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The function of a protein is strongly dependent on its structure. During evolution, proteins acquire new functions through mutations in the amino-acid sequence. Given the advance in deep mutational scanning, recent findings have found functional change to be position dependent, notwithstanding the chemical properties of mutant and mutated amino acids. This could indicate that structural properties of a given position are potentially responsible for the functional relevance of a mutation. Here, we looked at the relation between structure and function of positions using five proteins with experimental data of functional change available. In order to measure structural change, we modeled mutated proteins via amino-acid networks and quantified the perturbation of each mutation. We found that structural change is position dependent, and strongly related to functional change. Strong changes in protein structure correlate with functional loss, and positions with functional gain due to mutations tend to be structurally robust. Finally, we constructed a computational method to predict functionally sensitive positions to mutations using structural change that performs well on all five proteins with a mean precision of 74.7% and recall of 69.3% of all functional positions.

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Introduction−The Job Shop problem With Skilled Operators (JSSO) is an extension of the classic Job Shop in which, an op-eration must be executed by a limited set of workers, aiming to minimize jobs total termination time or Makespan. This situation can represent different applications in daily life. JSSO is a complex problem and its classified as NP-HARD..Objective−In this article, the JSSO problem is addressed. It is made by adapting an algorithm known as Squirrel Search Algorithm (SSA).Methodology−A discrete encoding scheme is proposed for the SSA algorithm and the Smallest Position Value (SPV) method are used. Also, solutions that can violate the prec-edent relationships are corrected with the Valid Particle Gen-erator (VPG) method, which guarantees feasible solutions. Two versions of the algorithm were tested in 28 instances proposed in the literature to valid their performance.Results−Computer experiments show that the proposed al-gorithms reach optimal solutions in 25 and 28 analyzed in-stances. In addition, for the instances where optimality was not achieved, the average gap does not exceed the 2% for both versions of the proposed algorithms.Conclusions−The proposed encoding scheme guarantees the discretization of the algorithms, generating solutions that converge towards the optimum. In addition, the proposed en-coding allows natural use of movement operators originally proposed for the algorithms used. Performance obtained by the algorithms is adequate and of high quality

Gene regulatory and signaling phenomena are known to be relevant players underlying the establishment of cellular phenotypes. It is also known that such regulatory programs are disrupted in cancer, leading to the onset and development of malignant phenotypes. Gene co-expression matrices have allowed us to compare and analyze complex phenotypes such as breast cancer (BrCa) and their control counterparts. Global co-expression patterns have revealed, for instance, that the highest gene-gene co-expression interactions often occur between genes from the same chromosome (cis-), meanwhile inter-chromosome (trans-) interactions are scarce and have lower correlation values. Furthermore, strength of cis- correlations have been shown to decay with the chromosome distance of gene couples. Despite this loss of long-distance co-expression has been clearly identified, it has been observed only in a small fraction of the whole co-expression landscape, namely the most significant interactions. For that reason, an approach that takes into account the whole interaction set results appealing. In this work, we developed a hybrid method to analyze whole-chromosome Pearson correlation matrices for the four BrCa subtypes (Luminal A, Luminal B, HER2+ and Basal), as well as adjacent normal breast tissue derived matrices. We implemented a systematic method for clustering gene couples, by using eigenvalue spectral decomposition and the k–medoids algorithm, allowing us to determine a number of clusters without removing any interaction. With this method we compared, for each chromosome in the five phenotypes: a) Whether or not the gene-gene co-expression decays with the distance in the breast cancer subtypes b) the chromosome location of cis- clusters of gene couples, and c) whether or not the loss of long-distance co-expression is observed in the whole range of interactions. We found that in the correlation matrix for the control phenotype, positive and negative Pearson correlations deviate from a random null model independently of the distance between couples. Conversely, for all BrCa subtypes, in all chromosomes, positive correlations decay with distance, and negative correlations do not differ from the null model. We also found that BrCa clusters are distance-dependent, meanwhile for the control phenotype, chromosome location does not determine the clustering. To our knowledge, this is the first time that a dependence on distance is reported for gene clusters in breast cancer. Since this method uses the whole cis- interaction geneset, combination with other -omics approaches may provide further evidence to understand in a more integrative fashion, the mechanisms that disrupt gene regulation in cancer.

