7, no. 8, pp. 6533–6544, Aug. 2013, doi: 10.1021/NN402411K.
-
B. Sen, S. Kuzu, E. Demir, E. Yıldırır, and F. Sen, “Highly efficient catalytic dehydrogenation of dimethyl ammonia borane via monodisperse palladium–nickel alloy nanoparticles assembled on PEDOT,” Int. J. Hydrogen Energy, vol. 42, no. 36, pp. 23307–23314, Sep. 2017, doi: 10.1016/J.IJHYDENE.2017.05.115.
-
F. Şen and G. Gökaǧaç, “Improving Catalytic Efficiency in the Methanol Oxidation Reaction by Inserting Ru in Face-Centered Cubic Pt Nanoparticles Prepared by a New Surfactant, tert-Octanethiol,” Energy and Fuels, vol. 22, no. 3, pp. 1858–1864, May 2008, doi: 10.1021/EF700575T.
-
E. Erken, Y. Yildiz, B. Kilbaş, and F. Şen, “Synthesis and Characterization of Nearly Monodisperse Pt Nanoparticles for C1 to C3 Alcohol Oxidation and Dehydrogenation of Dimethylamine-borane (DMAB),” J. Nanosci. Nanotechnol., vol. 16, no. 6, pp. 5944–5950, Jun. 2016, doi: 10.1166/JNN.2016.11683.
-
B. Şen, A. Aygün, A. Şavk, S. Akocak, and F. Şen, “Bimetallic palladium–iridium alloy nanoparticles as highly efficient and stable catalyst for the hydrogen evolution reaction,” Int. J. Hydrogen Energy, vol. 43, no. 44, pp. 20183–20191, Nov. 2018, doi: 10.1016/J.IJHYDENE.2018.07.081.
-
E. Demir, A. Savk, B. Sen, and F. Sen, “A novel monodisperse metal nanoparticles anchored graphene oxide as Counter Electrode for Dye-Sensitized Solar Cells,” Nano-Structures & Nano-Objects, vol. 12, pp. 41–45, Oct. 2017, doi: 10.1016/J.NANOSO.2017.08.018.
-
G. G. Wallace, M. J. Higgins, S. E. Moulton, and C. Wang, “Nanobionics: the impact of nanotechnology on implantable medical bionic devices,” Nanoscale, vol. 4, no. 15, pp. 4327–4347, 2012.
-
R. Prasad, Plant Nanobionics. Springer Nature, 2019.
-
M. K. Enamala, B. Kolapalli, P. D. Sruthi, S. Sarkar, C. Kuppam, and M. Chavali, “Applications of nanomaterials and future prospects for nanobionics,” in Plant Nanobionics, Springer, 2019, pp. 177–197.
-
Y. Liu, S. Qin, and Y. Luo, “Nanotechnology-enabled drug delivery systems guided by artificial intelligence,” Adv. Drug Deliv. Rev., vol. 167, pp. 104–122.
-
V. Kozhukharov and M. Machkova, “Nanomaterials and nanotechnology: European initiatives, status and strategy,” J. Chem. Technol. Metall., vol. 48, no. 3, 2013.
-
G. A. Mansoori, “An introduction to nanoscience and nanotechnology,” in Nanoscience and Plant--Soil Systems, Springer, 2017.
-
S. Tripathy et al., “Artificial Intelligence-Based Portable Bioelectronics Platform for SARS-CoV-2 Diagnosis with Multi-nucleotide Probe Assay for Clinical Decisions,” Anal. Chem., vol. 93, no. 45, pp. 14955–14965, Nov. 2021, doi: 10.1021/ACS.ANALCHEM.1C01650.
-
M. Bakshi and P. C. Abhilash, “Nanotechnology for soil remediation: Revitalizing the tarnished resource,” Nano-Materials as Photocatal. Degrad. Environ. Pollut. Challenges Possibilities, pp. 345–370, Dec. 2019, doi: 10.1016/B978-0-12-818598-8.00017-1.
-
A. Ranjan, V. D. Rajput, A. Kumari, S. S. Mandzhieva, and S. Sushkova, “Nanobionics in Crop Production : An Emerging Approach to,” Plant, vol. 11, pp. 1–16, 2022.
-
M. Usman et al., “Nanotechnology in agriculture: Current status, challenges and future opportunities,” Sci. Total Environ., vol. 721, Jun. 2020, doi: 10.1016/J.SCITOTENV.2020.137778.
-
P. S. Tourinho, C. A. M. van Gestel, S. Lofts, C. Svendsen, A. M. V. M. Soares, and S. Loureiro, “Metal-based nanoparticles in soil: Fate, behavior, and effects on soil invertebrates,” Environ. Toxicol. Chem., vol. 31, no. 8, pp. 1679–1692, Aug. 2012, doi: 10.1002/ETC.1880.
-
H. Chen, “Metal based nanoparticles in agricultural system: Behavior, transport, and interaction with plants,” Chem. Speciat. Bioavailab., vol. 30, no. 1, pp. 123–134, Jan. 2018, doi: 10.1080/09542299.2018.1520050.
-
A. Ranjan, V. D. Rajput, T. Minkina, T. Bauer, A. Chauhan, and T. Jindal, “Nanoparticles induced stress and toxicity in plants,” Environ. Nanotechnology, Monit. Manag., vol. 15, May 2021, doi: 10.1016/J.ENMM.2021.100457.
-
R. Liu and R. Lal, “Potentials of engineered nanoparticles as fertilizers for increasing agronomic productions,” Sci. Total Environ., vol. 514, pp. 131–139, May 2015, doi: 10.1016/J.SCITOTENV.2015.01.104.
-
V. D. Rajput et al., “Coping with the challenges of abiotic stress in plants: New dimensions in the field application of nanoparticles,” Plants, vol. 10, no. 6, Jun. 2021, doi: 10.3390/PLANTS10061221.
-
J. P. Giraldo et al., “Erratum: Plant nanobionics approach to augment photosynthesis and biochemical sensing (Nature Materials (2014) 13 (400-408)),” Nat. Mater., vol. 13, no. 5, p. 530, 2014, doi: 10.1038/NMAT3947.
-
N. Terry, “Limiting Factors in Photosynthesis,” Plant Physiol., vol. 65, no. 1, pp. 114–120, Jan. 1980, doi: 10.1104/PP.65.1.114.
-
R. E. al Abdalla-Aslan et al., “Nanotechnology for soil remediation: Revitalizing the tarnished resource,” ACS Nano, vol. 2, no. 3, p. 100561, Oct. 2023, doi: 10.1021/acsomega.3c09191.
-
Y. Zhu, S. Murali, W. Cai, X. Li, and S. J. W. diğerleri, “Grafen ve grafen oksit: Sentez, özellikler ve uygulamalar,” Gelişmiş Malzemeler, vol. 22, no. 35, pp. 3906–3924.
-
B. Sanchez-Lengeling and A. Aspuru-Guzik, “Inverse molecular design using machine learning:Generative models for matter engineering,” Science (80-. )., vol. 361, no. 6400, pp. 360–365, Jul. 2018, doi: 10.1126/SCIENCE.AAT2663;WEBSITE:WEBSITE:AAAS-SITE;JOURNAL:JOURNAL:SCIENCE;WGROUP:STRING:PUBLICATION.
-
S. He, J. S. Abarrategi, H. Bediaga, S. Arrasate, and H. González-Díaz, “On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems,” Beilstein J Nanotechnol, vol. 15, no. 1, pp. 535–55.
-
R. Qureshi et al., “AI in drug discovery and its clinical relevance,” Heliyon, vol. 9, p. 17575.
-
A. Blanco-González et al., “The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies,” Pharmaceuticals, vol. 16, p. 891.
-
J. S. Ahn et al., “Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine,” J. Breast Cancer, vol. 26, no. 5, pp. 405–435, Oct. 2023, doi: 10.4048/JBC.2023.26.E45.
-
D. Zheng, X. He, and J. Jing, “Overview of Artificial Intelligence in Breast Cancer Medical Imaging,” J. Clin. Med, vol. 12, p. 419.
-
A. Al Kuwaiti et al., “A Review of the Role of Artificial Intelligence in Healthcare,” J. Pers. Med., vol. 13, no. 6, Jun. 2023, doi: 10.3390/JPM13060951.
-
M. A. Darwish, W. Abd-Elaziem, A. Elsheikh, and A. A. Zayed, “Advancements in nanomaterials for nanosensors: A comprehensive review,” Nanoscale Adv, vol. 6, pp. 4015–4046.
-
M. Javaid, A. Haleem, R. P. Singh, S. Rab, and R. Suman, “Exploring the potential of nanosensors: A brief overview,” Sensors Int, vol. 2, p. 100130.
-
T. Adam and S. C. Gopinath, “Nanosensors: Recent perspectives on attainments and future promise of downstream applications,” Process. Biochem, vol. 117, pp. 153–173.
-
M. Eissa, “Nanosensors for Early Detection and Diagnosis of Cancer: A Review of Recent Advances,” J. Cancer Res. Rev., vol. 1, no. 1, p. 1, 2024, doi: 10.5455/JCRR.20240205070256.
-
S. Gulati, R. Yadav, V. Kumari, S. Nair, C. Gupta, and M. Aishwari, “Nanosensors in healthcare: transforming real-time monitoring and disease management with cutting-edge nanotechnology,” RSC Pharm., vol. 2, no. 5, pp. 1003–1018, Sep. 2025, doi: 10.1039/D5PM00125K.
-
X. Tang, Y. Zhu, W. Guan, W. Zhou, and P. Wei, “Advances in nanosensors for cardiovascular disease detection,” Life Sci, vol. 305, p. 120733.
-
X. Lin et al., “Portable dual-mode microfluidic sensor for rapid and sensitive detection of DPA on chip,” Adv. Compos. Hybrid Mater., vol. 8, no. 3, Jun. 2025, doi: 10.1007/S42114-025-01320-2.
-
R. Das, S. Nag, and P. Banerjee, “Electrochemical Nanosensors for Sensitization of Sweat Metabolites: From Concept Mapping to Personalized Health Monitoring,” Molecules, p. 28.
-
S. Liu et al., “Evaluation of the Multidimensional Enhanced Lateral Flow Immunoassay in Point-of-Care Nanosensors,” ACS Nano, vol. 18, no. 40, pp. 27167–27205, Oct. 2024, doi: 10.1021/ACSNANO.4C06564.
-
H. Tavakoli, S. Mohammadi, X. Li, G. Fu, and X. Li, “Microfluidic platforms integrated with nano-sensors for point-of-care bioanalysis,” TrAC Trends Anal. Chem, vol. 157, p. 116806.
-
C. Yang, Q. Wang, Y. Xiang, R. Yuan, and Y. Chai, “Target-induced strand release and thionine-decorated gold nanoparticle amplification labels for sensitive electrochemical aptamer-based sensing of small molecules,” Sensors Actuators, B Chem., vol. 197, pp. 149–154, Jul. 2014, doi: 10.1016/J.SNB.2014.02.036.
-
H. Chen, O. Engkvist, Y. Wang, M. Olivecrona, and T. Blaschke, “The rise of deep learning in drug discovery,” Drug Discov. Today, vol. 23, no. 6, pp. 1241–1250, Jun. 2018, doi: 10.1016/j.drudis.2018.01.039.
-
S. A. H. Hassan et al., “Development of Nanotechnology by Artificial Intelligence: A Comprehensive Review,” J. Nanostruct, vol. 13, pp. 915–932.
-
A. Fallah, S. A. Havaei, H. Sedighian, R. Kachuei, and A. A. I. Fooladi, “Prediction of aptamer affinity using an artificial intelligence approach,” J. Mater. Chem. B, vol. 12, pp. 8825–8842.
-
J. W. Lowdon et al., “Identifying potential machine learning algorithms for the simulation of binding affinities to molecularly imprinted polymers,” Computation, vol. 9, no. 10, Dec. 2021, doi: 10.3390/COMPUTATION9100103.
-
S. Hamedi, H. D. Jahromi, and A. Lotfiani, “Artificial intelligence-aided nanoplasmonic biosensor modeling,” Eng. Appl. Artif. Intell., vol. 118, Feb. 2023, doi: 10.1016/J.ENGAPPAI.2022.105646.
-
H. Haick and N. Tang, “Artificial Intelligence in Medical Sensors for Clinical Decisions,” ACS Nano, vol. 15, no. 3, pp. 3557–3567, Mar. 2021, doi: 10.1021/ACSNANO.1C00085.
-
K. Tafadzwa Mpofu and P. Mthunzi-Kufa, “Recent Advances in Artificial Intelligence and Machine Learning Based Biosensing Technologies,” Mar. 2025, doi: 10.5772/INTECHOPEN.1009613.
-
S. Yin and S. Yin, “Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects,” Biosens. 2025, Vol. 15, vol. 15, no. 10, p. 656, Oct. 2025, doi: 10.3390/BIOS15100656.
-
C. D. Flynn and D. Chang, “Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities,” Diagnostics, vol. 14, no. 11, Jun. 2024, doi: 10.3390/DIAGNOSTICS14111100.
-
F.-G. Banica, Chemical Sensors and Biosensors:Fundamentals and Applications. Chichester, UK: John Wiley & Sons, 2012.
-
C. Dincer et al., Disposable Sensors in Diagnostics, Food, and Environmental Monitoring, vol. 31, no. 30. Wiley-VCH Verlag, 2019. doi: 10.1002/adma.201806739.
-
A. P. F. . Turner, I. Karube, and G. S. . Wilson, Biosensors:Fundamentals and Applications. Oxford, UK: Oxford University Press, 1987.
-
U. Kökbaş, L. Kayrın, A. Tuli, and Ç. Üniversitesi Tıp Fakültesi Tıbbi Biyokimya ABD, “Biyosensörler ve Tıpta Kullanım Alanları,” Arch. Med. Rev. J., vol. 22, no. 4, pp. 499–513, Dec. 2013.
-
Y. S. Choi et al., “Real-Time Monitoring of Volatile Organic Compound-Mediated Plant Intercommunication Using Surface-Enhanced Raman Scattering Nanosensor,” Adv. Sci., vol. 12, no. 7, p. 2412732, Feb. 2025, doi: 10.1002/ADVS.202412732;PAGE:STRING:ARTICLE/CHAPTER.
-
Z. Tüylek, “Biyoteknolojide Biyosensör ve Biyoçip Uygulamaları,” Int. J. Life Sci. Biotechnol., vol. 4, no. 3, pp. 468–490, Dec. 2021, doi: 10.38001/IJLSB.876231.
