Recent advances of NFATc1 in rheumatoid arthritis-related bone destruction: mechanisms and potential therapeutic targets.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease characterized by inflammation of the synovial tissue and joint bone destruction, often leading to significant disability. The main pathological manifestation of joint deformity in RA patients is bone destruction, which occurs due to the differentiation and proliferation of osteoclasts. The transcription factor nuclear factor-activated T cell 1 (NFATc1) plays a crucial role in this process. The regulation of NFATc1 in osteoclast differentiation is influenced by three main factors. Firstly, NFATc1 is activated through the upstream nuclear factor kappa-B ligand (RANKL)/RANK signaling pathway. Secondly, the Ca2+-related co-stimulatory signaling pathway amplifies NFATc1 activity. Finally, negative regulation of NFATc1 occurs through the action of cytokines such as B-cell Lymphoma 6 (Bcl-6), interferon regulatory factor 8 (IRF8), MAF basic leucine zipper transcription factor B (MafB), and LIM homeobox 2 (Lhx2). These three phases collectively govern NFATc1 transcription and subsequently affect the expression of downstream target genes including TRAF6 and NF-κB. Ultimately, this intricate regulatory network mediates osteoclast differentiation, fusion, and the degradation of both organic and inorganic components of the bone matrix. This review provides a comprehensive summary of recent advances in understanding the mechanism of NFATc1 in the context of RA-related bone destruction and discusses potential therapeutic agents that target NFATc1, with the aim of offering valuable insights for future research in the field of RA. To assess their potential as therapeutic agents for RA, we conducted a drug-like analysis of potential drugs with precise structures.

Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed computational miniature mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

Rapid analysis of the chemical constituents in Qiangli Dingxuan tablets using ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry.

Qiangli Dingxuan (QLDX) tablet is a widely recognized traditional Chinese medicine formula that has been extensively used in China for decades to treat vertigo, tinnitus, and dizziness owing to its outstanding therapeutic outcomes. However, the complexity of the chemical components in this tablet makes it challenging to separate and identify these components. This study presented an effective and sensitive strategy for the rapid separation and simultaneous structural identification of QLDX tablet components using ultra-performance liquid chromatography with quadrupole time-of-flight tandem mass spectrometry and the UNIFI platform. Based on retention times, accurate masses, fragment ions, related literature, and authentic standards, 119 compounds were identified or tentatively characterized; these included 9 iridoids, 12 lignans, 21 phenylpropanoids, 27 flavonoids, 7 phthalides, and 43 others. Among them, 36 were confirmed using reference standards. The representative compounds with various chemical structures were studied by analyzing their fragmentation patterns and characteristic ions. In conclusion, this study established a rapid approach for characterizing the chemical constituents in QLDX tablet. The proposed approach provides a basis for qualitative analysis and quality control in the manufacturing process and is beneficial for advancing investigations into the efficacy and mechanism of action of this tablet.

Effects of vitamin C on the pharmacokinetics and pharmacodynamics of nimodipine in rats.

In recent years, researchers have shown a growing interest in the interactions between different pharmaceutical agents. An intriguing instance lies in the possible interaction between nimodipine and vitamin C. To investigate the pharmacokinetic and pharmacodynamic effects of vitamin C on nimodipine in rats, rats were randomly divided into a nimodipine only group and a combination group (nimodipine + vitamin C). The two groups were given intragastric administration and nimodipine blood concentrations were determined using high-performance liquid chromatography-tandem mass spectrum at different time points. Blood pressure and heart rate were measured via carotid artery cannulation. Pharmacokinetic differences were observed between the nimodipine only group and the combination group at the same dose. Compared with the nimodipine only group, the combination group's main pharmacokinetic parameters of peak concentration and area under the curve increased significantly, and the difference was statistically significant (p < 0.05); furthermore, the combination group exhibited a significant reduction in average blood pressure, while no significant effects on heart rate were observed. Vitamin C did not affect the activity of CYP450 in rat liver. The pharmacokinetic characteristics and pharmacodynamics of nimodipine were changed by vitamin C administration in rats.

