Retinoic Acid-Platinum (II) Complex [RT-Pt(II)] Protects Against Rheumatoid Arthritis in Mice via MEK/Nuclear Factor kappa B (NF-κB) Pathway Downregulation.

BACKGROUND Rheumatoid arthritis (RA) is an inflammatory disorder that is present in approximately 1% of the world's population. This study was aimed to investigate the effect of retinoic acid-platinum (II) complex [RT-Pt(II)] on rheumatoid arthritis (RA) and to explore the mechanism involved. MATERIAL AND METHODS MH7A cell viability was determined by MTT assay and apoptosis was assessed using FACSCalibur flow cytometry. RT-PCR and Western blot assays were used for assessment of mRNA and proteins levels. RESULTS Treatment of rheumatoid arthritis with RT-Pt(II) significantly reduced the levels of IL­1ß, IL-6, IL-8, MMP-1, and MMP-13 in synovial fluid of mice in a dose-dependent manner. The expression of iNOS and COX-2 mRNA and protein in rheumatoid arthritis rats was also significantly inhibited by treatment with RT-Pt(II). The TNF-alpha-induced proliferation of MH7A cells was alleviated by RT-Pt(II) treatment in a concentration-dependent manner. Moreover, RT-Pt(II) treatment induced apoptosis and caused arrest of cell cycle in MH7A cells. The activation of MEK/NF-kappaB pathway was downregulated by RT-Pt(II) treatment in MH7A cells. CONCLUSIONS In summary, the present study demonstrated that RT-Pt(II) inhibits TNF-alpha-induced inflammatory response, suppresses cell viability, and induces apoptosis in rheumatoid arthritis synovial cells. Moreover, RT-Pt(II) exhibited its effect through targeting the MEK/NF-kappaB pathway. Therefore, RT-Pt(II) can be used for the development of treatments for rheumatoid arthritis.

Endocrine disrupting chemicals: A promoter of non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent liver disorder. With the improvement in human living standards, the prevalence of NAFLD has been increasing in recent years. Endocrine-disrupting chemicals (EDCs) are a class of exogenous chemicals that simulate the effects of hormones in the body. There has been growing evidence regarding the potential effects of EDCs on liver health, especially in NAFLD. This paper aims to summarize the major EDCs that contribute to the growing burden of NAFLD and to raise public awareness regarding the hazards posed by EDCs with the objective of reducing the incidence of NAFLD.

An alternative excitation method for electrical impedance tomography.

Electrical impedance tomography (EIT) can be utilized to image the conductivity distribution of material under test. The EIT measurements depend on the quality in the current injection and voltage measuring circuits. The current source plays a vital role in the EIT instruments. In most of the research studies, the push-pull current sources were employed for the source and sink signal generation. It usually requires frequent calibration to achieve proper functioning, especially for the sweeping frequency measurements. In this paper, an alternative excitation method has been proposed for simplifying the design of the current source in EIT instruments, which aims to achieve the performance of the push-pull current source by using a single-ended current source. It could offer the following advantages: (1) hardware simplification and (2) reduced requirements on current source calibration. The corrected measurements could be consistent with that using push-pull excitation, as confirmed by the numerical simulations. In addition, the reconstructed images have also been investigated to illustrate the effectiveness of the proposed method.

A fast Tikhonov regularization method based on homotopic mapping for electrical resistance tomography.

Electrical resistance tomography (ERT) is considered a novel sensing technique for monitoring conductivity distribution. Image reconstruction of ERT is an ill-posed inverse problem. In this paper, an improved regularization reconstruction method is presented to solve this issue. We adopted homotopic mapping to choose the regularization parameter of the iterative Tikhonov algorithm. The standard normal distribution function was used to continuously adjust the regularization parameter. Subsequently, the resultant image vector was deployed as the initial value of the iterative Tikhonov algorithm to improve the image quality. Finally, the improved method was combined with a projection algorithm based on the Krylov subspace, which was also effective in reducing the computational time. Both simulation and experimental results indicated that the new algorithm could improve the real-time performance and imaging quality.

