Biosensors and Bioelectronics最新文献

Prostaglandin E2 (PGE2), an eicosane, regulates the physiological activity of inflammatory cells and represents a potential therapeutic target for facilitating tissue repair in vivo. In our work, an electrochemical immunosensor employing Ketjen black-Au nanoparticles (KB–Au) and poly tannic acid nanospheres conjugated with anti-PGE2 polyclonal antibody (PTAN-Ab) was designed to ultra-sensitively analyze PGE2 levels secreted by living cells and tissues. Antibody assembly strategies were explored to achieve signal amplification. Moreover, we studied the therapy effects of docosahexaenoic acid (DHA), arachidonic acid (AA), hyaluronic acid (HA), and small molecule 15-hydroxyprostaglandin dehydrogenase inhibitor (SW033291) on inflammation and evaluated the protective functions of HA and SW033291 in a murine model subjected to colitis induced by dextran sulfate sodium (DSS) using the developed sensor. The sensor exhibited a linear range of 10⁻⁵–10⁶ fg/mL and a detection limit (LOD) of 10⁻⁵ fg/mL. Fetal bovine serum (FBS) samples were used to achieve high recovery of target analytes. This study not only presents an effective strategy for ultra-sensitively monitoring PGE2 but also provides valuable insights into assessing the degree of inflammation and the therapeutic effect of related drugs. Research on human health monitoring and regenerative medicine could greatly benefit from the findings.

In the food industry, sulfides are commonly used as preservatives and flavor regulators. However, long-term excessive intake of sulfides can lead to serious health problems. Therefore, developing efficient sulfide detection methods is particularly important. Here, we have effectively synthesized a novel bifunctional copper hydroxide nitrate (Cu₂(OH)₃NO₃) nanozyme with outstanding peroxidase-like and laccase-like behaviors in basic deep eutectic solvents (DES). Because the various types of sulfides have diverse regulatory effects on the two catalytic behaviors of Cu₂(OH)₃NO₃, a two channel nanozyme sensor array based on the peroxidase-like and laccase-like behaviors of Cu₂(OH)₃NO₃ was constructed and successfully used for the identification of six kinds of sulfides (Na₂S, Na₂S₂O₃, Na₂SO₃, Na₂SO₄, NaHSO₃, and Na₂S₂O₈). Remarkably, the sensor array has achieved successful discrimination among six sulfides present in wine, egg, and milk samples. Finally, the sensor array has successfully distinguished and differentiated three actual samples (wine, egg, and milk). This study is of great significance in promoting the efficient construction of array units and improving the effective identification of sulfides in complex food samples.

Most multiplexed photoelectrochemical (PEC) sensors require additional instrumentation and cumbersome electrode modification and surface partitioning, which limits their portability and instrument miniaturization. Herein, a pH-responsive programmable triple DNA nanomachine was developed for constructing a reconfigurable multiplex PEC sensing platform. By programming the base sequence, T-A·T-riched triple DNA was designed to construct integrated nano-controlled release machine (INCRM) for simultaneous recognition of multiple targets. The INCRM enables to recognize two targets in one step, and sequentially separate the signal labels by pH adjustment. The detached signal label catalyzes glucose to produce gluconic acid, causing the C-riched DNA fold into a triple structure on the electrode surface. As a result, one target can be detected relying on the enhanced photocurrent due to accelerated electron transfer between the CdS QD labeled at the end of C-riched DNA and the electrode. The triplex DNA dissociation in pH 7.4 buffer reconfigures the electrode interface, which can be continued to detect another target. The feasibility of the multiplexed sensor is verified by the detection of extensively coexisting antibiotics enrofloxacin (ENR) and ciprofloxacin (CIP). Under the optimal conditions, wide linear range (10 fg/mL ∼ 1 μg/mL) and low detection limit (3.27 fg/mL and 9.60 fg/mL) were obtained. The pH-regulated programmable triplex DNA nanomachine-based sensing platform overcomes the technical difficulties of conventional multiplexed PEC assay, which may open the way for miniaturization of multiplexed PEC sensors.

The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases.

