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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.

Traditional static analysis cannot effectively explain the issue of the sealing performance of the premium connection being decreased due to the vibration of the tubing, leading to the failure of the connection sealing. In this paper, based on the energy dissipation theory and considering the influence of the micro contact slip of the sealing surface under the vibration of the tubing, a finite element model of the premium connection is established. The natural frequency and vibration mode are obtained through modal analysis experiments, and the accuracy of the finite element model is verified. The results show that the first five natural frequencies are mainly concentrated in the axial direction of the tubing, with the amplitude of the radial vibration mode being small. The vibration mode results are applied to the model as boundary conditions. It is found that an increase in the axial displacement amplitude leads to an increase in the energy dissipation of the sealing surface of the premium connection, which reduces the normal contact pressure and the effective length of the sealing surface, ultimately leading to a decrease in the sealing performance. [ABSTRACT FROM AUTHOR]

The premium connection is an important section of the tubing column. Under intricate downhole conditions, axial vibration generates alternating loads that cause energy dissipation between the sealing surfaces of the premium connection, reducing sealing performance. To investigate this issue, the mutual conversion process of sticking, slipping, and macroscopic slipping stages between the sealing surfaces of the premium connection under axial loads must be assessed. In this study, a finite element analysis model of a taper–taper Φ88.9 mm × 6.45 mm P110 premium connection is developed based on the discrete Iwan model's ontological relationship, and the sealing surface's force–displacement hysteresis curve is obtained. The equivalent Iwan model for this particular premium connection is constructed by discretizing the hysteresis curve and identifying the model's four sets of parameters. The correctness of the parameter identification method of the equivalent Iwan model is verified by comparing and analyzing the similarity of the two models. The energy dissipation in the sealing surfaces of the premium connection for different working conditions under dynamic loading is analyzed. This study reveals that the area similarity of the hysteresis curves of the two models is more than 92%, while the positional error is less than 2%. The sealing surface displacement amplitude of the premium connection is between 0.04 mm and 0.07 mm, while the sealing surface energy dissipation increases linearly, which may lead to a decline in sealing performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The fluctuation and intermittency of wind power generation seriously affect the stability and security of power grids. Aiming at smoothing wind power fluctuations, this paper proposes a flywheel–battery hybrid energy storage system (HESS) based on optimal variational mode decomposition (VMD). Firstly, the grid-connected power and charging–discharging power of the HESS are determined based on the sliding average algorithm. Secondly, the VMD algorithm, optimized using long short-term memory (LSTM), is used to decompose the hybrid energy storage power (HESP) into a series of sub-modes with frequencies from low to high. Then, the state of charge of the battery energy storage system and the speed of the flywheel energy storage system are monitored in real time, and the primary power of the HESS is modified according to the rules formulated by fuzzy control. Finally, through a simulation example, it is concluded that the method meets the requirements of smoothing wind power fluctuations and gives full play to the characteristics of energy storage battery and flywheel energy storage to ensure the stable operation of the energy storage system. The method presented in this paper can provide a reference for HESP configuration and control operation strategy formulation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Competing interests The authors declare that they have no competing interests.

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.