Biosensors and Bioelectronics最新文献

Squamous cell carcinoma antigen (SCCA) is one of the most commonly detected cancer biomarkers for a variety of cancers. In this paper, a microfiber ring laser biosensor with a graphene oxide linking layer for SCCA detection was proposed and experimentally demonstrated. SCCA antibody immobilized on graphene oxide surface binds specifically to SCCA, and induces refractive index variation over the surface of the microfiber biosensor, which leads to a wavelength shift of the microfiber ring laser biosensor. The experimental results show that the proposed laser biosensor can detect SCCA with concentrations from 0.01 to 50 ng/mL, and the calculated detection limit can be as low as 1.3 pg/mL. Additionally, the label-free quantitative detection of SCCA using the proposed microfiber biosensor was verified experimentally according to the corresponding regression equation, and the results agree well with clinical examination detection. This constructed microfiber biosensor may have promising practical applications in analytical detection, medical diagnostics, etc.

Cerebrospinal fluid (CSF)-based pathogen or biochemical testing is the standard approach for clinical diagnosis of various meningitis. However, misdiagnosis and missed diagnosis always occur due to the shortages of unusual clinical manifestations and time-consuming shortcomings, low sensitivity, and poor specificity. Here, for the first time, we propose a simple and reliable CSF-induced SERS platform assisted with machine learning (ML) for the diagnosis and identification of various meningitis. Stable and reproducible SERS spectra are obtained within 30 s by simply mixing the colloidal silver nanoparticles (Ag NPs) with CSF sample, and the relative standard deviation of signal intensity is achieved as low as 2.1%. In contrast to conventional salt agglomeration agent-induced irreversible aggregation for achieving Raman enhancement, a homogeneous and dispersed colloidal solution is observed within 1 h for the mixture of Ag NPs/CSF (containing 110–140 mM chloride), contributing to excellent SERS stability and reproducibility. In addition, the interaction processes and potential enhancement mechanisms of different Ag colloids-based SERS detection induced by CSF sample or conventional NaCl agglomeration agents are studied in detail through in-situ UV–vis absorption spectra, SERS analysis, SEM and optical imaging. Finally, an ML-assisted meningitis classification model is established based on the spectral feature fusion of characteristic peaks and baseline. By using an optimized KNN algorithm, the classification accuracy of autoimmune encephalitis, novel cryptococcal meningitis, viral meningitis, or tuberculous meningitis could be reached 99%, while an accuracy value of 68.74% is achieved for baseline-corrected spectral data. The CSF-induced SERS detection has the potential to provide a new type of liquid biopsy approach in the fields of diagnosis and early detection of various cerebral ailments.

Cardiac organoids differentiated from induced pluripotent stem cells are emerging as a promising platform for pre-clinical drug screening, assessing cardiotoxicity, and disease modelling. However, it is challenging to simultaneously measure mechanical contractile forces and electrophysiological signals of cardiac organoids in real-time and in-situ with the existing methods. Here, we present a biting-inspired sensory system based on a resistive skin sensor and a microelectrode array. The bite-like contact can be established with a micromanipulator to precisely position the resistive skin sensor on the top of the cardiac organoid while the 3D microneedle electrode array probes from underneath. Such reliable contact is key to achieving simultaneous electro-mechanical measurements. We demonstrate the use of our system for modelling cardiotoxicity with the anti-cancer drug doxorubicin. The electro-mechanical parameters described here elucidate the acute cardiotoxic effects induced by doxorubicin. This integrated electro-mechanical system enables a suite of new diagnostic options for assessing cardiac organoids and tissues.

