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Introduction Pyroptosis, also known as inflammatory necrosis, is a programmed cell death caused by inflammation. Multiple studies suggest that Mycobacterium tuberculosis (MTB) infection causes tissue pyroptosis. However, there are currently no protective drugs against inflammatory damage caused by pyroptosis. In this study, the anti-pyroptotic effects of the natural compound Astaxanthin (ASTA) in a simulated pulmonary tuberculosis-associated inflammatory environment were explored. Methods In the established LPS-induced apoptosis co-culture system of MEL-12 cells and RAW246.7 macrophages，we detected the expression of key proteins Caspase4, Caspase11 and Gasdermin D in the pyroptosis pathway.Results The results showed that ASTA was able to maintain the stability of MLE-12 lung epithelial cell numbers in the inflammatory environment established by LPS. But the reason is not to promote cell proliferation, but to inhibit LPS-induced pyroptosis. The results showed that ASTA significantly inhibited the expression of key proteins in the Caspase4/11-Gasdermin D pathway and the release of pyroptosis-related inflammatory mediators. Conclusion Therefore, ASTA inhibits inflammation-induced pyroptosis by inhibiting the G pathway, and has the potential to protect lung tissue from tuberculosis-related inflammatory injury. ASTA, a functional food component, is a promising candidate for protection against tuberculosis-associated inflammatory lung injury.

Binarization has always been a very challenging task for degraded document images because of the variety and complexity of degradations. This paper proposed a nonlinear diffusion equation with selective source for restoration of degraded document images, followed by a binary projection for binarization. The source is composed of two parts: one is used for the restoration of contaminated background, and another is responsible for the fidelity of texts. The evolution equation is numerically solved by the simplest finite differencing. The proposed method is tested on publicly available datasets (DIBCO (Document Image Binarization Competition) 2009–2014, 2016) and is compared with five models based on partial differential equation (PDE). The experimental results show that the proposed method is effective for degraded document images and has achieved generally the best performance of binarization compared to the other five PDE methods.

In this paper, we propose a noise-robust online convolutional coding model for image representation, which can use the noisy images as training data. Then an alternating algorithm is utilized to convert the model into two sub-problems, the image pursuit problem and the dictionary learning problem. For the image pursuit problem, the Gauss elimination method is used to solve the equation set which is derived by the Euler equation and discrete Fourier transform. For the dictionary learning problem, a gradient-descent flow is derived to solve it. Experimental results show that our method can output more meaningful feature representations compared to the related models while the training data was corrupted by Poisson noise.

Sepsis-associated encephalopathy (SAE) is characterized by the activation of inflammatory cascades, in which microglia play a key role. The activation of Recombinant Sirtuin 3 (SIRT3) was shown to significantly reduce the susceptibility of microglia to inflammatory stress. The purpose of this study is to determine whether miRNA-494 can regulate the activation and oxidative stress of SAE microglia through SIRT3.An SAE rat model was established, and the expression of Ionized calcium bindingadaptor molecule-1 (Iba-1) in rat brain tissue was detected by immunohistochemistry. Enzyme-linked immuno sorbent assay was performed to detect the expression of tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) in brain tissue. Real time quantitative PCR was performed to detect the relative expression of SIRT3 and related miRNAs, while Western blot was used to detect the protein expression of SIRT3. Rat microglia cells were culturedThe results showed that the expression of Iba-1 in the brain tissue of the SAE model group increased, and the expression of inflammatory factors TNF-α and IL-6 increased significantly (P0.01). The expression of SIRT3 protein and mRNA in the brain tissue of the SAE model group also significantly increased (P0.05). The relative expression of miRNA-494 in the SAE model group was significantly lower than that in the control group (P0.01). After miRNA-494 was transfected into microglia, cells were treated with lipopolysaccharide. In the miRNA transfection group, the expression levels of TNF-α and IL-6 were significantly lower than those in the negative control (NC) group (P0.01), and the protein expression levels of Iba-1 and SIRT3 were also significantly lower than those in the NC (P0.01).MiRNA-494 may further regulate the activation of microglia in SAE by regulating mitochondrial function, providing basic research data for the development of new SAE treatment methods.

