Exploring the effect of Gouqi Nuzhen Liuhe decoction on the PI3K/mTOR signaling pathway for premature ovarian insufficiency based on system pharmacology.

Objective: To explore the effect of Gouqi Nuzhen Liuhe Decoction (GNLHD) on the PI3K/mTOR Signaling Pathway for Premature Ovarian Insufficiency (POI) based on system pharmacology. Methods: First, the system pharmacology approach was used to predict the mechanism of GNLHD. Then, mice were randomly divided into model group, positive group, GNLHD high-dose group, GNLHD medium-dose group, and GNLHD low-dose group. Hematoxylin-eosin (HE) staining was used to observe the pathological changes of ovarian tissue under light microscope. The expression levels of estradiol (E2), follicle-stimulating hormone (FSH) and luteinizing hormone (LH) were detected by enzyme-linked immunosorbent assay. The expressions of PI3K, AKT1 and mTOR proteins in ovarian tissue were detected by immunohistochemistry. Results: The results of system pharmacology showed that GNLHD may regulate biological processes and signaling pathways such as: reproductive structure development, reproductive system development, Oocyte meiosis and so on. Compared with the model group, the levels of E2 in the GNLHD group were increased, and the levels of FSH and LH were decreased (P < 0.05). Compared with the model group, the number of mature follicles in the GNLHD group was significantly increased, the number of atretic follicles was relatively decreased, and the expressions of PI3K, AKT1, and MTOR proteins in the GNLHD group were significantly increased (P < 0.05). Conclusion: GNLHD may improve the ovarian function of POI mice by affecting the expression of PI3K, AKT1 and mTOR proteins, promote the growth and development of follicles, increase the E2 level, reduce FSH and LH level, and maintain the stability of the ovarian internal environment.

Real-Time Cardiac Abnormality Monitoring and Nursing for Patient Using Electrocardiographic Signals.

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.