9 resultados para Multivariate Linkage Analysis

em Repositório da Produção Científica e Intelectual da Unicamp


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There is an increasing rate of papillary thyroid carcinomas that may never progress to cause symptoms or death. Predicting outcome and determining tumour aggressiveness could help diminish the number of patients submitted to aggressive treatments. We aimed to evaluate whether markers of the immune system response and of tumour-associated inflammation could predict outcome of differentiated thyroid cancer (DTC) patients. Retrospective cohort study. We studied 399 consecutive patients, including 325 papillary and 74 follicular thyroid carcinomas. Immune cell markers were evaluated using immunohistochemistry, including tumour-associated macrophages (CD68) and subsets of tumour-infiltrating lymphocytes (TIL), such as CD3, CD4, CD8, CD16, CD20, CD45RO, GRANZYME B, CD69 and CD25. We also investigated the expression of cyclooxygenase 2 (COX2) in tumour cells and the presence of concurrent lymphocytic infiltration characterizing chronic thyroiditis. Concurrent lymphocytic infiltration characterizing chronic thyroiditis was observed in 29% of the cases. Among all the immunological parameters evaluated, only the enrichment of CD8+ lymphocytes (P = 0·001) and expression of COX2 (P =0·01) were associated with recurrence. A multivariate model analysis identified CD8+ TIL/COX2 as independent risk factor for recurrence. A multivariate analysis using Cox's proportional-hazards model adjusted for the presence of concurrent chronic thyroiditis demonstrated that the presence of concurrent chronic thyroiditis had no effect on prognostic prediction mediated by CD8+ TIL and COX2. In conclusion, we suggest the use of a relatively simple pathology tool to help select cases that may benefit of a more aggressive approach sparing the majority of patients from unnecessary procedures.

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Schistosomiasis is a common tropical disease caused by Schistosoma species Schistosomiasis' pathogenesis is known to vary according to the worms' strain. Moreover, high parasitical virulence is directly related to eggs release and granulomatous inflammation in the host's organs. This virulence might be influenced by different classes of molecules, such as lipids. Therefore, better understanding of the metabolic profile of these organisms is necessary, especially for an increased potential of unraveling strain virulence mechanisms and resistance to existing treatments. In this report, direct-infusion electrospray high-resolution mass spectrometry (ESI(+)-HRMS) along with the lipidomic platform were employed to rapidly characterize and differentiate two Brazilian S. mansoni strains (BH and SE) in three stages of their life cycle: eggs, miracidia and cercariae, with samples from experimental animals (Swiss/SPF mice). Furthermore, urine samples of the infected and uninfected mice were analyzed to assess the possibility of direct diagnosis. All samples were differentiated using multivariate data analysis, PCA, which helped electing markers from distinct lipid classes; phospholipids, diacylglycerols and triacylglycerols, for example, clearly presented different intensities in some stages and strains, as well as in urine samples. This indicates that biochemical characterization of S. mansoni may help narrowing-down the investigation of new therapeutic targets according to strain composition and aggressiveness of disease. Interestingly, lipid profile of infected mice urine varies when compared to control samples, indicating that direct diagnosis of schistosomiasis from urine may be feasible.

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Matrix-assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOF MS) has been widely used for the identification and classification of microorganisms based on their proteomic fingerprints. However, the use of MALDI-TOF MS in plant research has been very limited. In the present study, a first protocol is proposed for metabolic fingerprinting by MALDI-TOF MS using three different MALDI matrices with subsequent multivariate data analysis by in-house algorithms implemented in the R environment for the taxonomic classification of plants from different genera, families and orders. By merging the data acquired with different matrices, different ionization modes and using careful algorithms and parameter selection, we demonstrate that a close taxonomic classification can be achieved based on plant metabolic fingerprints, with 92% similarity to the taxonomic classifications found in literature. The present work therefore highlights the great potential of applying MALDI-TOF MS for the taxonomic classification of plants and, furthermore, provides a preliminary foundation for future research.

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Descriptors in multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) are pixels of bidimensional images of chemical structures (drawings), which were used to model the trichomonicidal activities of a series of benzimidazole derivatives. The MIA-QSAR model showed good predictive ability, with r², q² and r val. ext.² of 0.853, 0.519 and 0.778, respectively, which are comparable to the best values obtained by CoMFA e CoMSIA for the same series. A MIA-based analysis was also performed by using images of alphabetic letters with the corresponding numeric ordering as dependent variables, but no correlation was found, supporting that MIA-QSAR is not arbitrary.

