Efficient healing of diabetic wounds by MSC-EV-7A composite hydrogel via suppression of inflammation and enhancement of angiogenesis.

Diabetes mellitus (DM) is characterized by prolonged hyperglycemia, impaired vascularization, and serious complications, such as blindness and chronic diabetic wounds. About 25% of patients with DM are estimated to encounter impaired healing of diabetic wounds, often leading to lower limb amputation. Multiple factors are attributed to the non-healing of diabetic wounds, including hyperglycaemia, chronic inflammation, and impaired angiogenesis. It is imperative to develop more efficient treatment strategies to tackle healing difficulties in diabetic wounds. Mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) are promising for diabetic wound healing considering their anti-inflammatory, pro-angiogenic and pro-proliferative activities. A histone deacetylase 7 (HDAC7)-derived 7-amino-acid peptide (7A) was shown to be highly effective for angiogenesis. However, it has never been investigated whether MSC-EVs are synergistic with 7A for the healing of diabetic wounds. Herein, we propose that MSC-EVs can be combined with 7A to greatly promote diabetic wound healing. The combination of EVs and 7A significantly improved the migration and proliferation of skin fibroblasts. Moreover, EVs alone significantly suppressed LPS-induced inflammation in macrophages, and notably, the combination treatment showed an even better suppression effect. Importantly, the in vivo study revealed that the combination therapy consisting of EVs and 7A in an alginate hydrogel was more efficient for the healing of diabetic wounds in rats than monotherapy using either EV or 7A hydrogels. The underlying mechanisms include suppression of inflammation, improvement of skin cell proliferation and migration, and enhanced collagen fiber disposition and angiogenesis in wounds. In summary, the MSC-EV-7A hydrogel potentially constitutes a novel therapy for efficient healing of chronic diabetic wounds.

Enhanced cartilage regeneration by icariin and mesenchymal stem cell-derived extracellular vesicles combined in alginate-hyaluronic acid hydrogel.

OBJECTIVE: Osteoarthritis (OA) is characterized by progressive cartilage degeneration and absence of curative therapies. Therefore, more efficient therapies are compellingly needed. Both mesenchymal stem cells (MSCs)-derived extracellular vesicles (EVs) and Icariin (ICA) are promising for repair of cartilage defect. This study proposes that ICA may be combined to potentiate the cartilage repair capacity of MSC-EVs. MATERIALS AND METHODS: MSC-EVs were isolated from sodium alginate (SA) and hyaluronic acid (HA) composite hydrogel (SA-HA) cell spheroid culture. EVs and ICA were combined in SA-HA hydrogel to test therapeutic efficacy on cartilage defect in vivo. RESULTS: EVs and ICA were synergistic for promoting both proliferation and migration of MSCs and inflammatory chondrocytes. The combination therapy led to strikingly enhanced repair on cartilage defect in rats, with mechanisms involved in the concomitant modulation of both cartilage degradation and synthesis makers. CONCLUSION: The MSC-EVs-ICA/SA-HA hydrogel potentially constitutes a novel therapy for cartilage defect in OA.

[Clinical efficacy of fire needling combined with cupping therapy on herpes zoster of acute stage and the effect on Th17/Treg cellular immune balance].