Hydrogel sensors have broad application prospects in human motion monitoring and sweat composition detection. However, hydrogel-based sensors are faced with challenges such as low accuracy and poor mechanical properties of analytes detection. Based on mussel-inspired chemistry, we synthesized mesoporous silica@polydopamine-Au (MPS@PDA-Au) nanomaterials and designed a self-healing nanocomposite hydrogel to monitor human movement and ascorbic acid detection in sweat. Mesoporous silica (MPS) possess orderly mesoporous structure. Dopamine (DA) polymerized on the surface of MPS to generate polydopamine (PDA), forming the composite material MPS@PDA-Au. This composite was then embedded into polyvinyl alcohol (PVA) hydrogels through a simple freeze-thaw cycle process. The hydrogels have achieved excellent deformable ability (508.6%), self-healing property (90.5%) and mechanical strength (2.9 MPa). The PVA/MPS@PDA-Au hydrogel sensors had the characteristics of fast response time (123.2 ms), wide strain sensing range (0–500%), excellent fatigue resistance and stability in human detection. The detection range of ascorbic acid (AA) in sweat was wide (8.0 μmol/L-100.0 μmol/L) and the detection limit was low (3.3 μmol/L). Therefore, these hydrogel sensors have outstanding application prospects in human motion monitoring and sweat composition detection.

A major bottleneck in the development of wearable ion-selective sensors is the inherent conditioning and calibration procedures at the user's end due to the signal's instability and non-uniformity. To address this challenge, we developed a strategy that integrates three interdependent materials and device engineering approaches to realize a Ready-to-use Wearable ElectroAnalytical Reporting system (r-WEAR) for reliable electrolytes monitoring. The strategy collectively utilized (1) finely-configured diffusion-limiting polymers to stabilize the electromotive force in the electrodes, (2) a uniform electrical induction in electrochemical cells to normalize the open-circuit potential (OCP), and (3) an electrical shunt to maintain the OCP across the entire sensor in the r-WEAR. The approaches jointly enable fabrication of homogeneously stable and uniform ion-selective sensors, eliminating common conditioning and calibration practices. As a result, the r-WEAR demonstrated a signal's variation down to ±1.99 mV with a signal drift of 0.5 % per hour (0.12 mV h⁻¹) during a 12-h continuous measurement of 10 sensors and a signal drift as low as 13.3 μV h⁻¹ during storage. On-body evaluations of the r-WEAR for four days without conditioning and re-/calibration further validated the sensor's performance in realistic settings, indicating its remarkable potential for practical usage in a user operation-free manner in wearable healthcare applications.

Nitroreductase (NTR) is widely regarded as a biomarker whose enzymatic activity correlates with the degree of hypoxia in solid malignant tumors. Herein, we utilized 2-dimethylamino-7-hydroxynaphthalene as fluorophore linked diverse nitroaromatic groups to obtain four NTR-activatable two-photon fluorescent probes based on covalent assembly strategy. With the help of computer docking simulation and in vitro assay, the sulfonate-based probe XN3 was proved to be able to identify NTR activity with best performances in rapid response, outstanding specificity, and sensitivity in comparison with the other three probes. Furthermore, XN3 could detect the degree of hypoxia by monitoring NTR activity in kinds of cancer cells with remarkable signal-to-noise ratios. In cancer tissue sections of the breast and liver in mice, XN3 had the ability to differentiate between healthy and tumorous tissues, and possessed excellent fluorescence stability, high tissue penetration and low tissue autofluorescence. Finally, XN3 was successfully utilized for in situ visualizing NTR activities in human transverse colon and rectal cancer tissues, respectively. The findings suggested that XN3 could directly identify the boundary between cancer and normal tissues by monitoring NTR activities, which provides a new method for imaging diagnosis and intraoperative navigation of tumor tissue.

Plant signaling molecules can be divided into plant messenger signaling molecules (such as calcium ions, hydrogen peroxide, Nitric oxide) and plant hormone signaling molecules (such as auxin (mainly indole-3-acetic acid or IAA), salicylic acid, abscisic acid, cytokinin, jasmonic acid or methyl jasmonate, gibberellins, brassinosteroids, strigolactone, and ethylene), which play crucial roles in regulating plant growth and development, and response to the environment. Due to the important roles of the plant signaling molecules in the plants, many methods were developed to detect them. The development of in-situ and real-time detection of plant signaling molecules and field-deployable sensors will be a key breakthrough for botanical research and agricultural technology. Electrochemical methods provide convenient methods for in-situ and real-time detection of plant signaling molecules in plants because of their easy operation, high sensitivity, and high selectivity. This article comprehensively reviews the research on electrochemical detection of plant signaling molecules reported in the past decade, which summarizes the various types electrodes of electrochemical sensors and the applications of multiple nanomaterials to enhance electrode detection selectivity and sensitivity. This review also provides examples to introduce the current research trends in electrochemical detection, and highlights the applicability and innovation of electrochemical sensors such as miniaturization, non-invasive, long-term stability, integration, automation, and intelligence in the future. In all, the electrochemical sensors can realize in-situ, real-time and intelligent acquisition of dynamic changes in plant signaling molecules in plants, which is of great significance for promoting basic research in botany and the development of intelligent agriculture.

