Repair of facial dog bite wound utilizing cryopreserved umbilical cord allograft.

BACKGROUND: In the United States, craniofacial injuries are the most frequently observed traumas in the pediatric population. Human birth tissue products contain growth factors, cytokines, and signaling molecules that can be potentially harnessed for tissue regeneration and wound healing. PURPOSE: A cryopreserved ultra-thick amniotic membrane (AM) allograft wound dressing was used in a patient with significant facial soft tissue loss due to a dog bite injury. METHODS: This is a single case report of a pediatric patient. After obtaining IRB exemption, operative and postoperative clinic notes were reviewed. RESULTS: A 10-year-old female presented to the emergency department after suffering a dog bite to her left cheek and upper lip, resulting in tissue loss. A cryopreserved ultra-thick AM allograft was used to cover the area of tissue loss as part of surgical reconstruction. The patient was followed up at 1 week, 3 weeks, 4 months, and 1 year after the graft was placed, and rapid healing and full epithelialization were achieved in addition to scar contracture due to wound location. CONCLUSION: In the setting of acute trauma and tissue loss, human birth tissue was found to promote epithelialization and regenerative healing of facial tissues.

Identifying and training deep learning neural networks on biomedical-related datasets.

This manuscript describes the development of a resources module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Size, growth, and density data for shallow-water sea urchins from Mexico to the Aleutian Islands, Alaska, 1956-2016.

Size, growth, and density have been studied for North American Pacific coast sea urchins Strongylocentrotus purpuratus, S. droebachiensis, S. polyacanthus, Mesocentrotus (Strongylocentrotus) franciscanus, Lytechinus pictus, Centrostephanus coronatus, and Arbacia stellata by various workers at diverse sites and for varying lengths of time from 1956 to present. Numerous peer-reviewed publications have used some of these data but some data have appeared only in graduate theses or the gray literature. There also are data that have never appeared outside original data sheets. Motivation for studies has included fisheries management and environmental monitoring of sewer and power plant outfalls as well as changes associated with disease epidemics. Studies also have focused on kelp restoration, community effects of sea otters, basic sea urchin biology, and monitoring. The data sets presented here are a historical record of size, density, and growth for a common group of marine invertebrates in intertidal and nearshore environments that can be used to test hypotheses concerning future changes associated with fisheries practices, shifts of predator distributions, climate and ecosystem changes, and ocean acidification along the Pacific Coast of North America and islands of the north Pacific. No copyright restrictions apply. Please credit this paper when using the data.

Tibiofemoral Osteoarthritis After Surgical or Nonsurgical Treatment of Anterior Cruciate Ligament Rupture: A Systematic Review.

OBJECTIVE: To determine if surgical or nonsurgical treatment of anterior cruciate ligament rupture affects the prevalence of posttraumatic tibiofemoral osteoarthritis (OA). DATA SOURCES: Studies published between 1983 and April 2012 were identified via EBSCOhost and OVID. Reference lists were then screened in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. STUDY SELECTION: Studies were included if (a) treatment outcomes focused on a direct comparison of surgical versus nonsurgical treatment of anterior cruciate ligament rupture, (b) the prevalence of tibiofemoral OA was reported, and (c) they were written in English. Studies were excluded if (a) the included patients were treated with cast immobilization after surgery, (b) the mean follow-up was less than 10 years, or (c) the patients underwent anterior cruciate ligament revision surgery. DATA EXTRACTION: Two independent investigators reviewed the included articles using the Newcastle-Ottawa Scale. Frequency of OA, surgical procedure, nonsurgical treatments, and participant characteristics were extracted and summarized. We calculated prevalence (%) and 95% confidence intervals for treatment groups for each individual study and overall. We developed 2 × 2 contingency tables to assess the association between treatment groups (exposed had surgery, referent was nonsurgical treatment) and the prevalence of OA. DATA SYNTHESIS: Four retrospective studies were identified (140 surgical patients, 240 nonsurgical patients). The mean Newcastle-Ottawa Scale score was 5 (range = 4-6 [of 10] points). Average length of follow-up was 11.8 years (range = 10-14 years). The prevalence of OA for surgically treated patients ranged from 32.6% to 51.2% (overall = 41.4%, 95% confidence interval = 35.0%, 48.1%) and for nonsurgical patients ranged from 24.5% to 42.3% (overall = 30.9%, 95% confidence interval = 24.4%, 38.3%). CONCLUSIONS: Although OA prevalence was higher in the surgical treatment group at a mean follow-up of 11.8 years, no definitive evidence supports surgical or nonsurgical treatment after anterior cruciate ligament injury to prevent posttraumatic OA. Current studies have been limited by small sample sizes, low methodologic quality, and a lack of data regarding confounding factors.