Richterrichter1246

Evaluate primary health care functions from the perspective of patients with tuberculosis from slums in the city of Buenos Aires, Argentina.

Cross-sectional observational study with adult patients with tuberculosis (TB) and without TB (NoTB), living in slums (S) and outside them (NoS). Participants' perceptions were evaluated using the Primary Care Assessment Tool for users (abbreviated version), which measures four main domains (first contact, ongoing care, coordination with specialists, and comprehensiveness) and selected secondary domains. A Likert scale was used, ranging from "No, not at all" (1 point) to "Yes, definitely" (4 points). Scores ≥ 3 were considered to indicate adequate performance of functions. Averages were calculated for each domain, as well as two overall scores with and without secondary domains.

83 participants were included (20 TB-S, 21 TB-NoS, 19 NoTB-S, and 23 NoTB-NoS). The evaluated functions were perceived as inadequate. The TB-S group gave the lowest overall scores, not reaching 3 points in any domain. There were no significant differences in domains or overall scores between groups. Participants with TB gave lower scores in all domains, except in family-centered care, where they gave a significantly higher score than NoTB participants. The overall score without secondary domains was lower for TB participants than for the NoTB groups.

According to the perception of participants with TB and without TB, primary health care functions are not satisfactory, either in slums or outside them.

According to the perception of participants with TB and without TB, primary health care functions are not satisfactory, either in slums or outside them.Mutations in methyl CpG binding protein 2 (MeCP2) are the major cause of Rett syndrome (RTT), a rare neurodevelopmental disorder with a notable period of developmental regression following apparently normal initial development. Such MeCP2 alterations often result in changes to DNA binding and chromatin clustering ability, and in the stability of this protein. Among other functions, MeCP2 binds to methylated genomic DNA, which represents an important epigenetic mark with broad physiological implications, including neuronal development. In this review, we will summarize the genetic foundations behind RTT, and the variable degrees of protein stability exhibited by MeCP2 and its mutated versions. Also, past and emerging relationships that MeCP2 has with mRNA splicing, miRNA processing, and other non-coding RNAs (ncRNA) will be explored, and we suggest that these molecules could be missing links in understanding the epigenetic consequences incurred from genetic ablation of this important chromatin modifier. Importantly, although MeCP2 is highly expressed in the brain, where it has been most extensively studied, the role of this protein and its alterations in other tissues cannot be ignored and will also be discussed. Finally, the additional complexity to RTT pathology introduced by structural and functional implications of the two MeCP2 isoforms (MeCP2-E1 and MeCP2-E2) will be described. Epigenetic therapeutics are gaining clinical popularity, yet treatment for Rett syndrome is more complicated than would be anticipated for a purely epigenetic disorder, which should be taken into account in future clinical contexts.Antimicrobial resistance (AMR) is a significant and growing public health threat. Sequencing of bacterial isolates is becoming more common, and therefore automatic identification of resistant bacterial strains is of pivotal importance for efficient, wide-spread AMR detection. To support this approach, several AMR databases and gene identification algorithms have been recently developed. A key problem in AMR detection, however, is the need for computational approaches detecting potential novel AMR genes or variants, which are not included in the reference databases. Toward this direction, here we study the relation between AMR and relative solvent accessibility (RSA) of protein variants from an in silico perspective. We show how known AMR protein variants tend to correspond to exposed residues, while on the contrary their susceptible counterparts tend to be buried. Based on these findings, we develop RSA-AMR, a novel relative solvent accessibility-based AMR scoring system. This scoring system can be applied to any protein variant to estimate its propensity of altering the relative solvent accessibility, and potentially conferring (or hindering) AMR. We show how RSA-AMR score can be integrated with existing AMR detection algorithms to expand their range of applicability into detecting potential novel AMR variants, and provide a ten-fold increase in Specificity. The two main limitations of RSA-AMR score is that it is designed on single point changes, and a limited number of variants was available for model learning.The Waddington landscape provides an intuitive metaphor to view development as a ball rolling down the hill, with distinct phenotypes as basins and differentiation pathways as valleys. Since, at a molecular level, cell differentiation arises from interactions among the genes, a mathematical definition for the Waddington landscape can, in principle, be obtained by studying the gene regulatory networks. For eukaryotes, gene regulation is inextricably and intimately linked to histone modifications. However, the impact of such modifications on both landscape topography and stability of attractor states is not fully understood. In this work, we introduced a minimal kinetic model for gene regulation that combines the impact of both histone modifications