Alzheimer’s disease (AD) is a neurodegenerative disease with progressive memory loss and the cognitive decline. AD is mainly caused by abnormal accumulation of misfolded amyloid β (Aβ), which leads to neurodegeneration via a number of possible mechanisms such as down-regulation of brain-derived neurotrophic factor-tropomyosin-related kinase B (BDNF-TRKB) signaling pathway. 7 ,8-Dihydroxyflavone (7,8-DHF), a TRKB agonist, has demonstrated potential to enhance BDNF-TRKB pathway in various neurodegenerative diseases. T o expand the capacity of flavones as TRKB agonists, two natural flavones quercetin and apigenin, were evaluated. With tryptophan fluorescence quenching assay, we illustrated the direct interaction between quercetin/ apigenin and TRKB extracellular domain. Employing Aβ folding reporter SH-SY5Y cells, we showed that quercetin and apigenin reduced Aβ-aggregation, oxidative stress, caspase-1 and acetylcholinesterase activities, as well as improved the neurite outgrowth. Treatments with quercetin and apigenin increased TRKB Tyr516 and Tyr817 and downstream cAMP-response-element binding protein (CREB) Ser133 to activate transcription of BDNF and BCL2 apoptosis regulator (BCL2), as well as reduced the expression of pro-apoptotic BCL2 associated X protein (BAX). Knockdown of TRKB counteracted the improvement of neurite outgrowth by quercetin and apigenin. Our results demonstrate that quercetin and apigenin are to work likely as a direct agonist on TRKB for their neuroprotective action, strengthening the therapeutic potential of quercetin and apigenin in treating AD.

Glycogen synthase kinase-3β (GSK-3β) is an important serine/threonine kinase that implicates in multiple cellular processes and links with the neurodegenerative diseases including Alzheimer’s disease (AD). In this study, structure-based virtual screening was performed to search database for compounds targeting GSK-3β from Enamine’s screening collection. Of the top-ranked compounds, 7 primary hits underwent a luminescent kinase assay and a cell assay using human neuroblastoma SH-SY5Y cells expressing Tau repeat domain (TauRD) with pro-aggregant mutation ΔK280. In the kinase assay for these 7 compounds, residual GSK-3β activities ranged from 36.1% to 90.0% were detected at the IC50 of SB-216763. In the cell assay, only compounds VB-030 and VB-037 reduced Tau aggregation in SH-SY5Y cells expressing ΔK280 TauRD-DsRed folding reporter. In SH-SY5Y cells expressing ΔK280 TauRD, neither VB-030 nor VB-037 increased expression of GSK-3α Ser21 or GSK-3β Ser9. Among extracellular signal-regulated kinase (ERK), AKT serine/threonine kinase 1 (AKT), mitogen-activated protein kinase 14 (P38) and mitogenactivated protein kinase 8 (JNK) which modulate Tau phosphorylation, VB-037 attenuated active phosphorylation of P38 Thr180/ Tyr182, whereas VB-030 had no effect on the phosphorylation status of ERK, AKT, P38 or JNK. However, both VB-030 and VB-037 reduced endogenous Tau phosphorylation at Ser202, Thr231, Ser396 and Ser404 in neuronally differentiated SH-SY5Y expressing ΔK280 TauRD. In addition, VB-030 and VB-037 further improved neuronal survival and/or neurite length and branch in mouse hippocampal primary culture under Tau cytotoxicity. Overall, through inhibiting GSK-3β kinase activity and/or p-P38 (Thr180/Tyr182), both compounds may serve as promising candidates to reduce Tau aggregation/cytotoxicity for AD treatment.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.