New triple therapy for the diagnosis of CKD-MBD: a cross-sectional study in Shanxi province.

OBJECTIVES: To seek a triple combination of biomarkers for early diagnosis of chronic kidney disease-mineral and bone metabolic disorder and to explore the diagnostic efficacy of ß2-microglobulin, parathyroid hormone and blood urea nitrogen in chronic kidney disease-mineral and bone metabolic disorder. PARTICIPANTS: We collected medical records of 864 patients with chronic kidney disease (without direct contact with patients) and divided them into two groups based on the renal bone disease manifestations of all patients. PRIMARY AND SECONDARY OUTCOME MEASURES: There were 148 and 716 subjects in the Chronic kidney disease-mineral and bone metabolic disorder and the control groups, respectively. The aggregated data included basic information and various clinical laboratory indicators, such as blood lipid profile, antibody and electrolyte levels, along with renal function-related indicators. RESULTS: It was observed that most renal osteopathy occurs in the later stages of chronic kidney disease. In the comparison of two clinical laboratory indicators, 16 factors were selected for curve analysis and compared. We discovered that factors with high diagnostic values were ß2-microglobulin, parathyroid hormone and blood urea nitrogen. CONCLUSIONS: The triple combination of ß2-microglobulin+parathyroid hormone+blood urea nitrogen indicators can play the crucial role of a sensitive indicator for the early diagnosis of chronic kidney disease-mineral and bone metabolic disorder and in preventing or delaying the progress of chronic kidney disease-mineral and bone metabolic disorder.

Myeloid­derived suppressor cells: Key immunosuppressive regulators and therapeutic targets in colorectal cancer (Review).

Globally, colorectal cancer (CRC) is the third most common type of cancer. CRC has no apparent symptoms in the early stages of disease, and most patients receive a confirmed diagnosis in the middle or late disease stages. The incidence of CRC continues to increase, and the affected population tends to be younger. Therefore, determining how to achieve an early CRC diagnosis and treatment has become a top priority for prolonging patient survival. Myeloid­derived suppressor cells (MDSCs) are a group of bone marrow­derived immuno­negative regulatory cells that are divided into two subpopulations, polymorphonuclear­MDSCs and monocytic­MDSCs, based on their phenotypic similarities to neutrophils and monocytes, respectively. These cells can inhibit the immune response and promote cancer cell metastasis in the tumour microenvironment (TME). A large aggregation of MDSCs in the TME is often a marker of cancer and a poor prognosis in inflammatory diseases of the intestine (such as colonic adenoma and ulcerative colitis). In the present review, the phenotypic classification of MDSCs in the CRC microenvironment are first discussed. Then, the amplification, role and metastatic mechanism of MDSCs in the CRC TME are described, focusing on genes, gene modifications, proteins and the intestinal microenvironment. Finally, the progress in CRC­targeted therapies that aim to modulate the quantity, function and structure of MDSCs are summarized in the hope of identifying potential screening markers for CRC and improving CRC prognosis and therapeutic options.

Bias-Switchable Dual-Mode Organic Photodetector with High Operational Stability Using Self-Trapped Cs3Cu2I5 Interfacial Layer.

Conventional photovoltaic (PV)-photodetectors are hard to detect fainted signals, while photomultiplication (PM)-capable devices indispensable for detecting weak light and are prone to degrade under strong light illumination and large bias, and it is urgent to realize highly efficient integrated detecting system with both PM and PV operation modes. In this work, one lead-free Cs3Cu2I5 nanocrystals with self-trapping exciton nature was introduced as interfacial layer adjacent to bulk and layer-by-layer heterojunction structure, and corresponding organic photodetectors with bias-switchable dual modes are demonstrated. The fabricated device exhibits low operating bias (0 V for PV mode and 0.8 V for PM mode), high specific detectivity (~1013 Jones), fast response speed as low as 1.59 µs, large bandwidth over 0.2 MHz and long-term operational stability last for 4 months in ambient condition. This synergy strategy also validated in different materials and device architectures, providing a convenient and scalable production process to develop highly efficient bias-switchable multi-functional organic optoelectrical applications.

Effect of the gut microbiome, plasma metabolome, peripheral cells, and inflammatory cytokines on obesity: a bidirectional two-sample Mendelian randomization study and mediation analysis.

