Aims Metaplastic breast carcinoma (MBC) is a rare subtype of breast carcinoma less responsive to conventional chemotherapy than ductal carcinoma. In molecular terms, MBCs usually cluster with triple-negative breast cancers (TNBCs), but have a worse prognosis than TNBCs. Studies investigating MBCs for specific biomarkers of therapy response are rare and limited by the methodological approaches. The aim of the present study was to characterise MBCs on a molecular level and test programmed death-ligand 1 (PD-L1) biomarker expression in MBCs for future therapeutic interventions. Methods We profiled 297 samples (MBC (n=75), TNBC (n=106), human epidermal growth factor receptor 2 (HER2)-positive breast cancers (n=32) and hormone-positive breast cancers (n=84)) by next-generation sequencing. Immunohistochemistry for PD-L1 and programmed cell death 1 (PD-1) expression was performed using automated procedures. Results The most commonly mutated genes in MBCs included TP53 (56%) and PIK3CA (23%). Pathogenic mutations in other genes, including HRAS, FBXW7, PTEN, AKT1 and SMAD4, were rare. PD-L1 expression was detected in a significantly higher proportion of MBCs (46%) than in other subtypes (6% each in hormone-positive and HER2-positive breast cancers, and 9% in TNBC, not otherwise specified, p<0.001). PD-1-positive tumour infiltrating lymphocytes (TILs) varied greatly in MBCs. Conclusions Comprehensive profiling of a large cohort of this rare subtype of breast carcinoma highlighted the predominance of TP53 mutation and increased PD-L1 expression in carcinoma cells. These results can be exploited in clinical trials using immune checkpoint inhibitors.

Analytic Hierarchy Process (AHP) is often adopted in survey-based research activities and the number of participants involved in AHP studies ranges from few experts to hundreds of interviewed people. A common goal of survey research is to collect data representative of a population and, to this end, determining sample size is essential. The question then is, what is the appropriate sample size to run AHP in a survey-based study? To the best of the authors’ knowledge, no previous study addressed the proposed research question in the field of AHP-based survey. The current study aimed to propose a simulation approach for addressing the question of appropriate sample size for AHPbased survey. The proposed approach and the related findings will be presented and discussed.

Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible for data with millions of features. Here, we introduce the first feature selection method for nonlinear learning problems that can scale up to large, ultra-high dimensional biological data. More specifically, we scale up the novel Hilbert-Schmidt Independence Criterion Lasso (HSIC Lasso) to handle millions of features with tens of thousand samples. The proposed method is guaranteed to find an optimal subset of maximally predictive features with minimal redundancy, yielding higher predictive power and improved interpretability. Its effectiveness is demonstrated through applications to classify phenotypes based on module expression in human prostate cancer patients and to detect enzymes among protein structures. We achieve high accuracy with as few as 20 out of one million features—a dimensionality reduction of 99.998 percent. Our algorithm can be implemented on commodity cloud computing platforms. The dramatic reduction of features may lead to the ubiquitous deployment of sophisticated prediction models in mobile health care applications.

This paper represents the process of an on-going participatory action research project within a city affected by the socio-economic crisis. The focus of the research is to explore the possibilities of transforming idle capacity of skilled professionals into job opportunities through service design and defining strategies for designing a new value creation system between members in a community. The existing complementary currency models serve as an inspiration and foundation for conducting the research in collaborative and creative spaces, using a bottom-up approach in designing this service with the potential users in order to create value for that community. This could be achieved by giving shape to a service with its evidences as a framework to adapt to current conditions in peer-to-peer interactions. If a successful long-term and not only crisis-driven model could be designed, prototyped and globally replicated, based on debt-credit system and knowledge economy with enormous benefits of access to products and services, then it could enhance economic efficiency and distribute social capital while promoting new forms of entrepreneurship.

Introduction. We report a case of a sixty-year-old man diagnosed with gluteal compartment syndrome caused by traumatic rupture of the superior gluteal artery associated with fracture of the inferior pubic ramus and blunt trauma. Case report. A patient was injured falling from a height of four meters. Signs of compartment syndrome and sciatic nerve compression developed three hours after the injury. The patient went through a computerized tomography (CT) scan procedure with contrast, which showed a hematoma in the gluteal region, but without signs of active bleeding. However, after observation and monitoring of the patient, CT angiography was performed which revealed a rupture of the superior gluteal artery. Fasciotomy and debridement were performed and the patient was diagnosed with gluteal compartment syndrome and rupture of the superior gluteal artery. Surgery resulted in a significant improvement of the patient’s condition. Conclusion. Traumatic gluteal compartment syndrome is a rare condition. Gluteal compartment syndrome should be taken into consideration in each patient with pelvic trauma and hematoma in the gluteal region whose neurological status is affected. Prompt diagnosis and fasciotomy are crucial in the treatment and fasciotomy presents the gold standard in the treatment.