Silicon nutrition improves the quality and yield of rice under dry cultivation.

BACKGROUND: Dry cultivation of rice is a water-saving, emission reduction and labor-saving rice farming method. However, the development of rice under dry cultivation is hampered by the limitations of dry cultivation on rice yield and rice quality. We hypothesized that additional silicon (Si) would be a measure to address these limitations or challenges. RESULTS: In the present study, we set up field trials with three treatments: flooded cultivation (W), dry cultivation (D) and dry cultivation plus Si. Yield and quality were reduced under D treatment compared to W treatment. The addition of Si promoted root development, increased plant height and leaf area, increased photosynthetic enzyme activity, net photosynthetic rate and SPAD values, and increased biomass under dry crop conditions. Under the drought conditions, silica up-regulated the expression of AGPSI, SBEI, SBEIIb, SSI and SSII-1 genes and the activities of ADP-glucose pyrophosphorylase (AGPase), soluble starch synthetase (SSS) and starch branching enzyme (SBE) enzymes, which reduced protein, amylose, chalkiness percentage and chalkiness degree, increased brown rice rate, milled rice rate and head milled rice rate, and also improved rice quality. In addition, the increase of AGPase, SSS and SBE enzyme activities promoted the filling rate and the number of spikes was guaranteed, whereas the yield was improved by promoting the seed setting rate and 1000-grain weight. CONCLUSION: The results of the present study indicate that adding appropriate amounts of Si fertilizer can improve the yield and quality of rice under dry cultivation by regulating source supply capacity and grain starch synthesis. © 2023 Society of Chemical Industry.

Fourier ptychographic topography.

Topography measurement is essential for surface characterization, semiconductor metrology, and inspection applications. To date, performing high-throughput and accurate topography remains challenging due to the trade-off between field-of-view (FOV) and spatial resolution. Here we demonstrate a novel topography technique based on the reflection-mode Fourier ptychographic microscopy, termed Fourier ptychograhpic topography (FPT). We show that FPT provides both a wide FOV and high resolution, and achieves nanoscale height reconstruction accuracy. Our FPT prototype is based on a custom-built computational microscope consisting of programmable brightfield and darkfield LED arrays. The topography reconstruction is performed by a sequential Gauss-Newton-based Fourier ptychographic phase retrieval algorithm augmented with total variation regularization. We achieve a synthetic numerical aperture (NA) of 0.84 and a diffraction-limited resolution of 750 nm, increasing the native objective NA (0.28) by 3×, across a 1.2 × 1.2 mm2 FOV. We experimentally demonstrate the FPT on a variety of reflective samples with different patterned structures. The reconstructed resolution is validated on both amplitude and phase resolution test features. The accuracy of the reconstructed surface profile is benchmarked against high-resolution optical profilometry measurements. In addition, we show that the FPT provides robust surface profile reconstructions even on complex patterns with fine features that cannot be reliably measured by the standard optical profilometer. The spatial and temporal noise of our FPT system is characterized to be 0.529 nm and 0.027 nm, respectively.

The application of machine learning to air pollution research: A bibliometric analysis.

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

Optical spatial filtering with plasmonic directional image sensors.

Photonics provides a promising approach for image processing by spatial filtering, with the advantage of faster speeds and lower power consumption compared to electronic digital solutions. However, traditional optical spatial filters suffer from bulky form factors that limit their portability. Here we present a new approach based on pixel arrays of plasmonic directional image sensors, designed to selectively detect light incident along a small, geometrically tunable set of directions. The resulting imaging systems can function as optical spatial filters without any external filtering elements, leading to extreme size miniaturization. Furthermore, they offer the distinct capability to perform multiple filtering operations at the same time, through the use of sensor arrays partitioned into blocks of adjacent pixels with different angular responses. To establish the image processing capabilities of these devices, we present a rigorous theoretical model of their filter transfer function under both coherent and incoherent illumination. Next, we use the measured angle-resolved responsivity of prototype devices to demonstrate two examples of relevant functionalities: (1) the visualization of otherwise invisible phase objects and (2) spatial differentiation with incoherent light. These results are significant for a multitude of imaging applications ranging from microscopy in biomedicine to object recognition for computer vision.

