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  <front>
    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Soft Sci.</journal-id>
      <journal-id journal-id-type="publisher-id">ss</journal-id>
      <journal-title-group>
        <journal-title>Soft Science</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2769-5441</issn>
      <publisher>
        <publisher-name>OAE Publishing Inc.</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.20517/ss.2025.149</article-id>
      <article-id pub-id-type="publisher-id">SS-2025-149</article-id>
      <article-categories>
        <subj-group>
          <subject>Review Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Wearable and implantable aptamer sensors for physiological monitoring</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Lee</surname>
            <given-names>Hak Beom</given-names>
          </name>
          <xref ref-type="aff" rid="I1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Lee</surname>
            <given-names>Daeho</given-names>
          </name>
          <xref ref-type="aff" rid="I2">
            <sup>2</sup>
          </xref>
          <xref ref-type="aff" rid="I1042">
            <sup>*</sup>
          </xref>
          <xref ref-type="corresp" rid="cor1">*</xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Ko</surname>
            <given-names>Seung Hwan</given-names>
          </name>
          <xref ref-type="aff" rid="I1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="I1042">
            <sup>*</sup>
          </xref>
          <xref ref-type="corresp" rid="cor1">*</xref>
        </contrib>
      </contrib-group>
      <aff id="I1"><sup>1</sup>Department of Mechanical Engineering, Seoul National University, Seoul 08826, Republic of Korea.</aff>
      <aff id="I2"><sup>2</sup>Department of Mechanical Engineering, Gachon University, Seongnam 13120, Republic of Korea.</aff>
      <author-notes>
        <corresp id="cor1"><sup id="I1042">*</sup>Correspondence to: Prof. Seung Hwan Ko, Department of Mechanical Engineering, Seoul National University, Seoul 08826, Republic of Korea. E-mail: <email>maxko@snu.ac.kr</email>; Prof. Daeho Lee, Department of Mechanical Engineering, Gachon University, Seongnam 13120, Korea. E-mail: <email>dhl@gachon.ac.kr</email></corresp>
        <fn fn-type="other">
          <p><bold>Received:</bold> 28 Dec 2025 | <bold>First Decision:</bold> 29 Jan 2026 | <bold>Revised:</bold> 31 Mar 2026 | <bold>Accepted:</bold> 14 Apr 2026 | <bold>Published:</bold> 12 Jun 2026</p>
        </fn>
        <fn fn-type="other">
          <p><bold>Academic Editor:</bold> YongAn Huang | <bold>Copy Editor:</bold> Shu-Yuan Duan | <bold>Production Editor:</bold> Shu-Yuan Duan</p>
        </fn>
      </author-notes>
      <pub-date pub-type="ppub">
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>12</day>
        <month>6</month>
        <year>2026</year>
      </pub-date>
      <volume>6</volume>
	  <issue>2</issue>
      <elocation-id>46</elocation-id>
      <permissions>
        <copyright-statement>© The Author(s) 2026.</copyright-statement>
        <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
          <license-p>© The Author(s) 2026.<bold>Open Access</bold>This article is licensed under a Creative Commons Attribution 4.0 International License (<uri xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</uri>), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.</license-p>
        </license>
      </permissions>
      <abstract>
        <p>The transition from reactive healthcare to proactive precision medicine necessitates technologies capable of real-time, continuous monitoring of physiological biomarkers. While enzymatic sensors have paved the way for metabolic tracking, they are intrinsically restricted to a narrow spectrum of redox-active analytes and often suffer from limited stability in complex biological environments. In contrast, electrochemical aptamer-based (E-AB) sensors have emerged as a revolutionary platform, offering a universal, reagent-free sensing mechanism driven by binding-induced conformational changes, independent of the target’s chemical reactivity. This review provides a comprehensive analysis of the technological trajectory of E-AB sensors across the physiological continuum of biofluids: blood, interstitial fluid (ISF), and sweat. We critically examine distinct applications ranging from implantable wires for intravascular therapeutic drug monitoring and microneedle arrays for minimally invasive ISF analysis to wearable devices for non-invasive sweat sensing. A central theme of this work is the pivotal role of “soft material” strategies in facilitating the seamless integration of rigid electronics with soft biological tissues. We highlight how functional materials - including anti-fouling coatings, hydrogel interfaces, and flexible porous nanocomposites - bridge mechanical and chemical mismatches to mitigate biofouling and ensure stable, long-term bio-interfaces. Finally, we outline the remaining hurdles for clinical translation, discussing strategies to achieve ultra-sensitivity, establish calibration-free methodologies, and integrate artificial intelligence (AI) for intelligent data processing, ultimately aiming for ubiquitous personalized healthcare.</p>
      </abstract>
      <kwd-group>
        <kwd>Electrochemical aptamer-based sensor</kwd>
        <kwd>biofluid monitoring</kwd>
        <kwd>soft materials</kwd>
        <kwd>wearable biosensors</kwd>
        <kwd>continuous monitoring</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec1">
      <title>INTRODUCTION</title>
      <p>High-risk diseases such as cancer and stroke exhibit high morbidity and mortality rates globally, posing a severe threat to human health<sup>[<xref ref-type="bibr" rid="B1">1</xref>-<xref ref-type="bibr" rid="B3">3</xref>]</sup>. Since these diseases often present with non-specific symptoms, early detection is challenging, and establishing precision treatment strategies is essential due to individual variability in drug response<sup>[<xref ref-type="bibr" rid="B4">4</xref>]</sup>. Consequently, the paradigm of modern healthcare is shifting beyond reactive treatment toward proactive, preventive medicine based on the continuous collection of physiological data.</p>
      <p>Recently, “Liquid Biopsy”, which analyzes circulating nucleic acids, proteins, and exosomes in the blood, has garnered significant attention as an innovative technology<sup>[<xref ref-type="bibr" rid="B5">5</xref>,<xref ref-type="bibr" rid="B6">6</xref>]</sup>. According to guidelines from the National Academy of Clinical Biochemistry (NACB), blood contains various tumor markers [e.g., Carcinoembryonic antigen (CEA), Cancer antigen 125 (CA125)] and inflammatory biomarkers [e.g., C-reactive protein (CRP), tumor necrosis factor-α (TNF-α)], providing critical clues for disease diagnosis<sup>[<xref ref-type="bibr" rid="B7">7</xref>-<xref ref-type="bibr" rid="B10">10</xref>]</sup>. However, current gold-standard methods like Enzyme-linked immunosorbent assay (ELISA) are costly and labor-intensive, severely limiting their accessibility in resource-limited settings [Low- and middle-income countries (LMICs)]<sup>[<xref ref-type="bibr" rid="B11">11</xref>,<xref ref-type="bibr" rid="B12">12</xref>]</sup>. Therefore, developing accessible Point-of-Care Testing (POCT) platforms remains an urgent priority.</p>
      <p>Technologies capable of continuous or semi-continuous measurement already exist for a small number of metabolites, such as glucose<sup>[<xref ref-type="bibr" rid="B13">13</xref>]</sup> and lactate<sup>[<xref ref-type="bibr" rid="B14">14</xref>]</sup>, and neurotransmitters like dopamine<sup>[<xref ref-type="bibr" rid="B15">15</xref>,<xref ref-type="bibr" rid="B16">16</xref>]</sup>, serotonin<sup>[<xref ref-type="bibr" rid="B17">17</xref>]</sup>, glutamate<sup>[<xref ref-type="bibr" rid="B18">18</xref>]</sup>, and acetylcholine<sup>[<xref ref-type="bibr" rid="B19">19</xref>]</sup>. However, these approaches rely heavily on the specific chemical reactivity of the target (e.g., redox chemistry or enzymatic oxidation). Due to this dependence on reactivity, these techniques cannot be generalized to sense many other physiologically and clinically relevant molecules, leaving a significant unmet need for strategies that support continuous sensing independent of target reactivity.</p>
      <p>Unfortunately, realizing this goal faces severe technical barriers<sup>[<xref ref-type="bibr" rid="B20">20</xref>,<xref ref-type="bibr" rid="B21">21</xref>]</sup>. First, to support continuous measurement, sensors must not rely on batch processing steps such as washing or separation. Second, for <italic>in-vivo</italic> applications, sensors must function without externally added reagents and maintain stability during prolonged exposure to blood or interstitial fluids. Conventional methods like chromatography or immunoassays require complex multistep batch processes, rendering them unsuitable for continuous sensing. Conversely, label-free sensors such as Surface Plasmon Resonance (SPR), Quartz Crystal Microbalance (QCM), and Field-Effect Transistors (FET) support continuous operation but often fail in blood (and by extension, <italic>in-vivo</italic>) because they cannot distinguish between specific target binding and the non-specific adsorption of proteins and cells<sup>[<xref ref-type="bibr" rid="B22">22</xref>-<xref ref-type="bibr" rid="B25">25</xref>]</sup>.</p>
      <p>In contrast, electrochemical aptamer-based (E-AB) sensors demonstrate the capability to support direct, continuous, real-time measurements <italic>in-vivo</italic>, serving as a platform independent of the target’s chemical reactivity<sup>[<xref ref-type="bibr" rid="B26">26</xref>]</sup>. E-AB sensors utilize an aptamer labeled with a redox reporter attached to an electrode; upon binding to a specific target, the aptamer undergoes a conformational change that alters the electron transfer rate between the reporter and the electrode surface<sup>[<xref ref-type="bibr" rid="B27">27</xref>]</sup> [<xref ref-type="fig" rid="fig1">Figure 1A</xref>]. This reversible signal transduction mechanism enables continuous, selective, and reagent-free measurements suitable for diverse targets, including pharmaceuticals<sup>[<xref ref-type="bibr" rid="B26">26</xref>,<xref ref-type="bibr" rid="B28">28</xref>,<xref ref-type="bibr" rid="B29">29</xref>]</sup>, drugs of abuse<sup>[<xref ref-type="bibr" rid="B30">30</xref>]</sup>, proteins<sup>[<xref ref-type="bibr" rid="B31">31</xref>-<xref ref-type="bibr" rid="B33">33</xref>]</sup>, and amino acids<sup>[<xref ref-type="bibr" rid="B26">26</xref>,<xref ref-type="bibr" rid="B31">31</xref>,<xref ref-type="bibr" rid="B34">34</xref>-<xref ref-type="bibr" rid="B36">36</xref>]</sup>. Indeed, E-AB sensors have proven their utility in living animal models by enabling real-time monitoring of various drugs and metabolites, and even facilitating feedback-controlled closed-loop drug delivery via real-time target quantification<sup>[<xref ref-type="bibr" rid="B28">28</xref>,<xref ref-type="bibr" rid="B37">37</xref>]</sup>.</p>
      <fig id="fig1" position="float">
        <label>Figure 1</label>
        <caption>
          <p>Overview of <italic>in-vivo</italic> &amp; on-body E-AB sensor. E-AB: Electrochemical aptamer-based.</p>
        </caption>
        <graphic xlink:href="ss50149.fig.1.jpg"/>
      </fig>
      <p>This review examines the translation of E-AB sensor technology from the benchtop to the physiological biofluid continuum: ‘Blood - Interstitial Fluid (ISF) - Sweat’. While blood serves as a rich information source, it entails risks associated with phlebotomy<sup>[<xref ref-type="bibr" rid="B38">38</xref>]</sup>. ISF has emerged as a compelling alternative, offering valuable insights into cancer progression via tumor-derived metabolites<sup>[<xref ref-type="bibr" rid="B39">39</xref>,<xref ref-type="bibr" rid="B40">40</xref>]</sup> and allowing for minimally invasive access. Furthermore, sweat offers a route for completely non-invasive monitoring, though challenges regarding biomarker variability remain<sup>[<xref ref-type="bibr" rid="B41">41</xref>]</sup>.</p>
