Electrochemical Detection of Myoglobin Using an Ultrasensitive Label-free Sensor Derived from Cubic-ZIF67@Au-rGOF-NH2 Composite of MOF and GOF.

Markers of myocardial injury, such as myoglobin (Mb), are substances swiftly released into the peripheral bloodstream upon myocardial cell injury or altered cardiac activity. During the onset of acute myocardial infarction, patients experience a significant surge in serum Mb levels. Given this, precise detection of Mb is essential, necessitating the development of innovative assays to optimize detection capabilities. This study introduces the synthesis of a three-dimensional hierarchical nanocomposite, Cubic-ZIF67@Au-rGOF-NH2, utilizing aminated reduced graphene oxide and zeolite imidazolium ester framework-67 (ZIF67) as foundational structures. Notably, this novel material, applied in a label-free electrochemical immunosensor, presents a groundbreaking approach for detecting myocardial injury markers. Experimental outcomes revealed ZIF67 and AuNPs exhibit enhanced affinity and growth on the 3D-rGOF-NH2 matrix, thus amplifying electrical conductivity while preserving the inherent electrochemical attributes of ZIF67. As a result, the Cubic-ZIF67@Au-rGOF-NH2 label-free electrochemical immunosensor exhibited a broad detection range and high sensitivity for Mb. The derived standard curve was ΔIp=16.67552lgC+275.245 (R=0.993) with a detection threshold of 3.47 fg/ml. Moreover, recoveries of standards spiked into samples ranged between 96.3% to 108.7%. Importantly, the devised immunosensor retained notable selectivity against non-target proteins, proving its potential clinical utility based on exemplary sample analysis performance.

Ultrasensitive and label-free electrochemical immunosensor for the detection of the ovarian cancer biomarker CA125 based on CuCo-ONSs@AuNPs nanocomposites.

Cancer antigen 125 (CA125) is pivotal as a tumor marker in early ovarian cancer prevention and diagnosis. In this work, we introduced an ultrasensitive label-free electrochemical immunosensor tailored for CA125 detection, leveraging nanogold-functionalized copper-cobalt oxide nanosheets (CuCo-ONSs@AuNPs) as nanocomposites. For the inaugural application, copper-cobalt oxide nanosheets delivered the requisite DPV electrochemical response for the immunosensors. Their large specific surface area and commendable electrical conductivity amplify electron transfer and enable significant gold nanoparticle loading. Concurrently, AuNPs offer a plethora of active sites, facilitating easy immobilization of biomolecules via the bond between amino groups and AuNPs. We employed scanning electron microscopy, transmission electron microscopy, and x-ray photoelectron spectroscopy to characterize the nanomaterials' surface morphology and elemental composition. The electrochemical sensor response signals were ascertained using differential pulse voltammetry. Under optimal conditions, the immunosensor exhibited a linear detection range from 1×10-7 U/mL to 1×10-3 U/mL and a detection limit of 3.9×10-8 U/mL (S/N=3). The proposed label-free electrochemical immunosensor furnishes a straightforward, dependable, and sensitive approach for CA125 quantification and stands as a promising method for clinical detection of other tumor markers.

Sandwich-type immunosensor based on aminated 3D-rGOF-NH2 and CMK-3-Fc-MgAl-LDH multilayer nanocomposites for detection of CA125.

Cancer antigen 125 (CA125)1 is the most important biological screening indicator used to monitor epithelial ovarian cancers, and it plays a vital role in distinguishing ovarian cancers from benign diseases. Biosensors show great potential in the analysis and detection of disease markers. In this study, we designed electrochemical sensors based on three-dimensional amino-functionalized reduced graphene oxide (3D-rGOF-NH2),2 MgAl layered double hydroxide nanocomposites containing ordered mesoporous carbon (CMK-3),3 and ferrocene carboxylic acids（Fc-COOH）4for the detection of CA125. 3D-rGOF-NH2 possesses good conductivity, a large surface area, and high porosity, enabling more immobilized nanoparticles to be deposited on its surface with excellent stability. CMK-3@Fc@MgAl-LDH nanocomposite was used as a carrier to enhance the immobilization of antibodies and the loading of Fc, conductors to enhance conductivity, and enhancers to gradually amplify the signal of Fc. The surface morphology, elemental composition, and surface groups of the materials were characterized using scanning electron microscopy (SEM),5 transmission electron microscopy (TEM),6 and X-ray diffraction (XRD)7 techniques. The response signal of the electrochemical sensor was measured by DPV. Under the optimal conditions, the electrochemical sensor obtained a linear detection range of 0.01 U/mL-100 U/mL with a detection limit of 0.00417 U/mL.

