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Yun-Long Zhang

Publications and source records attributed to Yun-Long Zhang.

2 recordsLinked to original sources
PubMed2005

[In vitro and in vivo pharmaceutical behaviors of lycopene microcapsules].

AIM: To evaluate in vitro release of lycopene microcapsules. Pharmacokinetic parameters of lycopene microcapsule and lycopene powder as reference were estimated after a single dose of oral administration to dogs. The relationship between in vitro dissolution and in vivo absorption was investigated. METHODS: The content of lycopene in the release medium was determined by UV spectroscopy method. Health hybrid male dogs were used as experiment subjects and lycopene powder used as standard to estimate the pharmacokinetics of lycopene microcapsules. HPLC method was used to assay the concentration of lycopene in dog plasma. Pharmacokinetics parameters were estimated by 3P87 program. The drug release percentage in stimulated intestinal fluid was compared with the absorption at a given time point. RESULTS: The release profiles of lycopene from microcapsule showed that the lycopene gelatin microcapsule exhibited enteric property. The pharmacokinetics parameters estimated after oral administration of lycopene powder and lycopene microcapsule in a single dose of 2.5 mg x kg(-1) body weight to dogs were 7.30 h, 15.06 h for T1/2alpha; 28.10 h, 46.76 h for T1/2beta; 22.32 h, 41.03 h for T(max); 1.67 microg x h x L(-1), 2.08 microg x h x L(-1) for AUC(0-infinity), respectively. The concentration-time curves could be fitted to a two-compartment model for both the lycopene powder and the lycopene microcapsule analyzed by 3P87 program. The relationship between in vitro dissolution and in vivo absorption was found to have good correlation (r = 0. 981 9) was found. CONCLUSION: It could be concluded that lycopene microcapsule was a sustained release dosage form. The result of release in vitro could be used to predict the absorption in vivo.

Administration, Oral↗
PubMed2003

Classification of protein quaternary structure with support vector machine.

MOTIVATION: Since the gap between sharply increasing known sequences and slow accumulation of known structures is becoming large, an automatic classification process based on the primary sequences and known three-dimensional structure becomes indispensable. The classification of protein quaternary structure based on the primary sequences can provide some useful information for the biologists. So a fully automatic and reliable classification system is needed. This work tries to look for the effective methods of extracting attribute and the algorithm for classifying the quaternary structure from the primary sequences. RESULTS: Both of the support vector machine (SVM) and the covariant discriminant algorithms have been first introduced to predict quaternary structure properties from the protein primary sequences. The amino acid composition and the auto-correlation functions based on the amino acid index profile of the primary sequence have been taken into account in the algorithms. We have analyzed 472 amino acid indices and selected the four amino acid indices as the examples, which have the best performance. Thus the five attribute parameter data sets (COMP, FASG, NISK, WOLS and KYTJ) were established from the protein primary sequences. The COMP attribute data set is composed of amino acid composition, and the FASG, NISK, WOLS and KYTJ attribute data sets are composed of the amino acid composition and the auto-correlation functions of the corresponding amino acid residue index. The overall accuracies of SVM are 78.5, 87.5, 83.2, 81.7 and 81.9%, respectively, for COMP, FASG, NISK, WOLS and KYTJ data sets in jackknife test, which are 19.6, 7.8, 15.5, 13.1 and 15.8%, respectively, higher than that of the covariant discriminant algorithm in the same test. The results show that SVM may be applied to discriminate between the primary sequences of homodimers and non-homodimers and the two protein sequence descriptors can reflect the quaternary structure information. Compared with previous Robert Garian's investigation, the performance of SVM is almost equal to that of the Decision tree models, and the methods of extracting feature vector from the primary sequences are superior to Robert's binning function method. AVAILABILITY: Programs are available on request from the authors.

Algorithms↗