Leptin Signaling in Obesity and Colorectal Cancer

Obesity and colorectal cancer (CRC) are among the leading diseases causing deaths in the world, showing a complex multifactorial pathology. Obesity is considered a risk factor in CRC development through inflammation, metabolic, and signaling processes. Leptin is one of the most important adipokines related to obesity and an important proinflammatory marker, mainly expressed in adipose tissue, with many genetic variation profiles, many related influencing factors, and various functions that have been ascribed but not yet fully understood and elucidated, the most important ones being related to energy metabolism, as well as endocrine and immune systems. Aberrant signaling and genetic variations of leptin are correlated with obesity and CRC, with the genetic causality showing both inherited and acquired events, in addition to lifestyle and environmental risk factors; these might also be related to specific pathogenic pathways at different time points. Moreover, mutation gain is a crucial factor enabling the genetic process of CRC. Currently, the inconsistent and insufficient data related to leptin’s relationship with obesity and CRC indicate the necessity of further related studies. This review summarizes the current knowledge on leptin genetics and its potential relationship with the main pathogenic pathways of obesity and CRC, in an attempt to understand the molecular mechanisms of these associations, in the context of inconsistent and contradictory data. The understanding of these mechanisms linking obesity and CRC could help to develop novel therapeutic targets and prevention strategies, resulting in a better prognosis and management of these diseases.     

In Vitro Assays to Identify Metabolism-Disrupting Chemicals with Diabetogenic Activity in a Human Pancreatic β-Cell Model

There is a need to develop identification tests for Metabolism Disrupting Chemicals (MDCs) with diabetogenic activity. Here we used the human EndoC-βH1 β-cell line, the rat β-cell line INS-1E and dispersed mouse islet cells to assess the effects of endocrine disruptors on cell viability and glucose-stimulated insulin secretion (GSIS). We tested six chemicals at concentrations within human exposure (from 0.1 pM to 1 µM). Bisphenol-A (BPA) and tributyltin (TBT) were used as controls while four other chemicals, namely perfluorooctanoic acid (PFOA), triphenylphosphate (TPP), triclosan (TCS) and dichlorodiphenyldichloroethylene (DDE), were used as “unknowns”. Regarding cell viability, BPA and TBT increased cell death as previously observed. Their mode of action involved the activation of estrogen receptors and PPARγ, respectively. ROS production was a consistent key event in BPA-and TBT-treated cells. None of the other MDCs tested modified viability or ROS production. Concerning GSIS, TBT increased insulin secretion while BPA produced no effects. PFOA decreased GSIS, suggesting that this chemical could be a “new” diabetogenic agent. Our results indicate that the EndoC-βH1 cell line is a suitable human β-cell model for testing diabetogenic MDCs. Optimization of the test methods proposed here could be incorporated into a set of protocols for the identification of MDCs.     

Systematic Review and Meta-Analysis of Mass Spectrometry Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Schizophrenia

Mass spectrometry (MS)-based techniques can be a powerful tool to identify neuropsychiatric disorder biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids of schizophrenia (SCZ) patients to identify disease biomarkers and relevant networks of biological pathways. Following PRISMA guidelines, a search was performed for studies that used MS proteomics approaches to identify proteomic differences between SCZ patients and healthy control groups (PROSPERO database: CRD42021274183). Nineteen articles fulfilled the inclusion criteria, allowing the identification of 217 differentially expressed proteins. Gene ontology analysis identified lipid metabolism, complement and coagulation cascades, and immune response as the main enriched biological pathways. Meta-analysis results suggest the upregulation of FCN3 and downregulation of APO1, APOA2, APOC1, and APOC3 in SCZ patients. Despite the proven ability of MS proteomics to characterize SCZ, several confounding factors contribute to the heterogeneity of the findings. In the future, we encourage the scientific community to perform studies with more extensive sampling and validation cohorts, integrating omics with bioinformatics tools to provide additional comprehension of differentially expressed proteins. The produced information could harbor potential proteomic biomarkers of SCZ, contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.     

Effects of Low-Intensity and Long-Term Aerobic Exercise on the Psoas Muscle of mdx Mice: An Experimental Model of Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a muscle disease characterized by the absence of the protein dystrophin, which causes a loss of sarcolemma integrity, determining recurrent muscle injuries, decrease in muscle function, and progressive degeneration. Currently, there is a need for therapeutic treatments to improve the quality of life of DMD patients. Here, we investigated the effects of a low-intensity aerobic training (37 sessions) on satellite cells, peroxisome proliferator-activated receptor-gamma coactivator (PGC)-1α protein (PGC-1α), and different types of fibers of the psoas muscle from mdx mice (DMD experimental model). Wildtype and mdx mice were randomly divided into sedentary and trained groups (n = 24). Trained animals were subjected to 37 sessions of low-intensity running on a motorized treadmill. Subsequently, the psoas muscle was excised and analyzed by immunofluorescence for dystrophin, satellite cells, myosin heavy chain (MHC), and PGC-1α content. The minimal Feret’s diameters of the fibers were measured, and light microscopy was applied to observe general morphological features of the muscles. The training (37 sessions) improved morphological features in muscles from mdx mice and caused an increase in the number of quiescent/activated satellite cells. It also increased the content of PGC-1α in the mdx group. We concluded that low-intensity aerobic exercise (37 sessions) was able to reverse deleterious changes determined by DMD.     

Forms of reasoning used by prospective physical sciences teachers when explaining and predicting natural phenomena: The case of air pressure

Abstract

The co‐ordination between theory and evidence is an outstanding characteristic of scientific thinking. Research indicates that students often have difficulty in explaining natural phenomena because they use their own theories to explain phenomena or they are unable to build a bridge between theory and evidence. Science teachers must teach students to collect and select evidence and to use theory to explain it. The objective of this study was to investigate the forms of reasoning used by prospective physical sciences teachers when they build up explanations and make predictions about natural phenomena. Thirty‐eight prospective teachers answered a questionnaire structured around three problems focusing on phenomena that can be explained through air‐pressure variation. The results seem to indicate a variation in the forms of reasoning used, depending on the problem and the type of request. The number of prospective teachers who consistently use a certain form of reasoning is higher within problems than across problems.     

化学纤维的发展特点及趋势

从化学纤维的发展历程来说,过去一段时期内其经历了快速发展,并将在未来很长时期内持续这一态势,在这一过程中这类纤维的发展使命已经发生了飞跃,其最新进展不仅保持了化学纤维的可持续性,还带来了性能上的提升。     

Improved diagnosis using enhanced fault dominance

Defect diagnosis can benefit from fault dominance relations to reduce the set of defect candidate sites. This paper presents new fault dominance collapsing operators that further reduce the set of candidates considered during the initial phase of diagnosis. In contrast to existing dominance-based methods which operate on pairs of faults, the proposed method operates on sets of faults. Fault-related entities are generated to guide the diagnosis process. The proposed collapsing operators can be used to accelerate effect-cause diagnosis. Experimental results demonstrate that the proposed method achieves a higher collapsing ratio than existing methods.     

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