There is a strong rationale and many theoretical advantages for neoadjuvant therapy in pancreatic cancer (PC). However, study results have varied significantly. In this study, a systematic review and meta-analysis of prospective studies were performed in order to evaluate safety and effectiveness of neoadjuvant therapy in PC. Thirty-nine studies were selected

Background Premature coronary artery disease (PCAD) has become more common in recent years and is often associated with poor outcomes. Triglyceride-glucose (TyG) index is a simple and reliable surrogate for insulin resistance (IR) and is an independent predictor of cardiovascular prognosis. However, the prognostic value of the TyG index in patients with PCAD remains uncertain. Thus, this study aimed to investigate the prognostic value and predictive performance of the TyG index in patients with PCAD. Methods A total of 526 young subjects (male < 45 years, female < 55 years) with angiographically proven CAD from January 2013 to December 2018 were included consecutively in this study. Their clinical and laboratory parameters were collected, and the TyG index was calculated as $$\mathrm{Ln}[\mathrm{fasting triglyceride }(\mathrm{TG}) (\mathrm{mg}/\mathrm{dL})\times \mathrm{fasting plasma glucose }(\mathrm{FPG}) (\mathrm{mg}/\mathrm{dL})/2]$$ Ln [ fasting triglyceride ( TG ) ( mg / dL ) × fasting plasma glucose ( FPG ) ( mg / dL ) / 2 ] . The follow-up time after discharge was 40–112 months (median, 68 months; interquartile range, 49‒83 months). The primary endpoint was the occurrence of the major adverse cardiovascular events (MACE), defined as the composite of all-cause death, non-fatal myocardial infarction (MI), coronary artery revascularization, and non-fatal stroke. Results The TyG index was significantly associated with traditional cardiovascular risk factors and the Gensini score (GS). Kaplan–Meier survival (MACE-free) curves by tertiles of the TyG index showed statistically significant differences (log-rank test, p = 0.001). In the fully adjusted Cox regression model, the Hazard ratio (95% CI) of MACE was 2.17 (1.15–4.06) in tertile 3 and 1.45 (1.11–1.91) for per SD increase in the TyG index. Time-dependent ROC analyses of the TyG for prediction of MACE showed the area under the curves (AUC) reached 0.631 at 3 years, 0.643 at 6 years, and 0.710 at 9 years. Furthermore, adding TyG index to existing risk prediction model could improve outcome prediction [C-statistic increased from 0.715 to 0.719, p = 0.007; continuous net reclassification improvement (NRI) = 0.101, p = 0.362; integrated discrimination improvement (IDI) = 0.011, p = 0.017]. Conclusion The TyG index is an independent predictor of MACE in patients with PCAD, suggesting that the TyG index has important clinical implications for risk stratification and early intervention of PCAD.

Purpose Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix‐assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray. Experimental design Formalin‐fixed‐paraffin‐embedded tissue of 20 patients with ovarian clear‐cell, 14 low‐grade serous, 19 high‐grade serous ovarian carcinomas, and 14 serous borderline tumors are analyzed using MALDI‐Imaging. Classifications are computed by linear discriminant analysis (LDA), support vector machines with linear (SVM‐lin) and radial basis function kernels (SVM‐rbf), a neural network (NN), and a convolutional neural network (CNN). Results MALDI‐Imaging and machine learning methods result in classification of EOC histotypes with mean accuracy of 80% for LDA, 80% SVM‐lin, 74% SVM‐rbf, 83% NN, and 85% CNN. Based on sensitivity (69–100%) and specificity (90–99%), CCN and NN are most suited to EOC classification. Conclusion and clinical relevance The pilot study demonstrates the potential of MALDI‐Imaging derived proteomic classifiers in combination with machine learning algorithms to discriminate EOC histotypes. Applications may support the development of new prognostic parameters in the assessment of EOC.