OBJECTIVE: To compare the clinical efficacy between the combined therapy of fire needling and cupping, and western medication on herpes zoster of acute stage, as well as the effects on Th17 and Treg cells and inflammatory factors, i.e. IL-10 and IL-17 in the peripheral blood. METHODS: Eighty patients with herpes zoster of acute stage were randomly divided into a combined therapy (fire needling plus cupping) group and a western medication group, 40 cases in each one. In the combined therapy group, the pricking and scattering techniques with fire needle were used at ashi points and Jiaji (EX-B 2) corresponding to the affected spinal segments; afterwards, cupping therapy was delivered. The combined treatment was given once daily. In the western medication group, valaciclovir hydrochloride tablet and vitamin B1 tablet were administered orally. The duration of treatment in each group was 10 days. Before each treatment from day 1 to day 10 and on day 11 , the score of symptoms and physical signs was observed in the two groups separately. Before each treatment from day 1 to day 10 and on day 11, 30, 60, the score of visual analogue scale (VAS) and skin lesion indexes were observed in the two groups. On day 60, the incidence of postherpetic neuralgia was recorded in the two groups. The levels of Th17 and Treg cells, Th17/Treg ratio in the peripheral blood, as well as serum levels of IL-10 and IL-17 were detected before and after treatment in the two groups. The clinical efficacy was compared between the two groups. RESULTS: From day 6 to day 10 during treatment and on day 11, the scores of symptoms and physical signs in the combined therapy group were lower than those of the western medication group (P<0.05, P<0.01). On day 3, day 6 to day 10 during treatment and day 11, day 30, VAS scores in the combined therapy group were lower than those of the western medication group (P<0.05, P<0.01). On day 60, the incidence of postherpetic neuralgia in the combined therapy group was lower compared with that in the western medication group (P<0.05). The blister arresting time and scabbing time in the combined therapy group were shorter than those of the western medication group (P<0.05). After treatment, the level of Th17, and Th17/Treg ratio in the peripheral blood, as well as the serum levels of IL-10 and IL-17 were all lower in comparison with those in the western medication group (P<0.05). The curative and remarkably effective rate was 82.5% (33/40) in the combined therapy group, higher than 62.5% (25/40) in the western medication group (P<0.05). CONCLUSION: The early application of fire needling combined with cupping therapy can effectively treat herpes zoster of acute stage, relieve pain, and reduce the incidence of postherpetic neuralgia, which may be related to reducing the levels of Th17 and Treg cells, and Th17/Treg ratio in the peripheral blood, as well as the serum levels of IL-10 and IL-17 so that the cellular immune balance is modulated.

A Combined Model Integrating Radiomics and Deep Learning Based on Contrast-Enhanced CT for Preoperative Staging of Laryngeal Carcinoma.

RATIONALE AND OBJECTIVES: Accurate staging of laryngeal carcinoma can inform appropriate treatment decision-making. We developed a radiomics model, a deep learning (DL) model, and a combined model (incorporating radiomics features and DL features) based on the venous-phase CT images and explored the performance of these models in stratifying patients with laryngeal carcinoma into stage I-II and stage III-IV, and also compared these models with radiologists. MATERIALS AND METHODS: Three hundreds and nineteen patients with pathologically confirmed laryngeal carcinoma were randomly divided into a training set (n = 223) and a test set (n = 96). In the training set, the radiomics features with inter- and intraclass correlation coefficients (ICCs)> 0.75 were screened by Spearman correlation analysis and recursive feature elimination (RFE); then support vector machine (SVM) classifier was applied to develop the radiomics model. The DL model was built using ResNet 18 by the cropped 2D regions of interest (ROIs) in the maximum tumor ROI slices and the last fully connected layer of this network served as the DL feature extractor. Finally, a combined model was developed by pooling the radiomics features and extracted DL features to predict the staging. RESULTS: The area under the curves (AUCs) for radiomics model, DL model, and combined model in the test set were 0.704 (95% confidence interval [CI]: 0.588-0.820), 0.724 (95% CI: 0.613-0.835), and 0.849 (95% CI: 0.755-0.943), respectively. The combined model outperformed the radiomics model and the DL model in discriminating stage I-II from stage III-IV (p = 0.031 and p = 0.020, respectively). Only the combined model performed significantly better than radiologists (p < 0.050 for both). CONCLUSION: The combined model can help tailor the therapeutic strategy for laryngeal carcinoma patients by enabling more accurate preoperative staging.

The impact of non-adiabatic effects on reaction dynamics: a study based on the adiabatic and non-adiabatic potential energy surfaces of CaH2.

