Vericiguat improves cardiac function and microcirculation of a male patient with Fabry disease: A case report.

Fabry disease (FD) is a rare X chromosome-linked disorder and can be easily misdiagnosed. Here, we report the case of a 69-year-old male patient with FD who developed heart failure and showed extremely high pulmonary artery pressure. His initial symptom was recurrent atrial fibrillation. The left and right atrial inner diameters were large, and the ventricular wall was thick. Gene analysis which showed GLA c.215T>C p.Met72Thr mutation and single photon emission computed tomography indicated the diagnosis of FD with coronary microvascular dysfunction. The patient was prescribed anti-heart failure drugs, including vericiguat. Following the treatment, his heart function and microvascular perfusion significantly improved, which might be due to the beneficial effects of vericiguat.

Indications affect neonatal outcomes following early rescue ICSI: a retrospective study.

PURPOSE: To investigate the impact of heterogeneity in patient indications or insemination protocols on neonatal outcomes of singletons following early rescue ICSI (rICSI) treatments. METHODS: A retrospective study was conducted. Propensity score matching and multivariable logistic regression were used to adjust for confounders and biases. RESULTS: A total of 9095 IVF patients, 2063 ICSI patients, and 642 early rICSI patients were included in the study. No differences were detected in neonatal outcomes except small for gestational age (SGA) which increased in early rICSI patients compared with both unmatched and matched IVF groups with the risk ratio (RR) of 1.31 (95% CI: 1.05, 1.64) and 1.49 (95% CI: 1.05, 2.12). Further analysis showed that SGA increased significantly in partial fertilization failure (PFF) cycles with RRs of 1.56 (95% CI: 1.08, 2.27) and 1.78 (95% CI: 1.22, 2.59) compared with both unmatched and matched IVF patients but not in TFF patients. A positive association between fertilization rate via IVF and birth weight z-score was revealed in the PFF patients. CONCLUSION: Early rICSI in patients with total fertilization failure (TFF) appeared to be safe in terms of neonatal outcomes. However, when expanding the indications of rICSI to PFF patients, the SGA in the offspring increased, suggesting a potential effect on long-term health. Since other treatment options, such as using only the IVF-origin embryos still exist for these patients, further studies were needed to confirm the optimal decision for these patients.

Paramyosin from field snail (Bellamya quadrata): Structural characteristics and its contribution to enhanced the gel properties of myofibrillar protein.

To assess the blending effect of field snails with grass carp muscle, the effects of paramyosin (PM) and actomyosin (AM) with different mixture ratios on the gel properties of the binary blend system were investigated in our work. The purified PM from field snail muscle was about 95 kDa on SDS-PAGE. Its main secondary structure was α-helix, which reached to 97.97 %. When the amount of PM increased in the binary blend system, their rheological indices and gel strength were improved. The water holding capacity (WHC) increased to 86.30 % at a mixture ratio of 2:8. However, the WHC and the area of immobile water (P22) dramatically decreased, and the area of free water (P23) increased when the mixture ratio exceeded 4:6. The low level of PM in binary blend system promoted the formation of a homogenous and dense gel network through non-covalent interactions as observed results of SEM and FTIR. When there were redundant PM molecules, the development of heterostructure via hydrophobic interaction of tail-tail contributed to the reduced gel properties of the binary blend system. These findings provided new insight into the binary blend system of PM and AM with different ratios to change the gel properties of myofibrillar protein.

An effective framework for predicting drug-drug interactions based on molecular substructures and knowledge graph neural network.

Drug-drug interactions (DDIs) play a central role in drug research, as the simultaneous administration of multiple drugs can have harmful or beneficial effects. Harmful interactions lead to adverse reactions, some of which can be life-threatening, while beneficial interactions can promote efficacy. Therefore, it is crucial for physicians, patients, and the research community to identify potential DDIs. Although many AI-based techniques have been proposed for predicting DDIs, most existing computational models primarily focus on integrating multiple data sources or combining popular embedding methods. Researchers often overlook the valuable information within the molecular structure of drugs or only consider the structural information of drugs, neglecting the relationship or topological information between drugs and other biological objects. In this study, we propose MSKG-DDI - a two-component framework that incorporates the Drug Chemical Structure Graph-based component and the Drug Knowledge Graph-based component to capture multimodal characteristics of drugs. Subsequently, a multimodal fusion neural layer is utilized to explore the complementarity between multimodal representations of drugs. Extensive experiments were conducted using two real-world datasets, and the results demonstrate that MSKG-DDI outperforms other state-of-the-art models in binary-class, multi-class, and multi-label prediction tasks under both transductive and inductive settings. Furthermore, the ablation analysis further confirms the practical usefulness of MSKG-DDI.

