ObjectivesPatients with type 2 diabetes (T2DM) are suggested to have a higher risk of developing pancreatic cancer. We used two models to predict pancreatic cancer risk among patients with T2DM.MethodsThe original data used for this investigation were retrieved from the National Health Insurance Research Database of Taiwan. The prediction models included the available possible risk factors for pancreatic cancer. The data were split into training and test sets: 97.5% of the data were used as the training set and 2.5% of the data were used as the test set. Logistic regression (LR) and artificial neural network (ANN) models were implemented using Python (Version 3.7.0). The F1, precision, and recall were compared between the LR and the ANN models. The areas under the receiver operating characteristic (ROC) curves of the prediction models were also compared.ResultsThe metrics used in this study indicated that the LR model more accurately predicted pancreatic cancer than the ANN model. For the LR model, the area under the ROC curve in the prediction of pancreatic cancer was 0.727, indicating a good fit.ConclusionUsing this LR model, our results suggested that we could appropriately predict pancreatic cancer risk in patients with T2DM in Taiwan.

Unplanned extubation (UE) can be associated with fatal outcome; however, an accurate model for predicting the mortality of UE patients in intensive care units (ICU) is lacking. Therefore, we aim to compare the performances of various machine learning models and conventional parameters to predict the mortality of UE patients in the ICU. A total of 341 patients with UE in ICUs of Chi-Mei Medical Center between December 2008 and July 2017 were enrolled and their demographic features, clinical manifestations, and outcomes were collected for analysis. Four machine learning models including artificial neural networks, logistic regression models, random forest models, and support vector machines were constructed and their predictive performances were compared with each other and conventional parameters. Of the 341 UE patients included in the study, the ICU mortality rate is 17.6%. The random forest model is determined to be the most suitable model for this dataset with F1 0.860, precision 0.882, and recall 0.850 in the test set, and an area under receiver operating characteristic (ROC) curve of 0.910 (SE: 0.022, 95% CI: 0.867–0.954). The area under ROC curves of the random forest model was significantly greater than that of Acute Physiology and Chronic Health Evaluation (APACHE) II (0.779, 95% CI: 0.716–0.841), Therapeutic Intervention Scoring System (TISS) (0.645, 95% CI: 0.564–0.726), and Glasgow Coma scales (0.577, 95%: CI 0.497–0.657). The results revealed that the random forest model was the best model to predict the mortality of UE patients in ICUs.

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Lonicera japonica Thunb. is a flower that is used in traditional Chinese medicine to prevent the common cold. The two primary active compounds of the flower bud are luteolin, a flavonoid, and chlorogenic acid, a phenolic acid. Both active compounds have demonstrated antioxidant activity. The interactions between chemicals in a plant heavily influences its total antioxidant activity. We attempted to investigate the antioxidant interactions between the two chemicals in the plant. This study aims to investigate if the antioxidants luteolin and chlorogenic acid have a synergistic effect to inhibit free radicals when combined. A 2,2-diphenyl-1-picrylhydrazyl (DPPH•) assay was performed. The half maximal inhibitory concentration (IC50) of luteolin and chlorogenic acid were first determined and then combined at a 1:1 ratio. The combined inhibition capacity was then compared with the sum of the individual inhibition capacities. The IC50 of luteolin is 26.304 μg·ml-1 ± 0.120 μg·ml-1 while the IC50 of chlorogenic acid is 85.529 μg·ml-1 ± 4.482 μg·ml-1. The combined solution produced a free radical percentage inhibition of 77.617% ± 5.470%, more than the percentage inhibition of the separate solutions. The experiment shows that luteolin and chlorogenic acid have a synergistic effect in inhibiting DPPH free radicals.

IntroductionEpigallocatechin-3-gallate (EGCG) is a chemical catechin, a natural organic compound found in green teas with strong antioxidative effects. EGCG degrades or epimerizes according to temperature, fluctuating its concentration in green tea (Camellia sinensis). This study is conducted to determine the specified correlation between EGCG and tea temperature, and to conclude with the optimal temperature for EGCG yield.MethodsEGCG concentrations in different solutions of green tea are analyzed using a high-performance liquid chromatography (HPLC), with a diode array detector (DAD). The solutions are created from green tea brewed in water from 20°C to 100°C at increments of 20°C and undergo an ultrasonic bath of 30 minutes before being analyzed.ResultsThere is a discernible difference between EGCG concentrations in all temperatures. At 20, 40, 60, 80 and 100°C, the concentrations are 6.18 μg/mL, 32.37 μg/mL, 57.36 μg/mL, 36.13 μg/mL, and 44.85 μg/mL, respectively. EGCG concentration maximizes at 60°C. The lowest EGCG concentration yield is at 20°C.ConclusionThe results of our experiments lead us to recommend hot brewing over cold brewing for green tea if one wishes to maximize the potential of the effects of EGCG due to its higher concentration.