Research has shown that plants have the ability to detect environmental changes and generate electrical signals in response. These electrical signals can regulate the physiological state of plants and produce corresponding feedback. This suggests that plants have the potential to be used as biosensors for monitoring environmental information. However, there are current challenges in linking environmental information with plant electrical signals, especially in collecting and classifying the corresponding electrical signals under soil moisture gradients. This study documented the electrical signals of clivia under different soil moisture gradients and created a dataset for classifying electrical signals. Subsequently, we proposed a lightweight convolutional neural network (CNN) model (PlantNet) for classifying the electrical signal dataset. Compared to traditional CNN models, our model achieved optimal classification performance with the lowest computational resource consumption. The model achieved an accuracy of 99.26%, precision of 99.31%, recall of 92.26%, F1-score of 99.21%, with 0.17M parameters, a size of 7.17MB, and 14.66M FLOPs. Therefore, this research provides scientific evidence for the future development of plants as biosensors for detecting soil moisture, and offers insight into developing plants as biosensors for detecting signals such as ozone, PM2.5, Volatile Organic Compounds(VOCs), and more. These studies are expected to drive the development of environmental monitoring technology and provide new pathways for better understanding the interaction between plants and the environment.

Neurotransmitters (NTs) are molecules produced by neurons that act as the body's chemical messengers. Their abnormal levels in the human system have been associated with many disorders and neurodegenerative diseases, which makes the monitoring of NTs fundamentally important. Specifically for clinical analysis and understanding of brain behavior, simultaneous detection of NTs at low levels quickly and reliably is imperative for disease prevention and early diagnosis. However, the methods currently employed are usually invasive or inappropriate for multiple NTs detection. Herein, we developed a MXene-based impedimetric electronic tongue (e-tongue) for sensitive NT monitoring, using Nb₂C, Nb₄C₃, Mo₂C, and Mo₂Ti₂C₃ MXenes as sensing units of the e-tongue, and Principal Component Analysis (PCA) as the data treatment method. The high specific surface area, distinct electrical properties, and chemical stability of the MXenes gave rise to high sensitivity and good reproducibility of the sensor array toward NT detection. Specifically, the e-tongue detected and differentiated multiple NTs (acetylcholine, dopamine, glycine, glutamate, histamine, and tyrosine) at concentrations as low as 1 nmol L⁻¹ and quantified NTs present in a mixture. Besides, analyses performed with interferents and actual samples confirmed the system's potential to be used in clinical diagnostics. The results demonstrate that the MXene-based e-tongue is a suitable, rapid, and simple method for NT monitoring with high accuracy and sensitivity.

To enhance the precision and reliability of early disease detection, especially in malignancies, an exhaustive investigation of multi-target biomarkers is essential. In this study, an advanced integrated electrochemical biosensor array that demonstrates exceptional performance was constructed. This biosensor was developed through a controllable porous-size mechanism and in-situ modification of carbon nanotubes (CNTs) to quantify multiplex biomarkers—specifically, C-reaction protein (CRP), carbohydrate antigen 125 (CA125), and carcinoembryonic antigen (CEA)—in human serum plasma. The fabrication process involved creating a highly ordered three-dimensional inverse-opal structure with the CNTs (pCNTs) modifier through microdroplet-based microfluidics, confined spatial self-assembly of nanoparticles, and chemical wet-etching. This innovative approach allowed for direct in-situ modification of nanomaterial onto the surface of electrode array, eliminating secondary transfer and providing exceptional control over structure and stability. The outstanding electrochemical performance was achieved through the synergistic effect of the pCNTs nanomaterial, aptamer, and horseradish peroxidase-labeled (HRP-) antibody. Additionally, the integrated biosensor array platform comprised multiple individually addressable electrode units (n = 11), enabling simultaneous multi-parallel/target testing, thereby ensuring accuracy and high throughput. Crucially, this integrated biosensor array accurately quantified multiplex biomarkers in human serum, yielding results comparable to commercial methods. This integrated technology holds promise for point-of-care testing (POCT) in early disease diagnosis and biological analysis.