      <p>Critically, this paper highlights Soft Material strategies as key enablers for achieving seamless mechanical and chemical integration between sensor systems and the human body. Functional material technologies that bridge mechanical mismatches and inhibit biofouling are essential requirements for accelerating the clinical translation of E-AB sensors. We analyze recent E-AB sensing applications across different biofluids and discuss future research directions for commercialization, including the attainment of high sensitivity and specificity, and the establishment of calibration-free systems.</p>
    </sec>
    <sec id="sec2">
      <title>FUNDAMENTAL CONSIDERATIONS FOR <italic>IN-VIVO</italic> AND ON-BODY SENSING</title>
      <p>Whole blood, ISF, and sweat do not exist as isolated biological compartments; rather, they function as an interconnected continuum regarding the migration path of analytes. Typically, biomolecules originate in the circulatory system, partition across the capillary barrier into the ISF, and ultimately permeate the sweat glands for excretion onto the skin surface. This trajectory is governed by a complex interplay of passive diffusion and diverse active transport mechanisms<sup>[<xref ref-type="bibr" rid="B42">42</xref>,<xref ref-type="bibr" rid="B43">43</xref>]</sup> [<xref ref-type="fig" rid="fig2">Figure 2A</xref>].</p>
      <fig id="fig2" position="float">
        <label>Figure 2</label>
        <caption>
          <p>Schematic illustration of biomarker partitioning across biofluid compartments and corresponding electrochemical sensing modalities. (A) Schematic of biomarker transport pathways (red arrows: paracellular, black arrows: transcellular) and analytes (blue circles: small molecules, green squares: larger analytes) across blood, ISF, and sweat compartments; (B) Illustrations of implantable, microneedle, and wearable electrochemical sensors. ISF: Interstitial fluid.</p>
        </caption>
        <graphic xlink:href="ss50149.fig.2.jpg"/>
      </fig>
      <p>Within this physiological sequence, ISF can be characterized as a fluid derived from its systemic progenitor, blood. The dense capillary network within the dermis serves as the primary gateway for solute propagation into the tissue, resulting in the presence of a vast majority of blood-borne analytes within the ISF<sup>[<xref ref-type="bibr" rid="B44">44</xref>-<xref ref-type="bibr" rid="B46">46</xref>]</sup>. Consequently, the efficiency of mass transport across these capillary boundaries is the primary determinant of the solute concentration and compositional profile of the ISF<sup>[<xref ref-type="bibr" rid="B1">1</xref>,<xref ref-type="bibr" rid="B47">47</xref>,<xref ref-type="bibr" rid="B48">48</xref>]</sup>.</p>
      <p>Analyte transfer from the vasculature to the interstitium is a highly selective permeation process rather than random distribution. It is strictly regulated by the structural properties of endothelial cells and the physicochemical attributes of the analytes, specifically molecular weight, charge, and polarity. Transport pathways are broadly categorized into paracellular and transcellular routes. Molecular size acts as a predominant filter; inter-endothelial junctions (IEJs), composed mainly of cadherins and tight junctions, regulate this flow. Small molecules (under 3 kDa) diffuse freely through cadherin junctions regardless of charge<sup>[<xref ref-type="bibr" rid="B2">2</xref>,<xref ref-type="bibr" rid="B49">49</xref>]</sup>. Conversely, intermediate-sized molecules (3-70 kDa) face partial restriction by tight junctions, necessitating a combination of paracellular and transcellular transport<sup>[<xref ref-type="bibr" rid="B3">3</xref>]</sup>. Furthermore, transport mechanisms diverge based on charge state and polarity<sup>[<xref ref-type="bibr" rid="B50">50</xref>,<xref ref-type="bibr" rid="B51">51</xref>]</sup>. For instance, while hydrophobic molecules can permeate directly across the plasma membrane via simple diffusion, hydrophilic macromolecules typically require active endocytosis and subsequent vesicular transport to traverse the cellular barrier<sup>[<xref ref-type="bibr" rid="B52">52</xref>]</sup>.</p>
      <p>Ultimately, ISF functions as a reservoir for blood-derived constituents that have successfully navigated these selective barriers. These solutes subsequently enter the sweat glands, driven by concentration gradients or active secretion mechanisms, to establish the final composition of sweat. Therefore, a fundamental comprehension of sweat and ISF biosensing requires a prior understanding of the dynamic partitioning mechanisms that govern analyte progression from whole blood through each biological barrier.</p>
      <sec id="sec2-1">
        <title>Blood</title>
        <p>Whole blood serves as a critical biological specimen containing essential data for monitoring overall physiological conditions. Although the majority of target biomolecules, including proteins, ions, and metabolites, are dissolved within the plasma<sup>[<xref ref-type="bibr" rid="B53">53</xref>]</sup>, the capability to detect these targets directly in a whole blood environment is a prerequisite for the application of implantable sensors as emphasized in the introduction.</p>
        <p>Once a sensor is introduced into the body, endogenous biological materials rapidly accumulate on its surface, a phenomenon termed “fouling”. This adsorption cascade initiates with water molecules, followed immediately by ions which establish an electrical double layer<sup>[<xref ref-type="bibr" rid="B54">54</xref>]</sup>. Subsequently, various proteins begin to adsorb based on their specific diffusivity and surface affinity, a process that eventually leads to the attachment of cellular components<sup>[<xref ref-type="bibr" rid="B54">54</xref>]</sup>.</p>
        <p>In particular, high-abundance proteins in the blood, such as Albumin, Immunoglobulin G (IgG), and Fibrinogen, rapidly coat the electrode surface. This protein layer physically impedes the conformational changes required for aptamer function and blocks electron transfer pathways, thereby significantly diminishing the sensor’s activation window<sup>[<xref ref-type="bibr" rid="B55">55</xref>,<xref ref-type="bibr" rid="B56">56</xref>]</sup>.</p>
        <p>Furthermore, this adsorbed protein layer triggers the adhesion and aggregation of platelets. Through complex protein-cell interactions, platelet aggregates cross-link with fibrin to generate thrombi on the electrode surface. Thrombus formation is a critical safety hazard that extends beyond mere sensor performance degradation, as it can lead to vascular occlusion or embolism<sup>[<xref ref-type="bibr" rid="B57">57</xref>-<xref ref-type="bibr" rid="B59">59</xref>]</sup>. Although pharmaceutical interventions are employed to manage thrombosis, these drugs carry their own risks of complications; therefore, thrombogenicity must be viewed as a safety issue rather than solely a sensor longevity problem<sup>[<xref ref-type="bibr" rid="B60">60</xref>,<xref ref-type="bibr" rid="B61">61</xref>]</sup>.</p>
        <p>In addition to thrombosis, the protein-conditioned surface facilitates bacterial colonization, leading to the development of a biofilm<sup>[<xref ref-type="bibr" rid="B62">62</xref>]</sup>. Biofilms are composed of bacteria encased within a self-produced matrix of insoluble extracellular polymeric substances (EPS). This 3D EPS matrix not only stabilizes the biofilm structure but also significantly enhances bacterial resistance against antibiotics and the host immune system. The biofilm matures as bacteria proliferate and EPS accumulates<sup>[<xref ref-type="bibr" rid="B63">63</xref>,<xref ref-type="bibr" rid="B64">64</xref>]</sup>. The most frequently identified pathogens associated with blood-contacting devices are coagulase-negative Staphylococcus, followed by Pseudomonas aeruginosa. Infections associated with biofilms can be up to 75% more prevalent than those caused by planktonic bacteria and exhibit substantial resistance to both immune responses and systemic antibiotic therapies. Consequently, biofilm-mediated infections are notoriously difficult to eradicate and have a profound negative impact on patient health and quality of life<sup>[<xref ref-type="bibr" rid="B65">65</xref>,<xref ref-type="bibr" rid="B66">66</xref>]</sup>.</p>
      </sec>
      <sec id="sec2-2">
        <title>Interstitial fluid</title>
        <p>Interstitial fluid (ISF), generated via the filtration of systemic blood, has emerged as a compelling target fluid for biosensor applications. While ISF is largely devoid of cellular components such as erythrocytes and platelets that typically induce severe biofouling in whole blood, it retains a rich profile of cellular metabolites and clinically relevant biomarkers<sup>[<xref ref-type="bibr" rid="B67">67</xref>-<xref ref-type="bibr" rid="B69">69</xref>]</sup>. Significant analytes identified in ISF include protein biomarkers such as CA-125, HER2, and PSA which present the possibility of cancer such as ovarian, breast and prostate cancer<sup>[<xref ref-type="bibr" rid="B70">70</xref>,<xref ref-type="bibr" rid="B71">71</xref>]</sup>, as well as nucleic acid markers including microRNAs (e.g., miR-10b, 21, 155, 16)<sup>[<xref ref-type="bibr" rid="B72">72</xref>]</sup> and exosomes<sup>[<xref ref-type="bibr" rid="B73">73</xref>,<xref ref-type="bibr" rid="B74">74</xref>]</sup>. The utility of ISF lies in its compatibility with direct analysis without extensive pretreatment, offering a minimally invasive alternative to conventional biofluids like blood, urine, or saliva for disease diagnosis, prevention, and therapeutic monitoring<sup>[<xref ref-type="bibr" rid="B75">75</xref>-<xref ref-type="bibr" rid="B77">77</xref>]</sup>.</p>
        <p>However, the matrix of ISF presents its own set of stability challenges. Although less aggressive than whole blood, ISF is susceptible to fouling through the non-specific adsorption of proteins and lipids. These contaminants can compromise sensor integrity, for instance, by accelerating the desorption of alkylthiolate self-assembled monolayers (SAMs), thereby shortening the operational lifespan of the device<sup>[<xref ref-type="bibr" rid="B78">78</xref>]</sup>. Furthermore, biological degradation poses a distinct threat; ISF is not devoid of nucleases, meaning that sensing elements such as aptamers are vulnerable to enzymatic degradation rather than simple physical fouling<sup>[<xref ref-type="bibr" rid="B79">79</xref>]</sup>. Consequently, robust anti-fouling strategies remain a prerequisite for ISF-based sensors, mirroring the requirements for whole blood analysis.</p>
        <p>A critical consideration in evaluating ISF for diagnostics is establishing the correlation between analyte concentrations in ISF versus whole blood. The distribution of target molecules in ISF is governed by their physicochemical properties, such as size and charge state, often resulting in lower concentrations compared to the bloodstream. Specifically, medium-sized, negatively charged molecules (3-70 kDa) face a transport barrier imposed by the negatively charged glycocalyx lining the luminal surface of the vascular endothelium. This electrostatic repulsion significantly impedes the partitioning of such analytes into the interstitial space<sup>[<xref ref-type="bibr" rid="B80">80</xref>]</sup>.</p>
        <p>In addition to concentration discrepancies, temporal dynamics, or “lag time”, must be considered for small hydrophilic molecules. For instance, glucose monitoring in ISF typically exhibits a measurement delay of 20-35 min relative to blood glucose levels due to transport kinetics<sup>[<xref ref-type="bibr" rid="B81">81</xref>]</sup>. However, this latency is not universal; certain small hydrophilic and hydrophobic species are known to reach equilibrium between blood and ISF relatively rapidly<sup>[<xref ref-type="bibr" rid="B76">76</xref>]</sup>.</p>
        <p>Conversely, the localized nature of ISF generation offers a distinct advantage: since ISF composition is heavily influenced by neighboring cells, it can contain significantly higher concentrations of disease-specific biomarkers than systemic circulation. Notably, biomarker concentrations within the ISF of a tumor microenvironment can exceed systemic blood levels by three orders of magnitude (1,000-1,500 times), providing superior sensitivity for localized pathology detection<sup>[<xref ref-type="bibr" rid="B82">82</xref>]</sup>.</p>
      </sec>
      <sec id="sec2-3">
        <title>Sweat</title>
