Hybrid Bayesian Optimization-Based Graphical Discovery for Methylation Sites Prediction.

Protein methylation is one of the most important reversible post-translational modifications (PTMs), playing a vital role in the regulation of gene expression. Protein methylation sites serve as biomarkers in cardiovascular and pulmonary diseases, influencing various aspects of normal cell biology and pathogenesis. Nonetheless, the majority of existing computational methods for predicting protein methylation sites (PMSP) have been constructed based on protein sequences, with few methods leveraging the topological information of proteins. To address this issue, we propose an innovative framework for predicting Methylation Sites using Graphs (GraphMethySite) that employs graph convolution network in conjunction with Bayesian Optimization (BO) to automatically discover the graphical structure surrounding a candidate site and improve the predictive accuracy. In order to extract the most optimal subgraphs associated with methylation sites, we extend GraphMethySite by coupling it with a hybrid Bayesian optimization (together named GraphMethySite +) to determine and visualize the topological relevance among amino-acid residues. We evaluated our framework on two extended protein methylation datasets, and empirical results demonstrate that it outperforms existing state-of-the-art methylation prediction methods.

Effect of cognitive behavior therapy training and psychological nursing on the midwifery process in the delivery room.

BACKGROUND: The severe physical and psychological impact of pain on the physical and mental health of women during labor leads to increased risks and complications during childbirth, presenting a major public health concern. Some studies have shown that cognitive behavioral therapy (CBT) has a positive effect on maternal psychology during delivery, reducing stress and shortening labor time. Thus, CBT training for mothers and delivery room staff may be beneficial in minimizing complications and adverse effects during natural birth. AIM: To investigate the clinical effects of CBT training and psychological care during delivery, and their therapeutic effects on women in labor. METHODS: This study used a retrospective analysis and included 140 mothers admitted to the maternity ward between January 2021 and January 2023. The study subjects were randomized into two groups: control (n = 70) and observation (n = 70). Routine care, CBT training, and psychological care were provided to mothers in both groups. Psychological status scores, delivery time, and satisfaction with care pre- and post-delivery were compared, and the incidence of complications after receiving care was analyzed between the two groups. RESULTS: Although the psychological state of both groups improved significantly in the late stages of labor, the psychological state scores of the mothers in the observation group were significantly lower than those of the mothers in the control group (P < 0.05). The duration of labor and incidence of complications in the observation group were significantly lower than those in the control group (P < 0.05). The mothers in the observation group were significantly more satisfied with nursing care during the course of labor than those in the control group (P < 0.05). CONCLUSION: CBT training and psychological care for mothers in the midwifery process can effectively improve anxiety and depression, shorten labor duration, reduce postnatal complications, and improve nursing satisfaction and nurse-patient relationships. Its clinical application is effective and has popularization value, providing a new way to protect maternal mental health.

The correlation between economic fluctuation, workforce employment and health expenditure in the BRICS countries.

In this paper, we use the Fourier ARDL method (data from 2000 to 2019) to examine whether there is a correlation between economic fluctuation, health expenditure, and employment rate among BRICS countries. Fourier ARDL's model, the same as Bootstrap ARDL model, is to test the long-term cointegration relationship of variables; when there is cointegration, it will test whether there is a causal relationship. When there is no cointegration, short-term Granger causality between variables is tested. Our study shows that, in the long-term, whether South Africa takes economic fluctuation, employment rate or health expenditure as the dependent variable, there is a cointegration relationship with the other two independent variables, but the causal relationship is not significant. In short-term Granger causality tests, the effects of economic fluctuation in Brazil, China, and South Africa on health expenditure lag significantly by one period. Economic fluctuation in Brazil, India and China had a negative effect on employment rate, while South Africa had a positive effect. Health expenditure in Russia and India has a negative effect on employment rate, while China has a positive effect. Employment rates in China and South Africa have a significant positive effect on economic fluctuation, while Russia has a negative effect. India's employment rate has a negative effect on health expenditure, while South Africa's has a positive effect. In short-term causality tests, different countries will exhibit different phenomena. Except for economic fluctuation, where health spending is positive, everything else is negatively correlated, and all of them are positive in South Africa. Finally, we make policy recommendations for the BRICS countries on economic fluctuation, employment rates, and health expenditure.

HMGA1 Promotes Macrophage Recruitment via Activation of NF-κB-CCL2 Signaling in Hepatocellular Carcinoma.

Background: Tumor-associated macrophages (TAMs) are known to generate an immune-suppressive tumor microenvironment (TME) and promote tumor progression. Hepatocellular carcinoma (HCC) is a devastating disease that evolves in the background of chronic inflammatory liver damage. In this study, we aimed to uncover the mechanism by which HCC cells recruit macrophages into the TME. Methods: Bioinformatic analysis was performed to identify differentially expressed genes related to macrophage infiltration. An orthotopic HCC xenograft model was used to determine the role of macrophages in HCC tumor growth. Clodronate liposomes were used to delete macrophages. Western blotting analysis, quantitative real-time PCR, and enzyme-linked immunosorbent assay were performed to determine the underlying mechanisms. Results: The high mobility group A1 (HMGA1) gene was identified as a putative modulator of macrophage infiltration in HCC. Deletion of macrophages with clodronate liposomes significantly abrogated the tumor-promoting effects of HMGA1 on HCC growth. Mechanistically, HMGA1 can regulate the expression of C-C Motif Chemokine Ligand 2 (CCL2), also referred to as monocyte chemoattractant protein 1 (MCP1), which is responsible for macrophage recruitment. Moreover, NF-κB was required for HMGA1-mediated CCL2 expression. Pharmacological or genetic inhibition of NF-κB largely blocked CCL2 levels in HMGA1-overexpressing HCC cells. Conclusions: This study reveals HMGA1 as a crucial regulator of macrophage recruitment by activating NF-κB-CCL2 signaling, proves that HMGA1-induced HCC aggressiveness dependents on the macrophage, and provide an attractive target for therapeutic interventions in HCC.

[A nonlinear multi-compartment lung model for optimization of breathing airflow pattern].

It is difficult to select the appropriate ventilation mode in clinical mechanical ventilation. This paper presents a nonlinear multi-compartment lung model to solve the difficulty. The purpose is to optimize respiratory airflow patterns and get the minimum of the work of inspiratory phrase and lung volume acceleration, minimum of the elastic potential energy and rapidity of airflow rate changes of expiratory phrase. Sigmoidal function is used to smooth the respiratory function of nonlinear equations. The equations are established to solve nonlinear boundary conditions BVP, and finally the problem was solved with gradient descent method. Experimental results showed that lung volume and the rate of airflow after optimization had good sensitivity and convergence speed. The results provide a theoretical basis for the development of multivariable controller monitoring critically ill mechanically ventilated patients.