Women are at a significantly higher risk of developing osteoarthritis (OA) compared to males. The pathogenesis of osteoarthritis (OA) in women is poorly understood. Extracellular vesicles (EVs) have been shown to play an essential role in numerous signaling processes during the pathogenesis of age-related diseases via paracrine signaling. Molecular profiling of the synovial fluid-derived EVs cargo in women may help in the discovery of novel biomarkers and therapeutics for the treatment of OA in women. Previously, we reported that synovial fluid-derived EV miRNA cargo differs in a sex-specific manner. This study aims to characterize synovial fluid-derived EV protein cargo in OA patients. Our data showed sex-specific EVs protein content in OA. We found haptoglobin, orosomucoid, and ceruloplasmin significantly up-regulated, whereas apolipoprotein down-regulated in female OA EVs. In males, we discovered β-2-glycoprotein, and complement component 5 proteins significantly up-regulated and Spt-Ada-Gcn5 acetyltransferase (SAGA)-associated factor 29 down-regulated in male OA EVs. Database for Annotation, Visualization, and Integrated Discovery (DAVID) and QuickGO analysis revealed OA-specific protein involvement in several biological, molecular, and cellular pathways, specifically in inflammatory processes. In conclusion, synovial fluid EV protein content is altered in a sex-specific manner with OA, explaining the increased prevalence and severity of OA in women.

Background: Gunshot-related fractures near the elbow are challenging, and available data to guide the practitioner are lacking. This report analyzes injury patterns and treatment strategies in a case series from a high-volume urban trauma center.Methods: All periarticular gunshot fractures near the elbow treated at a level 1 trauma center from 2014 to 2018 were retrospectively reviewed. Fracture location, patient demographics, concomitant injuries, treatment modalities, and complications were analyzed.Results: Twenty-four patients were identified. All patients received prophylactic antibiotics upon admission and underwent urgent surgical debridement. Open reduction and internal fixation (ORIF) was performed with initial debridement in 22 of 24 patients. Seven patients sustained distal humerus fractures, 10 patients sustained isolated proximal ulna or proximal radius fractures, and seven had combined fracture patterns. Eleven patients presented with nerve palsy, and two had transected nerves. Two patients had vascular injury requiring repair. One patient required a temporary elbow-spanning external fixator and underwent staged debridement followed by ORIF. One patient with a grade IIIC fracture developed a deep infection that precluded ORIF. One patient required revision ORIF due to fracture displacement.Conclusions: This investigation reports on management of ballistic fractures near the elbow at a busy urban level I trauma center. Our management centered on rapid debridement, early definitive fixation, and intravenous antibiotic administration. We report on associated neurovascular injury, bone loss, and other challenges in this patient population.Level of evidence: IV.

Trigger finger is a common yet vastly understudied fibroproliferative hand pathology, severely affecting patients’ quality of life. Consistent trauma due to inadequate positioning within the afflicted finger’s tendon/pulley system leads to cellular dysregulation and eventual fibrosis. While the genetic characteristics of the fibrotic tissue in the trigger finger have been studied, the pathways that govern the initiation and propagation of fibrosis are still unknown. The complete gene expression profile of the trigger finger has never been explored. Our study has used the Nanostring nCounter gene expression assay to investigate the molecular signaling involved in trigger finger pathogenesis. We collected samples from patients undergoing trigger finger (n = 4) release surgery and compared the gene expression to carpal tunnel tissue (n = 4). Nanostring nCounter analysis identified 165 genes that were differentially regulated; 145 of these genes were upregulated, whereas 20 genes were downregulated. We found that several collagen genes were significantly upregulated, and a regulatory matrix metalloproteinase (MMP), MMP-3, was downregulated. Bioinformatic analysis revealed that several known signaling pathways were dysregulated, such as the TGF-β1 and Wnt signaling pathways. We also found several novel signaling pathways (e.g., PI3K, MAPK, JAK-STAT, and Notch) differentially regulated in trigger finger. The outcome of our study helps in understanding the molecular signaling pathway involved in the pathogenesis of the trigger finger.

Hypothesis: Large language models (LLMs) like ChatGPT have increasingly been used as online resources for patients with orthopedic conditions. Yet there is a paucity of information assessing the ability of LLMs to accurately and completely answer patient questions. The present study comparatively assessed both ChatGPT 3.5 and GPT-4 responses to frequently asked questions on common elbow pathologies, scoring for accuracy and completeness. It was hypothesized that ChatGPT 3.5 and GPT-4 would demonstrate high levels of accuracy for the specific query asked, but some responses would lack completeness, and GPT-4 would yield more accurate and complete responses than ChatGPT 3.5. Methods: ChatGPT was queried to identify five most common elbow pathologies (lateral epicondylitis, medial epicondylitis, cubital tunnel syndrome, distal biceps rupture, elbow arthritis). ChatGPT was then queried on the five most frequently asked questions for each elbow pathology. These 25 total questions were then individually asked of ChatGPT 3.5 and GPT-4. Responses were recorded and scored on 6-point Likert scale for accuracy and 3-point Likert scale for completeness by three fellowship-trained upper extremity orthopedic surgeons. ChatGPT 3.5 and GPT-4 responses were compared for each pathology using two-tailed t -tests. Results: Average accuracy scores for ChatGPT 3.5 ranged from 4.80 to 4.87. Average GPT-4 accuracy scores ranged from 4.80 to 5.13. Average completeness scores for ChatGPT 3.5 ranged from 2.13 to 2.47, and average completeness scores for GPT-4 ranged from 2.47 to 2.80. Total average accuracy for ChatGPT 3.5 was 4.83, and total average accuracy for GPT-4 was 5.0 ( p = 0.05). Total average completeness for ChatGPT 3.5 was 2.35, and total average completeness for GPT-4 was 2.66 ( p = 0.01). Conclusion: ChatGPT 3.5 and GPT-4 are capable of providing accurate and complete responses to frequently asked patient questions, with GPT-4 providing superior responses. Large language models like ChatGPT have potential to serve as a reliable online resource for patients with elbow conditions.