        <p>Sweat serves as a premier biofluid for continuous, non-invasive biomarker monitoring, distinguishing itself by the vast availability of sampling sites across the epidermis and its inherent accessibility<sup>[<xref ref-type="bibr" rid="B83">83</xref>,<xref ref-type="bibr" rid="B84">84</xref>]</sup>. Furthermore, when a sensor interface is properly sealed against skin, it creates an isolated microenvironment. This configuration effectively shields the sensing area from external fluids and contaminants, mitigating fouling issues typically associated with <italic>ex-vivo</italic> detection.</p>
        <p>However, the passive nature of perspiration poses a significant challenge; without a continuous secretory flow, stagnant sweat may accumulate within the sensor interface, exacerbating contamination artifacts originating from the skin surface<sup>[<xref ref-type="bibr" rid="B85">85</xref>]</sup>. To prevent the compromise of real-time monitoring capabilities, active stimulation via iontophoresis is frequently employed. This technique utilizes electrical current to deliver pilocarpine into the localized dermis, agonizing muscarinic receptors within the sweat glands to induce secretory activity<sup>[<xref ref-type="bibr" rid="B86">86</xref>,<xref ref-type="bibr" rid="B87">87</xref>]</sup>. While perspiration can also be triggered by thermal, physical, or psychological stimuli - each resulting in slightly varying fluid compositions<sup>[<xref ref-type="bibr" rid="B42">42</xref>]</sup> - this study primarily focuses on iontophoresis-mediated generation to ensure controlled parameters for sensor design.</p>
        <p>As an excretory fluid, sweat exhibits a broad range of salinity, typically fluctuating between 10 and 100 mM<sup>[<xref ref-type="bibr" rid="B83">83</xref>]</sup>. Such variations in ionic strength can fundamentally alter the thermodynamics governing the binding-induced conformational switching that drives signaling in E-AB sensors<sup>[<xref ref-type="bibr" rid="B88">88</xref>]</sup>. Consequently, compensatory mechanisms using ion-selective electrodes may be requisite. Additionally, dynamic pH shifts (spanning 4.5 to 7.0) can distort the concentration readouts of weak acids and bases<sup>[<xref ref-type="bibr" rid="B83">83</xref>]</sup>. For instance, exercise-induced sweat often manifests with lower pH, variable electrolyte profiles, and susceptibility to dilution. Conversely, naturally secreted sweat, characterized by extremely low flow rates, is highly vulnerable to evaporation and cutaneous contamination. Therefore, rigorous control over sweat collection dynamics is critical to ensure analytical accuracy<sup>[<xref ref-type="bibr" rid="B42">42</xref>]</sup>.</p>
        <p>From a mass transport perspective, sweat generally presents lower biomarker concentrations than blood or ISF due to filtration mechanisms. Transport pathways are strictly governed by molecular properties: small hydrophobic species (e.g., steroid hormones and many drugs) readily traverse the epithelium via transcellular diffusion. In contrast, small hydrophilic solutes (e.g., metabolites) are relegated to the more restrictive and dilution-prone paracellular entry path<sup>[<xref ref-type="bibr" rid="B42">42</xref>]</sup>. Macromolecules, such as proteins, are hypothesized to migrate via a combination of paracellular transport and transcytosis<sup>[<xref ref-type="bibr" rid="B42">42</xref>,<xref ref-type="bibr" rid="B43">43</xref>]</sup>, undergoing significant filtration; albumin, for instance, is diluted by at least three orders of magnitude relative to blood<sup>[<xref ref-type="bibr" rid="B89">89</xref>]</sup>. Consequently, biomarkers present at pM levels in circulation are exceptionally difficult to quantify in sweat<sup>[<xref ref-type="bibr" rid="B43">43</xref>,<xref ref-type="bibr" rid="B86">86</xref>]</sup>.</p>
        <p>Despite these limitations, certain analytes demonstrate high clinical utility. Glucose levels, for instance, appear largely decoupled from sweat generation rates and exhibit trends correlating with blood glucose<sup>[<xref ref-type="bibr" rid="B85">85</xref>,<xref ref-type="bibr" rid="B90">90</xref>]</sup>. Similarly, cortisol, a marker for acute and chronic stress, shows a concentration difference of roughly one order of magnitude between blood and sweat, making it a viable target for sweat-based detection<sup>[<xref ref-type="bibr" rid="B91">91</xref>]</sup>. However, complications persist with analytes like lactate, which is generated endogenously by the sweat gland metabolism, thereby reducing its correlation with systemic levels<sup>[<xref ref-type="bibr" rid="B42">42</xref>,<xref ref-type="bibr" rid="B92">92</xref>]</sup>. Collectively, while promising, sweat-based biomarker detection faces distinct physiological hurdles compared to blood or ISF analysis.</p>
        <p>Beyond the physicochemical properties of the biofluids themselves, the continuum framework also dictates specific requirements for aptamer selection. Aptamers deployed in blood must prioritize nuclease resistance and selectivity within high-protein matrices, motivating the use of chemically modified backbones such as 2′-O-methyl RNA<sup>[<xref ref-type="bibr" rid="B93">93</xref>]</sup>. In ISF, where analyte flux across the dermis is limited, high-affinity (low K_D) aptamers are preferred to maximize productive binding events per available target molecule<sup>[<xref ref-type="bibr" rid="B94">94</xref>]</sup>, alongside truncated sequences tailored for microneedle geometries<sup>[<xref ref-type="bibr" rid="B95">95</xref>,<xref ref-type="bibr" rid="B96">96</xref>]</sup>. Finally, in sweat, the wide fluctuations in ionic strength (10-100 mM) and pH (4.5-7.0) can readily destabilize aptamer secondary structures, necessitating sequences with highly robust folding thermodynamics<sup>[<xref ref-type="bibr" rid="B97">97</xref>]</sup>. Ultimately, these biofluid-specific aptamer requirements are an inseparable component of the continuum sensing design framework discussed throughout this review.</p>
      </sec>
      <sec id="sec2-4">
        <title>Scope of biofluids for continuous E-AB monitoring: opportunities and limitations</title>
        <p>As discussed in the preceding subsections, blood, ISF, and sweat currently represent the most viable physiological mediums for realizing real-time, continuous monitoring using E-AB sensors. Blood provides the gold standard for direct therapeutic drug monitoring (TDM), while ISF and sweat offer minimally invasive (via microneedle arrays) and non-invasive (via epidermal patches) alternatives, respectively. A shared, critical characteristic of these three biofluids is their physical compatibility with continuous sensor integration.</p>
        <p>While other biofluids, most notably urine and saliva, are undeniably valuable for conventional clinical diagnostics, they present fundamental physiological and engineering barriers to the continuous, <italic>in-situ</italic> monitoring framework targeted in this review. Urine, for instance, is inherently intermittent in its external availability, which fundamentally precludes the continuous, real-time tracking that wearable or implantable E-AB sensors aim to achieve. Furthermore, the renal filtration process substantially alters biomarker concentrations relative to systemic circulation, potentially diminishing physiological fidelity for real-time dynamic tracking<sup>[<xref ref-type="bibr" rid="B98">98</xref>,<xref ref-type="bibr" rid="B99">99</xref>]</sup>.</p>
        <p>Similarly, although saliva is a non-invasive fluid, intraoral sensor integration is hindered by severe mechanical and user-compliance limitations. More critically from an electrochemical perspective, the oral environment induces rapid and aggressive biofouling due to pellicle formation and bacterial biofilm metabolites, which can compromise the binding-induced conformational changes of aptamers within hours. The complex salivary matrix, rich in proteins, mucins, and food residues, introduces significant electrochemical interference. Moreover, the correlation between salivary and blood analyte concentrations often remains inconsistent and highly dependent on environmental factors<sup>[<xref ref-type="bibr" rid="B100">100</xref>-<xref ref-type="bibr" rid="B102">102</xref>]</sup>.</p>
        <p>Therefore, to maintain a cohesive focus on technologies capable of uninterrupted, long-term continuous monitoring and their associated material engineering challenges, the primary scope of this review is intentionally directed toward E-AB sensor platforms designed for blood, ISF, and sweat. Nevertheless, to provide a comprehensive perspective for future sensor designs, a comparative analysis of the merits, shortcomings, and key engineering challenges associated with these five distinct biofluids is summarized in <xref ref-type="table" rid="t1">Table 1</xref>.</p>
        <table-wrap id="t1">
          <label>Table 1</label>
          <caption>
            <p>Comparison of biofluids for continuous E-AB sensor applications</p>
          </caption>
          <table frame="hsides" rules="groups">
  <tbody>
    <tr>
      <td>
        <bold>Biofluid</bold>
      </td>
      <td>
        <bold>Key merits &amp; characteristics</bold>
      </td>
      <td>
        <bold>Shortcomings</bold>
        <break/>
        <bold>(Limitations)</bold>
      </td>
      <td>
        <bold>Engineering challenges for E-AB sensors</bold>
      </td>
    </tr>
    <tr>
      <td>Blood</td>
      <td>Gold standard for therapeutic drug (TDM)<sup>[<xref ref-type="bibr" rid="B162">162</xref>]</sup><break/>Contains the majority of systemic target biomolecules (proteins, ions, metabolites)<sup>[<xref ref-type="bibr" rid="B132">132</xref>]</sup></td>
      <td>High abundance of proteins (Albumin, IgG, Fibrinogen)<sup>[<xref ref-type="bibr" rid="B185">185</xref>,<xref ref-type="bibr" rid="B186">186</xref>]</sup><break/>Risk of thrombosis (platelet aggregation and fibrin cross-linking)<sup>[<xref ref-type="bibr" rid="B187">187</xref>,<xref ref-type="bibr" rid="B188">188</xref>]</sup></td>
      <td>Severe biofouling blocking electron transfer and aptamer changes<sup>[<xref ref-type="bibr" rid="B186">186</xref>]</sup><break/>Preventing device-associated biofilm formation<sup>[<xref ref-type="bibr" rid="B189">189</xref>,<xref ref-type="bibr" rid="B190">190</xref>]</sup><break/>Managing thrombogenicity as a safety hazard<sup>[<xref ref-type="bibr" rid="B187">187</xref>]</sup></td>
    </tr>
    <tr>
      <td>ISF</td>
      <td>Minimally invasive continuous monitoring<sup>[<xref ref-type="bibr" rid="B69">69</xref>,<xref ref-type="bibr" rid="B80">80</xref>,<xref ref-type="bibr" rid="B117">117</xref>,<xref ref-type="bibr" rid="B123">123</xref>]</sup><break/>Devoid of large cellular components (erythrocytes, platelets)<sup>[<xref ref-type="bibr" rid="B117">117</xref>,<xref ref-type="bibr" rid="B191">191</xref>,<xref ref-type="bibr" rid="B192">192</xref>]</sup><break/>Can contain highly concentrated localized biomarkers (e.g., up to 1,500X in tumors)<sup>[<xref ref-type="bibr" rid="B193">193</xref>]</sup></td>
      <td>Temporal lag time (e.g., 20-35 min for glucose) compared to blood<sup>[<xref ref-type="bibr" rid="B81">81</xref>]</sup><break/>Concentration barriers for medium-sized/negatively charged molecules (due to glycocalyx)<sup>[<xref ref-type="bibr" rid="B185">185</xref>,<xref ref-type="bibr" rid="B194">194</xref>]</sup><break/>Presence of nucleases <sup>[<xref ref-type="bibr" rid="B93">93</xref>]</sup></td>
      <td>Preventing enzymatic degradation of aptamers by nucleases<sup>[<xref ref-type="bibr" rid="B93">93</xref>]</sup><break/>Mitigation non-specific adsorption of proteins and lipids that compromises SAMs<sup>[<xref ref-type="bibr" rid="B177">177</xref>]</sup></td>
    </tr>
    <tr>
      <td>Sweat</td>
      <td>Non-invasive and broadly accessible across the epidermis<sup>[<xref ref-type="bibr" rid="B195">195</xref>,<xref ref-type="bibr" rid="B196">196</xref>]</sup><break/>Sealed sensor interface can prevent external <italic>ex-vivo</italic> contamination<sup>[<xref ref-type="bibr" rid="B97">97</xref>,<xref ref-type="bibr" rid="B141">141</xref>]</sup></td>
      <td>Requires active stimulation (e.g., iontophoresis) due to stagnant flow<sup>[<xref ref-type="bibr" rid="B97">97</xref>,<xref ref-type="bibr" rid="B141">141</xref>]</sup><break/>Macromolecules (e.g., albumin) are highly diluted via filtration barrier<sup>[<xref ref-type="bibr" rid="B43">43</xref>]</sup><break/>Endogenous generation (e.g., lactate) lowers systemic correlation<sup>[<xref ref-type="bibr" rid="B197">197</xref>]</sup></td>