Periodontitis is a common inflammatory disease of infectious origins that often evolves into a chronic condition. Aside from its importance as a stomatologic ailment, chronic periodontitis has gained relevance since it has been shown that it can develop into a systemic condition characterized by unresolved hyper-inflammation, disruption of the innate and adaptive immune system, dysbiosis of the oral, gut and other location's microbiota and other system-wide alterations that may cause, coexist or aggravate other health issues associated to elevated morbi-mortality. The relationships between the infectious, immune, inflammatory, and systemic features of periodontitis and its many related diseases are far from being fully understood and are indeed still debated. However, to date, a large body of evidence on the different biological, clinical, and policy-enabling sources of information, is available. The aim of the present work is to summarize many of these sources of information and contextualize them under a systemic inflammation framework that may set the basis to an integral vision, useful for basic, clinical, and therapeutic goals.

Introduction: Musculoskeletal disorders could be associated with metabolic disorders that are common after kidney transplantation, which could reduce the quality of life of patients. The aim of this study was to assess the prevalence of both musculoskeletal and metabolic disorders in kidney transplant patients. Methods: MEDLINE, CINAHL, Cochrane Library, EMBASE and Web of Science were searched from their inception up to June 2023. DerSimonian and Laird random-effects method was used to calculate pooled prevalence estimates and their 95% confidence intervals (CIs). Results: 21,879 kidney transplant recipients from 38 studies were analysed. The overall proportion of kidney transplant patients with musculoskeletal disorders was 27.2% (95% CI: 18.4–36.0), with low muscle strength (64.5%; 95% CI: 43.1–81.3) being the most common disorder. Otherwise, the overall proportion of kidney transplant patients with metabolic disorders was 37.6% (95% CI: 21.9–53.2), with hypovitaminosis D (81.8%; 95% CI: 67.2–90.8) being the most prevalent disorder. Conclusion: The most common musculoskeletal disorders were low muscle strength, femoral osteopenia, and low muscle mass. Hypovitaminosis D, hyperparathyroidism, and hyperuricemia were also the most common metabolic disorders. These disorders could be associated with poorer quality of life in kidney transplant recipients. [ABSTRACT FROM AUTHOR]

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El objetivo del presente artículo es divulgar el proceso metodológico y los resultados de una investigación que permitió determinar el perfil musical de los autores colombianos Alejandro Villalobos Arenas y del padre Fray Juan de Jesús Anaya Prada. La investigación se basó en la aplicación de un modelo metodológico que incluyó como estrategias fundamentales la elaboración de cronologías, esbozos biográficos, la caracterización de obras mediante inventarios y el análisis musical y la transcripción a notación musical. Teniendo en cuenta las particularidades de cada autor, el modelo fue adaptado durante el proceso para finalmente obtener la reconstrucción de las trayectorias musicales, los inventarios de las obras encontradas durante el proceso, la caracterización y el análisis de las mismas, así como la transcripción en partitura de material musical proveniente de diversas fuentes.