Binarization has always been a challenging problem in document image processing because of various types of degradation. In this paper, we present a nonlinear reaction–diffusion model for binarization of bleed-through document images, which is the Perona–Malik equation involving diffusion coefficient based on structure tensor along with a nonlinear reaction term. The Perona–Malik diffusion is utilized to selectively smooth document images with bleed-through removal. Meanwhile, the nonlinear reaction term takes the responsibility for the desired binarization. In order to solve our model numerically, we develop a parallel–series splitting algorithm by combining finite differencing with two kinds of splitting methods in the literature. Our algorithm is tested on seven publicly available datasets (DIBCO 2009 to 2014 and 2016). The experimental results show that our method averagely outperforms six relevant models for the nineteen document images with bleed-through in the DIBCO series datasets.

This paper presents an adaptive enhancement model with selective smoothing for restoration of degraded document images with blur, noise and bleed-through, which involves adaptive shock filtering and selective diffusion processes. A novel hybrid scheme is developed to solve the proposed model numerically, which combines explicit finite difference and exponential smoothing. Numerical experiments show that the proposed model is very effective for restoration of degraded document images with blur, noise and bleed-through, and has averagely the best performance on the DIBCO (Document Image Binarization Competition) series datasets, compared to five PDE (partial differential equation)-based models for restoration of degraded document images.

Recently, deep-learning based methods have been widely used for computed tomography (CT) reconstruction. However, most of these methods need extra steps to convert the sinogrmas into CT images and so their networks are not end-to-end. In this paper, we propose an end-to-end deep network for CT image reconstruction, which directly maps sparse sinogramss to CT images. Our network has three cascaded blocks, where the first block is used to denoise and interpolate the sinograms, the second to map the sinograms to CT images and the last to denoise the CT images. The second block of our network implements the filter backprojection (FBP) algorithm or the Feldkamp-Davis-Kress (FDK) algorithm, where the filter step is implemented by a one-dimensional convolution layer and the backprojection is implemented by a sparse matrix multiplication. By incorporating the FBP/FDK algorithm into our network, training a fully connected layer to convert the sinograms to CT images is avoided and the number of weights of our network is decreased. Our network is trained with two labels, the sinograms and CT images, and can reconstruct good CT images even if the input sinograms are very sparse. Experimental results show that our network outperforms the state-of-the-art approaches on test datasets for the sparse CT reconstruction under fan beam and circular cone beam scanning geometry.

Tissue engineering techniques have enabled to replicate the geometrical architecture of native tissues but usually fail to reproduce their exact cellular arrangements during the fabricating process, while it is critical for manufacturing physiologically relevant tissues. To address this problem, a "sewing-like" method of controlling cellular alignment during the fabricating process is reported here. By integrating the stretching step into the fabricating process, a static mechanical environment is created which, in turn, regulates the subsequent cellular alignment, elongation, and differentiation in the generated tissues. With this method, patterned cellular constructs can be fabricated with controlled cellular alignment. Moreover, this method shows a potent capability to fabricate physiologically relevant skeletal muscle constructs in vitro by mechanically inducing myoblast fusion and maturation. As a potential clinical application, aligned myofibers are directly fabricated onto injured muscles in vivo, which repair the damaged tissues effectively. This study shows that the "sewing-like" method can produce engineered tissues with precise control of cellular arrangements and more clinically viable functionalities. This study was supported by the Major International Cooperation Project of National Natural Science Foundation of China (No. 62120106004) and the Key R&D project of Ministry of Science and Technology of China (No. 2021YFB3200801).