-
A. P. F. Turner, “Biosensors: sense and sensibility,” Chem. Soc. Rev., vol. 42, no. 8, pp. 3184–3196, Mar. 2013, doi: 10.1039/C3CS35528D.
-
J. E. N. Dolatabadi et al., “Optical and electrochemical DNA nanobiosensors,” TrAC Trends Anal. Chem., vol. 30, no. 3, pp. 459–472, Mar. 2011, doi: 10.1016/J.TRAC.2010.11.010.
-
P. Mehrotra, “Biosensors and their applications – A review,” J. Oral Biol. Craniofacial Res., vol. 6, no. 2, pp. 153–159, May 2016, doi: 10.1016/J.JOBCR.2015.12.002.
-
V. Ergül, S. Çakir, and M. Bilgisi, “Nanoteknolojinin Sektörel Uygulamaları Üzerine Bir Değerlendirme,” J. Def. Sci., vol. 1, no. 43, pp. 1–22, May 2023, doi: 10.17134/KHOSBD.1081519.
-
E. Alotaibi and N. Nassif, “Yapay Zekayı Keşfedin Araştırma Çevresel izlemede yapay zeka: derinlemesine analiz”, doi: 10.1007/s44163-024-00198-1.
-
S. V. Di̇ji̇talleşme Yapay Zekâ, Ö. Üyesi Betül AKALIN, and Ü. Veranyurt, “Cilt: 2, Sayı: 2, ss,” pp. 131–141.
-
E. Üniversitesi, Z. Fakültesi, B. Koruma Bölümü, and G. Tarihi, “Biyosensörler ve Tarım Alanında Kullanımı Burçin BOZ, İsmail Can PAYLAN, Mehmet Zeki KIZMAZ, Semih ERKAN,” J. Agric. Mach. Sci., vol. 2017, no. 3, pp. 141–148.
-
K. Sağlık Yüksek Okulu, N. Hastanesi yanı, K. Tıp Dergisi, A. Kocatepe Üniversitesi, M. Özata, and Ş. Aslan, “Başvuru 10 Eylül,” 2003.
-
L. P. X. Yong et al., “Artificial Intelligence Applications in Emergency Toxicology: Advancements and Challenges,” J. Med. Internet Res., vol. 27, no. 1, p. e73121, Aug. 2025, doi: 10.2196/73121.
-
“biyomarkörlerin toksikolojide kullanımı.”
-
İ. Özer, H. TEZEL, S. SANAJOU, … A. Y.-J. of L., and undefined 2022, “Biyosensörler ve Kullanım Alanları: Geleneksel Derleme.,” Res. Özer, H TEZEL, S SANAJOU, A Yirün, T Baydar, P ErkekoğluJournal Lit. Pharm. Sci. 2022•researchgate.net.
-
N. A. Buckley, I. M. Whyte, and A. H. Dawson, “Diagnostic data in clinical toxicology - Should we use a Bayesian approach?,” J. Toxicol. - Clin. Toxicol., vol. 40, no. 3, pp. 213–222, 2002, doi: 10.1081/CLT-120005491;JOURNAL:JOURNAL:ICTX18;WGROUP:STRING:PUBLICATION.
-
V. Garzón, D. G. Pinacho, R. H. Bustos, G. Garzón, and S. Bustamante, “Optical Biosensors for Therapeutic Drug Monitoring,” Biosens. 2019, Vol. 9, Page 132, vol. 9, no. 4, p. 132, Nov. 2019, doi: 10.3390/BIOS9040132.
-
R. J. S. Banicod, N. Tabassum, D. M. Jo, A. Javaid, Y. M. Kim, and F. Khan, “Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens,” Biosens. 2025, Vol. 15, Page 690, vol. 15, no. 10, p. 690, Oct. 2025, doi: 10.3390/BIOS15100690.
-
H. Sezginer, F. Dane, T. Üniversitesi, F. Fakültesi, and B. Bölümü, “Toksik Maddelerin Genotoksik Analiz Yöntemleri,” Türk Bilim. Derlemeler Derg., vol. 9, no. 1, pp. 50–55, 2016.
-
M. S. Islam, K. Sazawa, K. Sugawara, and H. Kuramitz, “Electrochemical Biosensor for Evaluation of Environmental Pollutants Toxicity,” Environ. 2023, Vol. 10, Page 63, vol. 10, no. 4, p. 63, Apr. 2023, doi: 10.3390/ENVIRONMENTS10040063.
-
M. Negahdary et al., “Recent electrochemical sensors and biosensors for toxic agents based on screen-printed electrodes equipped with nanomaterials,” Microchem. J., vol. 185, p. 108281, Feb. 2023, doi: 10.1016/J.MICROC.2022.108281.
-
M. Javaid, A. Haleem, R. P. Singh, S. Rab, and R. Suman, “Exploring the potential of nanosensors: A brief overview,” Sensors Int., vol. 2, Jan. 2021, doi: 10.1016/J.SINTL.2021.100130.
-
X. Chen and Q. Wan, “Ru-Doped MoS2 Monolayer for Exhaled Breath Detection on Early Lung Cancer Diagnosis,” ACS Omega, vol. 9, no. 12, pp. 13951–13959, 2024, doi: 10.1021/acsomega.3c09191.
-
G. Shang et al., “Chemiresistive Sensor Array with Nanostructured Interfaces for Detection of Human Breaths with Simulated Lung Cancer VOCs,” ACS Sensors, vol. 8, no. 3, pp. 1328–1338, 2023, doi: 10.1021/acssensors.2c02839.
-
A. W. Adamson and A. P. Gast, Physical Chemistry of Surfaces, 6th ed. New York: John Wiley \& Sons, 1997.
-
G. Rong, S. R. Corrie, and H. A. Clark, “In Vivo Biosensing: Progress and Perspectives,” ACS Sensors, vol. 2, pp. 327–338, 2017.
-
S. Gulati, R. Yadav, V. Kumari, S. Nair, C. Gupta, and M. Aishwari, “Nanosensors in healthcare: Transforming real-time monitoring and disease management with cutting-edge nanotechnology,” RSC Pharm, vol. 2, pp. 1003–10018.
-
K. P. Mulaudji, K. V Mokwebo, F. Q. De Bruin, K. Pokpas, and N. Ross, “Advances in electrochemical sensing of chloramphenicol in complex matrices,” Talanta Open, vol. 12, p. 100561, 2025.
-
A. Pantelopoulos and N. G. Bourbakis, “Wearable sensor-based systems for health monitoring and prognosis,” IEEE Trans. Syst. Man, Cybern. Part C, vol. 40, pp. 1–12, 2010.
-
Z. Asefy, S. Hoseinnejhad, and Z. Ceferov, “Nanoparticles approaches in neurodegenerative diseases diagnosis and treatment,” Neurol Sci, vol. 42, no. 7, pp. 2653–60, Jul. 2021, doi: 10.1007/s10072-021-05234-x.
-
Global Cancer Observatory, “Cancer Today.” 2021.
-
A. Mohsin et al., “Nanomaterial interfaces for sensing applications,” ACS Nano, vol. 7, pp. 8924–8931, 2013.
-
O. Parlak, A. P. F. Turner, and A. Tiwari, “On-chip electrochemical biosensing,” Adv. Mater., vol. 26, pp. 482–486, 2014.
-
T. Kuila, S. Bose, P. Khanra, A. K. Mishra, N. H. Kim, and J. H. Lee, “Graphene-based biosensors,” Biosens. Bioelectron., vol. 26, pp. 4637–4648, 2011.
-
S. Qu, X. Wang, Q. Lu, X. Liu, and L. Wang, “Carbon quantum dots,” Angew. Chemie, vol. 124, pp. 12381–12384, 2012.
-
Z. Ku, Y. Rong, M. Xu, T. Liu, and H. Han, “Nanostructures for sensing,” Sci. Rep., vol. 3, p. 3132, 2013.
[
M. Kokabi, M. N. Tahir, D. Singh, and M. Javanmard, “Biosensors and machine learning synergy for early cancer diagnosis,” Biosensors, vol. 13, p. 884, 2023.
-
Y. Lei et al., “Microwave biosensor with machine learning for CEA detection,” Biosens. Bioelectron., vol. 269, p. 116908, 2025.
-
R. Miotto, L. Li, B. A. Kidd, and J. T. Dudley, “Deep patient: An unsupervised representation to predict the future of patients,” Sci. Rep., vol. 6, 2016.
-
D. Roffman, G. Hart, M. Girardi, C. J. Ko, and J. Deng, “Prediction of non-melanoma skin cancer with neural networks,” Sci. Rep., vol. 8, 2018.
-
B. J. Nartowt, G. R. Hart, W. Muhammad, Y. Liang, G. F. Stark, and J. Deng, “Robust machine learning for colorectal cancer risk prediction,” Front. Big Data, 2020.
-
J. E. al Jumper, “Highly accurate protein structure prediction with AlphaFold,” Nature.
-
K.-K. Mak and M. R. Pichika, “Artificial intelligence in drug development: present status and future prospects,” Drug Discov. Today, vol. 24, no. 3, pp. 773–780, 2019, doi: https://doi.org/10.1016/j.drudis.2018.11.014.
-
M. Shirzad et al., “Artificial Intelligence-Assisted Design of Nanomedicines for Breast Cancer Diagnosis and Therapy: Advances, Challenges, and Future Directions,” BioNanoScience 2025 153, vol. 15, no. 3, pp. 354-, May 2025, doi: 10.1007/S12668-025-01980-W.
-
M. Alavinejad, “Smart nanomedicines powered by artificial intelligence: A breakthrough in lung cancer diagnosis and treatment,” Med. Oncol., vol. 42, no. 5, p. 134.
-
L. Rao, Y. Yuan, X. Shen, G. Yu, and X. Chen, “Designing nanotheranostics with machine learning,” Nat Nanotechnol, vol. 19, no. 12, pp. 1769–78, Dec. 2024, doi: 10.1038/s41565-024-01753-8.
-
L. K. Vora, A. D. Gholap, K. Jetha, R. R. S. Thakur, H. K. Solanki, and V. P. Chavda, “Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design,” Pharmaceutics, vol. 15, no. 7, Jul. 2023, doi: 10.3390/PHARMACEUTICS15071916.
-
V. M. Cambuli and G. M, “Baroni, Intelligent insulin vs Artificial intelligence for type 1 diabetes: Will the real winner please stand up?,” Int. J. Mol. Sci., vol. 24, no. 17, p. 13139.
-
M. K. Jayasinghe and others, “The role of in silico research in developing nanoparticle-based therapeutics,” Front. Digit. Heal., vol. 4, p. 838590, 2022.
-
P. Hassanzadeh, F. Atyabi, and R. Dinarvand, “The significance of artificial intelligence in drug delivery system design,” Adv. Drug Deliv. Rev., vol. 151, pp. 169–190.
-
W. Zhan, M. Alamer, and X. Y. Xu, “Computational modelling of drug delivery to solid tumour,” Adv. Drug Deliv. Rev., vol. 132, pp. 81–103, 2018.
-
C. H. Cheng and S. S. Shi, “Artificial intelligence in cancer: applications, challenges, and future perspectives,” Mol. Cancer, vol. 24, no. 1, 2025, doi: 10.1186/s12943-025-02450-3.
-
M. Soltani and others, “Enhancing clinical translation of cancer using nanoinformatics,” Cancers (Basel)., vol. 13, p. 2481, 2021.
-
B. Zhang, H. Shi, and H. Wang, “Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach,” J Multidiscip Heal., vol. 16, pp. 1779–91.
-
M. J. Iqbal, Z. Javed, H. Sadia, I. A. Qureshi, A. Irshad, and R. Ahmed, “Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future,” Cancer Cell Int, vol. 21, no. 270.
-
R. Fjelland, “Why general artificial intelligence will not be realized,” Humanit Soc Sci Commun, vol. 7, no. 10.
-
B. Govindan, M. A. Sabri, A. Hai, F. Banat, and M. A. Haija, “A review of advanced multifunctional magnetic nanostructures for cancer diagnosis and therapy integrated into an artificial intelligence approach,” Pharmaceutics, vol. 15, no. 868.
-
M. Xu and others, “Nanorobots mediated drug delivery for brain cancer,” Discov. Nano, vol. 19, p. 183, 2024.
-
T. Wasilewski, W. Kamysz, and J. Gębicki, “AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring,” Biosensors, vol. 14, no. 7, Jul. 2024, doi: 10.3390/BIOS14070356.
-
K. P. Das and J. Chandra, “Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges,” Front. Med. Technol., vol. 4, p. 1067144, Jan. 2022, doi: 10.3389/FMEDT.2022.1067144/BIBTEX.
-
L. Zhang, J. Tan, D. Han, and H. Zhu, “Deep learning models for nanomaterial design and environmental remediation,” Nano Today, vol. 40, p. 101280.
-
E. J. Topol, “High-performance medicine: the convergence of human and artificial intelligence”, doi: 10.1038/s41591-018-0300-7.
-
Food and D. Administration, “Artificial Intelligence and Machine Learning in Drug Development.”
-
“Guideline on the use of artificial intelligence in the medicinal product lifecycle.”
-
R. Kurzweil, The Singularity Is Near: When Humans Transcend Biology. Viking.
-
R. Yuste and G. M. Church, “The new frontier of brain–computer interfaces: Neurotechnology and society,” Neuron, vol. 108, no. 2, pp. 218–232.
-
M. C. Roco and W. S. Bainbridge, The New World of Discoveries: Convergence of Knowledge, Technology, and Society (CKTS. Springer.
-
A. Clark, Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence. Oxford University Press.
-
D.-M. rasca et al., “Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology,” Pharmaceutics, vol. 17, no. 12, doi: 10.3390/pharmaceutics17121564.
-
S. Yin, “Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects,” Biosensors, vol. 15, no. 10, p. 656, doi: 10.3390/bios15100656.
-
I. F. Akyildiz, M. Pierobon, S. Balasubramaniam, and Y. Koucheryavy, “The internet of Bio-Nano things,” IEEE Commun. Mag., vol. 53, no. 3, pp. 32–40, Mar. 2015, doi: 10.1109/MCOM.2015.7060516.
-
L. Cao et al., “Carbon dots for multiphoton bioimaging,” J. Am. Chem. Soc., vol. 129, no. 37, pp. 11318–11319, Sep. 2007, doi: 10.1021/JA073527L.
-
C. Hu, M. Chen, and C. Xing, “Towards efficient video chunk dissemination in peer-to-peer live streaming,” Comput. Networks, vol. 57, no. 15, pp. 3009–3024, Oct. 2013, doi: 10.1016/J.COMNET.2013.07.003.
