Toward morphologically relevant extracellular matrix: nanofiber-hydrogel composites for tumor cell culture.

The natural extracellular matrix (ECM) consists of a continuous integrated fibrin network and a negatively charged proteoglycan-based matrix. In this work, we report a novel three-dimensional nanofiber hydrogel composite that mimics the natural ECM structure, exhibiting both degradability and mechanical characteristics comparable to that of tumor tissue. The embedded nanofiber improves the hydrogel mechanical properties, and varying the fiber density can match the elastic modulus of different tumor tissues (1.51-10.77 kPa). The degradability of the scaffold gives sufficient space for tumor cells to secrete and remodel the ECM. The expression levels of cancer stem cell markers confirmed the development of aggressive and metastatic phenotypes of prostate cancer cells in the 3D scaffold. Similar results were obtained in terms of anticancer resistance of prostate cancer cells in 3D scaffolds showing stem cell-like properties, suggesting that the current bionic 3D scaffold tumor model has broad potential in the development of effective targeted agents.

Numerical simulations of the effect of lateral malleolus fracture malunion on ankle biomechanics: Different offset directions and offsets.

INTRODUCTION: Ankle fractures account for approximately 10 % of all fractures. Approximately 5-68 % of patients with ankle fractures may suffer from malunion. Besides, suboptimal reduction of fracture fragments can affect the biomechanics of the ankle joint, ultimately leading to damage to the ankle joint. However, there are certain controversies over the conclusion of previous cadaveric studies. METHODS: In this study, a three-dimensional model of the ankle joint was established based on CT image data. In addition, the effects of backward offset (1-2 mm) and outward offset (0.5-1 mm) of the fracture fragment on the contact area, contact pressure, and ligament force of the ankle joint were investigated via the finite element method. Moreover, lateral malleolus fracture malunion in five ankle positions (neutral, 10° dorsiflexion, 10° plantarflexion, 20° dorsiflexion, and 20° plantarflexion) was investigated. RESULTS: This model predicted an overall increased contact area in the ankle joint in patients with lateral malleolus fracture malunion compared with the normal ankle joint. The results demonstrated that the outward offset had a more significant effect than the backward one. The larger the dorsiflexion-plantarflexion angle, the more pronounced the effect of malunion. Further, an outward offset can cause the fibula to lose its function. CONCLUSION: Post-traumatic osteoarthritis occurs under the action of unaccustomed cartilage forces due to altered tibial talar joint contact patterns, rather than increased contact pressure reported in previous studies. Malunion leads to an increase or decrease in force on the affected ligament, while the cause of malunion can be envisioned based on a decrease in the force on the ligaments.

Integrated Transcriptomic and Metabolomic Analyses Reveal the Effects of Grafting on Special Metabolites of Acanthopanax senticosus Leaves.

Acanthopanax senticosus (A. senticosus) is a member of Acanthopanax Miq. and is used in traditional Chinese medicine, and it has been found that grafting technology can be used to alter plant metabolite composition and transcriptome characteristics. In this study, shoots of A. senticosus were grafted onto the rootstocks of the vigorous Acanthopanax sessiliflorus (A. sessiliflorus) to improve its varietal characteristics. In order to investigate the changes in metabolites and transcriptional patterns in grafted A. senticosus leaves (GSCL), fresh leaves were collected from 2-year-old grafted A. senticosus scions, while self-rooted seedling A. senticosus leaves (SCL) were used as controls to analyse the transcriptome and metabolome. Metabolic profiles and gene expression patterns were further identified and correlated in special metabolite target pathways. The content of chlorogenic acid and triterpenoids in the GSCL was higher than in the control, while the quercetin content was lower. All these metabolic changes were associated with changes in the expression pattern of transcripts. Our results revealed the transcriptome and metabolome characteristics of GSCL. This may help to improve leaf quality in A. senticosus cultivation, suggesting that it is feasible to improve the medicinal quality of GSCL through asexual propagation, but the long-term effects need further investigation. In conclusion, this dataset provides a useful resource for future studies on the effects of grafting on medicinal plants.

Species identification of poisonous medicinal plant using DNA barcoding.

The aim is to select a universal DNA barcode for identifying all poisonous medicinal plants in Chinese pharmacopoeia and their poisonous related species or adulterants. We chose 4 commonly used regions as candidate DNA barcodes (ITS2, psbA-trnH, matK and rbcL) and compared their identification efficiency in 106 species from 27 families and 65 genera totally. Data analysis was performed including the information of sequence alignment, inter/intra-specific genetic distance and data distribution, identification efficiency and the situation of Neighbor-Joining (NJ) phylogenetic trees. We found ITS2 sequence region had high variation, stable genetic distance and identification efficiency relatively. The topological structure of NJ phylogenetic tree showed monophyletic. Our findings show that ITS2 can be applied as a universal barcode for identifying poisonous medicinal plants in Chinese pharmacopoeia and their poisonous related species or adulterants.

