Effects of lovastatin on hepatic expression of the low-density lipoprotein receptor in nephrotic rats.

To investigate the effect of the HMG-CoA reductase inhibitor lovastatin on the expression of the receptor for hepatic low-density lipoprotein (LDL) in a rat model with kidney disease, and to identify the mechanisms in statin treatment of nephrotic syndrome with hyperlipidemia, a rat model with nephrotic syndrome was established. Thirty male Sprague-Dawley rats were treated with lovastatin for 2 weeks using gavage. The expression of protein and mRNA of the LDL receptor in the rat liver was detected with Western blot and RT-PCR, respectively, and blood-biochemical indices were also recorded for each group. Compared with the untreated control group, lovastatin treatment significantly decreased the levels of serum total cholesterol, LDL cholesterol, triglycerides, and urinary protein. In addition, lovastatin treatment significantly increased the levels of serum albumin and hepatic LDL receptor proteins, but had no effect on the expression of hepatic LDL receptor mRNA. Treatment with lovastatin markedly increased the expression of the hepatic LDL receptor in rats with nephrotic syndrome, which was accompanied by significantly improved hyperlipidemia.

Differential proteomic analysis of HeLa cells treated with Honokiol using a quantitative proteomic strategy.

Honokiol (HNK) is an active component purified from Magnolia officinalis. HNK exhibits antitumor effects by inducing apoptosis and inhibiting the growth of many cancer cell lines, while proteins involved in antitumor effects in proteomic level are still unclear. In our study, HNK could inhibit HeLa cell proliferation and induce apoptosis in a concentration- and time-dependent manner. We utilized a quantitative proteomic technique termed SILAC (Stable isotope labeling with amino acids in cell culture)-MS (mass spectrometry) to study the differential proteomic profiling of HeLa cells treated by HNK. A total of 85 proteins were changed after HeLa cells were treated with 12 microg/ml HNK for 8 h, and 8 proteins showed up-regulation while 77 proteins down-regulated. The changed proteins were classified into 9 different categories, which covered a broad variety of cellular functions. In conclusion, HNK performs cytotoxicity to HeLa cells through co-operating of many proteins and different pathways.

Semi automated transformation to OWL formatted files as an approach to data integration. A feasibility study using environmental, disease register and primary care clinical data.

INTRODUCTION: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". BACKGROUND: Data heterogeneity is one of the critical problems in analysing, reusing, sharing or linking datasets. Metadata, whilst adding semantic description to data, adds an additional layer of complexity in the heterogeneity of metadata descriptors themselves. This can be managed by using a pre-defined model to extract the metadata, but this can reduce the richness of the data extracted. OBJECTIVES: to link the South London Stroke Register (SLSR), the London Air Pollution toolkit (LAP) and the Clinical Practice Research Datalink (CPRD) while transforming data into the Web Ontology Language (OWL) format. METHODS: We used a four-step transformation approach to prepare meta-descriptions, convert data, generate and update meta-classes and generate OWL files. We validated the correctness of the transformed OWL files by issuing queries and assessing results against the original source data. RESULTS: We have transformed SLSR LAP and CPRD into OWL format. The linked SLSR and CPRD OWL file contains 3644 male and 3551 female patients. The linked SLSR and LAP OWL file shows that there are 17 out of 35 outward postcode areas, where no overlapping data can support further analysis between SLSR and LAP. CONCLUSIONS:  Our approach generated a resultant set of transformed OWL formatted files, which are in a query-able format to run individual queries, or can be easily converted into other more suitable formats for further analysis, and the transformation was faithful with no loss or anomalies. Our results have shown that the proposed method provides a promising general approach to address data heterogeneity.

Model-based synthesis of plucked string instruments by using a class of scattering recurrent networks.

A physical modeling method for electronic music synthesis of plucked-string tones by using recurrent networks is proposed. A scattering recurrent network (SRN) which is used to analyze string dynamics is built based on the physics of acoustic strings. The measured vibration of a plucked string is employed as the training data for the supervised learning of the SRN. After the network is well trained, it can be regarded as the virtual model for the measured string and used to generate tones which can be very close to those generated by its acoustic counterpart. The "virtual string" corresponding to the SRN can respond to different "plucks" just like a real string, which is impossible using traditional synthesis techniques such as frequency modulation and wavetable. The simulation of modeling a cello "A"-string demonstrates some encouraging results of the new music synthesis technique. Some aspects of modeling and synthesis procedures are also discussed.

A growth velocity standard for Chinese children in Hong Kong.

Racial differences in the size of children make it questionable that a widely used Caucasian velocity reference standard can be used for Chinese children. In the present study, the growth velocity in 935 healthy Chinese children aged 4-6 was examined, showing that height velocity was significantly lower than Tanner's references. With the Caucasian velocity standard, Chinese children might be misinterpreted as growing abnormally slowly. In order to detect growth and endocrine disorders of childhood and adolescence, a Chinese growth velocity standard for children of 0 to 18 years must be established.

Monitoring driver's alertness based on the driving performance estimation and the EEG power spectrum analysis.

Preventing accidents caused by drowsiness behind the steering wheel is highly desirable but requires techniques for continuously estimating driver's abilities of perception, recognition and vehicle control abilities. This paper proposes methods for drowsiness estimation that combine the electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Results show that it is feasible to quantitatively monitor driver's alertness with concurrent changes in driving performance in a realistic driving simulator.