Exploring the underlying molecular mechanism of liver cancer cells under hypoxia based on RNA sequencing.

BACKGROUND: The aim of our study was to use the differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs) to illustrate the underlying mechanism of hypoxia in liver cancer. METHODS: In this study, a cell model of hypoxia was established, and autophagy activity was measured with western blotting and transmission electron microscopy. The effect of hypoxia conditions on the invasion of liver cancer cell was evaluated. RNA sequencing was used to identify DEmRNAs and DEmiRNAs to explore the mechanism of hypoxia in liver cancer cells. RESULTS: We found that autophagy activation was triggered by hypoxia stress and hypoxia might promote liver cancer cell invasion. In addition, a total of 407 shared DEmRNAs and 57 shared DEmiRNAs were identified in both HCCLM3 hypoxia group and SMMC-7721 hypoxia group compared with control group. Furthermore, 278 DEmRNAs and 24 DEmiRNAs were identified as cancer hypoxia-specific DEmRNAs and DEmiRNAs. Finally, we obtained 19 DEmiRNAs with high degree based on the DEmiRNA-DEmRNA interaction network. Among them, hsa-miR-483-5p, hsa-miR-4739, hsa-miR-214-3p and hsa-miR-296-5p may be potential gene signatures related to liver cancer hypoxia. CONCLUSIONS: Our study may help to understand the potential molecular mechanism of hypoxia in liver cancer.

Survey of signal clamping for digital phase sensitive detector.

Electrical capacitance tomography (ECT) is a multiphase flow detection technology, which has the advantages of nonradioactive, noninvasive, and on-line visualization measurements. In ECT measurements, the digital phase sensitive demodulation (PSD) plays an important role in guaranteeing the accuracy of small capacitance measurements. Generally, the clamping circuit is implemented to protect the A/D converter. In the measuring circuit, a programmable gain amplifier is usually employed to expand the dynamic range of PSD. However, the measured signal may exceed the measurement range from time to time, which induces the signal clamping effect. In this case, the digital PSD will present large errors in the capacitance measurements. In consideration of the frequency spectrum characteristic of the clamped signal, two digital PSD methods have been proposed. The proposed methods have been implemented in a field programmable gate array based measurement system. The experimental results show that the proposed multisine PSD method could expand the dynamic range by a factor of 5, at a relatively low error, i.e., ≤2%.

Uncovering the potential differentially expressed miRNAs as diagnostic biomarkers for hepatocellular carcinoma based on machine learning in The Cancer Genome Atlas database.

The present study aimed to identify novel diagnostic differentially expressed microRNAs (miRNAs/miRs) in order to understand the molecular mechanisms underlying hepatocellular carcinoma. The expression data of miRNA and mRNA were downloaded for differential expression analysis. Optimal diagnostic differentially expressed miRNA biomarkers were identified via a random forest algorithm. Classification models were established to distinguish patients with hepatocellular carcinoma and normal individuals. A regulatory network between optimal diagnostic differentially expressed miRNA and differentially expressed mRNAs was then constructed. The GSE63046 dataset and in vitro experiments were used to validate the expression of the optimal diagnostic differentially expressed miRNAs identified. In addition, diagnostic and prognostic analyses of optimal diagnostic differentially expressed miRNAs were performed. In total, 14 differentially expressed miRNAs (all upregulated) and 2,982 differentially expressed mRNAs (1,989 upregulated and 993 downregulated) were identified. hsa­miR­10b­5p, hsa­miR­10b­3p, hsa­miR­224­5p, hsa­miR­183­5p and hsa­miR­182­5p were considered as the optimal diagnostic biomarkers for hepatocellular carcinoma. The mRNAs targeted by these five miRNAs included secreted frizzled related protein 1 (SFRP1), endothelin receptor type B (EDNRB), nuclear receptor subfamily 4 group A member 3 (NR4A3), four and a half LIM domains 2 (FHL2), NK3 homeobox 1 (NKX3­1), interleukin 6 signal transducer (IL6ST) and forkhead box O1 (FOXO1). 'Bile acid biosynthesis and cholesterol' was the most enriched signaling pathways of these target mRNAs. The expression validation of the five miRNAs was consistent with the present bioinformatics analysis. Notably, hsa­miR­10b­5p and hsa­miR­10b­3p had a significant prognosis value for patients with hepatocellular carcinoma. In conclusion, the five differentially expressed miRNAs may be considered as diagnostic biomarkers for patients with hepatocellular carcinoma. In addition, the differential expression levels of the targets of these five mRNAs, including SFRP1, EDNRB, NR4A3, FHL2, NKX3­1, IL6ST and FOXO1, may be involved in hepatocellular carcinoma tumorigenesis.