      <td>Compensating for dynamic pH shifts (4.5-7.0) and salinity (10-100 mM) affecting E-AB thermodynamics <sup>[<xref ref-type="bibr" rid="B94">94</xref>,<xref ref-type="bibr" rid="B195">195</xref>]</sup><break/>Preventing evaporation and cutaneous contamination<sup>[<xref ref-type="bibr" rid="B43">43</xref>]</sup></td>
    </tr>
    <tr>
      <td>Urine</td>
      <td>Value for conventional clinical diagnostics<sup>[<xref ref-type="bibr" rid="B198">198</xref>]</sup></td>
      <td>Inherently intermittent availability, fundamentally precluding continuous real-time tracking<sup>[<xref ref-type="bibr" rid="B198">198</xref>]</sup><break/>Renal filtration substantially alters physiological fidelity relative to blood<sup>[<xref ref-type="bibr" rid="B99">99</xref>]</sup></td>
      <td>Not viable for the uninterrupted, continuous <italic>in-situ</italic> monitoring framework<sup>[<xref ref-type="bibr" rid="B99">99</xref>,<xref ref-type="bibr" rid="B198">198</xref>]</sup></td>
    </tr>
    <tr>
      <td>Saliva</td>
      <td>Non-invasive diagnostic value</td>
      <td>Inconsistent correlation with blood analyte concentrations depending on environment<sup>[<xref ref-type="bibr" rid="B199">199</xref>]</sup><break/>Intraoral integration faces severe mechanical and user-compliance limitations<sup>[<xref ref-type="bibr" rid="B200">200</xref>]</sup></td>
      <td>Overcoming rapid/aggressive biofouling from pellicle and bacterial biofilms<sup>[<xref ref-type="bibr" rid="B200">200</xref>]</sup><break/>Mitigating electrochemical interference from complex matrix (proteins, mucins, food)<sup>[<xref ref-type="bibr" rid="B199">199</xref>]</sup></td>
    </tr>
  </tbody>
</table>
          <table-wrap-foot>
            <fn id="t1FN1">
              <p>E-AB: Electrochemical aptamer-based; TDM: therapeutic drug monitoring; ISF: interstitial fluid; SAM: self-assembled monolayer; IgG: Immunoglobulin G.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>	
      </sec>
    </sec>
    <sec id="sec3">
      <title><italic>IN-VIVO</italic> E-AB SENSOR</title>
      <sec id="sec3-1">
        <title>Implantable blood analysis applications using wire-based E-AB sensor</title>
        <p>For real-time drug monitoring within the bloodstream of live animal models, the sensor’s form factor is a critical determinant of success. Consequently, implantable E-AB sensors have predominately adopted a wire-based electrode configuration [<xref ref-type="fig" rid="fig2">Figure 2B</xref>]. This prevalence is due to the wire geometry being structurally optimal for intravascular insertion and deep tissue penetration while ensuring minimal invasiveness during deployment<sup>[<xref ref-type="bibr" rid="B103">103</xref>]</sup>.</p>
        <p>Studies involving the implantation of sensors into the vasculature and tissues of live rats have substantiated the clinical validity of E-AB sensors<sup>[<xref ref-type="bibr" rid="B28">28</xref>]</sup> [<xref ref-type="fig" rid="fig3">Figure 3A</xref>]. Notably, continuous tracking of <italic>in-vivo</italic> pharmacokinetics has been successfully achieved in the veins of awake, ambulatory animals without the need for anesthesia<sup>[<xref ref-type="bibr" rid="B26">26</xref>]</sup>. This methodology represents a significant evolution from earlier strategies that relied on <italic>ex-vivo</italic> microfluidic devices utilizing stacked laminar flows to prevent whole blood contact<sup>[<xref ref-type="bibr" rid="B104">104</xref>]</sup>. By coating the sensor with a biocompatible polymer membrane to minimize fouling effects in whole blood, researchers have enabled stable monitoring in freely moving subjects (Note: Fouling shielding mechanisms are detailed in the subsequent section; this section focuses on the review of real-time <italic>in-vivo</italic> measurement outcomes). This approach provides precise physiological data by eliminating stress factors associated with anesthesia. Furthermore, the versatility of E-AB sensors has been confirmed through the successful detection of various distinct drugs, including doxorubicin, kanamycin, gentamicin, and tobramycin. Expanding beyond the bloodstream, the T. Soh research group demonstrated the utility of E-AB sensors for monitoring drug concentrations within tumor tissues, offering a new direction for drug design by analyzing chemotherapeutic efficacy as a function of tissue depth<sup>[<xref ref-type="bibr" rid="B105">105</xref>]</sup> [<xref ref-type="fig" rid="fig3">Figure 3B</xref>].</p>
        <fig id="fig3" position="float">
          <label>Figure 3</label>
          <caption>
            <p>Wire-based implantable E-AB sensor platforms and sensing mechanisms. (A) Intra-vascular monitoring. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B28">28</xref>]</sup>. Copyright 2019, American Chemical Society; (B) Intra-tumor sensing. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B105">105</xref>]</sup>. Copyright 2022, American Association for the Advancement of Science; (C) Nanoporous Gold (NPG) configuration and SEM images. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B107">107</xref>]</sup>. Copyright 2021, American Chemical Society; (D) Conceptual illustration of reaction kinetics. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B106">106</xref>]</sup>. Copyright 2021, John Wiley and Sons. PK: Pharmacokinetic; PTFE: polytetrafluoroethylene; E-AB: electrochemical aptamer-based; SEM: scanning electron microscope.</p>
          </caption>
          <graphic xlink:href="ss50149.fig.3.jpg"/>
        </fig>
        <p>While miniaturizing electrodes to the micrometer scale is essential for intravascular insertion, such reduction inherently decreases the physical surface area, leading to a degraded signal-to-noise (S/N) ratio and reduced utility for molecular diagnostics<sup>[<xref ref-type="bibr" rid="B106">106</xref>]</sup>. To overcome this trade-off, extensive research has focused on maximizing the effective electrode surface area using Nanoporous Gold (NPG)<sup>[<xref ref-type="bibr" rid="B107">107</xref>-<xref ref-type="bibr" rid="B111">111</xref>]</sup>. NPG leverages a 3D nanostructure to dramatically increase the effective surface area relative to the geometric area, thereby securing sufficient electrochemical signals even in miniaturized sensors<sup>[<xref ref-type="bibr" rid="B107">107</xref>,<xref ref-type="bibr" rid="B108">108</xref>]</sup>. Building on initial designs that increased microscopic surface area via surface roughening<sup>[<xref ref-type="bibr" rid="B111">111</xref>]</sup>, the Plaxco team successfully amplified the methylene blue redox signal by approximately 25-fold. This was achieved by employing cyclic voltammetry for the alternating deposition and stripping (alloying/dealloying) of zinc (Zn), which increased the microscopic surface area of the wire by up to 100-fold<sup>[<xref ref-type="bibr" rid="B107">107</xref>]</sup> [<xref ref-type="fig" rid="fig3">Figure 3C</xref>]. Moreover, it has been proven that nanostructured electrodes improve sensor sensitivity and reaction kinetics not merely by increasing area, but by accelerating electron transfer rates due to reduced charge screening effects<sup>[<xref ref-type="bibr" rid="B106">106</xref>,<xref ref-type="bibr" rid="B111">111</xref>]</sup> [<xref ref-type="fig" rid="fig3">Figure 3D</xref>]. Additionally, the pore size and morphology of the NPG thin film directly influence sensor performance by dictating analyte accessibility. It has been observed that the optimal signaling frequency varies depending on the film morphology. By creating libraries of various NPG morphologies to identify optimal structures, the signaling efficiency of nucleic acid-based biosensors can be optimized<sup>[<xref ref-type="bibr" rid="B108">108</xref>,<xref ref-type="bibr" rid="B110">110</xref>]</sup>. Electrochemical roughening processes are also presented as an effective method for fabricating high-surface-area electrodes<sup>[<xref ref-type="bibr" rid="B111">111</xref>]</sup>.</p>
      </sec>
      <sec id="sec3-2">
        <title>Implantable ISF analysis applications using microneedle-based E-AB sensor</title>
        <p>Monitoring ISF located beneath the epidermis has emerged as a promising alternative to blood-based monitoring. ISF contains physiological information analogous to blood yet remains accessible via microneedles in a minimally invasive manner. Recent advancements have integrated E-AB technology into these microneedle platforms, evolving them into systems capable of real-time, reagent-free detection of specific analytes. Currently, the most successful implementation of microneedle-based sensors is the Continuous Glucose Monitoring (CGM) device. Commercialized by companies such as Dexcom, this technology enables the continuous tracking of glucose levels in ISF through microscopic needles<sup>[<xref ref-type="bibr" rid="B112">112</xref>-<xref ref-type="bibr" rid="B114">114</xref>]</sup>. However, to transcend the limitations of enzymatic sensors and detect a broader range of drugs and biomarkers without reagents, recent research is increasingly pivoting toward incorporating E-AB technology into microneedle platforms<sup>[<xref ref-type="bibr" rid="B115">115</xref>]</sup> [<xref ref-type="fig" rid="fig4">Figure 4A</xref>].</p>
        <fig id="fig4" position="float">
          <label>Figure 4</label>
          <caption>
            <p>Microneedle-based implantable E-AB sensor platforms and sensing mechanisms. (A) Wearable microneedle-based E-AB sensor schematic. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B115">115</xref>]</sup>. Copyright 2023, Elsevier; (B) Conventional and wearable TDM comparison, with μNEAB-patch illustration and process flow. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B95">95</xref>]</sup>. Copyright 2022, American Association for the Advancement of Science; (C) Dual-sensing microneedle platforms: HMN-Flex and HMN-pH. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B118">118</xref>]</sup>. Copyright 2024, John Wiley and Sons; (D) Point-of-care biosensing strategy for multi-analytes. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B121">121</xref>]</sup>. Copyright 2023 John Wiley and Sons; (E) μSMART system for CRP detection. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B122">122</xref>]</sup>. Copyright 2025, American Chemical Society. TDM: Therapeutic drug monitoring; ISF: interstitial fluid; MB: methylene blue; HMN: hydrogel microneedle; HMN-RR: hydrogel microneedle-phenol red; DAHA: dopamine-based hyaluronic acid; CRP: C-reactive protein; E-AB: electrochemical aptamer-based.</p>
          </caption>
          <graphic xlink:href="ss50149.fig.4.jpg"/>
        </fig>
        <p>A primary application of microneedle E-AB sensors is TDM for pharmaceuticals with narrow therapeutic windows. For instance, studies have reported the successful acquisition of pharmacokinetic (PK) profiles by attaching 3D-printed microneedle sensor arrays to live rats to measure drug concentrations in ISF in real-time<sup>[<xref ref-type="bibr" rid="B95">95</xref>,<xref ref-type="bibr" rid="B116">116</xref>,<xref ref-type="bibr" rid="B117">117</xref>]</sup>. Specifically, for antibiotics requiring precise dosage control, microneedle E-AB patch modules have been designed to achieve high-resolution sensing<sup>[<xref ref-type="bibr" rid="B95">95</xref>]</sup> [<xref ref-type="fig" rid="fig4">Figure 4B</xref>]. These technologies successfully differentiated and detected representative antibiotics such as tobramycin and vancomycin within their respective therapeutic ranges of 4-21 μM and 6-35 μM in an ISF environment. Furthermore, recent developments include hyaluronic acid-based hydrogel microneedles integrated with flexible electrodes. These systems allow for the simultaneous precision detection of antibiotics and colorimetric monitoring of pH changes at infection sites, thereby providing a comprehensive evaluation of therapeutic efficacy<sup>[<xref ref-type="bibr" rid="B118">118</xref>]</sup> [<xref ref-type="fig" rid="fig4">Figure 4C</xref>].</p>
        <p>The scope of application is expanding beyond drugs to include metabolites and hormone monitoring. A technology termed the ‘Wearable Aptalyzer’, based on methacrylated hyaluronic acid (MeHA) hydrogel microneedle arrays, successfully tracked glucose and lactate levels in the dermal ISF of live diabetic rats via daily electrochemical measurements over a three-day period<sup>[<xref ref-type="bibr" rid="B96">96</xref>]</sup>. Notably, this study confirmed the multiplexing potential of E-AB sensors for metabolic disease management by maintaining stable simultaneous detection without severe biofouling. Additionally, sensor arrays for the continuous real-time monitoring of L-Tryptophan, a key amino acid linked to depression<sup>[<xref ref-type="bibr" rid="B119">119</xref>]</sup>, and technologies detecting the stress hormone cortisol transdermally using conductive microneedles<sup>[<xref ref-type="bibr" rid="B120">120</xref>]</sup>, are extending the domain of healthcare into mental health management.</p>