-
S. Ivanov, D. Botvich, and S. Balasubramaniam, “Enzyme-based circuit design for nano-scale computing,” Nano Commun. Netw., vol. 3, no. 3, pp. 168–174, Sep. 2012, doi: 10.1016/J.NANCOM.2012.09.002.
-
Y. Long et al., “PSO-SVM-based online locomotion mode identification for rehabilitation robotic exoskeletons,” Sensors (Switzerland), vol. 16, no. 9, Sep. 2016, doi: 10.3390/S16091408.
-
V. Naresh and N. Lee, “A review on biosensors and recent development of nanostructured materials-enabled biosensors,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–35, Feb. 2021, doi: 10.3390/S21041109.
-
G. Sliwoski, S. Kothiwale, J. Meiler, and E. W. Lowe, “Computational methods in drug discovery,” Pharmacol. Rev., vol. 66, no. 1, pp. 334–395, Jan. 2014, doi: 10.1124/PR.112.007336.
-
A. Lavecchia, “Machine-learning approaches in drug discovery: Methods and applications,” Drug Discov. Today, vol. 20, no. 3, pp. 318–331, 2015, doi: 10.1016/J.DRUDIS.2014.10.012.
-
R. H. Williams and T. Riedemann, “Development, diversity and death of mge-derived cortical interneurons,” Int. J. Mol. Sci., vol. 22, no. 17, Sep. 2021, doi: 10.3390/IJMS22179297.
-
S. Ekins, J. Mestres, and B. Testa, “In silico pharmacology for drug discovery: Applications to targets and beyond,” Br. J. Pharmacol., vol. 152, no. 1, pp. 21–37, Sep. 2007, doi: 10.1038/SJ.BJP.0707306.
-
R. Yuste et al., “Four ethical priorities for neurotechnologies and AI,” Nature, vol. 551, no. 7679, pp. 159–163, Nov. 2017, doi: 10.1038/551159A.
-
B. D. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, and L. Floridi, “The ethics of algorithms: Mapping the debate,” Big Data Soc., vol. 3, no. 2, Dec. 2016, doi: 10.1177/2053951716679679.
-
J. Burrell, “How the machine ‘thinks’: Understanding opacity in machine learning algorithms,” Big Data Soc., vol. 3, no. 1, Jan. 2016, doi: 10.1177/2053951715622512.
-
M. Finicelli, G. Peluso, and T. Squillaro, “Cellular Senescence in Physiological and Pathological Processes,” Int. J. Mol. Sci., vol. 23, no. 21, Nov. 2022, doi: 10.3390/IJMS232113342.
-
W. Zhao et al., “Superparamagnetic enhancement of thermoelectric performance,” Nature, vol. 549, no. 7671, pp. 247–251, Sep. 2017, doi: 10.1038/nature23667.
-
S. Dvorácskó et al., “Novel high affinity sigma-1 receptor Ligands from minimal ensemble docking-based virtual screening,” Int. J. Mol. Sci., vol. 22, no. 15, Aug. 2021, doi: 10.3390/IJMS22158112.
-
D. Seo et al., “Wireless Recording in the Peripheral Nervous System with Ultrasonic Neural Dust,” Neuron, vol. 91, no. 3, pp. 529–539, Aug. 2016, doi: 10.1016/J.NEURON.2016.06.034.
-
L. R. Hochberg et al., “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm,” Nature, vol. 485, no. 7398, pp. 372–375, May 2012, doi: 10.1038/NATURE11076.
-
J. C. G. Esteves da Silva and H. M. R. Gonçalves, “Analytical and bioanalytical applications of carbon dots,” TrAC - Trends Anal. Chem., vol. 30, no. 8, pp. 1327–1336, Sep. 2011, doi: 10.1016/J.TRAC.2011.04.009.
-
D. Mircsof et al., “Mutations in NONO lead to syndromic intellectual disability and inhibitory synaptic defects,” Nat. Neurosci., vol. 18, no. 12, pp. 1731–1736, Nov. 2015, doi: 10.1038/nn.4169.
-
C. Pandarinath et al., “High performance communication by people with paralysis using an intracortical brain-computer interface,” Elife, vol. 6, Feb. 2017, doi: 10.7554/ELIFE.18554.
-
M. Carè, M. Chiappalone, and V. R. Cota, “Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function,” Front. Neurosci., vol. 18, 2024, doi: 10.3389/FNINS.2024.1363128.
-
S. Ghosh, J. K. Sinha, S. Ghosh, H. Sharma, R. Bhaskar, and K. B. Narayanan, “A Comprehensive Review of Emerging Trends and Innovative Therapies in Epilepsy Management,” Brain Sci., vol. 13, no. 9, Sep. 2023, doi: 10.3390/BRAINSCI13091305.
-
J. Li, B. E. F. De Ávila, W. Gao, L. Zhang, and J. Wang, “Micro/nanorobots for Biomedicine: Delivery, surgery, sensing, and detoxification,” Sci. Robot., vol. 2, no. 4, Mar. 2017, doi: 10.1126/SCIROBOTICS.AAM6431.
-
I. Dobrzyńska, B. Szachowicz-Petelska, A. Wroński, I. Jarocka-Karpowicz, and E. Skrzydlewska, “Changes in the physicochemical properties of blood and skin cell membranes as a result of psoriasis vulgaris and psoriatic arthritis development,” Int. J. Mol. Sci., vol. 21, no. 23, pp. 1–17, Dec. 2020, doi: 10.3390/IJMS21239129.
-
O. A. Dambri, A. Azarnoush, D. Makrakis, G. Levesque, M. Witter, and A. S. Hafid, “Design and Implementation of a Simulation Framework for a Bio–Neural Dust System,” Model. 2025, Vol. 6, Page 8, vol. 6, no. 1, p. 8, Jan. 2025, doi: 10.3390/MODELLING6010008.
-
S. Majd, J. Power, and Z. Majd, “Alzheimer’s Disease and Cancer: When Two Monsters Cannot Be Together,” Front. Neurosci., vol. 13, Mar. 2019, doi: 10.3389/FNINS.2019.00155.
-
M. Ienca and R. Andorno, “Towards new human rights in the age of neuroscience and neurotechnology,” Life Sci. Soc. Policy, vol. 13, no. 1, Dec. 2017, doi: 10.1186/S40504-017-0050-1.
-
A. Dutta, “Bidirectional interactions between neuronal and hemodynamic responses to transcranial direct current stimulation (tDCS): Challenges for brain-state dependent tDCS,” Front. Syst. Neurosci., vol. 9, no. AUGUST, Aug. 2015, doi: 10.3389/FNSYS.2015.00107.
-
W. Han et al., “Integrated Control of Predatory Hunting by the Central Nucleus of the Amygdala,” Cell, vol. 168, no. 1–2, pp. 311-324.e18, Jan. 2017, doi: 10.1016/J.CELL.2016.12.027.
-
D. Moussa and H. Moussa, “The Architecture of Immortality Through Neuroengineering,” Philosophies, vol. 9, no. 6, Dec. 2024, doi: 10.3390/PHILOSOPHIES9060163.
-
W. Barfield and A. Williams, “Cyborgs and enhancement technology,” Philosophies, vol. 2, no. 1, Mar. 2017, doi: 10.3390/PHILOSOPHIES2010004.
-
F. Battaglia, “Agency, responsibility, selves, and the mechanical mind,” Philosophies, vol. 6, no. 1, Mar. 2021, doi: 10.3390/PHILOSOPHIES6010007.
-
A. K. Adamczyk and P. Zawadzki, “The Memory-Modifying Potential of Optogenetics and the Need for Neuroethics,” Nanoethics, vol. 14, no. 3, pp. 207–225, Dec. 2020, doi: 10.1007/S11569-020-00377-1.
-
J. Shaw, S. Pyreddy, C. Rosendahl, C. Lai, E. Ton, and R. Carter, “Current Neuroethical Perspectives on Deep Brain Stimulation and Neuromodulation for Neuropsychiatric Disorders: A Scoping Review of the Past 10 Years,” Diseases, vol. 13, no. 8, Aug. 2025, doi: 10.3390/DISEASES13080262.
-
M. M. Ahmed et al., “Integrating Digital Health Innovations to Achieve Universal Health Coverage: Promoting Health Outcomes and Quality Through Global Public Health Equity,” Healthc., vol. 13, no. 9, May 2025, doi: 10.3390/HEALTHCARE13091060.
-
P. A. Pinera, P. C. Kim, F. A. Pinera, and J. J. Shen, “Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals?,” Int. J. Environ. Res. Public Health, vol. 22, no. 2, Feb. 2025, doi: 10.3390/IJERPH22020294.
-
A. Lazăr and L. Azamfirei, “Personalized Medicine for the Critically Ill Patient: A Narrative Review,” Processes, vol. 10, p. 1200, 2022, doi: 10.3390/pr10061200.
-
K. Fesko, “Comparison of L-threonine aldolase variants in the aldol and retro-aldol reactions,” Front. Bioeng. Biotechnol., vol. 7, no. MAY, 2019, doi: 10.3389/FBIOE.2019.00119.
-
A. M. George, “The national security implications of cyberbiosecurity,” Front. Bioeng. Biotechnol., vol. 7, no. MAR, 2019, doi: 10.3389/FBIOE.2019.00051.
-
C. Loo et al., “Nanoshell-Enabled Photonics-Based Imaging and Therapy of Cancer,” Technol. Cancer Res. Treat., vol. 3, no. 1, pp. 33–40, 2004, doi: 10.1177/153303460400300104.
-
K. D. Apostolidis and G. A. Papakostas, “Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning,” J. Imaging, vol. 8, no. 6, Jun. 2022, doi: 10.3390/JIMAGING8060155.
-
W. Nam, K. Kim, H. Moon, H. Noh, J. Park, and H. Kil, “RISOPA: Rapid Imperceptible Strong One-Pixel Attacks in Deep Neural Networks,” Mathematics, vol. 12, no. 7, Apr. 2024, doi: 10.3390/MATH12071083.
-
J. Su, D. V. Vargas, and K. Sakurai, “Attacking convolutional neural network using differential evolution,” IPSJ Trans. Comput. Vis. Appl., vol. 11, no. 1, Dec. 2019, doi: 10.1186/S41074-019-0053-3.
-
J. Korpihalkola, T. Sipola, S. Puuska, and T. Kokkonen, “One-Pixel Attack Deceives Computer-Assisted Diagnosis of Cancer,” Nov. 2021, doi: 10.1145/3483207.3483224.
-
J. Allyn, N. Allou, C. Vidal, A. Renou, and C. Ferdynus, “Adversarial attack on deep learning-based dermatoscopic image recognition systems: Risk of misdiagnosis due to undetectable image perturbations,” Med. (United States), vol. 99, no. 50, p. E23568, Dec. 2020, doi: 10.1097/MD.0000000000023568.
-
X. Ma et al., “Understanding adversarial attacks on deep learning based medical image analysis systems,” Pattern Recognit., vol. 110, Feb. 2021, doi: 10.1016/j.patcog.2020.107332.
-
M. J. Tsai, P. Y. Lin, and M. E. Lee, “Adversarial Attacks on Medical Image Classification,” Cancers (Basel)., vol. 15, no. 17, Sep. 2023, doi: 10.3390/CANCERS15174228.
-
Y. Li and S. Liu, “The Threat of Adversarial Attack on a COVID-19 CT Image-Based Deep Learning System,” Bioengineering, vol. 10, no. 2, Feb. 2023, doi: 10.3390/BIOENGINEERING10020194.
-
M. Gasson, R. Edwards, and M. Ashcroft, “Invasive Brain–Computer Interfaces: Security, Privacy and Safety Considerations,” J. Neural Eng, vol. 9, p. 16005, doi: 10.1088/1741-2560/9/1/016005.
-
T. Bonaci et al., “Towards Safer Implantable Neural Devices: Wireless Security and Privacy Challenges,” Sensors, vol. 17, doi: 10.3390/s17071670.
-
D. Halperin et al., “Pacemakers and Implantable Cardiac Defibrillators: Software Radio Attacks and Zero-Power Defenses,” IEEE Secur. Priv., vol. 6, no. 3, pp. 38–49, doi: 10.1109/MSP.2008.66.
-
M. Li, F. Yang, Q. Liu, J. Chen, and W. Zhou, “BrainJacking: Risks of Unauthorized Access to Neural Implants,” Electronics, vol. 11, p. 3452, doi: 10.3390/electronics11193452.
-
H. Xu, C. Ma, Y. Li, S. Wang, and J. Qiu, “Security Analysis of Wireless Neuro-Implants: Vulnerabilities, Attacks, and Countermeasures,” Appl. Sci, vol. 11, p. 11042, doi: 10.3390/app112311042.
-
H. Xu, C. Ma, Y. Li, S. Wang, and J. Qiu, “*Security Analysis of Wireless Neuro-Implants: Vulnerabilities,” Attacks, Countermeas. Appl. Sci, vol. 11, p. 11042.
-
M. A. Al Faruque, S. Mustafa, V. Muthukkumarasamy, and S. Tariq, “*Cybersecurity in Internet of Bio-Nano Things: Threats,” Attacks, Countermeas. Sensors.
-
J. Fernandes, J. J. P. C. Rodrigues, I. Torre, and J. Lloret, “*Security-by-Design in IoT and Bio-Nano Networks: Approaches and Biometric Encryption Methods.*,” Appl. Sci, vol. 12, p. 7890.
-
Y. Zhang, H. Sun, R. Wang, and L. Zhao, “*Biometric-Based Security Solutions for Wireless Medical Devices: Preserving Data Integrity in IoBNT Systems.*,” Electronics, vol. 12, p. 654.
-
B. Fadeel, A. Fornara, M. S. Toprak, and M. P. Monopoli, “*Nanotoxicology: Principles and Approaches for Assessing Nanomaterial Safety in Biological Systems.* Appl,” Sci, vol. 10, p. 3245.
-
A. Nel, T. Xia, L. Mädler, and N. Li, “*Toxic Potential of Materials at the Nanolevel.*,” Science (80-. )., vol. 311, pp. 622–627.
-
Y. Wang, C. Chen, X. Hu, and Y. Zhao, “*Challenges in Traditional Toxicology for Assessing Nanomaterial Safety: Emerging Alternative Approaches.* Appl,” Sci, vol. 12, p. 5321.
-
J. Yan, R. Li, H. Sun, Y. Liu, and J. Zhang, “*Nanomaterial-Biological System Interactions: Toxicological Implications for Nano-Biomedical Devices.*,” Nanomaterials, vol. 11.
