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Introduction: Cardiovascular disease nursing is a critical clinical application that necessitates real-time monitoring models. Previous models required the use of multi-lead signals and could not be customized as needed. Traditional methods relied on manually designed supervised algorithms, based on empirical experience, to identify waveform abnormalities and classify diseases, and were incapable of monitoring and alerting abnormalities in individual waveforms. Methods: This research reconstructed the vector model for arbitrary leads using the phase space-time-delay method, enabling the model to arbitrarily combine signals as needed while possessing adaptive denoising capabilities. After employing automatically constructed machine learning algorithms and designing for rapid convergence, the model can identify abnormalities in individual waveforms and classify diseases, as well as detect and alert on abnormal waveforms. Result: Effective noise elimination was achieved, obtaining a higher degree of loss function fitting. After utilizing the algorithm in Section 3.1 to remove noise, the signal-to-noise ratio increased by 8.6%. A clipping algorithm was employed to identify waveforms significantly affected by external factors. Subsequently, a network model established by a generative algorithm was utilized. The accuracy for healthy patients reached 99.2%, while the accuracy for APB was 100%, for LBBB 99.32%, for RBBB 99.1%, and for P-wave peak 98.1%. Conclusion: By utilizing a three-dimensional model, detailed variations in electrocardiogram signals associated with different diseases can be observed. The clipping algorithm is effective in identifying perturbed and damaged waveforms. Automated neural networks can classify diseases and patient identities to facilitate precision nursing. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of the high-precision control of substrate tension in an accumulator during the roll-to-roll coating process. First, a coupling model for tension errors in the substrate within the accumulator is established, along with dynamic models for the input–output rollers, carriage, and the thrust model of the ball screw. Based on these models, a simulation model is built in MATLAB/Simulink to analyze the main causes of substrate tension errors in the accumulator under uncontrolled conditions. Next, to tackle the tension errors caused by carriage displacement, a nonlinear proportional–integral–derivative (PID) controller is proposed, and a control strategy for substrate tension in the accumulator is designed. Finally, based on the established simulation model, experiments are conducted using the proposed nonlinear PID controller and the designed tension control strategy, and their performance is compared with that of a classical PID controller. The simulation results show that both the nonlinear PID controller and the classical PID controller, when combined with the proposed tension error control strategy, can reduce tension errors in the accumulator substrate. However, the nonlinear PID controller is more suitable for controlling substrate tension errors in the accumulator. On the one hand, the nonlinear PID controller has better anti-disturbance capability. In the anti-disturbance experiment, under PID control, the substrate tension error remains stable at around −1.6 N, with tension disturbances of ±0.2 N occurring at approximately 185 s and 135 s. On the other hand, the nonlinear PID controller demonstrates better robustness. In the robustness experiment, under the nonlinear PID controller, the substrate tension error fluctuates within the range of 0 to 0.02 N, showing excellent robustness. [ABSTRACT FROM AUTHOR]

Breast cancer poses a serious threat to women's health globally. Current radiotherapy and chemotherapy regimens can induce drug-resistance effects in cancer tissues, such as anti-apoptosis, anti-pyroptosis, and anti-necroptosis, leading to poor clinical outcomes in the treatment of breast cancer. Ferroptosis is a novel programmed cell death modality characterized by iron overload, excessive generation of reactive oxygen species, and membrane lipid peroxidation. The occurrence of ferroptosis results from the imbalance between intracellular peroxidation mechanisms (executive system) and antioxidant mechanisms (defensive system), specifically involving iron metabolism pathways, amino acid metabolism pathways, and lipid metabolism pathways. In recent years, it has been found that ferroptosis is associated with the progression of various diseases, including tumors, hypertension, diabetes, and Alzheimer's disease. Studies have confirmed that triggering ferroptosis in breast cancer cells can significantly inhibit cancer cell proliferation and invasion, and improve cancer cell sensitivity to radiotherapy and chemotherapy, making induction of ferroptosis a potential strategy for the treatment of breast cancer. This paper reviews the development of the concept of ferroptosis, the mechanisms of ferroptosis (including signaling pathways such as GSH-GPX4, FSP1-CoQ1, DHODH-CoQ10, and GCH1-BH4) in breast cancer disease, the latest research progress, and summarizes the research on ferroptosis in breast cancer disease within the framework of metabolism, reactive oxygen biology, and iron biology. The key regulatory factors and mechanisms of ferroptosis in breast cancer disease, as well as important concepts and significant open questions in the field of ferroptosis and related natural compounds, are introduced. It is hoped that future research will make further breakthroughs in the regulatory mechanisms of ferroptosis and the use of ferroptosis in treating breast cancer cells. Meanwhile, natural compounds may also become a new direction for potential drug development targeting ferroptosis in breast cancer treatment. This provides a theoretical basis and opens up a new pathway for research and the development of drugs for the prevention and treatment of breast cancer. [ABSTRACT FROM AUTHOR]

