Toxicity prediction using target, interactome, and pathway profiles as descriptors.

In silico methods are essential to the safety evaluation of chemicals. Computational risk assessment offers several approaches, with data science and knowledge-based methods becoming an increasingly important sub-group. One of the substantial attributes of data science is that it allows using existing data to find correlations, build strong hypotheses, and create new, valuable knowledge that may help to reduce the number of resource intensive experiments.

Restoration of elective spine surgery during the first wave of COVID-19 : a UK-wide British Association of Spine Surgeons (BASS) prospective, multicentre, observational study.

Aims: With resumption of elective spine surgery services in the UK following the first wave of the COVID-19 pandemic, we conducted a multicentre British Association of Spine Surgeons (BASS) collaborative study to examine the complications and deaths due to COVID-19 at the recovery phase of the pandemic. The aim was to analyze the safety of elective spinal surgery during the pandemic.

Methods: A prospective observational study was conducted from eight spinal centres for the first month of operating following restoration of elective spine surgery in each individual unit.

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop.

We report the major conclusions of the online open-access workshop "Computational Applications in Secondary Metabolite Discovery (CAiSMD)" that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads.

Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors.

In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays.

Five Year Follow up of Retrospective Cohort Comparing Structural and Functional Outcome of Arthroscopic Single-row ersus Double-row Suture Bridge Repair of Large Posterosuperior Rotator Cuff Tear in Patients Less than or Equal to 70 Years.

Background: High re-tear rates after repairing large-sized posterosuperior rotator cuff tears remain a significant concern which may affect the clinical outcome. The most optimal type of repair (single versus double-row suture bridge) suited for large size tear remains debatable.

Methods: In a retrospective cohort study with a minimum of five years follow up, the structural and functional outcome of 103 patients with large size cuff tear repaired with single row (SR) or double row suture bridge (DRSB) were evaluated.