Correction to: inflammation is a target of medical treatment for lower urinary tract symptoms associated with benign prostatic hyperplasia

The article “Inflammation is a target of medical treatment for lower urinary tract symptoms associated with benign prostatic hyperplasia”, written by Cosimo De Nunzio, Andrea Salonia, Mauro Gacci and Vincenzo Ficarra was originally published electronically on the publisher’s internet portal on 14 February 2020 without open access

Inflammation is a target of medical treatment for lower urinary tract symptoms associated with benign prostatic hyperplasia

Purpose: To review the role of a persistent prostatic inflammatory status (PIS) in the development and progression of benign prostatic hyperplasia (BPH) associated with lower urinary tract symptoms (LUTS) and which medical therapies approved for LUTS/BPH may reduce persistent PIS. Methods: Literature search in PubMed up to July 2019. Results: The cause of histologically defined persistent PIS or chronic prostatic inflammation is multifactorial. It is evident in many men with LUTS/BPH, particularly in older men and in men with a large prostate volume or more severe (storage) LUTS. Additionally, persistent PIS is associated with an increased risk of acute urinary retention and symptom worsening. Of medical therapies approved for LUTS/BPH, the current evidence for a reduction of persistent PIS is greatest for the hexanic extract of Serenoa repens (HESr). This treatment relieves LUTS to the same extent as α1-adrenoceptor antagonists and short-term 5α-reductase inhibitors. Limited evidence is available on the effect of other mainstream LUTS/BPH treatments on persistent PIS. Conclusions: Persistent PIS plays a central role in both the development and progression of LUTS/BPH. In men with LUTS/BPH who have a high chance of harbouring persistent PIS, HESr will not only improve LUTS, but also reduce (underlying) inflammation. Well-designed clinical studies, with a good level of evidence, are required to better evaluate the impact of BPH/LUTS medical therapies on persistent PIS

Breast cancer mass detection in dce-mri using deep-learning features followed by discrimination of infiltrative vs. in situ carcinoma through a machine-learning approach

Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumor can be categorized into two main groups-in situ and infiltrative, with the latter being the most common malignant lesions. The current use of magnetic resonance imaging (MRI) was shown to provide the highest sensitivity in the detection and discrimination between benign vs. malignant lesions, when interpreted by expert radiologists. In this article, we present the prototype of a computer-aided detection/diagnosis (CAD) system that could provide valuable assistance to radiologists for discrimination between in situ and infiltrating tumors. The system consists of two main processing levels-(1) localization of possibly tumoral regions of interest (ROIs) through an iterative procedure based on intensity values (ROI Hunter), followed by a deep-feature extraction and classification method for false-positive rejection; and (2) characterization of the selected ROIs and discrimination between in situ and invasive tumor, consisting of Radiomics feature extraction and classification through a machine-learning algorithm. The CAD system was developed and evaluated using a DCE-MRI image database, containing at least one confirmed mass per image, as diagnosed by an expert radiologist. When evaluating the accuracy of the ROI Hunter procedure with respect to the radiologist-drawn boundaries, sensitivity to mass detection was found to be 75%. The AUC of the ROC curve for discrimination between in situ and infiltrative tumors was 0.70

Automated detection of lung nodules in low-dose computed tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness (~300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very high (75% range) even at 1-6 FP/scanComment: 4 pages, 2 figures: Proceedings of the Computer Assisted Radiology and Surgery, 21th International Congress and Exhibition, Berlin, Volume 2, Supplement 1, June 2007, pp 357-35

Hardware prototyping and validation of a W-ΔDOR digital signal processor

Microwave tracking, usually performed by on ground processing of the signals coming from a spacecraft, represents a crucial aspect in every deep-space mission. Various noise sources, including receiver noise, affect these signals, limiting the accuracy of the radiometric measurements obtained from the radio link. There are several methods used for spacecraft tracking, including the Delta-Differential One-Way Ranging (ΔDOR) technique. In the past years, European Space Agency (ESA) missions relied on a narrowband ΔDOR system for navigation in the cruise phase. To limit the adverse effect of nonlinearities in the receiving chain, an innovative wideband approach to ΔDOR measurements has recently been proposed. This work presents the hardware implementation of a new version of the ESA X/Ka Deep Space Transponder based on the new tracking technique named Wideband ΔDOR (W-ΔDOR). The architecture of the new transponder guarantees backward compatibility with narrowband ΔDOR

Flavonoid and non-flavonoid compounds of autumn royal and egnatia grape skin extracts affect membrane PUFA's profile and cell morphology in human colon cancer cell lines

Grapes contain many flavonoid and non-flavonoid compounds with anticancer effects. In this work we fully characterized the polyphenolic profile of two grape skin extracts (GSEs), Autumn Royal and Egnatia, and assessed their effects on Polyunsaturated Fatty Acid (PUFA) membrane levels of Caco2 and SW480 human colon cancer cell lines. Gene expression of 15-lipoxygenase-1 (15-LOX-1), and peroxisome proliferator-activated receptor gamma (PPAR-γ), as well as cell morphology, were evaluated. The polyphenolic composition was analyzed by Ultra-High-Performance Liquid Chromatography/Quadrupole-Time of Flight mass spectrometry (UHPLC/QTOF) analysis. PUFA levels were evaluated by gas chromatography, and gene expression levels of 15-LOX-1 and PPAR-γ were analyzed by real-time Polymerase Chain Reaction (PCR). Morphological cell changes caused by GSEs were identified by field emission scanning electron microscope (FE-SEM) and photomicrograph examination. We detected a different profile of flavonoid and non-flavonoid compounds in Autumn Royal and Egnatia GSEs. Cultured cells showed an increase of total PUFA levels mainly after treatment with Autumn Royal grape, and were richer in flavonoids when compared with the Egnatia variety. Both GSEs were able to affect 15-LOX-1 and PPAR-γ gene expression and cell morphology. Our results highlighted a new antitumor mechanism of GSEs that involves membrane PUFAs and their downstream pathways

Patient{'}s adherence on pharmacological therapy for benign prostatic hyperplasia (BPH)-associated lower urinary tract symptoms (LUTS) is different: Is combination therapy better than monotherapy

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