Semi-supervised enhanced discriminative local constraint preserving projection for dimensionality reduction of medical hyperspectral images.

Microscopic hyperspectral images has the advantage of containing rich spatial and spectral information. However, the large number of spectral bands provides a significant amount of spectral features, but also leads to data redundancy and noise, which seriously affect the recognition and classification performance of the images, as well as increasing the requirements for computation and storage. To address this issue, we propose a dimensionality reduction algorithm named enhanced discriminant local constraint preserving projection (EDLCPP). Specifically, the global spectral attention mechanism focuses on important bands, the high discriminability sample selection module measures the discriminability of samples using a modified average neighborhood margin, the graph construction module preserves the local geometric relationship and discriminant information, and the graph embedding module embeds the constructed graphs into a low-dimensional space to obtain the projection matrices. Experimental results on eight cholangiocarcinoma (CCA) hyperspectral images, Bloodcell1-3, and Bloodcell2-2 datasets have demonstrated the effectiveness of the proposed method.

Few-shot remote sensing scene classification based on multi subband deep feature fusion.

Recently, convolutional neural networks (CNNs) have performed well in object classification and object recognition. However, due to the particularity of geographic data, the labeled samples are seriously insufficient, which limits the practical application of CNN methods in remote sensing (RS) image processing. To address the problem of small sample RS image classification, a discrete wavelet-based multi-level deep feature fusion method is proposed. First, the deep features are extracted from the RS images using pre-trained deep CNNs and discrete wavelet transform (DWT) methods. Next, a modified discriminant correlation analysis (DCA) approach is proposed to distinguish easily confused categories effectively, which is based on the distance coefficient of between-class. The proposed approach can effectively integrate the deep feature information of various frequency bands. Thereby, the proposed method obtains the low-dimensional features with good discrimination, which is demonstrated through experiments on four benchmark datasets. Compared with several state-of-the-art methods, the proposed method achieves outstanding performance under limited training samples, especially one or two training samples per class.

Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space.

BACKGROUND AND OBJECTIVES: Compared with traditional RGB images, medical hyperspectral imagery (HSI) has numerous continuous narrow spectral bands, which can provide rich information for cancer diagnosis. However, the abundant spectral bands also contain a large amount of redundancy information and increase computational complexity. Thus, dimensionality reduction (DR) is essential in HSI analysis. All vector-based DR methods ignore the cubic nature of HSI resulting from vectorization. To overcome the disadvantage of vector-based DR methods, tensor-based techniques have been developed by employing multi-linear algebra. METHODS: To fully exploit the structure features of medical HSI and enhance computational efficiency, a novel method called unsupervised dimensionality reduction via tensor-based low-rank collaborative graph embedding (TLCGE) is proposed. TLCGE introduces entropy rate superpixel (ERS) segmentation algorithm to generate superpixels. Then, a low-rank collaborative graph weight matrix is constructed on each superpixel, greatly improving the efficiency and robustness of the proposed method. After that, TLCGE reduces dimensions in tensor space to well preserve intrinsic structure of HSI. RESULTS: The proposed TLCGE is tested on cholangiocarcinoma microscopic hyperspectral data sets. To further demonstrate the effectiveness of the proposed algorithm, other machine learning DR methods are used for comparison. Experimental results on cholangiocarcinoma microscopic hyperspectral data sets validate the effectiveness of the proposed TLCGE. CONCLUSIONS: The proposed TLCGE is a tensor-based DR method, which can maintain the intrinsic 3-D data structure of medical HSI. By imposing the low-rank and sparse constraints on the objective function, the proposed TLCGE can fully explore the local and global structures within each superpixel. The computational efficiency of the proposed TLCGE is better than other tensor-based DR methods, which can be used as a preprocessing step in real medical HSI classification or segmentation.