Abstract Transcriptional co-expression networks represent the concerted gene regulation programs by means of statistical inference of co-expression patterns. The rich phenomenology of transcriptional processes behind complex phenotypes such as cancer, is often captured (at least partially) in the connectivity structure of transcriptional co-expression networks. By analyzing the community structure of these networks, we may develop a deeper understanding of that phenomenology. We identified the modular structure of a transcriptional co-expression network obtained from breast cancer gene expression as well as a non-cancer adjacent breast tissue network as a control. We then analyzed the biological functions associated to the resulting communities by means of enrichment analysis. We also generated two projected networks for both, tumor and control networks: The first one is a projection to a network in which nodes are communities and edges represent topologically adjacent communities, indicating co-expression patterns between them. For the second projection, a bipartite network was generated containing a layer of modules and a layer of biological processes, with links between modules and the functions in which they are enriched; from this bipartite network, a projection to the community layer was obtained. From the analysis of the communities and projections, we were able to discern distinctive patterns of regulation between tumors and controls. Even though the connectivity structure of transcriptional co-expression networks is quite different, the topology of the projected networks is somehow similar, indicating functional compartmentalization, in both tumor and control conditions. However, the biological functions represented in the corresponding modules resulted notably different, with the tumor network comprising functional modules enriched for well-known hallmarks of cancer.

Abstract Background Currently, thanks to the growing number of public database resources, most evidence on planning and management, healthcare institutions, policies and practices is becoming available to everyone. However, one of the limitations for the advancement of data and literature-driven research has been the lack of flexibility of the methodological resources used in qualitative research. There is a need to incorporate friendly, cheaper and faster tools for the systematic, unbiased analysis of large data corpora, in particular regarding the qualitative aspects of the information (often overlooked). Methods This article proposes a series of novel techniques, exemplified by the case of the role of Institutional Committees of Bioethics to (1) massively identify the documents relevant to a given issue, (2) extract the fundamental content, focusing on qualitative analysis, (3) synthesize the findings in the published literature, (4) categorize and visualize the evidence, and (5) analyse and report the results. Results A critical study of the institutional role of public health policies and practices in Institutional Committees of Bioethics was used as an example application of the method. Interactive strategies were helpful to define and conceptualise variables, propose research questions and refine research interpretation. These methods are additional aids to systematic reviews, pre-coding schemes and construction of a priori diagrams to survey and analyse social science literature. Conclusions These novel methods have proven to facilitate the formulation and testing of hypotheses on the subjects to be studied. Such tools may allow important advances going from descriptive approaches to decision-making and even institutional assessment and policy redesign, by pragmatic reason of time and costs.

Staphylococcal exfoliative toxins (ETs) are glutamyl endopeptidases that specifically cleave the Glu381-Gly382 bond in the ectodomains of desmoglein 1 (Dsg1) via complex action mechanisms. To date, four ETs have been identified in different Staphylococcus aureus strains and ETE is the most recently characterized. The unusual properties of ETs have been attributed to a unique structural feature, i.e., the 180° flip of the carbonyl oxygen (O) of the nonconserved residue 192/186 (ETA/ETE numbering), not conducive to the oxyanion hole formation. We report the crystal structure of ETE determined at 1.61 Å resolution, in which P186(O) adopts two conformations displaying a 180° rotation. This finding, together with free energy calculations, supports the existence of a dynamic transition between the conformations under the tested conditions. Moreover, enzymatic assays showed no significant differences in the esterolytic efficiency of ETE and ETE/P186G, a mutant predicted to possess a functional oxyanion hole, thus downplaying the influence of the flip on the activity. Finally, we observed the formation of ETE homodimers in solution and the predicted homodimeric structure revealed the participation of a characteristic nonconserved loop in the interface and the partial occlusion of the protein active site, suggesting that monomerization is required for enzymatic activity.

Introducción: La hiperprolactinemia es el trastorno más común del eje hipotálamo-hipofisario. Se debe al aumento de la hormona prolactina en sangre. Objetivo: Caracterizar el comportamiento de la hiperprolactinemia tumoral e idiopática. Método: Se realizó un estudio de serie de casos en la consulta de Endocrinología del Hospital Universitario Vladimir Ilich Lenin, Holguín, Cuba, de enero 2018 a junio 2019. El universo estuvo integrado por 89 mujeres con hiperprolactinemia idiopática y tumoral; la muestra, constituida por 76 pacientes, se seleccionó de forma intencional. Se aplicó la prueba de hipótesis para comparación de dos proporciones y chi cuadrado. Resultados: Predominaron la hiperprolactinemia idiopática (57,9%), la infertilidad (22,4%) y, en el caso de la tumoral, los trastornos menstruales y la galactorrea (15,8%), con prolactinas superiores a 1000 mUI/mL. En ambos grupos existió una media de 32 años al diagnóstico. A los 6 meses de tratamiento existió eficacia bioquímica con la bromocriptina (5 mg/día) en el 73,5%, y con la cabergolina (1 mg/semana) en el 90,5%. Prevalecieron los síntomas digestivos como reacciones adversas (44,8%). Conclusiones: La hiperprolactinemia idiopática predominó sobre la tumoral, sin diferencias en cuanto a la edad al diagnóstico. Las cifras más elevadas de prolactina se relacionaron con la causa tumoral, siendo en ésta, el trastorno menstrual y la galactorrea los síntomas más frecuentes, mientras que en la idiopática, resultó la infertilidad. La bromocriptina (5 mg/día) y cabergolina (1 mg/semana) fueron más efectivas respecto al resto de las dosis empleadas en cada uno de estos medicamentos. Los efectos adversos más comunes fueron síntomas digestivos. Palabras clave: hiperprolactinemia, idiopática, tumoral.

There has been limited study of Native American whole genome diversity to date, which impairs effective implementation of personalized medicine and a detailed description of its demographic history. Here we report high coverage whole genome sequencing of 76 unrelated individuals, from 27 indigenous groups across Mexico, with more than 97% average Native American ancestry. On average, each individual has 3.26 million Single Nucleotide Variants and short indels, that together comprise a catalog of 9,737,152 variants, 44,118 of which are novel. We report 497 common Single Nucleotide Variants (with allele frequency > 5%) mapped to drug responses and 316,577 in enhancer or promoter elements; interestingly we found some of these enhancer variants in PPARG, a nuclear receptor involved in highly prevalent health problems in Mexican population, such as obesity, diabetes, and insulin resistance. By detecting signals of positive selection we report 24 enriched key pathways under selection, most of them related to immune mechanisms. No missense variants in ACE2, the receptor responsible for the entry of the SARS CoV-2 virus, were found in any individual. Population genomics and phylogenetic analyses demonstrated stratification in a Northern-Central-Southern axis, with major substructure in the Central region. The Seri, a northern group with the most genetic divergence in our study, showed a distinctive genomic context with the most novel variants, and the most population specific genotypes. Genome-wide analysis showed that the average haplotype blocks are longer in Native Mexicans than in other world populations. With this dataset we describe previously undetected population level variation in Native Mexicans, helping to reduce the gap in genomic data representation of such groups.

The field of Unmanned Aerial Vehicles (UAVs) has progressed greatly in the last years. UAVs are now used for many applications and are often flown automatically. One commonly implemented feature in an automatic flight is that of following a mission at a stable altitude. However, this altitude is almost always referenced from the take-off location and does not take terrain profile levels into account. This is a critical and dangerous issue because if the terrain level changes abruptly (e.g., mountain regions or buildings in a city), this can lead to crashes or an unintended (illegal) high altitude. Our aim for this work is to provide a solution such that a constant altitude above ground level is maintained. To this end, we make use of the readily available Digital Elevation Models (DEMs). These models, which contain the terrain elevation, help us in dynamically adjusting the VTOL UAV altitude so that it remains nearly constant in relation to the ground. Results have shown that with the use of our method, the altitude can be maintained sufficiently constant while introducing a limited increase in flight time and battery consumption that is proportional to the terrain’s irregularity. In a moderately changing terrain, the error could be reduced to just ±5 m.

