[Community reorientation of primary care in a health area: ROCAP project].

Aim: Evaluate the effectiveness of an intervention to incorporate group and community activities on a sustained basis in all the Basic Health Zones (ZBS) of a health area.

Design: During January and February 2019, two members of the research team traveled to each ZBS to interview the medical and nursing coordinators who had previously received an ad hoc initial assessment questionnaire (QAI) by email.

Place: The scope is the 11 ZBS of a health area.

[Adequacy of the safety of metamizole and agranulocytosis].

Objective: To analyze whether the drug safety update issued by the Spanish Agency of Medicines and Healthcare Products (AEMPS), dated October 30, 2018, on agranulocytosis and metamizole contains accurate and necessary information to protect patients from the presentation of this adverse reaction (AR) and if the official documentation of medicines containing metamizole for doctors, pharmacists and the general population conforms to the guidelines of the AEMPS to reduce this risk.

Setting And Participants: Drug safety update, bibliographic search, information at the European Medicines Agency on metamizole drugs marketed in Spain, technical datasheets, leaflets, Bot PLUS Health Information Database and Catalog of Pharmaceutical Specialties. Notification of 4cases of agranulocytosis due to metamizole after the drug safety update was issued.

Artificial neural networks and linear discriminant analysis: a valuable combination in the selection of new antibacterial compounds.

A set of topological descriptors has been used to discriminate between antibacterial and nonantibacterial drugs. Topological descriptors are simple integers calculated from the molecular structure represented in SMILES format. The methods used for antibacterial activity discrimination were linear discriminant analysis (LDA) and artificial neural networks of a multilayer perceptron (MLP) type.

Drugs and nondrugs: an effective discrimination with topological methods and artificial neural networks.

A set of topological and structural descriptors has been used to discriminate general pharmacological activity. To that end, we selected a group of molecules with proven pharmacological activity including different therapeutic categories, and another molecule group without any activity. As a method for pharmacological activity discrimination, an artificial neural network was used, dividing molecules into active and inactive, to train the network and externally validate it.

Discrimination and selection of new potential antibacterial compounds using simple topological descriptors.

The aim of the work was to discriminate between antibacterial and non-antibacterial drugs by topological methods and to select new potential antibacterial agents from among new structures. The method used for antibacterial activity selection was a linear discriminant analysis (LDA). It is possible to obtain a QSAR interpretation of the information contained in the discriminant function.