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Current guidelines have advised against the performance of (131)I-iodide diagnostic whole body scintigraphy (dxWBS) to minimize the occurrence of stunning, and to guarantee the efficiency of radioiodine therapy (RIT). The aim of the study was to evaluate the impact of stunning on the efficacy of RIT and disease outcome. This retrospective analysis included 208 patients with differentiated thyroid cancer managed according to a same protocol and followed up for 12-159 months (mean 30 ± 69 months). Patients received RIT in doses ranging from 3,700 to 11,100 MBq (100 mCi to 300 mCi). Post-RIT-whole body scintigraphy images were performed 10 days after RIT in all patients. In addition, images were also performed 24-48 hours after therapy in 22 patients. Outcome was classified as no evidence of disease (NED), stable disease (SD) and progressive disease (PD). Thyroid stunning occurred in 40 patients (19.2%), including 26 patients with NED and 14 patients with SD. A multivariate analysis showed no association between disease outcome and the occurrence of stunning (p = 0.3476). The efficacy of RIT and disease outcome do not seem to be related to thyroid stunning.

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To investigate the degree of T2 relaxometry changes over time in groups of patients with familial mesial temporal lobe epilepsy (FMTLE) and asymptomatic relatives. We conducted both cross-sectional and longitudinal analyses of T2 relaxometry with Aftervoxel, an in-house software for medical image visualization. The cross-sectional study included 35 subjects (26 with FMTLE and 9 asymptomatic relatives) and 40 controls; the longitudinal study was composed of 30 subjects (21 with FMTLE and 9 asymptomatic relatives; the mean time interval of MRIs was 4.4 ± 1.5 years) and 16 controls. To increase the size of our groups of patients and relatives, we combined data acquired in 2 scanners (2T and 3T) and obtained z-scores using their respective controls. General linear model on SPSS21® was used for statistical analysis. In the cross-sectional analysis, elevated T2 relaxometry was identified for subjects with seizures and intermediate values for asymptomatic relatives compared to controls. Subjects with MRI signs of hippocampal sclerosis presented elevated T2 relaxometry in the ipsilateral hippocampus, while patients and asymptomatic relatives with normal MRI presented elevated T2 values in the right hippocampus. The longitudinal analysis revealed a significant increase in T2 relaxometry for the ipsilateral hippocampus exclusively in patients with seizures. The longitudinal increase of T2 signal in patients with seizures suggests the existence of an interaction between ongoing seizures and the underlying pathology, causing progressive damage to the hippocampus. The identification of elevated T2 relaxometry in asymptomatic relatives and in patients with normal MRI suggests that genetic factors may be involved in the development of some mild hippocampal abnormalities in FMTLE.

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Hevea brasiliensis (Willd. Ex Adr. Juss.) Muell.-Arg. is the primary source of natural rubber that is native to the Amazon rainforest. The singular properties of natural rubber make it superior to and competitive with synthetic rubber for use in several applications. Here, we performed RNA sequencing (RNA-seq) of H. brasiliensis bark on the Illumina GAIIx platform, which generated 179,326,804 raw reads on the Illumina GAIIx platform. A total of 50,384 contigs that were over 400 bp in size were obtained and subjected to further analyses. A similarity search against the non-redundant (nr) protein database returned 32,018 (63%) positive BLASTx hits. The transcriptome analysis was annotated using the clusters of orthologous groups (COG), gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Pfam databases. A search for putative molecular marker was performed to identify simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). In total, 17,927 SSRs and 404,114 SNPs were detected. Finally, we selected sequences that were identified as belonging to the mevalonate (MVA) and 2-C-methyl-D-erythritol 4-phosphate (MEP) pathways, which are involved in rubber biosynthesis, to validate the SNP markers. A total of 78 SNPs were validated in 36 genotypes of H. brasiliensis. This new dataset represents a powerful information source for rubber tree bark genes and will be an important tool for the development of microsatellites and SNP markers for use in future genetic analyses such as genetic linkage mapping, quantitative trait loci identification, investigations of linkage disequilibrium and marker-assisted selection.

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The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50μgcm(-2).

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Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.