-
T. Puzyn, J. Leszczynski, and M. T. D. Cronin, “*Computational Nanotoxicology: In Silico Methods for Predicting Nanomaterial Toxicity.* Wiley Interdiscip,” Rev. Nanomed. Nanobiotechnol, vol. 3, pp. 463–477.
-
Z. Wang, X. Wei, X. Li, Q. Zhou, and R. Liu, “*Nano-SAR Modeling for Predicting Cytotoxicity of Engineered Nanomaterials.*,” Nanomaterials, vol. 11.
-
K. T. Ho, J. H. Lin, and S. Y. Chen, “*Machine Learning Approaches in Nanotoxicology: Predicting Cytotoxicity and Genotoxicity of Nanomaterials.* Appl,” Sci, vol. 12, p. 6789.
-
A. Gajewicz, B. Rasulev, and T. Puzyn, “*From QSAR to Nano-SAR: In Silico Prediction of Nanomaterial Toxicity Using Physicochemical Descriptors.*,” Nanomaterials, vol. 5, pp. 199–224.
-
X. R. Xia, N. A. Monteiro-Riviere, and J. E. Riviere, “An index for characterization of nanomaterials in biological systems,” Nat. Nanotechnol., vol. 5, no. 9, pp. 671–675, 2010, doi: 10.1038/NNANO.2010.164.
-
M. P. Monopoli, C. Åberg, A. Salvati, and K. A. Dawson, “*Biomolecular Coronas Provide the Biological Identity of Nanosized Materials.* Nat,” Nanotechnol, vol. 7, pp. 779–786.
-
J. H. Shannahan, R. Podila, and J. M. Brown, “*Protein Corona Formation on Nanomaterials: Implications for Biological Identity and Toxicity.* Appl,” Sci, vol. 10, p. 5152.
-
A. B. Raies and V. B. Bajic, “*In Silico Toxicology: Computational Methods for the Prediction of Chemical Toxicity.* Wiley Interdiscip,” Rev. Comput. Mol. Sci, vol. 6, pp. 147–172.
-
A. Mayr, G. Klambauer, T. Unterthiner, and S. Hochreiter, “DeepTox: Toxicity prediction using deep learning,” Front. Environ. Sci, vol. 3, no. FEB, Feb, doi: 10.3389/FENVS.2015.00080.
-
T. Puzyn et al., “Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles,” Nat. Nanotechnol., vol. 6, no. 3, pp. 175–178, 2011, doi: 10.1038/NNANO.2011.10.
-
B. Fadeel and A. E. Garcia-Bennett, “*Better Safe than Sorry: Understanding Nanomaterial Toxicity and the Importance of Chronic Exposure Studies.*,” Nano Today, vol. 5, pp. 328–331.
-
X. Zhang, N. A. Monteiro-Riviere, and J. E. Riviere, “*Predictive Models for Nanomaterial Toxicity: Integrating Nano-SAR and Machine Learning Approaches.*,” Int. J. Mol. Sci, vol. 21, p. 9123.
-
B. Fadeel, L. Farcal, and B. Hardy, “*Keeping it Safe: Nanomaterial Safety Assessment and Lifecycle Considerations.*,” Nano Today, vol. 21, pp. 1–16.
-
S. F. Ali, S. Hussain, and S. Ubaid, “*Nanowaste: Environmental and Health Implications of Engineered Nanomaterials at the End of Life.* Environ,” Sci. Pollut. Res, vol. 28, pp. 45230–45249.
-
I. Khan, K. Saeed, and I. Khan, “*Nanoparticles: Properties, Applications and Toxicities.* Arab,” J. Chem, vol. 12, pp. 908–931.
-
S. Arora, J. Jain, J. M. Rajwade, and K. M. Paknikar, “*Cellular Responses Induced by Silver Nanoparticles: In Vitro Toxicity Studies and Environmental Implications.* Toxicol,” Lett, vol. 185, pp. 34–40.
-
R. Roy, S. Bhattacharya, and M. Ghosh, “*Life Cycle Assessment of Engineered Nanomaterials: Environmental Fate and Toxicity Considerations.* Appl,” Sci, vol. 11, p. 4567.
-
C. J. Murphy, A. M. Vartanian, and F. M. Geiger, “*Nanomaterial Environmental Fate: Integrating Human and Ecosystem Health Perspectives.* Environ,” Sci. Nano, vol. 7, pp. 1234–1249.
-
F. Gottschalk and B. Nowack, “*The Release of Engineered Nanomaterials to the Environment.*,” J. Environ. Monit., vol. 13, pp. 1145–1155.
-
M. A. Maurer-Jones, I. L. Gunsolus, C. J. Murphy, and C. L. Haynes, “*Toxicity of Engineered Nanoparticles in the Environment.* Anal,” Chem, vol. 85, pp. 3036–3049.
-
A. B. A. Boxall, K. Tiede, and Q. Chaudhry, “*Engineered Nanoparticles in Soils and Water: Behaviour, Fate and Environmental Risk.* Environ,” Pollut, vol. 150, pp. 5–22.
-
N. U. M. Nizam, M. M. Hanafiah, and K. S. Woon, “A Content Review of Life Cycle Assessment of Nanomaterials : Current Practices , Challenges , and Future Prospects,” pp. 1–27, 2021.
Nanobiyonik Sistemlerde Yapay Zeka Entegrasyonu: Toksikoloji ve Tıbbi Uygulamalar için Yeni Yaklaşımlar
Yıl 2025,
Cilt: 1 Sayı: 4, 34 - 61, 28.12.2025
N. Abdullah Cengiz
,
Hamdi Kabasakal
,
Rümeysa Kurt
,
Melike Erik
,
Fatma Nur Maran
,
İrem Türk
Öz
Bu rapor, yapay zeka (AI), nanoteknoloji ve biyomedikal bilimler gibi ortak bilgi alanlarının birleşimini ifade eden "Biyo-Nano-Siber Yakınsama" paradigmasını ve bunun ilaç geliştirme endüstrisinden nanobiyonik toksikolojiye, "Biyo-Nano-Nano Nesnelerin İnterneti" (IoBNT) mimarisinden sinirsel arayüzlerin oluşturduğu siber-biyolojik güvenlik risklerine kadar uzanan modern tıp üzerindeki sistem yeniden şekillendirme etkilerini, literatürdeki en son veriler ışığında dikkatle incelemektedir. Araştırma, derin makine öğrenimi yaklaşımlarının ilaç keşif süreçlerini ve moleküler tasarım çalışmalarını nasıl radikal bir şekilde iyileştirdiğini, AlphaFold gibi sistemlerin yapısal biyolojide neden olduğu dönüşümü, çevresel ve klinik toksikolojide nanobiyonik sensörlerin sağladığı yüksek hassasiyetli izlemeyi ve "sessiz toksisite" risklerini belirlemede "in silico" tahmin modellerinin (QSAR/Nano-SAR) etkisini ortaya koymaktadır. Ayrıca, "Neural Dust" gibi kablosuz beyin-bilgisayar arayüzlerinin sunduğu terapötik fırsatların, "tek piksel saldırıları", "beyin kaçırma" ve "nano uçurum" gibi olağanüstü etik ve güvenlik tehditleriyle "Tek Sağlık" perspektifinden nasıl uyumlu hale getirilmesi gerektiğini incelemektedir.
Kaynakça
-
E. Erken et al., “New Pt(0) Nanoparticles as Highly Active and Reusable Catalysts in the C1–C3 Alcohol Oxidation and the Room Temperature Dehydrocoupling of Dimethylamine-Borane (DMAB),” J. Clust. Sci. 2015 271, vol. 27, no. 1, pp. 9–23, Jun. 2015, doi: 10.1007/S10876-015-0892-8.
-
B. Sen, S. Kuzu, E. Demir, S. Akocak, and F. Sen, “Polymer-graphene hybride decorated Pt nanoparticles as highly efficient and reusable catalyst for the dehydrogenation of dimethylamine–borane at room temperature,” Int. J. Hydrogen Energy, vol. 42, no. 36, pp. 23284–23291, Sep. 2017, doi: 10.1016/J.IJHYDENE.2017.05.112.
-
A. Hojjati-Najafabadi et al., “Bacillus thuringiensis Based Ruthenium/Nickel Co-Doped Zinc as a Green Nanocatalyst: Enhanced Photocatalytic Activity, Mechanism, and Efficient H2 Production from Sodium Borohydride Methanolysis,” Ind. Eng. Chem. Res., vol. 62, no. 11, pp. 4655–4664, Mar. 2023, doi: 10.1021/ACS.IECR.2C03833.
-
E. Demir, A. Savk, B. Sen, and F. Sen, “A novel monodisperse metal nanoparticles anchored graphene oxide as Counter Electrode for Dye-Sensitized Solar Cells,” Nano-Structures & Nano-Objects, vol. 12, pp. 41–45, Oct. 2017, doi: 10.1016/J.NANOSO.2017.08.018.
-
Y. Yıldız, İ. Esirden, E. Erken, E. Demir, M. Kaya, and F. Şen, “Microwave (Mw)-assisted Synthesis of 5-Substituted 1H-Tetrazoles via [3+2] Cycloaddition Catalyzed by Mw-Pd/Co Nanoparticles Decorated on Multi-Walled Carbon Nanotubes,” ChemistrySelect, vol. 1, no. 8, pp. 1695–1701, Jun. 2016, doi: 10.1002/SLCT.201600265.
-
F. Şen and G. Gökağaç, “Pt nanoparticles synthesized with new surfactants: Improvement in C 1-C3 alcohol oxidation catalytic activity,” J. Appl. Electrochem., vol. 44, no. 1, pp. 199–207, Jan. 2014, doi: 10.1007/S10800-013-0631-5/FIGURES/7.
-
F. Sen, A. A. Boghossian, S. Sen, Z. W. Ulissi, J. Zhang, and M. S. Strano, “Observation of oscillatory surface reactions of riboflavin, trolox, and singlet oxygen using single carbon nanotube fluorescence spectroscopy,” ACS Nano, vol. 6, no. 12, pp. 10632–10645, Dec. 2012, doi: 10.1021/NN303716N/ASSET/IMAGES/MEDIUM/NN-2012-03716N_0011.GIF.
-
H. Göksu, H. Burhan, S. D. Mustafov, and F. Şen, “Oxidation of Benzyl Alcohol Compounds in the Presence of Carbon Hybrid Supported Platinum Nanoparticles (Pt@CHs) in Oxygen Atmosphere,” Sci. Rep., vol. 10, no. 1, pp. 1–8, Dec. 2020, doi: 10.1038/S41598-020-62400-5;TECHMETA=131,140,145,146;SUBJMETA=45,638,639,77,884;KWRD=BIOCHEMISTRY,CATALYST+SYNTHESIS.
-
A. Cherif, R. Nebbali, J. W. Sheffield, N. Doner, and F. Sen, “Numerical investigation of hydrogen production via autothermal reforming of steam and methane over Ni/Al2O3 and Pt/Al2O3 patterned catalytic layers,” Int. J. Hydrogen Energy, vol. 46, no. 75, pp. 37521–37532, Oct. 2021, doi: 10.1016/J.IJHYDENE.2021.04.032.
-
B. Şen et al., “High-performance graphite-supported ruthenium nanocatalyst for hydrogen evolution reaction,” J. Mol. Liq., vol. 268, pp. 807–812, Oct. 2018, doi: 10.1016/J.MOLLIQ.2018.07.117.
-
R. Darabi et al., “Simultaneous determination of ascorbic acid, dopamine, and uric acid with a highly selective and sensitive reduced graphene oxide/polypyrrole-platinum nanocomposite modified electrochemical sensor,” Electrochim. Acta, vol. 457, p. 142402, Jul. 2023, doi: 10.1016/J.ELECTACTA.2023.142402.
-
B. Sen, B. Demirkan, B. Şimşek, A. Savk, and F. Sen, “Monodisperse palladium nanocatalysts for dehydrocoupling of dimethylamineborane,” Nano-Structures & Nano-Objects, vol. 16, pp. 209–214, Oct. 2018, doi: 10.1016/J.NANOSO.2018.07.008.
-
J. Lin et al., “Phyto-mediated synthesis of nanoparticles and their applications on hydrogen generation on NaBH4, biological activities and photodegradation on azo dyes: Development of machine learning model,” Food Chem. Toxicol., vol. 163, p. 112972, May 2022, doi: 10.1016/J.FCT.2022.112972.
-
B. Sen, E. Kuyuldar, A. Şavk, H. Calimli, S. Duman, and F. Sen, “Monodisperse rutheniumcopper alloy nanoparticles decorated on reduced graphene oxide for dehydrogenation of DMAB,” Int. J. Hydrogen Energy, vol. 44, no. 21, pp. 10744–10751, Apr. 2019, doi: 10.1016/J.IJHYDENE.2019.02.176.
-
A. Şavk et al., “Highly monodisperse Pd-Ni nanoparticles supported on rGO as a rapid, sensitive, reusable and selective enzyme-free glucose sensor,” Sci. Reports 2019 91, vol. 9, no. 1, pp. 1–9, Dec. 2019, doi: 10.1038/s41598-019-55746-y.
-
A. Hojjati-Najafabadi, S. Salmanpour, F. Sen, P. N. Asrami, M. Mahdavian, and M. A. Khalilzadeh, “A Tramadol Drug Electrochemical Sensor Amplified by Biosynthesized Au Nanoparticle Using Mentha aquatic Extract and Ionic Liquid,” Top. Catal., vol. 65, no. 5–6, pp. 587–594, Apr. 2022, doi: 10.1007/S11244-021-01498-X/TABLES/2.
-
S. Eris, Z. Daşdelen, and F. Sen, “Investigation of electrocatalytic activity and stability of Pt@f-VC catalyst prepared by in-situ synthesis for Methanol electrooxidation,” Int. J. Hydrogen Energy, vol. 43, no. 1, pp. 385–390, Jan. 2018, doi: 10.1016/J.IJHYDENE.2017.11.063.
-
H. Burhan et al., “Highly efficient carbon hybrid supported catalysts using nano-architecture as anode catalysts for direct methanol fuel cells,” Int. J. Hydrogen Energy, vol. 48, no. 17, pp. 6657–6665, Feb. 2023, doi: 10.1016/J.IJHYDENE.2021.12.141.
-
N. H. Khand et al., “A new electrochemical method for the detection of quercetin in onion, honey and green tea using Co3O4 modified GCE,” J. Food Meas. Charact. 2021 154, vol. 15, no. 4, pp. 3720–3730, May 2021, doi: 10.1007/S11694-021-00956-0.
