The Fusion of New Materials and Smart Technologies: Redefining How We Detect Antibiotic Resistance

Ao Qin; Yuhan Shen; Chengfeng Gao; Baosheng Chen; Guocai Li

doi:10.53941/sen.2026.100009

Abstract

The global spread of antimicrobial resistance (AMR) poses an urgent threat to modern public health, driven by multiple, interacting mechanisms including enzyme inactivation, target modification, efflux pump overexpression, and biofilm formation. While conventional antimicrobial susceptibility testing (AST) and molecular techniques remain the clinical standard, their practical limitations, including time-consuming procedures, reliance on culture, inability to reveal underlying mechanisms, and poor quantification of resistance levels, highlight the urgent need for innovative solutions. This review proposes a paradigm shift in drug resistance diagnostics from static, single-indicator, endpoint detection to dynamic, multi-parameter, predictive diagnostics by integrating advanced functional materials and artificial intelligence (AI). Nanomaterials, metal-organic frameworks, graphene oxide, and microfluidic platforms allow rapid, culture-free detection of resistance enzymes, genes, and biofilms with exceptional sensitivity. Meanwhile, AI architectures—including convolutional neural networks, random forests, and graph neural networks—enable precise signal quantification, multi-modal data fusion, and accurate resistance phenotype prediction. However, clinical translation remains hindered by material reproducibility, biofouling, regulatory gaps, and cost constraints. This review provides a forward-looking roadmap for developing globally deployable, intelligent AMR diagnostic systems within a “One Health” framework.

References

1.
Davies, J.; Davies, D. Origins and evolution of antibiotic resistance. Microbiol. Mol. Biol. Rev. 2010, 74, 417–433. https://doi.org/10.1128/mmbr.00016-10.
2.
Naghavi, M.; Vollset, S.E.; Ikuta, K.; et al. SGlobal burden of bacterial antimicrobial resistance 1990–2021: A systematic analysis with forecasts to 2050. Lancet 2024, 404, 1199–1226.
3.
Marino, A.; Maniaci, A.; Lentini, M.; et al. The Global Burden of Multidrug-Resistant Bacteria. Epidemiologia 2025, 6, 21.
4.
Daruka, L.; Czikkely, M.S.; Szili, P.; et al. ESKAPE pathogens rapidly develop resistance against antibiotics in development in vitro. Nat. Microbiol. 2025, 10, 313–331. https://doi.org/10.1038/s41564-024-01891-8.
5.
Sati, H.; Carrara, E.; Savoldi, A.; et al. The WHO Bacterial Priority Pathogens List 2024: A prioritisation study to guide research, development, and public health strategies against antimicrobial resistance. Lancet Infect. Dis. 2025, 25, 1033–1043.
6.
Bertagnolio, S.; Dobreva, Z.; Centner, C.M.; et al. WHO global research priorities for antimicrobial resistance in human health. Lancet Microbe 2024, 5, 100902. https://doi.org/10.1016/s2666-5247(24)00134-4.
7.
Okeke, I.N.; de Kraker, M.E.A.; Van Boeckel, T.P.; et al. The scope of the antimicrobial resistance challenge. Lancet 2024, 403, 2426–2438.
8.
Wiegand, I.; Hilpert, K.; Hancock, R.E. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat. Protoc. 2008, 3, 163–175. https://doi.org/10.1038/nprot.2007.521.
9.
Heatley, N.G. A method for the assay of penicillin. Biochem. J. 1944, 38, 61–65. https://doi.org/10.1042/bj0380061.
10.
Nonhoff, C.; Rottiers, S.; Struelens, M.J. Evaluation of the Vitek 2 system for identification and antimicrobial susceptibility testing of Staphylococcus spp. Clin. Microbiol. Infect. 2005, 11, 150–153.
11.
Rhoads, S.; Marinelli, L.; Imperatrice, C.A.; et al. Comparison of MicroScan WalkAway system and Vitek system for identification of gram-negative bacteria. J. Clin. Microbiol. 1995, 33, 3044–3046. https://doi.org/10.1128/jcm.33.11.3044-3046.1995.
12.
Hotchkiss, R.S.; Moldawer, L.L.; Opal, S.M.; et al. Sepsis and septic shock. Nature reviews Disease primers 2016, 2, 16045. https://doi.org/10.1038/nrdp.2016.45.
13.
Depka, D.; Mikucka, A.; Bogiel, T.; et al. Conventional and Real-Time PCR Targeting bla(OXA) Genes as Reliable Methods for a Rapid Detection of Carbapenem-Resistant Acinetobacter baumannii Clinical Strains. Antibiotics 2022, 11, 455.
14.
Heid, C.A.; Stevens, J.; Livak, K.J.; et al. Real time quantitative PCR. Genome Res. 1996, 6, 986–994. https://doi.org/10.1101/gr.6.10.986.
15.
Elnifro, E.M.; Ashshi, A.M.; Cooper, R.J.; et al. Multiplex PCR: Optimization and application in diagnostic virology. Clin. Microbiol. Rev. 2000, 13, 559–570. https://doi.org/10.1128/cmr.13.4.559.
16.
Li, C.; Kang, N.; Ye, S.; et al. All-In-One OsciDrop Digital PCR System for Automated and Highly Multiplexed Molecular Diagnostics. Adv. Sci. 2024, 11, e2309557.
17.
Eldin, C.; Parola, P.; Raoult, D. Limitations of diagnostic tests for bacterial infections. Med. Mal. Infect. 2019, 49, 98–101. https://doi.org/10.1016/j.medmal.2018.12.004.
18.
Yee, R.; Dien Bard, J.; Simner, P.J. The Genotype-to-Phenotype Dilemma: How Should Laboratories Approach Discordant Susceptibility Results? J. Clin. Microbiol. 2021, 59. https://doi.org/10.1128/jcm.00138-20.
19.
Analytical Methods Committee. PCR—The polymerase chain reaction. Anal. Methods 2014, 6, 333–336.
20.
Angus, D.C.; van der Poll, T. Severe sepsis and septic shock. N. Engl. J. Med. 2013, 369, 840–851. https://doi.org/10.1056/nejmra1208623.
21.
Burnham, C.D.; Leeds, J.; Nordmann, P.; et al. Diagnosing antimicrobial resistance. Nat. Rev. Microbiol. 2017, 15, 697–703. https://doi.org/10.1038/nrmicro.2017.103.
22.
Vergauwe, F.; De Waele, G.; Sass, A.; et al. Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms. NPJ Biofilms Microbiomes 2025, 11, 205. https://doi.org/10.1038/s41522-025-00833-4.
23.
Bush, K.; Bradford, P.A. Epidemiology of β-Lactamase-Producing Pathogens. Clin. Microbiol. Rev. 2020, 33. https://doi.org/10.1128/cmr.00047-19.
