Evaluation of Mid-term Outcomes Following Shoulder Hemiarthroplasty for Avascular Necrosis of the Humeral Head in Patients 40 Years and Younger.

In order to evaluate postoperative function and failure rates among younger patients undergoing hemiarthroplasty for humeral head avascular necrosis (AVN), data from patients < 40 years treated between December 2008 - January 2018 was retrospectively analyzed. Pain was assessed preoperatively and at final follow up using a visual analogue scale (VAS). The American Shoulder and Elbow Surgeons (ASES) standardized assessment, single assessment numeric evaluation (SANE) score, and patient satisfaction were assessed at final follow up, as well as surgical revision rates. In total, eight shoulders were included in the final analysis, with a follow up of 6.6 + 3.6 years. Analysis indicated a statistical improvement in VAS pain (p = 0.001), while comparison of postoperative function between surgical and non-surgical limbs did not demonstrate statistical differences in SANE or ASES averages (p > 0.05). At final follow up, 25% of patients expressed dissatisfaction; however, there were no cases of revision surgery. In conclusion, younger patients undergoing hemiarthroplasty for humeral head AVN experienced pain improvement and no revisions at short-to-mid-term follow up, but one-in-four indicated dissatisfaction. Level of evidence: IV, case series. (Journal of Surgical Orthopaedic Advances 32(2):118-121, 2023).

A RESTful API for accessing microbial community data for MG-RAST.

Metagenomic sequencing has produced significant amounts of data in recent years. For example, as of summer 2013, MG-RAST has been used to annotate over 110,000 data sets totaling over 43 Terabases. With metagenomic sequencing finding even wider adoption in the scientific community, the existing web-based analysis tools and infrastructure in MG-RAST provide limited capability for data retrieval and analysis, such as comparative analysis between multiple data sets. Moreover, although the system provides many analysis tools, it is not comprehensive. By opening MG-RAST up via a web services API (application programmers interface) we have greatly expanded access to MG-RAST data, as well as provided a mechanism for the use of third-party analysis tools with MG-RAST data. This RESTful API makes all data and data objects created by the MG-RAST pipeline accessible as JSON objects. As part of the DOE Systems Biology Knowledgebase project (KBase, http://kbase.us) we have implemented a web services API for MG-RAST. This API complements the existing MG-RAST web interface and constitutes the basis of KBase's microbial community capabilities. In addition, the API exposes a comprehensive collection of data to programmers. This API, which uses a RESTful (Representational State Transfer) implementation, is compatible with most programming environments and should be easy to use for end users and third parties. It provides comprehensive access to sequence data, quality control results, annotations, and many other data types. Where feasible, we have used standards to expose data and metadata. Code examples are provided in a number of languages both to show the versatility of the API and to provide a starting point for users. We present an API that exposes the data in MG-RAST for consumption by our users, greatly enhancing the utility of the MG-RAST service.

A metagenomics portal for a democratized sequencing world.

The democratized world of sequencing is leading to numerous data analysis challenges; MG-RAST addresses many of these challenges for diverse datasets, including amplicon datasets, shotgun metagenomes, and metatranscriptomes. The changes from version 2 to version 3 include the addition of a dedicated gene calling stage using FragGenescan, clustering of predicted proteins at 90% identity, and the use of BLAT for the computation of similarities. Together with changes in the underlying software infrastructure, this has enabled the dramatic scaling up of pipeline throughput while remaining on a limited hardware budget. The Web-based service allows upload, fully automated analysis, and visualization of results. As a result of the plummeting cost of sequencing and the readily available analytical power of MG-RAST, over 78,000 metagenomic datasets have been analyzed, with over 12,000 of them publicly available in MG-RAST.