-
F. Gulbagça et al., “Facile bio-fabrication of Pd-Ag bimetallic nanoparticles and its performance in catalytic and pharmaceutical applications: Hydrogen production and in-vitro antibacterial, anticancer activities, and model development,” Chem. Eng. Res. Des., vol. 180, pp. 254–264, Apr. 2022, doi: 10.1016/J.CHERD.2022.02.024.
-
R. Ulus, Y. Yıldız, S. Eriş, B. Aday, F. Şen, and M. Kaya, “Functionalized Multi-Walled Carbon Nanotubes (f-MWCNT) as Highly Efficient and Reusable Heterogeneous Catalysts for the Synthesis of Acridinedione Derivatives,” ChemistrySelect, vol. 1, no. 13, pp. 3861–3865, Aug. 2016, doi: 10.1002/SLCT.201600719.
-
M. S. Nas, M. H. Calimli, H. Burhan, M. Yılmaz, S. D. Mustafov, and F. Sen, “Corrigendum to ‘Synthesis, characterization, kinetics and adsorption properties of Pt-Co@GO nano-adsorbent for methylene blue removal in the aquatic mediums using ultrasonic process systems’. [J. Mol. Liquids 296 (2019) 112100],” J. Mol. Liq., vol. 340, p. 117289, Oct. 2021, doi: 10.1016/J.MOLLIQ.2021.117289.
-
F. Ameen et al., “Synthesis and characterization of activated carbon supported bimetallic Pd based nanoparticles and their sensor and antibacterial investigation,” Environ. Res., vol. 221, p. 115287, Mar. 2023, doi: 10.1016/J.ENVRES.2023.115287.
-
K. Arikan, H. Burhan, R. Bayat, and F. Sen, “Glucose nano biosensor with non-enzymatic excellent sensitivity prepared with nickel–cobalt nanocomposites on f-MWCNT,” Chemosphere, vol. 291, p. 132720, Mar. 2022, doi: 10.1016/J.CHEMOSPHERE.2021.132720.
-
H. Seckin, R. N. E. Tiri, I. Meydan, A. Aygun, M. K. Gunduz, and F. Sen, “An environmental approach for the photodegradation of toxic pollutants from wastewater using Pt–Pd nanoparticles: Antioxidant, antibacterial and lipid peroxidation inhibition applications,” Environ. Res., vol. 208, p. 112708, May 2022, doi: 10.1016/J.ENVRES.2022.112708.
-
I. Meydan, H. Burhan, T. Gür, H. Seçkin, B. Tanhaei, and F. Sen, “Characterization of Rheum ribes with ZnO nanoparticle and its antidiabetic, antibacterial, DNA damage prevention and lipid peroxidation prevention activity of in vitro,” Environ. Res., vol. 204, p. 112363, Mar. 2022, doi: 10.1016/J.ENVRES.2021.112363.
-
N. Korkmaz, Y. Ceylan, P. Taslimi, A. Karadağ, A. S. Bülbül, and F. Şen, “Biogenic nano silver: Synthesis, characterization, antibacterial, antibiofilms, and enzymatic activity,” Adv. Powder Technol., vol. 31, no. 7, pp. 2942–2950, Jul. 2020, doi: 10.1016/J.APT.2020.05.020.
-
F. Şen, G. Gökağaç, and S. Şen, “High performance Pt nanoparticles prepared by new surfactants for C 1 to C3 alcohol oxidation reactions,” J. Nanoparticle Res., vol. 15, no. 10, pp. 1–9, Oct. 2013, doi: 10.1007/S11051-013-1979-5/FIGURES/6.
-
Y. Yıldız, İ. Esirden, E. Erken, E. Demir, M. Kaya, and F. Şen, “Microwave (Mw)-assisted Synthesis of 5-Substituted 1H-Tetrazoles via [3+2] Cycloaddition Catalyzed by Mw-Pd/Co Nanoparticles Decorated on Multi-Walled Carbon Nanotubes,” ChemistrySelect, vol. 1, no. 8, pp. 1695–1701, Jun. 2016, doi: 10.1002/SLCT.201600265;WGROUP:STRING:PUBLICATION.
-
F. Sen, A. A. Boghossian, S. Sen, Z. W. Ulissi, J. Zhang, and M. S. Strano, “Observation of Oscillatory Surface Reactions of Riboflavin, Trolox, and Singlet Oxygen Using Single Carbon Nanotube Fluorescence Spectroscopy,” 2012, doi: 10.1021/NN303716N.
-
D. E. Mazouzi et al., “Auto-combustion designed BiFeO3/Bi2O3 photocatalyst for improved photodegradation of nitrobenzene under visible light and sunlight irradiation,” Surfaces and Interfaces, vol. 44, p. 103581, Jan. 2024, doi: 10.1016/J.SURFIN.2023.103581.
-
B. Çelik et al., “Retracted Article: Highly monodisperse Pt(0)@AC NPs as highly efficient and reusable catalysts: the effect of the surfactant on their catalytic activities in room temperature dehydrocoupling of DMAB,” Catal. Sci. Technol., vol. 6, no. 6, pp. 1685–1692, Mar. 2016, doi: 10.1039/C5CY01371B.
-
Y. Liang et al., “Facile synthesis of biogenic palladium nanoparticles using biomass strategy and application as photocatalyst degradation for textile dye pollutants and their in-vitro antimicrobial activity,” Chemosphere, vol. 306, p. 135518, Nov. 2022, doi: 10.1016/J.CHEMOSPHERE.2022.135518.
-
Y. Wu et al., “Hydrogen generation from methanolysis of sodium borohydride using waste coffee oil modified zinc oxide nanoparticles and their photocatalytic activities,” Int. J. Hydrogen Energy, vol. 48, no. 17, pp. 6613–6623, Feb. 2023, doi: 10.1016/J.IJHYDENE.2022.04.177.
-
B. Sen, B. Demirkan, A. Şavk, S. Karahan Gülbay, and F. Sen, “Trimetallic PdRuNi nanocomposites decorated on graphene oxide: A superior catalyst for the hydrogen evolution reaction,” Int. J. Hydrogen Energy, vol. 43, no. 38, pp. 17984–17992, Sep. 2018, doi: 10.1016/J.IJHYDENE.2018.07.122.
-
K. Nesrin et al., “Biogenic silver nanoparticles synthesized from Rhododendron ponticum and their antibacterial, antibiofilm and cytotoxic activities,” J. Pharm. Biomed. Anal., vol. 179, p. 112993, Feb. 2020, doi: 10.1016/J.JPBA.2019.112993.
-
H. Goksu, Y. Yildiz, B. Çelik, M. Yazici, B. Kilbas, and F. Sen, “Eco-friendly hydrogenation of aromatic aldehyde compounds by tandem dehydrogenation of dimethylamine-borane in the presence of a reduced graphene oxide furnished platinum nanocatalyst,” Catal. Sci. Technol., vol. 6, no. 7, pp. 2318–2324, Apr. 2016, doi: 10.1039/C5CY01462J.
-
N. Lolak, E. Kuyuldar, H. Burhan, H. Goksu, S. Akocak, and F. Sen, “Composites of Palladium-Nickel Alloy Nanoparticles and Graphene Oxide for the Knoevenagel Condensation of Aldehydes with Malononitrile,” ACS Omega, vol. 4, no. 4, pp. 6848–6853, Apr. 2019, doi: 10.1021/ACSOMEGA.9B00485/SUPPL_FILE/AO9B00485_SI_001.PDF.
-
S. Ertan, F. Şen, S. Şen, and G. Gökağaç, “Platinum nanocatalysts prepared with different surfactants for C1-C3 alcohol oxidations and their surface morphologies by AFM,” J. Nanoparticle Res., vol. 14, no. 6, pp. 1–12, Jun. 2012, doi: 10.1007/S11051-012-0922-5/FIGURES/8.
-
H. Göksu, Y. Yıldız, B. Çelik, M. Yazıcı, B. Kılbaş, and F. Şen, “Highly Efficient and Monodisperse Graphene Oxide Furnished Ru/Pd Nanoparticles for the Dehalogenation of Aryl Halides via Ammonia Borane,” ChemistrySelect, vol. 1, no. 5, pp. 953–958, Apr. 2016, doi: 10.1002/SLCT.201600207.
-
S. Günbatar, A. Aygun, Y. Karataş, M. Gülcan, and F. Şen, “Carbon-nanotube-based rhodium nanoparticles as highly-active catalyst for hydrolytic dehydrogenation of dimethylamineborane at room temperature,” J. Colloid Interface Sci., vol. 530, pp. 321–327, Nov. 2018, doi: 10.1016/J.JCIS.2018.06.100.
-
A. Aygun et al., “Highly active PdPt bimetallic nanoparticles synthesized by one-step bioreduction method: Characterizations, anticancer, antibacterial activities and evaluation of their catalytic effect for hydrogen generation,” Int. J. Hydrogen Energy, vol. 48, no. 17, pp. 6666–6679, Feb. 2023, doi: 10.1016/J.IJHYDENE.2021.12.144.
-
B. Şen, A. Aygün, T. O. Okyay, A. Şavk, R. Kartop, and F. Şen, “Monodisperse palladium nanoparticles assembled on graphene oxide with the high catalytic activity and reusability in the dehydrogenation of dimethylamine-borane,” Int. J. Hydrogen Energy, vol. 43, no. 44, pp. 20176–20182, Nov. 2018, doi: 10.1016/J.IJHYDENE.2018.03.175.
-
J. T. Abrahamson et al., “Excess Thermopower and the Theory of Thermopower Waves,” ACS Nano, vol. 7, no. 8, pp. 6533–6544, Aug. 2013, doi: 10.1021/NN402411K.
-
B. Sen, S. Kuzu, E. Demir, E. Yıldırır, and F. Sen, “Highly efficient catalytic dehydrogenation of dimethyl ammonia borane via monodisperse palladium–nickel alloy nanoparticles assembled on PEDOT,” Int. J. Hydrogen Energy, vol. 42, no. 36, pp. 23307–23314, Sep. 2017, doi: 10.1016/J.IJHYDENE.2017.05.115.
-
F. Şen and G. Gökaǧaç, “Improving Catalytic Efficiency in the Methanol Oxidation Reaction by Inserting Ru in Face-Centered Cubic Pt Nanoparticles Prepared by a New Surfactant, tert-Octanethiol,” Energy and Fuels, vol. 22, no. 3, pp. 1858–1864, May 2008, doi: 10.1021/EF700575T.
-
E. Erken, Y. Yildiz, B. Kilbaş, and F. Şen, “Synthesis and Characterization of Nearly Monodisperse Pt Nanoparticles for C1 to C3 Alcohol Oxidation and Dehydrogenation of Dimethylamine-borane (DMAB),” J. Nanosci. Nanotechnol., vol. 16, no. 6, pp. 5944–5950, Jun. 2016, doi: 10.1166/JNN.2016.11683.
-
B. Şen, A. Aygün, A. Şavk, S. Akocak, and F. Şen, “Bimetallic palladium–iridium alloy nanoparticles as highly efficient and stable catalyst for the hydrogen evolution reaction,” Int. J. Hydrogen Energy, vol. 43, no. 44, pp. 20183–20191, Nov. 2018, doi: 10.1016/J.IJHYDENE.2018.07.081.
-
E. Demir, A. Savk, B. Sen, and F. Sen, “A novel monodisperse metal nanoparticles anchored graphene oxide as Counter Electrode for Dye-Sensitized Solar Cells,” Nano-Structures & Nano-Objects, vol. 12, pp. 41–45, Oct. 2017, doi: 10.1016/J.NANOSO.2017.08.018.
-
G. G. Wallace, M. J. Higgins, S. E. Moulton, and C. Wang, “Nanobionics: the impact of nanotechnology on implantable medical bionic devices,” Nanoscale, vol. 4, no. 15, pp. 4327–4347, 2012.
-
R. Prasad, Plant Nanobionics. Springer Nature, 2019.
-
M. K. Enamala, B. Kolapalli, P. D. Sruthi, S. Sarkar, C. Kuppam, and M. Chavali, “Applications of nanomaterials and future prospects for nanobionics,” in Plant Nanobionics, Springer, 2019, pp. 177–197.
-
Y. Liu, S. Qin, and Y. Luo, “Nanotechnology-enabled drug delivery systems guided by artificial intelligence,” Adv. Drug Deliv. Rev., vol. 167, pp. 104–122.
-
V. Kozhukharov and M. Machkova, “Nanomaterials and nanotechnology: European initiatives, status and strategy,” J. Chem. Technol. Metall., vol. 48, no. 3, 2013.
-
G. A. Mansoori, “An introduction to nanoscience and nanotechnology,” in Nanoscience and Plant--Soil Systems, Springer, 2017.
-
S. Tripathy et al., “Artificial Intelligence-Based Portable Bioelectronics Platform for SARS-CoV-2 Diagnosis with Multi-nucleotide Probe Assay for Clinical Decisions,” Anal. Chem., vol. 93, no. 45, pp. 14955–14965, Nov. 2021, doi: 10.1021/ACS.ANALCHEM.1C01650.
-
M. Bakshi and P. C. Abhilash, “Nanotechnology for soil remediation: Revitalizing the tarnished resource,” Nano-Materials as Photocatal. Degrad. Environ. Pollut. Challenges Possibilities, pp. 345–370, Dec. 2019, doi: 10.1016/B978-0-12-818598-8.00017-1.
-
A. Ranjan, V. D. Rajput, A. Kumari, S. S. Mandzhieva, and S. Sushkova, “Nanobionics in Crop Production : An Emerging Approach to,” Plant, vol. 11, pp. 1–16, 2022.
-
M. Usman et al., “Nanotechnology in agriculture: Current status, challenges and future opportunities,” Sci. Total Environ., vol. 721, Jun. 2020, doi: 10.1016/J.SCITOTENV.2020.137778.
-
P. S. Tourinho, C. A. M. van Gestel, S. Lofts, C. Svendsen, A. M. V. M. Soares, and S. Loureiro, “Metal-based nanoparticles in soil: Fate, behavior, and effects on soil invertebrates,” Environ. Toxicol. Chem., vol. 31, no. 8, pp. 1679–1692, Aug. 2012, doi: 10.1002/ETC.1880.
-
H. Chen, “Metal based nanoparticles in agricultural system: Behavior, transport, and interaction with plants,” Chem. Speciat. Bioavailab., vol. 30, no. 1, pp. 123–134, Jan. 2018, doi: 10.1080/09542299.2018.1520050.
-
A. Ranjan, V. D. Rajput, T. Minkina, T. Bauer, A. Chauhan, and T. Jindal, “Nanoparticles induced stress and toxicity in plants,” Environ. Nanotechnology, Monit. Manag., vol. 15, May 2021, doi: 10.1016/J.ENMM.2021.100457.
