176 research outputs found

    Outcomes of polytrauma patients with diabetes mellitus.

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    BACKGROUND: The impact of diabetes mellitus in patients with multiple system injuries remains obscure. This study was designed to increase knowledge of outcomes of polytrauma in patients who have diabetes mellitus. METHODS: Data from the Trauma Audit and Research Network was used to identify patients who had suffered polytrauma during 2003 to 2011. These patients were filtered to those with known outcomes, then separated into those with diabetes, those known to have other co-morbidities but not diabetes and those known not to have any co-morbidities or diabetes. The data were analyzed to establish if patients with diabetes had differing outcomes associated with their diabetes versus the other groups. RESULTS: In total, 222 patients had diabetes, 2,558 had no past medical co-morbidities (PMC), 2,709 had PMC but no diabetes. The diabetic group of patients was found to be older than the other groups (P <0.05). A higher mortality rate was found in the diabetic group compared to the non-PMC group (32.4% versus 12.9%), P <0.05). Rates of many complications including renal failure, myocardial infarction, acute respiratory distress syndrome, pulmonary embolism and deep vein thrombosis were all found to be higher in the diabetic group. CONCLUSIONS: Close monitoring of diabetic patients may result in improved outcomes. Tighter glycemic control and earlier intervention for complications may reduce mortality and morbidity

    Microbes Breaking Down Plastic: Insights for Sustainable Waste Management

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    This research investigates the microbial degradation of low-density polyethylene (LDPE) and high-density polyethylene (HDPE) plastics by Bacillus sp., Proteus sp., Pseudomonas sp., and Salmonella sp. The study employs a systematic approach, isolating microorganisms from plastic-contaminated soil and subjecting them to a series of biochemical tests for identification. The research evaluates the weight loss of LDPE and HDPE over two months, revealing varying degrees of degradation among the bacterial strains. Results suggest a potential greater susceptibility of HDPE to microbial degradation. Scanning Electron Microscopy (SEM) analysis provides high-resolution images of the plastic surface, indicating structural changes and biofilm formation during degradation. The findings highlight the unique enzymatic capabilities of each strain and underscore the significance of SEM in elucidating microbial interactions with plastics. The study prompts discussions on optimization, synergistic effects, and the identification of key enzymes in plastic degradation, emphasizing the importance of microbial strategies for waste management. Overall, this research contributes valuable insights into the potential of bacterial strains for addressing plastic pollution challenges

    Management of Acute and Recurrent Gout: A Clinical Practice Guideline From the American College of Physicians

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    Description: The American College of Physicians (ACP) developed this guideline to present the evidence and provide clinical recommendations on the management of gout. Methods: Using the ACP grading system, the committee based these recommendations on a systematic review of randomized, controlled trials; systematic reviews; and large observational studies published between January 2010 and March 2016. Clinical outcomes evaluated included pain, joint swelling and tenderness, activities of daily living, patient global assessment, recurrence, intermediate outcomes of serum urate levels, and harms. Target Audience and Patient Population: The target audience for this guideline includes all clinicians, and the target patient population includes adults with acute or recurrent gout. Recommendation 1: ACP recommends that clinicians choose corticosteroids, nonsteroidal anti-inflammatory drugs (NSAIDs), or colchicine to treat patients with acute gout. (Grade: strong recommendation, high-quality evidence). Recommendation 2: ACP recommends that clinicians use low-dose colchicine when using colchicine to treat acute gout. (Grade: strong recommendation, moderate-quality evidence). Recommendation 3: ACP recommends against initiating long-term urate-lowering therapy in most patients after a first gout attack or in patients with infrequent attacks. (Grade: strong recommendation, moderate-quality evidence). Recommendation 4: ACP recommends that clinicians discuss benefits, harms, costs, and individual preferences with patients before initiating urate-lowering therapy, including concomitant prophylaxis, in patients with recurrent gout attacks. (Grade: strong recommendation, moderate-quality evidence)

    A framework for feature extraction from hospital medical data with applications in risk prediction

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    Background: Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Results: Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). Conclusions: The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks

    The association between blood glucose levels and matrix-metalloproteinase-9 in early severe sepsis and septic shock

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    BACKGROUND: Hyperglycemia is a frequent and important metabolic derangement that accompanies severe sepsis and septic shock. Matrix-Metalloproteinase 9 (MMP-9) has been shown to be elevated in acute stress hyperglycemia, chronic hyperglycemia, and in patient with sepsis. The objective of this study was to examine the clinical and pathogenic link between MMP-9 and blood glucose (BG) levels in patients with early severe sepsis and septic shock. METHODS: We prospectively examined 230 patients with severe sepsis and septic shock immediately upon hospital presentation and before any treatment including insulin administration. Clinical and laboratory data were obtained along with blood samples for the purpose of this study. Univariate tests for mean and median distribution using Spearman correlation and analysis of variance (ANOVA) were performed. A p value ≤ 0.05 was considered statistically significant. RESULTS: Patients were grouped based on their presenting BG level (mg/dL): BG <80 (n = 32), 80–120 (n = 53), 121–150 (n = 38), 151–200 (n = 23), and > 201 (n = 84). Rising MMP-9 levels were significantly associated with rising BG levels (p = 0.043). A corresponding increase in the prevalence of diabetes for each glucose grouping from 6.3 to 54.1 % (p = 0.0001) was also found. As MMP-9 levels increased a significantly (p < 0.001) decreases in IL-8 (pg/mL) and ICAM-1 (ng/mL) were noted. CONCLUSION: This is the first study in humans demonstrating a significant and early association between MMP-9 and BG levels in in patients with severe sepsis and septic shock. Neutrophil affecting biomarkers such as IL-8 and ICAM-1 are noted to decrease as MMP-9 levels increase. Clinical risk stratification using MMP-9 levels could potentially help determine which patients would benefit from intensive versus conventional insulin therapy. In addition, antagonizing the up-regulation of MMP-9 could serve as a potential treatment option in severe sepsis or septic shock patients
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