[Establishment of immortalized lymphoblastoid cell bank of a keloid pedigree].

Objective: To investigate a method for establishing immortalized lymphoblastoid cell bank of keloid pedigree so as to provide a long-term source of specimens for keloid research.

Methods: With Epstein-Barr virus transformation, fresh and frozen blood samples collected from all members of the keloid pedigree were used respectively to establish the immortalized lymphoblastoid cell lines of B lymphocytes.

Results: Twenty-seven immortalized lymphoblastoid cell lines of the keloid pedigree were obtained successfully, and all cell lines survived cryopreservation in liquid nitrogen.

[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.

[Expression of human T-cell immunoglobulin mucin 3(TIM-3) on eukaryotic cells and establishment of stable transfectant cell line].

Aim: To construct eukaryotic expression vector of human T-cell immunoglobulin mucin 3(TIM-3) and transfect mammalian cells to establish stable cell line.

Methods: The whole coding region of TIM-3 was amplified by PCR and inserted into eukaryotic expression vector pIRES2EGFP. The recombinant plasmid was transfected into mammalian cells by Lipofectamine.

[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.

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.

[In vitro experimental study of gene therapy for ovarian cancer with thymidine kinase gene of herpes simplex virus mediated by a non-viral GE7 delivery system].

Objective: To investigate gene transfer efficiency of a novel target non-viral vector GE7 and effects of herpes simplex virus thymidine kinase (HSV(1)-tk)/ganciclovir (GCV) mediated by it in vitro.

Methods: The epidermal growth factor receptor (EGF-R) target gene delivery system GE7 was constructed. Human ovarian cancer cell line CAOV3 was transfected in vitro with beta-galactosidase (beta-gal) as reporter gene and HSV(1)-tk gene as therapeutic gene using this gene delivery system.

[Clinical study of Photofrin photodynamic therapy for advanced cancers].

Objective: To evaluate the clinical efficacy and adverse effects of Photofrin photodynamic therapy (PDT) in patients with advanced cancers.

Methods: Forty patients with advanced cancers in stage IV with lumen obstruction, who failed to respond positively to other treatment regimens, received intravenous administration of Photofrin as the photosensitizer at the dose of 2 mg/kg.b.

[Investigation of p53 gene mutations in keloids using PCR-SSCP].

Objective: To detect gene mutations of p53 gene (exon 4-6) in fibroblasts.

Methods: Samples of keloids were taken from 15 patients. The mutations of p53 gene were detected using polymerase chain reaction, the single-strand conformational polymorphism(SSCP) analysis and DNA sequencing.

[Constant scaled-energy spectroscopy of Rydberg atoms in a static electric field].

In the past years, scaled energy spectroscopy is under active investigation because this method can simplify the analysis of atomic spectra in the external field based on classic mechanics. A fully computer-controlled experimental system to study the constant scaled-energy spectroscopy was established and described in this paper. The excitation energy E and the strength of the external electric field F were controlled synchronously to keep the scaled-energy epsilon = E/square root of F constant.

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.

[Expression of cyclin A in adult patients with acute leukemia].

Objective: To investigate the clinical significance of cyclin A expression in adult patients with acute leukemia (AL).

Methods: 4 ml bone marrow was extracted from 100 AL patients and 10 normal controls to isolate the mononuclear cells (MNCs). The cyclin A mRNA levels in these MNCs were measured by RT-PCR.

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.

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.

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.

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.

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.

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.

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.

Support vector machines for predicting the specificity of GalNAc-transferase.

Support Vector Machines (SVMs) which is one kind of learning machines, was applied to predict the specificity of GalNAc-transferase. The examination for the self-consistency and the jackknife test of the SVMs method were tested for the training dataset (305 oligopeptides), the correct rate of self-consistency and jackknife test reaches 100% and 84.9%, respectively.

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.

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.