-
R. Liu and R. Lal, “Potentials of engineered nanoparticles as fertilizers for increasing agronomic productions,” Sci. Total Environ., vol. 514, pp. 131–139, May 2015, doi: 10.1016/J.SCITOTENV.2015.01.104.
-
V. D. Rajput et al., “Coping with the challenges of abiotic stress in plants: New dimensions in the field application of nanoparticles,” Plants, vol. 10, no. 6, Jun. 2021, doi: 10.3390/PLANTS10061221.
-
J. P. Giraldo et al., “Erratum: Plant nanobionics approach to augment photosynthesis and biochemical sensing (Nature Materials (2014) 13 (400-408)),” Nat. Mater., vol. 13, no. 5, p. 530, 2014, doi: 10.1038/NMAT3947.
-
N. Terry, “Limiting Factors in Photosynthesis,” Plant Physiol., vol. 65, no. 1, pp. 114–120, Jan. 1980, doi: 10.1104/PP.65.1.114.
-
R. E. al Abdalla-Aslan et al., “Nanotechnology for soil remediation: Revitalizing the tarnished resource,” ACS Nano, vol. 2, no. 3, p. 100561, Oct. 2023, doi: 10.1021/acsomega.3c09191.
-
Y. Zhu, S. Murali, W. Cai, X. Li, and S. J. W. diğerleri, “Grafen ve grafen oksit: Sentez, özellikler ve uygulamalar,” Gelişmiş Malzemeler, vol. 22, no. 35, pp. 3906–3924.
-
B. Sanchez-Lengeling and A. Aspuru-Guzik, “Inverse molecular design using machine learning:Generative models for matter engineering,” Science (80-. )., vol. 361, no. 6400, pp. 360–365, Jul. 2018, doi: 10.1126/SCIENCE.AAT2663;WEBSITE:WEBSITE:AAAS-SITE;JOURNAL:JOURNAL:SCIENCE;WGROUP:STRING:PUBLICATION.
-
S. He, J. S. Abarrategi, H. Bediaga, S. Arrasate, and H. González-Díaz, “On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems,” Beilstein J Nanotechnol, vol. 15, no. 1, pp. 535–55.
-
R. Qureshi et al., “AI in drug discovery and its clinical relevance,” Heliyon, vol. 9, p. 17575.
-
A. Blanco-González et al., “The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies,” Pharmaceuticals, vol. 16, p. 891.
-
J. S. Ahn et al., “Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine,” J. Breast Cancer, vol. 26, no. 5, pp. 405–435, Oct. 2023, doi: 10.4048/JBC.2023.26.E45.
-
D. Zheng, X. He, and J. Jing, “Overview of Artificial Intelligence in Breast Cancer Medical Imaging,” J. Clin. Med, vol. 12, p. 419.
-
A. Al Kuwaiti et al., “A Review of the Role of Artificial Intelligence in Healthcare,” J. Pers. Med., vol. 13, no. 6, Jun. 2023, doi: 10.3390/JPM13060951.
-
M. A. Darwish, W. Abd-Elaziem, A. Elsheikh, and A. A. Zayed, “Advancements in nanomaterials for nanosensors: A comprehensive review,” Nanoscale Adv, vol. 6, pp. 4015–4046.
-
M. Javaid, A. Haleem, R. P. Singh, S. Rab, and R. Suman, “Exploring the potential of nanosensors: A brief overview,” Sensors Int, vol. 2, p. 100130.
-
T. Adam and S. C. Gopinath, “Nanosensors: Recent perspectives on attainments and future promise of downstream applications,” Process. Biochem, vol. 117, pp. 153–173.
-
M. Eissa, “Nanosensors for Early Detection and Diagnosis of Cancer: A Review of Recent Advances,” J. Cancer Res. Rev., vol. 1, no. 1, p. 1, 2024, doi: 10.5455/JCRR.20240205070256.
-
S. Gulati, R. Yadav, V. Kumari, S. Nair, C. Gupta, and M. Aishwari, “Nanosensors in healthcare: transforming real-time monitoring and disease management with cutting-edge nanotechnology,” RSC Pharm., vol. 2, no. 5, pp. 1003–1018, Sep. 2025, doi: 10.1039/D5PM00125K.
-
X. Tang, Y. Zhu, W. Guan, W. Zhou, and P. Wei, “Advances in nanosensors for cardiovascular disease detection,” Life Sci, vol. 305, p. 120733.
-
X. Lin et al., “Portable dual-mode microfluidic sensor for rapid and sensitive detection of DPA on chip,” Adv. Compos. Hybrid Mater., vol. 8, no. 3, Jun. 2025, doi: 10.1007/S42114-025-01320-2.
-
R. Das, S. Nag, and P. Banerjee, “Electrochemical Nanosensors for Sensitization of Sweat Metabolites: From Concept Mapping to Personalized Health Monitoring,” Molecules, p. 28.
-
S. Liu et al., “Evaluation of the Multidimensional Enhanced Lateral Flow Immunoassay in Point-of-Care Nanosensors,” ACS Nano, vol. 18, no. 40, pp. 27167–27205, Oct. 2024, doi: 10.1021/ACSNANO.4C06564.
-
H. Tavakoli, S. Mohammadi, X. Li, G. Fu, and X. Li, “Microfluidic platforms integrated with nano-sensors for point-of-care bioanalysis,” TrAC Trends Anal. Chem, vol. 157, p. 116806.
-
C. Yang, Q. Wang, Y. Xiang, R. Yuan, and Y. Chai, “Target-induced strand release and thionine-decorated gold nanoparticle amplification labels for sensitive electrochemical aptamer-based sensing of small molecules,” Sensors Actuators, B Chem., vol. 197, pp. 149–154, Jul. 2014, doi: 10.1016/J.SNB.2014.02.036.
-
H. Chen, O. Engkvist, Y. Wang, M. Olivecrona, and T. Blaschke, “The rise of deep learning in drug discovery,” Drug Discov. Today, vol. 23, no. 6, pp. 1241–1250, Jun. 2018, doi: 10.1016/j.drudis.2018.01.039.
-
S. A. H. Hassan et al., “Development of Nanotechnology by Artificial Intelligence: A Comprehensive Review,” J. Nanostruct, vol. 13, pp. 915–932.
-
A. Fallah, S. A. Havaei, H. Sedighian, R. Kachuei, and A. A. I. Fooladi, “Prediction of aptamer affinity using an artificial intelligence approach,” J. Mater. Chem. B, vol. 12, pp. 8825–8842.
-
J. W. Lowdon et al., “Identifying potential machine learning algorithms for the simulation of binding affinities to molecularly imprinted polymers,” Computation, vol. 9, no. 10, Dec. 2021, doi: 10.3390/COMPUTATION9100103.
-
S. Hamedi, H. D. Jahromi, and A. Lotfiani, “Artificial intelligence-aided nanoplasmonic biosensor modeling,” Eng. Appl. Artif. Intell., vol. 118, Feb. 2023, doi: 10.1016/J.ENGAPPAI.2022.105646.
-
H. Haick and N. Tang, “Artificial Intelligence in Medical Sensors for Clinical Decisions,” ACS Nano, vol. 15, no. 3, pp. 3557–3567, Mar. 2021, doi: 10.1021/ACSNANO.1C00085.
-
K. Tafadzwa Mpofu and P. Mthunzi-Kufa, “Recent Advances in Artificial Intelligence and Machine Learning Based Biosensing Technologies,” Mar. 2025, doi: 10.5772/INTECHOPEN.1009613.
-
S. Yin and S. Yin, “Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects,” Biosens. 2025, Vol. 15, vol. 15, no. 10, p. 656, Oct. 2025, doi: 10.3390/BIOS15100656.
-
C. D. Flynn and D. Chang, “Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities,” Diagnostics, vol. 14, no. 11, Jun. 2024, doi: 10.3390/DIAGNOSTICS14111100.
-
F.-G. Banica, Chemical Sensors and Biosensors:Fundamentals and Applications. Chichester, UK: John Wiley & Sons, 2012.
-
C. Dincer et al., Disposable Sensors in Diagnostics, Food, and Environmental Monitoring, vol. 31, no. 30. Wiley-VCH Verlag, 2019. doi: 10.1002/adma.201806739.
-
A. P. F. . Turner, I. Karube, and G. S. . Wilson, Biosensors:Fundamentals and Applications. Oxford, UK: Oxford University Press, 1987.
-
U. Kökbaş, L. Kayrın, A. Tuli, and Ç. Üniversitesi Tıp Fakültesi Tıbbi Biyokimya ABD, “Biyosensörler ve Tıpta Kullanım Alanları,” Arch. Med. Rev. J., vol. 22, no. 4, pp. 499–513, Dec. 2013.
-
Y. S. Choi et al., “Real-Time Monitoring of Volatile Organic Compound-Mediated Plant Intercommunication Using Surface-Enhanced Raman Scattering Nanosensor,” Adv. Sci., vol. 12, no. 7, p. 2412732, Feb. 2025, doi: 10.1002/ADVS.202412732;PAGE:STRING:ARTICLE/CHAPTER.
-
Z. Tüylek, “Biyoteknolojide Biyosensör ve Biyoçip Uygulamaları,” Int. J. Life Sci. Biotechnol., vol. 4, no. 3, pp. 468–490, Dec. 2021, doi: 10.38001/IJLSB.876231.
-
A. P. F. Turner, “Biosensors: sense and sensibility,” Chem. Soc. Rev., vol. 42, no. 8, pp. 3184–3196, Mar. 2013, doi: 10.1039/C3CS35528D.
-
J. E. N. Dolatabadi et al., “Optical and electrochemical DNA nanobiosensors,” TrAC Trends Anal. Chem., vol. 30, no. 3, pp. 459–472, Mar. 2011, doi: 10.1016/J.TRAC.2010.11.010.
-
P. Mehrotra, “Biosensors and their applications – A review,” J. Oral Biol. Craniofacial Res., vol. 6, no. 2, pp. 153–159, May 2016, doi: 10.1016/J.JOBCR.2015.12.002.
-
V. Ergül, S. Çakir, and M. Bilgisi, “Nanoteknolojinin Sektörel Uygulamaları Üzerine Bir Değerlendirme,” J. Def. Sci., vol. 1, no. 43, pp. 1–22, May 2023, doi: 10.17134/KHOSBD.1081519.
-
E. Alotaibi and N. Nassif, “Yapay Zekayı Keşfedin Araştırma Çevresel izlemede yapay zeka: derinlemesine analiz”, doi: 10.1007/s44163-024-00198-1.
-
S. V. Di̇ji̇talleşme Yapay Zekâ, Ö. Üyesi Betül AKALIN, and Ü. Veranyurt, “Cilt: 2, Sayı: 2, ss,” pp. 131–141.
-
E. Üniversitesi, Z. Fakültesi, B. Koruma Bölümü, and G. Tarihi, “Biyosensörler ve Tarım Alanında Kullanımı Burçin BOZ, İsmail Can PAYLAN, Mehmet Zeki KIZMAZ, Semih ERKAN,” J. Agric. Mach. Sci., vol. 2017, no. 3, pp. 141–148.
-
K. Sağlık Yüksek Okulu, N. Hastanesi yanı, K. Tıp Dergisi, A. Kocatepe Üniversitesi, M. Özata, and Ş. Aslan, “Başvuru 10 Eylül,” 2003.
-
L. P. X. Yong et al., “Artificial Intelligence Applications in Emergency Toxicology: Advancements and Challenges,” J. Med. Internet Res., vol. 27, no. 1, p. e73121, Aug. 2025, doi: 10.2196/73121.
-
“biyomarkörlerin toksikolojide kullanımı.”
-
İ. Özer, H. TEZEL, S. SANAJOU, … A. Y.-J. of L., and undefined 2022, “Biyosensörler ve Kullanım Alanları: Geleneksel Derleme.,” Res. Özer, H TEZEL, S SANAJOU, A Yirün, T Baydar, P ErkekoğluJournal Lit. Pharm. Sci. 2022•researchgate.net.
-
N. A. Buckley, I. M. Whyte, and A. H. Dawson, “Diagnostic data in clinical toxicology - Should we use a Bayesian approach?,” J. Toxicol. - Clin. Toxicol., vol. 40, no. 3, pp. 213–222, 2002, doi: 10.1081/CLT-120005491;JOURNAL:JOURNAL:ICTX18;WGROUP:STRING:PUBLICATION.
-
V. Garzón, D. G. Pinacho, R. H. Bustos, G. Garzón, and S. Bustamante, “Optical Biosensors for Therapeutic Drug Monitoring,” Biosens. 2019, Vol. 9, Page 132, vol. 9, no. 4, p. 132, Nov. 2019, doi: 10.3390/BIOS9040132.
-
R. J. S. Banicod, N. Tabassum, D. M. Jo, A. Javaid, Y. M. Kim, and F. Khan, “Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens,” Biosens. 2025, Vol. 15, Page 690, vol. 15, no. 10, p. 690, Oct. 2025, doi: 10.3390/BIOS15100690.
-
H. Sezginer, F. Dane, T. Üniversitesi, F. Fakültesi, and B. Bölümü, “Toksik Maddelerin Genotoksik Analiz Yöntemleri,” Türk Bilim. Derlemeler Derg., vol. 9, no. 1, pp. 50–55, 2016.
-
M. S. Islam, K. Sazawa, K. Sugawara, and H. Kuramitz, “Electrochemical Biosensor for Evaluation of Environmental Pollutants Toxicity,” Environ. 2023, Vol. 10, Page 63, vol. 10, no. 4, p. 63, Apr. 2023, doi: 10.3390/ENVIRONMENTS10040063.
-
M. Negahdary et al., “Recent electrochemical sensors and biosensors for toxic agents based on screen-printed electrodes equipped with nanomaterials,” Microchem. J., vol. 185, p. 108281, Feb. 2023, doi: 10.1016/J.MICROC.2022.108281.
-
M. Javaid, A. Haleem, R. P. Singh, S. Rab, and R. Suman, “Exploring the potential of nanosensors: A brief overview,” Sensors Int., vol. 2, Jan. 2021, doi: 10.1016/J.SINTL.2021.100130.
-
X. Chen and Q. Wan, “Ru-Doped MoS2 Monolayer for Exhaled Breath Detection on Early Lung Cancer Diagnosis,” ACS Omega, vol. 9, no. 12, pp. 13951–13959, 2024, doi: 10.1021/acsomega.3c09191.