Dissolution thermodynamics and preferential solvation of genistein in some (ethanol + water) mixtures at different temperatures.

The solubility of genistein was measured in the binary system of ethanol and water at temperatures ranging from 288.2 to 328.2 K. The obtained data were correlated with the modified Apelblat model, Yalkowsky model, λh model, CNIBS/R-K model, Jouyban-Acree-van't Hoff model, and modified Wilson model and their prediction accuracy was evaluated by calculating the mean relative deviation. The thermodynamic functions, Gibbs energy, enthalpy, and entropy of solution were determined using van't Hoff equation. Moreover, the preferential solvation was analyzed by using the solubility data at 298.2 K. The solubility of genistein in the system increased with an increase in temperature and mole fraction of ethanol in the solvent mixtures. The values for solubility of genistein are ranging from 0.47 obtained in neat water at T = 288.2 K to 5.02 obtained in absolute ethanol at T = 328.2 K. The values of ΔsolnG,0 ΔsolnH0 and ΔsolnH0 for the dissolution of genistein in mixtures are positive, whereas the values of ΔsolnH0 in neat water and absolute ethanol are negative. The thermodynamic properties of dissolution suggest that the dissolution process is non-spontaneous and endergonic. The modified Apelblat model can provide more accurate predictive solubility of genistein in the water and ethanol mixtures, whereas Yalkowsky model calculates solubility of genistein with large deviations. Genistein is preferentially solvated by water in water-rich mixtures (0 < x2 < 0.24) but preferential solvation by ethanol in the region of 0.24 < x2 < 1. Overall, this work could be applied for designing and optimizing the extraction, purification, and crystallization process of genistein.

PM2.5 and water-soluble inorganic ion concentrations decreased faster in urban than rural areas in China.

We investigated variations of PM2.5 and water-soluble inorganic ions chemical characteristics at nine urban and rural sites in China using ground-based observations. From 2015 to 2019, mean PM2.5 concentration across all sites decreased by 41.9 µg/m3 with a decline of 46% at urban sites and 28% at rural sites, where secondary inorganic aerosol (SIAs) contributed to 21% (urban sites) and 17% (rural sites) of the decreased PM2.5. SIAs concentrations underwent a decline at urban locations, while sulfate (SO42-), nitrate (NO3-), and ammonium (NH4+) decreased by 49.5%, 31.3% and 31.6%, respectively. However, only SO42- decreased at rural sites, NO3- increased by 21% and NH4+ decreased slightly. Those changes contributed to an overall SIAs increase in 2019. Higher molar ratios of NO3- to SO42- and NH4+ to SO42- were observed at urban sites than rural sites, being highest in the heavily polluted days. Mean molar ratios of NH3/NHx were higher in 2019 than 2015 at both urban and rural sites, implying increasing NHx remained as free NH3. Our observations indicated a slower transition from sulfate-driven to nitrate-driven aerosol pollution and less efficient control of NOx than SO2 related aerosol formation in rural regions than urban regions. Moreover, the common factor at urban and rural sites appears to be a combination of lower SO42- levels and an increasing fraction of NO3- to PM2.5 under NH4+-rich conditions. Our findings imply that synchronous reduction in NOx and NH3 emissions especially rural areas would be effective to mitigate NO3--driven aerosol pollution.

Machine learning-based prediction of survival prognosis in cervical cancer.

