Estimating complex cortical networks via surface recordings-A critical note

We discuss potential caveats when estimating topologies of 3D brain networks from surface recordings. It is virtually impossible to record activity from all single neurons in the brain and one has to rely on techniques that measure average activity at sparsely located (non-invasive) recording sites Effects of this spatial sampling in relation to structural network measures like centrality and assortativity were analyzed using multivariate classifiers A simplified model of 3D brain connectivity incorporating both short- and long-range connections served for testing. To mimic M/EEG recordings we sampled this model via non-overlapping regions and weighted nodes and connections according to their proximity to the recording sites We used various complex network models for reference and tried to classify sampled versions of the ""brain-like"" network as one of these archetypes It was found that sampled networks may substantially deviate in topology from the respective original networks for small sample sizes For experimental studies this may imply that surface recordings can yield network structures that might not agree with its generating 3D network. (C) 2010 Elsevier Inc All rights reserved

The oscillatory bioelectrical signal from plants explained by a simulated electrical model and tested using Lempel-Ziv complexity

This paper demonstrates the oscillatory characteristics of electrical signals acquired from two ornamental plant types (Epipremnum pinnatum and Philodendron scandens - Family Araceae), using a noninvasive acquisition system. The electrical signal was recorded using Ag/AgCl superficial electrodes inside a Faraday cage. The presence of the oscillatory electric generator was shown using a classical power spectral density. The Lempel and Ziv complexity measurement showed that the plant signal was not noise despite its nonlinear behavior. The oscillatory characteristics of the signal were explained using a simulated electrical model that establishes that for a frequency range from 5 to 15 Hz, the oscillatory characteristic is higher than for other frequency ranges. All results show that non-invasive electrical plant signals can be acquired with improvement of signal-to-noise ratio using a Faraday cage, and a simple electrical model is able to explain the electrical signal being generated. (C) 2010 Elsevier B.V. All rights reserved.

Expression of metallothionein in ameloblastoma. A regulatory molecule?

BACKGROUND: Ameloblastoma is a benign odontogenic tumor, exhibiting local invasiveness and high rate of recurrence. Metallothionein is a protein associated with tumorigenesis, serving as prognostic factor in different neoplasms. We are interested in mechanisms underlying ameloblastoma local invasiveness. Thus, we decided to analyze expression of metallothionein in this tumor. MATERIALS AND METHODS: An immunohistochemical evaluation of metallothionein in ameloblastoma was carried out. As control, we assessed expression of the same molecule in calcifying cystic odontogenic tumor (CCOT), a non-invasive odontogenic neoplasm with ameloblastomatous epithelium. RESULTS: We studied 12 cases of solid/multicystic ameloblastomas. Metallothionein was observed in all samples. This molecule was observed in columnar cells in the periphery and in central polyhedral cells. CCOT (four cases) also showed the presence of metallothionein. Morphometry of stained areas showed that expression of metallothionein in ameloblastoma was significantly higher compared to CCOT (P < 0.0001). CONCLUSIONS: This protein may have an impact on ameloblastoma behavior. Metallothionein would act as a zinc reservoir for important proteases related to ameloblastoma biology, such as MMPs. This protein could also display pro-mitotic and anti-apoptotic features in the tumor. J Oral Pathol Med (2011) 40: 516-519

Matrix metalloproteinases, TIMPs and growth factors regulating ameloblastoma behaviour

Aims: Ameloblastoma is an odontogenic neoplasm with local invasiveness and recurrence. We have previously suggested that growth factors and matrix metalloproteinases (MMPs) influence ameloblastoma invasiveness(1). The aim was to study expression of MMPs, tissue inhibitor of metalloproteinases (TIMPs) and growth factors in ameloblastoma. Methods and results: Thirteen cases of solid/multicystic ameloblastoma were examined. As a control, calcifying cystic odontogenic tumour (CCOT), a non-invasive odontogenic neoplasm with ameloblastomatous epithelium was also studied. Immunohistochemistry detected MMPs, TIMPs and growth factors in ameloblastoma and CCOT. The labelling index (LI) of MMP-9 and TIMP-2 was significantly higher in ameloblastoma compared with CCOT. The LI of epidermal growth factor (EGF), transforming growth factor (TGF)-alpha and epidermal growth factor receptor (EGFR) was also increased in ameloblastoma. This neoplasm showed greater expression of MMPs, TIMPs and growth factors compared with CCOT. We then analysed these molecules in ameloblastoma cells and stroma. Ameloblastoma cells exhibited increased LI of MMP-1, -2 and EGFR. We found a positive correlation between EGF and TIMP-1, and between TGF-alpha and TIMP-2. It is known that signals generated by growth factors are transduced by the ERK pathway. Ameloblastoma stroma exhibited the phosphorylated (activated) form of ERK. Conclusions: These results suggest an interplay involving growth factors MMPs and TIMPs that may contribute to ameloblastoma behaviour. Signals generated by this molecular network would be transduced by ERK 1/2 pathway.

Fluorescence spectroscopy to diagnose hepatic steatosis in a rat model of fatty liver

Steatosis is diagnosed on the basis of the macroscopic aspect of the liver evaluated by the surgeon at the time of organ extraction or by means of a frozen biopsy. In the present study, the applicability of laser-induced fluorescence (LIF) spectroscopy was investigated as a method for the diagnosis of different degrees of steatosis experimentally induced in rats. Rats received a high-lipid diet for different periods of time. The animals were divided into groups according to the degree of induced steatosis diagnosis by histology. The concentration of fat in the liver was correlated with LIF by means of the steatosis fluorescence factor (SFF). The histology classification, according to liver fat concentration was, Severe Steatosis, Moderate Steatosis, Mild Steatosis and Control (no liver steatosis). Fluorescence intensity could be directly correlated with fat content. It was possible to estimate an average of fluorescence intensity variable by means of different confidence intervals (P=95%) for each steatosis group. SFF was significantly higher in the Severe Steatosis group (P < 0.001) compared with the Moderate Steatosis, Mild Steatosis and Control groups. The various degrees of steatosis could be directly correlated with SFF. LIF spectroscopy proved to be a method capable of identifying the degree of hepatic steatosis in this animal model, and has the potential of clinical application for non-invasive evaluation of the degree of steatosis.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.