Osteogenic parameters surrounding trabecular tantalum metal implants in osteotomies prepared via osseodensification drilling

Surgical fixation of implants into bone for the correction of bone deformities or defects is a traditional approach for skeletal stabilization. Important measures of efficacy of implants include implant stability and osseointegration?the direct interaction between living bone and an implant. Osseointegration depends on successful implant placement and subsequent bone remodeling. This study utilized osseodensification drilling (OD) in a low bone density model using trabecular metal (TM) implants. Three osteotomy sites, Regular, OD-CW (clockwise), and OD-CCW (counterclockwise), were prepared in each ilium of three female sheep. Drilling was performed at 1100rpm with saline irrigation. Trabecular metal (TM) (Zimmer®, Parsippany, NJ, USA) implants measuring 3.7mm in diameter x 10mm length were placed into respective osteotomies. A three-week period post-surgery was given to allow for healing to take place after which all three sheep were euthanized and the ilia were collected. Samples were prepared, qualitatively and quantitatively analyzed using histology micrographs and image analysis software (ImageJ, NIH, Bethesda, MD). Bone-to-implant contact (BIC) and bone area fraction occupancy (BAFO) were quantified to evaluate the osseointegration parameters. All implants exhibit successful bone formation in the peri-implant environment as well as within the open spaces of the trabecular network. Osseointegration within the TM (quantified by %BIC) as a function of drilling technique was more pronounced in OD samples(p>0.05). The %BAFO however shows a significant difference (p=0.036) between the CCW and R samples. Greater bone volume and frequency of bone chips are observed in OD samples. The utilization of OD as a design for improved fixation of hardware was supported by increased levels of stability, both primary and secondary. Histological data with OD provided notably different results from those of the regular drilling method

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages