Discovery of a chalcone derivative as an anti-fibrotic agent targeting transforming growth factor-ß1 signaling: Potential therapy of renal fibrosis.

As a final common pathway of renal injuries, renal fibrosis leads to chronic kidney disease (CKD). Currently, there is no safe and effective therapy to prevent the progression of renal fibrosis to CKD. Inhibition of transforming growth factor-ß1 (TGF-ß1) pathway is proposed as one of the most promising approaches for anti-renal fibrosis therapies. This study aimed to identify novel anti-fibrotic agents using the TGF-ß1-induced fibrosis in renal proximal tubule epithelial cells (RPTEC) and characterize their mechanism of action as well as in vivo efficacy. By screening 362 natural product-based compounds for their ability to reduce collagen accumulation assessed by picro-sirius red (PSR) staining in RPTEC cells, a chalcone derivative AD-021 was identified as an anti-fibrotic agent with IC50 of 14.93 µM. AD-021 suppressed TGF-ß1-induced collagen production, expression of pro-fibrotic proteins (fibronectin and α-smooth muscle actin (αSMA)), and Smad-dependent and Smad-independent signaling pathways via suppression of TGF-ß receptor II (TGFßRII) phosphorylation in RPTEC cells. Furthermore, TGF-ß1-induced mitochondrial fission in RPTEC cells was ameliorated by AD-021 via mechanisms involving inhibition of Drp1 phosphorylation. In a mouse model of unilateral ureteral obstruction (UUO)-induced renal fibrosis, AD-021 reduced plasma TGF-ß1, ameliorated renal fibrosis and improved renal function. Collectively, AD-021 represents a novel class of natural product-based anti-fibrotic agent that has therapeutic potential in the prevention of fibrosis-associated renal disorders including CKD.

Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images.

Cytological screening plays a vital role in the diagnosis of cancer from the microscope slides of pleural effusion specimens. However, this manual screening method is subjective and time-intensive and it suffers from inter- and intra-observer variations. In this study, we propose a novel Computer Aided Diagnosis (CAD) system for the detection of cancer cells in cytological pleural effusion (CPE) images. Firstly, intensity adjustment and median filtering methods were applied to improve image quality. Cell nuclei were extracted through a hybrid segmentation method based on the fusion of Simple Linear Iterative Clustering (SLIC) superpixels and K-Means clustering. A series of morphological operations were utilized to correct segmented nuclei boundaries and eliminate any false findings. A combination of shape analysis and contour concavity analysis was carried out to detect and split any overlapped nuclei into individual ones. After the cell nuclei were accurately delineated, we extracted 14 morphometric features, 6 colorimetric features, and 181 texture features from each nucleus. The texture features were derived from a combination of color components based first order statistics, gray level cooccurrence matrix and gray level run-length matrix. A novel hybrid feature selection method based on simulated annealing combined with an artificial neural network (SA-ANN) was developed to select the most discriminant and biologically interpretable features. An ensemble classifier of bagged decision trees was utilized as the classification model for differentiating cells into either benign or malignant using the selected features. The experiment was carried out on 125 CPE images containing more than 10500 cells. The proposed method achieved sensitivity of 87.97%, specificity of 99.40%, accuracy of 98.70%, and F-score of 87.79%.

Comparative Study on Automated Cell Nuclei Segmentation Methods for Cytology Pleural Effusion Images.

Automated cell nuclei segmentation is the most crucial step toward the implementation of a computer-aided diagnosis system for cancer cells. Studies on the automated analysis of cytology pleural effusion images are few because of the lack of reliable cell nuclei segmentation methods. Therefore, this paper presents a comparative study of twelve nuclei segmentation methods for cytology pleural effusion images. Each method involves three main steps: preprocessing, segmentation, and postprocessing. The preprocessing and segmentation stages help enhancing the image quality and extracting the nuclei regions from the rest of the image, respectively. The postprocessing stage helps in refining the segmented nuclei and removing false findings. The segmentation methods are quantitatively evaluated for 35 cytology images of pleural effusion by computing five performance metrics. The evaluation results show that the segmentation performances of the Otsu, k-means, mean shift, Chan-Vese, and graph cut methods are 94, 94, 95, 94, and 93%, respectively, with high abnormal nuclei detection rates. The average computational times per image are 1.08, 36.62, 50.18, 330, and 44.03 seconds, respectively. The findings of this study will be useful for current and potential future studies on cytology images of pleural effusion.

Effects of Power and Time on Ablation Size Produced by Radiofrequency Ablation: In vitro Study in Fresh Human Placenta.

INTRODUCTION: Radiofrequency (RF) current has been clinically used to coagulate some solid tumors. We investigated the effects of RF on fresh placenta to determine the possibility of its prenatal application in placental tumors. MATERIALS AND METHODS: Total 196 fresh placentae were interstitially coagulated with a 2-cm RF needle at 14 power-duration combinations. We compared the horizontal length of coagulated area using ultrasound and microscopic measurements. Histological changes were also described. RESULTS: We did not observe any significant change in lesion size from different power levels (p = 0.104 and 0.242 for ultrasound and microscopic measurements, respectively). Mean ± SD lesion length after 5 and 10 min of exposure measured by microscopy are 16.00 ± 2.22 and 17.00 ± 1.82 mm, respectively (p < 0.001). Ultrasound consistently over-measured lesion size by 2.87 ± 3.45 mm. We also observed a collapse of large vessels on chorionic plate adjacent to the RF site. CONCLUSION: Our in vitro experiment demonstrated placental tissue coagulation and collapse of chorionic vessels from RF. Projecting the area of placental coagulation should be based on duration of RF exposure, and not on the power level. An in vivo animal study is needed before these data are translated into clinical practice.