Microfluidic Continuous Synthesis of Size- and Facet-Controlled Porous Bi2O3 Nanospheres for Efficient CO2 to Formate Catalysis.

Bismuth-based catalysts are effective in converting carbon dioxide into formate via electrocatalysis. Precise control of the morphology, size, and facets of bismuth-based catalysts is crucial for achieving high selectivity and activity. In this work, an efficient, large-scale continuous production strategy is developed for achieving a porous nanospheres Bi2O3-FDCA material. First-principles simulations conducted in advance indicate that the Bi2O3 (111)/(200) facets help reduce the overpotential for formate production in electrocatalytic carbon dioxide reduction reaction (ECO2RR). Subsequently, using microfluidic technology and molecular control to precisely adjust the amount of 2, 5-furandicarboxylic acid, nanomaterials rich in (111)/(200) facets are successfully synthesized. Additionally, the morphology of the porous nanospheres significantly increases the adsorption capacity and active sites for carbon dioxide. These synergistic effects allow the porous Bi2O3-FDCA nanospheres to stably operate for 90 h in a flow cell at a current density of ≈250 mA cm- 2, with an average Faradaic efficiency for formate exceeding 90%. The approach of theoretically guided microfluidic technology for the large-scale synthesis of finely structured, efficient bismuth-based materials for ECO2RR may provide valuable references for the chemical engineering of intelligent nanocatalysts.

CDCS-TCM: A framework based on complex network theory to analyze the causality and dynamic correlation of substances in the metabolic process of traditional Chinese medicine.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese medicine, with the feature of synergistic effects of multi-component, multi-pathway and multi-target, plays an important role in the treatment of cancer, cardiovascular and cerebrovascular diseases, etc. However, chemical components in traditional Chinese medicine are complex and most of the pharmacological mechanisms remain unclear, especially the relationships of chemical components change during the metabolic process. AIM OF STUDY: Our aim is to provide a method based on complex network theory to analyze the causality and dynamic correlation of substances in the metabolic process of traditional Chinese medicine. MATERIALS AND METHODS: We proposed a framework named CDCS-TCM to analyze the causality and dynamic correlation between substances in the metabolic process of traditional Chinese medicine. Our method mainly consists two parts. The first part is to discover the local and global causality by the causality network. The second part is to investigate the dynamic correlations and identify the essential substance by dynamic substance correlation network. RESULTS: We developed a CDCS-TCM method to analyze the causality and dynamic correlation of substances. Using the XiangDan Injection for ischemic stroke as an example, we have identified the important substances in the metabolic process including substance pairs with strong causality and the dynamic changes of the core effector substance clusters. CONCLUSION: The proposed framework will be useful for exploring the correlations of active ingredients in traditional Chinese medicine more effectively and will provide a new perspective for the elucidation of drug action mechanisms and the new drug discovery.

DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features.

Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years. However, how to make full use of multiview sequence information to predict thermostability effectively is still a challenge. In this study, we proposed a deep learning-based classifier named DeepPPThermo that fuses features of classical sequence features and deep learning representation features for classifying thermophilic and mesophilic proteins. In this model, deep neural network (DNN) and bi-long short-term memory (Bi-LSTM) are used to mine hidden features. Furthermore, local attention and global attention mechanisms give different importance to multiview features. The fused features are fed to a fully connected network classifier to distinguish thermophilic and mesophilic proteins. Our model is comprehensively compared with advanced machine learning algorithms and deep learning algorithms, proving that our model performs better. We further compare the effects of removing different features on the classification results, demonstrating the importance of each feature and the robustness of the model. Our DeepPPThermo model can be further used to explore protein diversity, identify new thermophilic proteins, and guide directed mutations of mesophilic proteins.

[Investigation of the mechanism of action and identification of candidate traditional Chinese medicines for the treatment of ischemic stroke in the Danshen-Jiangxiang pair based on drug-target-disease association network].

The therapeutic efficacy of Danshen and Jiangxiang in the treatment of ischemic stroke (IS) is relatively significant. Studying the mechanism of action of Danshen and Jiangxiang in the treatment of IS can effectively identify candidate traditional Chinese medicines (TCM) with efficacy. However, it is challenging to analyze the effector substances and explain the mechanism of action of Danshen-Jiangxiang from a systematic perspective using traditional pharmacological approaches. In this study, a systematic study was conducted based on the drug-target-symptom-disease association network using complex network theory. On the basis of the association information about Danshen, Jiangxiang and IS, the protein-protein interaction (PPI) network and the "drug pair-pharmacodynamic ingredient-target-IS" network were constructed. The different topological features of the networks were analyzed to identify the core pharmacodynamic ingredients including formononetin in Jiangxiang, cryptotanshinone and tanshinone IIA in Danshen as well as core target proteins such as prostaglandin G/H synthase 2, retinoic acid receptor RXR-alpha, sodium channel protein type 5 subunit alpha, prostaglandin G/H synthase 1 and beta-2 adrenergic receptor. Further, a method for screening IS candidates based on TCM symptoms was proposed to identify key TCM symptoms and syndromes using the "drug pair-TCM symptom-syndrome-IS" network. The results showed that three TCMs, namely Puhuang, Sanleng and Zelan, might be potential therapeutic candidates for IS, which provided a theoretical reference for the development of drugs for the treatment of IS.

Mechanism analysis and improved molecular modification: Design of high efficiency and environmentally friendly triazole fungicide substitutes.

The adverse effects of triazole fungicides (TFs) on the soil and the environmental damage caused by their residues have attracted the attention of the international community. To effectively prevent and control the above problems, this paper designed 72 substitutes of TFs with significantly better molecular functionality (>40%) using Paclobutrazol (PBZ) as the template molecule. Then, the comprehensive scores for environmental effects calculated after normalization by "extreme value method-entropy weight method-weighted average method" was the dependent variable, the structural parameters of TFs molecules was the independent variable (PBZ-214 was the template molecule) to construct the 3D-QSAR model of integrated environmental effects of TFs with high degradability, low bioenrichment, low endocrine disruption effects, and low hepatotoxicity and designed 46 substitutes of TFs with significantly better comprehensive environmental effects (>20%). After confirming the above effects of TFs and assessing human health risk and the universality of biodegradation and endocrine disruption, we screened PBZ-319-175 as the eco-friendly substitute of TF, which had high efficiency (improved functionality) and better environmental effects than those of the target molecule by 51.63% and 36.09%, respectively. Finally, the results of the molecular docking analysis showed that non-bonding interactions (hydrogen bonding, electrostatic, or polar force) predominantly affected the association between PBZ-319-175 and its biodegradable protein, and the hydrophobic effect of the amino acids distributed around PBZ-319-175 played a significant role. Additionally, we determined the microbial degradation path of PBZ-319-175 and found that the steric hindrance of the substituent group after molecular modification promoted its biodegradability. In this study, we enhanced molecular functionality twice and also reduce the major damage of TFs to the environment by performing iterative modifications. This paper provided theoretical support for the development and application of high-performance, eco-friendly substitutes of TFs.

WITHDRAWN: The WHO and the ICTV rename viruses such as monkeypox.

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