A Simple Method for the Measurement of Young's Moduli of Bilayer Thin Films Based on the Electrostatic Drive Approach.

This paper presents a simple method for the in situ determination of Young's moduli of surface-micromachined bilayer thin films. The test structure consists of a cantilever, a bottom drive electrode located near the anchor, and a bottom contact electrode placed below the free end of the cantilever. The cantilever is driven by applying a voltage sweep between the cantilever and the drive electrode, and bends due to the electrostatic force. A novel theoretical model is derived to relate Young's modulus with the applied voltage and structure dimensions. The theoretical model is validated by finite element simulation. Test structures for Au/polysilicon thin films are fabricated by the PolyMUMPsand tested with the current-voltage measurement system. The measured Young modulus of polysilicon ranges from 152.344 GPa to 154.752 GPa, and the measured Young modulus of Au ranges from 71.794 GPa to 74.880 GPa. Compared with existing extraction methods, the proposed method is featured with simple operation, good repeatability, relatively high precision, and low requirements for equipment. It can be used alongside the application of a process control monitor (PCM) in surface-micromachining process lines.

A Novel Electrochemical Immunosensor Based on COF-LZU1 as Precursor to Form Heteroatom-Doped Carbon Nanosphere for CA19-9 Detection.

Porous carbon sphere materials have a large variety of applications in several fields due to the large surface area, adaptable porosity, and good conductivity they possess. Obtaining a steady carbon sphere using the green synthesis method remains a significant challenge. In this experiment, covalent organic frameworks (COFs) were used as a precursor and Fe3O4NPs were integrated into the precursor in order to synthesize a porous carbon sphere material using the one-step pyrolysis method. COFs have an ordered porous structure, perpetual porosity, large surface area, and low density and display good environmental tolerance. These properties make them an excellent precursor for synthesizing porous carbon sphere, which maintains good morphology at high temperatures, and it is not involved in the removal of dangerous reagent and small size restrictions during the synthesis process. In addition to the formation of a porous carbon sphere, transition metal carbon material that contains N element can be an active catalyst. The composites exhibit better activity when Fe is doped into carbon materials containing N element than that of other doped transition metals including Mn and Co. In this situation, the integration of Fe3O4NPs and N element in the COF precursor exposed the active sites of the composites and the two substances synergistically improved the electrocatalytic properties, and the composites were named Fe3O4@NPCS. The constructed Fe3O4@NPCS/GCE immunosensor was applied as a means of detecting CA19-9 antigen and presented a wide linear range from 0.00001 to 10 U/mL with a low detection limit of 2.429 µU/mL (S/N = 3). In addition, the prepared immunosensor was utilized for detecting CA19-9 antigen in the real human serum, and the recovery rates were in the range from 95.24% to 106.38%. Therefore, a porous carbon sphere prepared by COFs as a precursor can be applied for the detection of CA19-9 antigen in real samples, which could be an excellent strategy for CA19-9 antigen detection and could potentially promote the development of COF materials in various electrochemical fields.

A 3D-2D Multibranch Feature Fusion and Dense Attention Network for Hyperspectral Image Classification.

In recent years, hyperspectral image classification (HSI) has attracted considerable attention. Various methods based on convolution neural networks have achieved outstanding classification results. However, most of them exited the defects of underutilization of spectral-spatial features, redundant information, and convergence difficulty. To address these problems, a novel 3D-2D multibranch feature fusion and dense attention network are proposed for HSI classification. Specifically, the 3D multibranch feature fusion module integrates multiple receptive fields in spatial and spectral dimensions to obtain shallow features. Then, a 2D densely connected attention module consists of densely connected layers and spatial-channel attention block. The former is used to alleviate the gradient vanishing and enhance the feature reuse during the training process. The latter emphasizes meaningful features and suppresses the interfering information along the two principal dimensions: channel and spatial axes. The experimental results on four benchmark hyperspectral images datasets demonstrate that the model can effectively improve the classification performance with great robustness.

Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification.

The convolutional neural network (CNN) has been proven to have better performance in hyperspectral image (HSI) classification than traditional methods. Traditional CNN on hyperspectral image classification is used to pay more attention to spectral features and ignore spatial information. In this paper, a new HSI model called local and hybrid dilated convolution fusion network (LDFN) was proposed, which fuses the local information of details and rich spatial features by expanding the perception field. The details of our local and hybrid dilated convolution fusion network methods are as follows. First, many operations are selected, such as standard convolution, average pooling, dropout and batch normalization. Then, fusion operations of local and hybrid dilated convolution are included to extract rich spatial-spectral information. Last, different convolution layers are gathered into residual fusion networks and finally input into the softmax layer to classify. Three widely hyperspectral datasets (i.e., Salinas, Pavia University and Indian Pines) have been used in the experiments, which show that LDFN outperforms state-of-art classifiers.

An ultrasensitive disposable sandwich-configuration electrochemical immunosensor based on OMC@AuNPs composites and AuPt-MB for alpha-fetoprotein detection.

Early finding and diagnosis are critical for prevention and treatment of hepatocellular carcinoma (HCC). Alpha-fetoprotein (AFP) is a typical biomarker of HCC. Since AFP level can reflect the severity of HCC, it is essential to ensure the accurate detection of AFP. In this study, through a combination of the advantages exhibited by ordered mesoporous carbon (OMC)@gold nanoparticles (AuNPs) composites and AuPt-methylene blue (AuPt-MB), a disposable ultrasensitive sandwich-configuration electrochemical immunosensor for determination of AFP was designed. Characterized by excellent conductivity, highly ordered pore distribution and great surface area, OMC can be effective in promoting electron transfer and loading a large number of AuNPs. In the meantime, AuNPs can also immobilize AFP-Ab1 through Au-N bonds. As a new redox-active species, rod-like AuPt-MB demonstrates high conductivity, uniform morphology and excellent biocompatibility, which makes it capable not only to fix AFP-Ab2, but also to release electrochemical signals. A wide linearity of 10 fg mL-1-100 ng mL-1 and a low detection limit of 3.33 fg mL-1 (S/N = 3) were obtained. Moreover, the proposed immunosensor exhibited acceptable selectivity, high stability and reproducibility. The excellent performance in detecting serum samples endows the proposed immunosensor with broad prospects of extensive application in the detection of disease biomarkers.

A sandwich-configuration electrochemiluminescence immunoassay based on Cu2O@OMC-Ru nanocrystals and OMC-MoS2 nanocomposites for determination of alpha-fetoprotein.

A sandwich-format electrochemiluminescence (ECL) immunosensor has been developed for alpha-fetoprotein (AFP) detection based on the use of ordered mesoporous carbon-molybdenum disulfide (OMC-MoS2) as a sensor platform and cuprous oxide @ ordered mesoporous carbon-Ru(bpy)32+ (Cu2O@OMC-Ru) composites as signal tags. OMC alongside MoS2 plays a synergistic role in improving the electrochemical performance of the electrode in the electron transfer process. The uniform cubic-shaped Cu2O@OMC-Ru nanocrystals display excellent luminous efficiency, with a signal amplification strategy of OMC-MoS2 synergistic enhancement and Cu2O@OMC which is capable of immobilizing more Ru(bpy)32+ serving as a tracing tag to label antibodies. A detectable ECL emission at a Cu2O@OMC-Ru nanocrystals modified electrode is initiated at an applied voltage of +1.15 V (scanning range: 0-1.2 V), in the presence of the tripropylamine (TPA) as coreactant. With the increase in AFP concentration, the loading of Cu2O@OMC-Ru at the electrode increases. Afterward, the ECL detection of AFP shows a wide linear range from 0.1 pg/mL to 10 ng/mL with a correlation coefficient of 0.9964 and a detection limit of 0.011 pg/mL (S/N = 3) under the optimal experimental conditions. The recoveries were in the range 91.2-97.1% with RSD varying from 4.8 to 8.5%. Overall, the novel immunosensor has been successfully applied to the analysis of human serum samples, indicating a great potential for application in clinical diagnostics.

One-step electrodeposition preparation of polyaniline/f-MWCNTs as electrochemical sensors for detection of 10-hydroxycamptothecine.