Background: Obesity is a metabolic and chronic inflammatory disease involving genetic and environmental factors. This study aimed to investigate the causal relationship among gut microbiota abundance, plasma metabolomics, peripheral cell (blood and immune cell) counts, inflammatory cytokines, and obesity. Methods: Summary statistics of 191 gut microbiota traits (N = 18,340), 1,400 plasma metabolite traits (N = 8,299), 128 peripheral cell counts (blood cells, N = 408,112; immune cells, N = 3,757), 41 inflammatory cytokine traits (N = 8,293), and 6 obesity traits were obtained from publicly available genome-wide association studies. Two-sample Mendelian randomization (MR) analysis was applied to infer the causal links using inverse variance-weighted, maximum likelihood, MR-Egger, weighted median, weighted mode, and Wald ratio methods. Several sensitivity analyses were also utilized to ensure reliable MR results. Finally, we used mediation analysis to identify the pathway from gut microbiota to obesity mediated by plasma metabolites, peripheral cells, and inflammatory cytokines. Results: MR revealed a causal effect of 44 gut microbiota taxa, 281 plasma metabolites, 27 peripheral cells, and 8 inflammatory cytokines on obesity. Among them, five shared causal gut microbiota taxa belonged to the phylum Actinobacteria, order Bifidobacteriales, family Bifidobacteriaceae, genus Lachnospiraceae UCG008, and species Eubacterium nodatum group. Furthermore, we screened 42 shared causal metabolites, 7 shared causal peripheral cells, and 1 shared causal inflammatory cytokine. Based on known causal metabolites, we observed that the metabolic pathways of D-arginine, D-ornithine, linoleic acid, and glycerophospholipid metabolism were closely related to obesity. Finally, mediation analysis revealed 20 mediation relationships, including the causal pathway from gut microbiota to obesity, mediated by 17 metabolites, 2 peripheral cells, and 1 inflammatory cytokine. Sensitivity analysis represented no heterogeneity or pleiotropy in this study. Conclusion: Our findings support a causal relationship among gut microbiota, plasma metabolites, peripheral cells, inflammatory cytokines, and obesity. These biomarkers provide new insights into the mechanisms underlying obesity and contribute to its prevention, diagnosis, and treatment.

A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators With Functional Group Information and Hypergraph Structure.

Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-specific. Recently, deep learning models based on graph neural networks have made remarkable progress in molecular representation learning. However, many graph-based approaches ignore molecular hierarchical structure modeling guided by domain knowledge. In chemistry, the functional groups of a molecule determine its interaction with specific targets. Therefore, we propose a hierarchical graph neural network framework (called HiGPPIM) for predicting PPIMs by integrating atom-level and functional group-level features of molecules. HiGPPIM constructs atom-level and functional group-level graphs based on chemical knowledge and learns graph representations using graph attention networks. Furthermore, a hypergraph attention network is designed in HiGPPIM to aggregate and transform two-level graph information. We evaluate the performance of HiGPPIM on eight PPI families and two prediction tasks, namely PPIM identification and potency prediction. Experimental results demonstrate that HiGPPIM achieves state-of-the-art performance on both tasks and that using functional group information to guide PPIM prediction is effective.

Orientation of graphene nanosheets in suspension under an electric field: theoretical model and molecular dynamic simulations.

Orientation regulation of nanoparticles in a suspension by an electric field is a powerful tool to tune its mechanical, thermal, optical, electrical properties etc. However, how molecular modification can affect the orientation of two-dimensional nanoparticles is still unclear. In this paper, the influence of molecular modification on the orientation of graphene nanosheets (GNS) in water was investigated through theoretical analyses and molecular dynamics (MD) simulations. Firstly, a new orientation angle model was proposed, which considers hydration effects, dipole moments and resistance torque. Then, MD simulations were conducted to investigate the effects of position, direction, type, and number of functional groups on the orientation of GNS. The trend observed in MD simulations is consistent with the proposed theoretical model. The results reveal that, under the combined influence of the dipole moment and hydration effects, the modification with hydrophilic functional groups can reduce the orientation angle from 21.31° to 8.34°, while the modification with hydrophobic functional groups increases it to 26.43°. Among the hydrophilic functional groups, orientation of hydroxylated GNS is the best. With an increase in the number of hydroxyl groups, orientation angle is decreased from 12.61° to 8.34°. This work can provide valuable guidance for the design of high-performance suspensions and composites, such as thermal smart materials with adjustable thermal conductivity and intelligent devices with tailored capabilities.

