The superstructures formed by the self-assembly of nanoparticles (NPs) can exhibit unique photonic collective properties (structural color, localized surface plasmon resonance [LSPR]), enhance the interaction between light and matter, and open up new possibilities for photonic sensing. Many photonic biosensors have addressed the limitations of current bioanalytical methods with their non-invasive nature, real-time monitoring, and high sensitivity. In recent years, the construction of photonic biosensors using super-structured materials could further enhance the sensors in terms of sensitivity, processing capacity, ease of use, and miniaturization. Superstructure-based photonic biosensors can analyze complex samples, but their development still needs to overcome limitations related to target binding specificity, long-term stability, and signal decoding efficiency. The development of artificial intelligence (AI) provides new opportunities to solve these problems. Deep learning (DL) algorithms can independently extract multi-dimensional data features such as spectra and images, distinguish weak biological signals from noise, optimize detection parameters, and achieve real-time dynamic calibration. In this review, we provide the photonic collective characteristics of superstructures and the applications of biosensors in intelligent diagnosis. The applications of superstructured photonic sensors in disease diagnosis, drug delivery, and cell imaging are summarized. The colorimetric, fluorescence-based sensor technologies assisted by DL are discussed along with challenges faced in integrating AI with superstructure-based photonic biosensors. As this field continues to evolve, the integration of AI and superstructure-based photonic biosensors will undoubtedly play a pivotal role in shaping the future of medical diagnostics and therapeutic interventions.
Biosensors inspired by biological sensory systems are valuable tools for detecting physiological and environmental stimuli with high degrees of specificity and sensitivity. An itch irritant biosensor to detect environmental changes or pruritogenic substances in human blood or tissues highly associated with inflammation and prevalent conditions like atopic dermatitis (AD) has not been developed. To address this gap, we developed a novel bioelectronic sensor by integrating the human itch receptor Mas-related G-protein-coupled receptor X2 (MRGPRX2) with a graphene field-effect transistor (GFET). This MRGPRX2-GFET biosensor covalently immobilizes functional receptors, enabling direct conversion of ligand-binding events into quantifiable electrical signals. We demonstrate that the sensor can detect known MRGPRX2 agonists with exceptional sensitivity and specificity, achieving a detection limit for SP at approximately 7 pM. Molecular dynamics (MD) simulations and mutational effects reveal that ligand binding induces cytoplasmic conformational rearrangements in MRGPRX2, strengthening receptor-graphene coupling and providing a mechanistic basis for signal transduction. Importantly, the biosensor effectively distinguishes plasma samples from AD patients and healthy controls by capturing different electrical signal responses. In our study, we establish a versatile platform for diagnosing and subtyping chronic itch disorders and offer a generalizable strategy for developing membrane receptor-based multiplexed "itch-print" biosensors.
Biosensors are pivotal for detecting foodborne and waterborne hazards due to their portability, low cost, and rapid response. However, performance often degrades in real samples, where complex matrices reduce sensitivity and specificity and increase false positives/negatives. This systematic review synthesizes recent advances in biosensor platforms for monitoring contaminants in food and water, emphasizing how matrix properties govern analytical reliability and field usability. We present a matrix-first benchmarking perspective that compares biosensor performance across low-biomass waters, high-organic wastewater, and complex food extracts (high fat/protein, high particulate load, acidic, or high-salt), and summarizes dominant interference modes (fouling, nonspecific binding, ionic-strength shifts, and optical turbidity) alongside practical mitigation workflows (dilution/filtration, cleanup extraction, antifouling coatings, and microfluidic preconcentration). Beyond bacteria and viruses, this revision integrates pesticides as a third hazard class, covering enzyme-inhibition, aptamer, immuno-, and molecularly imprinted polymer sensing strategies, with representative case studies including glyphosate/AMPA, paraquat/diquat, chlorpyrifos, and atrazine. Overall, the matrix-first framework highlights design and workflow choices most likely to translate biosensors from proof-of-concept to deployable, multi-hazard monitoring tools for food and water safety.
