Acute gastric volvulus is a life threatening condition requiring early diagnosis and aggressive management. Diagnosis of gastric volvulus remains a challenge for clinicians due to variable, non-specific clinical presentation, which requires a high level of suspicion. It should be considered in patients presenting with chest pain and/or epigastric pain, especially in the elderly population. Endoscopic de-rotation could be initially attempted as a therapeutic modality especially in patients who cannot undergo surgery. However, surgery remains the main stay of treatment. Delay in diagnosis can lead to complications like mucosal ischemia, necrosis or perforation, shock, which substantially increase the morbidity and mortality.

Traditionally, the elucidation of genes involved in maturation and aging has been studied in a temporal fashion by examining gene expression at different time points in an organism's life as well as by knocking out, knocking in, and mutating genes thought to be involved. Here, we propose an in silico method to combine clinical electronic medical record (EMR) data and gene expression measurements in the context of disease to identify genes that may be involved in the process of human maturation and aging. First we show that absolute lymphocyte count may serve as a biomarker for maturation by using statistical methods to compare trends among different clinical laboratory tests in response to an increase in age. We then propose using the rate of decay for absolute lymphocyte count across 12 diseases as a proxy for differences in aging. We correlate the differing rates with gene expression across the same diseases to find maturation/aging related genes. Among the 53 genes with strongest correlations between expression profile and change in rate of decay, we found genes previously implicated in the process of aging, including MGMT (DNA repair), TERF2 (telomere stability), POLD1 (DNA replication and repair), and POLG (mtDNA replication).

Bioinformatics methods that leverage the vast amounts of clinical data promises to provide insights into underlying molecular mechanisms that help explain human physiological processes. One of these processes is adolescent development. The utility of predictive aging models generated from cross-sectional cohorts and their applicability to separate populations, including the clinical population, has yet to be completely explored. In order to address this, we built regression models predictive of adolescent chronological age from 2001–2002 National Health and Nutrition Examination Survey (NHANES) data and validated them against independent 2003–2004 NHANES data and clinical data from an academic tertiary-care pediatric hospital. The results indicate distinct differences between male and female models with both alkaline phosphatase and creatinine as predictive biomarkers for all genders, hematocrit and mean cell volume for males, and total serum globulin for females. We also suggest that the models are generalizable, are clinically relevant, and imply underlying molecular and clinical differences between males and females that may affect prediction accuracy. The integration of both epidemiological and clinical data promises to create more robust models that shed new light on physiological processes.

BackgroundDiagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another. Treatment based on noticeable symptoms, however, is limited to the types of clinical biomarkers collected, and is prone to overlooking dysfunctions in physiological factors not easily evident to medical practitioners. We used a vector-based representation of patient clinical biomarkers, or clinarrays, to search for latent physiological factors that underlie human diseases directly from clinical laboratory data. Knowledge of these factors could be used to improve assessment of disease severity and help to refine strategies for diagnosis and monitoring disease progression.ResultsApplying Independent Component Analysis on clinarrays built from patient laboratory measurements revealed both known and novel concomitant physiological factors for asthma, types 1 and 2 diabetes, cystic fibrosis, and Duchenne muscular dystrophy. Serum sodium was found to be the most significant factor for both type 1 and type 2 diabetes, and was also significant in asthma. TSH3, a measure of thyroid function, and blood urea nitrogen, indicative of kidney function, were factors unique to type 1 diabetes respective to type 2 diabetes. Platelet count was significant across all the diseases analyzed.ConclusionsThe results demonstrate that large-scale analyses of clinical biomarkers using unsupervised methods can offer novel insights into the pathophysiological basis of human disease, and suggest novel clinical utility of established laboratory measurements.