Singular value decomposition based impulsive noise reduction in multi-frequency phase-sensitive demodulation of electrical impedance tomography.

As an important means in electrical impedance tomography (EIT), multi-frequency phase-sensitive demodulation (PSD) can be viewed as a matched filter for measurement signals and as an optimal linear filter in the case of Gaussian-type noise. However, the additive noise usually possesses impulsive noise characteristics, so it is a challenging task to reduce the impulsive noise in multi-frequency PSD effectively. In this paper, an approach for impulsive noise reduction in multi-frequency PSD of EIT is presented. Instead of linear filters, a singular value decomposition filter is employed as the pre-stage filtering module prior to PSD, which has advantages of zero phase shift, little distortion, and a high signal-to-noise ratio (SNR) in digital signal processing. Simulation and experimental results demonstrated that the proposed method can effectively eliminate the influence of impulsive noise in multi-frequency PSD, and it was capable of achieving a higher SNR and smaller demodulation error.

Amplitude demodulation for electrical capacitance tomography based on singular value decomposition.

Amplitude demodulation is essential in image reconstruction for electrical capacitance tomography (ECT). In this paper, an amplitude demodulation method is proposed based on singular value decomposition (SVD), which can substitute the role of phase-sensitive demodulation in ECT. First, an M × N Hankel matrix is constructed based on a set of discrete samples. Then, SVD operation is performed on the matrix. Finally, the mathematical expression between the sinusoid amplitude and effective singular values is given; i.e., the first two singular values are used to estimate the amplitude information of the acquired signal. The proposed method has the following advantages: (1) since no reference signals are needed, the synchronization between the acquired and reference signals is not necessary; (2) this method can obtain the amplitude information of the acquired signal with a high signal-to-noise ratio (SNR), even in the case of non-integrity period sampling; and (3) SVD itself can also implement the filtering function; thus, no additional low-pass filters are required in the signal conditioning module. The demodulation accuracy and feasibility of the proposed method were verified by numerical simulations and experiments, indicating that it can provide amplitude demodulation with excellent SNR and robust performances.

Modified sparse regularization for electrical impedance tomography.

Electrical impedance tomography (EIT) aims to estimate the electrical properties at the interior of an object from current-voltage measurements on its boundary. It has been widely investigated due to its advantages of low cost, non-radiation, non-invasiveness, and high speed. Image reconstruction of EIT is a nonlinear and ill-posed inverse problem. Therefore, regularization techniques like Tikhonov regularization are used to solve the inverse problem. A sparse regularization based on L1 norm exhibits superiority in preserving boundary information at sharp changes or discontinuous areas in the image. However, the limitation of sparse regularization lies in the time consumption for solving the problem. In order to further improve the calculation speed of sparse regularization, a modified method based on separable approximation algorithm is proposed by using adaptive step-size and preconditioning technique. Both simulation and experimental results show the effectiveness of the proposed method in improving the image quality and real-time performance in the presence of different noise intensities and conductivity contrasts.

Accelerated reconstruction of electrical impedance tomography images via patch based sparse representation.