Microbiota in colorectal cancer related to liver metastasis.

The prevalence of colorectal cancer (CRC) is increasing annually and metastasis is the principal cause of death in patients with CRC, with the liver being the most frequently affected site. Many studies have shown a strong interplay between the gut flora, particularly Fusobacterium nucleatum (F. nucleatum), Escherichia coli, and Bacteroides fragilis, and the development of gut tumors. Some strains can induce gut inflammation and produce toxins that directly harm gut epithelial cells, ultimately accelerating the onset and progression of CRC. However, little clinical evidence exists on the specific interplay between the gut microflora and colorectal cancer liver metastasis (CRLM). Some research showed the existence of viable F. nucleatum in distant metastasis of CRC. Subsequently, gut microbiota products, such as lipopolysaccharides, sodium butyrate, and protein cathepsin K, were also found to affect the development of CRC. This article summarizes the mechanism and research status of the interplay between gut microflora and CRLM, discusses the importance of gut microflora in the treatment of CRLM, and proposes a new approach to understanding the mechanism of CRLM and potential treatments for the microbiome. It is anticipated that the gut microbiota will be a formidable therapeutic and prophylactic tool for treating and preventing CRLM.

PPII-AEAT: Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training.

Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods.

Silica-induced macrophage pyroptosis propels pulmonary fibrosis through coordinated activation of relaxin and osteoclast differentiation signaling to reprogram fibroblasts.

Silica nanoparticle (SiNP) exposure induces severe pulmonary inflammation and fibrosis, but the pathogenesis remains unclear, and effective therapies are currently lacking. To explore the mechanism underlying SiNPs-induced pulmonary fibrosis, we constructed in vivo silica exposure animal models and in vitro models of silica-induced macrophage pyroptosis and fibroblast transdifferentiation. We found that SiNP exposure elicits upregulation of pulmonary proteins associated with pyroptosis, including NLRP3, ASC, IL-1ß, and GSDMD, while the immunofluorescence staining co-localized NLRP3 and GSDMD with macrophage-specific biomarker F4/80 in silica-exposed lung tissues. However, the NLRP3 inhibitor MCC950 and classical anti-fibrosis drug pirfenidone (PFD) were found to be able to alleviate silica-induced collagen deposition in the lungs. In in vitro studies, we exposed the fibroblast to a conditioned medium from silica-induced pyroptotic macrophages and found enhanced expression of α-SMA, suggesting increased transdifferentiation of fibroblast to myofibroblast. In line with in vivo studies, the combined treatment of MCC950 and PFD was demonstrated to inhibit the expression of α-SMA and attenuate fibroblast transdifferentiation. Mechanistically, we adopted high throughput RNA sequencing on fibroblast with different treatments and found activated signaling of relaxin and osteoclast differentiation pathways, where the expression of the dysregulated genes in these two pathways was examined and found to be consistently altered both in vitro and in vivo. Collectively, our study demonstrates that SiNP exposure induces macrophage pyroptosis, which subsequently causes fibroblast transdifferentiation to myofibroblasts, in which the relaxin and osteoclast differentiation signaling pathways play crucial roles. These findings may provide valuable references for developing new therapies for pulmonary fibrosis.

Effectiveness of a novel rat model of off-target PLA2R1 knockout to renal impairment.

Phospholipase A2 receptor 1 (PLA2R1) plays a crucial role in various diseases, including membranous nephropathy. However, the precise implications of PLA2R1 deficiency remain poorly understood. In this study, we created PLA2R1 knockout rats to explore potential consequences resulting from the loss of the PLA2R1 gene. Unexpectedly, our PLA2R1 knockout rats exhibited symptoms resembling those of chronic kidney disease after an 8-week observation period. Notably, several rats developed persistent proteinuria, a hallmark of renal dysfunction. Immunohistochemical and immunofluorescence analyses revealed insignificant glomerular fibrosis, reduced podocyte count, and augmented glomerular expression of complement C3 (C3) compared to immunoglobin A (IgA) and immunoglobin G(IgG) in the rat model. These findings suggest that the loss of PLA2R1 may contribute to the pathogenesis of membranous nephropathy and related conditions. Our knockout rat model provides a valuable tool for investigating the underlying pathology of PLA2R1-associated diseases, and may facilitate the development of targeted therapies for membranous nephropathy and other related disorders.