24.
Queenan, A.M.; Bush, K. Carbapenemases: The versatile beta-lactamases. Clin. Microbiol. Rev. 2007, 20, 440–458. https://doi.org/10.1128/cmr.00001-07.
25.
Bush, K. The ABCD’s of β-lactamase nomenclature. J. Infect. Chemother. 2013, 19, 549–559.
26.
Sirot, D.; Sirot, J.; Labia, R.; et al. Transferable resistance to third-generation cephalosporins in clinical isolates of Klebsiella pneumoniae: Identification of CTX-1, a novel beta-lactamase. J. Antimicrob. Chemother. 1987, 20, 323–334. https://doi.org/10.1093/jac/20.3.323.
27.
Walther-Rasmussen, J.; Høiby, N. Class A carbapenemases. J. Antimicrob. Chemother. 2007, 60, 470–482. https://doi.org/10.1093/jac/dkm226.
28.
Chen, L.; Mathema, B.; Chavda, K.D.; et al. Carbapenemase-producing Klebsiella pneumoniae: Molecular and genetic decoding. Trends Microbiol. 2014, 22, 686–696. https://doi.org/10.1016/j.tim.2014.09.003.
29.
Cornaglia, G.; Giamarellou, H.; Rossolini, G.M. Metallo-β-lactamases: A last frontier for β-lactams? Lancet Infect. Dis. 2011, 11, 381–393. https://doi.org/10.1016/s1473-3099(11)70056-1.
30.
Jacoby, G.A. AmpC beta-lactamases. Clin. Microbiol. Rev. 2009, 22, 161–182. https://doi.org/10.1128/cmr.00036-08.
31.
Evans, B.A.; Amyes, S.G. OXA β-lactamases. Clin. Microbiol. Rev. 2014, 27, 241–263. https://doi.org/10.1128/cmr.00117-13.
32.
Park, S.R.; Park, J.W.; Ban, Y.H.; et al. 2-Deoxystreptamine-containing aminoglycoside antibiotics: Recent advances in the characterization and manipulation of their biosynthetic pathways. Nat. Prod. Rep. 2013, 30, 11–20. https://doi.org/10.1039/c2np20092a.
33.
Ramirez, M.S.; Tolmasky, M.E. Aminoglycoside modifying enzymes. Drug Resist. Updates 2010, 13, 151–171.
34.
Azucena, E.; Mobashery, S. Aminoglycoside-modifying enzymes: Mechanisms of catalytic processes and inhibition. Drug Resist. Updates 2001, 4, 106–117.
35.
Shepherd, M.J.; Fu, T.; Harrington, N.E.; et al. Ecological and evolutionary mechanisms driving within-patient emergence of antimicrobial resistance. Nat. Rev. Microbiol. 2024, 22, 650–665. https://doi.org/10.1038/s41579-024-01041-1.
36.
Feng, S.; Wu, Z.; Liang, W.; et al. Prediction of Antibiotic Resistance Evolution by Growth Measurement of All Proximal Mutants of Beta-Lactamase. Mol. Biol. Evol. 2022, 39, msac086. https://doi.org/10.1093/molbev/msac086.
37.
Blair, J.M.; Webber, M.A.; Baylay, A.J.; et al. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 2015, 13, 42–51. https://doi.org/10.1038/nrmicro3380.
38.
Wang, Q.; Wei, S.; Silva, A.F.; et al. Cooperative antibiotic resistance facilitates horizontal gene transfer. ISME J. 2023, 17, 846–854. https://doi.org/10.1038/s41396-023-01393-1.
39.
Connell, S.R.; Tracz, D.M.; Nierhaus, K.H.; et al. Ribosomal protection proteins and their mechanism of tetracycline resistance. Antimicrob. Agents Chemother. 2003, 47, 3675–3681. https://doi.org/10.1128/aac.47.12.3675-3681.2003.
40.
Dönhöfer, A.; Franckenberg, S.; Wickles, S.; et al. Structural basis for TetM-mediated tetracycline resistance. Proc. Natl. Acad. Sci. USA 2012, 109, 16900–16905. https://doi.org/10.1073/pnas.1208037109.
41.
Li, W.; Atkinson, G.C.; Thakor, N.S.; et al. Mechanism of tetracycline resistance by ribosomal protection protein Tet(O). Nat. Commun. 2013, 4, 1477. https://doi.org/10.1038/ncomms2470.
42.
Kittl, S.; Brodard, I.; Tresch, M.; et al. Novel tetracycline resistance gene tet(65) located on a multi-resistance Corynebacterium plasmid. J. Antimicrob. Chemother. 2024, 79, 1023–1029. https://doi.org/10.1093/jac/dkae066.
43.
Zhu, Y.; Wang, C.; Schwarz, S.; et al. Identification of a novel tetracycline resistance gene, tet(63), located on a multiresistance plasmid from Staphylococcus aureus. J. Antimicrob. Chemother. 2021, 76, 576–581. https://doi.org/10.1093/jac/dkaa485.
44.
Tomlinson, J.H.; Kalverda, A.P.; Calabrese, A.N. Fusidic acid resistance through changes in the dynamics of the drug target. Proc. Natl. Acad. Sci. USA 2020, 117, 25523–25531. https://doi.org/10.1073/pnas.2008577117.
45.
Leclercq, R.; Courvalin, P. Bacterial resistance to macrolide, lincosamide, and streptogramin antibiotics by target modification. Antimicrob. Agents Chemother. 1991, 35, 1267–1272. https://doi.org/10.1128/aac.35.7.1267.
46.
Martínez-Trejo, A.; Ruiz-Ruiz, J.M.; Gonzalez-Avila, L.U.; et al. Evasion of Antimicrobial Activity in Acinetobacter baumannii by Target Site Modifications: An Effective Resistance Mechanism. Int. J. Mol. Sci. 2022, 23, 6582. https://doi.org/10.3390/ijms23126582.
47.
Lade, H.; Joo, H.S.; Kim, J.S. Molecular Basis of Non-β-Lactam Antibiotics Resistance in Staphylococcus aureus. Antibiotics 2022, 11, 1378.
48.
Lopatkin, A.J.; Bening, S.C.; Manson, A.L.; et al. Clinically relevant mutations in core metabolic genes confer antibiotic resistance. Science 2021, 371, eaba0862.
49.
Campbell, E.A.; Korzheva, N.; Mustaev, A.; et al. Structural mechanism for rifampicin inhibition of bacterial rna polymerase. Cell 2001, 104, 901–912. https://doi.org/10.1016/s0092-8674(01)00286-0.
50.
