Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy

Background: A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification. Methods: This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications. The patients were divided into two groups according to micro-histopathology: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted for each patient, and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by using four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them. Results: The best performance was achieved using the RF classifier (AUC = 0.59, 95% confidence interval 0.57–0.60; sensitivity = 0.56, 95% CI 0.54–0.58; specificity = 0.61, 95% CI 0.59–0.63; accuracy = 0.58, 95% CI 0.57–0.59). Conclusions: DBT-based radiomic analysis seems to have only limited potential in discriminating benign from malignant microcalcifications

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy

Background: A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification. Methods: This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications. The patients were divided into two groups according to micro-histopathology: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted for each patient, and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by using four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them. Results: The best performance was achieved using the RF classifier (AUC = 0.59, 95% confidence interval 0.57–0.60; sensitivity = 0.56, 95% CI 0.54–0.58; specificity = 0.61, 95% CI 0.59–0.63; accuracy = 0.58, 95% CI 0.57–0.59). Conclusions: DBT-based radiomic analysis seems to have only limited potential in discriminating benign from malignant microcalcifications

Sea Water Contamination in the Vicinity of the Italian Minor Islands Caused by Microplastic Pollution

The abundance and distribution of microplastics (MP) were evaluated in six “clean” sites (Italian minor islands) and in two “polluted” areas (near the mouth of two major Italian rivers). Samples of MP, plankton and persistent organic pollutants (POPs) were collected using a manta trawl (MA) and a plankton net (WP2), both lined with a 333 µm mesh net. MP have been confirmed to be ubiquitous since they were found at each site, showing an average density of 0.3 ± 0.04 items/m3 (values ranged from 0.641 to 0.119 ). When comparing the clean sites with the polluted ones, a significantly higher value of MP was found near the river mouths. The most common types of MP were synthetic filaments (50.24%), followed by fragments (30.39%), thin plastic films (16.98%) and spheres (2.39%). Infrared spectroscopy analysis highlighted that the most abundant polymers were polyethylene (PE-26%), polypropylene (PP-11%), polyethylene-terephthalate/polyester (PET/PEST-8%) and ethylene-vinyl-acetate (EVA-5%). Polychlorinated biphenyls and organochlorine pesticides were detected in all the samples with a high variability among sites and depths. This study adds to the existing information on the distribution of contaminants across the Mediterranean Sea, and is useful to policy makers who wish to implement effective measures to reduce MP pollution

Yeast killer toxin-like candidacidal Ab6 antibodies elicited through the manipulation of the idiotypic cascade

A mouse anti-anti-anti-idiotypic (Id) IgM monoclonal antibody (mAb K20, Ab4), functionally mimicking a Wyckerhamomyces anomalus (Pichia anomala) killer toxin (KT) characterized by fungicidal activity against yeasts presenting specific cell wall receptors (KTR) mainly constituted by beta-1,3-glucan, was produced from animals presenting anti-KT Abs (Ab3) following immunization with a rat IgM anti-Id KT-like mAb (mAb K10, Ab2). MAb K10 was produced by immunization with a KTneutralizing mAb (mAb KT4, Ab1) bearing the internal image of KTR. MAb K20, likewise mAb K10, proved to be fungicidal in vitro against KT-sensitive Candida albicans cells, an activity neutralized by mAb KT4, and was capable of binding to beta-1,3- glucan. MAb K20 and mAb K10 competed with each other and with KT for binding to C. albicans KTR. MAb K20 was used to identify peptide mimics of KTR by the selection of phage clones from random peptide phage display libraries. Using this strategy, four peptides (TK 1-4) were selected and used as immunogen in mice in the form of either keyhole limpet hemocyanin (KLH) conjugates or peptide-encoding minigenes. Peptide and DNA immunization could induce serum Abs characterized by candidacidal activity, which was inhibited by laminarin, a soluble beta-1,3-glucan, but not by pustulan, a beta-1,6- glucan. These findings show that the idiotypic cascade can not only overcome the barrier of animal species but also the nature of immunogens and the type of technology adopted