A Yolo-Based Model for Breast Cancer Detection in Mammograms

This work aims to implement an automated data-driven model for breast cancer detection in mammograms to support physicians' decision process within a breast cancer screening or detection program. The public available CBIS-DDSM and the INbreast datasets were used as sources to implement the transfer learning technique on full-field digital mammography proprietary dataset. The proprietary dataset reflects a real heterogeneous case study, consisting of 190 masses, 46 asymmetries, and 71 distortions. Several Yolo architectures were compared, including YoloV3, YoloV5, and YoloV5-Transformer. In addition, Eigen-CAM was implemented for model introspection and outputs explanation by highlighting all the suspicious regions of interest within the mammogram. The small YoloV5 model resulted in the best developed solution obtaining an mAP of 0.621 on proprietary dataset. The saliency maps computed via Eigen-CAM have proven capable solution reporting all regions of interest also on incorrect prediction scenarios. In particular, Eigen-CAM produces a substantial reduction in the incidence of false negatives, although accompanied by an increase in false positives. Despite the presence of hard-to-recognize anomalies such as asymmetries and distortions on the proprietary dataset, the trained model showed encouraging detection capabilities. The combination of Yolo predictions and the generated saliency maps represent two complementary outputs for the reduction of false negatives. Nevertheless, it is imperative to regard these outputs as qualitative tools that invariably necessitate clinical radiologic evaluation. In this view, the model represents a trusted predictive system to support cognitive and decision-making, encouraging its integration into real clinical practice

Transmission of Group B Streptococcus in late-onset neonatal disease: a narrative review of current evidence

Group B streptococcus (GBS) late-onset disease (LOD, occurring from 7 through 89 days of life) is an important cause of sepsis and meningitis in infants. The pathogenesis and modes of transmission of LOD to neonates are yet to be elucidated. Established risk factors for the incidence of LOD include maternal GBS colonisation, young maternal age, preterm birth, HIV exposure and African ethnicity. The mucosal colonisation by GBS may be acquired perinatally or in the postpartum period from maternal or other sources. Growing evidence has demonstrated the predominant role of maternal sources in the transmission of LOD. Intrapartum antibiotic prophylaxis (IAP) to prevent early-onset disease reduces neonatal GBS colonisation during delivery; however, a significant proportion of IAP-exposed neonates born to GBS-carrier mothers acquire the pathogen at mucosal sites in the first weeks of life. GBS-infected breast milk, with or without presence of mastitis, is considered a potential vehicle for transmitting GBS. Furthermore, horizontal transmission is possible from nosocomial and other community sources. Although unfrequently reported, nosocomial transmission of GBS in the neonatal intensive care unit is probably less rare than is usually believed. GBS disease can sometime recur and is usually caused by the same GBS serotype that caused the primary infection. This review aims to discuss the dynamics of transmission of GBS in the neonatal LOD

Disease activity states, reasons for discontinuation and adverse events in 1038 Italian children with juvenile idiopathic arthritis treated with etanercept

The advent of biologic medications has increased considerably the potential for treatment benefit in juvenile idiopathic arthritis (JIA), with clinical remission being now achievable in a substantial proportion of patients. However, there is a need of data from the real world of clinical practice to evaluate thoroughly the efficacy and safety profile of the biologic agents currently approved