Blaskovich, M.A.T.; Hansford, K.A.; Butler, M.S.; et al. Developments in Glycopeptide Antibiotics. ACS Infect. Dis. 2018, 4, 715–735. https://doi.org/10.1021/acsinfecdis.7b00258.
51.
Stogios, P.J.; Savchenko, A. Molecular mechanisms of vancomycin resistance. Protein Sci. 2020, 29, 654–669.
52.
Piddock, L.J. Clinically relevant chromosomally encoded multidrug resistance efflux pumps in bacteria. Clin. Microbiol. Rev. 2006, 19, 382–402. https://doi.org/10.1128/cmr.19.2.382-402.2006.
53.
Wong, K.; Ma, J.; Rothnie, A.; et al. Towards understanding promiscuity in multidrug efflux pumps. Trends Biochem. Sci. 2014, 39, 8–16. https://doi.org/10.1016/j.tibs.2013.11.002.
54.
Hirakata, Y.; Srikumar, R.; Poole, K.; et al. Multidrug efflux systems play an important role in the invasiveness of Pseudomonas aeruginosa. J. Exp. Med. 2002, 196, 109–118. https://doi.org/10.1084/jem.20020005.
55.
Pradel, E.; Pagès, J.M. The AcrAB-TolC efflux pump contributes to multidrug resistance in the nosocomial pathogen Enterobacter aerogenes. Antimicrob. Agents Chemother. 2002, 46, 2640–2643. https://doi.org/10.1128/aac.46.8.2640-2643.2002.
56.
Chevalier, S.; Bouffartigues, E.; Bodilis, J.; et al. Structure, function and regulation of Pseudomonas aeruginosa porins. FEMS Microbiol. Rev. 2017, 41, 698–722. https://doi.org/10.1093/femsre/fux020.
57.
Bell, A.; Bains, M.; Hancock, R.E. Pseudomonas aeruginosa outer membrane protein OprH: Expression from the cloned gene and function in EDTA and gentamicin resistance. J. Bacteriol. 1991, 173, 6657–6664. https://doi.org/10.1128/jb.173.21.6657-6664.1991.
58.
Nie, D.; Hu, Y.; Chen, Z.; et al. Outer membrane protein A (OmpA) as a potential therapeutic target for Acinetobacter baumannii infection. J. Biomed. Sci. 2020, 27, 26. https://doi.org/10.1186/s12929-020-0617-7.
59.
Scribano, D.; Cheri, E.; Pompilio, A.; et al. Acinetobacter baumannii OmpA-like porins: Functional characterization of bacterial physiology, antibiotic-resistance, and virulence. Commun. Biol. 2024, 7, 948. https://doi.org/10.1038/s42003-024-06645-0.
60.
Lister, P.D.; Wolter, D.J.; Hanson, N.D. Antibacterial-resistant Pseudomonas aeruginosa: Clinical impact and complex regulation of chromosomally encoded resistance mechanisms. Clin. Microbiol. Rev. 2009, 22, 582–610. https://doi.org/10.1128/cmr.00040-09.
61.
Roemhild, R.; Bollenbach, T.; Andersson, D.I. The physiology and genetics of bacterial responses to antibiotic combinations. Nat. Rev. Microbiol. 2022, 20, 478–490. https://doi.org/10.1038/s41579-022-00700-5.
62.
O’Toole, G.; Kaplan, H.B.; Kolter, R. Biofilm formation as microbial development. Annu. Rev. Microbiol. 2000, 54, 49–79. https://doi.org/10.1146/annurev.micro.54.1.49.
63.
Hall, C.W.; Mah, T.F. Molecular mechanisms of biofilm-based antibiotic resistance and tolerance in pathogenic bacteria. FEMS Microbiol. Rev. 2017, 41, 276–301. https://doi.org/10.1093/femsre/fux010.
64.
Taylor, P.K.; Yeung, A.T.; Hancock, R.E. Antibiotic resistance in Pseudomonas aeruginosa biofilms: Towards the development of novel anti-biofilm therapies. J. Biotechnol. 2014, 191, 121–130. https://doi.org/10.1016/j.jbiotec.2014.09.003.
65.
Harms, A.; Maisonneuve, E.; Gerdes, K. Mechanisms of bacterial persistence during stress and antibiotic exposure. Science 2016, 354, aaf4268.
66.
Rossi, E.; La Rosa, R.; Bartell, J.A.; et al. Pseudomonas aeruginosa adaptation and evolution in patients with cystic fibrosis. Nat. Rev. Microbiol. 2021, 19, 331–342. https://doi.org/10.1038/s41579-020-00477-5.
67.
Bassetti, M.; Poulakou, G.; Ruppe, E.; et al. Antimicrobial resistance in the next 30 years, humankind, bugs and drugs: A visionary approach. Intensive Care Med. 2017, 43, 1464–1475. https://doi.org/10.1007/s00134-017-4878-x.
68.
Dina, N.E.; Tahir, M.A.; Bajwa, S.Z.; et al. SERS-based antibiotic susceptibility testing: Towards point-of-care clinical diagnosis. Biosens. Bioelectron. 2023, 219, 114843. https://doi.org/10.1016/j.bios.2022.114843.
69.
Jorgensen, J.H.; Ferraro, M.J. Antimicrobial susceptibility testing: A review of general principles and contemporary practices. Clin. Infect. Dis. 2009, 49, 1749–1755.
70.
Belanger, C.R.; Hancock, R.E.W. Testing physiologically relevant conditions in minimal inhibitory concentration assays. Nat. Protoc. 2021, 16, 3761–3774. https://doi.org/10.1038/s41596-021-00572-8.
71.
Berkow, E.L.; Lockhart, S.R.; Ostrosky-Zeichner, L. Antifungal Susceptibility Testing: Current Approaches. Clin. Microbiol. Rev. 2020, 33. https://doi.org/10.1128/cmr.00069-19.
72.
Baquer, F.; Ali Sawan, A.; Auzou, M.; et al. Broth Microdilution and Gradient Diffusion Strips vs. Reference Agar Dilution Method: First Evaluation for Clostridiales Species Antimicrobial Susceptibility Testing. Antibiotics 2021, 10, 975.
73.
Hanson, C.W.; Martin, W.J. Modified agar dilution method for rapid antibiotic susceptibility testing of anaerobic bacteria. Antimicrob. Agents Chemother. 1978, 13, 383–388. https://doi.org/10.1128/aac.13.3.383.
74.
Brook, I.; Wexler, H.M.; Goldstein, E.J. Antianaerobic antimicrobials: Spectrum and susceptibility testing. Clin. Microbiol. Rev. 2013, 26, 526–546. https://doi.org/10.1128/cmr.00086-12.
75.