BACKGROUND: Accurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model. RESULTS: The expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally clustered into four groups with three different 5-years survival outcome (≥ 90%, ≈ 65%, ≤ 40%) by K-means clustering algorithm base on top 10 survival-related miRNAs. According to the K-means clustering result, a prediction model with high performance was established. The pathways analysis indicated that the miRNAs used play roles involved in the regulation of cancer stem cells. CONCLUSION: A miRNAs-based machine learning cervical cancer survival prediction model was developed that robustly stratifies cervical cancer patients into high survival rate (5-years survival rate ≥ 90%), moderate survival rate (5-years survival rate ≈ 65%), and low survival rate (5-years survival rate ≤ 40%).

High-energy micrometre-scale pixel direct conversion X-ray detector.

The objective of this work was to fabricate and characterize a new X-ray imaging detector with micrometre-scale pixel dimensions (7.8 µm) and high detection efficiency for hard X-ray energies above 20 keV. A key technology component consists of a monolithic hybrid detector built by direct deposition of an amorphous selenium film on a custom designed CMOS readout integrated circuit. Characterization was carried out at the synchrotron beamline 1-BM-B at the Advanced Photon Source of Argonne National Laboratory. The direct conversion detector demonstrated micrometre-scale spatial resolution with a 63 keV modulation transfer function of 10% at Nyquist frequency. In addition, spatial resolving power down to 8 µm was determined by imaging a transmission bar target at 21 keV. X-ray signal linearity, responsivity and lag were also characterized in the same energy range. Finally, phase contrast edge enhancement was observed in a phase object placed in the beam path. This amorphous selenium/CMOS detector technology can address gaps in commercially available X-ray detectors which limit their usefulness for existing synchrotron applications at energies greater than 50 keV; for example, phase contrast tomography and high-resolution imaging of nanoscale lattice distortions in bulk crystalline materials using Bragg coherent diffraction imaging. The technology will also facilitate the creation of novel synchrotron imaging applications for X-ray energies at or above 20 keV.

Displacement-agnostic coherent imaging through scatter with an interpretable deep neural network.

Coherent imaging through scatter is a challenging task. Both model-based and data-driven approaches have been explored to solve the inverse scattering problem. In our previous work, we have shown that a deep learning approach can make high-quality and highly generalizable predictions through unseen diffusers. Here, we propose a new deep neural network model that is agnostic to a broader class of perturbations including scatterer change, displacements, and system defocus up to 10× depth of field. In addition, we develop a new analysis framework for interpreting the mechanism of our deep learning model and visualizing its generalizability based on an unsupervised dimension reduction technique. We show that our model can unmix the scattering-specific information and extract the object-specific information and achieve generalization under different scattering conditions. Our work paves the way to a robust and interpretable deep learning approach to imaging through scattering media.

Theoretical and Experimental Analysis of Dynamic Characteristics for a Valve Train System.

The valve train is one of the main sources of engine vibration, and its dynamic performance is crucial for output power and fuel consumption. The flexibilities of slender bars and beams should be emphasised in the design of valve trains to develop high-power and high-speed engines with industrial applications. A flexible dynamic model of a valve train system is proposed. In the proposed model, the components, except the cam and gear bodies, are modelled as flexible bodies with multidirectional deformations. The gyroscopic effects of the camshaft, cams and gear discs are also considered to predict dynamic responses at high speeds accurately. Gear meshing, the friction of the cam-tappet pair, the centrifugal force of the cams and valve clearance are also considered. Experiments on housing vibration and pushrod stress are conducted to validate the proposed model. Results show that the proposed model can predict the dynamic stress of the flexible components well and predict the trend shown by the housing vibration. The proposed model shows that excessive cam rotation speed and valve clearance will cause intense bounce and jump phenomena. The proposed model can be an important reference for designing engine work speed, adjusting valve clearance and improving component durability.

Diffuser-based computational imaging funduscope.