Arsenic contamination in food and water remains a major global health issue, particularly across South and Southeast Asia. Although advanced analytical methods such as ICP-MS and GFAAS offer high sensitivity and precision, they are costly, laboratory-bound, and require skilled operators. In contrast, electrochemical nano-biosensors have emerged as a practical and cost-effective alternative, offering low cost, rapid detection, and portability suitable for field applications. This review recent advances in developing green-engineered nanomaterials for electrochemical arsenic detection, emphasizing eco-friendly synthesis routes and environmentally responsible design principles. Green fabrication of metal and metal-oxide nanoparticles, carbon-based materials, and hybrid composites has significantly improved sensor performance by enhancing surface area, conductivity, and catalytic activity while minimizing environmental impact. Key biosensing strategies are discussed, including aptamer-, enzyme-, and whole-cell-based systems capable of distinguishing between arsenic species. The review also highlights innovations in biodegradable polymers, waste-derived carbons, and renewable substrates that align with circular-economy principles. Finally, future perspectives highlight the integration of Internet of Things (IoT) and artificial intelligence (AI) technologies for real-time, decentralized monitoring. Collectively, these green nano-biosensing systems represent a critical step toward accessible, and efficient arsenic detection for global water safety.
Nucleic acid biomarkers are critical targets for early diagnosis of disease, public health surveillance and environmental safety. However, their low abundance and the complexity of the sample matrix pose strict requirements for the high sensitivity and portability of detection technologies. Although traditional clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) systems exhibit advantages such as high specificity, operational simplicity, and compatibility with mild reaction conditions, their reliance on pre-amplification steps elevates the risk of false-positive results and hinders their broader application. To overcome these limitations, amplification-free CRISPR/Cas technologies have emerged and undergone extensive development. These approaches enable highly sensitive nucleic acid detection without the need for pre-amplification and are more amenable to integration with portable devices, thereby offering promising avenues for point-of-care testing (POCT). This review systematically examines the fundamental principles and design strategies underlying amplification-free CRISPR/Cas biosensors and summarizes recent advances in detection platforms based on autocatalytic signal enhancement, nanomaterial-coupled amplification, and integrated high-sensitivity readout systems, while also outlining their practical applications in POCT settings. Furthermore, the key technical challenges and future development directions of amplification-free CRISPR technologies are discussed based on current advances in the field. These insights and perspectives aim to provide a systematic reference for further research and to facilitate the expanded application of amplification-free CRISPR/Cas systems in POCT.
Rapid and reliable wastewater monitoring has become a global necessity as water pollution, urbanization, and industrial expansion continue to strain freshwater resources worldwide. Conventional biochemical oxygen demand (BOD) tests require five days, making them unsuitable for real-time decision-making in treatment plants. Microbial fuel cell (MFC)-based biosensors are increasingly explored as rapid, in situ alternatives to the BOD test, yet their sensitivity, response time, linear range, and robustness in real wastewater remain major challenges. This structured review synthesized peer-reviewed primary studies from 2020 to 2025, identified through structured searches of Scopus and Web of Science, using predefined eligibility criteria confined to MFC-based BOD sensing. Reported operating windows span low-level effluents (e.g., 5-100 mg L⁻1) to high-strength wastewaters (up to ~800 mg L⁻1), with limits of detection as low as ~3 mg L⁻1 depending on inoculum source; response times range from minutes in micro-MFCs to hours in bench-scale systems. Accuracy is strongly conditioned by the combined effects of temperature, pH, external resistance, cathode chemistry, inoculum/biofilm state, and substrate composition, which modulate electron-recovery efficiency relative to true BOD. Notably, several studies validate performance in municipal and industrial wastewater, while long-term operation (≥800 days) has been demonstrated under stable calibration in single-chamber formats. Collectively, this review clarifies the design-performance trade-offs and operational determinants of accuracy.
