Our objective is to identify novel coronavirus disease 2019 (COVID-19) patients with a diagnosis of sudden sensorineural hearing loss (SSNHL) with an aim to describe possible mechanisms. A systematic review was conducted using PubMed and Google Scholar. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed^. Our search terms included: "Sensorineural Hearing Loss" + "COVID-19" or "Sensorineural Hearing Loss" + "SARS-CoV-2" or "Sensorineural Hearing Loss" + "Coronavirus". Studies that adhered to the inclusion and exclusion criteria were included in the review. Of the 20 articles identified in the initial search, five met the inclusion criteria. The included articles consisted of four case studies and one letter to the editor, with seven total patients analyzed. All patients were COVID-19 positive and exhibited SSNHL, either unilateral or bilateral. Four patients reported tinnitus and two patients experienced vertigo. One patient was treated with hydroxychloroquine and one patient was treated with a variety of medications. Four patients were treated with intravenous and/or oral steroids intended to treat the SSNHL. The current literature describing SSNHL in COVID-19 patients is insufficient to characterize the pattern of hearing loss or advise about the treatment or outcomes. Future studies require a larger database or population study.

Hepatocellular carcinoma is the third leading cause of cancer-related deaths worldwide. Many patients are not eligible for curative therapies, such as surgical resection of the tumor or a liver transplant. Transarterial embolization is one therapy clinically used in these cases; however, this requires a long procedure and careful placement of an intraarterial catheter. Gas embolization has been proposed as a fast, easily administered, more spatially selective, and less invasive alternative. Here, we demonstrate the feasibility and efficacy of using acoustic droplet vaporization to noninvasively generate gas emboli within vasculature. Intravital microscopy experiments were performed using the rat cremaster muscle to visually observe the formation of occlusions. Large gas emboli were produced within the vasculature in the rat cremaster, effectively occluding blood flow. Following these experiments, the therapeutic efficacy of gas embolization was investigated in an ectopic xenograft model of hepatocellular carcinoma in mice. The treatment group exhibited a significantly lower final tumor volume (ANOVA, p = 0.008) and growth rate than control groups – tumor growth was completely halted. Additionally, treated tumors exhibited significant necrosis as determined by histological analysis. To our knowledge, this study is the first to demonstrate the therapeutic efficacy of gas embolotherapy in a tumor model.

Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for GBM metabolism and assess recent advancements. A literature search was performed using predetermined search terms. Articles describing the use of an ML algorithm for GBM metabolism were included. Ten studies met the inclusion criteria for analysis: diagnostic (n = 3, 30%), prognostic (n = 6, 60%), or both (n = 1, 10%). Most studies analyzed data from multiple databases, while 50% (n = 5) included additional original samples. At least 2536 data samples were run through an ML algorithm. Twenty-seven ML algorithms were recorded with a mean of 2.8 algorithms per study. Algorithms were supervised (n = 24, 89%), unsupervised (n = 3, 11%), continuous (n = 19, 70%), or categorical (n = 8, 30%). The mean reported accuracy and AUC of ROC were 95.63% and 0.779, respectively. One hundred six metabolic markers were identified, but only EMP3 was reported in multiple studies. Many studies have identified potential biomarkers for GBM diagnosis and prognostication. These algorithms show promise; however, a consensus on even a handful of biomarkers has not yet been made.