Poor access to eye care is a major global challenge that could be ameliorated by low-cost, portable, and easy-to-use diagnostic technologies. Diffuser-based imaging has the potential to enable inexpensive, compact optical systems that can reconstruct a focused image of an object over a range of defocus errors. Here, we present a diffuser-based computational funduscope that reconstructs important clinical features of a model eye. Compared to existing diffuser-imager architectures, our system features an infinite-conjugate design by relaying the ocular lens onto the diffuser. This offers shift-invariance across a wide field-of-view (FOV) and an invariant magnification across an extended depth range. Experimentally, we demonstrate fundus image reconstruction over a 33° FOV and robustness to ±4D refractive error using a constant point-spread-function. Combined with diffuser-based wavefront sensing, this technology could enable combined ocular aberrometry and funduscopic screening through a single diffuser sensor.

Development of a beam propagation method to simulate the point spread function degradation in scattering media.

Scattering is one of the main issues that limit the imaging depth in deep tissue optical imaging. To characterize the role of scattering, we have developed a forward model based on the beam propagation method and established the link between the macroscopic optical properties of the media and the statistical parameters of the phase masks applied to the wavefront. Using this model, we have analyzed the degradation of the point-spread function of the illumination beam in the transition regime from ballistic to diffusive light transport. Our method provides a wave-optic simulation toolkit to analyze the effects of scattering on image quality degradation in scanning microscopy. Our open-source implementation is available at https://github.com/BUNPC/Beam-Propagation-Method.

Enzyme Mimic Based on a Self-Assembled Chitosan/DNA Hybrid Exhibits Superior Activity and Tolerance.

Nature has evolved enzymes with exquisite active sites that catalyze biotransformations with high efficiency. However, the exploitation of natural enzymes is often hampered by poor stability, and natural enzyme production and purification are costly. Supramolecular self-assembly allows the construction of biomimetic active sites, although it is challenging to produce such artificial enzymes with catalytic activity and stability that rival those of natural enzymes. We report herein a strategy to produce a horseradish peroxidase (HRP) mimic based on the assembly of chitosan with a G-quadruplex DNA (G-DNA)/hemin complex. A network-like morphology of the assembled nanomaterial was observed together with a remarkable enhancement of peroxidase activity induced by the chitosan and G-DNA components. The turnover frequency and catalytic efficiency of the enzyme-mimicking material reached or even surpassed those of HRP. Moreover, the catalytic complex exhibited higher tolerance than HRP to harsh environments, such as extremely low pH or high temperatures. In accord with the experimental and simulated results, it is concluded that the spatial distribution of the G-DNA and chitosan components and the exposure of the catalytic center may facilitate the coordination of substrates by the hemin iron, leading to the superior activity of the material. Our work provides a simple and affordable avenue to produce highly active and robust enzyme-mimicking catalytic nanomaterials.

Deep learning approach for Fourier ptychography microscopy.

Convolutional neural networks (CNNs) have gained tremendous success in solving complex inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct video sequences of dynamic live cells captured using a computational microscopy technique, Fourier ptychographic microscopy (FPM). The unique feature of the FPM is its capability to reconstruct images with both wide field-of-view (FOV) and high resolution, i.e. a large space-bandwidth-product (SBP), by taking a series of low resolution intensity images. For live cell imaging, a single FPM frame contains thousands of cell samples with different morphological features. Our idea is to fully exploit the statistical information provided by these large spatial ensembles so as to make predictions in a sequential measurement, without using any additional temporal dataset. Specifically, we show that it is possible to reconstruct high-SBP dynamic cell videos by a CNN trained only on the first FPM dataset captured at the beginning of a time-series experiment. Our CNN approach reconstructs a 12800×10800 pixel phase image using only ∼25 seconds, a 50× speedup compared to the model-based FPM algorithm. In addition, the CNN further reduces the required number of images in each time frame by ∼ 6×. Overall, this significantly improves the imaging throughput by reducing both the acquisition and computational times. The proposed CNN is based on the conditional generative adversarial network (cGAN) framework. We further propose a mixed loss function that combines the standard image domain loss and a weighted Fourier domain loss, which leads to improved reconstruction of the high frequency information. Additionally, we also exploit transfer learning so that our pre-trained CNN can be further optimized to image other cell types. Our technique demonstrates a promising deep learning approach to continuously monitor large live-cell populations over an extended time and gather useful spatial and temporal information with sub-cellular resolution.