Gene signatures to therapeutics: Assessing the potential of ivermectin against t(4;14) multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow. The translocation, (t)(4;14), results in high-risk MM with limited treatment alternatives. Thus, there is an urgent need for identification and validation of potential treatments for this MM subtype. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets. AIM: To elucidate the molecular basis and search for potential effective drugs of t(4;14) MM subtype by employing a comprehensive approach. METHODS: The transcriptional signature of t(4;14) MM was sourced from the Gene Expression Omnibus. Two datasets, GSE16558 and GSE116294, which included 17 and 15 t(4;14) MM bone marrow samples, and five and four normal bone marrow samples, respectively. After the differentially expressed genes were identified, the Cytohubba tool was used to screen for hub genes. Then, the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Using the STRING database and Cytoscape, protein-protein interaction networks and core targets were identified. Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis, respectively. RESULTS: In this study, a total of 258 differentially expressed genes with enriched functions in cancer pathways, namely cytokine receptor interactions, nuclear factor (NF)-κB signaling pathway, lipid metabolism, atherosclerosis, and Hippo signaling pathway, were identified. Ten hub genes (cd45, vcam1, ccl3, cd56, app, cd48, btk, ccr2, cybb, and cxcl12) were identified. Nine drugs, including ivermectin, deforolimus, and isoliquiritigenin, were predicted by the Connectivity Map database to have potential therapeutic effects on t (4;14) MM. In molecular docking, ivermectin showed strong binding affinity to all 10 identified targets, especially cd45 and cybb. Ivermectin inhibited t(4;14) MM cell growth via the NF-κB pathway and induced MM cell apoptosis in vitro. Furthermore, ivermectin increased reactive oxygen species accumulation and altered the mitochondrial membrane potential in t(4;14) MM cells. CONCLUSION: Collectively, the findings offer valuable molecular insights for biomarker validation and potential drug development in t(4;14) MM diagnosis and treatment, with ivermectin emerging as a potential therapeutic alternative.

Diagnostic value of serum dia in acute myocardial infarction and aortic dissection in athletic patients and those with optimal physical health

Objective: To explore the relationship between serum lipoprotein (a) levels and acute myocardial infarction (AMI) and aortic dissection in athletic patients and those with optimal physical health. Methods: This study involved 216 athletic patients admitted to a Chinese hospital for AMI who underwent Percutaneous Coronary Intervention (PCI) between 2018 and 2019. These patients, characterized by their athletic background and optimal physical health, were divided based on their serum lipoprotein (a) levels: 133 in the low-lipoprotein (a) group (<300 mg/L) and 83 in the high-lipoprotein (a) group (≥300 mg/L). Data including baseline demographics, laboratory tests, and details of interventional treatment were collected from medical records. All patients were followed up for two years post-discharge to record Major Adverse Cardiac Events (MACE). Factors influencing MACE were analyzed using univariate and multivariate logistic regression. Results: The low lipoprotein (a) group exhibited lower age, reduced Killip grades III-IV, lower LDL-C levels, and fewer diseased vessels than the high lipoprotein (a) group (P><0.05). The incidence of MACE was significantly lower in the low lipoprotein (a) group (5.3%, 7/133) compared to the high lipoprotein (a) group (27.87%, 51/183) (P><0.05). Univariate analysis identified significant differences in age, post-surgery β-blocker use, LDL-C levels, serum lipoprotein (a) levels, revascularization strategies, and the> <3 00 mg/L) and 83 in the high-lipoprotein (a) group (≥300 mg/L). Data including baseline demographics, laboratory tests, and details of interventional treatment were collected from medical records. All patients were followed up for two years post-discharge to record Major Adverse Cardiac Events (MACE). Factors influencing MACE were analyzed using univariate and multivariate logistic regression (AU)

ConPep: Prediction of peptide contact maps with pre-trained biological language model and multi-view feature extracting strategy.