Nijs, A.; Cartuyvels, R.; Mewis, A.; et al. Comparison and evaluation of Osiris and Sirscan 2000 antimicrobial susceptibility systems in the clinical microbiology laboratory. J. Clin. Microbiol. 2003, 41, 3627–3630. https://doi.org/10.1128/jcm.41.8.3627-3630.2003.
76.
Kustimur, S.; Kalkanci, A.; Mansuroglu, H.; et al. Evaluation of the disc diffusion method with a comparison study for fluconazole susceptibility of Candida strains. Chin. Med. J. 2003, 116, 633–636.
77.
Citron, D.M.; Ostovari, M.I.; Karlsson, A.; et al. Evaluation of the E test for susceptibility testing of anaerobic bacteria. J. Clin. Microbiol. 1991, 29, 2197–2203. https://doi.org/10.1128/jcm.29.10.2197-2203.1991.
78.
Jönsson, A.; Jacobsson, S.; Foerster, S.; et al. Performance characteristics of newer MIC gradient strip tests compared with the Etest for antimicrobial susceptibility testing of Neisseria gonorrhoeae. Apmis 2018, 126, 822–827.
79.
Huang, M.B.; Baker, C.N.; Banerjee, S.; et al. Accuracy of the E test for determining antimicrobial susceptibilities of staphylococci, enterococci, Campylobacter jejuni, and gram-negative bacteria resistant to antimicrobial agents. J. Clin. Microbiol. 1992, 30, 3243–3248. https://doi.org/10.1128/jcm.30.12.3243-3248.1992.
80.
Jorgensen, J.H.; Ferraro, M.J.; McElmeel, M.L.; et al. Detection of penicillin and extended-spectrum cephalosporin resistance among Streptococcus pneumoniae clinical isolates by use of the E test. J. Clin. Microbiol. 1994, 32, 159–163. https://doi.org/10.1128/jcm.32.1.159-163.1994.
81.
O’Hara, C.M. Manual and automated instrumentation for identification of Enterobacteriaceae and other aerobic gram-negative bacilli. Clin. Microbiol. Rev. 2005, 18, 147–162.
82.
Rodríguez, L.A.; Vivas, J.; Gallardo, C.S.; et al. Identification of Hafnia alvei with the MicroScan WalkAway system. J. Clin. Microbiol. 1999, 37, 4186–4188. https://doi.org/10.1128/jcm.37.12.4186-4188.1999.
83.
Steward, C.D.; Mohammed, J.M.; Swenson, J.M.; et al. Antimicrobial susceptibility testing of carbapenems: Multicenter validity testing and accuracy levels of five antimicrobial test methods for detecting resistance in Enterobacteriaceae and Pseudomonas aeruginosa isolates. J. Clin. Microbiol. 2003, 41, 351–358. https://doi.org/10.1128/jcm.41.1.351-358.2003.
84.
Zhou, X.; Dai, Y.Y.; Ma, X.L. Be alert to the alterations in the biological characteristics in heterogeneous vancomycin-intermediate Staphylococcus aureus. Indian J. Med. Microbiol. 2012, 30, 215–217. https://doi.org/10.4103/0255-0857.96696.
85.
Renders, N.H.; Kluytmans, J.A.; Verbrugh, H.A. Clinical impact of rapid in vitro susceptibility testing and bacterial identification. J. Clin. Microbiol. 1995, 33, 508. https://doi.org/10.1128/jcm.33.2.508-508.1995.
86.
Llor, C.; Bjerrum, L. Antimicrobial resistance: Risk associated with antibiotic overuse and initiatives to reduce the problem. Ther. Adv. Drug Saf. 2014, 5, 229–241. https://doi.org/10.1177/2042098614554919.
87.
Zafar, A.; Khursheed, N.; Adnan, F.; et al. Determination of Inducible Clindamycin Resistance and Correlation with Vitek2 Inducible Clindamycin Resistance Test in Staphylococcus aureus Isolated from Clinical Samples. J. Coll. Physicians Surg. 2024, 34, 183–186. https://doi.org/10.29271/jcpsp.2024.02.183.
88.
Tsakris, A.; Poulou, A.; Bogaerts, P.; et al. Evaluation of a new phenotypic OXA-48 disk test for differentiation of OXA-48 carbapenemase-producing Enterobacteriaceae clinical isolates. J. Clin. Microbiol. 2015, 53, 1245–1251. https://doi.org/10.1128/jcm.03318-14.
89.
Itahashi, M.; Higaki, S.; Fukuda, M.; et al. Detection and quantification of pathogenic bacteria and fungi using real-time polymerase chain reaction by cycling probe in patients with corneal ulcer. Arch. Ophthalmol. 2010, 128, 535–540.
90.
Kurkela, S.; Brown, D.W.G. Molecular diagnostic techniques. Medicine 2009, 37, 535–540.
91.
Yang, B.; Xin, X.; Cao, X.; et al. Phenotypic and genotypic perspectives on detection methods for bacterial antimicrobial resistance in a One Health context: Research progress and prospects. Arch. Microbiol. 2024, 206, 409. https://doi.org/10.1007/s00203-024-04131-z.
92.
Abd El-Aziz, N.; Abd El-Hamid, M.; Bendary, M.; et al. Existence of vancomycin resistance among methicillin resistant s. aureus recovered from animal and human sources in Egypt. Slov. Vet. Res. 2018, 55, 221–230.
93.
Pulido, M.R.; García-Quintanilla, M.; Martín-Peña, R.; et al. Progress on the development of rapid methods for antimicrobial susceptibility testing. J. Antimicrob. Chemother. 2013, 68, 2710–2717. https://doi.org/10.1093/jac/dkt253.
94.
Lupo, A.; Papp-Wallace, K.M.; Sendi, P.; et al. Non-phenotypic tests to detect and characterize antibiotic resistance mechanisms in Enterobacteriaceae. Diagn. Microbiol. Infect. Dis. 2013, 77, 179–194. https://doi.org/10.1016/j.diagmicrobio.2013.06.001.
95.
Kim, K.J.; Yun, S.G.; Cho, Y.; et al. Rapid Direct Identification of Microbial Pathogens and Antimicrobial Resistance Genes in Positive Blood Cultures Using a Fully Automated Multiplex PCR Assay. J. Korean Med. Sci. 2024, 39, e157. https://doi.org/10.3346/jkms.2024.39.e157.
96.
Mirabile, A.; Sangiorgio, G.; Bonacci, P.G.; et al. Advancing Pathogen Identification: The Role of Digital PCR in Enhancing Diagnostic Power in Different Settings. Diagnostics 2024, 14, 1598.
97.
Zhu, J.; Liu, B.; Shu, X.; et al. A novel mutation of walK confers vancomycin-intermediate resistance in methicillin-susceptible Staphylococcus aureus. Int. J. Med. Microbiol. 2021, 311, 151473. https://doi.org/10.1016/j.ijmm.2021.151473.