Temporal-spatial characteristics and sources of heavy metals in bulk deposition across China.

With the rapid development of industries, agriculture, and urbanization (including transportation and population growth), there has been a significant alteration in the emission and atmospheric deposition of heavy metal pollutants. This has consequently given rise to a range of ecological and environmental health issues. In this study, we conducted a comprehensive two-year investigation on the temporal and spatial distribution characteristics of heavy metals in atmospheric deposition across China based on the Nationwide Nitrogen Deposition Monitoring Network (NNDMN). The atmospheric bulk deposition of Lead (Pb), Arsenic (As), Nickel (Ni), Selenium (Se), Chromium (Cr) and Cadmium (Cd) were 6.32 ± 1.59, 4.49 ± 0.57, 1.31 ± 0.21, 1.05 ± 0.16, 0.60 ± 0.06 and 0.21 ± 0.03 mg m-2 yr-1, respectively, with a large variation among the different regions of China. The order for atmospheric deposition flux was Southwest China > Southeast China > North China > Northeast China > Qinghai-Tibet Plateau and rural area > urban area > background area. The concentrations of heavy metals in bulk deposition exhibit seasonal variation with higher levels observed during winter compared to summer and spring, which are closely associated with anthropogenic activities. The Positive Matrix Factorization (PMF) results indicated that combustion, industrial emissions and traffic are the primary contributors to atmospheric deposition of heavy metals. The single factor pollution index (Pi) of heavy metals is consistently below 1, and the composite pollution index (Ni) is 0.16 across China, indicating that atmospheric heavy metal deposition is at a pollution-free level. The comprehensive potential ecological risk index of heavy metals is 11.8, with Cd exhibiting the highest single factor potential ecological risk index at 7.09, suggesting that more attention should be paid to Cd deposition in China. The present study reveals the spatial-temporal distribution pattern of atmospheric heavy metals deposition in China, identifying regional source characteristics and providing a theoretical foundation and strategies for reducing emissions of atmospheric pollutants.

Development of double-layer poly (amino acid) modified electrochemical sensor for sensitive and direct detection of betamethasone in cosmetics.

Screening for illegal use of glucocorticoids (GCs) in cosmetics by electrochemical methods is extremely challenging due to the poor electrochemical activity of GCs. In this study, poly-L-Serine/poly-Taurine modified electrode (P(Tau)/P(L-Ser)/GCE) was prepared for sensitive and direct determination of betamethasone in cosmetics by a simple two-step in situ electropolymerization reaction. The relevant parameters of preparation and electroanalytical conditions were respectively studied, including the concentration of polymerization solution, the number of scanning circles and the scanning rate. The SEM and EDS mapping demonstrated successful preparation of P(Tau)/P(L-Ser)/GCE. The electro-catalytic properties of the obtained electrodes were investigated using cyclic voltammetry and differential pulse voltammetry methods, showing a remarkable improvement of sensitivity for the detection of betamethasone due to the synergic effect of both P(L-Ser) and P(Tau). In addition, we investigated the electrochemical reduction of betamethasone on the surface of modified electrode. It was found that the process was controlled by diffusion effect and involved the transfer of two electrons and two protons. Then the electrochemical sensor method based on P(Tau)/P(L-Ser)/GCE was established and delivered a linear response to betamethasone concentration from 0.5 to 20 µg mL-1 with a limit of detection of 32.2 ng mL-1, with excellent recoveries (98.1%-106.8%) and relative standard deviations (＜4.8%). Furthermore, the established electrochemical sensor method was compared with conventional HPLC method. The results showed that both of them were comparable. Moreover, the established electrochemical sensor method was with the merits of short analysis time, environmentally friendly, low cost and easy to achieve in-site detection.