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Objective: To develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients. Methods: Clinical data for PTC patients who underwent primary thyroidectomy at our hospital between January 2015 and December 2020, with pathologically confirmed presence or absence of any LLNM finding, were retrospectively reviewed. We built all models from a training set (80%) and assessed them in a test set (20%), using algorithms including decision tree, XGBoost, random forest, support vector machine, neural network, and K-nearest neighbor algorithm. Their performance was measured against a previously established nomogram using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, F1 score, specificity, and sensitivity. Interpretable machine learning was used for identifying potential relationships between variables and LLNM, and a web-based tool was created for use by clinicians. Results: A total of 1135 (62.53%) out of 1815 PTC patients enrolled in this study experienced LLNM episodes. In predicting LLNM, the best algorithm was random forest. In determining feature importance, the AUC reached 0.80, with an accuracy of 0.74, sensitivity of 0.89, and F1 score of 0.81. In addition, DCA showed that random forest held a higher clinical net benefit. Random forest identified tumor size, lymph node microcalcification, age, lymph node size, and tumor location as the most influentials in predicting LLNM. Conclusion: The results showed that machine learning can be used to enable accurate prediction for LLNM in PTC patients, and that the web tool allowed for LLNM risk assessment at the individual level. [ABSTRACT FROM AUTHOR]

Microglial activation has been widely demonstrated to mediate inflammatory processes that are crucial in several neurodegenerative disorders. Pharmaceuticals that can deliver direct inhibitory effects on microglia are therefore considered as a potential strategy to counter balance neurodegenerative progression. Caffeic acid phenethyl ester (CAPE), a natural phenol in honeybee propolis, is known to possess antioxidant, anti-inflammatory and anti-microbial properties. Accordingly, the current study intended to probe the effects of CAPE on microglia activation by using in vitro and in vivo models. Western blot and Griess reaction assay revealed CAPE significantly inhibited the expressions of inducible nitric oxide synthase (NOS), cyclooxygenase (COX)-2 and the production of nitric oxide (NO). Administration of CAPE resulted in increased expressions of hemeoxygenase (HO)-1and erythropoietin (EPO) in microglia. The phosphorylated adenosine monophosphate-activated protein kinase (AMPK)-α was further found to regulate the anti-inflammatory effects of caffeic acid. In vivo results from immunohistochemistry along with rotarod test also revealed the anti-neuroinflammatory effects of CAPE in microglia activation. The current study has evidenced several possible molecular determinants, AMPKα, EPO, and HO-1, in mediating anti-neuroinflammatory responses in microglial cells. [ABSTRACT FROM AUTHOR]