The accurate prediction of peptide contact maps remains a challenging task due to the difficulty in obtaining the interactive information between residues on short sequences. To address this challenge, we propose ConPep, a deep learning framework designed for predicting the contact map of peptides based on sequences only. To sufficiently incorporate the sequential semantic information between residues in peptide sequences, we use a pre-trained biological language model and transfer prior knowledge from large scale databases. Additionally, to extract and integrate sequential local information and residue-based global correlations, our model incorporates Bidirectional Gated Recurrent Unit and attention mechanisms. They can obtain multi-view features and thus enhance the accuracy and robustness of our prediction. Comparative results on independent tests demonstrate that our proposed method significantly outperforms state-of-the-art methods even with short peptides. Notably, our method exhibits superior performance at the sequence level, suggesting the robust ability of our model compared with the multiple sequence alignment (MSA) analysis-based methods. We expect it can be meaningful research for facilitating the wide use of our method.

Prediction of cancer recurrence based on compact graphs of whole slide images.

Cancer recurrence is one of the primary causes of patient mortality following treatment, indicating increased aggressiveness of cancer cells and difficulties in achieving a cure. A critical step to improve patients' survival is accurately predicting recurrence status and giving appropriate treatment. Whole Slide Images (WSIs) are a common type of image data in the field of digital pathology, containing high-resolution tissue information. Furthermore, WSIs of primary tumors contain microenvironmental information directly associated with the growth of tumor cells. To effectively utilize this microenvironmental information. Firstly, we represented microenvironmental features of histopathological images as compact graphs. Secondly, this work aims to develop an enhanced lightweight graph neural network called the Adaptive Graph Clustering Network (AGCNet) for predicting cancer recurrence. Experiments are conducted on three cancer datasets from The Cancer Genome Atlas (TCGA), and AGCNet achieved an accuracy of 81.81% in BLCA, 69.66% in PAAD, and 81.96% in STAD. These results indicated that AGCNet is an effective model for predicting cancer recurrence and is expected to be applied in clinical applications.

RETRACTED ARTICLE: Analysis of fractured soft rock characteristics in fault rupture zones and laneway shoring.

Fault rupture is a common phenomenon in geotechnical engineering. To prevent rupture, laneway shoring is performed, prior to which, convergence deformation, failure criteria, and fracture development in soft rocks in the fault rupture zone are carefully analyzed. Then, a supporting structure corresponding to the actual situation of the soft rock in the rupture zone is created. Herein, the water-rich laneway shoring through the fault rupture zone of the Hongqingliang coal mine located in the Inner Mongolia Autonomous Region is taken as the research object. Then, the fracture development and characteristics of argillaceous siltstones and laneway shoring cross-fault rupture zone are studied. Site inspection, indoor and field tests, theoretical analysis, numerical simulation, and field monitoring were used for systematic fracture analysis. Results indicated that laneway shoring through the fault fracture zone in the Hongqingliang coal mine could help prevent disasters. This method was extended to laneway supports built through the fault rupture zones in mines in other areas of China.

Preparation and structural analysis of fucomannogalactan and ß-1,6-glucan from Grifola frondosa mycelium.

Introduction: Polysaccharides, key components present in Grifola frondosa, can be divided into those derived from fruiting bodies, mycelium, and fermentation broth based on their source. The structure of G. frondosa fruiting body-derived polysaccharides has been fully characterized. However, the structure of G. frondosa mycelium-derived polysaccharides remains to be elucidated. Methods: In this study, we obtained mycelia from G. frondosa by liquid fermentation and extracted them with water and alkaline solution. Then, the mycelia were isolated and purified to obtain homogeneity and systematically characterized by methylation and FT infrared (FT-IR) and nuclear magnetic resonance (NMR) spectroscopy. Results and discussion: Structural analysis showed that two neutral fractions (WGFP-N-a and AGFP-N-a1) have a common backbone composed of α-1,6-D-Me-Galp and α-1,6-D-Galp that were substituted at O-2 by 1,2-Manp, α-1,3-L-Fucp, and α-T-D-Manp and thus are identified as fucomannogalactans. WGFP-A-a, AGFP-A-b, and AGFP-A-c are ß-1,6-glucans with different molecular weights and are branched with ß-1,3-D-Glcp and T-D-Glcp at the O-3 of Glc. Our results provide important structural information about G. frondosa mycelium-derived polysaccharides and provide the basis for their further development and application.