98.
Ruiying, F.; Lianchao, W.; Xutian, Y.; et al. Defects-Engineered Metal-Organic Frameworks for Supercapacitor Platform. Sustain. Eng. Novit 2025, 1, 2.
99.
Gn, Y.; Yn, C.; Wn, R.; et al. Hybrid and Flow-Electrode Capacitive Deionization: Materials Design, Multispecies Removal, and Smart Regulation. Sustain. Eng. Novit 2025, 1, 7.
100.
Hang, Y.; Wang, A.; Wu, N. Plasmonic silver and gold nanoparticles: Shape- and structure-modulated plasmonic functionality for point-of-caring sensing, bio-imaging and medical therapy. Chem. Soc. Rev. 2024, 53, 2932–2971. https://doi.org/10.1039/d3cs00793f.
101.
Palani, S.; Kenison, J.P.; Sabuncu, S.; et al. Multispectral Localized Surface Plasmon Resonance (msLSPR) Reveals and Overcomes Spectral and Sensing Heterogeneities of Single Gold Nanoparticles. ACS Nano 2023, 17, 2266–2278.
102.
Mayaka, R.K.; Alocilja, E.C. Genomic nano-biosensor for rapid detection of the carbapenem-resistant gene bla (NDM-1) in carbapenemase-producing bacteria. Nanoscale Adv. 2025, 7, 2518–2527. https://doi.org/10.1039/d4na00798k.
103.
Kao, K.; Alocilja, E.C. Integrated Sample to Detection of Carbapenem-Resistant Bacteria Extracted from Water Samples Using a Portable Gold Nanoparticle-Based Biosensor. Sensors 2025, 25, 5293.
104.
Kao, K.; Alocilja, E.C. Parallel Detection of the Unamplified Carbapenem Resistance Genes bla(NDM-1) and bla(OXA-1) Using a Plasmonic Nano-Biosensor with a Field-Portable DNA Extraction Method. Biosensors 2025, 15, 112. https://doi.org/10.3390/bios15020112.
105.
Wu, J.; Lin, H.; Moss, D.J.; et al. Graphene oxide for photonics, electronics and optoelectronics. Nat. Rev. Chem. 2023, 7, 162–183. https://doi.org/10.1038/s41570-022-00458-7.
106.
Gao, L.; Lian, C.; Zhou, Y.; et al. Graphene oxide–DNA based sensors. Biosens. Bioelectron. 2014, 60, 22–29. https://doi.org/10.1016/j.bios.2014.03.039.
107.
Wang, P.; Dimitrijevic, N.M.; Chang, A.Y.; et al. Photoinduced electron transfer pathways in hydrogen-evolving reduced graphene oxide-boosted hybrid nano-bio catalyst. ACS Nano 2014, 8, 7995–8002.
108.
Xiao, X.; Zhang, Y.; Zhou, L.; et al. Photoluminescence and Fluorescence Quenching of Graphene Oxide: A Review. Nanomaterials 2022, 12, 2444.
109.
Kang, J.; Nguyen, V.T.; Kim, M.S. Rapid and Sensitive Detection of Antibiotic Resistance Genes by Utilizing TALEs as a Diagnostic Probe with 2D-Nanosheet Graphene Oxide. Anal. Chem. 2023, 95, 9505–9512. https://doi.org/10.1021/acs.analchem.3c00647.
110.
Biesold, G.M.; Liang, S.; Brettmann, B.; et al. Tailoring Optical Properties of Luminescent Semiconducting Nanocrystals through Hydrostatic, Anisotropic Static, and Dynamic Pressures. Angew. Chem. Int. Ed. 2021, 60, 9772–9788. https://doi.org/10.1002/anie.202008395.
111.
Lu, H.; Li, W.; Dong, H.; et al. Graphene Quantum Dots for Optical Bioimaging. Small 2019, 15, e1902136.
112.
Hu, O.; Li, Z.; He, Q.; et al. Fluorescence Biosensor for One-Step Simultaneous Detection of Mycobacterium tuberculosis Multidrug-Resistant Genes Using nanoCoTPyP and Double Quantum Dots. Anal. Chem. 2022, 94, 7918–7927. https://doi.org/10.1021/acs.analchem.2c00723.
113.
Medintz, I.L.; Uyeda, H.T.; Goldman, E.R.; et al. Quantum dot bioconjugates for imaging, labelling and sensing. Nat. Mater. 2005, 4, 435–446. https://doi.org/10.1038/nmat1390.
114.
Escamilla, P.; Bartella, L.; Sanz-Navarro, S.; et al. Degradation of Penicillinic Antibiotics and β-Lactamase Enzymatic Catalysis in a Biomimetic Zn-Based Metal-Organic Framework. Chemistry 2023, 29, e202301325.
115.
Huo, Y.P.; Liu, S.; Gao, Z.X.; et al. State-of-the-art progress of switch fluorescence biosensors based on metal-organic frameworks and nucleic acids. Mikrochim. Acta 2021, 188, 168. https://doi.org/10.1007/s00604-021-04827-9.
116.
Yao, J.; Lu, Y.; Sun, H.; et al. Engineering for Covalent Organic Framework Membranes. Chem. Res. Chin. Univ. 2022, 38, 364–372. https://doi.org/10.1007/s40242-022-1507-1.
117.
Ma, J.; Lu, X.; Liu, S.; et al. Fabrication of fluorescence sensor array for discrimination subtypes of aminoglycosides leveraging MOF-based inhibition reactions and thiol-response metal nanoclusters. Biosens. Bioelectron. 2025, 287, 117652. https://doi.org/10.1016/j.bios.2025.117652.
118.
Yang, L.; Fu, Z.; Xie, J.; et al. Portable sensing of hydrogen peroxide using MOF-based nanozymes. Food Res. Int. 2024, 197, 115272. https://doi.org/10.1016/j.foodres.2024.115272.
119.
Li, R.; Qian, G.; Shen, H.; et al. MOF@MOF nanozyme for ultrasensitive and low-background detection of multidrug-resistant bacteria. Microchem. J. 2025, 209, 112695. https://doi.org/10.1016/j.microc.2025.112695.
120.
Li, R.; Fan, H.; Shen, H.; et al. Versatile MOF@COF catalyzed and magnetically multivalent aptamer assisted biosensing platform for rapid and ultrasensitive dual-mode detection of multidrug-resistant bacteria. Sens. Actuators B Chem. 2024, 410, 135719. https://doi.org/10.1016/j.snb.2024.135719.
121.
Chi, H.; Xiao, Y.; Ning, H.; et al. Thiol-ene click reaction aptamer sensor based on MWCNT-COOH/MOF-818 composite for highly sensitive detection of foodborne pathogenic bacteria. Curr. Res. Food Sci. 2025, 11, 101193. https://doi.org/10.1016/j.crfs.2025.101193.