        <p>Building on the same MeHA hydrogel microneedle platform, the same research group demonstrated a versatile point-of-care biosensing strategy by integrating graphene oxide-nucleic acid (GO·NA) probes directly within the hydrogel matrix<sup>[<xref ref-type="bibr" rid="B121">121</xref>]</sup> [<xref ref-type="fig" rid="fig4">Figure 4D</xref>]. Upon skin insertion, ISF diffuses into the swollen microneedles, whereupon target binding induces aptamer conformational changes that dissociate the aptamer-graphene oxide complex and restore fluorescence in a reagentless, turn-on manner. This approach was validated across four analytes (glucose, uric acid, insulin, and serotonin) spanning both small molecules and proteins, with <italic>ex vivo</italic> accuracy confirmed alongside a portable fluorescence reader for on-site use. Together with the electrochemical Wearable Aptalyzer, these studies illustrate that hydrogel microneedle-aptamer platforms can be adapted across orthogonal transduction modalities to meet diverse clinical monitoring needs.</p>
        <p>E-AB sensors are also being utilized for diagnostic purposes, particularly for bacterial infections. An integrated aptasensing platform based on methacrylate hyaluronic acid sensor design has been reported, which allows for the immediate, reagentless, on-site monitoring of specific bacterial infections such as S. aureus and the assessment of treatment responses<sup>[<xref ref-type="bibr" rid="B122">122</xref>]</sup> [<xref ref-type="fig" rid="fig4">Figure 4E</xref>]. Beyond bacterial infections, E-AB sensors are applicable to allergy diagnosis. Microneedle E-AB sensors capable of detecting IgE antibodies through the skin have opened new avenues for easily diagnosing food allergy sensitization without the need for blood draws<sup>[<xref ref-type="bibr" rid="B123">123</xref>]</sup>.</p>
        <p>In conclusion, microneedle E-AB sensors have progressed beyond animal models to the stage of continuously monitoring molecules within human dermal ISF<sup>[<xref ref-type="bibr" rid="B124">124</xref>]</sup>. Although challenges such as limited ISF flow rates and biological responses remain, multifaceted material and structural innovations - such as the adoption of 3D printing processes<sup>[<xref ref-type="bibr" rid="B116">116</xref>]</sup> and functionalization using hydrogels<sup>[<xref ref-type="bibr" rid="B118">118</xref>]</sup> - are solidifying their potential as next-generation continuous monitoring platforms.</p>
      </sec>
      <sec id="sec3-3">
        <title>Soft materials for anti-fouling</title>
        <p>To ensure that E-AB sensors function without losing signal accuracy in complex biofluids, designing an interface based on “soft materials” to protect the electrode surface from fouling is essential. An ideal anti-fouling surface should be hydrophilic, act simultaneously as a hydrogen bond donor and acceptor, and possess electrically neutral characteristics<sup>[<xref ref-type="bibr" rid="B103">103</xref>]</sup> [<xref ref-type="fig" rid="fig5">Figure 5A</xref>]. These molecular properties, realizable on gold electrodes via Self-Assembled Monolayers (SAMs), facilitate the strong binding of water molecules to the surface, creating a highly hydrated layer that serves as an effective "water barrier" against contaminant adsorption. Furthermore, increasing the packing density of surface molecules is a critical factor in maximizing hydrogen bonding to enhance surface hydration<sup>[<xref ref-type="bibr" rid="B125">125</xref>]</sup>.</p>
        <fig id="fig5" position="float">
          <label>Figure 5</label>
          <caption>
            <p>Soft material strategies for anti-fouling and long-term stability of E-AB sensors. (A) Anti-fouling SAM. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B103">103</xref>]</sup>. Copyright 2001, American Chemical Society. (B) Charge selectivity. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B128">128</xref>]</sup>. Copyright 2021, American Chemical Society. (C) Polymer coating (PEG). Reprinted with permission<sup>[<xref ref-type="bibr" rid="B131">131</xref>]</sup>. Copyright 2018, American Chemical Society. (D) Size-selective hydrogel barrier. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B133">133</xref>]</sup>. Copyright 2023, American Chemical Society. MCH: 6-mecapto-1-hexanol; AC: ammonium choline; PC: phorsphorycholine; OEG: oligoethylene glycol; SP: sulfopropylmethacrylate potassium; AP: ammonio propionate; WE: working electrode; PBS: phosphate buffered saline; E-AB: electrochemical aptamer-based; SAM: self-assembled monolayer.</p>
          </caption>
          <graphic xlink:href="ss50149.fig.5.jpg"/>
        </fig>
        <p>Beyond fouling prevention, tuning surface charge is an effective strategy for enhancing selectivity toward target analytes. In cases where structurally similar drugs (e.g., aminoglycosides) must be distinguished, tuning the surface charge using mixed SAMs can exclude interference via electrostatic repulsion<sup>[<xref ref-type="bibr" rid="B126">126</xref>,<xref ref-type="bibr" rid="B127">127</xref>]</sup> [<xref ref-type="fig" rid="fig5">Figure 5B</xref>]. Additionally, optimizing the chemical structure of SAM end-groups or introducing zwitterionic moieties aids in maintaining the chemical stability of the monolayer itself within biological fluids<sup>[<xref ref-type="bibr" rid="B128">128</xref>-<xref ref-type="bibr" rid="B130">130</xref>]</sup>.</p>
        <p>However, as monolayer-level control alone is insufficient to fully overcome harsh <italic>in-vivo</italic> environments, soft coating strategies utilizing functional polymers have recently emerged as key solutions<sup>[<xref ref-type="bibr" rid="B131">131</xref>]</sup> [<xref ref-type="fig" rid="fig5">Figure 5C</xref>]. Notably, introducing hydrophilic polymers such as polyethylene glycol (PEG) is a powerful method for extending sensor lifespan. Chen <italic>et al.</italic>  designed a NPG electrode mimicking the structure of intestinal villi and applied a PEG-based coating to effectively protect the sensor surface<sup>[<xref ref-type="bibr" rid="B132">132</xref>]</sup>. This approach successfully suppressed baseline drift and dramatically improved signal stability<sup>[<xref ref-type="bibr" rid="B132">132</xref>]</sup>. Through this protective coating, the sensor achieved a breakthrough result: it maintained robust sensitivity for kanamycin in undiluted human serum <italic>in vitro</italic> over four weeks with intermittent sampling at specific days. Furthermore, the system demonstrated exceptional <italic>in-vivo</italic> translation, sustaining its continuous sensing functionality for up to one week when implanted within the femoral vein of free-moving rats.</p>
        <p>In addition to PEG, the application of 3D polymer networks, such as hydrogels, as physical barriers completes the sensor’s durability [<xref ref-type="fig" rid="fig5">Figure 5D</xref>]. The presented biocompatible hydrogel coating technology maximizes the advantages of soft materials in a similar context to PEG coatings. Hydrogels physically block the access of large enzymes, such as proteins present in the blood, thereby preventing aptamer degradation and inhibiting fouling. At the same time, they allow the free diffusion of small drug molecules, enabling stable, long-term continuous drug monitoring across multiple <italic>in-vivo</italic> sites, including blood vessels and tissues<sup>[<xref ref-type="bibr" rid="B133">133</xref>]</sup>. Thus, soft material technologies like PEG and hydrogels are essential protective measures for translating E-AB sensors from laboratory settings to real-world biological applications.</p>
        <p>Furthermore, anionic polymer coatings such as Nafion act as charge-selective barriers. They repel negatively charged interfering species in the blood while selectively allowing the permeation of target molecules, thereby contributing to the improvement of the signal-to-noise ratio<sup>[<xref ref-type="bibr" rid="B134">134</xref>]</sup>.</p>
        <p>The sensors discussed above operate on the principle of direct immersion within the body, a strategy we term ‘<italic>in-vivo</italic>’, characterized by intimate contact with internal biofluids such as blood and ISF. This architecture grants access to the richest molecular information but imposes the most stringent biological constraints: chronic biofouling from protein adsorption and cellular adhesion, thrombus formation on electrode surfaces, and the mechanical demands of long-term tissue cohabitation. Together, these challenges necessitate the soft-material and anti-fouling strategies detailed in Section <bold>Soft materials for anti-fouling.</bold></p>
        <p>In contrast, the sensors described in the following section operate ‘on-body’, interfaced with the outer surface of the body rather than its interior. By confining the sensing interface to the skin surface and leveraging sweat as the sampling medium, this approach avoids the safety risks and surgical burden of implantation. The trade-off, however, is a fundamentally different set of constraints: lower and more variable biomarker concentrations due to filtration across biological barriers, intermittent and sweat-rate-dependent analyte delivery, and susceptibility to environmental contamination and motion artifacts. The soft-material strategies required here, such as breathable substrates, superwetting microfluidics, and adhesive biocomposites, are accordingly distinct from those demanded <italic>in-vivo</italic>.</p>
      </sec>
    </sec>
    <sec id="sec4">
      <title>ON-BODY E-AB SENSOR</title>
      <sec id="sec4-1">
        <title>Wearable sweat analysis applications using E-AB sensor</title>
        <p>Recent breakthroughs in sweat monitoring sensor technology have paved the way for innovative real-time tracking of physiological states directly linked to human performance and health, such as dehydration, fatigue, and disease<sup>[<xref ref-type="bibr" rid="B90">90</xref>,<xref ref-type="bibr" rid="B97">97</xref>,<xref ref-type="bibr" rid="B135">135</xref>-<xref ref-type="bibr" rid="B140">140</xref>]</sup>. Sweat presents an attractive alternative to blood for analysis, offering non-invasive access with relatively lower risks of biofouling. However, the majority of existing wearable sensors rely on enzymatic sensing mechanisms, which inherently suffer from sensitivity to environmental factors like temperature and humidity, as well as limited long-term stability due to enzyme degradation. Consequently, E-AB sensors, known for their superior stability and selectivity, are emerging as a next-generation solution for sweat analysis.</p>
        <p>A primary application of sweat-based E-AB sensors lies in the monitoring of stress and sex hormones. To non-invasively detect cortisol, a key biomarker of stress, skin-interfaced aptamer sensors integrated with FETs have recently been developed [<xref ref-type="fig" rid="fig6">Figure 6A</xref>]. These sensors demonstrate a wide dynamic range spanning from picomolar (pM) to micromolar (μM) levels. Through the standardized Trier Social Stress Test (TSST), researchers have successfully validated the correlation between sweat and salivary cortisol levels, proving the sensor’s reliability in biofluid environments<sup>[<xref ref-type="bibr" rid="B141">141</xref>]</sup>. Furthermore, Ye <italic>et al.</italic> has pioneered personalized reproductive healthcare by developing a wearable sensor targeting estradiol<sup>[<xref ref-type="bibr" rid="B97">97</xref>]</sup>. This study stands as a landmark innovation, successfully detecting sub-nanomolar levels of hormones in human sweat, thereby demonstrating the feasibility of monitoring menstrual cycles via wearable E-AB technology.</p>
        <fig id="fig6" position="float">
          <label>Figure 6</label>
          <caption>
