Sleep Quality, Nutrient Intake, and Social Development Index Predict Metabolic Syndrome in the Tlalpan 2020 Cohort: A Machine Learning and Synthetic Data Study.

This study investigated the relationship between Metabolic Syndrome (MetS), sleep disorders, the consumption of some nutrients, and social development factors, focusing on gender differences in an unbalanced dataset from a Mexico City cohort. We used data balancing techniques like SMOTE and ADASYN after employing machine learning models like random forest and RPART to predict MetS. Random forest excelled, achieving significant, balanced accuracy, indicating its robustness in predicting MetS and achieving a balanced accuracy of approximately 87%. Key predictors for men included body mass index and family history of gout, while waist circumference and glucose levels were most significant for women. In relation to diet, sleep quality, and social development, metabolic syndrome in men was associated with high lactose and carbohydrate intake, educational lag, living with a partner without marrying, and lack of durable goods, whereas in women, best predictors in these dimensions include protein, fructose, and cholesterol intake, copper metabolites, snoring, sobbing, drowsiness, sanitary adequacy, and anxiety. These findings underscore the need for personalized approaches in managing MetS and point to a promising direction for future research into the interplay between social factors, sleep disorders, and metabolic health, which mainly depend on nutrient consumption by region.

Comorbidity patterns in cardiovascular diseases: the role of life-stage and socioeconomic status.

Cardiovascular diseases stand as a prominent global cause of mortality, their intricate origins often entwined with comorbidities and multimorbid conditions. Acknowledging the pivotal roles of age, sex, and social determinants of health in shaping the onset and progression of these diseases, our study delves into the nuanced interplay between life-stage, socioeconomic status, and comorbidity patterns within cardiovascular diseases. Leveraging data from a cross-sectional survey encompassing Mexican adults, we unearth a robust association between these variables and the prevalence of comorbidities linked to cardiovascular conditions. To foster a comprehensive understanding of multimorbidity patterns across diverse life-stages, we scrutinize an extensive dataset comprising 47,377 cases diagnosed with cardiovascular ailments at Mexico's national reference hospital. Extracting sociodemographic details, primary diagnoses prompting hospitalization, and additional conditions identified through ICD-10 codes, we unveil subtle yet significant associations and discuss pertinent specific cases. Our results underscore a noteworthy trend: younger patients of lower socioeconomic status exhibit a heightened likelihood of cardiovascular comorbidities compared to their older counterparts with a higher socioeconomic status. By empowering clinicians to discern non-evident comorbidities, our study aims to refine therapeutic designs. These findings offer profound insights into the intricate interplay among life-stage, socioeconomic status, and comorbidity patterns within cardiovascular diseases. Armed with data-supported approaches that account for these factors, clinical practices stand to be enhanced, and public health policies informed, ultimately advancing the prevention and management of cardiovascular disease in Mexico.

Functional impact of multi-omic interactions in lung cancer.

Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

Topological Data Analysis in Cardiovascular Signals: An Overview.

Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes.

Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.

Exploring disparities and novel insights into metabolic and cardiovascular comorbidities among COVID-19 patients in Mexico.

Introduction: The COVID-19 pandemic, especially its early stages, sparked extensive discussions regarding the potential impact of metabolic and cardiovascular comorbidities on the severity and fatality of SARS-CoV-2 infection, yielding inconclusive outcomes. In this study, we delve into the prevalence of metabolic and cardiovascular comorbidities within COVID-19 patients in Mexico. Methods: Employing a retrospective observational study design, we collected data from official databases encompassing COVID-19 patients admitted to both public and private hospitals in Mexico City. Results: Our investigation unveiled a noteworthy incongruity in the prevalence of metabolic and cardiovascular comorbidities among COVID-19 patients, with a particular emphasis on obesity, hypertension, and diabetes. This incongruity manifests as location-dependent phenomena, where the prevalence of these comorbidities among COVID-19 patients significantly deviates from the reported values for the general population in each specific location. Discussion: These findings underscore the critical importance of screening for metabolic and cardiovascular comorbidities in COVID-19 patients and advocate for the necessity of tailored interventions for this specific population. Furthermore, our study offers insights into the intricate interplay between COVID-19 and metabolic and cardiovascular comorbidities, serving as a valuable foundation for future research endeavors and informing clinical practice.

Computational single cell oncology: state of the art.

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Real-World Evaluation of a Pharmacoinvasive Strategy for STEMI in Latin America: A Cost-Effective Approach with Short-Term Benefits.

