Redefining the R1 resection for pancreatic ductal adenocarcinoma: tumour lymph nodal burden and lymph node ratio are the only prognostic factors associated with survival

Introduction: The presence of positive nodal disease (LND) and the number of lymph nodes involved (LNB) are known to be significant prognostic markers for resected adenocarcinoma of the pancreas. In addition, the ratio of the number of involved nodes to the number of nodes resected known as the lymph node ratio (LNR) is emerging as an important prognostic marker. The role of the resection margin (RM) as presently defined (R1 ≤ 1 mm) is unclear as results differ based on the dataset. The aim of this study was to assess the impact of nodal disease and a redefined RM on outcome.Material and methods: Retrospective analysis of pancreatic head resections for adenocarcinomas from 2003–2009. The RM was re-analysed based on tumour clearance and categorized into: histopathological evidence of a tumour; ≤0.5 mm, ≤1 mm, ≤1.5 mm, or ≤2.0 mm of the actual surgical resection margin. The impact of histopathological variables on cancer-specific survival (CSS) and disease-free survival (DFS) was analysed.Results: LND, LNB and LNR were independent prognostic markers for CSS (P = 0.048, 0.003, 0.016) but, did not influence DFS. A LNR < 0.143 was associated with a higher CSS [38.16 ± 4.69 versus 20.59 ± 2.20 months, P = 0.0042, hazard ratio (HR) 3.74 (95% confidence interval (CI) 1.52–9.23)]. An R1 RM was not associated with CSS or DFS on multivariate analysis, irrespective of the distance. LNB and LNR maintained independent significance irrespective of the size of the RM.Conclusion: LNB and LNR are the only prognostic factors for CSS in patients with pancreatic head adenocarcinoma, but do not predict recurrence. Microscopic RMs does not seem to influence the outcome even when redefined. Further prospective studies are indicated to substantiate these findings.     

Chemometric Evaluation of Near Infrared,Fourier Transform Infrared,and Raman Spectroscopic Models for the Prediction of Nimodipine Polymorphs

The objective of this study was to assess the performance of the chemometric model to predict the proportion of the recrystallized polymorphs of nimodipine from the cosolvent formulations. Ranging from 100% to 0% (w/w) of polymorph I, the two polymorphs mixtures were prepared and characterized spectroscopically using Fourier transformed infrared spectroscopy (FTIR), near-infrared spectroscopy (NIR), and Raman spectroscopy. Instrumental responses were treated to construct multivariate calibration model using principal component regression (PCR) and partial least square regression approaches. Treated data showed better model fitting than without treatment, which demonstrated higher correlation coefficient (R²) and lower root mean square of standard error (RMSE) and standard error (SE). Multiple scattering correction and standard normal variate exhibited higher R² and lower RMSE and SE values than second derivative. Goodness of fit for FTIR and NIR (R² ~ 0.99) data was better than Raman (R² ~ 0.95). Furthermore, the models were applied on the recrystallized polymorphs obtained by storing nimodipine-cosolvent formulations at selected stability conditions. The relative composition of the polymorphs differed with storage conditions. NIR-chemical imaging on recrystallized sample of nimodipine at 15°C qualitatively corroborated the model-based prediction of the two polymorphs. Therefore, these studies strongly suggest the importance of the potential utility of the chemometric model in predicting nimodipine polymorphs.     

Prophylactic Peritoneal Dialysis After the Arterial Switch Operation: A Retrospective Cohort Study

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Novel Risk Factors for Progression of Diabetic and Nondiabetic CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study

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