Survival of the firm and territory.

El objetivo de este trabajo es evaluar el riesgo de mortalidad empresarial. Para ello, en una muestra de más de 11.741 empresas pertenecientes al sector textil-confección se ha estudiado, junto con otros factores, la influencia de la proximidad geográfica (efecto distrito) y la actividad productiva principal (efecto subsector) en la supervivencia de las empresas textiles españolas. Desde un punto de vista teórico-práctico esta industria es especialmente relevante para realizar ese tipo de estudios al menos por dos motivos: 1) porque después de la liberalización del comercio textil se ha visto inmersa en una profunda crisis en las economías más desarrolladas, y 2) porque tiende a agruparse geográficamente en torno a clusters o distritos industriales. Los resultados obtenidos sugieren que el riesgo de mortalidad empresarial se ve reducido por aspectos relativos a la empresa como la antigüedad y el subsector de actividad y, bajo determinadas circunstancias, por la localización en un distrito industrial. PALABRAS CLAVE: mortalidad empresarial, textil, distrito industrial

Future medical applications of single-cell sequencing in cancer

Advances in whole genome amplification and next-generation sequencing methods have enabled genomic analyses of single cells, and these techniques are now beginning to be used to detect genomic lesions in individual cancer cells. Previous approaches have been unable to resolve genomic differences in complex mixtures of cells, such as heterogeneous tumors, despite the importance of characterizing such tumors for cancer treatment. Sequencing of single cells is likely to improve several aspects of medicine, including the early detection of rare tumor cells, monitoring of circulating tumor cells (CTCs), measuring intratumor heterogeneity, and guiding chemotherapy. In this review we discuss the challenges and technical aspects of single-cell sequencing, with a strong focus on genomic copy number, and discuss how this information can be used to diagnose and treat cancer patients

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, high-throughput data, bioinformatics and systems biology

Update of the keratin gene family: evolution, tissue-specific expression patterns, and relevance to clinical disorders.

Intermediate filament (IntFil) genes arose during early metazoan evolution, to provide mechanical support for plasma membranes contacting/interacting with other cells and the extracellular matrix. Keratin genes comprise the largest subset of IntFil genes. Whereas the first keratin gene appeared in sponge, and three genes in arthropods, more rapid increases in keratin genes occurred in lungfish and amphibian genomes, concomitant with land animal-sea animal divergence (~ 440 to 410 million years ago). Human, mouse and zebrafish genomes contain 18, 17 and 24 non-keratin IntFil genes, respectively. Human has 27 of 28 type I "acidic" keratin genes clustered at chromosome (Chr) 17q21.2, and all 26 type II "basic" keratin genes clustered at Chr 12q13.13. Mouse has 27 of 28 type I keratin genes clustered on Chr 11, and all 26 type II clustered on Chr 15. Zebrafish has 18 type I keratin genes scattered on five chromosomes, and 3 type II keratin genes on two chromosomes. Types I and II keratin clusters-reflecting evolutionary blooms of keratin genes along one chromosomal segment-are found in all land animal genomes examined, but not fishes; such rapid gene expansions likely reflect sudden requirements for many novel paralogous proteins having divergent functions to enhance species survival following sea-to-land transition. Using data from the Genotype-Tissue Expression (GTEx) project, tissue-specific keratin expression throughout the human body was reconstructed. Clustering of gene expression patterns revealed similarities in tissue-specific expression patterns for previously described "keratin pairs" (i.e., KRT1/KRT10, KRT8/KRT18, KRT5/KRT14, KRT6/KRT16 and KRT6/KRT17 proteins). The ClinVar database currently lists 26 human disease-causing variants within the various domains of keratin proteins

Cancer drug therapy and stochastic modelling of “nano-motors”

Controlled inhibition of kinesin motor proteins is highly desired in the field of oncology. Among other interventions, the selective Eg5 competitive and allosteric inhibitors is the most successful targeted chemotherapeutic regime/options, inducing cancer cell apoptosis and tumor regression with improved safety profile. Though promising, this approach is under clinical trials, for the discovery of efficient and least harmful Eg5 inhibitors. The aim of present research is to bridge the computational modelling approach with drug design and therapy of cancer cells. Thus a computational model, interfaced with the clinical data of “Eg5 dynamics” and “inhibitors” via special functions is presented in this article. Comparisons are made for the drug efficacy and the threshold values are predicted through numerical simulations

A general piecewise multi-state survival model: Application to breast cancer

Multi-state models are considered in the field of survival analysis for modelling illnesses that evolve through several stages over time. Multi-state models can be developed by applying several techniques, such as non-parametric, semi-parametric and stochastic processes, particularly Markov processes. When the development of an illness is being analysed, its progression is tracked periodically. Medical reviews take place at discrete times, and a panel data analysis can be formed. In this paper, a discrete-time piecewise non-homogeneous Markov process is constructed for modelling and analysing a multi-state illness with a general number of states. The model is built, and relevant measures, such as survival function, transition probabilities, mean total times spent in a group of states and the conditional probability of state change, are determined. A likelihood function is built to estimate the parameters and the general number of cut-points included in the model. Time-dependent covariates are introduced, the results are obtained in a matrix algebraic form and the algorithms are shown. The model is applied to analyse the behaviour of breast cancer. A study of the relapse and survival times of 300 breast cancer patients who have undergone mastectomy is developed. The results of this paper are implemented computationally with MATLAB and R.Ministerio de Economía y Competitividad FQM-307European Regional Development Fund (ERDF) MTM2017-88708-PUniversity of Milano-Bicocca 2014-ATE-022