Distinct mutational processes shape selection of MHC class I and class II mutations across primary and metastatic tumors.

Disruption of antigen presentation via loss of major histocompatibility complex (MHC) expression is a strategy whereby cancer cells escape immune surveillance and develop resistance to immunotherapy. Here, we develop the personalized genomics algorithm Hapster and accurately call somatic mutations within the MHC genes of 10,001 primary and 2,199 metastatic tumors, creating a catalog of 1,663 non-synonymous mutations that provide key insights into MHC mutagenesis. We find that MHC class I genes are among the most frequently mutated genes in both primary and metastatic tumors, while MHC class II mutations are more restricted. Recurrent deleterious mutations are found within haplotype- and cancer-type-specific hotspots associated with distinct mutational processes. Functional classification of MHC residues reveals significant positive selection for mutations disruptive to the B2M, peptide, and T cell binding interfaces, as well as to MHC chaperones.

Identification of immunity-related lncRNAs and construction of a ceRNA network of potential prognostic biomarkers in acute myeloid leukemia.

Objective: Using bioinformatics analyses, this study aimed to identify lncRNAs related to the immune status of acute myeloid leukemia (AML) patients and ascertain the potential impact in immunity-related competing endogenous RNA (ceRNA) networks on AML prognosis. Methods: AML-related RNA-seq FPKM data, AML-related miRNA expression microarray data, and gene sets associated with immunity-related pathways were, respectively, obtained from the TCGA, GEO, and ImmReg databases. An immunity-related ceRNA network was then constructed according to the predicted interactions between AML-related mRNAs, lncRNAs, and miRNAs. After performing LASSO and multivariate Cox regression analyses, lncRNAs in the ceRNA network were used to establish an AML prognostic model. According to mutual regulatory relationships and consistent trends of expression among candidate ceRNAs, two ceRNA subnetworks related to the AML prognostic model were determined. Finally, the correlation between the expression levels of mRNAs, lncRNAs, and miRNAs in each ceRNA subnetwork and immune cell infiltration (assessed by combining the ESTIMATE and CIBERSORT methods and ssGSEA) was analyzed. Results: A total of 424 immunity-related differentially expressed (IR-DE) mRNAs (IR-DEmRNAs), 191 IR-DElncRNAs, and 69 IR-DEmiRNAs were obtained, and a ceRNA network of 20 IR-DElncRNAs, 6 IR-DEmRNAs, and 3 IR-DEmiRNAs was established. Univariate Cox regression analysis was conducted on 20 IR-DElncRNAs, and 7 of these were identified to be significantly correlated with the overall survival (OS) time in AML patients. Then, two IR-DElncRNAs (MEG3 and HCP5) were screened as independent OS-related factors by LASSO and multivariable Cox regression analyses, and a prognostic model was constructed to evaluate the survival risk in AML patients. Survival analyses indicated that the OS of patients was often poor in the high-risk group. Additionally, from this model, two ceRNA regulatory pathways, namely, MEG3/miR-125a-5p/SEMA4C and HCP5/miR-125b-5p/IL6R, which were potentially involved in the immune regulation of AML prognosis were identified. Conclusion: lncRNAs HCP5 and MEG3 may act as key ceRNAs in the pathogenesis in AML by regulating immune cell representation as part of the regulatory lncRNA-miRNA-mRNA axes. The candidate mRNAs, lncRNAs, and miRNAs included in the ceRNA network identified here may serve as useful prognostic biomarkers and immunotherapeutic targets for AML.

miR-125b-5p alleviates the damage of myocardial infarction by inhibiting the NFAT2 to reduce F2RL2 expression.