The two-state non-adiabatic potential energy matrices of the CaH2+ system are calculated via a diabatization approach by using a neural network model. Subsequently, the adiabatic and non-adiabatic potential energy surfaces (PESs) are constructed based on these non-adiabatic potential energy matrices. Furthermore, based on the adiabatic and non-adiabatic PESs, the Ca+(4s2S) + H2(X1Σ+g) → H(2S) + CaH+(X1Σ+) reaction is studied using the time-dependent wave packet method. Comparative analysis of the experimental and theoretical integral reaction cross-sections (ICSs) indicates that the maximum deviation between the results obtained from the adiabatic PES and the corresponding experimental value is 12.7 bohr2; in contrast, the maximum discrepancy between the theoretical result derived from the non-adiabatic PES and the experimental value is merely 0.42 bohr2. The potential well along the reaction path acts as a 'filter', selectively guiding intermediates with longer lifetimes in the potential well back to the reactant channel. This phenomenon indicates that the non-adiabatic effects significantly influence the reaction dynamics of the CaH2+ system.

The effect of the CPP size on the nonlinear optical properties of the new necklace-type molecules formed by carborane and [n]Cycloparaphenylenes(n = 8-11).

The new necklace-type molecules were formed by [8-13]CPP and carborane, which further manipulated the size of the macroring, revealing the effect of size on its luminescence behavior. In this work, the effects of ring size on the absorption spectrum, electron excitation and nonlinear optical properties of the compounds were investigated in detail, aiming to reveal an effective way to improve the optical properties of these necklace-type compounds. The absorption spectra of the compounds showed that the size of the CPP ring had little effect on the spectral shape and position, but the electron transition information showed that there were the significant charge transfer within the CPP ring and a gradual enhancement of interfragment charge transfer from the CPP ring to carborane. The increasing order of polarizability, first and second hyperpolarizability values of these compounds with the increase of CPP size indicated that increasing the size of the CPP ring was an effective way to increase the nonlinear optical properties of necklace-type molecules. Among the frequency dependent hyperpolarizability values, the γ(-ω;ω,0,0) value increased by a factor of 4 from complex 1 to 6 with the increase of CPP ring size, which indicated that increasing the size of the CPP ring was an effective way to increase the optical Kerr effect of necklace-type molecules. Therefore, these the new necklace-type nolecules formed by carborane and [n]Cycloparaphenylenes would be excellent nonlinear optical materials in the field of the all-optical switch.

Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study.

OBJECTIVES: To develop deep learning-assisted diagnosis models based on CT images to facilitate radiologists in differentiating benign and malignant parotid tumors. METHODS: Data from 573 patients with histopathologically confirmed parotid tumors from center 1 (training set: n = 269; internal-testing set: n = 116) and center 2 (external-testing set: n = 188) were retrospectively collected. Six deep learning models (MobileNet V3, ShuffleNet V2, Inception V3, DenseNet 121, ResNet 50, and VGG 19) based on arterial-phase CT images, and a baseline support vector machine (SVM) model integrating clinical-radiological features with handcrafted radiomics signatures were constructed. The performance of senior and junior radiologists with and without optimal model assistance was compared. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the clinical benefit of using the optimal model. RESULTS: MobileNet V3 had the best predictive performance, with sensitivity increases of 0.111 and 0.207 (p < 0.05) in the internal- and external-testing sets, respectively, relative to the SVM model. Clinical benefit and overall efficiency of junior radiologist were significantly improved with model assistance; for the internal- and external-testing sets, respectively, the AUCs improved by 0.128 and 0.102 (p < 0.05), the sensitivity improved by 0.194 and 0.120 (p < 0.05), the NRIs were 0.257 and 0.205 (p < 0.001), and the IDIs were 0.316 and 0.252 (p < 0.001). CONCLUSIONS: The developed deep learning models can assist radiologists in achieving higher diagnostic performance and hopefully provide more valuable information for clinical decision-making in patients with parotid tumors. KEY POINTS: • The developed deep learning models outperformed the traditional SVM model in predicting benign and malignant parotid tumors. • Junior radiologist can obtain greater clinical benefits with assistance from the optimal deep learning model. • The clinical decision-making process can be accelerated in patients with parotid tumors using the established deep learning model.

