Assessment of a new MRI classification of spinal stenosis based on cross-sectional morphology of the dural sac : FM2

Introduction: Quantitative measures of degree of lumbar spinal stenosis (LSS) such as antero-posterior diameter of the canal or dural sac cross sectional area vary widely and do not correlate with clinical symptoms or results of surgical decompression. In an effort to improve quantification of stenosis we have developed a grading system based on the morphology of the dural sac and its contents as seen on T2 axial images. The grading comprises seven categories ranging form normal to the most severe stenosis and takes into account the ratio of rootlet/CSF content. Material and methods: Fifty T2 axial MRI images taken at disc level from twenty seven symptomatic lumbar spinal stenosis patients who underwent decompressive surgery were classified into seven categories by five observers and reclassified 2 weeks later by the same investigators. Intra- and inter-observer reliability of the classification were assessed using Cohen's and Fleiss' kappa statistics, respectively. Results: Generally, the morphology grading system itself was well adopted by the observers. Its success in application is strongly influenced by the identification of the dural sac. The average intraobserver Cohen's kappa was 0.53 ± 0.2. The inter-observer Fleiss' kappa was 0.38 ± 0.02 in the first rating and 0.3 ± 0.03 in the second rating repeated after two weeks. Discussion: In this attempt, the teaching of the observers was limited to an introduction to the general idea of the morphology grading system and one example MRI image per category. The identification of the dimension of the dural sac may be a difficult issue in absence of complete T1 T2 MRI image series as it was the case here. The similarity of the CSF to possibly present fat on T2 images was the main reason of mismatch in the assignment of the cases to a category. The Fleiss correlation factors of the five observers are fair and the proposed morphology grading system is promising.

On the efficiency and sensitivity of a pyramidal classification algorithm

In this paper we propose a Pyramidal Classification Algorithm,which together with an appropriate aggregation index producesan indexed pseudo-hierarchy (in the strict sense) withoutinversions nor crossings. The computer implementation of thealgorithm makes it possible to carry out some simulation testsby Monte Carlo methods in order to study the efficiency andsensitivity of the pyramidal methods of the Maximum, Minimumand UPGMA. The results shown in this paper may help to choosebetween the three classification methods proposed, in order toobtain the classification that best fits the original structureof the population, provided we have an a priori informationconcerning this structure.

Peroxisome proliferator-activated receptors: finding the orphan a home.

The peroxisome proliferator-activated receptor (PPAR) is a member of the steroid hormone receptor superfamily and is activated by a variety of fibrate hypolipidaemic drugs and non-genotoxic rodent hepatocarcinogens that are collectively termed peroxisome proliferators. A key marker of peroxisome proliferator action is the peroxisomal enzyme acyl CoA oxidase, which is elevated about ten fold in the livers of treated rodents. Additional peroxisome proliferator responsive genes include other peroxisomal beta-oxidation enzymes and members of the cytochrome P450 IVA family. A peroxisome proliferator response element (PPRE), consisting of an almost perfect direct repeat of the sequence TGACCT spaced by a single base pair, has been identified in the upstream regulatory sequences of each of these genes. The retinoid X receptor (RXR) forms a heterodimer with PPAR and binds to the PPRE. Furthermore, the RXR ligand, 9-cis retinoic acid, enhances PPAR action. Retinoids may therefore modulate the action of peroxisome proliferators and PPAR may interfere with retinoid action, perhaps providing one mechanism to explain the toxicity of peroxisome proliferators. Interestingly, a variety of fatty acids can activate PPAR supporting the suggestion that fatty acids, or their acyl CoA derivatives, may be the natural ligands of PPAR and that the physiological role of PPAR is to regulate fatty acid homeostasis. Taken together, the discovery of PPAR has opened up new opportunities in understanding how lipid homeostasis is regulated, how the fibrate hypolipidaemic drugs may act and should lead to improvements in the assessment of human risk from peroxisome proliferators based upon a better understanding of their mechanism of action.

Classification of cell death: recommendations of the Nomenclature Committee on Cell Death 2009

Different types of cell death are often defined by morphological criteria, without a clear reference to precise biochemical mechanisms. The Nomenclature Committee on Cell Death (NCCD) proposes unified criteria for the definition of cell death and of its different morphologies, while formulating several caveats against the misuse of words and concepts that slow down progress in the area of cell death research. Authors, reviewers and editors of scientific periodicals are invited to abandon expressions like 'percentage apoptosis' and to replace them with more accurate descriptions of the biochemical and cellular parameters that are actually measured. Moreover, at the present stage, it should be accepted that caspase-independent mechanisms can cooperate with (or substitute for) caspases in the execution of lethal signaling pathways and that 'autophagic cell death' is a type of cell death occurring together with (but not necessarily by) autophagic vacuolization. This study details the 2009 recommendations of the NCCD on the use of cell death-related terminology including 'entosis', 'mitotic catastrophe', 'necrosis', 'necroptosis' and 'pyroptosis'.

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Inactivation of Notch 1 in immature thymocytes does not perturb CD4 or CD8T cell development.