            <p>Diverse applications of wearable E-AB sensors for non-invasive sweat analysis. (A) Hormonal health monitoring. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B141">141</xref>]</sup>. Copyright 2022, American Association for the Advancement of Science; (B) Illicit drug detection. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B142">142</xref>]</sup>. Copyright 2022, American Chemical Society; (C) Chronic wound management. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B146">146</xref>]</sup>. Copyright 2021, American Association for the Advancement of Science; (D) Ultra-sensitive &amp; reusable. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B147">147</xref>]</sup>. Copyright 2024, American Chemical Society. ACTH: adrenocorticotropic hormone; CRH: corticotropin-releasing hormone; LCD: liquid crystal display; IL-6: Interleukin-6; IL-8: Interleukin-8; CE: counter electrode; RE: reference electrode; TNF-α: tumor necrosis factor-α; TGF-β1: transforming growth factor–β1; PANI: polyaniline; MB: methylene blue; E-AB: electrochemical aptamer-based.</p>
          </caption>
          <graphic xlink:href="ss50149.fig.6.jpg"/>
        </fig>
        <p>E-AB sensors also exhibit exceptional performance in detecting small-molecule drugs and protein biomarkers that are challenging for enzymatic sensors. In the context of preventing drug abuse, sensor arrays have been reported to detect multiple illicit drugs in sweat with high sensitivity (hundreds of pM) while distinguishing between target drugs and interferents via distinct electrochemical fingerprints<sup>[<xref ref-type="bibr" rid="B142">142</xref>]</sup> [<xref ref-type="fig" rid="fig6">Figure 6B</xref>]. Expanding on this, multiplexed sensor arrays for monitoring multiple anticancer drugs (e.g., cyclophosphamide, cisplatin, and docetaxel) have been developed as a step toward personalized chemotherapy. While these systems demonstrated robust shelf-life stability over 15 days, their precise drug discrimination and quantification capabilities were validated using discrete measurements within artificial sweat <italic>in vitro</italic><sup>[<xref ref-type="bibr" rid="B143">143</xref>]</sup>, laying the groundwork for future continuous on-body tracking.</p>
        <p>Beyond general health management, innovations in form factors are enabling the early detection of severe diseases. A smart ring sensor designed for atherosclerosis management simultaneously detects C-reactive protein (CRP), cholesterol, and potassium ions (K+), tracking patient trends via a mobile application<sup>[<xref ref-type="bibr" rid="B144">144</xref>]</sup>. Additionally, research on cytokine detection to prevent life-threatening inflammatory storms is gaining momentum. A system integrating a Janus membrane with a graphene-FET successfully detected cytokines in undiluted sweat with a range of 0.5-500 pM<sup>[<xref ref-type="bibr" rid="B145">145</xref>]</sup>.</p>
        <p>The advancement of these wearable electrochemical sensing platforms is expanding the scope of analysis beyond sweat to include other cutaneous biofluids such as wound exudate<sup>[<xref ref-type="bibr" rid="B146">146</xref>]</sup>. Recently, a flexible multiplexed immunosensor for point-of-care applications was developed, presenting new possibilities for chronic wound management. This system utilizes microfluidic channels to collect exudate from the wound site and simultaneously monitors inflammatory cytokines [TNF-α, Interleukin-6 (IL-6), IL-1β] alongside physicochemical parameters critical for wound healing (uric acid, pH, and temperature) in real-time [<xref ref-type="fig" rid="fig6">Figure 6C</xref>]. This innovation serves as a significant example demonstrating that wearable sensor technology can transcend simple health monitoring to precisely diagnose and manage pathological conditions in localized areas, even within complex biofluid environments.</p>
        <p>Furthermore, a study utilizing Nafion and Molybdenum Disulfide (MoS2) FETs incorporated a stepwise wettability structure to enable automatic sweat collection and impurity blocking<sup>[<xref ref-type="bibr" rid="B147">147</xref>]</sup> [<xref ref-type="fig" rid="fig6">Figure 6D</xref>]. This system achieved ultra-high sensitivity (down to 10 fM) and, notably, was designed for reusability, highlighting it as a significant alternative to overcome the economic and environmental limitations of disposable wearable sensors.</p>
      </sec>
      <sec id="sec4-2">
        <title>Soft materials for skin adhesion of sensor</title>
        <p>To ensure the collection of precise and reliable biosignals via wearable sensors, it is essential to minimize mechanical mismatch and maintain conformal contact by accommodating the dynamic curvature and deformation of the skin<sup>[<xref ref-type="bibr" rid="B148">148</xref>]</sup>. Considering that substrate materials and biorecognition layers must possess biocompatibility and non-reactivity in direct contact with the skin, various flexible and stretchable sweat biosensors have been developed using elastomers such as polydimethylsiloxane (PDMS), polystyrene (PS), styrene-ethylene-butylene-styrene (SEBS), polyurethane (PU), and other silicone-based polymers<sup>[<xref ref-type="bibr" rid="B148">148</xref>,<xref ref-type="bibr" rid="B149">149</xref>]</sup> [<xref ref-type="fig" rid="fig7">Figure 7A</xref>].</p>
        <fig id="fig7" position="float">
          <label>Figure 7</label>
          <caption>
            <p>Advanced soft material strategies for conformal skin adhesion and stable bio-interfaces. (A) Conformal contact elastomer substrate. PDMS and polystyrene structures were obtained from Wikimedia Commons (Public Domain); (B) Smart wettability. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B153">153</xref>]</sup>. Copyright 2024, American Chemical Society; (C) Exudate management. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B135">135</xref>]</sup>. Copyright 2025, American Association for the Advancement of Science; (D) Self-adhesive platform. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B156">156</xref>]</sup>. Copyright 2024, American Chemical Society; (E) All-printed &amp; chip-less. Reprinted with permission<sup>[<xref ref-type="bibr" rid="B157">157</xref>]</sup>. Copyright 2025, Springer Nature. PDMS: Polydimethylsiloxane; SEBS: styrene-ethylene-butylene-styrene; PVDF-HFP: poly(vinylidene fluoride-hexafluoropropylene); PET: polyethylene terephthalate; PEIE: polyethylenimine; NP: nanoparticle; SWCNT: single-walled carbon nanotube; MWCNT: mutil-walled carbon nanotube.</p>
          </caption>
          <graphic xlink:href="ss50149.fig.7.jpg"/>
        </fig>
        <p>Breathability is another critical factor in establishing a stable skin-sensor interface. In this regard, recently developed phase-separated porous nanocomposites offer excellent breathability and ultra-flexibility while maintaining a low percolation threshold. These materials are gaining attention as key components for wireless bioelectronic devices, as they prevent skin inflammation and adhesion loss caused by sweat accumulation during long-term wear<sup>[<xref ref-type="bibr" rid="B150">150</xref>]</sup>.</p>
        <p>However, sweat secreted in real-world scenarios acts as a primary factor in weakening adhesion and increasing interfacial impedance<sup>[<xref ref-type="bibr" rid="B151">151</xref>]</sup>. Therefore, functional soft material technologies capable of actively managing fluids like sweat, beyond mere physical flexibility, are required<sup>[<xref ref-type="bibr" rid="B149">149</xref>]</sup>. As a solution, bio-inspired open microfluidic systems with superwetting properties have been proposed. These systems induce spontaneous sweat transport, thereby preventing sensor surface contamination and creating a stable adhesion environment<sup>[<xref ref-type="bibr" rid="B152">152</xref>]</sup>. Furthermore, stretchable and smart wettable sensing patch technologies ensure the accuracy of multiplexed <italic>in situ</italic> analysis by effectively guiding fluid flow even under skin stretching<sup>[<xref ref-type="bibr" rid="B153">153</xref>]</sup> [<xref ref-type="fig" rid="fig7">Figure 7B</xref>], while flexible superwettable band platforms enhance sensor utility by simultaneously performing efficient sweat collection and sensing<sup>[<xref ref-type="bibr" rid="B154">154</xref>]</sup>. Expanding the scope of biofluid management, a recent study introduced a microfluidic wearable device specifically designed for the complex environment of human chronic wounds<sup>[<xref ref-type="bibr" rid="B135">135</xref>]</sup>. This system utilizes a soft, biocompatible interface to effectively manage wound exudate - a fluid significantly more viscous and biologically complex than sweat [<xref ref-type="fig" rid="fig7">Figure 7C</xref>]. By continuously removing excess exudate, the device not only prevents skin maceration and sensor detachment but also enables the real-time <italic>in situ</italic> analysis of inflammatory biomarkers, demonstrating that advanced fluid-handling soft materials are essential for maintaining stable bio-interfaces on compromised skin.</p>
        <p>Material innovations aimed at enhancing adhesion strength itself are also actively progressing. Adhesive biocomposite electrodes capable of maintaining robust attachment even on sweaty skin have enabled long-term continuous electrophysiological monitoring<sup>[<xref ref-type="bibr" rid="B155">155</xref>]</sup>. Additionally, self-adhesive and biocompatible microfluidic platforms utilizing liquid metal to form erasable hotspots have successfully detected glucose in sweat by stably adhering to the skin without additional adhesives<sup>[<xref ref-type="bibr" rid="B156">156</xref>]</sup> [<xref ref-type="fig" rid="fig7">Figure 7D</xref>]. Pushing the boundaries of soft material integration, an all-printed, chip-less wearable neuromorphic system has recently been developed<sup>[<xref ref-type="bibr" rid="B157">157</xref>]</sup> [<xref ref-type="fig" rid="fig7">Figure 7E</xref>]. This approach eliminates the need for rigid silicon chips by utilizing functional material inks to print both the sensors and the computing elements directly onto flexible substrates. This system achieves efficient multimodal physicochemical monitoring (analyzing both sweat biomarkers and physical signals) while maintaining a seamless, soft interface with the skin, demonstrating that soft materials can now support complex on-sensor data processing.</p>
        <p>Moreover, graphene-hydrogel interfaces combine high conductivity with tissue similarity, significantly improving mechanical and electrical coupling in both wearable and implantable devices<sup>[<xref ref-type="bibr" rid="B158">158</xref>]</sup>. When combined with active sweat induction systems such as iontophoresis, these high-performance adhesive materials serve as a crucial foundation for enabling continuous analyte analysis even at rest<sup>[<xref ref-type="bibr" rid="B159">159</xref>]</sup>. In conclusion, soft material technologies for next-generation wearable sensors are evolving to establish stable bio-interfaces in dynamic skin environments by integrally implementing breathability, wettability control, and bio-friendly adhesion beyond simply securing mechanical flexibility.</p>
      </sec>
    </sec>
    <sec id="sec5">
      <title>CHALLENGES AND PERSPECTIVES</title>
      <p>To quantitatively contextualize both the progress made and the performance gaps that remain, <xref ref-type="table" rid="t2">Table 2</xref> summarizes key analytical parameters of representative E-AB sensors reported across the three target biofluids, alongside the dominant technical hurdles specific to each matrix. This comparative overview reveals that, despite advances in electrode nano-structuring and soft-material integration, a consistent performance ceiling persists, particularly in achieving sub-picomolar sensitivity within undiluted biological matrices and sustaining signal fidelity beyond 72 h of continuous deployment. The following subsections dissect the three most critical challenge domains and propose experimentally grounded pathways toward their resolution.</p>
      <table-wrap id="t2">
        <label>Table 2</label>
        <caption>
          <p>Performance comparison of representative E-AB sensors across blood, ISF, and sweat, highlighting key analytical parameters and dominant technical challenges specific to each biological matrix</p>
        </caption>
        <table frame="hsides" rules="groups">
  <tbody>
    <tr>
      <td>
        <bold>Biofluid</bold>
