Chemical constituent characterization and determination of Quisqualis fructus based on UPLC-Q-TOF-MS and HPLC combined with fingerprint and chemometric analysis.

Quisqualis fructus (QF) is a traditional Chinese medicine (TCM) that it has a long history in the therapeutic field of killing parasites, eliminating accumulation, and stopping diarrhea. However, the therapeutic material basis of QF is remaining ambiguous nowadays. The geographical origin differences of QF are also usually ignored in the process of medication. In this study, the alcohol-aqueous soluble constituents in QF from different origins were systematically characterized and accurately measured by ultra-high performance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and high-performance liquid chromatography (HPLC) respectively. Chemometric analysis was performed for origin differentiation and screening of potential quality marker (Q-marker). Finally, A total of 106 constituents were tentatively characterized in positive and negative ion modes, including 29 fatty acids, 26 organic acids, 11 amino acids and derivatives, 10 glycosides, 9 alkaloids and derivatives, and 21 other constituents. QF from different origins were effectively distinguished and 16 constituents were selected as the potential Q-markers subsequently. Four representative components (trigonelline, adenosine, ellagic acid, and 3,3'-di-O-methylellagic acid) in QF samples were simultaneously determined. HPLC fingerprint analysis indicated that the similarity between 16 batches of QF was in the range of 0.870-0.999. The above results provide some insights for the research on the pharmacodynamic constituents, quality control, and geographical discrimination of QF.

Integrating ICP-MS and Chemometrics for Profiling Inorganic Elements in Lianhua Qingwen Capsules and Evaluating Health Risk.

Lianhua Qingwen capsule (LHQWC) is composed of 13 traditional Chinese herbs. In this study, we employed inductively coupled plasma mass spectrometry (ICP-MS) to quantify the concentrations of 26 inorganic elements (Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Ag, Cd, Cs, Ba, Hg, Tl, Pb, U) across 22 batches of LHQWC. These results were complemented with Chemometrics analysis and health risk assessment of selected hazardous elements. Chemometric analysis revealed significant quality variations among the 22 batches of LHQWC, identifying U, Cs, Tl, Rb, Mn, As, Mg, and Al as characteristic elements influencing formulation consistency. Moreover, the health risk assessment indicated that while levels of Cu, As, Cd, Pb, Cr, and Hg in LHQWC were within acceptable limits, concerns arose regarding vanadium levels in certain batches. These findings underscore the necessity of comprehensive elemental analysis and health risk assessment to ensure the safety and quality of LHQWC. Our study provides valuable insights for both quality evaluation and regulatory considerations in the production of LHQWC and similar herbal formulations.

An Optimized Microwave-Assisted Digestion Method to Analyze the Amino Acids Profile of Quisqualis Fructus from Different Planted Origins.

This study aims to establish a rapid and convenient microwave-assisted digestion method for sample pretreatment to determine amino acid profiles in natural products. This method was applied to analyze the amino acid profiles of Quisqualis Fructus (QF) from different planted origins. The microwave-assisted digestion conditions were optimized by a response surface methodology (RSM), and 17 amino acids in different planted origins of QF were determined by an automatic amino acid analyzer according to the optimized digestion conditions. The contents of 17 amino acids in QF from different planted origins were further analyzed by fingerprint and chemometric analysis. The temperature of microwave digestion at 167 °C, time of microwave digestion at 24 min, and a solid-liquid ratio of 46.5 g/mL was selected as the optimal digestion conditions. The total content of 17 amino acids in QF from different planted origins ranged from 71.88 to 91.03 mg/g. Amino acid composition and nutritional evaluation indicated that the content of medicinal amino acids was higher than aromatic amino acids. The results of fingerprint analysis reflected that the similarity between the 16 batches of QF ranged from 0.889 to 0.999, while chemometrics analysis indicated amino acid content in QF varied from different planted origins, and six important differential amino acids were screened. Compared with the traditional extraction method, microwave-assisted digestion with response surface optimized has the advantages of rapidity, convenience, and reliability, which could be used to study the amino acid profiles in natural products. The amino acid profile of QF indicated that it has a rich medicinal nutritional value. Different planted origins of QF have a high degree of similarity and could be effectively distinguished by chemometric analysis.

Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations.

Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.

Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network representation learning and convolutional neural networks to predict disease miRNAs, called CNNMDA. CNNMDA deeply integrates the similarity information of miRNAs and diseases, miRNA-disease associations, and representations of miRNAs and diseases in low-dimensional feature space. The new framework based on deep learning was built to learn the original and global representation of a miRNA-disease pair. First, diverse biological premises about miRNAs and diseases were combined to construct the embedding layer in the left part of the framework, from a biological perspective. Second, the various connection edges in the miRNA-disease network, such as similarity and association connections, were dependent on each other. Therefore, it was necessary to learn the low-dimensional representations of the miRNA and disease nodes based on the entire network. The right part of the framework learnt the low-dimensional representation of each miRNA and disease node based on non-negative matrix factorization, and these representations were used to establish the corresponding embedding layer. Finally, the left and right embedding layers went through convolutional modules to deeply learn the complex and non-linear relationships among the similarities and associations between miRNAs and diseases. Experimental results based on cross validation indicated that CNNMDA yields superior performance compared to several state-of-the-art methods. Furthermore, case studies on lung, breast, and pancreatic neoplasms demonstrated the powerful ability of CNNMDA to discover potential disease miRNAs.

Drug repositioning through integration of prior knowledge and projections of drugs and diseases.

MOTIVATION: Identifying and developing novel therapeutic effects for existing drugs contributes to reduction of drug development costs. Most of the previous methods focus on integration of the heterogeneous data of drugs and diseases from multiple sources for predicting the candidate drug-disease associations. However, they fail to take the prior knowledge of drugs and diseases and their sparse characteristic into account. It is essential to develop a method that exploits the more useful information to predict the reliable candidate associations. RESULTS: We present a method based on non-negative matrix factorization, DisDrugPred, to predict the drug-related candidate disease indications. A new type of drug similarity is firstly calculated based on their associated diseases. DisDrugPred completely integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different levels including the chemical structures of drugs, the target proteins of drugs, the diseases associated with drugs and the side effects of drugs. The prior knowledge of drugs and diseases and the sparse characteristic of drug-disease associations provide a deep biological perspective for capturing the relationships between drugs and diseases. Simultaneously, the possibility that a drug is associated with a disease is also dependant on their projections in the low-dimension feature space. Therefore, DisDrugPred deeply integrates the diverse prior knowledge, the sparse characteristic of associations and the projections of drugs and diseases. DisDrugPred achieves superior prediction performance than several state-of-the-art methods for drug-disease association prediction. During the validation process, DisDrugPred also can retrieve more actual drug-disease associations in the top part of prediction result which often attracts more attention from the biologists. Moreover, case studies on five drugs further confirm DisDrugPred's ability to discover potential candidate disease indications for drugs. AVAILABILITY AND IMPLEMENTATION: The fourth type of drug similarity and the predicted candidates for all the drugs are available at https://github.com/pingxuan-hlju/DisDrugPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Design and preparation of efficient hydroisomerization catalysts by the formation of stable SAPO-11 molecular sieve nanosheets with 10-20 nm thickness and partially blocked acidic sites.

SAPO-11 nanosheets with partially filled micropores (N-SAPO-11) and a thickness of 10-20 nm were synthesized using polyhexamethylene biguanide hydrochloride (PHMB) as a mesoporogen and di-n-propylamine (DPA) as a microporous template. After Pt loading (0.5 wt%), the Pt/N-SAPO-11 catalyst exhibits higher selectivity for the isomers and lower selectivity for cracking products than conventional Pt/SAPO-11 catalysts in the hydroisomerization of n-dodecane.

A Hierarchical Bipyridine-Constructed Framework for Highly Efficient Carbon Dioxide Capture and Catalytic Conversion.

As a C1 feedstock, CO2 has the potential to be uniquely highly economical in both a chemical and a financial sense. Porous materials bearing particular binding and active sites that can capture and convert CO2 simultaneously are promising candidates for CO2 utilization. In this work, a bipyridine-constructed polymer featuring a high surface area, a hierarchical porous structure, and excellent stability was synthesized through free-radical polymerization. After metalation, the resultant catalysts exhibited superior activities in comparison with those of their homogeneous counterparts in the cycloaddition of CO2 to epoxides. The high performance of the heterogeneous catalysts originates from cooperative effects between the CO2 -philic polymer and the embedded metal species. In addition, the catalysts showed excellent stabilities and are readily recyclable; thus, they are promising for practical utilization for the conversion of CO2 into value-added chemicals.

