Alzheimer's Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction.

Background: Alzheimer's disease (AD), a progressive neurodegenerative disorder, continues to increase in prevalence without any effective treatments to date. In this context, knowledge graphs (KGs) have emerged as a pivotal tool in biomedical research, offering new perspectives on drug repurposing and biomarker discovery by analyzing intricate network structures. Our study seeks to build an AD-specific knowledge graph, highlighting interactions among AD, genes, variants, chemicals, drugs, and other diseases. The goal is to shed light on existing treatments, potential targets, and diagnostic methods for AD, thereby aiding in drug repurposing and the identification of biomarkers. Results: We annotated 800 PubMed abstracts and leveraged GPT-4 for text augmentation to enrich our training data for named entity recognition (NER) and relation classification. A comprehensive data mining model, integrating NER and relationship classification, was trained on the annotated corpus. This model was subsequently applied to extract relation triplets from unannotated abstracts. To enhance entity linking, we utilized a suite of reference biomedical databases and refine the linking accuracy through abbreviation resolution. As a result, we successfully identified 3,199,276 entity mentions and 633,733 triplets, elucidating connections between 5,000 unique entities. These connections were pivotal in constructing a comprehensive Alzheimer's Disease Knowledge Graph (ADKG). We also integrated the ADKG constructed after entity linking with other biomedical databases. The ADKG served as a training ground for Knowledge Graph Embedding models with the high-ranking predicted triplets supported by evidence, underscoring the utility of ADKG in generating testable scientific hypotheses. Further application of ADKG in predictive modeling using the UK Biobank data revealed models based on ADKG outperforming others, as evidenced by higher values in the areas under the receiver operating characteristic (ROC) curves. Conclusion: The ADKG is a valuable resource for generating hypotheses and enhancing predictive models, highlighting its potential to advance AD's disease research and treatment strategies.

State of energy estimation of lithium-ion battery based on long short-term memory optimization Adaptive Cubature Kalman filter.

State of energy (SOE) is an important parameter to ensure the safety and reliability of lithium-ion battery (LIB) system. The safety of LIBs, the development of artificial intelligence, and the increase in computing power have provided possibilities for big data computing. This article studies SOE estimation problem of LIBs, aiming to improve the accuracy and adaptability of the estimation. Firstly, in the SOE estimation process, adaptive correction is performed by iteratively updating the observation noise equation and process noise equation of the Adaptive Cubature Kalman Filter (ACKF) to enhance the adaptive capability. Meanwhile, the adoption of high-order equivalent models further improves the accuracy and adaptive ability of SOE estimation. Secondly, Long Short-term Memory (LSTM) is introduced to optimize Ohmic internal resistance (OIR) and actual energy (AE), further improving the accuracy of SOE estimation. Once again, in the process of OIR and AE estimation, the iterative updating of the observation noise equation and process noise equation of ACKF were also adopted to perform adaptive correction and enhance the adaptive ability. Finally, this article establishes a SOE estimation method based on LSTM optimized ACKF. Validate the LSTM optimized ACKF method through simulation experiments and compare it with individual ACKF methods. The results show that the ACKF estimation method based on LSTM optimization has an SOE estimation error of less than 0.90% for LIB, regardless of the SOE at 100%, 65%, and 30%, which is more accurate than the SOE estimation error of ACKF alone. It can be seen that this study has improved the accuracy and adaptability of LIB's SOE estimation, providing more accurate data support for ensuring the safety and reliability of lithium batteries.

Agrimol B alleviates cisplatin-induced acute kidney injury by activating the Sirt1/Nrf2 signaling pathway in mice.