Ovarian carcinoma (OvCa) poses a severe threat to women's health due to its high mortality rate and lack of efficient early diagnosis approach. There is evidence to suggest that nanosized small extracellular vesicles (sEVs) which carrying cell-specific components from OvCa can serve as potential diagnostic biomarkers. Herein, we reported a Surface-enhanced Raman Scattering (SERS)-multichannel microchip for sEVs (S-MMEV) assay to investigate the phenotype changes of sEVs. The microchip composed of seven microchannels, which enabled the parallel detection of multiple biomarkers to improve the detection accuracy. Using SERS probes conjugated with antibodies recognizing different biomarkers including ubiquitous EV biomarkers (i.e., tetraspanins; CD9, CD81) and putative OvCa tumor biomarkers (i.e. EpCAM, CD24, CA125, EGFR), we successfully analyzed the phenotypic changes of sEVs and accurately differentiated OvCa patients from healthy controls, even at early stage (I-II), with high sensitivity, high specificity and an area under the curve value of 0.9467. Additionally, the proposed approach exhibited higher sensitivity than conventional methods, demonstrating the efficiency of precise detection from cell culture and clinical samples. Collectively, the developed EV phenotyping approach S-MMEV could serve as a potential tool to achieve the early clinical diagnosis of OvCa for further precise diagnosis and personal treatment monitoring.

In response to the pervasive issue of herbicide pollution in environmental water bodies, particularly from herbicides used extensively in agriculture, traditional chemical-based water quality analysis methods have proven costly and time-consuming, often failing to meet regulatory standards. To overcome these limitations, global environmental agencies have turned to rapidly-growing species like duckweed as bioindicators for herbicide and pesticide contamination. However, conventional biological assessment methods, such as the 168-h duckweed growth inhibition test, are slow and lack real-time monitoring capabilities. To address this challenge, we developed an innovative approach by integrating opto-mechanical technology with duckweed to create a cost-effective biosensor for herbicide detection, priced under $10 USD per system. This advancement allows for the rapid detection of herbicide impacts on duckweed growth within just 48 h, significantly improving upon traditional methods. Our biosensor achieves detection limits of 10 ppm (p < 0.05) for glyphosate and 1 ppm (p < 0.05) for glufosinate, both prominent herbicides globally. This mini-biosensing platform offers a practical alternative to the official method, which requires 168 h and higher thresholds (36.4 ppm for glyphosate and 34.0 ppm for glufosinate) for routine environmental analysis. Thus, these duckweed-based optical biosensors represent a promising advancement in environmental monitoring, enhancing accessibility and efficacy for widespread adoption globally.

Pectobacterium carotovorum subsp. carotovorum (PCC) is a notorious plant pathogen responsible for severe soft rot in kimchi cabbage, which results in significant economic losses. To detect PCC rapidly and accurately in kimchi cabbage, we developed a surface-enhanced Raman scattering (SERS) substrate on which silver nanospheres (AgNSs), nanowires (AgNWs), and nanoseeds are combined on a polydimethylsiloxane (PDMS) platform. The incorporation of Ag nanoseeds creates a higher density of hotspots, which ensures a low detection limit of 1.001 CFU/mL. Electron microscopy and spectroscopic analyses confirmed the successful fabrication of the substrate and its enhanced sensitivity. The SERS substrate exhibits excellent selectivity by effectively distinguishing PCC from other bacteria commonly found in kimchi cabbage. The substrate gives rise to strong Raman signals across PCC concentrations ranging from 10¹ to 10⁶ CFU/mL. Additionally, a predictive model was developed for accurately detecting PCC in real kimchi cabbage samples, and the results were validated by polymerase chain reaction measurements. A sensitive, selective, and rapid approach for PCC detection in kimchi cabbage that offers a promising improvement over existing methodologies is presented.