Rational Design of Zirconium-doped Titania Photocatalysts with Synergistic Brønsted Acidity and Photoactivity.

The preparation of photocatalysts with high activities under visible-light illumination is challenging. We report the rational design and construction of a zirconium-doped anatase catalyst (S-Zr-TiO2 ) with Brønsted acidity and photoactivity as an efficient catalyst for the degradation of phenol under visible light. Electron microscopy images demonstrate that the zirconium sites are uniformly distributed on the sub-10 nm anatase crystals. UV-visible spectrometry indicates that the S-Zr-TiO2 is a visible-light-responsive catalyst with narrower band gap than conventional anatase. Pyridine-adsorption infrared and acetone-adsorption 13 C NMR spectra confirm the presence of Brønsted acidic sites on the S-Zr-TiO2 sample. Interestingly, the S-Zr-TiO2 catalyst exhibits high catalytic activity in the degradation of phenol under visible-light illumination, owing to a synergistic effect of the Brønsted acidity and photoactivity. Importantly, the S-Zr-TiO2 shows good recyclability.

Atom-economical synthesis of a high silica CHA zeolite using a solvent-free route.

A high silica CHA zeolite is successfully synthesized in the presence of a small amount of N,N,N-dimethylethylcyclohexylammonium bromide under solvent-free conditions. Catalytic tests for the selective catalytic reduction of NOx with NH3 (NH3-SCR) and methanol-to-olefins (MTO) show that the sample from the solvent-free route exhibits comparable catalytic properties to that from the conventional route.

Importance of platinum particle size for complete oxidation of toluene over Pt/ZSM-5 catalysts.

Size-controllable Pt nanoparticles ranging from 1.3 to 2.3 nm were successfully loaded onto ZSM-5 (Pt-x/ZSM-5, where x is the mean diameter of the Pt nanoparticles). Catalytic tests in complete oxidation of toluene as a model for VOC removal show that Pt-1.9/ZSM-5 has the highest activity, due to a balance of Pt dispersion and Pt(0) proportion.

Solvent-free synthesis of zeolites from anhydrous starting raw solids.

Development of sustainable routes for synthesis of zeolites is very important because of wide applications of zeolites at large scale in the fields of catalysis, adsorption, and separation. Here we report a novel and generalized route for synthesis of zeolites in the presence of NH4F from grinding the anhydrous starting solid materials and heating at 140-240 °C. Accordingly, zeolites of MFI, BEA*, EUO, and TON structures have been successfully synthesized. The presence of F(-) drives the crystallization of these zeolites from amorphous phase. Compared with conventional hydrothermal synthesis, the synthesis in this work not only simplifies the synthesis process but also significantly enhances the zeolite yields. These features should be potentially of great importance for industrial production of zeolites at large scale in the future.

Porous organic ligands (POLs) for synthesizing highly efficient heterogeneous catalysts.

We reported a universal route for synthesizing porous organic ligands (POLs) via solvothermal polymerization. The POLs were obtained quantitatively, showing high surface area, large pore volume, hierarchical porosity, and superior stability. The POL bearing a triphenylphosphine supported rhodium catalyst (Rh/POL-PPh3) exhibits high activity and excellent recyclability in 1-octene hydroformylation.

Sustainable synthesis of zeolites without addition of both organotemplates and solvents.