Electrical impedance tomography (EIT) reconstruction is a nonlinear and ill-posed problem. Exact reconstruction of an EIT image inverts a high dimensional mathematical model to calculate the conductivity field, which causes significant problems regarding that the computational complexity will reduce the achievable frame rate, which is considered as a major advantage of EIT imaging. The single-step method, state estimation method, and projection method were always used to accelerate reconstruction process. The basic principle of these methods is to reduce computational complexity. However, maintaining high resolution in space together with not much cost is still challenging, especially for complex conductivity distribution. This study proposes an idea to accelerate image reconstruction of EIT based on compressive sensing (CS) theory, namely, CSEIT method. The novel CSEIT method reduces the sampling rate through minimizing redundancy in measurements, so that detailed information of reconstruction is not lost. In order to obtain sparse solution, which is the prior condition of signal recovery required by CS theory, a novel image reconstruction algorithm based on patch-based sparse representation is proposed. By applying the new framework of CSEIT, the data acquisition time, or the sampling rate, is reduced by more than two times, while the accuracy of reconstruction is significantly improved.

An alternating direction algorithm for two-phase flow visualization using gamma computed tomography.

In order to build high-speed imaging systems with low cost and low radiation leakage, the number of radioactive sources and detectors in the multiphase flow computed tomography (CT) system has to be limited. Moreover, systematic and random errors are inevitable in practical applications. The limited and corrupted measurement data have made the tomographic inversion process the most critical part in multiphase flow CT. Although various iterative reconstruction algorithms have been developed based on least squares minimization, the imaging quality is still inadequate for the reconstruction of relatively complicated bubble flow. This paper extends an alternating direction method (ADM), which is originally proposed in compressed sensing, to image two-phase flow using a low-energy γ-CT system. An l(1) norm-based regularization technique is utilized to treat the ill-posedness of the inverse problem, and the image reconstruction model is reformulated into one having partially separable objective functions, thereafter a dual-based ADM is adopted to solve the resulting problem. The feasibility is demonstrated in prototype experiments. Comparisons between the ADM and the conventional iterative algorithms show that the former has obviously improved the space resolution in reasonable time.

Reconstruction of electrical impedance tomography (EIT) images based on the expectation maximum (EM) method.

Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing.

Electrical capacitance tomography using an accelerated proximal gradient algorithm.

Image reconstruction in electrical capacitance tomography requires a solution of an ill-posed inverse problem. This paper applies an accelerated proximal gradient (APG) singular value thresholding algorithm, which is originally proposed for the matrix completion problem, to image two-phase flow. Aiming to improve the image quality, a nuclear norm-based regularization technique is adopted to treat the ill-posedness of the inverse problem, and a simple updating technique is used to update the sensitivity matrix. Both typical and complicated distributions (e.g., "sun-rise" and cross-shape), have been examined based on a 16-electrode configuration. The results showed that the APG algorithm with updated sensitivity matrix could produce higher quality images when compared to the algorithm based on the typical sensitivity matrix. Both simulation and experiment results indicate that the algorithm developed has been able to achieve good quality reconstructed images with relativity fast computation speed for the cases tested in this paper.

Image reconstruction based on L1 regularization and projection methods for electrical impedance tomography.

Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction in EIT is a nonlinear and ill-posed inverse problem. The Tikhonov method with L(2) regularization is always used to solve the EIT problem. However, the L(2) method always smoothes the sharp changes or discontinue areas of the reconstruction. Image reconstruction using the L(1) regularization allows addressing this difficulty. In this paper, a sum of absolute values is substituted for the sum of squares used in the L(2) regularization to form the L(1) regularization, the solution is obtained by the barrier method. However, the L(1) method often involves repeatedly solving large-dimensional matrix equations, which are computationally expensive. In this paper, the projection method is combined with the L(1) regularization method to reduce the computational cost. The L(1) problem is mainly solved in the coarse subspace. This paper also discusses the strategies of choosing parameters. Both simulation and experimental results of the L(1) regularization method were compared with the L(2) regularization method, indicating that the L(1) regularization method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages. Furthermore, the projected L(1) method can also effectively reduce the computational time without affecting the quality of reconstructed images.