Somatosensory Evoked Potentials suppression due to remifentanil during spinal operations; a prospective clinical study

<p>Abstract</p> <p>Background</p> <p>Somatosensory evoked potentials (SSEP) are being used for the investigation and monitoring of the integrity of neural pathways during surgical procedures. Intraoperative neurophysiologic monitoring is affected by the type of anesthetic agents. Remifentanil is supposed to produce minimal or no changes in SSEP amplitude and latency. This study aims to investigate whether high doses of remifentanil influence the SSEP during spinal surgery under total intravenous anesthesia.</p> <p>Methods</p> <p>Ten patients underwent spinal surgery. Anesthesia was induced with propofol (2 mg/Kg), fentanyl (2 mcg/Kg) and a single dose of cis-atracurium (0.15 mg/Kg), followed by infusion of 0.8 mcg/kg/min of remifentanil and propofol (30-50 mcg/kg/min). The depth of anesthesia was monitored by Bispectral Index (BIS) and an adequate level (40-50) of anesthesia was maintained. Somatosensory evoked potentials (SSEPs) were recorded intraoperatively from the tibial nerve (P37) 15 min before initiation of remifentanil infusion. Data were analysed over that period.</p> <p>Results</p> <p>Remifentanil induced prolongation of the tibial SSEP latency which however was not significant (p > 0.05). The suppression of the amplitude was significant (p < 0.001), varying from 20-80% with this decrease being time related.</p> <p>Conclusion</p> <p>Remifentanil in high doses induces significant changes in SSEP components that should be taken under consideration during intraoperative neuromonitoring.</p

Automated detection of regions of interest for tissue microarray experiments: an image texture analysis

BACKGROUND: Recent research with tissue microarrays led to a rapid progress toward quantifying the expressions of large sets of biomarkers in normal and diseased tissue. However, standard procedures for sampling tissue for molecular profiling have not yet been established. METHODS: This study presents a high throughput analysis of texture heterogeneity on breast tissue images for the purpose of identifying regions of interest in the tissue for molecular profiling via tissue microarray technology. Image texture of breast histology slides was described in terms of three parameters: the percentage of area occupied in an image block by chromatin (B), percentage occupied by stroma-like regions (P), and a statistical heterogeneity index H commonly used in image analysis. Texture parameters were defined and computed for each of the thousands of image blocks in our dataset using both the gray scale and color segmentation. The image blocks were then classified into three categories using the texture feature parameters in a novel statistical learning algorithm. These categories are as follows: image blocks specific to normal breast tissue, blocks specific to cancerous tissue, and those image blocks that are non-specific to normal and disease states. RESULTS: Gray scale and color segmentation techniques led to identification of same regions in histology slides as cancer-specific. Moreover the image blocks identified as cancer-specific belonged to those cell crowded regions in whole section image slides that were marked by two pathologists as regions of interest for further histological studies. CONCLUSION: These results indicate the high efficiency of our automated method for identifying pathologic regions of interest on histology slides. Automation of critical region identification will help minimize the inter-rater variability among different raters (pathologists) as hundreds of tumors that are used to develop an array have typically been evaluated (graded) by different pathologists. The region of interest information gathered from the whole section images will guide the excision of tissue for constructing tissue microarrays and for high throughput profiling of global gene expression

The Efficiency of the Human CD8+ T Cell Response: How Should We Quantify It, What Determines It, and Does It Matter?

Multidisciplinary techniques, in particular the combination of theoretical and experimental immunology, can address questions about human immunity that cannot be answered by other means. From the turnover of virus-infected cells in vivo, to rates of thymic production and HLA class I epitope prediction, theoretical techniques provide a unique insight to supplement experimental approaches. Here we present our opinion, with examples, of some of the ways in which mathematics has contributed in our field of interest: the efficiency of the human CD8+ T cell response to persistent viruses

Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.National Institutes of Health (U.S.) (Grant GM081871

Rodent models of focal cerebral ischemia: procedural pitfalls and translational problems

Rodent models of focal cerebral ischemia are essential tools in experimental stroke research. They have added tremendously to our understanding of injury mechanisms in stroke and have helped to identify potential therapeutic targets. A plethora of substances, however, in particular an overwhelming number of putative neuroprotective agents, have been shown to be effective in preclinical stroke research, but have failed in clinical trials. A lot of factors may have contributed to this failure of translation from bench to bedside. Often, deficits in the quality of experimental stroke research seem to be involved. In this article, we review the commonest rodent models of focal cerebral ischemia - middle cerebral artery occlusion, photothrombosis, and embolic stroke models - with their respective advantages and problems, and we address the issue of quality in preclinical stroke modeling as well as potential reasons for translational failure