Study on outcome of fracture shaft of the humerus treated non-operatively with a functional brace.

BACKGROUND: Non-operatively treated fractures of the humeral diaphysis have a high rate of union with good functional results. The objective of this study is to find out the outcome of fractures of the humeral diaphysis treated with a functional brace that permits motion of shoulder and elbow joints and progressive use of the injured extremity. MATERIALS AND METHODS: This was a descriptive analytical study in patients of 16 years and above with closed fracture shaft of humerus treated with a functional brace that permits the motion of shoulder and elbow joints. The fracture arms were initially stabilized with U slab or hanging cast for an average of 11 days before application of brace. Radiographs were made at each follow-up visit until the fracture union occured. Angulation at fracture site, motion at shoulder and elbow joint were measured at the time of removal of brace. RESULTS: One hundred and five out of 108 fractures (97.2 %) were united with mean duration of 12.16 weeks (range, 7.5-19.3 weeks). Radial nerve injury was present in 6 cases (5.5 %). Varus angulation of ≤15° was present in 90.9 % out of 99 patients, while no angulation was present in 6 cases (5.7 %) out of 105 patients. Apex anterior angulation of ≤10° was present in 100 % out of 48 patients, whereas apex posterior angulation of ≤10° was present in 94.1 % out of 51 patients. CONCLUSION: Functional bracing for the treatment of fractures of the humeral diaphysis is associated with a high rate of union with nearly normal elbow motion and some restriction of shoulder motion.

REDIAL-2020: A suite of machine learning models to estimate Anti-SARS-CoV-2 activities.

Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The "best models" are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (http://drugcentral.org/Redial). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (viral entry, viral replication, and live virus infectivity) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (https://github.com/sirimullalab/redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.