ABSTRACT
Gout treatment has evolved rapidly in recent decades, and various drugs have been designed for acute and chronic management. Three medications used to treat gout include pegloticase, colchicine, and febuxostat. When prescribing these drugs, important factors to consider include pharmacokinetics, pharmacodynamics, population specifics, benefits, and contraindications. Pharmacokinetic considerations of each drug include absorption, distribution, metabolism, and elimination factors. Pharmacodynamics factors are assessed by their potential for toxicity and effects on serum uric acid levels. Additionally, the drug's targeted population must be considered to avoid unwanted complications in certain pre-existing conditions such as cardiovascular disease or glucose-6-dehydrogenase (G6PD) deficiency. In this paper, we aim to provide insight into the gout medications, pegloticase, colchicine, and febuxostat. This review will include their pharmacokinetics, pharmacodynamics, population specifics, benefits, and contraindications.
ABSTRACT
Ganglioneuroblastomas (GNBs) are a rare subtype of neoplastic tumors that arise from the autonomic nervous system and contain both mature gangliocytes and immature neuroblasts. The primary age group affected by GNBs is the pediatric population, with less than 50 cases of adult GNBs existing in the literature. To the authors' best knowledge, only 21 cases of GNBs arising in the adrenal glands of adults have been reported. Herein we present a literature review examining the symptoms, treatment type, age, and sex of adults, and the presence of tumor metastases and calcification from the 21 cases reported in the literature.
ABSTRACT
OBJECTIVE: Clinical trials are an essential part of the effort to find safe and effective prevention and treatment for COVID-19. Given the rapid growth of COVID-19 clinical trials, there is an urgent need for a better clinical trial information retrieval tool that supports searching by specifying criteria, including both eligibility criteria and structured trial information. MATERIALS AND METHODS: We built a linked graph for registered COVID-19 clinical trials: the COVID-19 Trial Graph, to facilitate retrieval of clinical trials. Natural language processing tools were leveraged to extract and normalize the clinical trial information from both their eligibility criteria free texts and structured information from ClinicalTrials.gov. We linked the extracted data using the COVID-19 Trial Graph and imported it to a graph database, which supports both querying and visualization. We evaluated trial graph using case queries and graph embedding. RESULTS: The graph currently (as of October 5, 2020) contains 3392 registered COVID-19 clinical trials, with 17 480 nodes and 65 236 relationships. Manual evaluation of case queries found high precision and recall scores on retrieving relevant clinical trials searching from both eligibility criteria and trial-structured information. We observed clustering in clinical trials via graph embedding, which also showed superiority over the baseline (0.870 vs 0.820) in evaluating whether a trial can complete its recruitment successfully. CONCLUSIONS: The COVID-19 Trial Graph is a novel representation of clinical trials that allows diverse search queries and provides a graph-based visualization of COVID-19 clinical trials. High-dimensional vectors mapped by graph embedding for clinical trials would be potentially beneficial for many downstream applications, such as trial end recruitment status prediction and trial similarity comparison. Our methodology also is generalizable to other clinical trials.