and transcription factors. We further developed an approximation scheme based on variational principles to solve the corresponding master equation in a second quantized framework. By analyzing the steady-state solutions at various parameter regimes, we found that histone modification kinetics can significantly alter the behavior of a genetic network, resulting in qualitative changes in gene expression profiles. The emerging epigenetic landscape captures the delicate interplay between transcription factors and histone modifications in driving cell-fate decisions.Lung adenocarcinoma (LUAD) is caused by multiple biological factors. Therefore, it will be more meaningful to study the prognosis from the perspective of omics integration. Given the significance of epigenetic modification and immunity in tumorigenesis and development, we tried to combine aberrant methylation and tumor infiltration CD8 T cell-related genes to build a prognostic model, to explore the key biomarkers of early-stage LUAD. On the basis of RNA-seq and methylation microarray data downloaded from The Cancer Genome Atlas (TCGA), differentially expressed genes and aberrant methylated genes were calculated with "DEseq2" and "ChAMP" packages, respectively. A Chi-square test was performed to obtain methylation driver genes. Weighted correlation network analysis (WGCNA) was utilized to mine cancer biomarkers related to CD8 T cells. With the consequences of univariate Cox proportional hazards analysis and least absolute shrinkage and selection operator (LASSO) COX regression analysis, the prognostic index based on 17 methylation driver genes (ZNF677, FAM83A, TRIM58, CLDN6, NKD1, NFE2L3, FKBP5, ITGA5, ASCL2, SLC24A4, WNT3A, TMEM171, PTPRH, ITPKB, ITGA2, SLC6A17, and CCDC81) and four CD8 T cell-related genes (SPDL1, E2F7, TK1, and TYMS) was successfully established, which could make valuable predictions for the survival risk of patients with early-stage LUAD.Circular RNAs (circRNAs), as a rising star in the RNA world, play important roles in various biological processes. Understanding the interactions between circRNAs and RNA binding proteins (RBPs) can help reveal the functions of circRNAs. For the past decade, the emergence of high-throughput experimental data, like CLIP-Seq, has made the computational identification of RNA-protein interactions (RPIs) possible based on machine learning methods. However, as the underlying mechanisms of RPIs have not been fully understood yet and the information sources of circRNAs are limited, the computational tools for predicting circRNA-RBP interactions have been very few. In this study, we propose a deep learning method to identify circRNA-RBP interactions, called DeCban, which is featured by hybrid double embeddings for representing RNA sequences and a cross-branch attention neural network for classification. To capture more information from RNA sequences, the double embeddings include pre-trained embedding vectors for both RNA segments and their converted amino acids. Meanwhile, the cross-branch attention network aims to address the learning of very long sequences by integrating features of different scales and focusing on important information. The experimental results on 37 benchmark datasets show that both double embeddings and the cross-branch attention model contribute to the improvement of performance. DeCban outperforms the mainstream deep learning-based methods on not only prediction accuracy but also computational efficiency. The data sets and source code of this study are freely available at https//github.com/AaronYll/DECban.After years of development, the complexity of the biological sequence alignment algorithm is gradually increasing, and the lack of high abstract level domain research leads to the complexity of its algorithm development and improvement. By applying the idea of software components to the design and development of algorithms, the development efficiency and reliability of biological sequence alignment algorithms can be effectively improved. The component assembly platform applies related assembly technology, which simplifies the operation difficulty of component assembly and facilitates the maintenance and optimization of the algorithm. At the same time, a friendly visual interface is used to intuitively complete the assembly of algorithm components, and an executable sequence alignment algorithm program is obtained, which can directly carry out alignment computing.Telomere shortening is a well-known hallmark of both cellular senescence and organismal aging. AZ20 An accelerated rate of telomere attrition is also a common feature of age-related diseases. Therefore, telomere length (TL) has been recognized for a long time as one of the best biomarkers of aging. Recent research findings, however, indicate that TL per se can only allow a rough estimate of aging rate and can hardly be regarded as a clinically important risk marker for age-related pathologies and mortality. Evidence is obtained that other indicators such as certain immune parameters, indices of epigenetic age, etc., could be stronger predictors of the health status and the risk of chronic disease. However, despite these issues and limitations, TL remains to be very informative marker in accessing the biological age when used along with other markers such as indices of homeostatic dysregulation, frailty index, epigenetic clock, etc. This review article is aimed at describing the current state of the art in the field and at discussing recent research findings and divergent viewpoints regarding the usefulness of leukocyte TL for estimating the human biological age.