CXCL16 promotes tumor metastasis by regulating angiogenesis in the tumor micro-environment of BRAF V600E mutant colorectal cancer.

Patients of colorectal cancer (CRC) with BRAF V600E mutation obtain poor prognosis. This study aimed to explore the role and mechanism of BRAF V600E mutation in angiogenesis of tumor micro-environment (TME). It has been reported that CXCL16 expression in TME is closely related to BRAF mutation. Clinicopathological features of CRC with BRAF V600E mutant or wild type were collected in this study. Immunohistochemistry (IHC) assays were conducted to test the expressions of vascular endothelial growth factor (VEGF), CD31 and CXCL16. ROC curve was used to determine the optimal cut off values of CXCL16. A total of 680 patients including 141 BRAF V600E type and 679 wild type were included. BRAF V600E mutant tumors were presented with significant worse clinicopathological features and a shorter overall survival (OS) than wild-type. Besides, chemokines CXCL16 was up-regulated in BRAF V600E mutant tissues and was associated with poorer prognosis. In addition, VEGF levels and vascular endothelial cell density was significantly increased in BRAF mutation. At last, CXCL16 was positively correlated with VEGF expression and vascular endothelial cell density. In conclusion, BRAF V600E mutations may promote metastasis of CRC by regulating CXCL16 expression and promoting angiogenesis in the TME.

Construction and application of a QSRR approach for identifying flavonoids.

A quantitative structure retention relationship (QSRR) method was developed to identify flavonoid isomers auxiliary using an ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method based on the linear relationships between the Ln(k') values of flavonoids and their hydrogen bonding energy (XAH) and dissolution energy (ES). Chromatographic separation was achieved with a Hypersil GOLD C18 (100 mm × 2.1 mm, 1.9 µm) column and Agilent SB-C18 (2.1 ×50 mm, 1.8 µm) column on a Dionex Ultimate 3000 RSLC chromatograph. Compounds were eluted isocratically using a mobile phase containing 0.1% formic acid/water solution and methanol at a ratio of 55:45 (v/v). Mass spectrometry was performed in the negative and positive ionization modes on a Thermo Fisher Q Exactive Orbitrap mass spectrometer equipped with an electrospray ionization interface. The established QSRR model was Ln(k') = 5.6163 + 0.0469ES - 0.0984XAH, with a determination coefficient (R2) of 0.9981, adjusted determination coefficient (adjR2) of 0.9976, and corrected root mean square error of 0.0682. The determination coefficient of the leave-one-out (LOO) cross-validation (Q2LOO) was 0.9976, and the cross-verification root mean square error was 0.0754. Simulated samples containing 7 flavonoids were used to validate the feasibility of the method. The classical method (UHPLC-MS/MS combined the CD software and the mzCloud, mzVault and Chemspider databases) was used to identify the seven flavonoids in the simulated samples. This classic identification strategy cannot provide accurate identification results, which provided multiple identification results for each compound in the simulated samples. On the basis of the results, the 7 flavonoids were accurately identified by the established QSRR model, and the reference standards were used to validate it. The relative error of retention time(RE(tR)) between the model calculation and experimental results was less than 10%. This method effectively complements and improves the classical methods, that UHPLC-MS/MS combined the CD software and the mass spectra databases were used to identify flavonoids identification.

Synergistic Microbial Inhibition and Quality Preservation for Grapes through High-Voltage Electric Field Cold Plasma and Nano-ZnO Antimicrobial Film Treatment.

To ensure their quality and safety, harvested grapes should be protected from microbial contamination before reaching consumers. For the first time, this study combined high-voltage electric field cold plasma (HVEF-CP) and nano-ZnO antimicrobial film to inhibit microbial growth on grapes. Using the response surface method, the optimal processing parameters of HVEF-CP (a voltage of 78 kV, a frequency of 110 Hz, and a time of 116 s) were identified to achieve 96.29% sterilization. The effects of co-processing with HVEF-CP and nano-ZnO antimicrobial film on the quality and safety of grapes during storage were explored. When stored at 4 °C and 20 °C, the co-processing extended the shelf life of grapes to 14 and 10 days, respectively. The co-processing increased the sterilization rate to 99.34%, demonstrating a synergistic effect between the two methods to ensure not only the safety of grapes but also their nutrient retention during storage. This novel approach is promising for the efficient, safe, and scalable preservation of grapes as well as other foods.