Frequent COPD exacerbations have a large impact on morbidity, mortality and health-care expenditures. By 2020, the World Health Organization expects COPD and COPD exacerbations to be the third leading cause of death world-wide. Furthermore, In 2005 it was estimated that COPD exacerbations cost the U.S. health-care system 38 billion dollars. Studies attempting to determine factors related to COPD readmissions are still very limited. Moreover, few have used a organized machine-learning, sensitivity analysis approach, such as a Random Forest (RF) statistical model, to analyze this problem. This study utilized the RF machine learning algorithm to determine factors that predict risk for multiple COPD exacerbations in a single year.This was a retrospective study with a data set of 106 patients. These patients were divided randomly into training (80%) and validating (20%) data-sets, 100 times, using approximately sixty variables intially, which in prior studies had been found to be associated with patient readmission for COPD exacerbation. In an interactive manner, an RF model was created using the training set and validated on the testing dataset. Mean area-under-curve (AUC) statistics, sensitivity, specificity, and negative/positive predictive values (NPV, PPV) were calculated for the 100 runs.The following variables were found to be important predictors of patients having at least two COPD exacerbations within one year: employment, body mass index, number of previous surgeries, administration of azithromycin/ceftriaxone/moxifloxacin, and admission albumin level. The mean AUC was 0.72, sensitivity of 0.75, specificity of 0.56, PPV of 0.7 and NPV of 0.63. Histograms were used to confirm consistent accuracy.The RF design has consistently demonstrated encouraging results. We expect to validate our results on new patient groups and improve accuracy by increasing our training dataset. We hope that identifying patients at risk for frequent readmissions will improve patient outcome and save valuable hospital resources.

Along with other etiological factors like genetics, family history, age, etc. there is growing scientific evidence that exposure to chemicals, including pesticides is associated with increased incidence of breast cancer among women. Various animal studies have demonstrated the carcinogenic effect of pesticides byacting as Xenoestrogen, interacting and disrupting estrogen receptors or by damaging breast tissue DNA inducing malignancy/catalyzing existing DNA mutation in susceptible individuals. Pesticide's role as a contributing etiological factor in growing incidence of breast cancer is of particular concern as pesticides is one of the chemicals to which humans get exposed every day in significant concentration. In this review we describe various kinds of pesticides and their respective associations to breast cancer.

Aim. To assess the effect of treating chronic hepatitis C virus (HCV) infection with direct acting antiviral drugs (DAAs) on glycemic control in patients with concomitant diabetes mellitus (DM). Methods. We performed a retrospective case-control study in a viral hepatitis ambulatory clinic in Shreveport, Louisiana, during the period 11/01/2014 to 12/31/2017. All the clinic patient ages 18 years and above with treatment-naïve/biopsy-proven chronic hepatitis C and DM (hemoglobin A1C level≥6.5%) who were eligible for treatment were included in the study. Of 118 such patients, 59 were treated with oral DAAs for 8-12 weeks with the goal of achieving a sustained virologic response (SVR). A control group of 59 patients did not receive treatment for their hepatitis C and was followed in the clinic. Patients in the control group did not receive treatment either due to insurance issues or refusal of hepatitis C treatment. Results. Fifty-five of the 59 patients treated with DAAs (93%) achieved a SVR. Six months after treatment completion, their mean±SEM HbA1C level had decreased by 1.1±0.03% (P<0.0001). Four of the 59 patients treated with DAAs did not achieve a SVR. Their mean HbA1C 6 months after treatment completion had increased by 0.8±0.2%. Furthermore, there was no improvement in HbA1C levels over time in the untreated group (mean HbA1C increase, 0.2±0.05%; P<0.0001 vs. the treatment group, which had a mean HbA1C decrease of 0.9±0.2%). Conclusion. This controlled study demonstrated that treatment of chronic hepatitis C with DAAs results in statistically significant and meaningful reductions in hemoglobin A1C levels in patients with coexisting diabetic mellitus if a SVR is achieved.

Delay in performing operating procedures is one of the key factors that hinders optimal flow of patients and also has a significant impact on the economic outcome of the hospital. These delays could arise at various points due to multiple personnel involved in the process of performing a surgery. The main aim of the study was to identify possible causes for delay in performing operating procedures on time and instituting effective interventions which would prevent such delays. Positive reinforcement was used to encourage the surgical teams to perform the procedures on time and avoid the delays. The mean number of cases starting on time has improved from about 10% in March 2009 to about 80% in March 2011. With the use of effective intervention strategies delay in performing operating procedures can be reduced which ultimately improves overall patient satisfaction along with improving hospital costs.

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