In this paper, we present an arc based fan-beam computed tomography (CT) reconstruction algorithm by applying Katsevich’s helical CT image reconstruction formula to 2D fan-beam CT scanning data. Specifically, we propose a new weighting function to deal with the redundant data. Our weighting function ϖ ( x _ , λ ) is an average of two characteristic functions, where each characteristic function indicates whether the projection data of the scanning angle contributes to the intensity of the pixel x _ . In fact, for every pixel x _ , our method uses the projection data of two scanning angle intervals to reconstruct its intensity, where one interval contains the starting angle and another contains the end angle. Each interval corresponds to a characteristic function. By extending the fan-beam algorithm to the circle cone-beam geometry, we also obtain a new circle cone-beam CT reconstruction algorithm. To verify the effectiveness of our method, the simulated experiments are performed for 2D fan-beam geometry with straight line detectors and 3D circle cone-beam geometry with flat-plan detectors, where the simulated sinograms are generated by the open-source software “ASTRA toolbox.” We compare our method with the other existing algorithms. Our experimental results show that our new method yields the lowest root-mean-square-error (RMSE) and the highest structural-similarity (SSIM) for both reconstructed 2D and 3D fan-beam CT images.

BACKGROUND: China ranks second in the incidence of tuberculosis (TB), and the virulence and infectivity of Mycobacterium tuberculosis (M.tb) in different lineages are different. The variation of virulence genes in the M.tb regions of difference (RD) may be the reason for differences in pathogenicity. Studying the relationship between virulence gene mutations in the RD region of clinical strains of M.tb and TB relapse can provide basic data for the study of TB prevention and control. METHODS: A total of 155 M.tb clinical strains were collected in Kashgar Prefecture. Whole-genome sequencing (WGS) was conducted, and mutations in virulence genes in the M.tb RD region were analyzed. The maximum likelihood method was implemented using IQ-TREE software. Logistic regression was used to analyze the relationship between lineage, RD region virulence gene variation, and patient relapse. RESULTS: The 155 strains of M.tb in Kashgar Prefecture belong to 3 M.tb lineages: L2 (45.80%), L3 (32.90%), and L4 (21.30%). In relapsed patients, L2 (70.83%, 17/24) was significantly higher than the other lineages (29.17%, 7/24; P

Total variation (TV) regularization model has the excellent performance in noise-removing and edge-preserving. However, it often yields staircase artifacts in the smooth region of the restorations. To attack this problem, we propose a generalized hybrid nonconvex variational regularization model in this paper, which utilizes two nonconvex regularizers to impose the priors to the two different components of the images, respectively. One is nonconvex TV regularizer that measures the piecewise constant component of the input image, and the other is nonconvex Laplacian regularizer that models the piecewise smooth component. New model inherits the advantages of the nonconvex regularization and the first- and second-order hybrid variational regularization, which can well remove the noises while preserving edges and reducing staircase artifacts. To solve this nonconvex minimization problem efficiently, we propose a first-order algorithm based on alternating direction method of multipliers (ADMM) combining with majorization–minimization (MM) scheme. In addition, a sufficient condition for the convergence of the proposed algorithm is provided. In the experiments, we compare the proposed model with several state-of-the-art image denoising models, numerical results illustrate the effectiveness of the proposed model and algorithm for both synthetic and real images in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM) indexes.

In this paper, we propose a variational level set model for multiscale image segmentation that can extract desired object from the background under various scales. The new model consists of three terms: A boundary extraction term, a regularization term and a multiscale representation term. The boundary extraction term is based on the Chan–Vese (CV) functional, which drives the active contour toward the boundaries of the objects. The regularization term penalizes the length of the active contour and keeps the level set function close to a signed distance function , which enables the active contour to maintain its stability during the evolution process. The multiscale representation term is based on the total variation (TV) energy that can provide a multiscale representation of the input image by tuning the scale parameter. We use an alternating iterative algorithm that combines the gradient descent algorithm and the alternating direction method of multipliers (ADMM) to numerically solve the model. The experimental results for both synthetic and real images demonstrate the robustness and effectiveness of the proposed model for multiscale image segmentation. In addition, the proposed model is a promising model for image denoising applications.

