Biomedical subjects

Xiao-Jun Liu

Publications and source records attributed to Xiao-Jun Liu.

18 recordsLinked to original sources

PubMed2006

Characteristics of occurrence for Han Chinese familial keloids.

AIM: To elucidate the characteristics of occurrence for Han Chinese familial keloids. METHOD: To find out their common characteristics of occurrence by studying the clinical and genetic information of six Han Chinese pedigrees with familial keloids with all the family members having no recorded marriage history to other races except Han people. RESULT: Most of these familial keloids occur spontaneously during puberty, often displaying symmetrically at chest, back and shoulder with respective specific shapes, presenting with variable clinical expression and incomplete clinical penetrance and showing the characteristics of an autosomal dominant mode of inheritance with reduced penetrance. CONCLUSION: These Han Chinese familial keloids present with many similar characteristics of occurrence differing from those of sporadic cases. The pattern of inheritance observed in these Han Chinese pedigrees with familial keloids is consistent with an autosomal dominant mode with incomplete clinical penetrance and variable expression.

Adolescent↗

PubMed2006

Atypical protein kinase C in glucose metabolism.

Type 2 diabetes mellitus is a multigenic disease with evident genetic predisposition, and complex pathogenesis in which environmental and genetic factors interact. The disorder of body utilization glucose is a crucial reason for causing diabetes. Atypical PKCs, belonging to Ser/Thr protein kinase, have many important biological functions in vivo, and may be involved in the pathogenesis of diabetes mellitus. APKCs participate in glucose metabolism by regulating glucose transport and absorption, glycogen synthesis, and insulin secretion. The exact mechanism by which aPKCs participate in glucose metabolism remains unclear. So far, the clarification of which will be helpful for the prevention and cure of type 2 diabetes.

Animals↗

PubMed2006

Protein kinase Czeta and glucose uptake.

Protein kinase Czeta (PKCzeta) is a member of the PKC family, serving downstream of insulin receptor and phosphatidylinositol (PI) 3-kinase. Many evidences suggest that PKCzeta plays a very important role in activating glucose transport response. Not only insulin but also glucose and exercise can activate PKCzeta through diverse pathways. PKCzeta activation and activity are impaired with insulin resistance in muscle and adipose tissues of type II diabetes individuals, but heightened in liver tissue, wherein it also increases lipid synthesis mediated by SREBP-1c (sterol-regulatory element-binding protein). Many studies have focused on linkage between PKCzeta and GLUT4 translocation and activation. Exploring the molecular mechanisms and pathways by which PKCzeta mediates glucose transport will highlight the insulin-signaling pathway.

Animals↗

PubMed2006

WT1 gene expression lowered by IL-12 In vitro in peripheral blood mononuclear cells from patients with leukemia or myelodysplastic syndromes.

Previous studies demonstrated that interleukin-12 (IL-12) enhances the non-MHC-restricted cytotoxic activity of NK cells and facilitate specific allogeneic human cytotoxic T lymphocyte responses against fresh leukemia cells and cell lines. The Wilms' tumor gene, WT1 mRNA, has been used as a marker of minimal residual disease (MRD) for evaluating therapeutic efficacy of patients with leukemia or myelodysplastic syndrome (MDS). This study was aimed to investigate whether in vitro IL-12 can lower WT1 gene expression in peripheral blood monuclear cells (PBMNC) from patients with leukemia or MDS. PBMNC from these 30 patients and 5 healthy volunteers were cultured at 5 x 10(5) cells/ml alone with or without 100 units/ml of IL-12 for 3 days. WT1 mRNA was measured by competitive reverse transcription polymerase chain reaction (RT-PCR) since WT1 mRNA is considered as a marker of minimal residual disease (MRD) in leukemia and MDS. The results demonstrated that WT1 mRNA in PBMNC of 5 healthy volunteers was less than 10(3) copies/microg of total RNA. Following the 3-day IL-12 treatment, mean WT1 mRNA of PBMNC was reduced from 10(4.8) to 10(4.2) copies/microg of total RNA in 6 CML patients, from 10(5.4) to 10(4.8) copies/microg in 12 MDS patients and from 10(5.0) to 10(4.2) copies/microg in 5 AML patients in CR, but not reduced in 5 of 7 AML in non-CR. It is concluded that IL-12 significantly decrease the quantity of leukemia cells in PBMNC of most patients with MDS, CML and AML in CR. IL-12 may be of considerable benefit in the elimination of MRD in patients with hematological malignancies.

Adult↗

PubMed2006

[Combined effect of recombinant mutant human TRAIL and daunorubicin in inducing apoptosis of leukemia cell and its mechanism].