Effects of drought-re-watering-drought on the photosynthesis physiology and secondary metabolite production of Bupleurum chinense DC.

KEY MESSAGE: Drastic changes in soil water content can activate the short-term high expression of key enzyme-encoding genes involved in secondary metabolite synthesis thereby increasing the content of secondary metabolites. Bupleurum chinense DC. is a traditional medicinal herb that is famous for its abundant saikosaponins. In the current study, the effects of drought-re-watering-drought on the photosynthesis physiology and biosynthesis of saikosaponins were investigated in 1-year-old B. chinense. The results showed that alterations in soil moisture altered the photosynthesis physiological process of B. chinense. The dry weight and fresh weight of the roots, photosynthesis capacity, chlorophyll fluorescence parameters, and SOD, POD and CAT activities were significantly reduced, and the contents of SP, soluble sugars, PRO and MDA increased. There were strong correlations between different physiological stress indices. All indices promoted and restricted each other, responded to soil moisture changes synergistically, maintained plant homeostasis and guaranteed normal biological activities. It was found that RW and RD_1 were the key stages of the water-control experiment affecting the expression of saikosaponin-related genes. At these two stages, the expression of multiple genes was affected by changes in soil moisture, with their expression levels reaching several-fold higher than those at the previous stage. We noticed that the expression of saikosaponin synthesis genes (which were rapidly upregulated at the RW and RD_1 stages) did not coincide with the rapid accumulation of saikosaponins (at the RD-2 stage), which were found to correspond to each other at the later stages of the water-control experiment. This finding indicates that there is a time lag between gene expression and the final product synthesis. Rapid changes in the external environment (RW to RD_1) have a short-term promoting effect on gene expression. This study reveals that short-term stress regulation may be an effective way to improve the quality of medicinal materials.

Anti-tumour effects of polysaccharide extracted from Acanthopanax senticosus and cell-mediated immunity.

Acanthopanax senticosus, also known as Siberian ginseng, is widely distributed throughout northern Asia and used in traditional Chinese medicine; it has been reported to prevent a number of diseases. However, the association between the antitumour and immunostimulatory activities of polysaccharide extracted from A. senticosus (ASPS) remains to be elucidated. The aim of the present study was to investigate the anti-tumour and immunomodulatory effects of polysaccharide extracted from ASPS on Crocker sarcoma S180, hepatic carcinoma H22 and uterine cervical carcinoma U14 tumour cell lines implanted in mice. High performance liquid chromatography, gas chromatography and infrared spectroscopy were used to analyse the monosaccharide composition of ASPS. The monosaccharide composition of ASPS (Arabic candy: Xylose: Glucose: Mannose) was 7.1:22.3:7.6:1.0. On day 0, female Kunming mice, were injected subcutaneously with 1×108 tumour cells in 0.2 ml. The inoculated mice were subsequently divided into five groups (10 mice/group) as follows: Model group, treated with normal saline; positive control group, treated with 30 mg/kg cyclophosphamide (CTX); and three treatment groups, treated with 200, 100 or 50 mg/kg ASPS. Non-inoculated mice were divided into the normal group, which was treated with normal saline, and the negative control group, which was treated with 200 mg/kg ASPS (n=10/group). CTX and ASPS were administered intragastrically once daily for 10 days. All mice were sacrificed on day 11. ASPS was observed to have an inhibitory effect on the growth of S180, H22 and U14 cells in solid and ascites tumour-bearing mice. Serum interleukin (IL)-2 and IL-12 levels were significantly increased in S180 solid tumour-bearing mice treated with 200 or 100 mg/kg ASPS compared with mice in the normal, control and model groups (P<0.05), whereas serum IL-2 and IL-12 levels were significantly decreased in the cyclophosphamide treatment group compared with the normal, control and model groups (P<0.05). No significant difference in serum levels of tumour necrosis factor-α level was observed between any groups. In S180 and U14 solid tumour-bearing mice, no significant differences in serum levels of interferon (INF)-γ level in were observed between groups; however, in H22 solid tumour-bearing mice, treatment with ASPS significantly increased serum INF-γ compared with the positive control group (P<0.05). The results may provide a basis for the potential application of ASPS in clinical treatment for cancer.

Isolation and identification of fourteen microsatellite markers in Clivia miniata and Clivia nobilis (Amaryllidaceae).

Clivia is a genus of great horticultural importance and has been widely cultivated as ornamental plants in all over the world. In order to assess the phylogenetic relationships and genetic diversity of the wild Clivia species and cultivars, we isolated AC-enriched repeats using FIASCO from a single clone each of C. miniata Regel. and Clivia nobilis Lindl. Of the fourteen repeats, 10 were polymorphic and 4 were monomorphic. The polymorphic marker loci were characterized using 61 Clivia accessions. The number of alleles ranged from two to six, observed heterozygosity ranged from 0.04 to 1.00 and expected heterozygosity ranged from 0.04 to 0.83. These microsatellite marker loci provide tools for future studies of Clivia species and cultivars.

QSPR modeling of nonionic surfactant cloud points: an update.

Quantitative structure-property relationship (QSPR) models for the cloud points of nonionic surfactants were developed based on CODESSA descriptors. Essentials accounting for a reliable model were considered carefully. Four descriptors were selected by a generic algorithm (GA) method to link the structures of nonionic surfactants to their corresponding cloud-point values. The descriptors were also analyzed using principal component analysis (PCA). Nonlinear models based on support vector machine (SVM) and projection pursuit regression (PPR) were also developed. All models were validated in two ways, i.e., internal cross-validation (CV) and a test set. The results are discussed in light of the main factors that influence the property under investigation and its modeling. In addition, an independent external data set of 16 nonionic surfactants was used to check the generalization ability of the optimum model.

3D-QSAR studies on the inhibitors of AP-1 and NF-kappaB mediated transcriptional activation.

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of 68 inhibitors of AP-1 and NF-kappaB mediated transcriptional activations. The CoMFA model produced statistically significant results with the cross-validated q(2) of 0.594 and the conventional correlation coefficient r(2) of 0.968. The best CoMSIA model was obtained by the combination use of steric, electrostatic, hydrogen-bond donor and acceptor fields. The corresponding q(2) and r(2) of CoMSIA model were 0.703 and 0.932, respectively. From the cross-validated results, it can be seen that the CoMSIA model has a better predictive ability than CoMFA model due to the importance of the hydrogen bonds for the activity of these inhibitors. The predictive abilities of the two models were further validated by a test set of 15 compounds. The models gave predicted correlation coefficient r(pred)(2) of 0.891 for CoMFA model and 0.810 for CoMSIA model. Based on the above results, we identified the key structural features that may help to design potent inhibitors with improved activities: (1) the NH linker at the position R(4) acts as important hydrogen-bond donor and any group on phenyl or 2-thienyl ring of R(1) substituent decreases inhibitory activity, (2)further structural modification of compound 50 on the phenyl ring of the quinazoline ring considering steric, electrostatic and hydrogen-bond acceptor properties will influence the inhibitory activity.

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) of thiazolone derivatives as hepatitis C virus NS5B polymerase allosteric inhibitors.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models for a series of thiazolone derivatives as novel inhibitors bound to the allosteric site of hepatitis C virus (HCV) NS5B polymerase were developed based on CoMFA and CoMSIA analyses. Two different conformations of the template molecule and the combinations of different CoMSIA field/fields were considered to build predictive CoMFA and CoMSIA models. The CoMFA and CoMSIA models with best predictive ability were obtained by the use of the template conformation from X-ray crystal structures. The best CoMFA and CoMSIA models gave q (2) values of 0.621 and 0.685, and r (2) values of 0.950 and 0.940, respectively for the 51 compounds in the training set. The predictive ability of the two models was also validated by using a test set of 16 compounds which gave r (pred) (2) values of 0.685 and 0.822, respectively. The information obtained from the CoMFA and CoMSIA 3D contour maps enables the interpretation of their structure-activity relationship and was also used to the design of several new inhibitors with improved activity.

Three-dimensional topographic index applied to the prediction of acyclic C5-C8 alkenes Kováts retention indices on polydimethylsiloxane and squalane columns.

A novel approach is described for the prediction of gas chromatographic Kováts retention indices of 150 acyclic C5-C8 alkenes on two stationary phases (polydimethylsiloxane, PDMS, and squalane, SQ). The heuristic method was used to build multiple linear regression models using descriptors calculated by MODLESLAB software and CODESSA program. The resulting quantitative structure-retention relationship (QSRR) models were well-correlated, with predictive R2 values of 0.970 and 0.958 for retention indices on PDMS and SQ columns, respectively. 1Omegap, a three-dimensional (3D) topographic index, was found to play the most important role in the description of the chromatographic retention behavior of the alkenes in these two stationary phases. Moreover, this index could completely distinguish different isomers of alkene. Therefore, it can also be extended to distinguish different isomers of other compounds so that can well describe their quantitative structure-retention relationships.

