Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Independent component analysis applied to raman spectra for classification of in vitro human coronary arteries

Optical diagnostic methods, such as near-infrared Raman spectroscopy allow quantification and evaluation of human affecting diseases, which could be useful in identifying and diagnosing atherosclerosis in coronary arteries. The goal of the present work is to apply Independent Component Analysis (ICA) for data reduction and feature extraction of Raman spectra and to perform the Mahalanobis distance for group classification according to histopathology, obtaining feasible diagnostic information to detect atheromatous plaque. An 830nm Ti:sapphire laser pumped by an argon laser provides near-infrared excitation. A spectrograph disperses light scattered from arterial tissues over a liquid-nitrogen cooled CCD to detect the Raman spectra. A total of 111 spectra from arterial fragments were utilized.

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

Agreement for Detecting Glaucoma Progression with the GDx Guided Progression Analysis, Automated Perimetry, and Optic Disc Photography

Purpose: To evaluate the ability of the GDx Variable Corneal Compensation (VCC) Guided Progression Analysis (GPA) software for detecting glaucomatous progression. Design: Observational cohort study. Participants: The study included 453 eyes from 252 individuals followed for an average of 46 +/- 14 months as part of the Diagnostic Innovations in Glaucoma Study. At baseline, 29% of the eyes were classified as glaucomatous, 67% of the eyes were classified as suspects, and 5% of the eyes were classified as healthy. Methods: Images were obtained annually with the GDx VCC and analyzed for progression using the Fast Mode of the GDx GPA software. Progression using conventional methods was determined by the GPA software for standard automated achromatic perimetry (SAP) and by masked assessment of optic disc stereophotographs by expert graders. Main Outcome Measures: Sensitivity, specificity, and likelihood ratios (LRs) for detection of glaucoma progression using the GDx GPA were calculated with SAP and optic disc stereophotographs used as reference standards. Agreement among the different methods was reported using the AC(1) coefficient. Results: Thirty-four of the 431 glaucoma and glaucoma suspect eyes (8%) showed progression by SAP or optic disc stereophotographs. The GDx GPA detected 17 of these eyes for a sensitivity of 50%. Fourteen eyes showed progression only by the GDx GPA with a specificity of 96%. Positive and negative LRs were 12.5 and 0.5, respectively. None of the healthy eyes showed progression by the GDx GPA, with a specificity of 100% in this group. Inter-method agreement (AC1 coefficient and 95% confidence intervals) for non-progressing and progressing eyes was 0.96 (0.94-0.97) and 0.44 (0.28-0.61), respectively. Conclusions: The GDx GPA detected glaucoma progression in a significant number of cases showing progression by conventional methods, with high specificity and high positive LRs. Estimates of the accuracy for detecting progression suggest that the GDx GPA could be used to complement clinical evaluation in the detection of longitudinal change in glaucoma. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references. Ophthalmology 2010; 117: 462-470 (C) 2010 by the American Academy of Ophthalmology.

Relationship between Optical Coherence Tomography, Pattern Electroretinogram and Automated Perimetry in Eyes with Temporal Hemianopia from Chiasmal Compression

PURPOSE. To evaluate the relationship between pattern electroretinogram (PERG) amplitude, macular and retinal nerve fiber layer (RNFL) thickness by optical coherence tomography (OCT), and visual field (VF) loss on standard automated perimetry (SAP) in eyes with temporal hemianopia from chiasmal compression. METHODS. Forty-one eyes from 41 patients with permanent temporal VF defects from chiasmal compression and 41 healthy subjects underwent transient full-field and hemifield (temporal or nasal) stimulation PERG, SAP and time domain-OCT macular and RNFL thickness measurements. Comparisons were made using Student`s t-test. Deviation from normal VF sensitivity for the central 18 of VF was expressed in 1/Lambert units. Correlations between measurements were verified by linear regression analysis. RESULTS. PERG and OCT measurements were significantly lower in eyes with temporal hemianopia than in normal eyes. A significant correlation was found between VF sensitivity loss and fullfield or nasal, but not temporal, hemifield PERG amplitude. Likewise a significant correlation was found between VF sensitivity loss and most OCT parameters. No significant correlation was observed between OCT and PERG parameters, except for nasal hemifield amplitude. A significant correlation was observed between several macular and RNFL thickness parameters. CONCLUSIONS. In patients with chiasmal compression, PERG amplitude and OCT thickness measurements were significant related to VF loss, but not to each other. OCT and PERG quantify neuronal loss differently, but both technologies are useful in understanding structure-function relationship in patients with chiasmal compression. (ClinicalTrials.gov number, NCT00553761.) (Invest Ophthalmol Vis Sci. 2009; 50: 3535-3541) DOI:10.1167/iovs.08-3093

Comparison of three systems of classification in predicting the outcome of diabetic foot ulcers in a Brazilian population

Objective: The aim was to compare there ulcer classification systems as predictors of the outcome of diabetic foot ulcers; the Wagner, the University of Texas (UT) and the size (area, depth), sepsis, arteriopathy, denervation system (S(AD)SAD) systems in specialist clinic in Brazil. Methods: Ulcer area, depth, appearance, infection and associated ischaemia and neuropathy were recorded in a consecutive series of 94 subjects. A novel score, the S(AD)SAD score, was derived from the sum of individual items of the S(AD)SAD system, and was evaluated. Follow-up was for at least 6 months. The primary outcome measure was the incidence of healing. Results: Mean age was 57.6 years; 57 (60.6%) were made. Forty-eight ulcers (51.1%) healed without surgery; 11 (12.2%) subjects underwent minor amputation. Significant differences in terms of healing were observed for depth (P = 0.002), infection (P = 0.006) and denervation (P = 0.002) using the S(AD)SAD system, for UT grade (P = 0.002) and stage (P = 0.032) and for Wagner grades (P = 0.002). Ulcers with an S(AD)SAD score of <= 9 (total possible 15) were 7.6 times more likely to heal than scores >= 10 (P < 0.001). Conclusions: All three systems predicted ulcer outcome. The S(AD)SAD score of ulcer severity could represent a useful addition to routine clinical practice. The association between outcome and ulcer depth confirms earlier reports. The association with infection was stronger than that reported from the centres in Europe or North America. The very strong association with neuropathy has only previously been observed in Tanzania. Studies designed to compare the outcome in different countries should adopt systems of classification, which are valid for the populations studied.

Transgendering shojo shosetsu: Girls' inter-text/sex-uality

This essay recognises the power of reading and intertextuality (embedding texts within texts) in fiction targeted at girls and young women.

Meningiomas and schwannomas: Molecular subgroup classification found by expression arrays

Microarray gene expression profiling is a high-throughput system used to identify differentially expressed genes and regulation patterns, and to discover new tumor markers. As the molecular pathogenesis of meningiomas and schwannomas, characterized by NF2 gene alterations, remains unclear and suitable molecular targets need to be identified, we used low density cDNA microarrays to establish expression patterns of 96 cancer-related genes on 23 schwannomas, 42 meningiomas and 3 normal cerebral meninges. We also performed a mutational analysis of the NF2 gene (PCR, dHPLC, Sequencing and MLPA), a search for 22q LOH and an analysis of gene silencing by promoter hypermethylation (MS-MLPA). Results showed a high frequency of NF2 gene mutations (40%), increased 22q LOH as aggressiveness increased, frequent losses and gains by MLPA in benign meningiomas, and gene expression silencing by hypermethylation. Array analysis showed decreased expression of 7 genes in meningiomas. Unsupervised analyses identified 2 molecular subgroups for both meningiomas and schwannomas showing 38 and 20 differentially expressed genes, respectively, and 19 genes differentially expressed between the two tumor types. These findings provide a molecular subgroup classification for meningiomas and schwannomas with possible implications for clinical practice.