A Robust Machine Learning Model for Diabetic Retinopathy Classification.

Ensemble learning is a process that belongs to the artificial intelligence (AI) field. It helps to choose a robust machine learning (ML) model, usually used for data classification. AI has a large connection with image processing and feature classification, and it can also be successfully applied to analyzing fundus eye images. Diabetic retinopathy (DR) is a disease that can cause vision loss and blindness, which, from an imaging point of view, can be shown when screening the eyes. Image processing tools can analyze and extract the features from fundus eye images, and these corroborate with ML classifiers that can perform their classification among different disease classes. The outcomes integrated into automated diagnostic systems can be a real success for physicians and patients. In this study, in the form image processing area, the manipulation of the contrast with the gamma correction parameter was applied because DR affects the blood vessels, and the structure of the eyes becomes disorderly. Therefore, the analysis of the texture with two types of entropies was necessary. Shannon and fuzzy entropies and contrast manipulation led to ten original features used in the classification process. The machine learning library PyCaret performs complex tasks, and the empirical process shows that of the fifteen classifiers, the gradient boosting classifier (GBC) provides the best results. Indeed, the proposed model can classify the DR degrees as normal or severe, achieving an accuracy of 0.929, an F1 score of 0.902, and an area under the curve (AUC) of 0.941. The validation of the selected model with a bootstrap statistical technique was performed. The novelty of the study consists of the extraction of features from preprocessed fundus eye images, their classification, and the manipulation of the contrast in a controlled way.

Correlation of XMAP and ELISA cytokine profiles; development and validation for immunotoxicological studies in vitro.

UNLABELLED: There is an emerging trend in immunotoxicological studies to use the multiplex technologies for testing the safety and the efficacy of new pharmaceuticals by using cytokines profiling as biomarker. The Luminex 200 xMAP (multi-analyte profiling) technology provides simultaneous measurement of multiple cytokines in small sample volumes, expressing rapidly the differences between various test compounds. The aim is to develop and validate the Luminex 200 multiplex immunoassays by correlation with ELISA (enzyme-linked immunosorbent assays) for implementation in evaluating cytokine profiling in immunotoxicological studies in vitro. METHODS: Human peripheral whole blood from healthy subject diluted 1+4 with RPMI 1640 was cultured 48 hours in 28 experimental variants: control, in presence of mitogens, bioflavonoid extracts (from Crataegus monogyna and Echinacea purpurea) as cytoprotectors and with a toxic compound [Pb(NO3)2]), separately or variously combined. IL-1beta and IL-2 were comparatively performed by xMAP and ELISA immunoassays from the same sample to initialize validation of multiplex cytokine panel: IL-1beta, IL-2, IL-4, IL-6, IL-8, IL-10, TNF-alpha, IFN-gamma, usually performed by Luminex 200 system in our immunotoxicological studies. The results indicate similarly typed trends of cytokine values obtained by both methods, with comparable relative changes in presence of mitogens, bioflavonoids and toxic, respectively. Although xMAP absolute cytokine values were higher than ELISA values, the correlation between multiplexed assay and ELISA was good for IL-1beta and IL-2 with positive correlation coefficients near to 1. Conclusions. Quantitative differences between absolute values for IL-1beta and IL-2 obtained by xMAP and ELISA assays are found, but the relative values are comparable and the two methods keep similar trends in similar exposure conditions. The performance parameters of the xMAP assay and the good correlation coefficients with the "gold standard" ELISA recommend to validate the multiplex assay for analyzing cytokine profiles in immunotoxicological studies in vitro.

Caveolin-1 overexpression correlates with tumour progression markers in pancreatic ductal adenocarcinoma.

The assessment of caveolin-1 (Cav-1) as a marker of tumor aggressiveness in pancreatic ductal adenocarcinoma (PDAC). In this study, we examined the expression of Cav-1 in 34 human PDAC tissue samples and the associated peritumoral tissues by immunohistochemistry and western blot. Additionally, we correlated Cav-1 expression with other tissue (Ki-67, p53) and serum (CA 19-9) tumor markers. In the tumor-derived tissue, both tumor cells and blood vessels expressed Cav-1. In contrast, in peritumoral tissue, Cav-1 expression was confined mainly to blood vessels and was only occasionally expressed in ductal or parenchymal cells. Western blot analysis confirmed the overexpression of Cav-1 in pancreatic tumors compared with peritumoral tissue. Cav-1 expression in tumor tissues was correlated with both the Ki-67 LI (r = 0.95, P < 0.0001) and p53 expression (chi2 = 9.91, P < 0.005). Overexpression of Cav-1 was associated with tumor size, grade and stage and Cav-1 expression in tumors was correlated with an increased serum level of CA 19-9 (r = 0.795, P < 0.001). Based on the results of this study, the inclusion of Cav-1 in a putative panel of biomarkers predicting pancreatic cancer aggressiveness is warranted.

Assessment of soluble angiogenic markers in pancreatic cancer.

UNLABELLED: Angiogenic markers such as VEGF/basic FGF (bFGF) can enlarge the diagnostic biomarkers panel for pancreatic cancer. MATERIALS & METHODS: Serum samples from 32 stage I-IV pancreatic cancer patients and 20 controls were analyzed for soluble VEGF/bFGF by ELISA and xMAP array. RESULTS: VEGF/bFGF serum levels were significantly increased in patients compared with controls (p < 0.0001). We report a correlation with tumor diameter (p < 0.01/p < 0.05), stage (p < 0.001), Ki67LI (p < 0.005/p < 0.05) and carbohydrate 19-9 antigen (p < 0.005/p < 0.001). VEGF/bFGF levels analyzed by xMAP array were comparable with the pattern (patient/control) outline obtained by ELISA tests. We obtained a good correlation between these two soluble angiogenic markers (p < 0.001). CONCLUSION: Data obtained for angiogenic markers qualifies them as important candidates in the pancreatic cancer biomarker panel.