Role of intraoperative neuromonitoring of the recurrent laryngeal nerve in high-risk thyroid surgery.

BACKGROUND: Thyroid surgeries associated with an elevated incidence of recurrent laryngeal nerve (RLN) injury are considered high-risk thyroidectomies. These high-risk operations include surgery for thyroid cancer, Graves disease, and recurrent goitre. In addition, the size of the goitre is an important risk factor for RLN injury. OBJECTIVE: In our retrospective study, we tried to evaluate the role of intraoperative neuromonitoring of RLN specifically in high-risk thyroid surgery with 421 nerves at risk. METHOD: Thyroid surgeries in our study were done by experienced surgeons in a high-volume provider centre.The overall percentage of RLN injury was 8.8% in the monitored group in comparison with 9.1% in the unmonitored group. The percentage of permanent nerve palsy in the monitored group was 3.9% of nerves at risk in comparison with 3.8% in the unmonitored group. No statistically significant difference was found between groups. CONCLUSION: Routine visual identification of the nerve by meticulous dissection is the best method to avoid RLN injury. The benefit of RLN neuromonitoring could be further assessed by performing a multicentre prospective study to compare the role of RLN neuromonitoring in high-risk thyroid surgeries.

Analyzing stochastic transcription to elucidate the nucleoid's organization.

BACKGROUND: The processes of gene transcription, translation, as well as the reactions taking place between gene products, are subject to stochastic fluctuations. These stochastic events are being increasingly examined as it emerges that they can be crucial in the cell's survival. In a previous study we had examined the transcription patterns of two bacterial species (Escherichia coli and Bacillus subtilis) to elucidate the nucleoid's organization. The basic idea is that genes that share transcription patterns, must share some sort of spatial relationship, even if they are not close to each other on the chromosome. We had found that picking any gene at random, its transcription will be correlated with genes at well-defined short - as well as long-range distances, leaving the explanation of the latter an open question. In this paper we study the transcription correlations when the only transcription taking place is stochastic, in other words, no active or "deterministic" transcription takes place. To this purpose we use transcription data of Sinorhizobium meliloti. RESULTS: Even when only stochastic transcription takes place, the co-expression of genes varies as a function of the distance between genes: we observe again the short-range as well as the regular, long-range correlation patterns. CONCLUSION: We explain these latter with a model based on the physical constraints acting on the DNA, forcing it into a conformation of groups of a few successive large and transcribed loops, which are evenly spaced along the chromosome and separated by small, non-transcribed loops. We discuss the question about the link between shared transcription patterns and physiological relationship and come to the conclusion that when genes are distantly placed along the chromosome, the transcription correlation does not imply a physiological relationship.

Comments on selected fundamental aspects of microarray analysis.

Microarrays are becoming a ubiquitous tool of research in life sciences. However, the working principles of microarray-based methodologies are often misunderstood or apparently ignored by the researchers who actually perform and interpret experiments. This in turn seems to lead to a common over-expectation regarding the explanatory and/or knowledge-generating power of microarray analyses. In this note we intend to explain basic principles of five (5) major groups of analytical techniques used in studies of microarray data and their interpretation: the principal component analysis (PCA), the independent component analysis (ICA), the t-test, the analysis of variance (ANOVA), and self organizing maps (SOM). We discuss answers to selected practical questions related to the analysis of microarray data. We also take a closer look at the experimental setup and the rules, which have to be observed in order to exploit microarrays efficiently. Finally, we discuss in detail the scope and limitations of microarray-based methods. We emphasize the fact that no amount of statistical analysis can compensate for (or replace) a well thought through experimental setup. We conclude that microarrays are indeed useful tools in life sciences but by no means should they be expected to generate complete answers to complex biological questions. We argue that even well posed questions, formulated within a microarray-specific terminology, cannot be completely answered with the use of microarray analyses alone.

Decoding the nucleoid organisation of Bacillus subtilis and Escherichia coli through gene expression data.