Prediction of ozone tropospheric degradation rate constants by projection pursuit regression.

Quantitative structure-property relationship (QSPR) models were developed to predict degradation rate constants of ozone tropospheric and to study the degradation reactivity mechanism of 116 diverse compounds. DUPLEX algorithm was utilized to design the training and test sets. Seven molecular descriptors selected by the heuristic method (HM) were used as inputs to perform multiple linear regression (MLR), support vector machine (SVM) and projection pursuit regression (PPR) studies. The PPR model performs best both in the fitness and in the prediction capacity. For the test set, it gave a predictive correlation coefficient (R) of 0.955, root mean square error (RMSE) of 1.041 and absolute average relative deviation (AARD, %) of 4.663, respectively. The results proved that PPR is a useful tool that can be used to solve the nonlinear problems in QSPR. In addition, methods used in this paper are simple, practical and effective for chemists to predict the ozone degradation rate constants of compounds in troposphere.

Prediction of binding affinities to beta1 isoform of human thyroid hormone receptor by genetic algorithm and projection pursuit regression.

Quantitative structure-activity relationship (QSAR) has been applied to a set of thyroid hormone receptor beta(1) (TRbeta(1)) antagonists, which are of special interest because of their potential role in safe therapies for nonthyroid disorders while avoiding the cardiac side effects. Using the calculated structural descriptors by CODESSA program, principal component analysis (PCA) was performed on the whole compounds to assist the separation of the data into the training set and the test set in QSAR analysis. Six molecular descriptors selected by genetic algorithm (GA) were used as inputs for a projection pursuit regression (PPR) study to develop a more accurate QSAR model. The PPR model performs well both in the fitting and prediction capacity. For the test set, it gave a predictive correlation coefficient (R) of 0.9450, root mean square error (RMSE) of 0.4498, and absolute average relative deviation (AARD) of 4.19%, respectively, confirming the ability of PPR for the prediction of the binding affinities of compounds to beta(1) isoform of human thyroid hormone receptor (TRbeta(1)).

An accurate QSRR model for the prediction of the GCxGC-TOFMS retention time of polychlorinated biphenyl (PCB) congeners.

Quantitative structure-retention relationship (QSRR) models were constructed for the GCxGC-TOFMS retention time of 209 polychlorinated biphenyl (PCB) congeners. Principal component analysis (PCA) was used to recognize groups of samples with similar behavior and assist the separation of the data into training and test sets. The best multi-linear regression (BMLR) method was used for the systematic development of multi-linear regression equations; the best regression model involved four descriptors which were related to GCxGC-TOFMS chromatographic retention of PCBs. The obtained model has good predictive ability. For the test set, it gave a predictive correlation coefficient (R) of 0.988 and an average absolute relative deviation (AARD) of 3.08%. Results of a six-fold cross-validation procedure, which were in accordance with those from validation of training and test sets, demonstrated that this model was reliable. Additionally, this paper provides a simple, practical, and effective method for analytical chemists to predict the retention times of PCBs in GC.

The accurate QSPR models for the prediction of nonionic surfactant cloud point.

Quantitative structure-property relationship models were developed to predict cloud points and study the cloud phenomena of nonionic surfactants in aqueous solution. Four descriptors were selected by the heuristic method as the inputs of multiplier linear regression and support vector machine (SVM) models. Very satisfactory results were obtained. SVM models performed better both in fitness and in prediction capacity. For the test set, they gave a predictive correlation coefficient (R) of 0.9882, root mean squared error of 4.2727, and absolute average relative deviation of 9.5490, respectively. The proposed models can identify and provide some insight into what structural features are related to the cloud points of compounds, i.e., the molecular size, structure, and isomerism of the hydrocarbon moiety and the degree of oxyethylation. They can also help to understand the cloud phenomena of nonionic surfactants in aqueous solution. Additionally, this paper provides two simple, practical, and effective methods for analytical chemists to predict the cloud points of nonionic surfactants in aqueous solution.

Classification study of skin sensitizers based on support vector machine and linear discriminant analysis.

The support vector machine (SVM), recently developed from machine learning community, was used to develop a nonlinear binary classification model of skin sensitization for a diverse set of 131 organic compounds. Six descriptors were selected by stepwise forward discriminant analysis (LDA) from a diverse set of molecular descriptors calculated from molecular structures alone. These six descriptors could reflect the mechanic relevance to skin sensitization and were used as inputs of the SVM model. The nonlinear model developed from SVM algorithm outperformed LDA, which indicated that SVM model was more reliable in the recognition of skin sensitizers. The proposed method is very useful for the classification of skin sensitizers, and can also be extended in other QSAR investigation.