Indomethacin-induced activation of the death receptor-mediated apoptosis pathway circumvents acquired doxorubicin resistance in SCLC cells

Small-cell lung cancers (SCLCs) initially respond to chemotherapy but are often resistant at recurrence. A potentially new method to overcome resistance is to combine classical chemotherapeutic drugs with apoptosis induction via tumour necrosis factor (TNF) death receptor family members such as Fas. The doxorubicin-resistant human SCLC cell line GLC(4)-Adr and its parental doxorubicin-sensitive line GLC(4) were used to analyse the potential of the Fas-mediated apoptotic pathway and the mitochondrial apoptotic pathway to modulate doxorubicin resistance in SCLC. Western blotting showed that all proteins necessary for death-inducing signalling complex formation and several inhibitors of apoptosis were expressed in both lines. The proapototic proteins Bid and caspase-8, however, were higher expressed in GLC(4)-Adr. In addition, GLC(4)-Adr expressed more Fas (3.1x) at the cell membrane. Both lines were resistant to anti-Fas antibody, but plus the protein synthesis inhibitor cycloheximide anti-Fas antibody induced 40% apoptosis in GLC(4)-Adr. Indomethacin, which targets the mitochondrial apoptotic pathway, induced apoptosis in GLC(4)-Adr but not in GLC(4) cells. Surprisingly, in GLC(4)-Adr indomethacin induced caspase-8 and caspase-9 activation as well as Bid cleavage, while both caspase-8 and caspase-9 specific inhibitors blocked indomethacin-induced apoptosis. In GLC(4)-Adr, doxorubicin plus indomethacin resulted in elevated caspase activity and a 2.7-fold enhanced sensitivity to doxorubicin. In contrast, no effect of indomethacin on doxorubicin sensitivity was observed in GLC(4). Our findings show that indomethacin increases the cytotoxic activity of doxorubicin in a doxorubicin-resistant SCLC cell line partly via the death receptor apoptosis pathway, independent of Fas

Evaluation and enhancement of medical knowledge competency by monthly tests: a single institution experience

Abdur Rahman Khan, Nauman Saleem Siddiqui, Raja Thotakura, Syed Shafae Hasan, Faraz Khan Luni, Thomas Sodeman, Bryan Hinch, Dinkar Kaw, Imad Hariri, Sadik Khuder, Ragheb Assaly Department of Internal Medicine, University of Toledo Medical Center, Toledo, OH, USA Background: In-training examination (ITE) has been used as a predictor of performance at the American Board of Internal Medicine (ABIM) certifying examination. ITE however may not be an ideal modality as it is held once a year and represents snapshots of performance as compared with a trend. We instituted monthly tests (MTs) to continually assess the performance of trainees throughout their residency. Objective: To determine the predictors of ABIM performance and to assess whether the MTs can be used as a tool to predict passing the ABIM examination. Methods: The MTs, core competencies, and ITE scores were analyzed for a cohort of graduates who appeared for the ABIM examination from 2010 to 2013. Logistic regression was performed to identify the predictors of a successful performance at the ABIM examination. Results: Fifty-one residents appeared for the ABIM examination between 2010 and 2013 with a pass rate of 84%. The MT score for the first year (odds ratio [OR] =1.302, CI =1.004–1.687, P=0.04) and second year (OR =1.125, CI =1.004–1.261, P=0.04) were independent predictors of ABIM performance along with the second-year ITE scores (OR =1.248, CI =1.096–1.420, P=0.001). Conclusion: The MT is a valuable tool to predict the performance at the ABIM examination. Not only it helps in the assessment of likelihood of passing the certification examination, it also helps to identify those residents who may require more assistance earlier during their residency. It may also highlight the areas of weakness in program curriculum and guide curriculum development. Keywords: American Board of Internal Medicine, certification examination, health care, board certification, residency progra

Supplementary Material for: Neural Network Analysis to Predict Mortality in End-Stage Renal Disease: Application to United States Renal Data System

<p>We examined whether we could develop models based on data provided to the United States Renal Data System (USRDS) to accurately predict survival. Records were obtained from patients beginning dialysis in 1990 through 2007. We developed linear and neural network models and optimized the fit of these models to the actual time to death. Next, we examined whether we could accurately predict survival in a dataset containing censored and uncensored patients. The results with these models were contrasted with those obtained with a Cox proportional hazards model fit to the entire dataset. The average C statistic over a 6-month to 10-year time range achieved with these models was approximately 0.7891 (linear model), 0.7804 (transformed dataset linear model), 0.7769 (neural network model), 0.7774 (transformed dataset neural network model), 0.8019 (Cox model), and 0.7970 (transformed dataset Cox model). When we used the Cox proportional hazards model, superior C statistic results were found at time points between 2 and 10 years but at earlier time points, the Cox model was slightly inferior. These results suggest that data provided to the USRDS can allow for predictive models which have a high degree of accuracy years following the initiation of dialysis.</p