Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction

Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US, underlining the importance of accurate ADRD risk prediction. While recent advancement in ADRD risk prediction have primarily relied on imaging analysis, yet not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. Our goal is to utilize Graph Neural Networks (GNNs) with claims data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative method to evaluate relationship importance and its influence on ADRD risk prediction, ensuring comprehensive interpretation. We employed Variationally Regularized Encoder-decoder Graph Neural Network (VGNN) for estimating ADRD likelihood. We created three scenarios to assess the model's efficiency, using Random Forest and Light Gradient Boost Machine as baselines. We further used our relation importance method to clarify the key relationships for ADRD risk prediction. VGNN surpassed other baseline models by 10% in the area under the receiver operating characteristic. The integration of the GNN model and relation importance interpretation could potentially play an essential role in providing valuable insight into factors that may contribute to or delay ADRD progression. Employing a GNN approach with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data

Antitumor activity of curcumin is involved in down-regulation of YAP/TAZ expression in pancreatic cancer cells

Pancreatic cancer (PC) is one of the most aggressive human malignancies worldwide and is the fourth leading cause of cancer-related deaths. Curcumin (diferuloylmethane) is a polyphenol derived from the Curcuma longa plant. Certain studies have demonstrated that curcumin exerts its anti-tumor function in a variety of human cancers including PC, via targeting multiple therapeutically important cancer signaling pathways. However, the detailed molecular mechanisms are not fully understood. Two transcriptional co-activators, YAP (Yes-associated protein) and its close paralog TAZ (transcriptional coactivator with PDZ-binding motif) exert oncogenic activities in various cancers. Therefore, in this study we aimed to determine the molecular basis of curcumin-induced cell proliferation inhibition in PC cells. First, we detected the anti-tumor effects of curcumin on PC cell lines using CTG assay, Flow cytometry, clonogenic assay, wound healing assay and Transwell invasion assay. We found that curcumin significantly suppressed cell growth, weakened clonogenic potential, inhibited migration and invasion, and induced apoptosis and cell cycle arrest in PC cells. We further measured that overexpression of YAP enhanced cell proliferation and abrogated the cytotoxic effects of curcumin on PC cells. Moreover, we found that curcumin markedly down-regulated YAP and TAZ expression and subsequently suppressed Notch-1 expression. Collectively, these findings suggest that pharmacological inhibition of YAP and TAZ activity may be a promising anticancer strategy for the treatment of PC patients

Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing

To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. Among three knowledge graph completion models, TransE outperformed the other two (MR = 13.45, Hits@1 = 0.306). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.Comment: Submitted to the BMC Bioinformatic

Inhibition of Notch-1 pathway is involved in rottlerin-induced tumor suppressive function in nasopharyngeal carcinoma cells

Recent studies have revealed that rottlerin is a natural chemical drug to exert its anti-cancer activity. However, the molecular mechanisms of rottlerin-induced tumor suppressive function have not been fully elucidated. Notch signaling pathway has been characterized to play a crucial role in tumorigenesis. Therefore, regulation of Notch pathway could be beneficial for the treatment of human cancer. The aims of our current study were to explore whether rottlerin could suppress Notch-1 expression, which leads to inhibition of cell proliferation, migration and invasion in nasopharyngeal carcinoma cells. We performed several approaches, such as CTG, Flow cytometry, scratch healing assay, transwell and Western blotting. Our results showed that rottlerin treatment inhibited cell growth, migration and invasion, and triggered apoptosis, and arrested cell cycle to G1 phase. Moreover, the expression of Notch-1 was obvious decreased in nasopharyngeal carcinoma cells after rottlerin treatment. Importantly, overexpression of Notch-1 promoted cell growth and invasion, whereas down-regulation of Notch-1 inhibited cell growth and invasion in nasopharyngeal carcinoma cells. Notably, we found the over-expression of Notch-1 could abrogate the anti-cancer function induced by rottlerin. Strikingly, our study implied that Notch-1 could be a useful target of rottlerin for the prevention and treatment of human nasopharyngeal carcinoma

Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and Validation

Background The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug‐eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. Methods and Results We developed and validated a new AI‐based pipeline using retrospective data of drug‐eluting stent‐treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de‐identified Clinformatics Data Mart Database (n=9978). The 36 months following drug‐eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI‐DAPT model. The AI‐DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%–92%] for ischemia and 84% [95% CI, 82%–87%] for bleeding predictions. Conclusions Our AI‐DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability

Conformational Change of Human Checkpoint Kinase 1 (Chk1) Induced by DNA Damage

Phosphorylation of Chk1 by ataxia telangiectasia-mutated and Rad3-related (ATR) is critical for checkpoint activation upon DNA damage. However, how phosphorylation activates Chk1 remains unclear. Many studies suggest a conformational change model of Chk1 activation in which phosphorylation shifts Chk1 from a closed inactive conformation to an open active conformation during the DNA damage response. However, no structural study has been reported to support this Chk1 activation model. Here we used FRET and bimolecular fluorescence complementary techniques to show that Chk1 indeed maintains a closed conformation in the absence of DNA damage through an intramolecular interaction between a region (residues 31–87) at the N-terminal kinase domain and the distal C terminus. A highly conserved Leu-449 at the C terminus is important for this intramolecular interaction. We further showed that abolishing the intramolecular interaction by a Leu-449 to Arg mutation or inducing ATR-dependent Chk1 phosphorylation by DNA damage disrupts the closed conformation, leading to an open and activated conformation of Chk1. These data provide significant insight into the mechanisms of Chk1 activation during the DNA damage response