      </td>
      <td>
        <bold>Platform or form factor</bold>
      </td>
      <td>
        <bold>Target analyte</bold>
      </td>
      <td>
        <bold>Limit of detection</bold>
      </td>
      <td>
        <bold>Operational stability (in biofluid)</bold>
      </td>
      <td>
        <bold>Response time</bold>
      </td>
      <td>
        <bold>Dominant technical challenges</bold>
      </td>
      <td>
        <bold>Ref.</bold>
      </td>
    </tr>
    <tr>
      <td>Blood</td>
      <td>Wire electrode (SAM modification)</td>
      <td>Vancomycin</td>
      <td>Not specified</td>
      <td>> 7 days in undiluted serum at 37 °C</td>
      <td>Seconds</td>
      <td>SAM oxidative degradation; redox-driven thiol desorption accelerated at body temperature</td>
      <td>[<xref ref-type="bibr" rid="B78">78</xref>]</td>
    </tr>
    <tr>
      <td>Blood</td>
      <td>Wire electrode (NPG + PEG hydrogel coating)</td>
      <td>Kanamycin</td>
      <td>μM ~ mM</td>
      <td>~ 1 month (<italic>in vitro</italic> serum); ~1 week (<italic>in-vivo</italic>, free-moving rats)</td>
      <td>Seconds</td>
      <td>Biofouling-induced baseline drift</td>
      <td>[<xref ref-type="bibr" rid="B132">132</xref>]</td>
    </tr>
    <tr>
      <td>Blood</td>
      <td>Wire electrode (NPG)</td>
      <td>Daunorubicin</td>
      <td>100 nM</td>
      <td>Multi-hour (<italic>in-vivo</italic>, rat models)</td>
      <td>Seconds</td>
      <td>Inter-individual pharmacokinetic variability; <italic>In-vivo</italic> baseline signal drift in complex biofluids</td>
      <td>[<xref ref-type="bibr" rid="B201">201</xref>]</td>
    </tr>
    <tr>
      <td>Blood</td>
      <td>Implantable microelectrode array (NPG, flexible)</td>
      <td>Doxorubicin (intratumoral)</td>
      <td>μM level</td>
      <td>Hours (<italic>in-vivo</italic>, tumor implantation)</td>
      <td>Seconds</td>
      <td>Tumor microenvironment heterogeneity; mechanical stress during tissue insertion</td>
      <td>[<xref ref-type="bibr" rid="B105">105</xref>]</td>
    </tr>
    <tr>
      <td>ISF</td>
      <td>3D-printed microneedle array (E-AB)</td>
      <td>Tobramycin / Vancomycin</td>
      <td>μM level</td>
      <td>60 min (<italic>in-vivo</italic>, rat)</td>
      <td>Sub-second</td>
      <td><italic>In-vivo</italic> environmental noise (precluding high-frequency KDM drift correction); progressive sensor degradation in ISF</td>
      <td>[<xref ref-type="bibr" rid="B117">117</xref>]</td>
    </tr>
    <tr>
      <td>ISF</td>
      <td>Acupuncture needle (AuNP-deposited)</td>
      <td>Tobramycin / Vancomycin</td>
      <td>μM level</td>
      <td>Hours (<italic>in-vivo</italic>, continuous)</td>
      <td>Sub-second</td>
      <td>Needle-to-skin contact stability; ISF–plasma lag time</td>
      <td>[<xref ref-type="bibr" rid="B95">95</xref>]</td>
    </tr>
    <tr>
      <td>ISF</td>
      <td>Hydrogel microneedle (MeHA, Wearable Aptalyzer)</td>
      <td>Glucose / Lactate</td>
      <td>mM level</td>
      <td>3 days (<italic>in-vivo</italic>, rat skin)</td>
      <td>20 min</td>
      <td>Signal lag time due to target diffusion through the hydrogel and slow aptamer dissociation kinetics</td>
      <td>[<xref ref-type="bibr" rid="B96">96</xref>]</td>
    </tr>
    <tr>
      <td>ISF</td>
      <td>Solid microneedle patch (human clinical trial)</td>
      <td>Vancomycin</td>
      <td>nM level</td>
      <td>12–24 h in human dermal ISF (primarily 12 h reliable)</td>
      <td>5 min</td>
      <td>Sensor degradation beyond 12 h; ISF–plasma pharmacokinetic equilibration delay</td>
      <td>[<xref ref-type="bibr" rid="B202">202</xref>]</td>
    </tr>
    <tr>
      <td>Sweat</td>
      <td>Skin patch (AuNP-MXene electrode, strand-displacement)</td>
      <td>Estradiol</td>
      <td>0.14 pM</td>
      <td>Hours (on-body, iontophoresis-induced)</td>
      <td>~10 min (field-assisted)</td>
      <td>Sub-pM target concentration in sweat; ionic strength / pH variability requiring multivariate calibration</td>
      <td>[<xref ref-type="bibr" rid="B97">97</xref>]</td>
    </tr>
    <tr>
      <td>Sweat</td>
      <td>Smartwatch (flexible FET array)</td>
      <td>Cortisol</td>
      <td>pM level</td>
      <td>Hours (on-body, continuous)</td>
      <td>seconds</td>
      <td>Detection of Ultralow Concentration Biomarkers; Debye screening in high ionic strength matrices</td>
      <td>[<xref ref-type="bibr" rid="B141">141</xref>]</td>
    </tr>
    <tr>
      <td>Sweat</td>
      <td>Wearable PET patch</td>
      <td>TNF-α</td>
      <td>0.31 pM</td>
      <td>Up to 15 days</td>
      <td>Not specified</td>
      <td>Trace-level biomarker detection limit; Deformation-induced electrical instability</td>
      <td>[<xref ref-type="bibr" rid="B145">145</xref>]</td>
    </tr>
  </tbody>
</table>
        <table-wrap-foot>
          <fn id="t2FN1">
            <p>E-AB: Electrochemical aptamer-based; ISF: interstitial fluid; SAM: self-assembled monolayer; NPG: Nanoporous Gold; PEG: polyethylene glycol; NP: nanoparticle; FET: Field-Effect Transistors; PET: TNF-α: tumor necrosis factor-α.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <sec id="sec5-1">
        <title>Simultaneous achievement of ultra-sensitivity and specificity</title>
        <p>First is the simultaneous achievement of ultra-sensitivity and specificity. Macromolecules such as proteins and nucleic acids in whole blood exist in trace amounts (sub-picomolar), demanding much higher sensitivity than metabolite analysis in the millimolar range. While nanostructured electrodes and various signal amplification strategies are being explored, the paradox of increased surface area leading to non-specific adsorption and reduced signal-to-noise (S/N) ratio must be resolved<sup>[<xref ref-type="bibr" rid="B160">160</xref>]</sup>. Furthermore, to exclude the influence of interferents within the complex matrix of real samples, anti-fouling performance verification in clinically relevant sample environments, rather than simple buffer solutions, must be prioritized. At a more fundamental level, sensitivity in E-AB sensors is intrinsically bounded by the equilibrium dissociation constant (K_D) of the aptamer-target interaction. As established by the population-shift thermodynamic framework, the signal gain of a structure-switching sensor is strictly coupled to its switching equilibrium constant, meaning that sensitivity and dynamic range cannot be simultaneously optimized<sup>[<xref ref-type="bibr" rid="B94">94</xref>]</sup>. This thermodynamic ceiling is further compounded in complex biological matrices, where competitive adsorption from high-abundance proteins such as albumin (~ 40 mg/mL in blood) compresses the available signal window. Electrode geometry improvements alone cannot resolve these constraints. Instead, orthogonal strategies must be co-engineered at the molecular and transduction levels, each tailored to the physicochemical demands of the target biofluid.</p>
        <p>Three experimentally validated pathways are proposed here, each matched to a specific biological matrix.</p>
        <p>(i) Blood: Aptamer structural engineering. For implantable blood sensors, pseudoknot-assisted two-state aptamer designs constrain the aptamer to only two discrete conformational states, eliminating partially folded intermediates that generate background noise. This approach improves effective sensitivity by nearly an order of magnitude without modifying the electrode or measurement protocol, which is a critical advantage given that the SAM of implantable wire sensors undergoes progressive thiol desorption during multi-day vascular deployment<sup>[<xref ref-type="bibr" rid="B78">78</xref>,<xref ref-type="bibr" rid="B161">161</xref>]</sup>.</p>
        <p>(ii) ISF: Probe density optimization. For microneedle ISF sensors, limited fluid volume and slow analyte diffusion across the dermis create a mass-transport bottleneck. Controlling aptamer surface density over an order-of-magnitude range modulates signal gain from 60% to 200%, with peak performance at the lowest densities where steric crowding is minimized<sup>[<xref ref-type="bibr" rid="B27">27</xref>]</sup>. This principle has guided the adoption of dendritic gold nanoparticle-decorated microneedle tips, which expand aptamer-accessible surface area without enlarging the electrode footprint<sup>[<xref ref-type="bibr" rid="B95">95</xref>,<xref ref-type="bibr" rid="B96">96</xref>]</sup>.</p>
        <p>(iii) Sweat: Electric field-assisted binding. For wearable sweat sensors, target hormones and cytokines often fall below the intrinsic K_D ceiling under passive diffusion conditions, and sweat ionic strength variability continuously perturbs aptamer folding thermodynamics. Applying a controlled electric potential during measurement electrokinetically concentrates charged target molecules at the electrode surface, locally exceeding the bulk concentration and driving occupancy beyond the thermodynamic equilibrium limit without any added reagent. This has been validated in human sweat estradiol sensing (LOD = 0.14 pM), with field-assisted operation reducing incubation time by more than half while preserving full sensor reversibility<sup>[<xref ref-type="bibr" rid="B97">97</xref>]</sup>.</p>
        <p>Across all three strategies, it is essential that sensitivity gains be validated in the target biological matrix rather than buffer alone. Improvements demonstrated in phosphate-buffered saline have repeatedly failed to translate to undiluted whole blood, ISF, or real human sweat, highlighting that the matrix is itself an inseparable component of the sensing problem<sup>[<xref ref-type="bibr" rid="B162">162</xref>]</sup>.</p>
      </sec>
      <sec id="sec5-2">
        <title>Establishment of calibration-free technologies</title>
        <p>Second is the establishment of calibration-free technologies. E-AB sensors often suffer from low reproducibility between sensors due to minute variations in electrode surface or probe density, necessitating cumbersome calibration processes prior to use<sup>[<xref ref-type="bibr" rid="B109">109</xref>,<xref ref-type="bibr" rid="B163">163</xref>-<xref ref-type="bibr" rid="B170">170</xref>]</sup>. This can be a critical drawback, particularly for continuous monitoring or implantable sensors. As a solution, novel signal mechanisms such as ratiometric approaches or dual-frequency measurements are being introduced<sup>[<xref ref-type="bibr" rid="B171">171</xref>-<xref ref-type="bibr" rid="B176">176</xref>]</sup>. These methods are emerging as promising alternatives to enable calibration-free measurements by canceling out sensor-to-sensor variations. To rigorously contextualize these calibration-free claims, it is critical to examine the specific mechanisms driving this correction, establish standardized performance metrics, and identify the unresolved physical limitations.</p>
        <p>(i) Mechanism of drift reduction and variability cancellation. Baseline drift in E-AB sensors primarily arises from two distinct physicochemical processes occurring at the electrode surface: biofouling (nonspecific protein and cellular adsorption that blocks aptamer access, causing a rapid, exponential loss in absolute peak current over the first ~ 1-2 h <italic>in-vivo</italic> or in whole blood) and thiol desorption (electrochemically driven desorption from the gold SAM, producing a slower, approximately linear signal decline thereafter)<sup>[<xref ref-type="bibr" rid="B177">177</xref>]</sup>. Critically, both processes reduce the absolute amplitude of the electrochemical signal at all square-wave voltammetry (SWV) frequencies in a nearly proportional manner, as they simply reduce the total number of accessible methylene blue reporters.</p>
        <p>Kinetic Differential Measurements (KDM) and ratiometric approaches exploit this symmetry. By taking the difference or ratio of normalized signals collected at two frequencies (one where target binding increases current and one where it decreases current), the drift term, which scales equally at both frequencies, cancels subtractively. The target-specific signal, however, responds differentially because it reflects a binding-induced change in aptamer electron transfer kinetics, not merely total current amplitude. Similarly, sensor-to-sensor fabrication variability, which manifests as differences in absolute baseline current due to variations in probe density or electrode surface area, also scales with absolute amplitude and is therefore canceled by the ratio operation, rendering the output unitless and independent of fabrication-batch variation<sup>[<xref ref-type="bibr" rid="B178">178</xref>]</sup>.</p>