In this paper, a vector total fractional-order variation (VTV-β) is proposed. Then, VTV-β model and Gβ(Ω) model are proposed for color image denoising and decomposition, respectively. Some properties of the VTV-β are investigated and an alternative algorithm is used to solve the two models. Some experimental results are given to show the effectiveness and advantages of our methods.

This paper proposes a nonlinear edge-preserving diffusion equation with an adaptive source term for binarization of degraded document images. The role of nonlinear diffusion term is to smooth images with preservation of text edges and corners, while the source term is responsible for the desired binarization. Unlike other binarization techniques (such as clustering-based and threshold-based), the idea behind the proposed method is that a sequence of gradually binarized images is obtained by solving the evolution equation starting with the image to be binarized, and tends to the slightly smoothed version of the desired binary image at infinity. A semi-implicit parallel splitting-up method is developed for solving the proposed model effectively. The proposed model with algorithm is tested on the DIBCO (Document Image Binarization Competitions) series datasets. The results show that it has generally the best performance, compared to four PDE (partial differential equation)-based binarization models, and six recent and benchmark binarization algorithms (non-PDE based).

In this paper, we put forward an idea of indirect diffusion and further develope an indirect diffusion-based level set model for image segmentation. This model is based on the dynamic process of diffusion that is posed indirectly on level set function by way of auxiliary function, coupled with a transition region-based force that exhibits the desired sign-changing property. It is formulated as a coupled system of two evolution equations, in which the first equation drives the motion of zero level set toward the object edges and makes it possible to set a termination criterion on the algorithm, while the second equation (indirect diffusion) smoothens the auxiliary function and keeps the auxiliary function as close to the level set function as possible. The derived model can effectively be solved purely by the simplest explicit finite difference. Experimental results show that the proposed model not only has the strong capability of noise immunity, but it also can much better conduce to extraction of deeply concave edges and preservation of sharp corners, compared with the direct diffusion-based counterpart.

Urolithiasis commonly occurs in kidney and ureteral, and may cause local organ/tissue damage, even kidney failure. The incidence of this disease is increasing worldwide, in which calcium oxalate is the major composition forming the urinary calculus. Therefore, to monitor this disease for the prevention and treatment, measuring the oxalate in the urine is of great significance. Here, a rapid and sensitive colorimetric method was developed based on 3,3',5,5'-tetramethylbenzidine-manganese dioxide (TMB-MnO2) nanosheets for oxalate detection. MnO2 nanosheets acted as an efficient biomimetic oxidase to catalyze the reaction with TMB and oxalate. Pale yellow TMB can be oxidized to blue oxide TMB catalyzed by BSA-stabilized MnO2 nanosheets, and oxalate can selectively inhibit this reaction by consuming and reacting with MnO2 nanosheets, thus achieving the quantitative detection of oxalate. Moreover, a home-made bionic electronic-eye (E-eye) system was developed as a portable in-situ detection platform to efficiently measure the oxalate concentrations in 10 s by direct photographing. By optimizing experimental conditions, this method shows a wide linear range (7.8 μM to 250 μM) and a low detection limit (0.91 μM) for oxalate detection. Besides, this method exhibits high selectivity even with 80-fold interfering chemicals. Furthermore, the performance of the method was validated by testing the artificial urine samples, indicating its great potential for monitoring and diagnosis of urolithiasis in point-of-care applications.

In this paper, we propose a variational model in binary level set framework for segmenting two-phase images with impulse noise. In this model, the contour is implicitly represented by the discontinuity of a binary level set function instead of the zero-level set. The energy functional consists of three terms, i.e., the data term that is defined by L1-norm metric, the regularization term that is defined by Dirichlet energy of level set function, and the penalty term that punishes the deviation of level set function from binary function. We design a three-step time-splitting scheme to solve the gradient descent flow of the proposed model efficiently, in which the flow equation is divided into three differential equations that are solved sequentially and alternatively until convergence. We present some experimental results that are performed on simulated and real images, which demonstrate that our model is very robust to impulse noise and has the best performance compared with five relevant models.

