Case report: Isolated eyelid metastasis of ccRCC 5 years after receiving radical nephrectomy.

Introduction: The most common sites of clear cell renal cell carcinoma(ccRCC) metastasis are the lung, bones, liver and brain; eyelid metastasis is a rare occurrence. Case presentation: We report a case of ccRCC metastasis to the left eyelid after radical nephrectomy, and remission after sunitinib treatment. Conclusions: Although the probability of eyelid metastasis rate is very low, tumor metastasis to the eyelid skin is possible after radical nephrectomy. Therefore, any rash like changes on the skin during the review procedure cannot be ignored by the physician.

Potential Prognosis and Diagnostic Value of AKT3, LSM12, MEF2C, and RAB30 in Exosomes in Colorectal Cancer on Spark Framework.

Colorectal cancer (CRC) is a common malignant tumor and one of the leading causes of cancer-related deaths worldwide. CRC progression is greatly affected by the local microenvironment. In the study, we proposed a deep computational-based model for the classification of mRNA, lncRNA, and circRNA in exosomes. We, first, analyzed mRNA expression levels in CRC tumors and normal tissues. Secondly, we used GO and KEGG to analyze their functional enrichment. Thirdly, we analyzed the composition of immune cells in all TCGA samples and then evaluated the prognostic value of tumor-infiltrating immune cells in CRC. Lastly, we combined the TCGA dataset, i.e., COADN = 449 and ROADN = 6, for analysis and found that the expression levels of AKT3, LSM12, MEF2C, and RAB30 in exosomes were significantly correlated with tumor immune infiltration levels. The performance evaluation has shown that the proposed model based on neural networks performs better as compared to the existing methods. The proposed model can be used as a potential tool for the immune infiltration level and their role in cancer metastasis and progression, which can help us to explore potential strategies for CRC diagnosis, therapy, and prognosis.

Chemical constituents from the stems of Acanthopanax senticosus with their inhibitory activity on α-glucosidase.

A new aurone named (2Z)-2-[(4'-hydroxy-3'-methoxyphenyl) methylene]-6-methoxy-7-prenyl-3(2H)-benzofurane (1), together with five known compounds (2-6), were isolated from EtOAc-soluble extract of the stems of Acanthopanax senticosus. The chemical structures were elucidated on the basis of spectroscopic analyses. All isolates were evaluated for in vitro inhibitory activity on α-glucosidase. Among them, compounds 1 and 4 were found to exhibit moderate inhibitory activity on α-glucosidase with IC50 value of 64.1 ± 1.2 and 48.9 ± 1.1 µM, respectively.[Formula: see text].

A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN.

BACKGROUND AND OBJECTIVES: Mammography analysis is an effective technology for early detection of breast cancer. Micro-calcification clusters (MCs) are a vital indicator of breast cancer, so detection of MCs plays an important role in computer aided detection (CAD) system, this paper proposes a new hybrid method to improve MCs detection rate in mammograms. METHODS: The proposed method comprises three main steps: firstly, remove label and pectoral muscle adopting the largest connected region marking and region growing method, and enhance MCs using the combination of double top-hat transform and grayscale-adjustment function; secondly, remove noise and other interference information, and retain the significant information by modifying the contourlet coefficients using nonlinear function; thirdly, we use the non-linking simplified pulse-coupled neural network to detect MCs. RESULTS: In our work, we choose 118 mammograms including 38 mammograms with micro-calcification clusters and 80 mammograms without micro-calcification to demonstrate our algorithm separately from two open and common database including the MIAS and JSMIT; and we achieve the higher specificity of 94.7%, sensitivity of 96.3%, AUC of 97.0%, accuracy of 95.8%, MCC of 90.4%, MCC-PS of 61.3% and CEI of 53.5%, these promising results clearly demonstrate that the proposed approach outperforms the current state-of-the-art algorithms. In addition, this method is verified on the 20 mammograms from the People's Hospital of Gansu Province, the detection results reveal that our method can accurately detect the calcifications in clinical application. CONCLUSIONS: This proposed method is simple and fast, furthermore it can achieve high detection rate, it could be considered used in CAD systems to assist the physicians for breast cancer diagnosis in the future.

Local sparsity enhanced compressed sensing magnetic resonance imaging in uniform discrete curvelet domain.

BACKGROUND: Compressed sensing(CS) has been well applied to speed up imaging by exploring image sparsity over predefined basis functions or learnt dictionary. Firstly, the sparse representation is generally obtained in a single transform domain by using wavelet-like methods, which cannot produce optimal sparsity considering sparsity, data adaptivity and computational complexity. Secondly, most state-of-the-art reconstruction models seldom consider composite regularization upon the various structural features of images and transform coefficients sub-bands. Therefore, these two points lead to high sampling rates for reconstructing high-quality images. METHODS: In this paper, an efficient composite sparsity structure is proposed. It learns adaptive dictionary from lowpass uniform discrete curvelet transform sub-band coefficients patches. Consistent with the sparsity structure, a novel composite regularization reconstruction model is developed to improve reconstruction results from highly undersampled k-space data. It is established via minimizing spatial image and lowpass sub-band coefficients total variation regularization, transform sub-bands coefficients l 1 sparse regularization and constraining k-space measurements fidelity. A new augmented Lagrangian method is then introduced to optimize the reconstruction model. It updates representation coefficients of lowpass sub-band coefficients over dictionary, transform sub-bands coefficients and k-space measurements upon the ideas of constrained split augmented Lagrangian shrinkage algorithm. RESULTS: Experimental results on in vivo data show that the proposed method obtains high-quality reconstructed images. The reconstructed images exhibit the least aliasing artifacts and reconstruction error among current CS MRI methods. CONCLUSIONS: The proposed sparsity structure can fit and provide hierarchical sparsity for magnetic resonance images simultaneously, bridging the gap between predefined sparse representation methods and explicit dictionary. The new augmented Lagrangian method provides solutions fully complying to the composite regularization reconstruction model with fast convergence speed.