ボールミリング法で改質したβ-TCPセメントの諸特性への粉液比の影響

The authors have developed a β-tricalcium-phosphate (β-TCP) powder modified mechano-chemically through the application of a ball-milling process (mβ-TCP). The resulting powder can be used in a calcium-phosphate-cement (CPC). In this study, the effects of the powder-to-liquid ratio (P/L ratio) on the properties of the CPCs were investigated, and an appropriate P/L ratio that would simultaneously improve injectability and strength was clarified. The mβ-TCP cement mixed at a P/L ratio of 2.5 and set in air exhibited sufficient injectability until 20 min after mixing, and strength similar to or higher than that mixed at a P/L ratio of 2.0 and 2.78. Although the mβ-TCP cements set in vivo and in SBF were found to exhibit a lower strength than those set in air, it did have an appropriate setting time and strength for clinical applications. In conclusion, P/L ratio optimization successfully improved the strength of injectable mβ-TCP cement

The metabolomic plasma profile of patients with Duchenne muscular dystrophy: providing new evidence for its pathogenesis

Abstract Background Duchenne muscular dystrophy (DMD) is a fatal genetic muscle-wasting disease that affects 1 in 5000 male births with no current cure. Despite great progress has been made in the research of DMD, its underlying pathological mechanism based on the metabolomics is still worthy of further study. Therefore, it is necessary to gain a deeper understanding of the mechanisms or pathogenesis underlying DMD, which may reveal potential therapeutic targets and/or biomarkers. Results Plasma samples from 42 patients with DMD from a natural history study and 40 age-matched healthy volunteers were subjected to a liquid chromatography-mass spectrometry-based non-targeted metabolomics approach. Acquired metabolic data were evaluated by principal component analysis, partial least squares-discriminant analysis, and metabolic pathway analysis to explore distinctive metabolic patterns in patients with DMD. Differentially expressed metabolites were identified using publicly available and integrated databases. By comparing the DMD and healthy control groups, 25 differential metabolites were detected, including amino acids, unsaturated fatty acids, carnitine, lipids, and metabolites related to the gut microbiota. Correspondingly, linoleic acid metabolism, D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, and alanine, aspartate, and glutamate metabolism were significantly altered in patients with DMD, compared with those of healthy volunteers. Conclusions Our study demonstrated the abnormal metabolism of amino acids, energy, and lipids in patients with DMD, consistent with pathological features, such as recurrent muscle necrosis and regeneration, interstitial fibrosis, and fat replacement. Additionally, we found that metabolites of intestinal flora were disordered in DMD patients, providing support for treatment of intestinal microbia disturbance in DMD diseases. Our study provides a new research strategy for understanding the pathogenesis of DMD

Prediction of protein-ligand binding affinity with deep learning

The prediction of binding affinities between target proteins and small molecule drugs is essential for speeding up the drug research and design process. To attain precise and effective affinity prediction, computer-aided methods are employed in the drug discovery pipeline. In the last decade, a variety of computational methods has been developed, with deep learning being the most commonly used approach. We have gathered several deep learning methods and classified them into convolutional neural networks (CNNs), graph neural networks (GNNs), and Transformers for analysis and discussion. Initially, we conducted an analysis of the different deep learning methods, focusing on their feature construction and model architecture. We discussed the advantages and disadvantages of each model. Subsequently, we conducted experiments using four deep learning methods on the PDBbind v.2016 core set. We evaluated their prediction capabilities in various affinity intervals and statistically and visually analyzed the samples of correct and incorrect predictions for each model. Through visual analysis, we attempted to combine the strengths of the four models to improve the Root Mean Square Error (RMSE) of predicted affinities by 1.6% (reducing the absolute value to 1.101) and the Pearson Correlation Coefficient (R) by 2.9% (increasing the absolute value to 0.894) compared to the current state-of-the-art method. Lastly, we discussed the challenges faced by current deep learning methods in affinity prediction and proposed potential solutions to address these issues

Tomato SlWRKY3 Negatively Regulates <i>Botrytis cinerea</i> Resistance via <i>TPK1b</i>

