Evaluation of an Antimicrobial L-Amino Acid Oxidase and Peptide Derivatives from Bothropoides mattogrosensis Pitviper Venom

Healthcare-associated infections (HAIs) are causes of mortality and morbidity worldwide. The prevalence of bacterial resistance to common antibiotics has increased in recent years, highlighting the need to develop novel alternatives for controlling these pathogens. Pitviper venoms are composed of a multifaceted mixture of peptides, proteins and inorganic components. L-amino oxidase (LAO) is a multifunctional enzyme that is able to develop different activities including antibacterial activity. In this study a novel LAO from Bothrops mattogrosensis (BmLAO) was isolated and biochemically characterized. Partial enzyme sequence showed full identity to Bothrops pauloensis LAO. Moreover, LAO here isolated showed remarkable antibacterial activity against Gram-positive and -negative bacteria, clearly suggesting a secondary protective function. Otherwise, no cytotoxic activities against macrophages and erythrocytes were observed. Finally, some LAO fragments (BmLAO-f1, BmLAO-f2 and BmLAO-f3) were synthesized and further evaluated, also showing enhanced antimicrobial activity. Peptide fragments, which are the key residues involved in antimicrobial activity, were also structurally studied by using theoretical models. The fragments reported here may be promising candidates in the rational design of new antibiotics that could be used to control resistant microorganisms

Coccidian Infection Causes Oxidative Damage in Greenfinches

The main tenet of immunoecology is that individual variation in immune responsiveness is caused by the costs of immune responses to the hosts. Oxidative damage resulting from the excessive production of reactive oxygen species during immune response is hypothesized to form one of such costs. We tested this hypothesis in experimental coccidian infection model in greenfinches Carduelis chloris. Administration of isosporan coccidians to experimental birds did not affect indices of antioxidant protection (TAC and OXY), plasma triglyceride and carotenoid levels or body mass, indicating that pathological consequences of infection were generally mild. Infected birds had on average 8% higher levels of plasma malondialdehyde (MDA, a toxic end-product of lipid peroxidation) than un-infected birds. The birds that had highest MDA levels subsequent to experimental infection experienced the highest decrease in infection intensity. This observation is consistent with the idea that oxidative stress is a causative agent in the control of coccidiosis and supports the concept of oxidative costs of immune responses and parasite resistance. The finding that oxidative damage accompanies even the mild infection with a common parasite highlights the relevance of oxidative stress biology for the immunoecological research

Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies