Plos One
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Adiponectin gene polymorphisms and acute respiratory distress syndrome susceptibility and mortality.
Adiponectin is an anti-inflammatory adipokine that is the most abundant gene product of adipose tissue. Lower levels have been observed in obesity, insulin resistance, and in critical illness. However, elevated levels early in acute respiratory failure have been associated with mortality. Polymorphisms in adiponectin-related genes (ADIPOQ, ADIPOR1, ADIPOR2) have been examined for relationships with obesity, insulin resistance and diabetes, cardiovascular disease, and to circulating adipokine levels, but many gaps in knowledge remain. The current study aims to assess the association between potentially functional polymorphisms in adiponectin-related genes with acute respiratory distress syndrome (ARDS) risk and mortality. ⋯ A common and potentially functional polymorphism in ADIPOQ may impact survival in ARDS. Further studies are required to replicate these results and to correlate genotype with circulating adiponectin levels.
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The need for an accurate, rapid, simple and affordable point-of-care (POC) test for Tuberculosis (TB) that can be implemented in microscopy centers and other peripheral health-care settings in the TB-endemic countries remains unmet. This manuscript describes preliminary results of a new prototype rapid lateral flow TB test based on detection of antibodies to immunodominant epitopes (peptides) derived from carefully selected, highly immunogenic M. tuberculosis cell-wall proteins. Peptide selection was initially based on recognition by antibodies in sera from TB patients but not in PPD-/PPD+/BCG-vaccinated individuals from TB-endemic settings. ⋯ Three peptides were further evaluated with sera from 400 subjects, including additional PPD-/PPD+/PPD-unknown healthy contacts, close hospital contacts and household contacts of untreated TB patients, patients with non-TB lung disease, and HIV+TB- patients. Combination of the 3 peptides provided sensitivity and specificity>90%. While the final fully optimized lateral flow POC test for TB is under development, these preliminary results demonstrate that an antibody-detection based rapid POC lateral flow test based on select combinations of immunodominant M. tb-specific epitopes may potentially replace microscopy for TB diagnosis in TB-endemic settings.
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To report the outcomes of a posterior hybrid decompression protocol for the treatment of cervical spondylotic myelopathy (CSM) associated with hypertrophic ligamentum flavum (HLF). ⋯ Hybrid laminectomy and autograft laminoplasty decompression using Centerpiece plates may facilitate bone healing and produce a comparatively satisfactory prognosis for CSM patients with HLF.
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To evaluate adherence to uncomplicated urinary tract infections (UTI) guidelines and UTI diagnostic accuracy in an emergency department (ED) setting before and after implementation of an antimicrobial stewardship intervention. ⋯ A stewardship intervention including an electronic order set and audit and feedback was associated with increased adherence to uncomplicated UTI guidelines and reductions in unnecessary antibiotic therapy and fluoroquinolone therapy for cystitis. Many diagnoses were rejected or deemed unlikely, suggesting a need for studies to improve diagnostic accuracy for UTI.
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By aggregating self-reported health statuses across millions of users, we seek to characterize the variety of health information discussed in Twitter. We describe a topic modeling framework for discovering health topics in Twitter, a social media website. This is an exploratory approach with the goal of understanding what health topics are commonly discussed in social media. ⋯ ATAM discovered 13 coherent clusters of Twitter messages, some of which correlate with seasonal influenza (r = 0.689) and allergies (r = 0.810) temporal surveillance data, as well as exercise (r = .534) and obesity (r = -.631) related geographic survey data in the United States. These results demonstrate that it is possible to automatically discover topics that attain statistically significant correlations with ground truth data, despite using minimal human supervision and no historical data to train the model, in contrast to prior work. Additionally, these results demonstrate that a single general-purpose model can identify many different health topics in social media.