RESUMEN El amparo universitario de la enfermería ha propiciado un avance significativo de la investigación en la última década. El logro del grado de Doctor, ya es posible para los/as enfermeros/as. Esto dará un mayor respaldo a la investigación enfermera, pero el camino no está exento de obstáculos. Entre ellos: las características del profesional de enfermería, las características de los documentos que presentan los resultados de la investigación, las características de las organizaciones, la alta presión asistencial, la escasa financiación a la investigación y la medicalización de la sanidad.

Clear cell renal carcinoma (ccRC) is a highly heterogeneous and progressively malignant disease. Analyzing ccRC progression in terms of modifications at the molecular and genetic level may help us to develop a broader understanding of its patho-physiology and may give us a glimpse toward improved therapeutics. In this work, by using TCGA data, we studied the molecular progression of the four main ccRC stages (i, ii, iii, iv) in two different yet complementary approaches: (a) gene expression and (b) gene co-expression. For (a) we analyzed the differential gene expression between each stage and the control non-cancer group. We compared the progression molecular signature between stages, and observed those genes that change their expression patterns through progression stages. For (b) we constructed and analyzed co-expression networks for the four ccRC progression stages, as well as for the control phenotype, to observe whether and how the co-expression landscape changes with progression. We separated genomic interactions into intra-chromosome (cis-) and inter-chromosome (trans-). Finally, we intersected those networks and performed functional enrichment analysis. All calculations were made over different network sizes, from the top 100 edges to top 1,000,000. We show that differential expression is quite similar between ccRC progression stages. However, interestingly, two genes, namely SLC6A19 and PLG show a significant progressive decrease in their expression according to ccRC stage, meanwhile two other genes, SAA2-SAA4 and CXCL13 show progressive increase. Despite the high similarity between gene expression profiles, all networks are substantially different between them in terms of their topological features. Control network has a larger proportion of trans- interactions, meanwhile for any stage, the amount of cis- interactions is higher, independent of the network cut-off. The majority of interactions in any network are phenotype-specific. Only 189 interactions are shared between the five networks, and 533 edges are ccRC-specific, independent of the stage. The small resulting connected components in both cases are formed by genes with the same differential expression trend, and are associated with important biological processes, such as cell cycle or immune system, suggesting that activity of these categories follows the differential expression trend. With this approach we have shown that, even if the expression program is similar during ccRC progression, the co-expression programs strongly differ. More research is needed to understand the delicate interplay between expression and co-expression, but this is a first approach to enclose both approaches in an integrative view aimed at a deeper understanding in gene regulation in tumor evolution.

Acanthophora spicifera is an invasive red alga that was recently detected in La Paz Bay, Baja California Sur, Mexico, where it has developed into a large biomass. Because it is a new species in the region, the characteristics and properties of the soluble polysaccharides (SPs) that it contains are unknown. To determine the content, chemical composition, and properties of SPs in A. spicifera, monthly samplings were carried out in 2013 at Point Roca Caimancito in La Paz Bay, and native and alkali-treated polysaccharides were extracted and characterized. The alkaline treatment produced lower yields and modified the composition of A. spicifera SPs. The polysaccharides obtained before or after the alkaline treatment had low viscosity and did not have gelling properties. In line with the obtained results, the molar ratio of components (galactose, 3,6-anhydrogalactose, sulfates [Gal:3,6-AG:sulfates]) for native (1.00:0.30:0.23) and alkali-treated (1.00:0.30:0.17) polysaccharides showed that A. spicifera SPs have a lower proportion of sulfates than that in polysaccharides belonging to carrageenans but greater than that in polysaccharides belonging to true agar. The Fourier-transform infrared spectra of SPs showed characteristic signals for sulfated galactans, with the presence of pyruvic acid; after the alkaline treatment, characteristic signals for agar-type polysaccharides (agaroid) were observed. Although A. spicifera polysaccharides have no gelling properties, additional studies are needed to clarify the structure of the SPs it contains to find appropriate uses for this resource.