Fluorine (F) substitution in organic semiconductors has been proven effective for enhancing the performance of organic photovoltaics (OPVs), organic field-effect transistors (OFETs) and organic thermoelectrics (OTEs). However, the effect of such substitution on conjugated polymers in organic electrochemical transistors (OECTs) has not been fully elucidated. Herein, two conjugated polymers (PNDI-2T and PNDI-2TF) with a naphthalenediimide (NDI)-bithiophene (2T) backbone, featuring amphipathic side chains and either bearing or lacking fluorine (F) substitution on the bithiophene unit, were designed and synthesized to investigate their effect on organic electrochemical transistors. By combining optical spectroscopy, density functional theory calculations, cyclic voltammetry, and water contact angle analysis, we reveal that fluorine substitution on the conjugated polymer, on one hand, increases the hydrophobicity, which impedes the ion penetration, on the other hand, reduces the LUMO energy level of the polymer. As a consequence, PNDI-2TF-based OECT devices exhibited lower geometry-normalized transconductance performance but with a significantly reduced V th to 0.232 V compared to PNDI-2T-based ones (0.386 V). Atomic force microscopy and 2D grazing-incidence wide-angle X-ray scattering reveal that fluorine substitution reduces polymer crystallinity, leading to decreased electron mobility and consequently inferior OECT performance in PNDI-2TF. In addition, we fabricated complementary inverters by pairing the p-type polymer (Pg2T-TT) with the n-type polymers (PNDI-2T or PNDI-2TF), achieving maximum voltage gains of 28.3 and 22.9 V/V for PNDI-2T and PNDI-2TF, respectively, at a supply voltage of 0.7 V. Furthermore, glucose sensors employing N-type conjugated polymers (PDNI-2T or PDNI-2TF) as the active layer exhibited both comparable and remarkably high sensitivity toward glucose sensing, together with an outstanding linear response over a wide concentration range of 1 µM to 20 mM, surpassing the performance of most reported electrochemical glucose sensors. Overall, this work not only elucidates the influence of fluorination on NDI-based polymers, an insight crucial for validating the fluorine substitution strategy in developing high-performance n-type organic mixed ionic-electronic conductors, but also demonstrates the promising prospects of NDI-based polymers in biological applications.
A LexA-mediated biosensor tailored for space applications was developed leveraging the desert cyanobacterium Chroococcidiopsis sp. CCMEE 029 engineered with a transcriptional fusion between an SOS-responsive promoter and a luciferase gene reporter. The SOS DNA repair system was investigated by bioinformatics analysis that identified a lexA gene in the genome CCMEE 029 encoding a protein sharing key structural features with other cyanobacterial homologs. Consensus LexA-binding motifs upstream genes involved in DNA-damage repair, photosynthesis, and oxidative-stress defense were computationally identified and the DNA binding of CCMEE 029' LexA was confirmed by molecular modeling and molecular dynamics simulations. The expression of lexA and recA after treatment for 30 min with 10 mM H2O2 was evaluated by RT-qPCR. Then Chroococcidiopsis sp. CCMEE 029 was transformed with a plasmid carrying a transcriptional fusion between the upstream region of the recA gene containing the LexA-binding motif and a firefly luciferase gene. The biosensor responsiveness was tested in response to DNA-damaging agents, after desiccation as well as under simulated microgravity conditions combined with γ-rays. Upon the addition of the luciferin substrate, Chroococcidiopsis transformants exposed to DNA-damaging agents (H2O2, ultra-violet C (UVC), and γ-ray irradiation) emitted a bioluminescent signal. A correlation was detected between increased UVC doses and the onset of detectable DNA damage. The biosensor responsiveness was confirmed under simulated microgravity conditions combined with γ-rays used as a proxy of the space environment. Notably, the biosensor responsiveness was retained after 4.5 months of air-dried storage as demonstrated by signal emission after rehydration and UVC and γ-ray irradiation. Although a more global stress management role for LexA remains to be investigated in Chroococcidiopsis sp. CCMEE 029. The tested biosensor's feature supports a future integration of the air-dried biosensor into satellites and its in-orbit reactivation for real-time monitoring of the effects of space conditions, thus advancing future space exploration.
Genetically encoded fluorescent biosensors frequently use Förster resonance energy transfer (FRET) between donor and acceptor fluorescent proteins (FPs) as readouts for monitoring molecular activities in live cells. Here, we present a protocol for determining FRET efficiency through the simultaneous imaging of multiple FRET biosensors alongside donor and acceptor FPs using spectrally orthogonal fluorescent cell barcodes. We describe steps for culturing and transfecting cells, acquiring and analyzing images, and calculating FRET efficiency. This approach facilitates simultaneous analysis of multiple FRET biosensors. For complete details on the use and execution of this protocol, please refer to Wu et al.1.