Aim: To explore the molecular mechanism of Sijunzi Decoction (SJZD) in the treatment of Parkinson's disease (PD) through the application of network pharmacology, molecular docking, and molecular dynamics simulations, complemented by experimental verification. Methods: The BATMAN-TCM, GeneCards, and DisGeNet databases were searched to screen the active components and therapeutic targets of SJZD. Cytoscape (3.7.1) was used to create a network diagram of the components and targets. The STRING platform was used to construct a protein-protein interaction (PPI) network. The Bioconductor database and RX64 (4.0.0) software were used to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the core target genes. The binding sites and binding energies between SJZD active components and the target were analyzed by molecular docking and dynamic simulation. Finally, the therapeutic effect and mechanism of SJZD were verified by Cell Counting Kit-8 (CCK-8) and Western blotting (WB). Results: This research identified 188 active compounds in SJZD, 1568 drug targets, 2069 PD targets, and 451 intersection targets related to PD. According to network analysis, Adenosine Triphosphate, Tridecanoic Acid, Hexadecanoic Acid, Pentadecanoic Acid, and Adenosine were identified as the core components of SJZD in the treatment of PD. The five targets with the highest Degree values in the PPI network were AKT1, INS, TNF, IL-6, and TP53. The GO and KEGG enrichment analyses, in turn, determined that the administration of SJZD for the treatment of PD may engage processes such as xenobiotic stimulation and biological stimulus response. Furthermore, AGE-RAGE and cAMP signaling pathways related to diabetic complications may be involved. Molecular docking and kinetic simulations showed that IL-6 and AKT1 bind best to Adenosine. Experimental results showed that SJZD significantly reduced 6-OHDA-induced apoptosis of SH⁃SY5Y cells by activating the PI3K/AKT signaling pathway and regulating the expression of apoptosis factors such as Bcl⁃2 and Bax. Conclusion: SJZD is essential in the processes of apoptosis and neuronal protection, acting through various components that target multiple pathways. Notably, the PI3K/AKT pathway is a verified SJZD-PD target, providing a reference for clinical precision drug use for PD. [ABSTRACT FROM AUTHOR]

MYC promotes tumor growth through multiple mechanisms. Here, we show that, in human glioblastomas, the variant MYC transcript encodes a 114–amino acid peptide, MYC pre-mRNA encoded protein (MPEP), from the upstream open reading frame (uORF) MPEP. Secreted MPEP promotes patient-derived xenograft tumor growth in vivo, independent of MYC through direct binding, and activation of tropomyosin receptor kinase B (TRKB), which induces downstream AKT-mTOR signaling. Targeting MPEP through genetic ablation reduced growth of patient-derived 4121 and 3691 glioblastoma stem cells. Administration of an MPEP-neutralizing antibody in combination with a small-molecule TRKB inhibitor reduced glioblastoma growth in patient-derived xenograft tumor–bearing mice. The overexpression of MPEP in surgical glioblastoma specimens predicted a poor prognosis, supporting its clinical relevance. In summary, our results demonstrate that tumor-specific translation of a MYC-associated uORF promotes glioblastoma growth, suggesting a new therapeutic strategy for glioblastoma. Editor's summary: Upstream open reading frames (uORFs) are RNA sequences encoding short peptides that mainly function as translational represssors. Here, Li et al. show that the uORF of the oncogene MYC is translated into a peptide (MPEP) that is secreted from glioblastoma stem cells (GSCs). MPEP expression promoted proliferation of GSCs in vitro and accelerated tumorigenesis in a mouse model. MPEP acted through canonical activation of tropomyosin receptor kinase B, which was corroborated by the increase in survival rates of glioblastoma mice undergoing combination treatment with an MPEP antibody and the TRK inhibitor entrecitinib. —Daniela Neuhofer [ABSTRACT FROM AUTHOR]