Botrytis cinerea is considered the second most important fungal plant pathogen, and can cause serious disease, especially on tomato. The TPK1b gene encodes a receptor-like kinase that can positively regulate plant resistance to B. cinerea. Here, we identified a tomato WRKY transcription factor SlWRKY3 that binds to the W-box on the TPK1b promoter. It can negatively regulate TPK1b transcription, then regulate downstream signaling pathways, and ultimately negatively regulate tomato resistance to B. cinerea. SlWRKY3 interference can enhance resistance to B. cinerea, and SlWRKY3 overexpression leads to susceptibility to B. cinerea. Additionally, we found that B. cinerea can significantly, and rapidly, induce the upregulation of SlWRKY3 expression. In SlWRKY3 transgenic plants, the TPK1b expression level was negatively correlated with SlWRKY3 expression. Compared with the control, the expression of the SA pathway marker gene PR1 was downregulated in W3-OE plants and upregulated in W3-Ri plants when inoculated with B. cinerea for 48 h. Moreover, SlWRKY3 positively regulated ROS production. Overall, SlWRKY3 can inhibit TPK1b transcription in tomato, and negatively regulate resistance to B. cinerea by modulating the downstream SA and ROS pathways

Recent Advances in Metal-Organic Framework (MOF)-Based Photocatalysts: Design Strategies and Applications in Heavy Metal Control

The heavy metal contamination of water systems has become a major environmental concern worldwide. Photocatalysis using metal-organic frameworks (MOFs) has emerged as a promising approach for heavy metal remediation, owing to the ability of MOFs to fully degrade contaminants through redox reactions that are driven by photogenerated charge carriers. This review provides a comprehensive analysis of recent developments in MOF-based photocatalysts for removing and decontaminating heavy metals from water. The tunable nature of MOFs allows the rational design of composition and features to enhance light harvesting, charge separation, pollutant absorptivity, and photocatalytic activities. Key strategies employed include metal coordination tuning, organic ligand functionalization, heteroatom doping, plasmonic nanoparticle incorporation, defect engineering, and morphology control. The mechanisms involved in the interactions between MOF photocatalysts and heavy metal contaminants are discussed, including light absorption, charge carrier separation, metal ion adsorption, and photocatalytic redox reactions. The review highlights diverse applications of MOF photocatalysts in treating heavy metals such as lead, mercury, chromium, cadmium, silver, arsenic, nickel, etc. in water remediation. Kinetic modeling provides vital insights into the complex interplay between coupled processes such as adsorption and photocatalytic degradation that influence treatment efficiency. Life cycle assessment (LCA) is also crucial for evaluating the sustainability of MOF-based technologies. By elucidating the latest advances, current challenges, and future opportunities, this review provides insights into the potential of MOF-based photocatalysts as a sustainable technology for addressing the critical issue of heavy metal pollution in water systems. Ongoing efforts are needed to address the issues of stability, recyclability, scalable synthesis, and practical reactor engineering

Wnt5a through noncanonical Wnt/JNK or Wnt/PKC signaling contributes to the differentiation of mesenchymal stem cells into type II alveolar epithelial cells in vitro.

The differentiation of mesenchymal stem cells (MSCs) into type II alveolar epithelial (AT II) cells is critical for reepithelization and recovery in acute respiratory distress syndrome (ARDS), and Wnt signaling was considered to be the underlying mechanisms. In our previous study, we found that canonical Wnt pathway promoted the differentiation of MSCs into AT II cells, however the role of the noncanonical Wnt pathway in this process is unclear. It was disclosed in this study that noncanonical Wnt signaling in mouse bone marrow-derived MSCs (mMSCs) was activated during the differentiation of mMSCs into AT II cells in a modified co-culture system with murine lung epithelial-12 cells and small airway growth media. The levels of surfactant protein (SP) C, SPB and SPD, the specific markers of AT II cells, increased in mMSCs when Wnt5a was added to activate noncanonical Wnt signaling, while pretreatment with JNK or PKC inhibitors reversed the promotion of Wnt5a. The differentiation rate of mMSCs also depends on their abilities to accumulate and survive in inflammatory tissue. We found that the Wnt5a supplement promoted the vertical and horizontal migration of mMSCs, ameliorated the cell death and the reduction of Bcl-2/Bax induced by H2O2. The effect of Wnt5a on the migration of mMSCs and their survival after H2O2 exposure were partially inhibited with PKC or JNK blockers. In conclusion, Wnt5a through Wnt/JNK signaling alone or both Wnt/JNK and Wnt/PKC signaling promoted the differentiation of mMSCs into AT II cells and the migration of mMSCs; through Wnt/PKC signaling, Wnt5a increased the survival of mMSCs after H2O2 exposure in vitro