122.
Wang, C.; Luo, Y.; Liu, X.; et al. The enhanced photocatalytic sterilization of MOF-Based nanohybrid for rapid and portable therapy of bacteria-infected open wounds. Bioact. Mater. 2022, 13, 200–211. https://doi.org/10.1016/j.bioactmat.2021.10.033.
123.
Wang, M.; Xiao, C.; Zhao, F.; et al. A label-free electrochemical sensor based on π-structured bipedal DNA walker-triggered hybridization chain reaction and AuPt NPs/Zr-MOF for OTA detection. Anal. Chim. Acta 2025, 1334, 343424. https://doi.org/10.1016/j.aca.2024.343424.
124.
Gao, Z.; Cheng, Y.; Long, C.; et al. Dual-Nanozyme Cascade for System-Wide Specific Colorimetric Detection of Aminoglycoside Antibiotics. Anal. Chem. 2025, 97, 6136–6144. https://doi.org/10.1021/acs.analchem.4c06854.
125.
Nordmann, P.; Helsens, N.; Kieffer, N.; et al. Rapid detection of β-lactamase activity using the rapid Amp NP test. Microbiol. Spectr. 2025, 13, e0078224. https://doi.org/10.1128/spectrum.00782-24.
126.
Yang, C.; Wang, Q.; Ding, W. Recent progress in the imaging detection of enzyme activities in vivo. RSC Adv. 2019, 9, 25285–25302. https://doi.org/10.1039/c9ra04508b.
127.
Chen, F.; Li, Y.; Peng, Y.; et al. Highly Sensitive In Vivo Imaging of Bacterial Infections with a Hydrophilicity-Switching, Self-Immobilizing, Near-Infrared Fluorogenic β-Lactamase Probe Enriched within Bacteria. Adv. Sci. 2025, 12, 2408559. https://doi.org/10.1002/advs.202408559.
128.
Kong, Y.; Yao, H.; Ren, H.; et al. Imaging tuberculosis with endogenous β-lactamase reporter enzyme fluorescence in live mice. Proc. Natl. Acad. Sci. USA 2010, 107, 12239–12244. https://doi.org/10.1073/pnas.1000643107.
129.
Heuker M. Bacteria-Targeted Infection Imaging. Ph.D. Thesis, University of Groningen, Groningen, The Netherlands, 2021.
130.
Yao, Y.; Zhang, Y.; Yan, C.; et al. Enzyme-activatable fluorescent probes for β-galactosidase: From design to biological applications. Chem. Sci. 2021, 12, 9885–9894. https://doi.org/10.1039/d1sc02069b.
131.
Wang, R.; Chen, J.; Gao, J.; et al. A molecular design strategy toward enzyme-activated probes with near-infrared I and II fluorescence for targeted cancer imaging. Chem. Sci. 2019, 10, 7222–7227. https://doi.org/10.1039/c9sc02093d.
132.
Yang, H.J.; Kong, Y.; Cheng, Y.; et al. Real-time Imaging of Mycobacterium tuberculosis, Using a Novel Near-Infrared Fluorescent Substrate. J. Infect. Dis. 2017, 215, 405–414.
133.
Nooshabadi, F.; Yang, H.J.; Cheng, Y.; et al. Intravital excitation increases detection sensitivity for pulmonary tuberculosis by whole-body imaging with β-lactamase reporter enzyme fluorescence. J. Biophotonics 2017, 10, 821–829. https://doi.org/10.1002/jbio.201600132.
134.
Xin, E.Y.H.; Kwek, G.; An, X.; et al. Enzymes in Synergy: Bacteria Specific Molecular Probe for Locoregional Imaging of Urinary Tract Infection in vivo. Angew. Chem. 2024, 63, e202406843.
135.
Zieliński, W.; Grabowska, I. Electrochemical biosensors as emerging alternatives to conventional detection of pathogens and antibiotic-resistance determinants. Talanta 2026, 301, 129323. https://doi.org/10.1016/j.talanta.2025.129323.
136.
Gunasekaran, D.; Rostovsky, I.; Taussig, D.; et al. A dual-channel electrochemical biosensor enables concurrent detection of pathogens and antibiotic resistance. Biosens. Bioelectron. 2024, 257, 116314. https://doi.org/10.1016/j.bios.2024.116314.
137.
Dutta, A.; Mukherjee, S.; Haldar, J.; et al. Augmenting Antimicrobial Resistance Surveillance: Rapid Detection of β-Lactamase-Expressing Drug-Resistant Bacteria through Sensitized Luminescence on a Paper-Supported Hydrogel. ACS Sens. 2024, 9, 351–360. https://doi.org/10.1021/acssensors.3c02065.
138.
Shukla, M.; Maiya, D.; Malaviya, R.; et al. Electrochemical β-lactamase immunostrip sensor with 3D hydrogel-paper scaffold for rapid detection & post-antibiotic therapy monitoring in drug-resistant bloodstream infections. Anal. Chim. Acta 2025, 1353, 343953. https://doi.org/10.1016/j.aca.2025.343953.
139.
Cui, H.S.; Wu, Z.R.; Shi, X.Y.; et al. CS/PVP Hydrogel-Based Nanocapillary for Monitoring Bacterial Growth and Rapid Antibiotic Susceptibility Testing. ACS Sens. 2024, 9, 3540–3548. https://doi.org/10.1021/acssensors.4c00381.
140.
Bottari, F.; Blust, R.; De Wael, K. Bio(inspired) strategies for the electro-sensing of β-lactam antibiotics. Curr. Opin. Electrochem. 2018, 10, 136–142. https://doi.org/10.1016/j.coelec.2018.05.015.
141.
Jamali, S.S.; Somerville, S.V.; Dief, E.M.; et al. Stochastic Electrochemical Measurement of a Biofouling Layer on Gold. Anal. Chem. 2024, 96, 7401–7410. https://doi.org/10.1021/acs.analchem.3c04868.
142.
Shan, S.; Lai, W.; Xiong, Y.; et al. Novel strategies to enhance lateral flow immunoassay sensitivity for detecting foodborne pathogens. J. Agric. Food Chem. 2015, 63, 745–753. https://doi.org/10.1021/jf5046415.
143.
Wang, Y.; Deng, C.; Qian, S.; et al. An ultrasensitive lateral flow immunoassay platform for foodborne biotoxins and pathogenic bacteria based on carbon-dots embedded mesoporous silicon nanoparticles fluorescent reporter probes. Food Chem. 2023, 399, 133970. https://doi.org/10.1016/j.foodchem.2022.133970.
144.