Quantifying cardiac contractile force is of paramount important in studying mechanical heart failure and screening therapeutic drugs. However, most existing methods can only measure the in-plane component of twitch force of cardiomyocytes, such that mismatching the centripetal compressive stress of heart beating in physiology. Here, a non-destructive method is developed for quantifying the compressive stress and mapping the distribution of the local stress within the 3D cardiac tissues. In detail, elastic gelatin microspheres labeled with fluorescence beads are fabricated by microfluidic chips with high throughput, and they serve as built-in pressure sensors which are wrapped by cardiomyocytes in 3D tissues. The deformation of microspheres and the displacements of fluorescent beads induced by the contraction of cardiomyocytes are demonstrated to characterize the amount and distribution of the centripetal compressive stress. Further, the method shows a potent capability to locally quantify contractile force variation of 3D cardiac tissues, which is induced by agonist (norepinephrine) and inhibitor (blebbistatin). On the whole, the method significantly improves the 3D measurement of mechanical force in vitro and provides a solution for locally quantifying the compressive stress within engineered cardiac tissues.

Bioprinting is a common method to replicate geometrical architecture of native tissues. However, it usually fails to modulate cellular arrangements, which is critical for the tissue’s functionality. To our knowledge, no method has successfully addressed this challenge. Here, we report a method of controlling cellular orientation during the bioprinting process by integrating a stretch process into a modified bioprinting frame. We demonstrate that the cellular orientation is a result of cells’ sensing and responding to the tensile stress, instead of shear stress or topographical patterns. Moreover, our method shows a potent capability to induce myoblast differentiation, fusion and maturation without the presence of differentiation medium. As a potential clinical application, we demonstrate that aligned myofibers directly printed onto injured muscle in vivo, can not only repair the structure of damaged tissue, but also recover the muscle functionalities effectively. This study shows that the new method can produce tissues with precise control of cellular arrangements and more clinically viable functionalities.Significance StatementDue to no method could reproduce the exact cellular arrangements of native tissues in engineered tissues, tissue engineering facing difficult in fabricating 3D tissues that possess desirable biological and mechanical functionalities for biomedical applications. For the first time, we report a method of controlling cellular orientation during 3D bio-printing process. This method can be used to produce engineered tissues with controlled cellular arrangement with several different cell types. Moreover, this method shows a potent capability of fabricating fully mature and aligned myofibers in vitro in the absence of differentiation medium. As potential clinical applications, with this method, engineered tissues could be directly printed in vivo with high efficacy of tissue repair and function recovery.

Airway smooth muscle (ASM) contraction is a major pathophysiological characteristic of asthma. Although β2-adrenoceptor (β2-AR) agonists are currently used as bronchodilators, they cause rapid effect and long-term agonist-induced desensitization. Thus, it is necessary to search for more effective and safer relaxant agents for ASM cells. In this work, bitter taste receptors (TAS2Rs) were demonstrated to be expressed in primary mouse ASM cells endogenously, and they were considered as new drug targets for asthma treatment. Traditional Chinese medicines (TCMs) contained a wide range of TAS2R agonists and some of them had the efficacy of relieving cough and asthma with less toxic side effects. Then the electronic cell-substrate impedance sensor (ECIS) was used for the first time to establish a method to detect the contraction/relaxation effects of ASM cells. Therefore, we introduced a biomimetic in vitro respiratory system using ASM cells on ECIS chips to screen for potential TCMs against asthma. Quinine, nobiletin, and picfeltarraenin IA screened in this study could effectively inhibit the ASM contraction in a concentration-dependent manner, showing potential value as novel anti-asthma drugs. Furthermore, the effective screening of anti-asthma drugs was realized based on 3D ASM cell arrays and gel imaging system. Consistent results were found and the reliability of the biomimetic in vitro respiratory system for the screening of TCMs against asthma was further verified. The biomimetic system designed in this study has the advantages of operation simplicity, high throughput, non-invasive, real-time, and high sensitivity, and therefore provides a promising drug screening platform for asthma disease.