Selective CDK9 knockdown sensitizes TRAIL response by suppression of antiapoptotic factors and NF-kappaB pathway.

The aberrantly up-regulated CDK9 can be targeted for cancer therapy. The CDK inhibitor dinaciclib (Dina) has been found to drastically sensitizes cancer response to TRAIL-expressing extracellular vesicle (EV-T). However, the low selectivity of Dina has limited its application for cancer. We propose that CDK9-targeted siRNA (siCDK9) may be a good alternative to Dina. The siCDK9 molecules were encapsulated into EV-Ts to prepare a complexed nanodrug (siEV-T). It was shown to efficiently suppress CDK9 expression and overcome TRAIL resistance to induce strikingly augmented apoptosis in lung cancer both in vitro and in vivo, with a mechanism related to suppression of both anti-apoptotic factors and nuclear factor-kappa B pathway. Therefore, siEV-T potentially constitutes a novel, highly effective and safe therapy for cancers.

IKBIP is a Predictive Biomarker Related to Immunosuppressive Microenvironment in Digestive System Malignancies.

OBJECTIVE: IKBKB-interacting protein (IKBIP) has rarely been reported in tumor research. This study aimed to evaluate IKBIP role in tumor progression. mRNA (messenger ribonucleic acid) expression, clinical characteristics and predictive values of IKBIP were assessed. METHODS: R package "clusterProfiler" was used to examine the potential mechanisms in which IKBIP may involve. Immune cell infiltration and its correlation with IKBIP was also analyzed. We further evaluated IKBIP influence on drug resistance. RESULTS: It was found that IKBIP was overexpressed and related to poorer survival in most types of tumors. IKBIP expression was strongly related to immunosuppressive cells in the TCGA (The Cancer Genome Atlas) pan-cancer samples. These immunosuppressive cells included tumor-related macrophages, tumor-related fibroblasts, and regulatory T cells. Moreover, immunosuppressive genes and immune checkpoints were positively related to IKBIP expression in several tumor types. Furthermore, patients with IKBIP overexpressed did not respond to most anti-cancer medications. It was also found that compared to control group, the number of invasive cells is four times that of IKBIP overexpression group, and the number of clone forming cells is six times that of IKBIP overexpression group. IKBIP overexpression promoted colon cancer cells invasiveness and clonogenesis by Transwell assay and colon formation assay. CONCLUSIONS: According to current findings, IKBIP is a probable oncogene and predictive marker for most of tumor types. High IKBIP expression is associated with tumor immunosuppression.

Effect of intermolecular interaction of the charge-transfer complex between molecular "tweezers" and C60/C70 on second-order nonlinear optical properties.

To enhance understanding of the correlation between the intermolecular interaction and second-order nonlinear optical (NLO) properties, we studied a "molecular tweezer" with two corannulene substituents linked by a tetrahydro[5]helicene imide, which enabled highly sensitive and selective complexation of C60/C70 through convex-concave π-π interactions. The geometric structure, molecular orbitals, intermolecular interactions, electron absorption spectra and second-order NLO properties of the charge-transfer (CT) complexes formed by molecular tweezers and C60/C70 were studied by density functional theory. Larger fullerenes helped to increase the intermolecular interaction and CT, thereby increasing the first hyperpolarizabilities of CT complexes. Embedding of lithium ions helped to enhance the electron-absorption ability of fullerenes, thereby increasing the intermolecular interaction and intermolecular CT and, thus, enhancing their first hyperpolarizability significantly. Our data indicated that, through structure adjustment (including increasing the volume of fullerene and embedding alkali metal ions), we could enhance intermolecular interactions and improve intermolecular CT significantly. These actions could improve the second-order NLO properties of CT complexes.