Food products are highly vulnerable to fungal contamination throughout storage, transportation, and processing, leading to the accumulation of mycotoxins that pose significant risks to food safety and public health. Therefore, there is an urgent need to develop sensitive, rapid, and reliable detection tools. Visual detection enables real-time monitoring of mycotoxin contamination using miniaturized equipment, offering advantages such as simple operation, user-friendliness, fast detection speed, and high sensitivity. As a result, it has gained increasing favor among researchers and practitioners across various fields. This review summarizes recent advances in visual detection methods for mycotoxins, including colorimetric, fluorescent, electrochemiluminescent, and photoelectrochromic visualization approaches. For each category, the design principles, representative applications, key advantages, and inherent limitations are systematically analyzed. Finally, the key scientific and technological challenges currently facing this field are critically examined, and the emerging opportunities arising from the integration of visual biosensing with big data analytics and machine learning are highlighted. This review is intended to serve as a valuable reference for the development of novel visual biosensors for mycotoxin detection and for the broader application of next-generation visual biosensors in on-site mycotoxin monitoring.
Nano-biosensors represent innovative analytical devices that couple nanotechnology with biomolecular recognition elements to detect specific targets with high sensitivity and selectivity. By incorporating nanomaterials such as gold, carbon, or metal oxides, these devices exhibit enhanced conductivity, larger surface areas, and improved electron transfer, thereby enabling rapid and accurate detection even at ultralow concentrations. Electrochemical nano-biosensors are particularly advantageous for medical diagnostics, health monitoring, and environmental applications because they convert bioreceptor-analyte interactions into measurable real-time electrical signals. Due to their ability to monitor key biomarkers, including neurotransmitters, these sensors hold immense promise for early disease diagnosis, therapeutic monitoring, and neurological research. This review highlights current trends in the development of nanomaterial-enhanced electrochemical sensors for neurotransmitter detection, focuses on their performance and clinical translation potential, and outlines future directions to address challenges in selectivity, stability, and large-scale manufacturing.
Lung cancer is a highly prevalent and lethal malignant tumor worldwide, accounting for approximately 25% of all cancer deaths. Early symptoms are often subtle, leading to late-stage diagnosis in the majority of patients and a missed window for optimal treatment. Therefore, more sensitive diagnostic approaches are urgently required to improve survival rates and reduce the disease burden. Compared with traditional imaging and tissue biopsy, novel biosensors have become important tools for lung cancer diagnosis due to their high sensitivity, non-invasiveness and rapid detection abilities. Aptamers, a class of single-stranded DNA/RNA molecules capable of specific target recognition, are ideal for the development of efficient lung cancer biosensors owing to their low synthesis cost, high stability, minimal immunogenicity, and ease of modification. This review focuses on the application of aptamers in lung cancer detection, summarizes current developments in aptamer-based sensors (aptasensors), and critically compares the analytical performance, clinical limitations and translational obstacles of five mainstream aptasensor platforms, then discusses their challenges and future perspectives, aiming to promote more accurate and efficient early-diagnostic techniques for lung cancer.
Hormones and metabolites jointly regulate physiology yet their dynamic interplay remains difficult to resolve due to the lack of technologies for continuous, multiplexed monitoring. Microneedle biosensors that access dermal interstitial fluid offer a minimally invasive approach to molecular profiling, but deployment is limited by trade-offs between fabrication scalability and precision, and by mechanical instability under skin deformation. Here we present a strain-isolated microneedle platform fabricated by combining two-photon polymerization with ultrasound-assisted moulding, enabling scalable replication of submicrometre-sharp microneedles (∼430-nm apex width) bearing hierarchical, high-surface-area microstructures. Nanostructured gold and platinum electrodes enable high-fidelity sensing at the single-needle level, providing a 4.7-fold higher peak current and a 9-fold larger electrochemical surface area, respectively. Mechanical decoupling of the sensing interface from tissue deformation preserves stable molecular access during motion. When integrated with battery-free wireless electronics and multiplexed aptameric and enzymatic sensors, the system enables continuous in vivo monitoring of serotonin and glucose for 12 h. In freely moving rats, the platform captures biomolecular dynamics associated with stress, feeding and circadian rhythms. This work establishes a mechanically robust, scalable microneedle biointerface for ambulatory molecular monitoring and context-aware assessment of physiological state.