Cardiovascular disease is currently a leading killer to human, while drug-induced cardiotoxicity remains the main cause of the withdrawal and attrition of drugs. Taking clinical correlation and throughput into account, cardiomyocyte is perfect as in vitro cardiac model for heart disease modeling, drug discovery, and cardiotoxicity assessment by accurately measuring the physiological multiparameters of cardiomyocytes. Remarkably, cardiomyocytes present both electrophysiological and biomechanical characteristics due to the unique excitation-contraction coupling, which plays a significant role in studying the cardiomyocytes. This review mainly focuses on the recent advances of biosensing technologies for the 2D and 3D cardiac models with three special properties: electrophysiology, mechanical motion, and contractile force. These high-performance multidimensional cardiac models are popular and effective to rebuild and mimic the heart in vitro. To help understand the high-quality and accurate physiologies, related detection techniques are highly demanded, from microtechnology to nanotechnology, from extracellular to intracellular recording, from multiple cells to single cell, and from planar to 3D models. Furthermore, the characteristics, advantages, limitations, and applications of these cardiac biosensing technologies, as well as the future development prospects should contribute to the systematization and expansion of knowledge.

Haze removal (or dehazing) is very important for many applications in computer vision. Because depth information and atmospheric light are usually unknown in practice, haze removal is a challenging problem, especially for single image dehazing. In this paper, we propose a new variational model for removing haze from a single input image. The proposed model combines Koschmieder’s law with Retinex assumption that an image is the product of illumination and reflection. We assume that scene depth and surface radiance are spatially piecewise smooth, total variation is thus used for regularization in our model. The proposed model is defined as a constrained optimization problem, which is solved by an alternating minimization scheme and a fast gradient projection algorithm. Theoretical analyses are given for the proposed model and algorithm. Some numerical examples are presented, which have shown that our model has the best visual effect and the highest average PSNR (Peak Signal-to-Noise Ratio) compared to six relevant models in the literature.

In this paper, we present a new variant of level set methods and then propose a ternary variational level set model involving L0 gradient regularizer and L0 function regularizer in discrete framework, following the Chan-Vese model for image segmentation. Different from the existing level set methods, we use the 0.5-level set of a ternary function whose values are within {0, 0.5, 1} to implicitly represent the interfaces between subregions and use L0 counting operator to discretely measure the length of interfaces and the area of foreground subregions. The proposed model can be regarded as a discrete form of the Chan-Vese model. Based on the half-quadratic splitting method, we design an alternating minimization algorithm to solve our model efficiently. Experimental results show that the proposed method has good performance for segmentation of images with severe noise, outliers or low contrast.

The segmentation of images with severe noise has always been a very challenging task because noise has great influence on the accuracy of segmentation. This paper proposes a robust variational level set model for image segmentation, involving the kernel metric based on the Gaussian radial basis function (GRBF) kernel as the data fidelity metric. The kernel metric can adaptively emphasize the contribution of pixels close to the mean intensity value inside (or outside) the evolving curve and so reduce the influence of noise. We prove that the proposed energy functional is strictly convex and has a unique global minimizer in B V ( Ω ) . A three-step time-splitting scheme, in which the evolution equation is decomposed into two linear differential equations and a nonlinear differential equation, is developed to numerically solve the proposed model efficiently. Experimental results show that the proposed method is very robust to some types of noise (namely, salt & pepper noise, Gaussian noise and mixed noise) and has better performance than six state-of-the-art related models.

