Cytological Characteristics of Postoperative Metastases of Papillary Thyroid Cancer During the Development of Secondary Radioiodine Refractoriness

Radioiodine refractoriness is the main problem in the diagnosis and treatment of papillary thyroid carcinoma. The aim of the study was to investigate the cytological and immunocytochemical changes of thyrocytes in fine-needle aspiration smears of thyroid papillary cancer metastases in the course of the development of secondary radioiodine resistance. A total of 70 postoperative metastases of thyroid papillary cancer (secondary radioiodine refractory metastases, previously responsive to radioiodine, that eventually loses the ability to radioiodine accumulation, radioiodine-avid metastases, primary radioiodine-refractory metastases), immunohistochemical staining of thyroid peroxidase, thyroglobulin, cytokeratin 17 and cytological analysis were performed. Revealing the presence of specific cellular phenotypes and structures in punctuates, a low percentage of thyroid peroxidase and thyroglobulin-positive thyrocytes allows the development of the method of cytological prediction of the radioiodine therapy effectiveness

Isolation and Characterization of Ischemia-Derived Astrocytes (IDAs) with Ability to Transactivate Quiescent Astrocytes

Reactive gliosis involving activation and proliferation of astrocytes and microglia, is a widespread but largely complex and graded glial response to brain injury. Astroglial population has a previously underestimated high heterogeneity with cells differing in their morphology, gene expression profile, and response to injury. Here, we identified a subset of reactive astrocytes isolated from brain focal ischemic lesions that show several atypical characteristics. Ischemia-derived astrocytes (IDAs) were isolated from early ischemic penumbra and core. IDA did not originate from myeloid precursors, butrather from pre-existing local progenitors. Isolated IDA markedly differ from primary astrocytes, as they proliferate in vitro with high cell division rate, show increased migratory ability, have reduced replicative senescence and grow in the presence of macrophages within the limits imposed by the glial scar. Remarkably, IDA produce a conditioned medium that strongly induced activation on quiescent primary astrocytes and potentiated the neuronal death triggered by oxygen-glucose deprivation. When re-implanted into normal rat brains, eGFP-IDA migrated around the injection site and induced focal reactive gliosis. Inhibition of gamma secretases or culture on quiescent primary astrocytes monolayers facilitated IDA differentiation to astrocytes. We propose that IDA represent an undifferentiated, pro-inflammatory, highly replicative and migratory astroglial subtype emerging from the ischemic microenvironment that may contribute to the expansion of reactive gliosis.Fil: Villarreal, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Rosciszewski, Gerardo Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Murta, Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Cadena, María Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Usach, Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; ArgentinaFil: Dodes Traian, Martín Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; ArgentinaFil: Setton, Clara Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; ArgentinaFil: Barbeito, Osvaldo Luis. Instituto Pasteur de Montevideo; UruguayFil: Ramos, Alberto Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; Argentin

Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

Genome-Wide Fine-Mapping Of Diabetic Traits

Type 2 diabetes results from both genes and the environment. Mapping genetic loci in animal models can help identify genes that are involved in type 2 diabetes to better understand the disease. Heterogeneous stock (HS) rats are derived from eight inbred founder strains and maintained in a breeding strategy that minimizes inbreeding. HS rats have a highly recombinant genome, which allows for rapid fine-mapping of complex traits genome-wide. However, this results in a complicated set of relationships between animals that is non-existent in traditional genetic mapping methods. To fine-map traits involved in type 2 diabetes, multiple diabetic phenotypes were collected in 1,038 HS male rats and these animals were genotyped using the Affymetrix 10K SNP array. Following ancestral haplotype reconstruction, a mixed modeling approach was used to identify genetic loci involved in two phenotypes suggestive of diabetes: fasting glucose and glucose area under the curve after a glucose tolerance test. Sibship was used as a random effect in the model to account for the complex family relationships. A genome-wide significant marker interval was detected on chromosome 11 for fasting glucose with a 95% confidence interval of 5.75 Mb. Genome-wide significant marker intervals were also detected on chromosomes 1,3, 10, and 13 for glucose area under the curve, with the average 95% confidence interval for these loci being only 3.15 Mb. A multilocus modeling technique involving resample model averaging was applied to the fasting glucose phenotype. This technique determines how frequently each locus is detected when resampling a portion of the original data-set, thus reducing potential false positives. Multilocus modeling results for fasting glucose coincided with the significant marker interval demonstrated in the mixed modeling approach. Both approaches are effective at detecting significant marker intervals that are expected to be involved in the phenotype of interest with a greater resolution over traditional methods

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure