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BackgroundCircTADA2A has been demonstrated to play critical roles in the occurrence and development of human cancer. However, the expression pattern and biological mechanisms of circTADA2A in melanoma remains largely unknown.MethodsCircTADA2A were detected by quantitative real-time RT-PCR (qRT-PCR) and validated by Sanger sequencing. Function of circTADA2A and its protein partner in melanoma cells was investigated using RNA interference and overexpression assays. Interaction of circTADA2A, CCHC-type zinc finger nucleic acid binding protein (CNBP) and solute carrier family 38 member 1 (SLC38A1) was confirmed by RNA immunoprecipitation, RNA pull-down, and dual-luciferase reporter assay. The expression of genes and proteins were detected by qRT-PCR and western blot assays.ResultsData from the investigation showed that a novel circRNA (circTADA2A, hsa_circ_0043278) was markedly downregulated in melanoma cells. Functionally, circTADA2A repressed cell proliferation, migration, invasion in melanoma cells. Mechanistically, circTADA2A interacted with CNBP, acting to suppress the binding of CNBP to the SLC38A1 promoter and subsequently restrained SLC38A1 transcription, which resulting in repression of melanoma progression.ConclusionsCircTADA2A suppresses melanoma progression by regulating CNBP/SLC38A1 axis, indicating a potential therapeutic target in melanoma.

Psoriatic keratinocytes express exaggerated levels of inflammatory cytokines, and show aberrant hyperproliferation and terminal differentiation in the pathogenesis of psoriasis. The antimicrobial protein hS100A7 (psoriasin) has been found highly expressed in psoriatic skin, but the mechanism and physiological function remain largely unknown. We observed that hS100A7 induces mature interleukin 1α (17kDa) expression in normal human epidermal keratinocytes, which is dependent on RAGE-p38 MAPK and calpain-1 as the inhibitors or knockdown of them completely decreased the expression of mature interleukin1α. Then, we proved mS100a7a15, mature IL-1α and calpain-1 were highly expressed in imquimod-induced psoriasis model and mouse IL-17a-neutralizing antibody treatment attenuated mS100a7a15 expression. At last, PD 151746 (calpain-1 inhibitor) treatment decreased epidermal thickness in imquimod-induced psoriasis model. Taken together, our results suggest that mature IL-1α induced by hS100A7 is via RAGE-p38 MAPK and calpain-1 pathway in keratinocyte and this mechanism may play an important role during psoriasis.

PurposeIn ultrasound-guided High Intensity Focused Ultrasound (HIFU) therapy, the target tissue (such as a tumor) often moves and/or deforms in response to an external force. This problem creates difficulties in treating patients and can lead to the destruction of normal tissue. In order to solve this problem, we present a novel method to model and predict the movement and deformation of the target tissue during ultrasound-guided HIFU therapy.MethodsOur method computationally predicts the position of the target tissue under external force. This prediction allows appropriate adjustments in the focal region during the application of HIFU so that the treatment head is kept aligned with the diseased tissue through the course of therapy. To accomplish this goal, we utilize the cow tissue as the experimental target tissue to collect spatial sequences of ultrasound images using the HIFU equipment. A Geodesic Localized Chan-Vese (GLCV) model is developed to segment the target tissue images. A 3D target tissue model is built based on the segmented results. A versatile particle framework is constructed based on Smoothed Particle Hydrodynamics (SPH) to model the movement and deformation of the target tissue. Further, an iterative parameter estimation algorithm is utilized to determine the essential parameters of the versatile particle framework. Finally, the versatile particle framework with the determined parameters is used to estimate the movement and deformation of the target tissue.ResultsTo validate our method, we compare the predicted contours with the ground truth contours. We found that the lowest, highest and average Dice Similarity Coefficient (DSC) values between predicted and ground truth contours were, respectively, 0.9615, 0.9770 and 0.9697.ConclusionOur experimental result indicates that the proposed method can effectively predict the dynamic contours of the moving and deforming tissue during ultrasound-guided HIFU therapy.

To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model's average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.