In our work, one-step electro-deposition method was adopted to produce polyaniline (PANI) and functional multiwalled carbon nanotubes (f-MWCNTs) films on glass carbon electrodes, and the modified electrodes were applied as an electrochemical sensor for determination of 10-hydroxycamptothecine (10-HCPT). The f-MWCNTs were handled by ultrasound processing in concentrated oxidizing acid solution, which can obtain a wonderful dissolution in water and attach new functional groups, such as -COOH and -OH. Then, aniline monomer could polymerize on the surface easily. The surface characterization was investigated using various techniques including scanning electron microscope, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and X-ray diffraction, and electro-catalytic properties were characterized by cyclic voltammetry and electrochemical impedance spectroscopy. Under optimal conditions, the resulting of PANI/f-MWCNTs sensor showed a wide linear range (3 × 10-9 to 7 × 10-7 mol L-1 ) and a low detection limit (1 × 10-9 mol L-1 ), which is attributing to its large special surface area and good conductivity. Moreover, the modified electrodes are convenient to fabricate, which can be used to detect 10-HCPT in urine samples successfully.

Anomalous Spin-Charge Separation in a Driven Hubbard System.

Spin-charge separation (SCS) is a striking manifestation of strong correlations in low-dimensional quantum systems, whereby a fermion splits into separate spin and charge excitations that travel at different speeds. Here, we demonstrate that periodic driving enables control over SCS in a Hubbard system near half filling. In one dimension, we predict analytically an exotic regime where charge travels slower than spin and can even become "frozen," in agreement with numerical calculations. In two dimensions, the driving slows both charge and spin and leads to complex interferences between single-particle and pair-hopping processes.

Spherical carrier amplification strategy for electrochemical immunosensor based on polystyrene-gold nanorods @L-cysteine/MoS2 for determination of tacrolimus.

A novel sensitive electrochemical immunosensor has been designed for determination of tacrolimus (Tac) based on spherical carrier amplification strategy. In this work, a spherical signal carrier was prepared by conjugating gold nanorods (AuNRs) functionalized L-cysteine (AuNRs@L-Cys) onto polystyrene nanoparticles (PS) to form a nanostructure, greatly increasing the amount of loaded capture antibodies. The PS was a stable spherical functional polymer with large specific surface area and was labeled with AuNRs@L-Cys via coupling reagent to increase active groups, biocompatibility and conductivity. The capture antibodies could be attached on the PS- AuNRs@L-Cys via linkage reagent glutaraldehyde (GA). Furthermore, single-layer molybdenum disulfide (MoS2) fixed by chitosan were utilized to construct the base of this immunosensor, increasing the carrying capacity and stability of the electrode. After electrochemical reduction, the conductivity and electron transfer rate of MoS2 were obviously improved, which offered a platform for this immunosensor and further amplified the signal. Under the optimized conditions, the proposed immunosensor revealed a linear Tac-concentration behavior from 1.0 to 30 ng mL-1 with a detection limit of 0.17 ng mL-1 (S/N = 3). The immunoassay was successfully applied to the quantification of Tac in serum samples with acceptable precision. The results indicated that a potentially analytical platform may be used to recognize the concentration of other drugs.

Photoinduced Electron Pairing in a Driven Cavity.

We demonstrate how virtual scattering of laser photons inside a cavity via two-photon processes can induce controllable long-range electron interactions in two-dimensional materials. We show that laser light that is red (blue) detuned from the cavity yields attractive (repulsive) interactions whose strength is proportional to the laser intensity. Furthermore, we find that the interactions are not screened effectively except at very low frequencies. For realistic cavity parameters, laser-induced heating of the electrons by inelastic photon scattering is suppressed and coherent electron interactions dominate. When the interactions are attractive, they cause an instability in the Cooper channel at a temperature proportional to the square root of the driving intensity. Our results provide a novel route for engineering electron interactions in a wide range of two-dimensional materials including AB-stacked bilayer graphene and the conducting interface between LaAlO_{3} and SrTiO_{3}.

Electrochemical strategy with zeolitic imidazolate framework-8 and ordered mesoporous carbon for detection of xanthine.