The Effect of Magnetic Resonance Imaging Based Radiomics Models in Discriminating stage I-II and III-IVa Nasopharyngeal Carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a common tumor in China. Accurate stages of NPC are crucial for treatment. We therefore aim to develop radiomics models for discriminating early-stage (I-II) and advanced-stage (III-IVa) NPC based on MR images. METHODS: 329 NPC patients were enrolled and randomly divided into a training cohort (n = 229) and a validation cohort (n = 100). Features were extracted based on axial contrast-enhanced T1-weighted images (CE-T1WI), T1WI, and T2-weighted images (T2WI). Least absolute shrinkage and selection operator (LASSO) was used to build radiomics signatures. Seven radiomics models were constructed with logistic regression. The AUC value was used to assess classification performance. The DeLong test was used to compare the AUCs of different radiomics models and visual assessment. RESULTS: Models A, B, C, D, E, F, and G were constructed with 13, 9, 7, 9, 10, 7, and 6 features, respectively. All radiomics models showed better classification performance than that of visual assessment. Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance (AUC: 0.847) in the training cohort. CE-T1WI showed the greatest significance for staging NPC. CONCLUSION: Radiomics models can effectively distinguish early-stage from advanced-stage NPC patients, and Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance.

MRI radiomics-based machine learning model integrated with clinic-radiological features for preoperative differentiation of sinonasal inverted papilloma and malignant sinonasal tumors.

Objective: To explore the best MRI radiomics-based machine learning model for differentiation of sinonasal inverted papilloma (SNIP) and malignant sinonasal tumor (MST), and investigate whether the combination of radiomics features and clinic-radiological features can produce a superior diagnostic performance. Methods: The database of 247 patients with SNIP (n=106) or MST (n=141) were analyzed. Dataset from scanner A were randomly divided into training set (n=135) and test set 1 (n=58) in a ratio of 7:3, and dataset from scanner B and C were used as an additional independent test set 2 (n=54). Fourteen clinic-radiological features were analyzed by using univariate analysis, and those with significant differences were applied to construct clinical model. Based on the radiomics features extracted from single sequence (T2WI or CE-T1WI) and combined sequence, four commonly used classifiers (logistic regression (LR), support vector machine (SVM), decision tree (DT) and k-nearest neighbor (KNN)) were employed to constitute twelve different machine learning models, and the best-performing one was confirmed as the optimal radiomics model. Furthermore, a combined model incorporated best radiomics feature subsets and clinic-radiological features was developed. The diagnostic performances of these models were assessed by the area under the receiver operating characteristic (ROC) curve (AUC) and the calibration curves. Results: Five clinic-radiological features (age, convoluted cerebriform pattern sign, heterogeneity, adjacent bone involvement and infiltration of surrounding tissue) were considered to be significantly different between the tumor groups (P < 0.05). Among the twelve machine learning models, the T2WI-SVM model exhibited optimal predictive efficacy for classification tasks on the two test sets, with the AUC of 0.878 and 0.914, respectively. For three types of diagnostic models, the combined model achieved highest AUC of 0.912 (95%CI: 0.807-0.970) and 0.927 (95%CI: 0.823-0.980) for differentiation of SNIP and MST in test 1 and test 2 sets, which performed prominently better than clinical model (P=0.011, 0.005), but not significantly different from the optimal radiomics model (P=0.100, 0.452). Conclusion: The machine learning model based on T2WI sequence and SVM classifier achieved best performance in differentiation of SNIP and MST, and the combination of radiomics features and clinic-radiological features significantly improved the diagnostic capability of the model.

Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors.

Objective: This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs). Methods: This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves. Results: Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888). Conclusion: Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.

Aggregation behavior of partially contacted graphene sheets in six-carbon alkanes: all-atom molecular dynamics simulation.