The development of sustainable and environmentally friendly techniques for synthesizing zeolites has attracted much attention, as the use of organic templates and solvents in the hydrothermal synthesis of zeolites is a major obstacle for realizing green and sustainable synthesis ways. Recently, the introduction of the organotemplate-free synthesis method allowed avoiding the use of organic templates, but water as solvent was still required; solvent-free routes on the other hand beared the potential to significantly reduce the amount of polluted wastewater, but organic templates were still present. In this work, we have demonstrated a combined strategy of both organotemplate- and solvent-free conditions to synthesize aluminosilicate zeolites Beta and ZSM-5 (S-Beta and S-ZSM-5), two of the most important zeolites relevant for industry. The samples are thoroughly characterized by XRD patterns, SEM images, N2 sorption isotherms, UV-Raman spectra, and (29)Si and (27)Al MAS NMR spectra. The results demonstrate that S-Beta and S-ZSM-5 zeolites exhibit almost the same textural parameters (e.g., BET surface area and pore volume) and catalytic performance in cumene cracking and m-xylene isomerization as those of conventional Beta and ZSM-5 zeolites synthesized under hydrothermal conditions (C-Beta and C-ZSM-5). The organotemplate- and solvent-free syntheses of S-Beta and S-ZSM-5 take place at a low-pressure regime and are free of harmful gases as well as give high product yields together with highly efficient consumption of the starting raw materials. These advantages plus the very simple procedures opened the pathway to a highly sustainable zeolite synthesis protocol compared to conventional methods currently employed for C-Beta and C-ZSM-5. Very interestingly, this simple synthesis is a good model for understanding zeolite crystallization. The detail characterizations indicate that the S-Beta crystals are formed from the assembly of zeolite building units, mainly 4MRs, while the 5MRs in the framework are just formed in the crystallization of S-ZSM-5, rather than existence in the starting solid mixture. During the crystallization processes, small traces of water play an important role for the hydrolysis and condensation of silica and/or aluminosilicate species.

Catalytically active and hierarchically porous SAPO-11 zeolite synthesized in the presence of polyhexamethylene biguanidine.

Hierarchically porous SAPO-11 zeolite (H-SAPO-11) is rationally synthesized from a starting silicoaluminophosphate gel in the presence of polyhexamethylene biguanidine as a mesoscale template. The sample is well characterized by XRD, N2 sorption, SEM, TEM, NMR, XPS, NH3-TPD, and TG techniques. The results show that the sample obtained has good crystallinity, hierarchical porosity (mesopores at ca. 10 nm and macropores at ca. 50-200 nm), high BET surface area (226 m(2)/g), large pore volume (0.25 cm(3)/g), and abundant medium and strong acidic sites (0.36 mmol/g). After loading Pt (0.5 wt.%) on H-SAPO-11 by using wet impregnation method, catalytic hydroisomerization tests of n-dodecane show that the hierarchical Pt/SAPO-11 zeolite exhibits high conversion of n-dodecane and enhanced selectivity for branched products as well as reduced selectivity for cracking products, compared with conventional Pt/SAPO-11 zeolite. This phenomenon is reasonably attributed to the presence of hierarchical porosity, which is favorable for access of reactants on catalytically active sites. The improvement in catalytic performance in long-chain paraffin hydroisomerization over Pt/SAPO-11-based catalyst is of great importance for its industrial applications in the future.

Highly mesoporous single-crystalline zeolite beta synthesized using a nonsurfactant cationic polymer as a dual-function template.

Mesoporous zeolites are useful solid catalysts for conversion of bulky molecules because they offer fast mass transfer along with size and shape selectivity. We report here the successful synthesis of mesoporous aluminosilicate zeolite Beta from a commercial cationic polymer that acts as a dual-function template to generate zeolitic micropores and mesopores simultaneously. This is the first demonstration of a single nonsurfactant polymer acting as such a template. Using high-resolution electron microscopy and tomography, we discovered that the resulting material (Beta-MS) has abundant and highly interconnected mesopores. More importantly, we demonstrated using a three-dimensional electron diffraction technique that each Beta-MS particle is a single crystal, whereas most previously reported mesoporous zeolites are comprised of nanosized zeolitic grains with random orientations. The use of nonsurfactant templates is essential to gaining single-crystalline mesoporous zeolites. The single-crystalline nature endows Beta-MS with better hydrothermal stability compared with surfactant-derived mesoporous zeolite Beta. Beta-MS also exhibited remarkably higher catalytic activity than did conventional zeolite Beta in acid-catalyzed reactions involving large molecules.

A significant enhancement of catalytic activities in oxidation with H2O2 over the TS-1 zeolite by adjusting the catalyst wettability.

Hydrophilic TS-1 (H-TS-1) with rich hydroxyl groups, which were confirmed by (29)Si and (1)H NMR techniques, exhibits much higher activities in the oxidation than conventional TS-1. This phenomenon is strongly related to the unique features of high enrichment of H2O2 on H-TS-1.