An accurate, safe, environmentally friendly, fast and sensitive electrochemical biosensors were developed to detect xanthine in serum. The metal-organic framework ZIF-8 was synthesised and elemental gold was supported on the surface of ZIF-8 by reduction method to synthesise Ag-ZIF-8. The mesoporous carbon material and the synthesised Ag-ZIF-8 were, respectively, applied to a glassy carbon electrode to construct biosensors. The constructed biosensor has a good linear relation in the range of 1-280 µmol l-1 of xanthine and the detection limit is 0.167 µmol l-1. The relative standard deviation value in serum samples was <5%, and the recoveries were 96-106%, indicating the good selectivity, stability and reproducibility of this electrochemical biosensor.

Electrochemical immunoassay for the carcinoembryonic antigen based on Au NPs modified zeolitic imidazolate framework and ordered mesoporous carbon.

An electrochemical immunoassay for the carcinoembryonic antigen is described. It is based on the use of Au NPs modified zeolitic imidazolate framework (ZIF-8) and ordered mesoporous carbon (OMC). Au NPs@ZIF-8 was synthesized by reduction of chloroauric acid. It serves as immobilization support nanocarrier to increase antibody loading due to its large surface area. OMC was dropped on a glassy carbon electrode to improve electrochemical signals due to enhanced electrical conductivity. Differential pulse voltammetry was carried out to record electrochemical responses (best measured at 0.26 V vs. Ag/AgCl). The immunosensor demonstrated excellent electrochemical performance with a linear determination range of 5 pg mL-1 to 400 ng mL-1 and a determination limit of 1.3 pg mL-1 (S/N = 3). The sensor also exhibited high selectivity, good stability, and acceptable reproducibility. Graphical abstract Scheme 1 Schematic representation of fabrication of the immunosensor for CEA determination.

A sandwich-type ECL immunosensor based on signal amplification using a ZnO nanorods-L-cysteine-luminol nanocomposite for ultrasensitive detection of prostate specific antigen.

An ultrasensitive sandwich-type electrochemiluminescence (ECL) immunosensor was developed for detection of prostate specific antigen (PSA) using an amplification strategy based on ZnO nanorods-l-cysteine-luminol nanocomposites and the biotin-streptavidin system. The biotin-streptavidin system served as a capture probe to increase the number of antibodies. ZnO nanorods not only acted as a nanocarrier that increased the number of luminol molecules and secondary antibodies, but also enhanced the ECL signal of luminol-H2O2 system by promoting H2O2 decomposition, which can further increase ECL intensity. Under optimized conditions, the proposed immunosensor demonstrated excellent analytical performance with a wide linear detection range of 0.03 pg mL-1 to 30 ng mL-1 and a detection limit of 0.01 pg mL-1 (the detection limit in real samples was 0.021 pg mL-1). This method exhibited excellent stability, reproducibility, and selectivity. In addition, the results of PSA determinations in human serum samples obtained using the proposed immunosensor were consistent with data collected using the commercial chemiluminescence immunoassay analyzer.

An Efficient Building Extraction Method from High Spatial Resolution Remote Sensing Images Based on Improved Mask R-CNN.

In this paper, we consider building extraction from high spatial resolution remote sensing images. At present, most building extraction methods are based on artificial features. However, the diversity and complexity of buildings mean that building extraction methods still face great challenges, so methods based on deep learning have recently been proposed. In this paper, a building extraction framework based on a convolution neural network and edge detection algorithm is proposed. The method is called Mask R-CNN Fusion Sobel. Because of the outstanding achievement of Mask R-CNN in the field of image segmentation, this paper improves it and then applies it in remote sensing image building extraction. Our method consists of three parts. First, the convolutional neural network is used for rough location and pixel level classification, and the problem of false and missed extraction is solved by automatically discovering semantic features. Second, Sobel edge detection algorithm is used to segment building edges accurately so as to solve the problem of edge extraction and the integrity of the object of deep convolutional neural networks in semantic segmentation. Third, buildings are extracted by the fusion algorithm. We utilize the proposed framework to extract the building in high-resolution remote sensing images from Chinese satellite GF-2, and the experiments show that the average value of IOU (intersection over union) of the proposed method was 88.7% and the average value of Kappa was 87.8%, respectively. Therefore, our method can be applied to the recognition and segmentation of complex buildings and is superior to the classical method in accuracy.