Notch proteins influence cell-fate decisions in many developing systems. Several gain-of-function studies have suggested a critical role for Notch 1 signaling in CD4-CD8 lineage commitment, maturation and survival in the thymus. However, we show here that tissue-specific inactivation of the gene encoding Notch 1 in immature (CD25+CD44-)T cell precursors does not affect subsequent thymocyte development. Neither steady-state numbers nor the rate of production of CD4+ and CD8+ mature thymocytes is perturbed in the absence of Notch 1. In addition, Notch 1-deficient thymocytes are normally sensitive to spontaneous or glucocorticoid-induced apoptosis. In contrast to earlier reports, these data formally exclude an essential role for Notch 1 in CD4-CD8 lineage commitment, maturation or survival.

Saving our science from ourselves: the plight of biological classification

Saving our science from ourselves: the plight of biological classification. Biological classification ( nomenclature, taxonomy, and systematics) is being sold short. The desire for new technologies, faster and cheaper taxonomic descriptions, identifications, and revisions is symptomatic of a lack of appreciation and understanding of classification. The problem of gadget-driven science, a lack of best practice and the inability to accept classification as a descriptive and empirical science are discussed. The worst cases scenario is a future in which classifications are purely artificial and uninformative.

A data-dependent skeleton estimate and a scale-sensitive dimension for classification

The classical binary classification problem is investigatedwhen it is known in advance that the posterior probability function(or regression function) belongs to some class of functions. We introduceand analyze a method which effectively exploits this knowledge. The methodis based on minimizing the empirical risk over a carefully selected``skeleton'' of the class of regression functions. The skeleton is acovering of the class based on a data--dependent metric, especiallyfitted for classification. A new scale--sensitive dimension isintroduced which is more useful for the studied classification problemthan other, previously defined, dimension measures. This fact isdemonstrated by performance bounds for the skeleton estimate in termsof the new dimension.

In susceptible mice, Leishmania major induce very rapid interleukin-4 production by CD4+ T cells which are NK1.1-.

Susceptibility of BALB/c mice to infection with Leishmania major is associated with a T helper type 2 (Th2) response. Since interleukin-4 (IL-4) is critically required early for Th2 cell development, the kinetics of IL-4 mRNA expression was compared in susceptible and resistant mice during the first days of infection. In contrast to resistant mice, susceptible mice exhibited a peak of IL-4 mRNA in their spleens 90 min after i.v. injection of parasites and in lymph nodes 16 h after s.c. injection. IL-12 and interferon-gamma (IFN-gamma) down-regulated this early peak of IL-4 mRNA; the effect of IL-12 was IFN-gamma dependent. Treatment of resistant C57BL/6 mice with anti-IFN-gamma allowed the expression of this early IL-4 response to L. major. The increased IL-4 mRNA expression occurred in V beta 8, 7, 2- CD4+ cells in BALB/c mice and NK1.1- CD4+ cells in anti-IFN-gamma treated C57BL/6 mice. These results show that the NK1.1+ CD4+ cells, responsible for the rapid burst of IL-4 production after i.v. injection of anti-CD3, do not contribute to the early IL-4 response to L. major.

Artifact-free coronary magnetic resonance angiography and coronary vessel wall imaging in the presence of a new, metallic, coronary magnetic resonance imaging stent.

BACKGROUND: Coronary in-stent restenosis cannot be directly assessed by magnetic resonance angiography (MRA) because of the local signal void of currently used stainless steel stents. The aim of this study was to investigate the potential of a new, dedicated, coronary MR imaging (MRI) stent for artifact-free, coronary MRA and in-stent lumen and vessel wall visualization. METHODS AND RESULTS: Fifteen prototype stents were deployed in coronary arteries of 15 healthy swine and investigated with a double-oblique, navigator-gated, free-breathing, T2-prepared, 3D cartesian gradient-echo sequence; a T2-prepared, 3D spiral gradient-echo sequence; and a T2-prepared, 3D steady-state, free-precession coronary MRA sequence. Furthermore, black-blood vessel wall imaging by a dual-inversion-recovery, turbo spin-echo sequence was performed. Artifacts of the stented vessel segment and signal intensities of the coronary vessel lumen inside and outside the stent were assessed. With all investigated sequences, the vessel lumen and wall could be visualized without artifacts, including the stented vessel segment. No signal intensity alterations inside the stent when compared with the vessel lumen outside the stent were found. CONCLUSIONS: The new, coronary MRI stent allows for completely artifact-free coronary MRA and vessel wall imaging.

Natural classification of knots

The principal objective of the knot theory is to provide a simple way of classifying and ordering all the knot types. Here, we propose a natural classification of knots based on their intrinsic position in the knot space that is defined by the set of knots to which a given knot can be converted by individual intersegmental passages. In addition, we characterize various knots using a set of simple quantum numbers that can be determined upon inspection of minimal crossing diagram of a knot. These numbers include: crossing number; average three-dimensional writhe; number of topological domains; and the average relaxation value