Molecular dynamics simulations are used to investigate the aggregation of the cross-contacted and non-cross-contacted graphene sheets in n-hexane, 2,3-dimethylbutane, and cyclohexane solvents. The results show that the main driving force of the graphene aggregation is the interaction between the graphene sheets, and the interaction between solvent molecules also contributes to the aggregation slightly. The initial graphene configurations and the solvent molecule structures both have effects on the graphene aggregation speed. Specifically, the cross-contacted graphene sheets aggregate faster than the non-cross-contacted configuration, since the interaction between the graphene sheets is larger and the direction of this interaction is conducive to pushing away the solvent molecules adsorbed on the graphene surface. The graphene aggregation speed is larger in n-hexane mainly since the mobility of the solvent molecules is higher than the other two solvents, while the interaction between graphenes/solvents has little influence for the systems used in this work. This work provides useful insights into the graphene aggregation in the solvents with different initial graphene configurations and solvent molecule structures.

Control one-dimensional length of rectangular pore on graphene membrane for better desalination performance.

At present, there is a general contradiction between permeability and selectivity of reverse osmosis (RO) membranes for desalination; a membrane with higher water permeability will give a lower salt rejection or selectivity, and vice versa. In this work, single-layer nanoporous graphene is used as RO membrane to investigate the effects of pore shape to reduce this contradiction by molecular dynamics simulations. Two kinds of pores (round and rectangular pores) with different sizes are simulated. For round pore, although the water permeability increases with the increase of the pore size, the salt rejection rate drops rapidly. For rectangular pore, reasonable designed pore structure can achieve improved water permeability and high salt rejection of graphene membrane by keeping one-dimensional length (i.e. the width) of the pore less than the size of the hydrated ions and increasing the other dimensional length. The restriction of one dimension can prevent the passage of hydrated ions through the pore effectively. This 'one-dimensional restriction' provides a simple strategy for designing RO membrane with variable pore structures to obtain a better desalination performance.

Impurity Controlled near Infrared Surface Plasmonic in AlN.

In this work, we used multi-scale computational simulation methods combined with density functional theory (DFT) and finite element analysis (FEA) in order to study the optical properties of substitutional doped aluminium nitride (AlN). There was strong surface plasmon resonance (SPR) in the near-infrared region of AlN substituted with different alkali metal doping configurations. The strongest electric field strength reached 109 V/m. There were local exciton and charge transfer exciton behaviours in some special doping configurations. These research results not only improve the application of multi-scale computational simulations in quantum surface plasmons, but also promote the application of AlN in the field of surface-enhanced linear and non-linear optical spectroscopy.

Assembling of Perylene, Naphthalene, and Pyromellitic Diimide-Based Materials and Their Third-Order Nonlinear Optical Properties.

π-conjugated aromatic diimides with chemical stability, heat resistance, and redox activity have attracted more attention due to their excellent fluorescence quantum yield in solution. The planar perylene diimide (PDI) derivatives generally have aggregation-induced emission quenching in the solid state, while the cyclic trimers based on pyromellitic diimides (PMDIs), naphthalene diimides (NDIs), and PDIs can increase the fluorescence quantum yield in the solid state and have large two-photon absorption cross section, which can be used as excellent nonlinear optical (NLO) materials. Therefore, this paper will study the effects of multiple assembly modes of the three monomers on the NLO responses of materials. It was found that the assembly modes of 2PMDI-1NDI and 2NDI-1PDI exhibit larger third-order NLO response (γ) values, which was due to the larger conjugate surface of PDI effectively reducing the energy gap between the HOMO and LUMO. Compared with other assembly methods, 2PMDI-1NDI and 2NDI-1PDI were conducive to causing redshifts (150 nm) in the absorption spectrum. Therefore, the larger conjugate surface of PDI and the assembly mode of the isosceles triangle were more favorable for intramolecular charge transfer, thus improving its NLO properties.

Ab initio study of spectroscopic properties and anharmonic force fields of MNH2 (M = Li, Na, K).

