Raman analysis of microcalcifications in male breast cancer.

Microcalcifications (MCs) are important disease markers for breast cancer. Many studies were conducted on their characterization in female breast cancer (FBC), but no information is available on their composition in male breast cancer (MBC). Raman spectroscopy (RS) is a molecular spectroscopy that can rapidly explore the biochemical composition of MCs without requiring any staining protocol. In this study, we optimized an algorithm to identify the mineral components present in MCs from Raman images. The algorithm was then used to study and compare MCs identified on breast cancer pieces from male and female patients. In total, we analyzed 41 MCs from 5 invasive MBC patients and 149 MCs from 13 invasive FBC patients. Results show that hydroxyapatite is the most abundant type of calcium both in MBC and FBC. However, some differences in the amount and distribution of calcium minerals are present between the two groups. Besides, we observed that MCs in MBC have a higher amount of organic material (collagen) than FBC. To the best of our knowledge, this study provides the first overview of the composition of MCs present in MBC patients; and suggests that these patients have specific features that differentiate them from the previously studied FBC. Our result support thus the need for studies designed explicitly to the understanding of MBC.

A Bioorthogonal Probe for Multiscale Imaging by 19F-MRI and Raman Microscopy: From Whole Body to Single Cells.

Molecular imaging techniques are essential tools for better investigating biological processes and detecting disease biomarkers with improvement of both diagnosis and therapy monitoring. Often, a single imaging technique is not sufficient to obtain comprehensive information at different levels. Multimodal diagnostic probes are key tools to enable imaging across multiple scales. The direct registration of in vivo imaging markers with ex vivo imaging at the cellular level with a single probe is still challenging. Fluorinated (19F) probes have been increasingly showing promising potentialities for in vivo cell tracking by 19F-MRI. Here we present the unique features of a bioorthogonal 19F-probe that enables direct signal correlation of MRI with Raman imaging. In particular, we reveal the ability of PERFECTA, a superfluorinated molecule, to exhibit a remarkable intense Raman signal distinct from cell and tissue fingerprints. Therefore, PERFECTA combines in a single molecule excellent characteristics for both macroscopic in vivo 19F-MRI, across the whole body, and microscopic imaging at tissue and cellular levels by Raman imaging.

Stable and scalable SERS tags conjugated with neutravidin for the detection of fibroblast activation protein (FAP) in primary fibroblasts.

SERS tags are a class of nanoparticles with great potential in advanced imaging experiments. The preparation of SERS tags however is complex, as they suffer from the high variability of the SERS signals observed even at the slightest sign of aggregation. Here, we developed a method for the preparation of SERS tags based on the use of gold nanostars conjugated with neutravidin. The SERS tags here obtained are extremely stable in all biological buffers commonly employed and can be prepared at a relatively large scale in very mild conditions. The obtained SERS tags have been used to monitor the expression of fibroblast activation protein alpha (FAP) on the membrane of primary fibroblasts obtained from patients affected by Crohn's disease. The SERS tags allowed the unambiguous identification of FAP on the surface of cells thus suggesting the feasibility of semi-quantitative analysis of the target protein. Moreover, the use of the neutravidin-biotin system allows to apply the SERS tags for any other marker detection, for example, different cancer cell types, simply by changing the biotinylated antibody chosen in the analysis.

Deep Learning to Unveil Correlations between Urban Landscape and Population Health.

The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary lifestyle. Healthcare providers deal with increasing new challenges, and thanks to fast-developing big data technologies, they can be faced with systems that provide direct support to citizens. In this context, within the EU-funded Participatory Urban Living for Sustainable Environments (PULSE) project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches, to jointly analyze maps and geospatial information with healthcare and air pollution data. In this paper we describe a component of such platforms, which couples deep learning analysis of urban geospatial images with healthcare indexes collected by the 500 Cities project. By applying a pre-learned deep Neural Network architecture, satellite images of New York City are analyzed and latent feature variables are extracted. These features are used to derive clusters, which are correlated with healthcare indicators by means of a multivariate classification model. Thanks to this pipeline, it is possible to show that, in New York City, health care indexes are significantly correlated to the urban landscape. This pipeline can serve as a basis to ease urban planning, since the same interventions can be organized on similar areas, even if geographically distant.