Exosomes are nanosized vesicles secreted by cells, with a lipid bilayer membrane and protein and nucleic acid contents. Here, we present the first method for the selective and quantitative analysis of exosomes by digital detection integrated with nucleic acid-based amplification in a microchip. An external biocompatible anchor molecule conjugated with DNA oligonucleotides was anchored in the lipid bilayer membrane of exosomes via surface self-assembly for total exosome analysis. Then, specific antibody-DNA conjugates were applied to label selective exosomes among the total exosomes. The DNA-anchored exosomes were distributed into microchip chambers with one or fewer exosomes per chamber. The signal from the DNA on the exosomes was amplified by a rapid isothermal nucleic acid detection assay. A chamber with an exosome exhibited a positive signal and was recorded as 1, while a chamber without an exosome presented a negative signal and was recorded as 0. The 10100101 digital signals give the number of positive chambers. According to the Poisson distribution, the exosome stock concentration was calculated by the observed fraction of positive chambers. The findings showed that nanoscale particles can be digitally detected via DNA-mediated signal amplification in a microchip with simple microscopic settings. This approach can be integrated with multiple types of established nucleic acid assays and provides a versatile platform for the quantitative detection of various nanosomes, from extracellular vesicles such as exosomes and enveloped viruses to inorganic and organic nanoparticles, and it is expected to have broad applications in basic research areas as well as disease diagnosis and therapy.

It is well known that an image is the pixel-wise multiplication of two components (illumination and reflectance) that are described as intrinsic images. To enhance the visual quality of a degraded image with uneven illumination and low local contrast, it is an effective way to extract the illumination or reflectance from the image and correct the unevenness of illumination. In this paper, we propose a variational model to estimate the illumination and reflectance from the degraded image for the uneven illumination correction. The proposed model is solved by the alternating minimization scheme and the augmented Lagrange method. We give some theoretic analysis of our model and convergence analysis of the algorithm. Experimental results show that the proposed model can achieve excellent performance in the correction of the uneven illumination.

Exosomes are common membrane-bound nanovesicles that contain diverse biomolecules, such as lipids, proteins, and nucleic acids. Exosomes are derived from cells through exocytosis, are ingested by target cells, and can transfer biological signals between local or distant cells. Exosome secretion is a constitutive phenomenon that is involved in both physiological and pathological processes and determines both the exosomal surface molecules and the contents. Hence, we can exploit exosomes as biomarkers, vaccines and drug carriers and modify them rationally for therapeutic interventions. However, it is still a challenge to identify, isolate and quantify exosomes accurately, efficiently and selectively. Further studies on exosomes will explore their potential in translational medicine and provide new avenues for the creation of effective clinical diagnostics and therapeutic strategies; the use of exosomes in these applications can be called exosome theranostics. This review describes the fundamental processes of exosome formation and uptake. In addition, the physiological and pathological roles of exosomes in biology are also illustrated with a focus on how exosomes can be exploited or engineered as powerful tools in translational medicine.

Cancer cells metastasize and are transported in the bloodstream, easily reaching any site in the body through the blood circulation. A method designed to assess the number of circulating tumor cells (CTCs) should be validated as a clinical tool for predicting the response to therapy and monitoring the disease progression in patients with cancer. Although CTCs are detectable in many cases, they remain unavailable for clinic usage because of their high testing cost, tedious operation, and poor clinical relevance. Herein, we developed a regeneratable microchip for isolating CTCs, which is available for robust cell heterogeneity assays on-site without the need for a sterile environment. The ivy-like hierarchical roughened zinc oxide (ZnO) nanograss interface was synthesized and directly integrated into the microfluidic devices and enables effective CTC capture and flexible, nontoxic CTC release during incubation in a mildly acidic solution, thus enabling cellular and molecular analyses. The microchip can be regenerated and recycled to capture CTCs with the remaining ZnO without affecting the efficiency, even after countless cycles of cell release. Moreover, microbial infection is avoided during its storage, distribution, and even in the open space usage, which ideally appeals to the demands of point-of-care (POC) and home testing and meets to the requirements for blood examinations in undeveloped or resource-limited settings. Furthermore, the findings generated using this platform based on the cocktail of antiepithelial cell adhesion molecule and antivimentin antibodies indicate that CTC capture was more precise and reasonable for patients with advanced cancer.

