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Homochiral metal-organic frameworks (MOFs) have attracted considerable attention in many fields of research, such as chiral catalysis and chiral chromatography. However, only few homochiral MOFs can be effectively used in capillary electrochromatography (CEC) and their performances are far from adequate. In this study, we successfully synthesized achiral nanocrystalline MIL-53. A facile post-synthetic modification strategy was then implemented to functionalize the product, yielding a homochiral MOF: l-His-NH-MIL-53. This MOF was then employed as a chiral coating in open-tubular CEC mode (OT-CEC), and, as such, it exhibited high enantioselectivities for several racemic drugs. The homochiral MOF and the fabricated capillary coating were systematically characterized using transmission electron microscopy, scanning electron microscopy (with energy-dispersive X-ray spectrometry), Fourier-transform infrared spectroscopy, X-ray diffractometry, thermogravimetric analysis, circular dichroism spectroscopy, Brunauer-Emmett-Teller surface area measurements, and X-ray photoelectron spectroscopy. This study is expected to provide a new strategy for the design and establishment of MOF-based chiral OT-CEC systems.

Background Glioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mechanisms of glioma based on protein-protein interaction network combined with machine learning methods. Key differentially expressed genes (DEGs) were screened and selected by using the protein-protein interaction (PPI) networks. Results As a result, 19 genes between grade I and grade II, 21 genes between grade II and grade III, and 20 genes between grade III and grade IV. Then, five machine learning methods were employed to predict the gliomas stages based on the selected key genes. After comparison, Complement Naive Bayes classifier was employed to build the prediction model for grade II-III with accuracy 72.8%. And Random forest was employed to build the prediction model for grade I-II and grade III-VI with accuracy 97.1% and 83.2%, respectively. Finally, the selected genes were analyzed by PPI networks, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the results improve our understanding of the biological functions of select DEGs involved in glioma growth. We expect that the key genes expressed have a guiding significance for the occurrence of gliomas or, at the very least, that they are useful for tumor researchers. Conclusion Machine learning combined with PPI networks, GO and KEGG analyses of selected DEGs improve our understanding of the biological functions involved in glioma growth.

Alzheimer's disease (AD) is primarily characterized by the production and deposit of β-amyloid protein (Aβ) in β-amyloid plaques (APs). On this basis, we investigated whether vascular endothelial growth factor (VEGF), a growth factor with important neuroprotective activity, may provide a therapeutic opportunity for treating AD. We initially found that the expression and production of VEGF was downregulated in the brains of Tg2576 mice during the course of AD development and progression. Restoring VEGF in the brains of Tg2576 mice antagonized the production and deposit of Aβ in Tg2576 mice. The addition of VEGF concurrently increased the expression of disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) and decreased the expression of β-site APP cleaving enzyme 1 (BACE1), which contributes to the enhanced clearance of Aβ in vivo. By decreasing the production and deposit of Aβ, VEGF improved the cognitive decline of Tg2576 mice. These observations provide a novel implication for VEGF as a therapeutic approach for the treatment of AD.

Avian influenza is a serious zoonotic infectious disease with huge negative impacts on local poultry farming, human health and social stability. Therefore, the design of new compounds against avian influenza has been the focus in this field. In this study, computational methods were applied to investigate the compounds with neuraminidase inhibitory activity. First, 2D-SAR model was built to recognize neuraminidase inhibitors (NAIs). As a result, the accuracy of 10 cross-validation and independent tests is 96.84% and 98.97%, respectively. Then, the Topomer CoMFA model was constructed to predict the inhibitory activity and analyses molecular fields. Two models were obtained by changing the cutting methods. The second model is employed to predict the activity (q2 = 0.784 and r2 = 0.982). Molecular docking was also used to further analyze the binding sites between NAIs and neuraminidase from human and avian virus. As a result, it is found that same binding Total Score has some differences, but the binding sites are basically the same. At last, some potential NAIs were screened and some optimal opinions were taken. It is expected that our study can assist to study and develop new types of NAIs., Graphical Abstract Unlabelled Image

In this study, a composite nanomaterial, gold nanoparticles-graphitic carbon nitride (GNPs-g-C3N4), composed of GNPs and g-C3N4 nanosheet was synthesized and utilized as capillary coating. The composites possess both the advantages of GNPs and g-C3N4, which has many advantages such as relatively large surface and ease-to-modify. After functionalization by sulfydryl-β-cycldextrin (HS-β-CD), the CD-GNPs-g-C3N4 was employed as chiral stationary phases (CSP) for enantioseparation in open tubular capillary electrochromatography (OT-CEC) mode. The composites nanomaterials and the functionalized capillaries ware characterized by scanning electron microscopy transmission electron microscopy, Fourier transform infrared spectroscopy and Brunauer-Emmett-Teller surface area measurement and N2 adsorption–desorption isotherm. Several basic drugs were used as the model to evaluate the enantioselectivity and enantiomeric resolutions of the CSP. Compared to CD-GNPs-column and CD-g-C3N4-column, significantly enhanced separation was obtained in this OT-CEC system. Several key factors (e.g., buffer pH and applied voltage) that affect CEC performance were systematically investigated and optimized. And the reproducibility of the CD-GNPs-g-C3N4-column was evaluated. Under the optimized conditions, the maximum column efficiency of model drugs could reach 120,000 plates/m. This work is expected to provide a novel approach for designing and fabricating composite nanomaterials for chiral separation in OT-CEC.