Aim: To explore the effect of miR-125b-5p/nuclear factor of activated T cells 1 (NFAT2)/F2RL2 on myocardial infarction (MI). Method: After establishment of MI mouse model and oxygen glucose deprivation (OGD)-induced cell model, the effects of NFAT2 on the process of MI were observed, the effects of miR-125b-5p/NFAT2/F2RL2 on the cell viability, apoptosis, and inflammatory factors levels were determined. Result: NFAT2 silencing relieved MI and inhibited the inflammation in MI model mice. In OGD-induced human coronary artery endothelial cells and human cardiac microvascular endothelial cells, miR-125b-5p enhanced cell viability, yet repressed cell apoptosis and inflammatory factors and NFAT2 levels. NFAT2 overexpression reversed the effects of miR-125b-5p, while F2RL2 silencing offset the effects of NFAT2 overexpression. Conclusion: MiR-125b-5p alleviates MI injury by inhibiting NFAT2 level to reduce F2RL2 expression.

This research proves that miR-125b-5p reduces the level of F2RL2 by preventing the activation of NFAT2 pathway, thereby reducing cardiogenic vascular endothelial cell damage and inflammation (heat, swelling and redness). This may provide a new treatment for heart attacks.

Targeted RASSF1A expression inhibits proliferation of HER2­positive breast cancer cells in vitro.

Human epidermal growth factor receptor 2-positive (HER2+) breast cancer, which accounts for 15-20% of all breast cancer, is associated with tumor recurrence and poor prognosis. RAS association domain family protein 1 subtype A (RASSF1A) is a tumor suppressor that is silenced in a variety of human cancers. The present study aimed to investigate the role of RASSF1A in HER2+ breast cancer and the therapeutic potential of RASSF1A-based targeted gene therapy for this malignancy. RASSF1A expression in human HER2+ breast cancer tissues and cell lines was evaluated by reverse transcription PCR and western blot analysis. The associations between tumorous RASSF1A level and tumor grade, TNM stage, tumor size, lymph node metastasis and five-year survival were examined. HER2+ and HER2-negative (HER2-) breast cancer cells were transfected with a lentiviral vector (LV-5HH-RASSF1A) that could express RASSF1A under the control of five copies of the hypoxia-responsive element (5HRE) and one copy of the HER2 promoter (HER2p). Cell proliferation was evaluated by the MTT and colony formation assays. It was found that tumorous RASSF1A level was negatively associated with tumor grade (P=0.014), TNM stage (P=0.0056), tumor size (P=0.014) and lymph node metastasis (P=0.029) and positively associated with five-year survival (P=0.038) in HER2+ breast cancer patients. Lentiviral transfection of HER2+ breast cancer cells resulted in increased RASSF1A expression and decreased cell proliferation, especially under hypoxic conditions. However, lentiviral transfection of HER2-breast cancer cells did not affect RASSF1A expression. In conclusion, these findings verified the clinical significance of RASSF1A as a tumor suppressor in HER2+ breast cancer and supported LV-5HH-RASSF1A as a potential targeted gene therapy for this malignancy.

Therapeutic mechanisms of the medicine and food homology formula Xiao-Ke-Yin on glucolipid metabolic dysfunction revealed by transcriptomics, metabolomics and microbiomics in mice.