Purpose: While pharmacoinvasive strategy (PI) is a safe and effective approach whenever access to primary percutaneous intervention (pPCI) is limited, data on each strategy's economic cost and impact on in-hospital stay are scarce. The objective is to compare the cost-effectiveness of a PI with that of pPCI for the treatment of ST-elevation myocardial infarction (STEMI) in a Latin-American country. Patients and Methods: A total of 1747 patients were included, of whom 470 (26.9%) received PI, 433 (24.7%) pPCI, and 844 (48.3%) NR. The study's primary outcome was the incremental cost-effectiveness ratio (ICER) for PI compared with those for pPCI and non-reperfused (NR), calculated for 30-day major cardiovascular events (MACE), 30-day mortality, and length of stay. Results: For PI, the ICER estimates for MACE showed a decrease of $-35.81/per 1% (95 confidence interval, -114.73 to 64.81) compared with pPCI and a decrease of $-271.60/per 1% (95% CI, -1086.10 to -144.93) compared with NR. Also, in mortality, PI had an ICER decrease of $-129.50 (95% CI, -810.57, 455.06) compared to pPCI and $-165.27 (-224.06, -123.52) with NR. Finally, length of stay had an ICER reduction of -765.99 (-4020.68, 3141.65) and -283.40 (-304.95, -252.76) compared to pPCI and NR, respectively. Conclusion: The findings of this study suggest that PI may be a more efficient treatment approach for STEMI in regions where access to pPCI is limited or where patient and system delays are expected.

Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seq.

It has been documented that variations in glycosylation on glycoprotein hormones, confer distinctly different biological features to the corresponding glycoforms when multiple in vitro biochemical readings are analyzed. We here applied next generation RNA sequencing to explore changes in the transcriptome of rat granulosa cells exposed for 0, 6, and 12 h to 100 ng/ml of four highly purified follicle-stimulating hormone (FSH) glycoforms, each exhibiting different glycosylation patterns: human pituitary FSH18/21 and equine FSH (eqFSH) (hypo-glycosylated), and human FSH24 and chinese-hamster ovary cell-derived human recombinant FSH (recFSH) (fully-glycosylated). Total RNA from triplicate incubations was prepared from FSH glycoform-exposed cultured granulosa cells obtained from DES-pretreated immature female rats, and RNA libraries were sequenced in a HighSeq 2500 sequencer (2 × 125 bp paired-end format, 10-15 × 106 reads/sample). The computational workflow focused on investigating differences among the four FSH glycoforms at three levels: gene expression, enriched biological processes, and perturbed pathways. Among the top 200 differentially expressed genes, only 4 (0.6%) were shared by all 4 glycoforms at 6 h, whereas 118 genes (40%) were shared at 12 h. Follicle-stimulating hormone glycocoforms stimulated different patterns of exclusive and associated up regulated biological processes in a glycoform and time-dependent fashion with more shared biological processes after 12 h of exposure and fewer treatment-specific ones, except for recFSH, which exhibited stronger responses with more specifically associated processes at this time. Similar results were found for down-regulated processes, with a greater number of processes at 6 h or 12 h, depending on the particular glycoform. In general, there were fewer downregulated than upregulated processes at both 6 h and 12 h, with FSH18/21 exhibiting the largest number of down-regulated associated processes at 6 h while eqFSH exhibited the greatest number at 12 h. Signaling cascades, largely linked to cAMP-PKA, MAPK, and PI3/AKT pathways were detected as differentially activated by the glycoforms, with each glycoform exhibiting its own molecular signature. These data extend previous observations demonstrating glycosylation-dependent differential regulation of gene expression and intracellular signaling pathways triggered by FSH in granulosa cells. The results also suggest the importance of individual FSH glycoform glycosylation for the conformation of the ligand-receptor complex and induced signalling pathways.

A machine learning approach to personalized predictors of dyslipidemia: a cohort study.