-
G. Shang et al., “Chemiresistive Sensor Array with Nanostructured Interfaces for Detection of Human Breaths with Simulated Lung Cancer VOCs,” ACS Sensors, vol. 8, no. 3, pp. 1328–1338, 2023, doi: 10.1021/acssensors.2c02839.
-
A. W. Adamson and A. P. Gast, Physical Chemistry of Surfaces, 6th ed. New York: John Wiley \& Sons, 1997.
-
G. Rong, S. R. Corrie, and H. A. Clark, “In Vivo Biosensing: Progress and Perspectives,” ACS Sensors, vol. 2, pp. 327–338, 2017.
-
S. Gulati, R. Yadav, V. Kumari, S. Nair, C. Gupta, and M. Aishwari, “Nanosensors in healthcare: Transforming real-time monitoring and disease management with cutting-edge nanotechnology,” RSC Pharm, vol. 2, pp. 1003–10018.
-
K. P. Mulaudji, K. V Mokwebo, F. Q. De Bruin, K. Pokpas, and N. Ross, “Advances in electrochemical sensing of chloramphenicol in complex matrices,” Talanta Open, vol. 12, p. 100561, 2025.
-
A. Pantelopoulos and N. G. Bourbakis, “Wearable sensor-based systems for health monitoring and prognosis,” IEEE Trans. Syst. Man, Cybern. Part C, vol. 40, pp. 1–12, 2010.
-
Z. Asefy, S. Hoseinnejhad, and Z. Ceferov, “Nanoparticles approaches in neurodegenerative diseases diagnosis and treatment,” Neurol Sci, vol. 42, no. 7, pp. 2653–60, Jul. 2021, doi: 10.1007/s10072-021-05234-x.
-
Global Cancer Observatory, “Cancer Today.” 2021.
-
A. Mohsin et al., “Nanomaterial interfaces for sensing applications,” ACS Nano, vol. 7, pp. 8924–8931, 2013.
-
O. Parlak, A. P. F. Turner, and A. Tiwari, “On-chip electrochemical biosensing,” Adv. Mater., vol. 26, pp. 482–486, 2014.
-
T. Kuila, S. Bose, P. Khanra, A. K. Mishra, N. H. Kim, and J. H. Lee, “Graphene-based biosensors,” Biosens. Bioelectron., vol. 26, pp. 4637–4648, 2011.
-
S. Qu, X. Wang, Q. Lu, X. Liu, and L. Wang, “Carbon quantum dots,” Angew. Chemie, vol. 124, pp. 12381–12384, 2012.
-
Z. Ku, Y. Rong, M. Xu, T. Liu, and H. Han, “Nanostructures for sensing,” Sci. Rep., vol. 3, p. 3132, 2013.
[
M. Kokabi, M. N. Tahir, D. Singh, and M. Javanmard, “Biosensors and machine learning synergy for early cancer diagnosis,” Biosensors, vol. 13, p. 884, 2023.
-
Y. Lei et al., “Microwave biosensor with machine learning for CEA detection,” Biosens. Bioelectron., vol. 269, p. 116908, 2025.
-
R. Miotto, L. Li, B. A. Kidd, and J. T. Dudley, “Deep patient: An unsupervised representation to predict the future of patients,” Sci. Rep., vol. 6, 2016.
-
D. Roffman, G. Hart, M. Girardi, C. J. Ko, and J. Deng, “Prediction of non-melanoma skin cancer with neural networks,” Sci. Rep., vol. 8, 2018.
-
B. J. Nartowt, G. R. Hart, W. Muhammad, Y. Liang, G. F. Stark, and J. Deng, “Robust machine learning for colorectal cancer risk prediction,” Front. Big Data, 2020.
-
J. E. al Jumper, “Highly accurate protein structure prediction with AlphaFold,” Nature.
-
K.-K. Mak and M. R. Pichika, “Artificial intelligence in drug development: present status and future prospects,” Drug Discov. Today, vol. 24, no. 3, pp. 773–780, 2019, doi: https://doi.org/10.1016/j.drudis.2018.11.014.
-
M. Shirzad et al., “Artificial Intelligence-Assisted Design of Nanomedicines for Breast Cancer Diagnosis and Therapy: Advances, Challenges, and Future Directions,” BioNanoScience 2025 153, vol. 15, no. 3, pp. 354-, May 2025, doi: 10.1007/S12668-025-01980-W.
-
M. Alavinejad, “Smart nanomedicines powered by artificial intelligence: A breakthrough in lung cancer diagnosis and treatment,” Med. Oncol., vol. 42, no. 5, p. 134.
-
L. Rao, Y. Yuan, X. Shen, G. Yu, and X. Chen, “Designing nanotheranostics with machine learning,” Nat Nanotechnol, vol. 19, no. 12, pp. 1769–78, Dec. 2024, doi: 10.1038/s41565-024-01753-8.
-
L. K. Vora, A. D. Gholap, K. Jetha, R. R. S. Thakur, H. K. Solanki, and V. P. Chavda, “Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design,” Pharmaceutics, vol. 15, no. 7, Jul. 2023, doi: 10.3390/PHARMACEUTICS15071916.
-
V. M. Cambuli and G. M, “Baroni, Intelligent insulin vs Artificial intelligence for type 1 diabetes: Will the real winner please stand up?,” Int. J. Mol. Sci., vol. 24, no. 17, p. 13139.
-
M. K. Jayasinghe and others, “The role of in silico research in developing nanoparticle-based therapeutics,” Front. Digit. Heal., vol. 4, p. 838590, 2022.
-
P. Hassanzadeh, F. Atyabi, and R. Dinarvand, “The significance of artificial intelligence in drug delivery system design,” Adv. Drug Deliv. Rev., vol. 151, pp. 169–190.
-
W. Zhan, M. Alamer, and X. Y. Xu, “Computational modelling of drug delivery to solid tumour,” Adv. Drug Deliv. Rev., vol. 132, pp. 81–103, 2018.
-
C. H. Cheng and S. S. Shi, “Artificial intelligence in cancer: applications, challenges, and future perspectives,” Mol. Cancer, vol. 24, no. 1, 2025, doi: 10.1186/s12943-025-02450-3.
-
M. Soltani and others, “Enhancing clinical translation of cancer using nanoinformatics,” Cancers (Basel)., vol. 13, p. 2481, 2021.
-
B. Zhang, H. Shi, and H. Wang, “Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach,” J Multidiscip Heal., vol. 16, pp. 1779–91.
-
M. J. Iqbal, Z. Javed, H. Sadia, I. A. Qureshi, A. Irshad, and R. Ahmed, “Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future,” Cancer Cell Int, vol. 21, no. 270.
-
R. Fjelland, “Why general artificial intelligence will not be realized,” Humanit Soc Sci Commun, vol. 7, no. 10.
-
B. Govindan, M. A. Sabri, A. Hai, F. Banat, and M. A. Haija, “A review of advanced multifunctional magnetic nanostructures for cancer diagnosis and therapy integrated into an artificial intelligence approach,” Pharmaceutics, vol. 15, no. 868.
-
M. Xu and others, “Nanorobots mediated drug delivery for brain cancer,” Discov. Nano, vol. 19, p. 183, 2024.
-
T. Wasilewski, W. Kamysz, and J. Gębicki, “AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring,” Biosensors, vol. 14, no. 7, Jul. 2024, doi: 10.3390/BIOS14070356.
-
K. P. Das and J. Chandra, “Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges,” Front. Med. Technol., vol. 4, p. 1067144, Jan. 2022, doi: 10.3389/FMEDT.2022.1067144/BIBTEX.
-
L. Zhang, J. Tan, D. Han, and H. Zhu, “Deep learning models for nanomaterial design and environmental remediation,” Nano Today, vol. 40, p. 101280.
-
E. J. Topol, “High-performance medicine: the convergence of human and artificial intelligence”, doi: 10.1038/s41591-018-0300-7.
-
Food and D. Administration, “Artificial Intelligence and Machine Learning in Drug Development.”
-
“Guideline on the use of artificial intelligence in the medicinal product lifecycle.”
-
R. Kurzweil, The Singularity Is Near: When Humans Transcend Biology. Viking.
-
R. Yuste and G. M. Church, “The new frontier of brain–computer interfaces: Neurotechnology and society,” Neuron, vol. 108, no. 2, pp. 218–232.
-
M. C. Roco and W. S. Bainbridge, The New World of Discoveries: Convergence of Knowledge, Technology, and Society (CKTS. Springer.
-
A. Clark, Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence. Oxford University Press.
-
D.-M. rasca et al., “Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology,” Pharmaceutics, vol. 17, no. 12, doi: 10.3390/pharmaceutics17121564.
-
S. Yin, “Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects,” Biosensors, vol. 15, no. 10, p. 656, doi: 10.3390/bios15100656.
-
I. F. Akyildiz, M. Pierobon, S. Balasubramaniam, and Y. Koucheryavy, “The internet of Bio-Nano things,” IEEE Commun. Mag., vol. 53, no. 3, pp. 32–40, Mar. 2015, doi: 10.1109/MCOM.2015.7060516.
-
L. Cao et al., “Carbon dots for multiphoton bioimaging,” J. Am. Chem. Soc., vol. 129, no. 37, pp. 11318–11319, Sep. 2007, doi: 10.1021/JA073527L.
-
C. Hu, M. Chen, and C. Xing, “Towards efficient video chunk dissemination in peer-to-peer live streaming,” Comput. Networks, vol. 57, no. 15, pp. 3009–3024, Oct. 2013, doi: 10.1016/J.COMNET.2013.07.003.
-
S. Ivanov, D. Botvich, and S. Balasubramaniam, “Enzyme-based circuit design for nano-scale computing,” Nano Commun. Netw., vol. 3, no. 3, pp. 168–174, Sep. 2012, doi: 10.1016/J.NANCOM.2012.09.002.
-
Y. Long et al., “PSO-SVM-based online locomotion mode identification for rehabilitation robotic exoskeletons,” Sensors (Switzerland), vol. 16, no. 9, Sep. 2016, doi: 10.3390/S16091408.
-
V. Naresh and N. Lee, “A review on biosensors and recent development of nanostructured materials-enabled biosensors,” Sensors (Switzerland), vol. 21, no. 4, pp. 1–35, Feb. 2021, doi: 10.3390/S21041109.
-
G. Sliwoski, S. Kothiwale, J. Meiler, and E. W. Lowe, “Computational methods in drug discovery,” Pharmacol. Rev., vol. 66, no. 1, pp. 334–395, Jan. 2014, doi: 10.1124/PR.112.007336.
-
A. Lavecchia, “Machine-learning approaches in drug discovery: Methods and applications,” Drug Discov. Today, vol. 20, no. 3, pp. 318–331, 2015, doi: 10.1016/J.DRUDIS.2014.10.012.
-
R. H. Williams and T. Riedemann, “Development, diversity and death of mge-derived cortical interneurons,” Int. J. Mol. Sci., vol. 22, no. 17, Sep. 2021, doi: 10.3390/IJMS22179297.
-
S. Ekins, J. Mestres, and B. Testa, “In silico pharmacology for drug discovery: Applications to targets and beyond,” Br. J. Pharmacol., vol. 152, no. 1, pp. 21–37, Sep. 2007, doi: 10.1038/SJ.BJP.0707306.
-
R. Yuste et al., “Four ethical priorities for neurotechnologies and AI,” Nature, vol. 551, no. 7679, pp. 159–163, Nov. 2017, doi: 10.1038/551159A.
-
B. D. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, and L. Floridi, “The ethics of algorithms: Mapping the debate,” Big Data Soc., vol. 3, no. 2, Dec. 2016, doi: 10.1177/2053951716679679.
-
J. Burrell, “How the machine ‘thinks’: Understanding opacity in machine learning algorithms,” Big Data Soc., vol. 3, no. 1, Jan. 2016, doi: 10.1177/2053951715622512.
-
M. Finicelli, G. Peluso, and T. Squillaro, “Cellular Senescence in Physiological and Pathological Processes,” Int. J. Mol. Sci., vol. 23, no. 21, Nov. 2022, doi: 10.3390/IJMS232113342.
-
W. Zhao et al., “Superparamagnetic enhancement of thermoelectric performance,” Nature, vol. 549, no. 7671, pp. 247–251, Sep. 2017, doi: 10.1038/nature23667.
-
S. Dvorácskó et al., “Novel high affinity sigma-1 receptor Ligands from minimal ensemble docking-based virtual screening,” Int. J. Mol. Sci., vol. 22, no. 15, Aug. 2021, doi: 10.3390/IJMS22158112.
-
D. Seo et al., “Wireless Recording in the Peripheral Nervous System with Ultrasonic Neural Dust,” Neuron, vol. 91, no. 3, pp. 529–539, Aug. 2016, doi: 10.1016/J.NEURON.2016.06.034.
-
L. R. Hochberg et al., “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm,” Nature, vol. 485, no. 7398, pp. 372–375, May 2012, doi: 10.1038/NATURE11076.
-
J. C. G. Esteves da Silva and H. M. R. Gonçalves, “Analytical and bioanalytical applications of carbon dots,” TrAC - Trends Anal. Chem., vol. 30, no. 8, pp. 1327–1336, Sep. 2011, doi: 10.1016/J.TRAC.2011.04.009.
-
D. Mircsof et al., “Mutations in NONO lead to syndromic intellectual disability and inhibitory synaptic defects,” Nat. Neurosci., vol. 18, no. 12, pp. 1731–1736, Nov. 2015, doi: 10.1038/nn.4169.
-
C. Pandarinath et al., “High performance communication by people with paralysis using an intracortical brain-computer interface,” Elife, vol. 6, Feb. 2017, doi: 10.7554/ELIFE.18554.
-
M. Carè, M. Chiappalone, and V. R. Cota, “Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function,” Front. Neurosci., vol. 18, 2024, doi: 10.3389/FNINS.2024.1363128.
-
S. Ghosh, J. K. Sinha, S. Ghosh, H. Sharma, R. Bhaskar, and K. B. Narayanan, “A Comprehensive Review of Emerging Trends and Innovative Therapies in Epilepsy Management,” Brain Sci., vol. 13, no. 9, Sep. 2023, doi: 10.3390/BRAINSCI13091305.
-
J. Li, B. E. F. De Ávila, W. Gao, L. Zhang, and J. Wang, “Micro/nanorobots for Biomedicine: Delivery, surgery, sensing, and detoxification,” Sci. Robot., vol. 2, no. 4, Mar. 2017, doi: 10.1126/SCIROBOTICS.AAM6431.