The spectroscopic properties and anharmonic force fields of NaNH2 are studied in present work by DFT (B3P86 and B3PW91) and MP2 methods in combination with 6-311++G(2d, 2p) and 6-311++G(3df, 2pd) basis sets. The calculated equilibrium geometry, ground state rotational constants and centrifugal distortion constants of NaNH2 at B3P86/6-311++G(3df, 2pd) theoretical level agree very well with the corresponding experimental values. Noteworthy, some spectroscopic constants and anharmonic force fields of NaNH2, which have not been experimentally measured, are firstly predicted. In addition, the spectroscopic properties of KNH2 are also predicted at the B3P86/6-311++G(3df, 2pd) level of theory. The influences of metal atoms on the equilibrium geometry, anharmonic constants, rotational constants, centrifugal distortion constants of MNH2 (M = Li, Na, K) are analyzed intuitively. One can find that the metal atoms affect the rotational constants, part of centrifugal distortion constants (DK, DJK, HK, and HKJ), M-N bond length and some anharmonic constants of MNH2.

The shape selectivity of corannulene dimers based on concave-convex and convex-convex shape complementarity as hosts for C60 and C70.

In the formation of noncovalent complexes, the stacking arrangements of corannulene and fullerene are diverse, most of which are combinations of multiple corannulenes and fullerene. Here, a composition ratio of 2 : 1 was selected for the complex between corannulene and fullerene (C60 and C70) to investigate the effects of different superposition modes, including concave-convex and convex-convex interactions, on the stability and third-order nonlinear optical (NLO) properties of the composite materials. It was found that the concave-convex interaction was stronger and it was reported to stabilize the charge-transfer (CT) complex more effectively than the convex-convex interaction. The dispersion range of the concave-convex interaction was larger than that of the convex-convex interaction, which is consistent with the interaction energy results. The packing design with the double convex-convex interactions exhibited the largest linear optical response and third-order NLO response, which showed that the convex-convex interaction was more likely to be excited and cause intermolecular CT as compared to the concave-convex interaction. This work confirmed that the packing arrangement significantly affected the NLO response and will advance the development of NLO crystals.

Network Pharmacology Identifies the Mechanisms of Sang-Xing-Zhi-Ke-Fang against Pharyngitis.

BACKGROUND: Sang-Xing-Zhi-Ke-Fang (SXZKF) demonstrates good therapeutic effect against pharyngitis. Nevertheless, the pharmacological mechanism underlying its effectiveness is still unclear. OBJECTIVE: To investigate the underlying mechanisms of SXZKF against pharyngitis using network pharmacology method. METHODS: Bioactive ingredients of SXZKF were collected and screened using published literature and two public databases. Using four public databases, the overlapping genes between these bioactive compound-related and pharyngitis-related genes were identified by Venn diagram. Protein-protein interaction (PPI) was obtained using "Search Tool for the Retrieval of Interacting Genes (STRING)" database. "Database for Annotation, Visualization, and Integrated Discovery ver. 6.8 (DAVID 6.8)" was used to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to explore the molecular mechanisms of SXZKF against pharyngitis. Finally, Cytoscape 3.7.2 software was used to construct and visualize the networks. RESULT: A total of 102 bioactive compounds were identified. Among them, 886 compounds-related and 6258 pharyngitis-related genes were identified, including 387 overlapping genes. Sixty-three core targets were obtained, including ALB, PPARγ, MAPK3, EGF, and PTGS2. Signaling pathways closely related to mechanisms of SXZKF for pharyngitis were identified, including serotonergic synapse, VEGF signaling pathway, Fc epsilon RI signaling pathway, Ras signaling pathway, MAPK signaling pathway, and influenza A. CONCLUSION: This is the first identification of in-depth study of SXZKF against pharyngitis using network pharmacology. This new evidence could be informative in providing new support on the clinical effects of SXZKF on pharyngitis and for the development of personalized medicine for pharyngitis.