Introduction: Mexico ranks second in the global prevalence of obesity in the adult population, which increases the probability of developing dyslipidemia. Dyslipidemia is closely related to cardiovascular diseases, which are the leading cause of death in the country. Therefore, developing tools that facilitate the prediction of dyslipidemias is essential for prevention and early treatment. Methods: In this study, we utilized a dataset from a Mexico City cohort consisting of 2,621 participants, men and women aged between 20 and 50 years, with and without some type of dyslipidemia. Our primary objective was to identify potential factors associated with different types of dyslipidemia in both men and women. Machine learning algorithms were employed to achieve this goal. To facilitate feature selection, we applied the Variable Importance Measures (VIM) of Random Forest (RF), XGBoost, and Gradient Boosting Machine (GBM). Additionally, to address class imbalance, we employed Synthetic Minority Over-sampling Technique (SMOTE) for dataset resampling. The dataset encompassed anthropometric measurements, biochemical tests, dietary intake, family health history, and other health parameters, including smoking habits, alcohol consumption, quality of sleep, and physical activity. Results: Our results revealed that the VIM algorithm of RF yielded the most optimal subset of attributes, closely followed by GBM, achieving a balanced accuracy of up to 80%. The selection of the best subset of attributes was based on the comparative performance of classifiers, evaluated through balanced accuracy, sensitivity, and specificity metrics. Discussion: The top five features contributing to an increased risk of various types of dyslipidemia were identified through the machine learning technique. These features include body mass index, elevated uric acid levels, age, sleep disorders, and anxiety. The findings of this study shed light on significant factors that play a role in dyslipidemia development, aiding in the early identification, prevention, and treatment of this condition.

Analysis of High-Risk Neuroblastoma Transcriptome Reveals Gene Co-Expression Signatures and Functional Features.

Neuroblastoma represents a neoplastic expansion of neural crest cells in the developing sympathetic nervous system and is childhood's most common extracranial solid tumor. The heterogeneity of gene expression in different types of cancer is well-documented, and genetic features of neuroblastoma have been described by classification, development stage, malignancy, and progression of tumors. Here, we aim to analyze RNA sequencing datasets, publicly available in the GDC data portal, of neuroblastoma tumor samples from various patients and compare them with normal adrenal gland tissue from the GTEx data portal to elucidate the gene expression profile and regulation networks they share. Our results from the differential expression, weighted correlation network, and functional enrichment analyses that we performed with the count data from neuroblastoma and standard normal gland samples indicate that the analysis of transcriptome data from 58 patients diagnosed with high-risk neuroblastoma shares the expression pattern of 104 genes. More importantly, our analyses identify the co-expression relationship and the role of these genes in multiple biological processes and signaling pathways strongly associated with this disease phenotype. Our approach proposes a group of genes and their biological functions to be further investigated as essential molecules and possible therapeutic targets of neuroblastoma regardless of the etiology of individual tumors.

[Social, demographic and morbimortality characteristics of the cases treated for COVID-19 at the Ignacio Chávez National Institute of Cardiology. A descriptive cross-sectional study]. / Características sociales, demográficas y de morbimortalidad de los casos atendidos por COVID-19 en el Instituto Nacional de Cardiología Ignacio Chávez. Un estudio transversal descriptivo.

Introduction: The COVID-19 pandemic brought with it a large number of adverse consequences for public health with serious socioeconomic repercussions. In this study we characterize the social, demographic, morbidity and mortality conditions of individuals treated for COVID-19 in one of the SARS-CoV-2 reference hospitals in Mexico City. Method: A descriptive cross-sectional study was carried out in 259 patients discharged from the Instituto Nacional de Cardiología Ignacio Chávez, between April 11, 2020 and March 14, 2021. A multivariate logistic regression model was used to identify the association between sociodemographic and clinical variables. An optimization was performed using maximum likelihood calculations to choose the best model compatible with the data. The maximum likelihood model was evaluated using ROC curves, goodnessof-fit estimators, and multicollinearity analysis. Statistically significant patterns of comorbidities were inferred by evaluating a hypergeometric test over the frequencies of co-occurrence of pairs of conditions. A network analysis was implemented to determine connectivity patterns based on degree centrality, between comorbidities and outcome variables. Results: The main social disadvantages of the studied population are related to the lack of social security (96.5%) and the lag in housing conditions (81%). Variables associated with the probability of survival were being younger (p < 0.0001), having more durable material goods (p = 0.0034) and avoiding: pneumonia (p = 0.0072), septic shock (p < 0.0001) and acute respiratory failure (p < 0.0001); (AUROC: 91.5%). The comorbidity network for survival cases has a high degree of connectivity between conditions such as cardiac arrhythmias and essential arterial hypertension (Degree Centrality = 90 and 78, respectively). Conclusions: Given that among the factors associated with survival to COVID-19 there are clinical, sociodemographic and social determinants of health variables, in addition to age; It is imperative to consider the various factors that may affect or modify the health status of a population, especially when addressing emerging epidemic phenomena such as the current COVID-19 pandemic.