Robust and accurate segmentation of oil slick regions from synthetic aperture radar satellite images plays a fundamental role for detecting and monitoring of oil spills. However, uneven intensity, high noise, and blurry boundary, which always exist in oil spill images, make the automatic segmentation of such images very difficult. In this paper, a two-stage method is developed for the segmentation of oil spill images. The first stage of our method is to obtain the enhanced image by suppressing the backscattering from an oil spill image. Once the enhanced image is obtained, then in the second stage, a variational segmentation model is presented for dealing with the enhanced image. The data term of the energy functional is constructed for the enhanced image in a piecewise constant way. In addition, a Cahn–Hilliard-type regularization term is introduced into the energy functional. The variational model is numerically solved by alternating minimization. Numerical experiments on $\text{65}$ oil spill images from ENVISAT show that the proposed method can obtain an overall accuracy of $\text{94} \%$ for dark spot segmentation and create limited false alarms and outperforms the two representative state-of-the-art methods in terms of the efficiency and accuracy.

In this letter, we present a fast and effective algorithm for solving the Poisson-modified total variation model proposed in [Le et al. , “A variational approach to reconstructing images corrupted by Poisson noise,” J. Math. Imag. Vis. , vol. 27, no 3, pp. 257–263, Apr. 2007]. The existence and uniqueness of solution for the model are proved by using a different method. A semi-implicit difference scheme is designed to discretize the derived gradient descent flow with a large time step. Different from the original numerical scheme, our scheme is conditional stable with a less stringent condition and can ensure that the numerical solution is strictly positive in image domain. Experimental results show the efficiency and effectiveness of our algorithm.

The detection of olfaction, gustation and toxicity has played an important role in industries and research. The key factor to realize this function is to detect different substances with the same sensitive element. In this paper, the human neuroblastoma SH-SY5Y cells endogenously expressed the human bitter receptor, T2R16. Meanwhile, an olfactory receptor, ODR-10, was transfected on the plasma membrane of the cells at the same time. T2R16 could specifically respond to bitter compounds, which can be monitored by cell-impedance sensor. The ODR-10 receptor can also specifically respond to diacetyl, which was cultured on the microelectrode arrays to establish a novel biomimetic sensor for odor detection. The results represent a dose-dependent responses in a certain concentration range. Moreover, the cell-impedance biosensor enabled a quick toxicity detection of salicin. In conclusion, the biomimetic sensors based on extracellular recording in vitro show great potential for use in both basic research and practical applications.

Electronic tongues (ETs) have been developed and widely used in food, beverage and pharmaceutical fields, but limited in sensitivity and specificity. In recent years, bioelectronic tongues (BioETs) integrating biological materials and various types of transducers are proposed to bridge the gap between ET system and biological taste. In this work, a bionic in vitro cell-based BioET is developed for bitter and umami detection, utilizing rat cardiomyocytes as a primary taste sensing element and microelectrode arrays (MEAs) as a secondary transducer for the first time. The primary cardiomyocytes of Sprague Dawley (SD) rats, which endogenously express bitter and umami taste receptors, were cultured on MEAs. Cells attached and grew well on the sensor surface, and syncytium was formed for potential conduction and mechanical beating, indicating the good biocompatibility of surface coating. The specificity of this BioET was verified by testing different tastants and bitter compounds. The results show that the BioET responds to bitter and umami compounds specifically among five basic tastants. For bitter recognition, only those can activate receptors in cardiomyocytes can be recognized by the BioET, and different bitter substances could be discriminated by principal component analysis (PCA). Moreover, the specific detections of two bitters (Denatonium Benzoate, Diphenidol) and an umami compound (Monosodium Glutamate) were realized with a detection limit of 10−6 M. The cardiomyocytes-based BioET proposed in this work provides a new approach for the construction of BioETs and has promising applications in taste detection and pharmaceutical study.