BACKGROUND: In recent decades, the prevalence of metabolic diseases, particularly diabetes, hyperlipidemia, obesity, and non-alcoholic fatty liver disease (NAFLD), has increased dramatically, causing great public health and economic burdens worldwide. Traditional Chinese medicine (TCM) serves as an effective therapeutic choice. Xiao-Ke-Yin (XKY) is a medicine and food homology TCM formula consisting of nine "medicine and food homology" herbs and is used to ameliorate metabolic diseases, such as insulin resistance, diabetes, hyperlipidemia and NAFLD. However, despite its therapeutic potential in metabolic disorders, the underlying mechanisms of this TCM remain unclear. This study aimed to evaluate the therapeutic effectiveness of XKY on glucolipid metabolism dysfunction and explore the potential mechanisms in db/db mice. METHODS: To verify the effects of XKY, db/db mice were treated with different concentrations of XKY (5.2, 2.6 and 1.3 g/kg/d) and metformin (0.2 g/kg/d, a hypoglycemic positive control) for 6 weeks, respectively. During this study, we detected the body weight (BW) and fasting blood glucose (FBG), oral glucose tolerance test (OGTT), insulin tolerance test (ITT), daily food intake and water intake. At the end of the animal experiment, blood samples, feces, liver and intestinal tissue of mice in all groups were collected. The potential mechanisms were investigated by using hepatic RNA sequencing, 16 S rRNA sequencing of the gut microbiota and metabolomics analysis. RESULTS: XKY efficiently mitigated hyperglycemia, IR, hyperlipidemia, inflammation and hepatic pathological injury in a dose dependent manner. Mechanistically, hepatic transcriptomic analysis showed that XKY treatment significantly reversed the upregulated cholesterol biosynthesis which was further confirmed by RT-qPCR. Additionally, XKY administration maintained intestinal epithelial homeostasis, modulated gut microbiota dysbiosis, and regulated its metabolites. In particular, XKY decreased secondary bile acid producing bacteria (Clostridia and Lachnospircaeae) and lowered fecal secondary bile acid (lithocholic acid (LCA) and deoxycholic acid (DCA)) levels to promote hepatic bile acid synthesis by inhibiting the LCA/DCA-FXR-FGF15 signalling pathway. Furthermore, XKY regulated amino acid metabolism including arginine biosynthesis, alanine, aspartate and glutamate metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and tryptophan metabolism likely by increasing Bacilli, Lactobacillaceae and Lactobacillus, and decreasing Clostridia, Lachnospircaeae, Tannerellaceae and Parabacteroides abundances. CONCLUSION: Taken together, our findings demonstrate that XKY is a promising "medicine food homology" formula for ameliorating glucolipid metabolism and reveal that the therapeutic effects of XKY may due to its downregulation of hepatic cholesterol biosynthesis and modulation of the dysbiosis of the gut microbiota and metabolites.

The model polysaccharide potato galactan is actually a mixture of different polysaccharides.

Commercially-supplied potato galactan (PG) is widely used as a model polysaccharide in various bioactivity studies. However, results using this galactan are not always consistent with the stated composition. Here, we assessed its composition by fractionating this commercial PG and purified its primary components: PG-A, PG-B and PG-Cp with weight-averaged molecular weights of 430, 93, and 11.3 kDa, respectively. PG-Cp consists of free ß-1,4-galactan chains, whereas PG-A and PG-B are type I rhamnogalacturonans with long ß-1,4-galactan side chains of up to 80 Gal residues and short ß-1,4-galactan side chains of 0 to 3 Gal residues that display a "trees in lawn" pattern. Structures of these polysaccharides correlate well with their activities in terms of galectin-3 binding and gut bacterial growth assays. Our study clarifies the confusion related to commercial PG, with purified fractions serving as better model polysaccharides in bioactivity investigations.

Single cell landscape of parietal epithelial cells in healthy and diseased states.

The biology and diversity of glomerular parietal epithelial cells (PECs) are important for understanding podocyte regeneration and crescent formation. Although protein markers have revealed the morphological heterogeneity of PECs, the molecular characteristics of PEC subpopulations remain largely unknown. Here, we performed a comprehensive analysis of PECs using single-cell RNA sequencing (scRNA-seq) data. Our analysis identified five distinct PEC subpopulations: PEC-A1, PEC-A2, PEC-A3, PEC-A4 and PEC-B. Among these subpopulations, PEC- A1 and PEC-A2 were characterized as podocyte progenitors while PEC-A4 represented tubular progenitors. Further dynamic signaling network analysis indicated that activation of PEC-A4 and the proliferation of PEC-A3 played pivotal roles in crescent formation. Analyses suggested that upstream signals released by podocytes, immune cells, endothelial cells and mesangial cells serve as pathogenic signals and may be promising intervention targets in crescentic glomerulonephritis. Pharmacological blockade of two such pathogenic signaling targets, proteins Mif and Csf1r, reduced hyperplasia of the PECs and crescent formation in anti-glomerular basement membrane glomerulonephritis murine models. Thus, our study demonstrates that scRNA-seq-based analysis provided valuable insights into the pathology and therapeutic strategies for crescentic glomerulonephritis.