Herein, by rationally introducing multi-interactions sites, a new rhodamine derivative BT was successfully designed and constructed. The BT can further self-assemble into a stable supramolecular organogel BTG and exhibits strong pink-white aggregation-induced emission (AIE). Interestingly, the BTG exhibits selective and ultrasensitive sensing property for Cu2+. The limit of lowest detection (LOD) of BTG for Cu2+ is approximately 0.829 nM. By adding Cu2+ into the BTG, a no fluorescence Cu2+ coordinated metallogel BTG-Cu formed. Due to the competitive coordination of Hg2+, the BTG-Cu exhibits promising sensing properties for Hg2+, such as high selectivity and ultra-sensitivity, fluorescence turn-on response, instant response to Hg2+ (

Edible vegetable oil is a necessity in people's daily diet, and its safety has attracted the attention of the government and consumers. In recent years, the safety incidents of edible vegetable oil are reported, and the safety problems of edible vegetable oil caused by chemical hazards such as benzopyrene, heavy metal and aflatoxin B1 are more serious. In this research, a risk assessment model through dietary exposure assessment and margin of exposure (MOE) were built to assess the health risks of benzopyrene, aflatoxin B1, and heavy metals in edible vegetable oils. And then an early warning model of food oil safety risk was established by using analytic hierarchy process (AHP) and back propagation (BP) neural network. According to the national standards and sampling data, the risk early warning model determined eight evaluation indexes of vegetable oil quality and safety, and calculated the risk value of chemical pollution in vegetable oil by Entropy Weight-Analytic Hierarchy Process(EW-AHP). The data of eight evaluation indexes were taken as the input of the model, and the comprehensive risk value was taken as the output of the model. The model learning process was carried out, and four algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Radial Basis Function Neural Network (RBF) and Neural Network (BP), were selected for construction and comparison of model. The daily sampling data of chemical hazard factors in edible vegetable oil were quantified into specific chemical hazard risk levels. Thus the goal of predicting chemical hazard levels was achieved in edible vegetable oil. This study provides targeted reference suggestions for the safety supervision of edible vegetable oil, so as to improve the efficiency of supervision and ensure the consumption safety of edible vegetable oil.

The selective and ultrasensitive detection of H2PO4−, critical component of biomolecules and key substrates for many biochemical reactions, is extremely important. Herein, a signal amplification strategy was successfully used by introducing metal ions into pillar[5]arene-based supramolecular gel (TB). Accordingly, the in-suit generated non-fluorescent metal coordinated supramolecular gel (TB-Fe) could serve as a fluorescence “turn-on” sensor to detect H2PO4− selectively and ultrasensitively, and the limit of detection (LOD) of TB-Fe for H2PO4− was calculated to be 2.82 nM.

The binding interactions of bovine lactoferrin (BLF) with two flavonoids dihydromyricetin (DMY) and myricetin (MY) were investigated by the multi-spectroscopic, microscale thermophoresis (MST) techniques, molecular docking, and then their antioxidant activities were studied by detection of free radical scavenging activity against DPPH. Results of UV-vis and fluorescence spectroscopies showed that DMY/MY and BLF formed the ground state complex through the static quenching mechanism. Moreover, MY with more planar stereochemical structure had higher affinity for BLF than DMY with twisted stereochemical structure, according to the binding constant (Kb), free energy change (ΔG°), dissociation constant (Kd) and donor-acceptor distance (r). Thermodynamic parameters revealed that hydrogen bond and van der Waals force were major forces in the formation of BLF-DMY complex, while hydrophobic interactions played major roles in the formation of BLF-DMY complex. The circular dichroism (CD) study indicated that MY induced more conformational change in BLF than DMY. Furthermore, molecular modeling provided insights into the difference of binding interactions between BLF and two flavonoids. Finally, the radical scavenging activity assays indicated the presence of BLF delayed the decrease in antioxidant capacities of two flavonoids. These results were helpful to understand the binding mechanism and biological effects of non-covalent BLF-flavonoid interaction.

