Tail proteins of phage T5: investigation of the effect of the His6-tag position, from expression to crystallisation.

Upon binding to its bacterial host receptor, the tail tip of phage T5 perforates, by an unknown mechanism, the heavily armoured cell wall of the host. This allows the injection of phage DNA into the cytoplasm to hijack the cell machinery and enable the production of new virions. In the perspective of a structural study of the phage tail, we have systematically overproduced eight of the eleven T5 tail proteins, with or without a N- or a C-terminal His6-tag. The widely used Hi6-tag is very convenient to purify recombinant proteins using immobilised-metal affinity chromatography. The presence of a tag however is not always innocuous. We combined automated gene cloning and expression tests to rapidly identify the most promising constructs for proteins of phage T5 tail, and performed biochemical and biophysical characterisation and crystallisation screening on available proteins. Automated small-scale purification was adapted for two highly expressed proteins. We obtained structural information for three of the proteins. We showed that the presence of a His6-tag can have drastic effect on protein expression, solubility, oligomerisation propensity and crystal quality.

Prediction of metabolic syndrome and its associated risk factors in patients with chronic kidney disease using machine learning techniques.

INTRODUCTION: Chronic kidney disease (CKD) and metabolic syndrome (MS) are recognized as public health problems which are related to overweight and cardiometabolic factors. The aim of this study was to develop a model to predict MS in people with CKD. METHODS: This was a prospective cross-sectional study of patients from a reference center in São Luís, MA, Brazil. The sample included adult volunteers classified according to the presence of mild or severe CKD. For MS tracking, the k-nearest neighbors (KNN) classifier algorithm was used with the following inputs: gender, smoking, neck circumference, and waist-to-hip ratio. Results were considered significant at p < 0.05. RESULTS: A total of 196 adult patients were evaluated with a mean age of 44.73 years, 71.9% female, 69.4% overweight, and 12.24% with CKD. Of the latter, 45.8% had MS, the majority had up to 3 altered metabolic components, and the group with CKD showed statistical significance in: waist circumference, systolic blood pressure, diastolic blood pressure, and fasting blood glucose. The KNN algorithm proved to be a good predictor for MS screening with 79% accuracy and sensitivity and 80% specificity (area under the ROC curve - AUC = 0.79). CONCLUSION: The KNN algorithm can be used as a low-cost screening method to evaluate the presence of MS in people with CKD.

Phenotypic Characterization of Chronic Kidney Patients Through Hierarchical Clustering.

Chronic kidney disease is a major public health problem around the world and this disease early diagnosis is still a great challenge as it is asymptomatic in its early stages. Thus, in order to identify variables capable of assisting CKD diagnosis and monitoring, machine learning techniques and statistical analysis use has shown itself to be extremely promising. For this work, unsupervised machine learning, statistical analysis techniques and discriminant analysis were used.Clinical Relevance - Discriminating variables characterization assist to differentiate groups of patients in different stages of Chronic Kidney Disease and it has important outcomes in the development of future models to aid clinical decision-making, as they can generate models with a greater predictive capacity for Chronic Kidney Disease, predominantly aiding the early diagnosis capacity of this pathology.