240 resultados para CORRELATION NETWORKS
Resumo:
Among 112 patients infected only by Plasmodium falciparum, WHO criteria of severity were compared with parasite load assessed by microscopy and quantitative PCR. Clinical severity was significantly correlated with higher parasite load as determined by microscopy (p < 0.001) and by PCR (p < 0.001). Hence, quantitative PCR might be useful to predict outcome.
Resumo:
Extreme prematurity and pregnancy conditions leading to intrauterine growth restriction (IUGR) affect thousands of newborns every year and increase their risk for poor higher order cognitive and social skills at school age. However, little is known about the brain structural basis of these disabilities. To compare the structural integrity of neural circuits between prematurely born controls and children born extreme preterm (EP) or with IUGR at school age, long-ranging and short-ranging connections were noninvasively mapped across cortical hemispheres by connection matrices derived from diffusion tensor tractography. Brain connectivity was modeled along fiber bundles connecting 83 brain regions by a weighted characterization of structural connectivity (SC). EP and IUGR subjects, when compared with controls, had decreased fractional anisotropy-weighted SC (FAw-SC) of cortico-basal ganglia-thalamo-cortical loop connections while cortico-cortical association connections showed both decreased and increased FAw-SC. FAw-SC strength of these connections was associated with poorer socio-cognitive performance in both EP and IUGR children.
Resumo:
AIMS: c-Met is an emerging biomarker in pancreatic ductal adenocarcinoma (PDAC); there is no consensus regarding the immunostaining scoring method for this marker. We aimed to assess the prognostic value of c-Met overexpression in resected PDAC, and to elaborate a robust and reproducible scoring method for c-Met immunostaining in this setting. METHODS AND RESULTS: c-Met immunostaining was graded according to the validated MetMab score, a classic visual scale combining surface and intensity (SI score), or a simplified score (high c-Met: ≥20% of tumour cells with strong membranous staining), in stage I-II PDAC. A computer-assisted classification method (Aperio software) was developed. Clinicopathological parameters were correlated with disease-free survival (DFS) and overall survival(OS). One hundred and forty-nine patients were analysed retrospectively in a two-step process. Thirty-seven samples (whole slides) were analysed as a pre-run test. Reproducibility values were optimal with the simplified score (kappa = 0.773); high c-Met expression (7/37) was associated with shorter DFS [hazard ratio (HR) 3.456, P = 0.0036] and OS (HR 4.257, P = 0.0004). c-Met expression was concordant on whole slides and tissue microarrays in 87.9% of samples, and quantifiable with a specific computer-assisted algorithm. In the whole cohort (n = 131), patients with c-Met(high) tumours (36/131) had significantly shorter DFS (9.3 versus 20.0 months, HR 2.165, P = 0.0005) and OS (18.2 versus 35.0 months, HR 1.832, P = 0.0098) in univariate and multivariate analysis. CONCLUSIONS: Simplified c-Met expression is an independent prognostic marker in stage I-II PDAC that may help to identify patients with a high risk of tumour relapse and poor survival.
Resumo:
Animal societies rely on interactions between group members to effectively communicate and coordinate their actions. To date, the transmission properties of interaction networks formed by direct physical contacts have been extensively studied for many animal societies and in all cases found to inhibit spreading. Such direct interactions do not, however, represent the only viable pathways. When spreading agents can persist in the environment, indirect transmission via 'same-place, different-time' spatial coincidences becomes possible. Previous studies have neglected these indirect pathways and their role in transmission. Here, we use rock ant colonies, a model social species whose flat nest geometry, coupled with individually tagged workers, allowed us to build temporally and spatially explicit interaction networks in which edges represent either direct physical contacts or indirect spatial coincidences. We show how the addition of indirect pathways allows the network to enhance or inhibit the spreading of different types of agent. This dual-functionality arises from an interplay between the interaction-strength distribution generated by the ants' movement and environmental decay characteristics of the spreading agent. These findings offer a general mechanism for understanding how interaction patterns might be tuned in animal societies to control the simultaneous transmission of harmful and beneficial agents.
Resumo:
PURPOSE: Because desmoid tumors exhibit an unpredictable clinical course, translational research is crucial to identify the predictive factors of progression in addition to the clinical parameters. The main issue is to detect patients who are at a higher risk of progression. The aim of this work was to identify molecular markers that can predict progression-free survival (PFS). EXPERIMENTAL DESIGN: Gene-expression screening was conducted on 115 available independent untreated primary desmoid tumors using cDNA microarray. We established a prognostic gene-expression signature composed of 36 genes. To test robustness, we randomly generated 1,000 36-gene signatures and compared their outcome association to our define 36-genes molecular signature and we calculated positive predictive value (PPV) and negative predictive value (NPV). RESULTS: Multivariate analysis showed that our molecular signature had a significant impact on PFS while no clinical factor had any prognostic value. Among the 1,000 random signatures generated, 56.7% were significant and none was more significant than our 36-gene molecular signature. PPV and NPV were high (75.58% and 81.82%, respectively). Finally, the top two genes downregulated in no-recurrence were FECH and STOML2 and the top gene upregulated in no-recurrence was TRIP6. CONCLUSIONS: By analyzing expression profiles, we have identified a gene-expression signature that is able to predict PFS. This tool may be useful for prospective clinical studies. Clin Cancer Res; 21(18); 4194-200. ©2015 AACR.