Tong, H.; Cao, C.; You, M.; et al. Artificial intelligence-assisted colorimetric lateral flow immunoassay for sensitive and quantitative detection of COVID-19 neutralizing antibody. Biosens. Bioelectron. 2022, 213, 114449. https://doi.org/10.1016/j.bios.2022.114449.
145.
Sulfianti, A.; Sopandi, V.T.; Isnaeni, I.; et al. Antibody-labelled gold nanoparticles synthesized by laser ablation to detect SARS-CoV-2 antigen spike. Admet Dmpk 2024, 12, 193–208.
146.
Kepceoğlu, A.; Gündoğdu, Y.; Sarilmaz, A.; et al. Rhenium/rhenium oxide nanoparticles production using femtosecond pulsed laser ablation in liquid. Turk. J. Chem. 2021, 45, 485–492. https://doi.org/10.3906/kim-2008-59.
147.
Bielskė, K.; Simanavičius, M.; Nuttens, J.; et al. Novel monoclonal antibodies for immunodetection of AmpC β-lactamases. PeerJ 2025, 13, e20036. https://doi.org/10.7717/peerj.20036.
148.
Moguet, C.; Gonzalez, C.; Naas, T.; et al. Multiplex Lateral Flow Immunoassay for the Detection of Expanded-Spectrum Hydrolysis and CTX-M Enzymes. Diagnostics 2022, 12, 190.
149.
Mancini, S.; Garcia-Verellen, L.; Seth-Smith, H.M.B.; et al. Diagnostic algorithm for the detection of carbapenemases and extended-spectrum β-lactamases in carbapenem-resistant Pseudomonas aeruginosa. Microbiol. Spectr. 2025, 13, e0319624. https://doi.org/10.1128/spectrum.03196-24.
150.
Herrera, A.; Zhou, J.; Song, M.S.; et al. Evolution of Cell-Type-Specific RNA Aptamers via Live Cell-Based SELEX. Methods Mol. Biol. 2023, 2666, 317–346.
151.
Gauger, M.; Duchardt-Ferner, E.; Halbritter, A.J.; et al. Investigating the Conformational Diversity of the TMR-3 Aptamer. J. Am. Chem. Soc. 2025, 147, 17497–17509. https://doi.org/10.1021/jacs.5c04576.
152.
Liu, R.; Yang, Z.; Guo, Q.; et al. Signaling-Probe Displacement Electrochemical Aptamer-based Sensor (SD-EAB) for Detection of Nanomolar Kanamycin A. Electrochim. Acta 2015, 182, 516–523. https://doi.org/10.1016/j.electacta.2015.09.140.
153.
Bao, Y.; Ding, G.; Li, Y.; et al. Signal-On, and Modification-Free Electrochemical Sensing Platform Based on Aptamer Switch Hydrogel. Anal. Chem. 2025, 97, 23331–23338. https://doi.org/10.1021/acs.analchem.5c04124.
154.
Cui, J.; Zhang, Y.; Lun, K.; et al. Sensitive detection of Escherichia coli in diverse foodstuffs by electrochemical aptasensor based on 2D porphyrin-based COF. Mikrochim. Acta 2023, 190, 421. https://doi.org/10.1007/s00604-023-05978-7.
155.
Zhang, J.; Zhu, M.; Yan, H.; et al. Split CRISPR/Cas systems: Pioneering solutions for molecular diagnostics challenges. Biosens. Bioelectron. 2026, 293, 118177. https://doi.org/10.1016/j.bios.2025.118177.
156.
Garneau, J.E.; Dupuis, M.; Villion, M.; et al. The CRISPR/Cas bacterial immune system cleaves bacteriophage and plasmid DNA. Nature 2010, 468, 67–71. https://doi.org/10.1038/nature09523.
157.
Li, X.; Zhang, Y.; He, M.; et al. An ultrasensitive and specific fluorescence split-aptasensor for D-VP detection based on target-induced self-propelled 3D DNA walkers coupled with CRISPR-Cas12a. Talanta 2025, 293, 128102. https://doi.org/10.1016/j.talanta.2025.128102.
158.
Huang, D.; He, Y.; Xu, C.; et al. DNAzyme-Triggered Equilibrium Transfer with Self-Activated CRISPR-Cas12a Biosensor Enables One-Pot Diagnosis of Nucleic Acids. Anal. Chem. 2025, 97, 3026–3035. https://doi.org/10.1021/acs.analchem.4c06066.
159.
Dziuba, A.; Dzierżak, S.; Sodo, A.; et al. Comparative study of virulence potential, phylogenetic origin, CRISPR-Cas regions and drug resistance of Escherichia coli isolates from urine and other clinical materials. Front. Microbiol. 2023, 14, 1289683. https://doi.org/10.3389/fmicb.2023.1289683.
160.
Shu, Y.; Liu, S.; Liu, J. Combining aptamers for thiamphenicol and chloramphenicol for detecting both antibiotics. Chem. Commun. 2025, 61, 18132–18135.
161.
Mason, H.G.; Hu, C.H.; Cordova, L.S.; et al. Rapid Prototyping of Microfluidic Devices with Stereolithographic 3D Printing. bioRxiv 2025, https://doi.org/10.1101/2025.07.10.662041.
162.
Tang, P.C.; Eriksson, O.; Sjögren, J.; et al. A Microfluidic Chip for Studies of the Dynamics of Antibiotic Resistance Selection in Bacterial Biofilms. Front. Cell. Infect. Microbiol. 2022, 12, 896149. https://doi.org/10.3389/fcimb.2022.896149.
163.
Tran, V.N.; Khan, F.; Han, W.; et al. Real-time monitoring of mono- and dual-species biofilm formation and eradication using microfluidic platform. Sci. Rep. 2022, 12, 9678. https://doi.org/10.1038/s41598-022-13699-9.
164.
Guliy, O.I.; Evstigneeva, S.S.; Bunin, V.D. Microfluidic bioanalytical system for biofilm formation indication. Talanta 2022, 247, 123541. https://doi.org/10.1016/j.talanta.2022.123541.
165.
Liu, Z.; Qin, S.; Chen, X.; et al. PDMS-PDMS Micro Channels Filled with Phase-Change Material for Chip Cooling. Micromachines 2018, 9, 165. https://doi.org/10.3390/mi9040165.
166.
Pholwat, S.; Liu, J.; Stroup, S.; et al. Integrated microfluidic card with TaqMan probes and high-resolution melt analysis to detect tuberculosis drug resistance mutations across 10 genes. mBio 2015, 6, e02273. https://doi.org/10.1128/mbio.02273-14.
167.
Kulshrestha, A.; Gupta, P.; Negi, S.S. Sustainable and optimized fabrication of microfluidic devices for electrochemical detection and monitoring of microbial biofilms. Microfluid. Nanofluidics 2025, 29, 34. https://doi.org/10.1007/s10404-025-02804-9.