Metal-organic frameworks (MOFs) are at the forefront of materials research due to their high area-to-volume ratio, tunable porosity, host-guest adsorption capabilities, and improved optical, fluorescent, conductive, and electromagnetic properties. Van der Waals heterostructures (VWHs) are 2D materials, including MOFs and graphene oxide (GO), among others, stacked like building blocks defined with one-atomic-plane precision by weak, reversible interactions that synergistically integrate their features to create new structures with enhanced properties. For example, MOF-based architectures enable the adsorption of numerous small molecules, such as electroactive probes and biomolecules, which can be linked to GO to form VWHs and stably coupled to an electrode surface, thereby enhancing the electrode's response and making them promising for the development of highly sensitive biosensors. In this work, VWHs were assembled by stacking Ni3(HITP2) MOF and GO, which were deposited on screen-printed carbon electrodes (SPCE) by drop-casting and characterized by electrochemistry, spectroscopy, and microscopy. They showed better conductive properties than bare and modified electrodes with the corresponding materials separately, thus offering a highly conductive surface area that permits the adsorption of toluidine blue O (TB), an in-situ electrochemical probe, and multiple anchoring points of procalcitonin capture antibodies. As a result, a label- and reagent-free nanoimmunosensor platform was developed for the detection of procalcitonin in one step, showing high sensitivity (0.099 μA*mL/pg) and high specificity over a linear range from 4.25 to 125.00 pg/mL, with a limit of detection (LOD) of 4.25 pg/mL by square wave voltammetry (SWV). The nanoimmunosensor performance was tested in spiked human serum, with recovery greater than 90%, and in real samples, demonstrating high discrimination between positive and negative samples and strong correlation with a gold standard method. Procalcitonin levels, in the context of a panel of infection biomarkers, provide information not only about diagnosis but also prognosis, infection severity, and sepsis risk.
Quorum sensing (QS) orchestrates virulence, biofilm maturation, and antimicrobial tolerance across clinically dominant pathogens, driving chronic infections and therapeutic failure. Although quorum-sensing inhibitors (QSIs) were developed to attenuate pathogenic coordination without bactericidal pressure, their clinical translation has been constrained by biochemical instability, narrow receptor specificity, limited pharmacokinetic robustness, and emerging adaptive resistance. Molecularly imprinted polymers (MIPs) provide a mechanistically distinct strategy based on structurally defined recognition cavities capable of physically sequestering or catalytically degrading autoinducers with measurable thermodynamic parameters, including the imprinting factor (IF), dissociation constant (K d), and binding capacity. This review critically synthesizes advances in molecularly imprinted polymer design for QS detection and modulation, emphasizing the role of monomer-template complementarity, cross-link density, porogen environment, polymerization strategy, and template removal in governing recognition fidelity and biological performance. Computational modeling has improved monomer selection and prepolymerization complex prediction, yet translational reliability requires integration of solvent dynamics, cross-linker effects, and matrix competition under physiologically relevant conditions. Compared with conventional biosensors and small-molecule QSIs, MIPs demonstrate nanomolar detection limits, resilience in complex media, and up to 80% biofilm inhibition through signal sequestration. Early in vivo studies further support their potential to attenuate the QS-dependent virulence. Despite these advances, barriers remain, including monomer cytotoxicity, nonspecific adsorption in biological fluids, incomplete biodegradation profiling, and the need for standardized in vivo validation frameworks. With rational engineering and regulatory alignment, MIPs represent a programmable materials platform for communication-based infection control, expanding the antivirulence paradigm beyond receptor antagonism toward structurally resilient quorum interception.
