Migration of Broken Dental Needle Through the Internal Jugular Vein in the Parapharyngeal Space.

Accidental broken dental needles during dental blocks have become a rare occurrence but still occur. Although the treatment for such occurrence is controversial, an increasing body of literature demonstrates that migration of such needles is possible. In this case, we report on a 48-year-old male with migration of a broken dental needle from an inferior alveolar block. Over the course of 2 years, we demonstrated radiological documentation of the course of migration with penetration of the internal jugular vein at the jugular foramen which was subsequently successfully retrieved through a transcervical approach without neurovascular injury. This case is unique given the location of migration to the skull base as well as radiologically documented time course. Furthermore, it highlights the need for prompt retrieval of broken dental needles given the high potential of migration and injury to neurovascular structures.

Detection of Head and Neck Cancer Based on Longitudinal Changes in Serum Protein Abundance.

BACKGROUND: Approximately 85% of the U.S. military active duty population is male and less than 50 years of age, with elevated levels of known risk factors for oropharyngeal squamous cell carcinoma (OPSCC), including smoking, excessive use of alcohol, and greater numbers of sexual partners, and elevated prevalence of human papilloma virus (HPV). Given the recent rise in incidence of OPSCC related to the HPV, the Department of Defense Serum Repository provides an unparalleled resource for longitudinal studies of OPSCC in the military for the identification of early detection biomarkers. METHODS: We identified 175 patients diagnosed with OPSCC with 175 matched healthy controls and retrieved a total of 978 serum samples drawn at the time of diagnosis, 2 and 4 years prior to diagnosis, and 2 years after diagnosis. Following immunoaffinity depletion, serum samples were analyzed by targeted proteomics assays for multiplexed quantification of a panel of 146 candidate protein biomarkers from the curated literature. RESULTS: Using a Random Forest machine learning approach, we derived a 13-protein signature that distinguishes cases versus controls based on longitudinal changes in serum protein concentration. The abundances of each of the 13 proteins remain constant over time in control subjects. The AUC for the derived Random Forest classifier was 0.90. CONCLUSIONS: This 13-protein classifier is highly promising for detection of OPSCC prior to overt symptoms. IMPACT: Use of longitudinal samples has significant potential to identify biomarkers for detection and risk stratification.