-
I. Dobrzyńska, B. Szachowicz-Petelska, A. Wroński, I. Jarocka-Karpowicz, and E. Skrzydlewska, “Changes in the physicochemical properties of blood and skin cell membranes as a result of psoriasis vulgaris and psoriatic arthritis development,” Int. J. Mol. Sci., vol. 21, no. 23, pp. 1–17, Dec. 2020, doi: 10.3390/IJMS21239129.
-
O. A. Dambri, A. Azarnoush, D. Makrakis, G. Levesque, M. Witter, and A. S. Hafid, “Design and Implementation of a Simulation Framework for a Bio–Neural Dust System,” Model. 2025, Vol. 6, Page 8, vol. 6, no. 1, p. 8, Jan. 2025, doi: 10.3390/MODELLING6010008.
-
S. Majd, J. Power, and Z. Majd, “Alzheimer’s Disease and Cancer: When Two Monsters Cannot Be Together,” Front. Neurosci., vol. 13, Mar. 2019, doi: 10.3389/FNINS.2019.00155.
-
M. Ienca and R. Andorno, “Towards new human rights in the age of neuroscience and neurotechnology,” Life Sci. Soc. Policy, vol. 13, no. 1, Dec. 2017, doi: 10.1186/S40504-017-0050-1.
-
A. Dutta, “Bidirectional interactions between neuronal and hemodynamic responses to transcranial direct current stimulation (tDCS): Challenges for brain-state dependent tDCS,” Front. Syst. Neurosci., vol. 9, no. AUGUST, Aug. 2015, doi: 10.3389/FNSYS.2015.00107.
-
W. Han et al., “Integrated Control of Predatory Hunting by the Central Nucleus of the Amygdala,” Cell, vol. 168, no. 1–2, pp. 311-324.e18, Jan. 2017, doi: 10.1016/J.CELL.2016.12.027.
-
D. Moussa and H. Moussa, “The Architecture of Immortality Through Neuroengineering,” Philosophies, vol. 9, no. 6, Dec. 2024, doi: 10.3390/PHILOSOPHIES9060163.
-
W. Barfield and A. Williams, “Cyborgs and enhancement technology,” Philosophies, vol. 2, no. 1, Mar. 2017, doi: 10.3390/PHILOSOPHIES2010004.
-
F. Battaglia, “Agency, responsibility, selves, and the mechanical mind,” Philosophies, vol. 6, no. 1, Mar. 2021, doi: 10.3390/PHILOSOPHIES6010007.
-
A. K. Adamczyk and P. Zawadzki, “The Memory-Modifying Potential of Optogenetics and the Need for Neuroethics,” Nanoethics, vol. 14, no. 3, pp. 207–225, Dec. 2020, doi: 10.1007/S11569-020-00377-1.
-
J. Shaw, S. Pyreddy, C. Rosendahl, C. Lai, E. Ton, and R. Carter, “Current Neuroethical Perspectives on Deep Brain Stimulation and Neuromodulation for Neuropsychiatric Disorders: A Scoping Review of the Past 10 Years,” Diseases, vol. 13, no. 8, Aug. 2025, doi: 10.3390/DISEASES13080262.
-
M. M. Ahmed et al., “Integrating Digital Health Innovations to Achieve Universal Health Coverage: Promoting Health Outcomes and Quality Through Global Public Health Equity,” Healthc., vol. 13, no. 9, May 2025, doi: 10.3390/HEALTHCARE13091060.
-
P. A. Pinera, P. C. Kim, F. A. Pinera, and J. J. Shen, “Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals?,” Int. J. Environ. Res. Public Health, vol. 22, no. 2, Feb. 2025, doi: 10.3390/IJERPH22020294.
-
A. Lazăr and L. Azamfirei, “Personalized Medicine for the Critically Ill Patient: A Narrative Review,” Processes, vol. 10, p. 1200, 2022, doi: 10.3390/pr10061200.
-
K. Fesko, “Comparison of L-threonine aldolase variants in the aldol and retro-aldol reactions,” Front. Bioeng. Biotechnol., vol. 7, no. MAY, 2019, doi: 10.3389/FBIOE.2019.00119.
-
A. M. George, “The national security implications of cyberbiosecurity,” Front. Bioeng. Biotechnol., vol. 7, no. MAR, 2019, doi: 10.3389/FBIOE.2019.00051.
-
C. Loo et al., “Nanoshell-Enabled Photonics-Based Imaging and Therapy of Cancer,” Technol. Cancer Res. Treat., vol. 3, no. 1, pp. 33–40, 2004, doi: 10.1177/153303460400300104.
-
K. D. Apostolidis and G. A. Papakostas, “Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning,” J. Imaging, vol. 8, no. 6, Jun. 2022, doi: 10.3390/JIMAGING8060155.
-
W. Nam, K. Kim, H. Moon, H. Noh, J. Park, and H. Kil, “RISOPA: Rapid Imperceptible Strong One-Pixel Attacks in Deep Neural Networks,” Mathematics, vol. 12, no. 7, Apr. 2024, doi: 10.3390/MATH12071083.
-
J. Su, D. V. Vargas, and K. Sakurai, “Attacking convolutional neural network using differential evolution,” IPSJ Trans. Comput. Vis. Appl., vol. 11, no. 1, Dec. 2019, doi: 10.1186/S41074-019-0053-3.
-
J. Korpihalkola, T. Sipola, S. Puuska, and T. Kokkonen, “One-Pixel Attack Deceives Computer-Assisted Diagnosis of Cancer,” Nov. 2021, doi: 10.1145/3483207.3483224.
-
J. Allyn, N. Allou, C. Vidal, A. Renou, and C. Ferdynus, “Adversarial attack on deep learning-based dermatoscopic image recognition systems: Risk of misdiagnosis due to undetectable image perturbations,” Med. (United States), vol. 99, no. 50, p. E23568, Dec. 2020, doi: 10.1097/MD.0000000000023568.
-
X. Ma et al., “Understanding adversarial attacks on deep learning based medical image analysis systems,” Pattern Recognit., vol. 110, Feb. 2021, doi: 10.1016/j.patcog.2020.107332.
-
M. J. Tsai, P. Y. Lin, and M. E. Lee, “Adversarial Attacks on Medical Image Classification,” Cancers (Basel)., vol. 15, no. 17, Sep. 2023, doi: 10.3390/CANCERS15174228.
-
Y. Li and S. Liu, “The Threat of Adversarial Attack on a COVID-19 CT Image-Based Deep Learning System,” Bioengineering, vol. 10, no. 2, Feb. 2023, doi: 10.3390/BIOENGINEERING10020194.
-
M. Gasson, R. Edwards, and M. Ashcroft, “Invasive Brain–Computer Interfaces: Security, Privacy and Safety Considerations,” J. Neural Eng, vol. 9, p. 16005, doi: 10.1088/1741-2560/9/1/016005.
-
T. Bonaci et al., “Towards Safer Implantable Neural Devices: Wireless Security and Privacy Challenges,” Sensors, vol. 17, doi: 10.3390/s17071670.
-
D. Halperin et al., “Pacemakers and Implantable Cardiac Defibrillators: Software Radio Attacks and Zero-Power Defenses,” IEEE Secur. Priv., vol. 6, no. 3, pp. 38–49, doi: 10.1109/MSP.2008.66.
-
M. Li, F. Yang, Q. Liu, J. Chen, and W. Zhou, “BrainJacking: Risks of Unauthorized Access to Neural Implants,” Electronics, vol. 11, p. 3452, doi: 10.3390/electronics11193452.
-
H. Xu, C. Ma, Y. Li, S. Wang, and J. Qiu, “Security Analysis of Wireless Neuro-Implants: Vulnerabilities, Attacks, and Countermeasures,” Appl. Sci, vol. 11, p. 11042, doi: 10.3390/app112311042.
-
H. Xu, C. Ma, Y. Li, S. Wang, and J. Qiu, “*Security Analysis of Wireless Neuro-Implants: Vulnerabilities,” Attacks, Countermeas. Appl. Sci, vol. 11, p. 11042.
-
M. A. Al Faruque, S. Mustafa, V. Muthukkumarasamy, and S. Tariq, “*Cybersecurity in Internet of Bio-Nano Things: Threats,” Attacks, Countermeas. Sensors.
-
J. Fernandes, J. J. P. C. Rodrigues, I. Torre, and J. Lloret, “*Security-by-Design in IoT and Bio-Nano Networks: Approaches and Biometric Encryption Methods.*,” Appl. Sci, vol. 12, p. 7890.
-
Y. Zhang, H. Sun, R. Wang, and L. Zhao, “*Biometric-Based Security Solutions for Wireless Medical Devices: Preserving Data Integrity in IoBNT Systems.*,” Electronics, vol. 12, p. 654.
-
B. Fadeel, A. Fornara, M. S. Toprak, and M. P. Monopoli, “*Nanotoxicology: Principles and Approaches for Assessing Nanomaterial Safety in Biological Systems.* Appl,” Sci, vol. 10, p. 3245.
-
A. Nel, T. Xia, L. Mädler, and N. Li, “*Toxic Potential of Materials at the Nanolevel.*,” Science (80-. )., vol. 311, pp. 622–627.
-
Y. Wang, C. Chen, X. Hu, and Y. Zhao, “*Challenges in Traditional Toxicology for Assessing Nanomaterial Safety: Emerging Alternative Approaches.* Appl,” Sci, vol. 12, p. 5321.
-
J. Yan, R. Li, H. Sun, Y. Liu, and J. Zhang, “*Nanomaterial-Biological System Interactions: Toxicological Implications for Nano-Biomedical Devices.*,” Nanomaterials, vol. 11.
-
T. Puzyn, J. Leszczynski, and M. T. D. Cronin, “*Computational Nanotoxicology: In Silico Methods for Predicting Nanomaterial Toxicity.* Wiley Interdiscip,” Rev. Nanomed. Nanobiotechnol, vol. 3, pp. 463–477.
-
Z. Wang, X. Wei, X. Li, Q. Zhou, and R. Liu, “*Nano-SAR Modeling for Predicting Cytotoxicity of Engineered Nanomaterials.*,” Nanomaterials, vol. 11.
-
K. T. Ho, J. H. Lin, and S. Y. Chen, “*Machine Learning Approaches in Nanotoxicology: Predicting Cytotoxicity and Genotoxicity of Nanomaterials.* Appl,” Sci, vol. 12, p. 6789.
-
A. Gajewicz, B. Rasulev, and T. Puzyn, “*From QSAR to Nano-SAR: In Silico Prediction of Nanomaterial Toxicity Using Physicochemical Descriptors.*,” Nanomaterials, vol. 5, pp. 199–224.
-
X. R. Xia, N. A. Monteiro-Riviere, and J. E. Riviere, “An index for characterization of nanomaterials in biological systems,” Nat. Nanotechnol., vol. 5, no. 9, pp. 671–675, 2010, doi: 10.1038/NNANO.2010.164.
-
M. P. Monopoli, C. Åberg, A. Salvati, and K. A. Dawson, “*Biomolecular Coronas Provide the Biological Identity of Nanosized Materials.* Nat,” Nanotechnol, vol. 7, pp. 779–786.
-
J. H. Shannahan, R. Podila, and J. M. Brown, “*Protein Corona Formation on Nanomaterials: Implications for Biological Identity and Toxicity.* Appl,” Sci, vol. 10, p. 5152.
-
A. B. Raies and V. B. Bajic, “*In Silico Toxicology: Computational Methods for the Prediction of Chemical Toxicity.* Wiley Interdiscip,” Rev. Comput. Mol. Sci, vol. 6, pp. 147–172.
-
A. Mayr, G. Klambauer, T. Unterthiner, and S. Hochreiter, “DeepTox: Toxicity prediction using deep learning,” Front. Environ. Sci, vol. 3, no. FEB, Feb, doi: 10.3389/FENVS.2015.00080.
-
T. Puzyn et al., “Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles,” Nat. Nanotechnol., vol. 6, no. 3, pp. 175–178, 2011, doi: 10.1038/NNANO.2011.10.
-
B. Fadeel and A. E. Garcia-Bennett, “*Better Safe than Sorry: Understanding Nanomaterial Toxicity and the Importance of Chronic Exposure Studies.*,” Nano Today, vol. 5, pp. 328–331.
-
X. Zhang, N. A. Monteiro-Riviere, and J. E. Riviere, “*Predictive Models for Nanomaterial Toxicity: Integrating Nano-SAR and Machine Learning Approaches.*,” Int. J. Mol. Sci, vol. 21, p. 9123.
-
B. Fadeel, L. Farcal, and B. Hardy, “*Keeping it Safe: Nanomaterial Safety Assessment and Lifecycle Considerations.*,” Nano Today, vol. 21, pp. 1–16.
-
S. F. Ali, S. Hussain, and S. Ubaid, “*Nanowaste: Environmental and Health Implications of Engineered Nanomaterials at the End of Life.* Environ,” Sci. Pollut. Res, vol. 28, pp. 45230–45249.
-
I. Khan, K. Saeed, and I. Khan, “*Nanoparticles: Properties, Applications and Toxicities.* Arab,” J. Chem, vol. 12, pp. 908–931.
-
S. Arora, J. Jain, J. M. Rajwade, and K. M. Paknikar, “*Cellular Responses Induced by Silver Nanoparticles: In Vitro Toxicity Studies and Environmental Implications.* Toxicol,” Lett, vol. 185, pp. 34–40.
-
R. Roy, S. Bhattacharya, and M. Ghosh, “*Life Cycle Assessment of Engineered Nanomaterials: Environmental Fate and Toxicity Considerations.* Appl,” Sci, vol. 11, p. 4567.
-
C. J. Murphy, A. M. Vartanian, and F. M. Geiger, “*Nanomaterial Environmental Fate: Integrating Human and Ecosystem Health Perspectives.* Environ,” Sci. Nano, vol. 7, pp. 1234–1249.
-
F. Gottschalk and B. Nowack, “*The Release of Engineered Nanomaterials to the Environment.*,” J. Environ. Monit., vol. 13, pp. 1145–1155.
-
M. A. Maurer-Jones, I. L. Gunsolus, C. J. Murphy, and C. L. Haynes, “*Toxicity of Engineered Nanoparticles in the Environment.* Anal,” Chem, vol. 85, pp. 3036–3049.
-
A. B. A. Boxall, K. Tiede, and Q. Chaudhry, “*Engineered Nanoparticles in Soils and Water: Behaviour, Fate and Environmental Risk.* Environ,” Pollut, vol. 150, pp. 5–22.
-
N. U. M. Nizam, M. M. Hanafiah, and K. S. Woon, “A Content Review of Life Cycle Assessment of Nanomaterials : Current Practices , Challenges , and Future Prospects,” pp. 1–27, 2021.