Background: Cardiovascular diseases are the leading causes of mortality worldwide. One reason behind this lethality lies in the fact that often cardiovascular illnesses develop into systemic failure due to the multiple connections to organismal metabolism. This in turn is associated with co-morbidities and multimorbidity. The prevalence of coexisting diseases and the relationship between the molecular origins adds to the complexity of the management of cardiovascular diseases and thus requires a profound knowledge of the genetic interaction of diseases.Objective: In order to develop a deeper understanding of this phenomenon, we examined the patterns of comorbidity as well as their genetic interaction of the diseases (or the lack of evidence of it) in a large set of cases diagnosed with cardiovascular conditions at the national reference hospital for cardiovascular diseases in Mexico.Methods: We performed a cross-sectional study of the National Institute of Cardiology. Socioeconomic information, principal diagnosis that led to the hospitalization and other conditions identified by an ICD-10 code were obtained for 34,099 discharged cases. With this information a cardiovascular comorbidity networks were built both for the full database and for ten 10-years age brackets. The associated cardiovascular comorbidities modules were found. Data mining was performed in the comprehensive ClinVar database with the disease names (as extracted from ICD-10 codes) to establish (when possible) connections between the genetic associations of the genetic interaction of diseases. The rationale is that some comorbidities may have a stronger genetic origin, whereas for others, the environment and other factors may be stronger.Results: We found that comorbidity networks are highly centralized in prevalent diseases, such as cardiac arrhythmias, heart failure, chronic kidney disease, hypertension, and ischemic diseases. Said comorbidity networks are actually modular on their connectivity. Modules recapitulate physiopathological commonalities, e.g., ischemic diseases clustering together. This is also the case of chronic systemic diseases, of congenital malformations and others. The genetic and environmental commonalities behind some of the relations in these modules were also found by resorting to clinical genetics databases and functional pathway enrichment studies.Conclusions: This methodology, hence may allow the clinician to look up for non-evident comorbidities whose knowledge will lead to improve therapeutically designs. By continued and consistent analysis of these types of patterns, we envisaged that it may be possible to acquire, strong clinical and basic insights that may further our advance toward a better understanding of cardiovascular diseases as a whole. Hopefully these may in turn lead to further development of better, integrated therapeutic strategies.

Rationale: The optimal modality, intensity, duration, frequency, and dose–response of exercise as a therapy for Parkinson's Disease (PD) are insufficiently understood.Objective: To assess the impact of a high-intensity tandem bicycle program on clinical severity, biomarkers, and functional MRI (fMRI) in PD.Methods: A single-center, parallel-group clinical trial was conducted. Thirteen PD patients aged 65 or younger were divided in two groups: a control group and an intervention group that incorporated a cycling program at 80% of each individual's maximum heart rate (HR) (≥80 rpm), three times a week, for 16 weeks. Both groups continued their conventional medications for PD. At baseline and at the end of follow-up, we determined in all participants the Unified Parkinson's Disease Rating Scale, anthropometry, VO2max, PD biomarkers, and fMRI.Results: VO2max improved in the intervention group (IG) (+5.7 ml/kg/min), while it slightly deteriorated in the control group (CG) (−1.6 ml/kg/min) (p = 0.041). Mean Unified Parkinson's Disease Rating Scale (UPDRS) went down by 5.7 points in the IG and showed a small 0.9-point increase in the CG (p = 0.11). fMRI showed activation of the right fusiform gyrus during the motor task and functional connectivity between the cingulum and areas of the frontal cortex, and between the cerebellar vermis and the thalamus and posterior temporal gyrus. Plasma brain-derived neurotrophic factor (BDNF) levels increased more than 10-fold in the IG and decreased in the CG (p = 0.028). Larger increases in plasma BDNF correlated with greater decreases in UPDRS (r = −0.58, p = 0.04).Conclusions: Our findings suggest that high-intensity tandem bicycle improves motor function and biochemical and functional neuroimaging variables in PD patients.Trial registration number: ISRCTN 13047118, Registered on February 8, 2018.

