Early detection of respiratory disease outbreaks through primary healthcare data.

Background: The emergence of coronavirus disease 2019 (COVID-19) in 2020 highlighted the relevance of surveillance systems in detecting early signs of potential outbreaks, thus enabling public health authorities to act before the pathogen becomes widespread. Syndromic digital surveillance through web applications has played a crucial role in monitoring the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. However, this approach requires expensive infrastructure, which is not available in developing countries.

In silico approaches for drug repurposing in oncology: Protocol for a scoping review of existing evidence.

Drug repurposing has been applied in the biomedical field to optimize the use of existing drugs, leading to a more efficient allocation of research resources. In oncology, this approach is particularly interesting, considering the high cost related to the discovery of new drugs with therapeutic potential. Computational methods have been applied to predict associations between drugs and their targets.

A whole genome bioinformatic approach to determine potential latent phase specific targets in Mycobacterium tuberculosis.

Current Tuberculosis treatment is long and expensive, faces the increasing burden of MDR/XDR strains and lack of effective treatment against latent form, resulting in an urgent need of new anti-TB drugs. Key to TB biology is its capacity to fight the host's RNOS mediated attack. RNOS are known to display a concentration dependent mycobactericidal activity, which leads to the following hypothesis "if we know which proteins are targeted by RNOS and kill TB, we we might be able to inhibit them with drugs resulting in a synergistic bactericidal effect".