Histologic subtyping affecting outcome of triple negative breast cancer: a large Sardinian population-based analysis.

Background: Triple Negative breast cancer (TNBC) includes a heterogeneous group of tumors with different clinico-pathological features, molecular alterations and treatment responsivity. Our aim was to evaluate the clinico-pathological heterogeneity and prognostic significance of TNBC histologic variants, comparing "special types" to high-grade invasive breast carcinomas of no special type (IBC-NST).

Methods: This study was performed on data obtained from TNBC Database, including pathological features and clinical records of 1009 TNBCs patients diagnosed between 1994 and 2015 in the four most important Oncology Units located in different hospitals in Sardinia, Italy.

Clinical and pathological factors influencing survival in a large cohort of triple-negative breast cancer patients.

Background: To provide further information on the clinical and pathological prognostic factors in triple-negative breast cancer (TNBC), for which limited and inconsistent data are available.

Methods: Pathological characteristics and clinical records of 841 TNBCs diagnosed between 1994 and 2015 in four major oncologic centers from Sardinia, Italy, were reviewed. Multivariate hazard ratios (HRs) for mortality and recurrence according to various clinicopathological factors were estimated using Cox proportional hazards models.

Fragment Prioritization on a Large Mutagenicity Dataset.

The identification of structural alerts is one of the simplest tools used for the identification of potentially toxic chemical compounds. Structural alerts have served as an aid to quickly identify chemicals that should be either prioritized for testing or for elimination from further consideration and use. In the recent years, the availability of larger datasets, often growing in the context of collaborative efforts and competitions, created the raw material needed to identify new and more accurate structural alerts.

A generalizable definition of chemical similarity for read-across.

Background: Methods that provide a measure of chemical similarity are strongly relevant in several fields of chemoinformatics as they allow to predict the molecular behavior and fate of structurally close compounds. One common application of chemical similarity measurements, based on the principle that similar molecules have similar properties, is the read-across approach, where an estimation of a specific endpoint for a chemical is provided using experimental data available from highly similar compounds.

Results: This paper reports the comparison of multiple combinations of binary fingerprints and similarity metrics for computing the chemical similarity in the context of two different applications of the read-across technique.

Implementing the "Best Template Searching" tool into Adenosiland platform.

Background: Adenosine receptors (ARs) belong to the G protein-coupled receptors (GCPRs) family. The recent release of X-ray structures of the human A2A AR (h A2A AR ) in complex with agonists and antagonists has increased the application of structure-based drug design approaches to this class of receptors. Among them, homology modeling represents the method of choice to gather structural information on the other receptor subtypes, namely A1, A2B, and A3 ARs.

Adenosiland: walking through adenosine receptors landscape.

Adenosine receptors (ARs) belong to the family of G protein-coupled receptors. Four distinct subtypes are known, termed adenosine A(1), A(2A), A(2B) and A(3). receptors and they are regulated by adenosine which is one of the most ancient and widespread chemical messengers in the animal and plant kingdoms.