Introducción: La pandemia de enfermedad por coronavirus 2019 (COVID-19) trajo aparejadas una gran cantidad de consecuencias adversas para la salud pública con serias repercusiones socioeconómicas. En este estudio caracterizamos las condiciones sociales, demográficas y de morbimortalidad de los casos atendidos por COVID-19 en uno de los hospitales de referencia de coronavirus 2 del síndrome respiratorio agudo grave (SARS-CoV-2) en la Ciudad de México. Método: Se llevó a cabo un estudio transversal descriptivo en 259 pacientes egresados del Instituto Nacional de Cardiología Ignacio Chávez, entre el 11 de abril de 2020 y el 14 de marzo de 2021. Se utilizó un modelo de regresión logística multivariante para identificar la asociación entre variables sociodemográficas y clínicas. Se realizó una optimización mediante cálculos de máxima verosimilitud para elegir el mejor modelo compatible con los datos. El modelo de máxima verosimilitud fue evaluado mediante curvas ROC, estimadores de bondad de ajuste y análisis de multicolinealidad. Se infirieron patrones de comorbilidades estadísticamente significativos mediante la evaluación de una prueba hipergeométrica en las frecuencias de coocurrencia de pares de condiciones. Se implementó un análisis de redes para determinar los patrones de conectividad basado en la centralidad de grado, entre algunas comorbilidades y las variables de desenlace. Resultados: Las principales desventajas sociales de la población estudiada se relacionan con la falta de seguridad social (96.5%) y el rezago en las condiciones de vivienda (81%). Las variables asociadas a la probabilidad de sobrevivir fueron tener una menor edad (p < 0.0001), contar con más bienes materiales durables (p = 0.0034) y evitar: la neumonía (p = 0.0072), el choque séptico (p < 0.0001) y la insuficiencia respiratoria aguda (p < 0.0001); (AUROC: 91.5%). Las red de comorbilidades para los casos de supervivencia tienen un alto grado de conectividad entre padecimientos como las arritmias cardiacas e hipertensión arterial esencial (centralidad de grado: 90 y 78 respectivamente). Conclusiones: En vista de que entre los factores asociados a supervivencia existen variables clínicas, sociodemográficas y determinantes sociales de la salud, además de la edad, resulta imperativo considerar los diversos factores que puedan incidir o modificar el estado de salud de una población, sobre todo al abordar los fenómenos epidémicos emergentes como es el caso de la actual pandemia de COVID-19.

Methylation-related genes involved in renal carcinoma progression.

Renal carcinomas are a group of malignant tumors often originating in the cells lining the small tubes in the kidney responsible for filtering waste from the blood and urine production. Kidney tumors arise from the uncontrolled growth of cells in the kidneys and are responsible for a large share of global cancer-related morbidity and mortality. Understanding the molecular mechanisms driving renal carcinoma progression results crucial for the development of targeted therapies leading to an improvement of patient outcomes. Epigenetic mechanisms such as DNA methylation are known factors underlying the development of several cancer types. There is solid experimental evidence of relevant biological functions modulated by methylation-related genes, associated with the progression of different carcinomas. Those mechanisms can often be associated to different epigenetic marks, such as DNA methylation sites or chromatin conformation patterns. Currently, there is no definitive method to establish clear relations between genetic and epigenetic factors that influence the progression of cancer. Here, we developed a data-driven method to find methylation-related genes, so we could find relevant bonds between gene co-expression and methylation-wide-genome regulation patterns able to drive biological processes during the progression of clear cell renal carcinoma (ccRC). With this approach, we found out genes such as ITK oncogene that appear hypomethylated during all four stages of ccRC progression and are strongly involved in immune response functions. Also, we found out relevant tumor suppressor genes such as RAB25 hypermethylated, thus potentially avoiding repressed functions in the AKT signaling pathway during the evolution of ccRC. Our results have relevant implications to further understand some epigenetic-genetic-affected roles underlying the progression of renal cancer.

Molecular mechanisms of multi-omic regulation in breast cancer.