Cisplatin (CDDP) is a widely used chemotherapeutic agent that has remarkable antineoplastic effects. However, CDDP can cause severe acute kidney injury (AKI), which limits its clinical application. Agrimol B is the main active ingredient found in Agrimonia pilosa Ledeb and has a variety of pharmacological activities. The effect of agrimol B on CDDP-induced renal toxicity has not been determined. To investigate whether agrimol B has a protective effect against CDDP-induced AKI, we first identify Sirtuin 1 (Sirt1) as a critical target protein of agrimol B in regulating AKI through network pharmacology analysis. Subsequently, the AKI mouse model is induced by administering a single dose of CDDP via intraperitoneal injection. By detecting the serum urea nitrogen and creatinine levels, as well as the histopathological changes, we confirm that agrimol B effectively reduces CDDP-induced AKI. In addition, treatment with agrimol B counteracts the increase in renal malondialdehyde level and the decrease in superoxide dismutase (SOD), catalase and glutathione levels induced by CDDP. Moreover, western blot results reveal that agrimol B upregulates the expressions of Sirt1, SOD2, nuclear factor erythroid2-related factor 2, and downstream molecules, including heme oxygenase 1 and NAD(P)H quinone dehydrogenase 1. However, administration of the Sirt1 inhibitor EX527 abolishes the effects of agrimol B. Finally, we establish a tumor-bearing mouse model and find that agrimol B has a synergistic antitumor effect with CDDP. Overall, agrimol B attenuates CDDP-induced AKI by activating the Sirt1/Nrf2 signaling pathway to counteract oxidative stress, suggesting that this compound is a potential therapeutic agent for the treatment of CDDP-induced AKI.

Variable selection for joint models of multivariate skew-normal longitudinal and survival data.

Many joint models of multivariate skew-normal longitudinal and survival data have been presented to accommodate for the non-normality of longitudinal outcomes in recent years. But existing work did not consider variable selection. This article investigates simultaneous parameter estimation and variable selection in joint modeling of longitudinal and survival data. The penalized splines technique is used to estimate unknown log baseline hazard function, the rectangle integral method is adopted to approximate conditional survival function. Monte Carlo expectation-maximization algorithm is developed to estimate model parameters. Based on local linear approximations to conditional expectation of likelihood function and penalty function, a one-step sparse estimation procedure is proposed to circumvent the computationally challenge in optimizing the penalized conditional expectation of likelihood function, which is utilized to select significant covariates and trajectory functions, and identify the departure from normality of longitudinal data. The conditional expectation of likelihood function-based Bayesian information criterion is developed to select the optimal tuning parameter. Simulation studies and a real example from the clinical trial are used to illustrate the proposed methodologies.

Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images.

Cardiovascular health interacts with cognitive and mental health in complex ways, yet little is known about the phenotypic and genetic links of heart-brain systems. We quantified heart-brain connections using multiorgan magnetic resonance imaging (MRI) data from more than 40,000 subjects. Heart MRI traits displayed numerous association patterns with brain gray matter morphometry, white matter microstructure, and functional networks. We identified 80 associated genomic loci (P < 6.09 × 10-10) for heart MRI traits, which shared genetic influences with cardiovascular and brain diseases. Genetic correlations were observed between heart MRI traits and brain-related traits and disorders. Mendelian randomization suggests that heart conditions may causally contribute to brain disorders. Our results advance a multiorgan perspective on human health by revealing heart-brain connections and shared genetic influences.

Inhibition of NAD kinase elevates the hepatic NAD+ pool and alleviates acetaminophen-induced acute liver injury in mice.

Acetaminophen (APAP) overdose induces acute liver injury (ALI), even acute liver failure (ALF). There is a significant unmet need to furtherly elucidate the mechanisms and find new therapeutic target. Recently, emerging evidence indicates that nicotinamide adenine dinucleotide (NAD+) plays a crucial role in APAP-induced ALI. Herein, we firstly investigated the protein expression of NAD kinase (NADK), as the rate-limiting enzyme converting NAD+ to nicotinamide adenine dinucleotide phosphate (NADP+), and found it was positively correlated with APAP-induced ALI in a dose- and time-dependent manner. Additionally, supplementation of N-acetylcysteine (NAC), known as an antidote of APAP, mitigated the ALI and downregulated the expression of NADK which was also in a dose-dependent manner. Moreover, pretreatment with methotrexate (MTX), the inhibitor of NADK, attenuated the levels of transaminases, alleviated morphological abnormalities, and improved oxidative stress triggered by APAP overdose, which was attributed to elevated hepatic NAD+ pool. Subsequently, the increased NAD+ upregulated the expression of Sirt1, SOD2 and attenuated DNA damage. Collectively, elevated expression of NADK is related to APAP-induced ALI, and inhibition of NADK alleviates the ALI through elevating liver NAD+ level and improving antioxidant capacity.