Introduction: Pancreatic cancer (PC) is one of the leading causes of cancer, with the lowest 5-year survival rate of all cancer types. Given the fast metastasis of PC and its resistance to surgery, radiotherapy, chemotherapy, and combinations thereof, it is imperative to develop more effective anti-PC drugs. Phillygenin (PHI) has been reported to exert anti-cancer, antibacterial, and anti-inflammatory properties. However, the mechanism of PHI in the development of PC is still unclear. Methods: The cytotoxicity of PHI in pancreatic cancer cells was evaluated by MTT assay, and clonogenic assay was used to test the anti-proliferation of PHI. The pro-apoptotic effect of PHI was detected by flow cytometry analysis. The changes of epithelial-mesenchymal transition (EMT) in pancreatic cancer cells treated with PHI were determined by Western blot. Transwell assay was used to test the migration and invasion of PC cells after treatment with PHI. Molecular docking was used to predict the potential binding site of candidate target with PHI. Results: PHI could inhibit the proliferation, migration, and EMT of PC cells (PANC-1 and SW1990) and induce its apoptosis. Analysis of the Cancer Genome Atlas database indicated that elevated MELK levels correlated with poor overall survival (OS) and disease-free survival (DFS) of PC patients. In addition, molecular modeling showed that PHI may potentially target the catalytic domain of maternal embryonic leucine zipper kinase (MELK). Overexpression of MELK muted the anti-PC effects of PHI. Conclusion: PHI holds promise as a potent candidate drug for the treatment of PC via targeted MELK.
Alzheimer's disease (AD) is a neurodegenerative disease that is difficult to be detected using convenient and reliable methods. The language change in patients with AD is an important signal of their cognitive status, which potentially helps in early diagnosis. In this study, we developed a transfer learning model based on speech and natural language processing (NLP) technology for the early diagnosis of AD. The lack of large datasets limits the use of complex neural network models without feature engineering, while transfer learning can effectively solve this problem. The transfer learning model is firstly pre-trained on large text datasets to get the pre-trained language model, and then, based on such a model, an AD classification model is performed on small training sets. Concretely, a distilled bidirectional encoder representation (distilBert) embedding, combined with a logistic regression classifier, is used to distinguish AD from normal controls. The model experiment was evaluated on Alzheimer's dementia recognition through spontaneous speech datasets in 2020, including the balanced 78 healthy controls (HC) and 78 patients with AD. The accuracy of the proposed model is 0.88, which is almost equivalent to the champion score in the challenge and a considerable improvement over the baseline of 75% established by organizers of the challenge. As a result, the transfer learning method in this study improves AD prediction, which does not only reduces the need for feature engineering but also addresses the lack of sufficiently large datasets.
Purpose: Berbamine (Ber), a bioactive constituent extracted from a traditional Chinese medicinal herb, has been shown to exhibit broad inhibitory activity on a panel of cancer cell types. However, its effects and the underlying molecular mechanisms on gastric cancer (GC) remain poorly understood. Methods: The anti-growth activity of Ber on two GC cell lines and normal gastric epithelial cell line were evaluated using MTS and clone formation assay. Flow cytometry analysis was employed to evaluate the cell cycle distribution and apoptosis of GC cells. Western blot and quantitative PCR (qPCR) analysis were employed to investigate the anti-GC mechanism of Ber. The inhibitory activity and binding affinity of Ber against BRD4 were evaluated by homogeneous time-resolved fluorescence (HTRF) and surface plasmon resonance (SPR) assay, respectively. Molecular docking and molecular simulations were conducted to predict the interaction mode between BRD4 and Ber. Results:The results demonstrated that Ber reduced the proliferation of GC cell lines SGC-7901 and BGC-823 and induced cell cycle arrest and apoptosis. Mechanistically, Ber was identified as a novel natural-derived BRD4 inhibitor through multiple experimental assay, and its anti-GC activity was probably mediated by BRD4 inhibition. Molecular modeling studies suggested that Ber might bind to BRD4 primarily through hydrophobic interactions. Conclusion: Our study uncovered the underlying anti-GC activity of Ber in vitro and suggested that Ber holds promise as a potential lead compound in the discovery of novel BRD4 inhibitors.
Background: Spoken responses can provide diagnostic markers as language impairment maybe an important early performance for dementia patients. In this study, an automatic assessment system was proposed to discriminate MCI and AD patients from their speeches so as to achieve the aim of speeding up treatment and slowing down disease progression.Methods: We integrated a group of acoustic, demographic, linguistic features and used machine learning algorithm to effectively predict MCI and AD patients. Additionally, in order to get the best result, comparison experiment is done effectively which includes three different feature extraction methods (e.g. acoustic, text and their combination) and four of the most popular algorithms, namely, Logistic Regression, SVM, Random Forest and LightGBM.Results: According to Iflytek’s dataset “Alzheimer’s disease prediction challenge competition” in 2019, the performance of LightGBM was especially better than other algorithms,the state-of-the-art AUC value of which was between 0.75 and 0.89 in binary classification and across 0.57 in ternary classification. The result also revealed that age had a significant impact on all the proposed cognitive factors in the meanwhile.Conclusions: The results indicate that our method is increasingly useful for assessing suspected AD and MCI by using multiple, complementary acoustic and linguistic measures.
Background: Alzheimer’s Disease (AD) is a common dementia which affects linguistic function, memory, cognitive and visual spatial ability of the patients. More and more studies have been done to access non-invasive, accessible, cost-effective methods for the detection of AD, Speech is proved to have relationship with AD, so a time that AD can be diagnosed in a doctor’s office is coming.Methods: In our study, the ADRess dataset in 2020 was used to detect AD which was balanced in gender and age. First we extract three categories of feature parameters: acoustic feature extracted by opensmile software, bert embeddings automatically and complicated linguistic feature extraction manually. Linguistic features are based on the POS tag, lexical Richness, fluency, semantic feature. Then seven different classifiers are used for identifying AD from normal controls, including SVM, Logistic Regress, Random forest, Extra Trees, Adaboost, LightGBM and a novel ensemble approach with majority voting strategy which is applied to overcome the error caused by a base classifier. Finally ten-fold cross validation is adopted for the evaluation of our approach. In addition, individual features and their combine features are fed to six base classifiers and ensemble of classifier. Results: We get top-performing classify result on the test set with ensemble of classifiers, the best accuracy of which is 85.4%. The best performance of feature sets are linguistic features, the accuracy of which is 85.6% with LightGBM classifier, and SFS approach is used to manifest seven discriminative linguistic features. Conclusions: The statistical and experimental results illustrates the feasibility by using speech to predict AD effectively based on acoustic and linguistic feature parameters. Stronger classifier and discriminate features are vital for the final results. We emphasise the best linguistic features for predicting AD disease are based on the POS tag, lexical Richness, fluency, semantic feature. Ensemble of classifiers usually has a better performance than single classifier.
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