Galectin-3. The impact on the clinical management of patients with thyroid nodules and future perspectives

Galectins (S-type lectins) are an evolutionarily-conserved family of lectin molecules, which can be expressed intracellularly and in the extracellular matrix, as well. Galectins bind β-galactose-containing glycoconjugates and are functionally active in converting glycan-related information into cell biological programs. Altered glycosylation notably occurring in cancer cells and expression of specific galectins provide, indeed, a fashionable mechanism of molecular interactions able to regulate several tumor relevant functions, among which are cell adhesion and migration, cell differentiation, gene transcription and RNA splicing, cell cycle and apoptosis. Furthermore, several galectin molecules also play a role in regulating the immune response. These functions are strongly dependent on the cell context, in which specific galectins and related glyco-ligands are expressed. Thyroid cancer likely represents the paradigmatic tumor model in which experimental studies on galectins' glycobiology, in particular on galectin-3 expression and function, contributed greatly to the improvement of cancer diagnosis. The discovery of a restricted expression of galectin-3 in well-differentiated thyroid carcinomas (WDTC), compared to normal and benign thyroid conditions, contributed also to promoting preclinical studies aimed at exploring new strategies for imaging thyroid cancer in vivo based on galectin-3 immuno-targeting. Results derived from these recent experimental studies promise a further improvement of both thyroid cancer diagnosis and therapy in the near future. In this review, the biological role of galectin-3 expression in thyroid cancer, the validation and translation to a clinical setting of a galectin-3 test method for the preoperative characterization of thyroid nodules and a galectin-3-based immuno-positron emission tomography (immuno-PET) imaging of thyroid cancer in vivo are presented and discussed

A Genotypic-oriented View of CFTR Genetics Highlights Specific Mutational Patterns Underlying Clinical Macro-categories of Cystic Fibrosis.

Cystic Fibrosis (CF) is a monogenic disease caused by mutations of the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) gene. The genotype-phenotype relationship in this disease is still unclear, and diagnostic, prognostic and therapeutic challenges persist. We enrolled 610 patients with different forms of CF and studied them from a clinical, biochemical, microbiological and genetic point of view. Overall, 125 different mutated alleles (11 of which with novel mutations and 10 of which complex) and 225 genotypes were found. A strong correlation between mutational patterns at the genotypic level and phenotypic macro-categories emerged. This specificity appears to be largely dependent on rare and individual mutations, as well as on the varying prevalence of common alleles in different clinical macro-categories. However, 19 genotypes appeared to underlie different clinical forms of the disease. The dissection of the pathway from the CFTR mutated genotype to the clinical phenotype allowed to identify at least two components of the variability usually found in the genotype - phenotype relationship. One component seems to depend on the genetic variation of CFTR, the other component on the cumulative effect of variations in other genes and cellular pathways independent from CFTR. The experimental dissection of the overall biological CFTR pathway appears to be a powerful approach for a better comprehension of the genotype - phenotype relationship. However, a change from an allele-oriented to a genotypic-oriented view of CFTR genetics is mandatory, as well as a better assessment of sources of variability within the CFTR pathway

Analysis of the human diseasome reveals phenotype modules across common, genetic, and infectious diseases

Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text- mining approach to identify the phenotypes (signs and symptoms) associated with over 8,000 diseases. We demonstrate that our method generates phenotypes that correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that share signs and symptoms cluster together, and we use this network to identify phenotypic disease modules

Harnessing rare category trinity for complex data

In the era of big data, we are inundated with the sheer volume of data being collected from various domains. In contrast, it is often the rare occurrences that are crucially important to many high-impact domains with diverse data types. For example, in online transaction platforms, the percentage of fraudulent transactions might be small, but the resultant financial loss could be significant; in social networks, a novel topic is often neglected by the majority of users at the initial stage, but it could burst into an emerging trend afterward; in the Sloan Digital Sky Survey, the vast majority of sky images (e.g., known stars, comets, nebulae, etc.) are of no interest to the astronomers, while only 0.001% of the sky images lead to novel scientific discoveries; in the worldwide pandemics (e.g., SARS, MERS, COVID19, etc.), the primary cases might be limited, but the consequences could be catastrophic (e.g., mass mortality and economic recession). Therefore, studying such complex rare categories have profound significance and longstanding impact in many aspects of modern society, from preventing financial fraud to uncovering hot topics and trends, from supporting scientific research to forecasting pandemic and natural disasters. In this thesis, we propose a generic learning mechanism with trinity modules for complex rare category analysis: (M1) Rare Category Characterization - characterizing the rare patterns with a compact representation; (M2) Rare Category Explanation - interpreting the prediction results and providing relevant clues for the end-users; (M3) Rare Category Generation - producing synthetic rare category examples that resemble the real ones. The key philosophy of our mechanism lies in "all for one and one for all" - each module makes unique contributions to the whole mechanism and thus receives support from its companions. In particular, M1 serves as the de-novo step to discover rare category patterns on complex data; M2 provides a proper lens to the end-users to examine the outputs and understand the learning process; and M3 synthesizes real rare category examples for data augmentation to further improve M1 and M2. To enrich the learning mechanism, we develop principled theorems and solutions to characterize, understand, and synthesize rare categories on complex scenarios, ranging from static rare categories to time-evolving rare categories, from attributed data to graph-structured data, from homogeneous data to heterogeneous data, from low-order connectivity patterns to high-order connectivity patterns, etc. It is worthy of mentioning that we have also launched one of the first visual analytic systems for dynamic rare category analysis, which integrates our developed techniques and enables users to investigate complex rare categories in practice

Actual State and Changes of Flora and Vegetation in the Broczówka Steppe Reserve

This paper presents floristic characterization of xerothermic plant associations and analysis of changes of flora within Broczówka steppe reserve. The floristic research was carried out in 2004-2009. Numerous species that were noted here almost 30 years ago were not found in the present study, the size of other populations decreased. Nevertheless, many plant species occurring in the reserve are rare, endangered or protected. Six major plant associations, impoverished form of two ones and one plant community are distinguished in the whole area of the reserve. Occurrence of two plant associations was not confirmed

Spatial Distribution of the Surface Geology and 1992 Land Use of the Buffalo River Watershed

The Buffalo River was established by Congress in 1972 as the first National River in the United States and is one of the few remaining free-flowing streams in Arkansas . The Buffalo River flows through the three major physiographic provinces of northern Arkansas, originating in the higher elevations of the Boston Mountains, and flowing generally northeastward to cut through the Springfield and Salem Plateaus. It drops from approximately 2000 feet in the headwaters to around 500 feet above sea level at its confluence with the White River in Marion County. The Buffalo River is considered to be one of Arkansas\u27 greatest natural treasures; thus there is strong interest in protecting it from undue anthropogenic influences. A general description of the area within the Buffalo River Watershed was given by Smith (1967)