The aim of study was to investigate the combined effect of recombinant mutant human TRAIL (rmhTRAIL) with daunorubicin (DNR) or alone on K562 and U937 leukemia cell lines and its mechanism. The fibroblasts (MRC-5) of normal-human embryonic lung were used as control cells. After being treated with rmhTRAIL and DNR or only with rmTRAIL, the cytotoxic effect and the apoptosis rate in K562, U937 cells were measured by MTT assay. The expression levels of TRAIL death receptor and TRAIL decoy receptor mRNA in these three cell lines were assayed by semiquantitive RT-PCR before and after treatment with DNR. The results indicated that K562 and U937 were sensitive to rmhTRIAL. DNR had synergistic inhibitory effect with rmhTRAIL on the growth of K562 and U937 cell lines (P < 0.05). The expression level of DR4 and DR5 mRNA was significantly higher in K562 and U937 with combined treatment of rmhTRAIL and DNR than that in those alone, while the expressions of DcR1 and DcR2 mRNA were not influenced. It is concluded that in vitro, rmhTRAIL alone or in combination with DNR can obviously inhibit the growth of leukemia cell lines and induce cell apoptosis, DNR and rmhTRAIL have a synergistic inhibitory effect on growth of K562 and U937. The mechanism may correlate with the up-regulation of DR4 and DR5 of K562 and U937.

Antibiotics, Antineoplastic↗

PubMed2005

Using supervised fuzzy clustering to predict protein structural classes.

Prediction of protein classification is both an important and a tempting topic in protein science. This is because of not only that the knowledge thus obtained can provide useful information about the overall structure of a query protein, but also that the practice itself can technically stimulate the development of novel predictors that may be straightforwardly applied to many other relevant areas. In this paper, a novel approach, the so-called "supervised fuzzy clustering approach" is introduced that is featured by utilizing the class label information during the training process. Based on such an approach, a set of "if-then" fuzzy rules for predicting the protein structural classes are extracted from a training dataset. It has been demonstrated through two different working datasets that the overall success prediction rates obtained by the supervised fuzzy clustering approach are all higher than those by the unsupervised fuzzy c-means introduced by the previous investigators [C.T. Zhang, K.C. Chou, G.M. Maggiora. Protein Eng. (1995) 8, 425-435]. It is anticipated that the current predictor may play an important complementary role to other existing predictors in this area to further strengthen the power in predicting the structural classes of proteins and their other characteristic attributes.

Algorithms↗

PubMed2005

[Craniofacial characteristics of the Class II division 1 malocclusion patients with mouth-breating: a posteroanterior].

OBJECTIVE: To evaluate the craniofacial characteristics of the Class II malocclusion patients with mouth-breating by posteroanterior cephalometry. METHODS: To measure craniofacial width of the 12 Class II malocclusion patients with mouth-breathing, and to compared these measures with corresponding measures in a group of normal children. RESULTS: The width of the maxillary base bone (J-J) was less than that in normal children significantly (P < 0.01). The mouth-breathing children's upper and lower arch width (at first molar and cuspid) were comparatively narrower, and lateronasal width (Lap-Lap) was narrower too. CONCLUSION: Mouth breathing may lead to craniofacial morphological abnormal development in craniofacial transverse structures.

Adolescent↗

PubMed2005

[The functional integrity of imaging diagnostic equipments].

On the basis of introduction and analysis of three new medical imaging equipments, the article discusses the superiority of the function integrity that is a new concept, and its application in the development of medical equipments in 21st century.

Diagnostic Equipment↗

PubMed2003

Prediction of protein secondary structure content by artificial neural network.

The neural network method was applied to the prediction of the content of protein secondary structure elements, including alpha-helix, beta-strand, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend, and random coil. The "pair-coupled amino acid composition" originally proposed by K. C. Chou [J Protein Chem 1999, 18, 473] was adopted as the input. Self-consistency and independent-dataset tests were used to appraise the performance of the neural network. Results of both tests indicated high performance of the method.

Algorithms↗

PubMed2003

Support vector machines for prediction of protein domain structural class.

The support vector machines (SVMs) method was introduced for predicting the structural class of protein domains. The results obtained through the self-consistency test, jack-knife test, and independent dataset test have indicated that the current method and the elegant component-coupled algorithm developed by Chou and co-workers, if effectively complemented with each other, may become a powerful tool for predicting the structural class of protein domains.

Algorithms↗

PubMed2003

Prediction of beta-turns with learning machines.

The support vector machine approach was introduced to predict the beta-turns in proteins. The overall self-consistency rate by the re-substitution test for the training or learning dataset reached 100%. Both the training dataset and independent testing dataset were taken from Chou [J. Pept. Res. 49 (1997) 120]. The success prediction rates by the jackknife test for the beta-turn subset of 455 tetrapeptides and non-beta-turn subset of 3807 tetrapeptides in the training dataset were 58.1 and 98.4%, respectively. The success rates with the independent dataset test for the beta-turn subset of 110 tetrapeptides and non-beta-turn subset of 30,231 tetrapeptides were 69.1 and 97.3%, respectively. The results obtained from this study support the conclusion that the residue-coupled effect along a tetrapeptide is important for the formation of a beta-turn.

Artificial Intelligence↗

PubMed2002

Support Vector Machines for predicting HIV protease cleavage sites in protein.

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired is useful for designing specific and efficient HIV protease inhibitors. The pace in searching for the proper inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this article, a Support Vector Machine is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of the study. Two hundred ninety-nine oligopeptides were chosen for the training set, while the other 63 oligopeptides were taken as a test set. Because of its high rate of self-consistency (299/299 = 100%), a good result in the jackknife test (286/299 = 95%) and correct prediction rate (55/63 = 87%), it is expected that the Support Vector Machine method can be referred to as a useful assistant technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the Support Vector Machine method can also be applied to analyzing the specificity of other multisubsite enzymes.

Amino Acid Sequence↗

PubMed2002

Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect.

Support Vector Machine (SVM), which is one class of learning machines, was applied to predict the subcellular location of proteins by incorporating the quasi-sequence-order effect (Chou [2000] Biochem. Biophys. Res. Commun. 278:477-483). In this study, the proteins are classified into the following 12 groups: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracellular, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane, and (12) vacuole, which account for most organelles and subcellular compartments in an animal or plant cell. Examinations for self-consistency and jackknife testing of the SVMs method were conducted for three sets consisting of 1,911, 2,044, and 2,191 proteins. The correct rates for self-consistency and the jackknife test values achieved with these protein sets were 94 and 83% for 1,911 proteins, 92 and 78% for 2,044 proteins, and 89 and 75% for 2,191 proteins, respectively. Furthermore, tests for correct prediction rates were undertaken with three independent testing datasets containing 2,148 proteins, 2,417 proteins, and 2,494 proteins producing values of 84, 77, and 74%, respectively.

Proteins↗

PubMed2002

Support vector machines for the classification and prediction of beta-turn types.

The support vector machines (SVMs) method is proposed because it can reflect the sequence-coupling effect for a tetrapeptide in not only a beta-turn or non-beta-turn, but also in different types of beta-turn. The results of the model for 6022 tetrapeptides indicate that the rates of self-consistency for beta-turn types I, I', II, II', VI and VIII and non-beta-turns are 99.92%, 96.8%, 98.02%, 97.75%, 100%, 97.19% and 100%, respectively. Using these training data, the rate of correct prediction by the SVMs for a given protein: rubredoxin (54 residues. 51 tetrapeptides) which includes 12 beta-turn type I tetrapeptides, 1 beta-turn type II tetrapeptide and 38 non-beta-turns reached 82.4%. The high quality of prediction of the SVMs implies that the formation of different beta-turn types or non-beta-turns is considerably correlated with the sequence of a tetrapeptide. The SVMs can save CPU time and avoid the overfitting problem compared with the neural network method.

Algorithms↗

PubMed2002

Artificial neural network model for predicting protein subcellular location.

The function of a protein is closely correlated to its subcellular location. Is it possible to utilize a bioinformatics method to predict the protein subcellular location? To explore this problem, proteins are classified into 12 groups (Protein Eng. 12 (1999) 107-118) according to their subcellular location: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracellular, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane and (12) vacuole. In this paper, the neural network method was proposed to predict the subcellular location of a protein according to its amino acid composition. Results obtained through self-consistency, cross-validation and independent dataset tests are quite high. Accordingly, the present method can serve as a complement tool for the existing prediction methods in this area.

Algorithms↗

PubMed2002

Prediction of protein structural classes by support vector machines.

In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a result, high rates of both self-consistency and jackknife test are obtained. This indicates that the structural class of a protein inconsiderably correlated with its amino and composition, and the support vector machine can be referred as a powerful computational tool for predicting the structural classes of proteins.

Artificial Intelligence↗

PubMed2002

Artificial neural network method for predicting protein secondary structure content.

In this paper, the neural network method was applied to predict the content of protein secondary structure elements that was based on 'pair-coupled amino acid composition', in which the sequence coupling effects are explicitly included through a series of conditional probability elements. The prediction was examined by a self-consistency test and an independent-dataset. Both indicated good results obtained when using the neural network method to predict the contents of alpha-helix, beta-sheet, parallel beta-sheet strand, antiparallel beta-sheet strand, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend, and random coil.

Neural Networks, Computer↗

PubMed2000

Cloning, Sequencing and Preliminary Expression of Human RP2 Gene.

RP2 is an X-linked retinitis pigmentosa gene, which was newly discovered by positional cloning. A polymerase chain reaction (PCR) was conducted to screen a full-length cDNA fragment, defined as hRP2a, which included the coding region of hRP2, in a human retina cDNA library. HRP2a gene was cloned into the pJLA503 vector and hRP2 gene was subcloned into the expression vector pP(RO)EX HTa. Polymorphism was demonstrated at two sites through DNA sequencing. The recombinant pP(RO)RP2 was transformed into Escherichia coli strain DH5alpha and the expression of a 6xHis tagged hRP2 fusion protein was induced by IPTG. Band density scanning of stained gel was performed to estimate the percentage of the recombinant protein in the total bacterial protein. The ratio was 7% when the expression was induced at 30 degrees and was 5.6 % at 37 degrees. The cloning and expression of hRP2 gene in E.coli established a basis for the further purification and studies of RP2 for its physiochemical identity, immunohistochemistry and structure-function relationship.

Journal Article↗