Breast carcinomas are characterized by anomalous gene regulatory programs. As is well-known, gene expression programs are able to shape phenotypes. Hence, the understanding of gene co-expression may shed light on the underlying mechanisms behind the transcriptional regulatory programs affecting tumor development and evolution. For instance, in breast cancer, there is a clear loss of inter-chromosomal (trans-) co-expression, compared with healthy tissue. At the same time cis- (intra-chromosomal) interactions are favored in breast tumors. In order to have a deeper understanding of regulatory phenomena in cancer, here, we constructed Gene Co-expression Networks by using TCGA-derived RNA-seq whole-genome samples corresponding to the four breast cancer molecular subtypes, as well as healthy tissue. We quantify the cis-/trans- co-expression imbalance in all phenotypes. Additionally, we measured the association between co-expression and physical distance between genes, and characterized the ratio of intra/inter-cytoband interactions per phenotype. We confirmed loss of trans- co-expression in all molecular subtypes. We also observed that gene cis- co-expression decays abruptly with distance in all tumors in contrast with healthy tissue. We observed co-expressed gene hotspots, that tend to be connected at cytoband regions, and coincide accurately with already known copy number altered regions, such as Chr17q12, or Chr8q24.3 for all subtypes. Our methodology recovered different alterations already reported for specific breast cancer subtypes, showing how co-expression network approaches might help to capture distinct events that modify the cell regulatory program.

Breast cancer is a disease that exhibits heterogeneity that goes from the genomic to the clinical levels. This heterogeneity is thought to be captured (at least partially) by the so-called breast cancer molecular subtypes. These molecular subtypes were initially defined based on the unsupervised clustering of gene expression and its correlate with histological, morphological, phenotypic and clinical features already known. Later, a 50-gene signature, PAM50, was defined in order to identify the biological subtype of a given sample within the clinical setting. The PAM50 signature was obtained by the use of unsupervised statistical methods, and therefore no limitation was set on the biological relevance (or lack of) of the selected genes beyond its predictive capacity. An open question that remains is what are the regulatory elements that drive the various expression behaviors of this set of genes in the different molecular subtypes. This question becomes more relevant as the measurement of more biological layers of regulation becomes accessible. In this work, we analyzed the gene expression regulation of the 50 genes in the PAM50 signature, in terms of (a) gene co-expression, (b) transcription factors, (c) micro-RNAs, and (d) methylation. Using data from the Cancer Genome Atlas (TCGA) for the Luminal A and B, Basal, and HER2-enriched molecular subtypes as well as normal tumor adjacent tissue, we identified predictors for gene expression through the use of an elastic net model. We compare and contrast the sets of identified regulators for the gene signature in each molecular subtype, and systematically compare them to current literature. We also identified a unique set of predictors for the expression of genes in the PAM50 signature associated with each of the molecular subtypes. Most selected predictors are exclusive for a PAM50 gene and predictors are not shared across subtypes. There are only 13 coding transcripts and 2 miRNAs selected for the four subtypes. MiR-21 and miR-10b connect almost all the PAM50 genes in all the subtypes and normal tissue, but do it in an exclusive manner, suggesting a cancer switch from miR-10b coordination in normal tissue to miR-21. The PAM50 gene sets of selected predictors that enrich for a function across subtypes, support that different regulatory molecular mechanisms are taking place. With this study we aim to a wider understanding of the regulatory mechanisms that differentiate the expression of the PAM50 signature, which in turn could perhaps help understand the molecular basis of the differences between the molecular subtypes.

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

Reseña del libro de Marta Bailón García – Ignacio Jordán Gimena, Cursus Publicus. El primer correo en Hispania (=Colección Ayudas a la Investigación 3), Madrid, Sociedad Estatal Correos y Telégrafos, S.A., 2019, 155 pp. [ISBN: 978-84-88841-49-0].