168.
Wang, H.; Yin, Y.; Zhu, Z.J. Encoding LC-MS-Based Untargeted Metabolomics Data into Images toward AI-Based Clinical Diagnosis. Anal. Chem. 2023, 95, 6533–6541. https://doi.org/10.1021/acs.analchem.2c05079.
169.
Liang, S.; Ma, J.; Wang, G.; et al. The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis. Front. Med. 2022, 9, 935080. https://doi.org/10.3389/fmed.2022.935080.
170.
Nguyen, H.A.; Peleg, A.Y.; Song, J.; et al. Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra. mSystems 2024, 9, e0078924.
171.
Li, Y.; Wang, J.; Pan, X.; et al. MRI-mediated intelligent multimodal imaging system: From artificial intelligence to clinical imaging diagnosis. Drug Discov. Today 2025, 30, 104399. https://doi.org/10.1016/j.drudis.2025.104399.
172.
Batisti Biffignandi, G.; Chindelevitch, L.; Corbella, M.; et al. Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae. Microb. Genom. 2024, 10, 001222.
173.
Ozcan, A.; Coudert, F.X.; Rogge, S.M.J.; et al. Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research. J. Am. Chem. Soc. 2025, 147, 23367–23380. https://doi.org/10.1021/jacs.5c08214.
174.
Jiang, Z.; Feng, J.; Wang, F.; et al. AI-Guided Design of Antimicrobial Peptide Hydrogels for Precise Treatment of Drug-resistant Bacterial Infections. Adv. Mater. 2025, 37, e2500043.
175.
Wulfert, L.; Kuhnel, J.; Krupp, L.; et al. AIfES: A Next-Generation Edge AI Framework. IEEE Trans. Pattern Anal. Mach. Intell. 2024, 46, 4519–4533. https://doi.org/10.1109/tpami.2024.3355495.
176.
Davis, A.M.; Tomitaka, A. Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images. Biosensors 2025, 15, 19. https://doi.org/10.3390/bios15010019.
177.
Zhang, Y.; Zhang, L.; Zhang, Z.; et al. Explainable AI multiomics analysis reveals shared and divergent host responses in COVID-19 and influenza. NPJ Digit. Med. 2026, 9, 111. https://doi.org/10.1038/s41746-025-02291-w.
178.
Tian, T.; Zhang, X.; Zhang, F.; et al. Harnessing AI for advancing pathogenic microbiology: A bibliometric and topic modeling approach. Front. Microbiol. 2024, 15, 1510139. https://doi.org/10.3389/fmicb.2024.1510139.
179.
Ong Ly, C.; Unnikrishnan, B.; Tadic, T.; et al. Shortcut learning in medical AI hinders generalization: Method for estimating AI model generalization without external data. NPJ Digit. Med. 2024, 7, 124.
180.
Ryan, K.; Kasun, M.; Roberts, L.W., et al. Information, collaboration, regulation: Physician and AI researcher views on ethical considerations in clinical AI integration. Big Data Soc. 2025, 12, 20539517251343853.
181.
Yang, M.; Shi, Y.; Wang, F.; et al. Hydrogel Microspheres as Versatile Platforms for Biomedical Research: Design, Properties, and Applications. MedComm 2025, 6, e70423. https://doi.org/10.1002/mco2.70423.
182.
Ma, C.; Zhang, H.; Rao, Y.; et al. AI-driven virtual cell models in preclinical research: Technical pathways, validation mechanisms, and clinical translation potential. NPJ Digit. Med. 2025, 9, 25. https://doi.org/10.1038/s41746-025-02198-6.
183.
Wang, T.; Su, E. Guardians of Future Food Safety: Innovative Applications and Advancements in Anti-biofouling Materials. J. Agric. Food Chem. 2024, 72, 21973–21985. https://doi.org/10.1021/acs.jafc.4c05156.
184.
Alazzam, I.; Ait-Mouheb, N.; Knapp, Y.; et al. Biofouling in milli-labyrinth channels of drip irrigation systems using reclaimed wastewater: A review of optical methods and numerical modelling. J. Environ. Manag. 2026, 397, 128377. https://doi.org/10.1016/j.jenvman.2025.128377.
185.
Sivakumar, A.D.; Sharma, R.; Wang, J.; et al. Dual-Channel Microfluidic Photoionization Detector. Anal. Chem. 2025, 97, 22397–22406. https://doi.org/10.1021/acs.analchem.5c05021.
186.
Pfaller, M.A.; Andes, D.; Diekema, D.J.; et al. Wild-type MIC distributions, epidemiological cutoff values and species-specific clinical breakpoints for fluconazole and Candida: Time for harmonization of CLSI and EUCAST broth microdilution methods. Drug Resist. Updates 2010, 13, 180–195. https://doi.org/10.1016/j.drup.2010.09.002.
187.
Zhang, K.; Khosravi, B.; Vahdati, S.; et al. FDA Review of Radiologic AI Algorithms: Process and Challenges. Radiology 2024, 310, e230242. https://doi.org/10.1148/radiol.230242.
188.
Hay, S.I.; Rao, P.C.; Dolecek, C.; et al. Measuring and mapping the global burden of antimicrobial resistance. BMC Med. 2018, 16, 78. https://doi.org/10.1186/s12916-018-1073-z.
189.
Fang, G.Y.; Mu, X.J.; Huang, B.W.; et al. Monitoring Longitudinal Trends and Assessment of the Health Risk of Shigella flexneri Antimicrobial Resistance. Environ. Sci. Technol. 2023, 57, 4971–4983. https://doi.org/10.1021/acs.est.2c08766.
190.
Arnold, K.E.; Laing, G.; McMahon, B.J.; et al. The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance. Lancet Planet. Health 2024, 8, e124–e133. https://doi.org/10.1016/s2542-5196(23)00278-4.
191.
Zhou, H.; Guo, W.; Hao, T.; et al. Electrochemical sensor for single-cell determination of bacteria based on target-triggered click chemistry and fast scan voltammetry. Food Chem. 2023, 417, 135906. https://doi.org/10.1016/j.foodchem.2023.135906.
192.
Chen, L.; Chen, F.; Liu, G.; et al. Superhydrophobic Functionalized Ti(3)C(2)T(x) MXene-Based Skin-Attachable and Wearable Electrochemical pH Sensor for Real-Time Sweat Detection. Anal. Chem. 2022, 94, 7319–7328. https://doi.org/10.1021/acs.analchem.2c00684.
193.
Gao, W.; Xu, H.; Sun, X.; et al. An integrated wearable photo-electrochemical sensor for visible light amplified uric acid monitoring in sweat. Biosens. Bioelectron. 2025, 289, 117892. https://doi.org/10.1016/j.bios.2025.117892.

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