A phase IV, prospective, observational study of the clinical safety of snake antivenoms.

Snake envenoming is a neglected tropical disease that affects more than 2.7 million people worldwide. The treatment is based on the administration of antivenom composed of heterologous immunoglobulins, species-specific therapy involving the possibility of adverse reactions due to activation of the immune system. Considering the scarcity of prospective studies evaluating the safety of snake antivenoms, this study aimed to describe and characterize adverse events after antivenom infusion in an observational, prospective, single-centre investigation conducted in a referral centre in Brazil. A total of 47 victims of snake envenoming were included in the study, who were mostly men (75%), with ages ranging from 2 to 83 years. Twenty-two participants (47%) presented manifestations compatible with infusion-related reactions (IRRs) during or up to two hours after F(ab')2 heterologous immunoglobulin infusion. The most common clinical manifestation related to the infusion was a diffuse cutaneous rash (82%), followed by respiratory manifestations (46%) and facial swelling (23%). In four cases (9%), IRR were considered serious adverse events (SAE), characterized by haemodynamic instability, airway obstruction or hypoxia. Only one patient developed symptoms compatible with serum sickness. Although almost half of the patients treated with antivenom sera experienced IRRs, the SAE rate was 9%; in all cases, the adverse reaction was reversible by using supportive treatment, and there were no deaths. The results have shown that there is much to improve in the antivenom production process to obtain a more purified and specific product. Even so, a timely antivenom serum administration managed by well-trained health teams is safe and prevents complications after snake-related accidents.

Rama: a machine learning approach for ribosomal protein prediction in plants.

Ribosomal proteins (RPs) play a fundamental role within all type of cells, as they are major components of ribosomes, which are essential for translation of mRNAs. Furthermore, these proteins are involved in various physiological and pathological processes. The intrinsic biological relevance of RPs motivated advanced studies for the identification of unrevealed RPs. In this work, we propose a new computational method, termed Rama, for the prediction of RPs, based on machine learning techniques, with a particular interest in plants. To perform an effective classification, Rama uses a set of fundamental attributes of the amino acid side chains and applies a two-step procedure to classify proteins with unknown function as RPs. The evaluation of the resultant predictive models showed that Rama could achieve mean sensitivity, precision, and specificity of 0.91, 0.91, and 0.82, respectively. Furthermore, a list of proteins that have no annotation in Phytozome v.10, and are annotated as RPs in Phytozome v.12, were correctly classified by our models. Additional computational experiments have also shown that Rama presents high accuracy to differentiate ribosomal proteins from RNA-binding proteins. Finally, two novel proteins of Arabidopsis thaliana were validated in biological experiments. Rama is freely available at http://inctipp.bioagro.ufv.br:8080/Rama .