        <p>(ii) Practical performance metrics. To standardize the reporting and benchmarking of these calibration-free platforms, specific quantitative metrics should be adopted. For drift, the raw signal loss should be expressed as the percentage of signal loss per hour under relevant deployment conditions (e.g., 37 °C undiluted whole blood or <italic>in-vivo</italic>). For context, when implanted in the jugular veins of anesthetized rats for continuous tobramycin monitoring with one scan every 10 s, conventional DNA-based SAM sensors lose approximately 48% of their baseline signal over 5 h. In contrast, 2′-O-methyl RNA (OMe RNA) aptamer analogs under identical conditions lose only ~ 7% over the same period, a 7-fold improvement<sup>[<xref ref-type="bibr" rid="B93">93</xref>]</sup>. For between-sensor variability, the coefficient of variation (CV) of the normalized output should be reported across ≥ 3 independently fabricated batches. Dual-frequency approaches have shown clinically relevant accuracy even with sensors fabricated without tight surface density control<sup>[<xref ref-type="bibr" rid="B178">178</xref>]</sup>. Finally, the recalibration interval should target ≥ 24 h without recalibration, a critical benchmark considering commercial continuous glucose monitors still require multiple daily recalibrations to maintain ±20% accuracy<sup>[<xref ref-type="bibr" rid="B172">172</xref>]</sup>.</p>
        <p>(iii) Remaining failure modes. Despite the effectiveness of dual-frequency correction, three fundamental failure modes remain unresolved. Progressive biofouling beyond what KDM can correct: severe or heterogeneous fouling can eventually alter the ratio of the two frequency responses themselves (e.g., by sterically blocking aptamer conformational switching differentially at different timescales), thereby corrupting the correction<sup>[<xref ref-type="bibr" rid="B177">177</xref>]</sup>. This explains why operational lifetimes <italic>in-vivo</italic> remain limited to hours rather than days. Furthermore, motion artifacts: mechanical deformation of the electrode-SAM interface during body movement or blood pulsation can transiently alter the electron transfer rate independently of target binding, generating spurious signals that KDM cannot distinguish from genuine target responses. And temperature dependence: the electron transfer kinetics of the methylene blue reporter shift with temperature, meaning a correction optimized at 37 °C may be inaccurate at body sites with lower or fluctuating temperatures. Real-time temperature compensation via co-integrated thermal sensors has been shown to reduce quantification error by up to 10% and is recommended as a mitigation strategy<sup>[<xref ref-type="bibr" rid="B179">179</xref>]</sup>.</p>
      </sec>
      <sec id="sec5-3">
        <title>Multiplexing and AI-assisted intelligentization</title>
        <p>Third is multiplexing and intelligentization. Accurate disease diagnosis often requires the simultaneous detection of complementary sets of biomarkers rather than a single marker<sup>[<xref ref-type="bibr" rid="B180">180</xref>,<xref ref-type="bibr" rid="B181">181</xref>]</sup>. This necessitates integration with microfluidic technologies or high-density array designs. Moreover, the convergence with artificial intelligence (AI) and machine learning (ML) algorithms holds the potential to dramatically enhance the accuracy of personalized diagnostics by denoising complex electrochemical signals and extracting meaningful patterns<sup>[<xref ref-type="bibr" rid="B182">182</xref>,<xref ref-type="bibr" rid="B183">183</xref>]</sup>. For multiplexed sensing, a practical near-term pathway is spatially encoded aptamer arrays on flexible substrates, where each electrode zone is independently functionalized for a different target and individually addressable via SWV frequency-based interrogation. This approach eliminates cross-talk between channels without increasing device footprint, which is already demonstrated in wearable E-AB platforms for simultaneous detection of multiple antibiotics in ISF<sup>[<xref ref-type="bibr" rid="B117">117</xref>]</sup> and multiple drugs in sweat<sup>[<xref ref-type="bibr" rid="B142">142</xref>]</sup>.</p>
        <p>For AI integration, the most critical, and currently underappreciated, prerequisite is the construction of longitudinal <italic>in-vivo</italic> datasets with paired ground-truth reference measurements. Without such structured training data, machine learning models are likely to overfit to bench-scale measurement artifacts rather than learn genuine physiological dynamics. To address the privacy concerns that inevitably accompany the collection of large-scale patient biosensor data, federated learning offers a principled solution: models are trained locally on each patient’s device, and only model parameters, not raw signals, are aggregated centrally, preserving patient confidentiality while enabling collaborative learning at scale<sup>[<xref ref-type="bibr" rid="B184">184</xref>]</sup>. </p>
      </sec>
    </sec>
    <sec id="sec6">
      <title>CONCLUSION AND OUTLOOK</title>
      <p>This review systematically examined E-AB sensor technology for continuous, real-time biomarker monitoring across the blood, ISF, and sweat physiological continuum, with a central focus on soft material strategies that resolve the mechanical and chemical mismatches at the sensor-body interface. As systematically compared in <xref ref-type="table" rid="t3">Table 3</xref>, the distinct biological constraints imposed by each biofluid underscore why conventional sensing paradigms, including enzymatic sensors and antibody-based immunosensors, fall short as platforms for continuous physiological monitoring, and why the binding-induced conformational change mechanism of E-AB sensors represents a uniquely enabling foundation. To address these biological hurdles, implantable sensors have overcome signal attenuation through nanoporous gold electrode architectures and anti-fouling soft material coatings, enabling real-time intravascular drug monitoring and continuous ISF analysis via microneedle integration, while wearable platforms have expanded non-invasive detection of hormones, drugs, and disease biomarkers through active fluid control technologies including Janus membranes and iontophoresis. Realizing the full clinical potential of this platform will ultimately require the co-engineering of aptamer structural design, calibration-free dual-frequency signal processing, and AI-assisted multiplexed interpretation within a unified, biofluid-aware framework, which is a convergence that positions E-AB sensors as a foundational technology for next-generation personalized precision medicine.</p>
      <table-wrap id="t3">
        <label>Table 3</label>
        <caption>
          <p>Comparison of key sensing technologies for continuous biomonitoring</p>
        </caption>
        <table frame="hsides" rules="groups">
  <tbody>
    <tr>
      <td>
        <bold>Criteria</bold>
      </td>
      <td>
        <bold>Enzymatic sensors</bold>
      </td>
      <td>
        <bold>Antibody-based immunosensors</bold>
      </td>
      <td>
        <bold>E-AB sensors</bold>
      </td>
    </tr>
    <tr>
      <td>Sensing mechanism</td>
      <td>Catalytic oxidation or reduction<sup>[<xref ref-type="bibr" rid="B162">162</xref>,<xref ref-type="bibr" rid="B203">203</xref>]</sup></td>
      <td>Affinity binding (irreversible)<sup>[<xref ref-type="bibr" rid="B185">185</xref>,<xref ref-type="bibr" rid="B203">203</xref>]</sup></td>
      <td>Binding-induced conformational change<sup>[<xref ref-type="bibr" rid="B117">117</xref>,<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
    </tr>
    <tr>
      <td>Target universality</td>
      <td>Narrow (redox-active substrates only)<sup>[<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
      <td>Broad (protein, small molecules)<sup>[<xref ref-type="bibr" rid="B203">203</xref>]</sup></td>
      <td>Broad (ions, small molecules, proteins, drugs)<sup>[<xref ref-type="bibr" rid="B117">117</xref>,<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
    </tr>
    <tr>
      <td>Reagent-free</td>
      <td>X (mediator or cofactor required)<sup>[<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
      <td>X (labeled secondary Ab often needed)<sup>[<xref ref-type="bibr" rid="B203">203</xref>]</sup></td>
      <td>O<sup>[<xref ref-type="bibr" rid="B117">117</xref>,<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
    </tr>
    <tr>
      <td>Regenerability</td>
      <td>Limited (substrate consumed)<sup>[<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
      <td>Poor (irreversible binding)<sup>[<xref ref-type="bibr" rid="B185">185</xref>,<xref ref-type="bibr" rid="B203">203</xref>]</sup></td>
      <td>Excellent (reversible binding)<sup>[<xref ref-type="bibr" rid="B117">117</xref>,<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
    </tr>
    <tr>
      <td>Continuous monitoring</td>
      <td>Until enzyme depletion<sup>[<xref ref-type="bibr" rid="B117">117</xref>,<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
      <td>X<sup>[<xref ref-type="bibr" rid="B203">203</xref>]</sup></td>
      <td>O<sup>[<xref ref-type="bibr" rid="B117">117</xref>,<xref ref-type="bibr" rid="B141">141</xref>,<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
    </tr>
    <tr>
      <td>Calibration-free potential</td>
      <td>X<sup>[<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
      <td>X<sup>[<xref ref-type="bibr" rid="B203">203</xref>]</sup></td>
      <td>O (signal-on, E-AB ratiometric) <sup>[<xref ref-type="bibr" rid="B162">162</xref>,<xref ref-type="bibr" rid="B178">178</xref>,<xref ref-type="bibr" rid="B179">179</xref>]</sup></td>
    </tr>
    <tr>
      <td>Chemical modification flexibility</td>
      <td>Low (limited enzyme engineering<sup>[<xref ref-type="bibr" rid="B162">162</xref>]</sup> </td>
      <td>Low (Ab production constraints)<sup>[<xref ref-type="bibr" rid="B162">162</xref>,<xref ref-type="bibr" rid="B203">203</xref>]</sup></td>
      <td>High (SELEX, chemical modification)<sup>[<xref ref-type="bibr" rid="B117">117</xref>,<xref ref-type="bibr" rid="B162">162</xref>]</sup></td>
    </tr>
  </tbody>
</table>
        <table-wrap-foot>
          <fn id="t3FN1">
            <p>E-AB: Electrochemical aptamer-based.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
    </sec>
  </body>
  <back>
    <sec>
      <title>DECLARATIONS</title>
      <sec>
        <title>Authors’ contributions</title>
        <p>Literature review, Manuscript conceptualization, Writing - original draft: Lee, H. B.</p>
        <p>Supervision, writing - review &amp; editing, project administration: Lee, D.; Ko, S. H.</p>
      </sec>
      <sec>
        <title>Availability of data and materials </title>
        <p>Not applicable.</p>
      </sec>
      <sec>
        <title>AI and AI-assisted tools statement</title>
        <p>During the preparation of this manuscript, the AI tool Google Gemini (version 3.0, released 2025-11-19) was used solely for language editing. The tool did not influence the study design, data collection, analysis, interpretation, or the scientific content of the work. All authors take full responsibility for the accuracy, integrity, and final content of the manuscript.</p>
      </sec>
      <sec>
        <title>Financial support and sponsorship</title>
        <p>This work was supported by National Research Foundation of Korea (grant number RS-2025-11092968, RS-2023-00216821, RS-2022-NR068144).</p>
      </sec>
      <sec>
        <title>Conflicts of interest</title>
        <p>Ko, S. H. is an Editorial Board Member of the journal <italic>Soft Science</italic> but is not involved in any steps of editorial processing, notably including reviewer selection, manuscript handling, or decision-making. The other authors declare that there are no conflicts of interest.</p>
      </sec>
      <sec>
        <title>Ethical approval and consent to participate</title>
        <p>Not applicable.</p>
      </sec>
      <sec>
        <title>Consent for publication</title>
        <p>Not applicable.</p>
      </sec>
      <sec>
        <title>Copyright</title>
        <p>© The Author(s) 2026.</p>
      </sec>
    </sec>
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