Biomedical subjects

Xinhui Liu

Publications and source records attributed to Xinhui Liu.

6 recordsLinked to original sources

PubMed2005

Collagen-based implants reinforced by chitin fibres in a goat shank bone defect model.

Tissue engineering is an increasingly popular method for repairing bone defects. However, repair of bone defects over 30 mm using tissue-engineering methods is a difficult clinical problem. In this study, we used a goat shank model to evaluate the bone-regenerating efficacy of a novel nano-hydroxyapatite/collagen/PLLA (nHACP) composite reinforced by chitin fibres. Forty adult male goats with 40 mm defects in shank at the same anatomic site were divided into four groups. The first group was the control, where nothing was implanted in the defect (defect group). The other three groups were implanted with porous pure PLLA, nHACP and nHACP reinforced by chitin fibres, respectively. Bone growth in each group was evaluated by radiography, histology, bone mineral density and mechanical strength, once every 5 weeks for 15 weeks. The results indicated that nHACP implants, both with and without chitin fibres, are better for repairing the defects than pure PLLA. However, only the reinforced implants showed nearly perfect recovery in 15 weeks after operation. So, the reinforced scaffold might be a candidate for bone tissue repair.

Animals↗

PubMed2003

Acute toxicity and quantitative structure-activity relationships of alpha-branched phenylsulfonyl acetates to Daphnia magna.

The acute toxicity (48 h-EC50, microM) of 20 alpha-substituted phenylsulfonyl acetates was measured using Daphnia magna with a static method. On the basis of physicochemical parameters (octanol/water partition coefficient logK(ow) and aqueous solubility logS(w)), the theoretical linear solvation energy relationships (TLSER) and Charge model descriptors, QSARs were calculated for the immobilization of D. magna. For the models with the physicochemical parameters logK(ow) and logS(w), the low squared correlation coefficients indicate that hydrophobicity plays a dominant role on the toxicity and hydrophobicity is not the only factor that influences the activity of the compounds. For the TLSER model and the Charge model, the great squared correlation coefficients suggest that the models have good predictive capability. The higher activity of the compounds can be explained with the disruption of van der Waals interactions between lipid and/or protein compounds within the membrane and the possibility of the compounds to form hydrogen bonds with the receptor molecules. The models may more completely illustrate the toxicity mechanisms.

Animals↗

PubMed2003

Three-dimensional quantitative structure-activity relationship study for phenylsulfonyl carboxylates using CoMFA and CoMSIA.

From both the comparative molecular field analysis (CoMFA) and the comparative molecular similarity indices analysis (CoMSIA), the paper describes two three-dimensional quantitative structure-activity relationship (3D-QSAR) models for the acute toxicity logEC50 (15 min-EC50 in micromoll(-1)) of 56 phenylsulfonyl carboxylates on Photobacterium phosphoreum. Two models yield the leave-one-out cross-validated correlation coefficient q2 values of 0.823 and 0.713, and the conventional correlation coefficient r2 values of 0.958 and 0.933, respectively. The achievement of higher q2 and r2 values of CoMFA model indicates the significance of correlation of steric and electrostatic fields with biological activities. The key features in the CoMFA contour maps are critical to trace the important properties and gain insight into the toxic mechanism of tested compounds. The quality of CoMSIA model is slightly lower than that of CoMFA in terms of q2 and r2 values. Not requiring molecular superposition, CoMSIA is faster than CoMFA in data processing.

Models, Chemical↗

PubMed2002

Prediction and application in QSPR of aqueous solubility of sulfur-containing aromatic esters using GA-based MLR with quantum descriptors.

Quantitative structure-property relationships (QSPR) were developed using a genetic algorithm (GA)-based variable-selection approach with quantum chemical descriptors derived from AM1-based calculations (MOPAC7.0). With the QSPR models, the aqueous solubility of 71 aromatic sulfur-containing carboxylates, including phenylthio, and phenylsulfonyl carboxylates were efficiently estimated and predicted. Using GA-based multivariate linear regression (MLR) with cross-validation procedure, the most important descriptors were selected from a pool of 28 quantum chemical semi-empirical descriptors, including steric and electronic types, to build QSPR models. The molecular descriptors included molecular surface (SA), charges on carboxyl group (Q(oc)), the magnitude of the difference between E(HOMO) of the solute and ELUMO of water, divided by 100 (E(B)), which were main factors affecting the aqueous solubility of the compounds of interest. The resulted coefficients R and R2 of 0.9571 and 0.9161 and the prediction residual error sum of squares (PRESS) of 13.1768, revealed that it was accurate and reliable for the model to predict the aqueous solubility of the investigated organic compounds. If two outliers were omitted from the dataset, the resulted coefficients R = 0.9619, R2 = 0.9253, and PRESS = 10.3875 were significantly improved. Compared with stepwise regression analysis, the results obtained in this work were better and more reasonable. The best QSPR model were obtained by GA-based MLR. Reasonable mechanisms for aqueous solubility of the sulfur-containing carboxylates were investigated and interpreted.

Algorithms↗

PubMedDate not supplied

Evaluation of three models for predicting newly determined octanol-water partition coefficients and mechanisms for substituted aromatic compounds.

Octanol-water partition coefficients (K(ow)) of 27 substituted aromatic compounds, including polyhalogenated aromatics, were determined. A molecular connectivity index (MCI), a theoretical linear solvation energy relationship, and a quantum chemical method were applied to model the property and study the partition mechanism. The multiple correction coefficients (r2(adj)) (> or = 0.870) and the standard errors (< or = 0.33) for log K(ow) indicated that the models were successful. Comparing the three models, the MCI method (including the nondisperse force factor) was the most satisfactory. However, the quantum chemical model based on the potential of the negative atomic charge, total energy, and molecular weight revealed that the molecular bulk properties and electrostatic interaction were the most important factors influencing the partition process.

Forecasting↗

PubMedDate not supplied

Three-dimensional, quantitative-structure-property-relationship study of aqueous solubility for phenylsulfonyl carboxylates using comparative-molecular-field analysis and comparative-molecular-similarity-indices analysis.

With both the comparative-molecular-field analysis (CoMFA) and the comparative-molecular-similarity-indices analysis (CoMSIA), the paper describes two five-component, three-dimensional, quantitative-structure-property-relationship (3D-QSPR) models for the aqueous solubility logSw (Sw, mol x L(-1)) of 52 phenylsulfonyl carboxylates. Two models yield the leave-one-out cross-validated correlation coefficient q2 values 0.851 and 0.821, and the conventional correlation coefficient r2 values 0.963 and 0.929, respectively. The achievement of high q2 and r2 values of the CoMFA model indicates the significance of correlation of steric and electrostatic fields with the aqueous solubility. The key features in the CoMFA contour maps are critical to trace the important properties and gain insight to the solvation mechanism of tested compounds. The quality of CoMSIA model is slightly lower than that of CoMFA in terms of q2 and r2 values. Not requiring molecular superposition, CoMSIA may be faster than CoMFA in data processing.

Models, Molecular↗