Elasmobranch species composition and spatiotemporal variability can often be characterized from landing surveys of local fisheries, with important management implications. In the present study, we describe the elasmobranch landing composition of 4 major artisanal fishing ports in northwestern Baja California, Mexico. Sampling was conducted opportunistically between 1995 and 2010 and included an effort of 684 fishing days. Species composition was gear-dependent and varied with the fishing area, with thresher sharks (Alopias vulpinus) and spiny dogfish (Squalus suckleyi) dominating the catches landed by coastal gill net fisheries, and blue sharks (Prionace glauca) and shortfin mako sharks (Isurus oxyrhinchus) dominating the catches landed by the offshore longline fishery. Size-structure analysis indicated that catches were composed primarily of juveniles for most species, although the catch of mature soupfin sharks (Galeorhinus galeus) increased in longline fisheries during the winter. The results indicate that minor regional differences in elasmobranch species composition occur along the Pacific coast of Baja California and are most likely related to regional oceanographic or habitat differences. The results of this study may form the basis for future efforts to monitor species composition changes over time, to more completely assess elasmobranch populations.

Metabolic deregulation is an emergent hallmark of cancer. Altered patterns of metabolic pathways result in exacerbated synthesis of macromolecules, increased proliferation, and resistance to treatment via alteration of drug processing. In addition, molecular heterogeneity creates a barrier to therapeutic options. In breast cancer, this broad variation in molecular metabolism constitutes, simultaneously, a source of prognostic and therapeutic challenges and a doorway to novel interventions. In this work, we investigated the metabolic deregulation landscapes in breast cancer molecular subtypes. Such landscapes are the regulatory signatures behind subtype-specific metabolic features. n = 735 breast cancer samples of the Luminal A, Luminal B, Her2+, and Basal subtypes, as well as n = 113 healthy breast tissue samples were analyzed. By means of a single-sample-based algorithm, deregulation for all metabolic pathways in every sample was determined. Deregulation levels match almost perfectly with the molecular classification, indicating that metabolic anomalies are closely associated with gene-expression signatures. Luminal B tumors are the most deregulated but are also the ones with higher within-subtype variance. We argued that this variation may underlie the fact that Luminal B tumors usually present the worst prognosis, a high rate of recurrence, and the lowest response to treatment in the long term. Finally, we designed a therapeutic scheme to regulate purine metabolism in breast cancer, independently of the molecular subtype. This scheme is founded on a computational tool that provides a set of FDA-approved drugs to target pathway-specific differentially expressed genes. By providing metabolic deregulation patterns at the single-sample level in breast cancer subtypes, we have been able to further characterize tumor behavior. This approach, together with targeted therapy, may open novel avenues for the design of personalized diagnostic, prognostic, and therapeutic strategies.

This work compares the tribological behavior of surface layers obtained by three different hardening processes. The layers were formed on the surface of AISI 4140 steel by applying three different thermochemical treatments. Wear resistance was evaluated using a standardized tribological machine for abrasive wear, according to the limits established by the ASTM G65 “Standard Test Method for Measuring Abrasion Using Dry Sand/Rubber Wheel Apparatus”. According to the results, the boride layers exhibited the highest wear resistance, as compared to nitrided and carburized layers. In contrast, the carburized layers presented the highest loss of volume. Scanning electron microscopy (SEM) was used to analyze the worn surfaces to examine the wear mechanisms. Abrasive wear was identified in all the samples, as the main abrasive wear mechanism. The mean values of the coefficient of friction (CoF) of the hardened surfaces were 0.39, 0.55, and 0.65 for carburizing, nitriding, and boriding samples, respectively, indicating that the wear process may not always be related to a low CoF. The results suggest that the highest hardness is normally associated with high wear resistance, but the coefficient of friction could be not directly related to the hardness of the materials. Finally, a statistical study demonstrates the random nature of the layers obtained by three different hardening processes.