Continuous monitoring of chronic diseases with mobile health tools is rapidly advancing to improve patient care. Wearable sensors placed on or near the skin remain the dominant paradigm for collecting physiological data noninvasively during activities of daily living. However, wearable sensors have their limitations. Epidermal sensors are fundamentally limited by the information barrier that the skin presents. Furthermore, environmental factors impact the stability and longevity of wearables. An alternative technology is the expanding class of minimally invasive subdermal sensors, also called insertables. Key applications for subdermal sensors are reviewed, including three current clinical devices: insertable cardiac monitors, insertable continuous glucose monitors, and sub-scalp epilepsy monitors. Next, common challenges impacting performance are discussed, including device migration, device encapsulation, and misalignment with wirelessly linked wearables. Finally, recent technological advancements to mitigate these challenges are covered. With state-of-the-art approaches for achieving biocompatibility, remote powering, and wireless data transmission, the next generation of subdermal biosensors can be developed for unmet needs in chronic disease monitoring.
Most clustered regularly interspaced short palindromic repeats (CRISPR)-based electrochemical biosensors lack controllable switching function and are vulnerable to false signals induced by intrinsic amplification signal crosstalk. Thus, exploring strategies that mitigate intrinsic amplification crosstalk with a precise switch would be extremely useful for reliable and accurate bioanalysis. Herein, we develop a photo-switchable CRISPR/Cas12a electrochemical (PSCE) system by introducing a photocleavable (PC) linker and adopting a light-responsive strategy, which enables output of de-interference electrochemical signals and achieves highly sensitive and accurate detection of gene mutations for non-small cell lung cancer (NSCLC). The system decouples nucleic acid amplification (NAA) from signal transduction and produces unique photo-switchable response signals by light irradiation to activate Cas12a activity. This architectural design further separates signal readout from sample pretreatment, suppressing intrinsic amplification-derived signal crosstalk rather than global biological interference to deliver an ultralow limit of detection (LoD). The PSCE achieves 98.1% sensitivity, 92.7% specificity, and 98.9% overall accuracy when tested with 67 clinical samples. Moreover, the exploration of PSCE system in flexible wearable electronics and machine-learning analysis of clinical patient samples demonstrates significant application potential clinical diagnosis of mutation-associated diseases.
Arboviruses remain a major global public health concern, particularly Dengue (DENV), Zika (ZIKV), and Chikungunya (CHIKV), whose co-circulation and overlapping symptoms lead to challenging diagnosis. Due to their sensitivity and compatibility with diverse biorecognition elements, electrochemical biosensors have become attractive tools for viral detection. Hybrid materials play a central role in biosensing by integrating conductivity, high surface area, and chemical functionality at a single interface. Lectin-carbohydrate interactions enable selective targeting of glycan motifs on viral surface proteins, providing an alternative route for virus identification. Here, we report a lectin-based impedimetric biosensor for differential electrochemical sensing of DENV-2, DENV-4, ZIKV, and CHIKV. The sensing interface consisted of an electropolymerized polypyrrole (PPy) film combined with 4-mercaptobenzoic acid-functionalized gold nanoparticles (AuNPs@MBA) for Concanavalin A (ConA) immobilization. Atomic force microscopy, cyclic voltammetry, and electrochemical impedance spectroscopy confirmed the successful assembly and electrochemical performance of the hybrid interface. The PPy-AuNPs@MBA platform enhanced electron transfer while providing a suitable surface for lectin immobilization. Distinct impedance responses were observed for the investigated arboviruses, consistent with differences in glycan presentation at the viral surface. The biosensor exhibited limits of detection of 0.578, 1.023, 0.620, and 0.761 PFU.mL⁻¹ for DENV-2, ZIKV, CHIKV, and DENV-4, respectively. These findings demonstrate the potential of the proposed hybrid platform as a lectin-based strategy for differential arbovirus sensing and support its further investigation as a complementary analytical tool for viral screening.
