Power doppler sonography: Anything to add to BI-RADS US in solid breast masses?

Objective: To evaluate the contribution of power Doppler ultrasonography (PDUS) to breast imaging reporting and data system ultrasonography (BI-RADS US) categorization of solid breast masses. Materials and methods: Totally 94 solid lesions with histopathological results in 49 patients were included in the study. US features of the lesions were classified according to American College of Radiologists (ACR) BI-RADS US lexicon. Lesions were evaluated qualitatively according to their PDUS properties and quantitatively with spectral analysis. Hypervascularity, penetration of vessels into the mass or branching-disordered course and resistivity index values higher than 0.85 were accepted as probable malignant criteria. Results: Fifty-five of 94 lesions were benign (58.5%), while 39 (41.5%) were malignant histopathologically. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of US and PDUS in the diagnosis of malignant lesions were 100%, 58.2%, 62.9%, 100% and 71.8%, 81.8%, 73.7%, 80.4%, respectively. Criteria used for the distinction of malignant and benign lesions like number of vessels (p < 0.05), distribution of tumoral vessels, morphology of vessels and resistivity index values higher than 0.85 showed statistically significant difference (p < 0.001). When sonographic findings were combined with PDUS and spectral analysis findings, sensitivity, specificity, PPV and NPV were 100%, 52.7%, 60% and 100%, respectively. Conclusion: PDUS and spectral analysis have no contribution to BI-RADS US. For the spectral analysis, when RI value is one or greater, malignancy risk significantly increases

In silico identification of potential inhibitors targeting n terminal of human replication protein a for cancer therapy

During replication stress, Replication Protein A (RPA) initiates the DDR activation by binding to single-stranded DNA (ssDNA), while it also mediates the DDR through recruiting protein partners. Given these pivotal roles, RPA has become an attractive target for cancer drug discovery. Hitherto, many efforts have been devoted to identify inhibitors of RPA, employing different methods ranging from high throughput screening to fragment-based approaches. Although these studies led to the identification of RPA inhibitors, large molecule databases are awaiting to be screened, marking a possibility to identify more potent inhibitor(s). To this end, here we report the virtual screening of the ZINC15 database which is composed of more than 700 million small molecules. Particularly the RPA70N domain is used as the target, enabling inhibition of the protein protein interactions (PPIs) formed by RPA. Prior to the screening, we assessed the performance of multiple docking tools by using benchmark ligand sets which were experimentally characterized. The best-performed tool, LeDock (r=0.745±0.08) was used for the large screen, and the ligands were filtered according to their docking scores and also the presence of a negatively charged group which was considered to be critical in binding to the positively charged amino acids located in the RPA70N cleft. 20 selected ligands were analyzed in molecular dynamics simulations followed by MM-PBSA prediction of binding free energy. The performance of the MM-PBSA method was also tested by using the benchmark set and the results showed a well-agreement (r=0.92) between the binding free energy predictions and experimental values. These validated tools led us to identify one promising ligand targeting RPA70N with a higher binding affinity and better drug-likeness features than any of the known inhibitors. Overall we surmise this ligand as being a inhibitor to target RPA70N more efficiently, reflecting its potential in reducing the side-effects associated with other RPA inhibitors

Optimal basis pursuit based on jaya optimization for adaptive fourier decomposition

The Adaptive Fourier Decomposition (AFD) is a novel signal decomposition algorithm that can describe an analytical signal through a linear combination of adaptive basis functions. At every decomposition step of the AFD, the basis function is determined by making a search in an over-complete dictionary. The decomposition continues until the difference between the energies of the original and reconstructed signals is to be less than a predefined tolerance. To reach the most accurate description of the signal, the AFD requires a large number of decomposition levels and a long duration because of using a sufficiently small tolerance and searching in a large dictionary. To make the AFD more practicable, we propose to combine it with Jaya algorithm for determining basis functions. The proposed approach does not require any dictionary and a tolerance for stopping decomposition. Furthermore, it enables to determine the decomposition level of the AFD automatically. © 2017 IEEE

Signal denoising based on adaptive fourier decomposition

Signal denoising based on the adaptive Fourier decomposition (AFD) is investigated and an approach, termed Jaya-based AFD combined with Savitzky-Golay filter, is offered to reconstruct the original signal under white Gaussian noise (WGN). Using the AFD, an analytic signal can be expressed via the summation of mono-components (MCs) whose energies are in decreasing order. Its ability to decompose signals according to their energy distributions makes the AFD useful for the signal reconstruction from noisy measurements with signal-to-noise ratios greater than zero in decibels. In every decomposition level, the conventional AFD requires an over-complete dictionary to determine the MCs. Without requiring such a dictionary, a metaheuristic optimization algorithm, termed Jaya, is used for determining the MCs. Savitzky-Golay filtering is then applied to the summation of MCs, which are obtained in every decomposition level of the noisy signal. Simulations performed on real-world signals show that the proposed approach provides satisfactory denoising performance. © 2017 Division of Signal Processing and Electronic Systems, Poznan University of Technology

Replacement Lipomatosis Of The Kidney: Mri Features

Renal replacement lipomatosis of the kidney is characterized by renal sinus and perirenal fat proliferation. It is associated with chronic infection and calculi, commonly central, often obstructing. The kidney may be large or small but is usually nonfunctioning. Most of the renal parenchyma has been replaced by fat, pararenal fascia are thickened, and there may be fistulae. We reported radiological findings of renal replacement lipomatosis in a 55 year-old man

Computerized Tomography Findings Of A Sternal Osteosarcoma

A 58-year-old female patient presented with chest pain and swelling in her neck. Computerized tomography showed an expansile mass containing lytic-sclerotic areas that caused destructions in the manibrium sterni and extended as far as to the thyroid gland at the lower cervical region and the upper mediastinum. Since osteosarcoma of the sternum is a extremely rare entity, we have thought to explain radiologic approach based on our experience