Breast cancer is a complex disease that is influenced by the concurrent influence of multiple genetic and environmental factors. Recent advances in genomics and other high throughput biomolecular techniques (-omics) have provided numerous insights into the molecular mechanisms underlying breast cancer development and progression. A number of these mechanisms involve multiple layers of regulation. In this review, we summarize the current knowledge on the role of multiple omics in the regulation of breast cancer, including the effects of DNA methylation, non-coding RNA, and other epigenomic changes. We comment on how integrating such diverse mechanisms is envisioned as key to a more comprehensive understanding of breast carcinogenesis and cancer biology with relevance to prognostics, diagnostics and therapeutics. We also discuss the potential clinical implications of these findings and highlight areas for future research. Overall, our understanding of the molecular mechanisms of multi-omic regulation in breast cancer is rapidly increasing and has the potential to inform the development of novel therapeutic approaches for this disease.

Effects of social confinement during the first wave of COVID-19 in Mexico City.

Background: The COVID-19 pandemic led to global social confinement that had a significant impact on people's lives. This includes changes such as increased loneliness and isolation, changes in sleep patterns and social habits, increased substance use and domestic violence, and decreased physical activities. In some cases, it has increased mental health problems, such as anxiety, depression, and post-traumatic stress disorder. Objective: The objective of this study is to analyze the living conditions that arose during social confinement in the first wave of COVID-19 within a group of volunteers in Mexico City. Methods: This is a descriptive and cross-sectional analysis of the experiences of volunteers during social confinement from 20 March 2020 to 20 December 2020. The study analyzes the impact of confinement on family life, work, mental health, physical activity, social life, and domestic violence. A maximum likelihood generalized linear model is used to determine the association between domestic violence and demographic and health-related factors. Results: The findings indicate that social confinement had a significant impact on the participants, resulting in difficulties within families and vulnerable conditions for individuals. Gender and social level differences were observed in work and mental health. Physical activity and social life were also modified. We found that suffering from domestic violence was significantly associated with being unmarried (OR = 1.4454, p-value = 0.0479), lack of self-care in feeding habits (OR = 2.3159, p-value = 0.0084), and most notably, having suffered from a symptomatic COVID-19 infection (OR = 4.0099, p-value = 0.0009). Despite public policy to support vulnerable populations during confinement, only a small proportion of the studied population reported benefiting from it, suggesting areas for improvement in policy. Conclusion: The findings of this study suggest that social confinement during the COVID-19 pandemic had a significant impact on the living conditions of people in Mexico City. Modified circumstances on families and individuals, included increased domestic violence. The results can inform policy decisions to improve the living conditions of vulnerable populations during times of social confinement.

CNVs in 8q24.3 do not influence gene co-expression in breast cancer subtypes.

Gene co-expression networks are a useful tool in the study of interactions that have allowed the visualization and quantification of diverse phenomena, including the loss of co-expression over long distances in cancerous samples. This characteristic, which could be considered fundamental to cancer, has been widely reported in various types of tumors. Since copy number variations (CNVs) have previously been identified as causing multiple genetic diseases, and gene expression is linked to them, they have often been mentioned as a probable cause of loss of co-expression in cancerous networks. In order to carry out a comparative study of the validity of this statement, we took 477 protein-coding genes from chromosome 8, and the CNVs of 101 genes, also protein-coding, belonging to the 8q24.3 region, a cytoband that is particularly active in the appearance of breast cancer. We created CNVS-conditioned co-expression networks of each of the 101 genes in the 8q24.3 region using conditional mutual information. The study was carried out using the four molecular subtypes of breast cancer (Luminal A, Luminal B, Her2, and Basal), as well as a case corresponding to healthy samples. We observed that in all cancer cases, the measurement of the Kolmogorov-Smirnov statistic shows that there are no significant differences between one and other values of the CNVs for any case. Furthermore, the co-expression interactions are stronger in all cancer subtypes than in the control networks. However, the control network presents a homogeneously distributed set of co-expression interactions, while for cancer networks, the highest interactions are more confined to specific cytobands, in particular 8q24.3 and 8p21.3. With this approach, we demonstrate that despite copy number alterations in the 8q24 region being a common trait in breast cancer, the loss of long-distance co-expression in breast cancer is not determined by CNVs.

Drug repurposing for Basal breast cancer subpopulations using modular network signatures.