BACKGROUND: Although the organisation of the bacterial chromosome is an area of active research, little is known yet on that subject. The difficulty lies in the fact that the system is dynamic and difficult to observe directly. The advent of massive hybridisation techniques opens the way to further studies of the chromosomal structure because the genes that are co-expressed, as identified by microarray experiments, probably share some spatial relationship. The use of several independent sets of gene expression data should make it possible to obtain an exhaustive view of the genes co-expression and thus a more accurate image of the structure of the chromosome. RESULTS: For both Bacillus subtilis and Escherichia coli the co-expression of genes varies as a function of the distance between the genes along the chromosome. The long-range correlations are surprising: the changes in the level of expression of any gene are correlated (positively or negatively) to the changes in the expression level of other genes located at well-defined long-range distances. This property is true for all the genes, regardless of their localisation on the chromosome. We also found short-range correlations, which suggest that the location of these co-expressed genes corresponds to DNA turns on the nucleoid surface (14-16 genes). CONCLUSION: The long-range correlations do not correspond to the domains so far identified in the nucleoid. We explain our results by a model of the nucleoid solenoid structure based on two types of spirals (short and long). The long spirals are uncoiled expressed DNA while the short ones correspond to coiled unexpressed DNA.

Familial correlations and inter-relationships of four asthma-associated quantitative phenotypes in 320 French EGEA families ascertained through asthmatic probands.

Asthma is a complex disease, associated with biological and physiological phenotypes including immunoglobulin E (IgE) levels, sum of positive skin prick tests to allergens (SPTQ), eosinophil counts (EOS) and percent predicted forced expiratory volume in 1 s (%FEV1). We investigated the patterns of familial correlations and the inter-relationships of these four quantitative phenotypes, using the general class D regressive model, in 320 French EGEA nuclear families ascertained through 204 offspring (set A) and 116 parents (set B). Familial correlations of IgE and SPTQ were consistent with a model including no spouse correlation and equal parent-offspring and sib-sib correlations (rhoPO = rhoSS = 0.25 for IgE and 0.15 for SPTQ), this model being compatible with an additive polygenic model in the whole sample and the two family subsets A and B. Different patterns of familial correlations of EOS and %FEV1 were observed in these two sets. In set A, the best fitting model included no spouse correlation and equality of parent-offspring and sib-sib correlations (rhoPO = rhoSS = 0.14 for EOS and 0.23 for %FEV1). In set B, EOS had only a significant rhoSS of 0.28, while %FEV1 had significant rhoMO of 0.28 and rhoSS of 0.16. Analysis of shared familial determinants between these phenotypes indicated an overlap of at most 30% in rhoFO for IgE and SPTQ and in both rhoFO and rhoMO for IgE and EOS, while determinants of %FEV1 and atopy-related phenotypes appear distinct. These results may have implications for further linkage and association studies with genetic markers.

The operons, a criterion to compare the reliability of transcriptome analysis tools: ICA is more reliable than ANOVA, PLS and PCA.

The number of statistical tools used to analyze transcriptome data is continuously increasing and no one, definitive method has so far emerged. There is a need for comparison and a number of different approaches has been taken to evaluate the effectiveness of the different statistical tools available for microarray analyses. In this paper, we describe a simple and efficient protocol to compare the reliability of different statistical tools available for microarray analyses. It exploits the fact that genes within an operon exhibit the same expression patterns. In order to compare the tools, the genes are ranked according to the most relevant criterion for each tool; for each tool we look at the number of different operons represented within the first twenty genes detected. We then look at the size of the interval within which we find the most significant genes belonging to each operon in question. This allows us to define and estimate the sensitivity and accuracy of each statistical tool. We have compared four statistical tools using Bacillus subtilis expression data: the analysis of variance (ANOVA), the principal component analysis (PCA), the independent component analysis (ICA) and the partial least square regression (PLS). Our results show ICA to be the most sensitive and accurate of the tools tested. In this article, we have used the protocol to compare statistical tools applied to the analysis of differential gene expression. However, it can also be applied without modification to compare the statistical tools developed for other types of transcriptome analyses, like the study of gene co-expression.