Many models can predict building occupants’ thermal comfort, but their accuracies were not always perfect due to the limited self-learning and self-correction capability when varying the application contexts. Advances in machine learning algorithms allow us to reveal the “hidden insights” behind a large amount of data, offering a great opportunity to understand more nuanced aspects of thermal comfort in buildings. This study applied the support vector machine (SVM) algorithm to the RP-884 thermal comfort database and developed a new model with self-learning and self-correction ability. We identified its application range according to the features of the SVM algorithm and the sample distribution characteristics of RP-884. With variables of indoor air temperature, clothing insulation, metabolic rate, air velocity and so forth, the model can largely reduce the previous models’ inaccuracy. Compared to the PMV model, the new model's sum of squares for residuals (SSE) reduced by 96.4%, and the fitting degree (Rnew) increased by 83.7%. It can also quantify the effects of each input variable on building occupants’ thermal sensation. Instead of using two separate models, the data-driven model can automatically distinguish occupants’ thermal comfort responses in natural ventilation (NV) and air-conditioning (AC) buildings. Using the new model, we determined thermally comfortable zones on the psychrometric chart for NV and AC buildings. Moreover, an open-access platform has been developed to help apply machine learning algorithms in thermal comfort data analysis. The work introduced in this paper can be a reference for a more comprehensive comfort model development.

Objective To clarify the rationality and to provide reasonable usage references of processed Corydalis Rhizoma (CR) in clinic by comparing respectively pharmacological actions including analgesic, anti-inflammation, antiplatelet aggregation and spasmolysis of Yuanhu Zhitong Tablet (YZT) and Yanhusuo Decoction (YD) constituted with raw CR (RCR) or processed CR (PCR). Methods The hot-board, acetic-acid-induced twisting experiments and mouse auricular swelling model by injecting xylene in the abdomen of mouse were adopted to investigate the analgesic and anti-inflammatory effects of the four formulas on Kunming mice; The experiment of contractile activity on isolated rat intestine smooth muscle on Wistar rats was applied to observing the spasmolysis effect of the four formulas; The experiments of platelet aggregation induced by ADP or collagen on Wistar rats by turbidimetry method were used to observe the anti-platelet aggregation effect of the four formulas. Results Compared with RCR in two prescriptions, PCR displayed better analgesic effect (P Conclusion PCR can improve the analgesic effect of prescription YZT and intensify the spasmolysis effect of prescription YD, which can be explained by the Chinese medicine processing regulation. Therefore, PCR is suggested to be chosen in YZT and YD used to cure pain symptoms.

Correctly predicting the site of O-glycosylation will greatly benefit the search and design of new specific and efficient GalNAc-transferase inhibitors. In this article, the site of O-glycosylation was studied using the correlation-based feature subset (CfsSubset) selection method combined with a wrapper method. Twenty-three important biochemical features were found based on a jackknife test from original data set containing 4779 features. By using the AdaBoost method with the twenty-three selected features, the prediction model yields an accuracy rate of 88.1% for the jackknife test and 87.5% for an independent set test, with increased accuracy over the original dataset by 8.5% and 10.42%, respectively. It is expected that our feature selection scheme can be referred to as a useful assistant technique for finding effective competitive inhibitors of GalNAc-transferase. An online predictor based on this research is available at http://chemdata.shu.edu.cn/gal_p/ .

Based on pseudo amino acid (PseAA) composition and a novel hybrid feature selection frame, this paper presents a computational system to predict the PPIs (protein-protein interactions) using 8796 protein pairs. These pairs are coded by PseAA composition, resulting in 114 features. A hybrid feature selection system, mRMR-KNNs-wrapper, is applied to obtain an optimized feature set by excluding poor-performed and/or redundant features, resulting in 103 remaining features. Using the optimized 103-feature subset, a prediction model is trained and tested in the k-nearest neighbors (KNNs) learning system. This prediction model achieves an overall accurate prediction rate of 76.18%, evaluated by 10-fold cross-validation test, which is 1.46% higher than using the initial 114 features and is 6.51% higher than the 20 features, coded by amino acid compositions. The PPIs predictor, developed for this research, is available for public use at http://chemdata.shu.edu.cn/ppi.

AdaBoost Learner is employed to investigate Structure–Activity Relationships of phenols based on molecular descriptors. In this paper, the performance of AdaBoost Learner is compared with support vector machine (SVM), artificial neural networks (ANNs) and K nearest neighbors (KNNs), which are the most common algorithms used for SARs analysis. AdaBoost Learner performed better than SVM, ANNs and KNNs in predicting the mechanism of toxicity of phenols based on molecular descriptors. It can be concluded that AdaBoost has a potential to improve the performance of SARs analysis. We believe that AdaBoost Learner will play an important and complementary role to the existing algorithms for the prediction of the mechanisms of toxicity based on SARs. We have developed an online web server for the prediction of ecotoxicity mechanisms of phenols, accessible at http://chemdata.shu.edu.cn/ecotoxity/ .