Breast cancer is characterized as being a heterogeneous pathology with a broad phenotype variability. Breast cancer subtypes have been developed in order to capture some of this heterogeneity. Each of these breast cancer subtypes, in turns retains varied characteristic features impacting diagnostic, prognostic and therapeutics. Basal breast tumors, in particular have been challenging in these regards. Basal breast cancer is often more aggressive, of rapid evolution and no tailor-made targeted therapies are available yet to treat it. Arguably, epigenetic variability is behind some of these intricacies. It is possible to further classify basal breast tumor in groups based on their non-coding transcriptome and methylome profiles. It is expected that these groups will have differences in survival as well as in sensitivity to certain classes of drugs. With this in mind, we implemented a computational learning approach to infer different subpopulations of basal breast cancer (from TCGA multi-omic data) based on their epigenetic signatures. Such epigenomic signatures were associated with different survival profiles; we then identified their associated gene co-expression network structure, extracted a signature based on modules within these networks, and use these signatures to find and prioritize drugs (in the LINCS dataset) that may be used to target these types of cancer. In this way we are introducing the analytical workflow for an epigenomic signature-based drug repurposing structure.

Relationship between the Social Development Index and Self-Reported Periodontal Conditions.

Inequalities in oral health are influenced by the social strata of the population. Few studies have focused on the multitude of factors related to social development as indicators of living conditions and periodontal health status. The aim of this study is to evaluate the association between self-reported periodontal conditions and the Social Development Index (SDI). A cross-sectional validated questionnaire was carried out among 1294 Mexican adults. Descriptive statistics and multivariate logistic regression models were used to identify the best predictors of self-reported periodontal conditions. Bone loss reporting was used as a proxy for the presence of periodontal disease. We found that higher global scores on the SDI and quality and available space in the home (QASH) increase the probability of having bone loss. Global SDI (OR = 7.27) and higher QASH (OR = 3.66) were indeed the leading societal factors related to periodontal disease. These results have pointed out how SDI and its indicators, in particular QASH, can be used to further explore inequities related to privileged access to dental care in the context of periodontal diseases.

A single-cell transposable element atlas of human cell identity.

Single cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex biological systems. However, most sequencing studies overlook the contribution of transposable element (TE) expression to the transcriptome. In both scRNA-seq and bulk tissue RNA sequencing (RNA-seq), quantification of TE expression is challenging due to repetitive sequence content and poorly characterized TE gene models. Here, we developed a tool and analysis pipeline for Single cell Transposable Element Locus Level Analysis of scRNA Sequencing (Stellarscope) that reassigns multi-mapped reads to specific genomic loci using an expectation-maximization algorithm. Using Stellarscope, we built an atlas of TE expression in human PBMCs. We found that locus-specific TEs delineate cell types and define new cell subsets not identified by standard mRNA expression profiles. Altogether, this study provides comprehensive insights into the influence of transposable elements in human biology.

Probability of hospitalisation and death among COVID-19 patients with comorbidity during outbreaks occurring in Mexico City.

Background: We compared the probability of hospitalization and death caused by COVID-19 in patients with comorbidities during three periods defined for this study: first-wave (FW), interwave period (IP), and second-wave (SW) observed in Mexico City. Methods: In this registry-based study, we included individuals over 20 years of age. During the FW (symptomatic), the IP, and the SW (symptomatic and asymptomatic), participants were diagnosed using nasopharyngeal swabs. Symptomatic individuals with risk factors for serious disease or death were referred to the hospital. SARS-CoV-2 infection was defined by RT-qPCR in all hospitalized patients. All data were added to the SISVER database. Bayesian analysis and False Discovery Rate were used for further evaluation. Results: The study included 2 260 156 persons (mean age of 43.1 years). Of these, 8.6% suffered from DM, 11.6% arterial hypertension, and 9.7% obesity. Of the total, 666 694 persons tested positive (29.5%). Of the infected persons, a total of 85 587 (12.8%) were hospitalized: 24 023 in the FW; 16 935 in the IP, and 44 629 in the SW. Of the hospitalized patients, there were 42 979 deaths (50.2%), in the FW, 11 964 (49.8%), in the IP, 6794 (40.1%), and in the SW 24 221 (54.3%). The probability of death among individuals hospitalized with or without comorbidities increased consistently in all age groups. A significant increase in the Fatality Rate was observed in individuals with comorbidities (1.36E-19< = FDR< = 3.36E-2). A similar trend was also observed in individuals without comorbidities (1.03E-44< = FDR< = 5.58E-4). Conclusions: The data from this study show a considerable increase in the number of detected cases of infection between the FW and SW. In addition, 12.8% of those infected were hospitalized for severe COVID-19. A high mortality rate was observed among hospitalized patients (>50%). An age-dependent probability of death was observed with a positive trend in hospitalized patients with and without comorbidities.