Sampling globally and locally correct RNA 3D structures using Ernwin, SPQR and experimental SAXS data.

The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray scattering can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent.

Patient self-reported pain and nausea via smartphone following day care surgery, first year results: An observational cohort study.

Contact with the hospital is usually limited for patients after day care surgery. Dedicated smartphone applications can improve communication and possibly enhance outcomes. The objective of this retrospective study was to evaluate patients' self-reported pain and nausea and assess the success of routine implementation of a smartphone application for outcome reporting.

The Multiscale Ernwin/SPQR RNA Structure Prediction Pipeline.

Aside from the well-known role in protein synthesis, RNA can perform catalytic, regulatory, and other essential biological functions which are determined by its three-dimensional structure. In this regard, a great effort has been made during the past decade to develop computational tools for the prediction of the structure of RNAs from the knowledge of their sequence, incorporating experimental data to refine or guide the modeling process. Nevertheless, this task can become exceptionally challenging when dealing with long noncoding RNAs, constituted by more than 200 nucleotides, due to their large size and the specific interactions involved.

Baseline and end-of-treatment host serum biomarkers predict relapse in adults with pulmonary tuberculosis.

Background: There is a need for new tools for monitoring of the response to TB treatment. Such tools may allow for tailored treatment regimens, and stratify patients initiating TB treatment into different risk groups. We evaluated combinations between previously published host biomarkers and new candidates, as tools for monitoring TB treatment response, and prediction of relapse.