Bacterial infections remain a significant threat to public health worldwide, driving an urgent need for rapid, accurate, and field-deployable diagnostic techniques. Point-of-care testing (POCT) has emerged as a transformative strategy, providing timely detection, operational simplicity, and portability. Recent studies have aimed at enhancing sensitivity, specificity, multiplexing capability, and automation through the integration of molecular diagnostics with microfluidics and lab-on-chip technologies, alongside the development of low-cost, portable devices equipped with smartphone-based readout and cloud connectivity for real-time surveillance in resource-limited settings. Nonetheless, evidence-based frameworks for selecting optimal detection targets-such as genomic sequences, conserved protein epitopes, or viable whole cells-and matching them to appropriate POCT modalities remain notably underrepresented in the literature. This review systematically summarizes recent advances in POCT strategies for bacterial detection, categorized according to three major types of detection targets, including cellular phenotypic characteristics, surface antigens, and nucleic acids. We discuss the principles, advantages, limitations, and representative applications of key POCT platforms, which include microscopy-based visualization, immunoassays, isothermal amplification, clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein (Cas) systems, and microfluidic biosensors. Critical challenges, such as sample pretreatment, detection sensitivity, and operational simplicity, have been partially addressed through recent innovations. Finally, we outline the main future research directions focused on the development of integrated, automated, and intelligent POCT systems for clinical deployment. 细菌感染已成为全球性重大公共卫生威胁，亟需发展快速、准确、可现场化的诊断技术。即时检测（POCT）技术凭借其检测及时、操作简便和便携等优势，已成为应对细菌感染的变革性技术。POCT通过将分子诊断与微流控芯片技术相结合，提升了细菌检测的灵敏度、特异性、多重检测能力和自动化水平;同时，伴随着低成本、便携式检测装置不断涌现，以及智能手机读取信号与云端传输数据等技术的融合，POCT为在资源受限的场景下实时监测病原菌提供了可行性。然而，如何选择最优检测靶标（例如，基因组序列、保守蛋白表位或存活细胞）并构建与之匹配的POCT技术，相关综述尚不充分。本综述系统总结了用于细菌检测的POCT策略的最新进展，并依据三类主要检测目标（细胞表型特征、表面抗原和核酸）进行分类;评述了可视化显微镜成像技术、免疫检测方法、等温核酸扩增技术、成簇规律间隔短回文重复序列及其关联蛋白系统（CRISPR-Cas）系统以及微流控生物传感器等POCT平台的检测原理、优势、局限性及代表性生物医学应用，展示了POCT技术在样品前处理、检测灵敏度和操作简便性等关键环节上取得的阶段性突破;最后提出了当前POCT技术面向临床实际需求所面临的挑战，并展望了未来聚焦于构建集成化、自动化、智能化POCT系统的发展趋势。.
Plant growth, stress adaptation, and productivity depend on the continuous exchange of information between hormone signaling and metabolic networks. Although major phytohormones have been widely studied through their biosynthesis, receptors, and downstream transcriptional modules, these pathways are still often presented as linear systems. This view does not fully explain how plants integrate carbon status, nutrient availability, redox balance, cellular energy, and developmental signals under changing environments. In this review, we present plant hormone signaling and metabolism as a reciprocal regulatory network in which hormones reshape metabolic flux, while metabolites feedback to control hormone biosynthesis, transport, perception, degradation, and signal output. We synthesized evidence across salicylic acid, gibberellins, auxin, abscisic acid, strigolactones, ethylene, jasmonates, cytokinins, brassinosteroids, and melatonin, showing that sugars, amino acids, organic acids, lipid derivatives, sulfur metabolites, and redox signals act as shared regulatory nodes. This integrated perspective reveals that core metabolic signals such as sucrose, trehalose-6-phosphate, 2-oxoglutarate, citrate, malate, reactive oxygen species, glutathione, ascorbate, cysteine, and tryptophan coordinate multiple hormone pathways across tissues, developmental stages, and stress contexts. We further highlight emerging tools, including hormone atlases, live biosensors, spatial omics, single-cell omics, isotope tracing, genome editing, and computational network modeling, as essential approaches for moving from descriptive pathway maps to predictive systems biology. By defining hormone metabolism crosstalk as a central principle in plant biochemistry and physiology, this review provides a conceptual framework for identifying engineering targets that can improve crop resilience, nutrient use efficiency, and growth stress balance under future agricultural conditions.