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The characteristic epigenetic profile of periodontitis found in peripheral leukocytes denotes its impact on systemic immunity. In fact, this profile not only stands for periodontitis as a low-grade inflammatory disease with systemic effects but also as an important source of potentially valuable clinical biomarkers of its systemic effects and susceptibility to other inflammatory conditions. Thus, we aimed to identify relevant genes tested as epigenetic systemic biomarkers in patients with periodontitis, based on the DNA methylation patterns and RNA expression profiles in peripheral immune cells. A detailed protocol was designed following the Preferred Reporting Items for Systematic Review and Meta-analysis -PRISMA guideline. Only cross-sectional and case-control studies that reported potential systemic biomarkers of periodontitis in peripheral immune cell types were included. DNA methylation was analyzed in leukocytes, and gene expression was in polymorphonuclear and mononuclear cells. Hypermethylation was found in TLR regulators genes: MAP3K7, MYD88, IL6R, RIPK2, FADD, IRAK1BP1, and PPARA in early stages of periodontitis, while advanced stages presented hypomethylation of these genes. TGFB1I1, VNN1, HLADRB4, and CXCL8 genes were differentially expressed in lymphocytes and monocytes of subjects with poorly controlled diabetes mellitus, dyslipidemia, and periodontitis in comparison with controls. The DAB2 gene was differentially overexpressed in periodontitis and dyslipidemia. Peripheral blood neutrophils in periodontitis showed differential expression in 163 genes. Periodontitis showed an increase in ceruloplasmin gene expression in polymorphonuclears in comparison with controls. Several genes highlight the role of the epigenetics of peripheral inflammatory cells in periodontitis that could be explored in blood as a source of biomarkers for routine testing.

We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of tractography data is not straightforward due to the geometrical complexity of the streamlines, the file format, and the size of the datasets, which may contain millions of fibers. Hence, we collected and structured state-of-the-art methods for the analysis of tractography and packed them into a Python library, to integrate and share tools for tractography analysis. Due to the high computational requirements, the most demanding modules were implemented in C/C++. Available functions include brain Bundle Segmentation (FiberSeg), Hierarchical Fiber Clustering (HClust), Fast Fiber Clustering (FFClust), normalization to a reference coordinate system, fiber sampling, calculation of intersection between sets of brain fibers, tools for cluster filtering, calculation of measures from clusters, and fiber visualization. The library tools were structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis). Phybers is freely available on a GitHub repository under the GNU public license for non-commercial use and open-source development, which provides sample data and extensive documentation. In addition, the library can be easily installed on both Windows and Ubuntu operating systems through the pip library.

BACKGROUND Education influences brain health and dementia. However, its impact across regions, specifically Latin America (LA) and the United States (US), is unknown. METHODS A total of 1412 participants comprising controls, patients with Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD) from LA and the US were included. We studied the association of education with brain volume and functional connectivity while controlling for imaging quality and variability, age, sex, total intracranial volume (TIV), and recording type. RESULTS Education influenced brain measures, explaining 24%–98% of the geographical differences. The educational disparities between LA and the US were associated with gray matter volume and connectivity variations, especially in LA and AD patients. Education emerged as a critical factor in classifying aging and dementia across regions. DISCUSSION The results underscore the impact of education on brain structure and function in LA, highlighting the importance of incorporating educational factors into diagnosing, care, and prevention, and emphasizing the need for global diversity in research. Highlights Lower education was linked to reduced brain volume and connectivity in healthy controls (HCs), Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD). Latin American cohorts have lower educational levels compared to the those in the United States. Educational disparities majorly drive brain health differences between regions. Educational differences were significant in both conditions, but more in AD than FTLD. Education stands as a critical factor in classifying aging and dementia across regions.

An appropriate design of a chiller plant is crucial to guarantee highly performing solutions. However, several design variables, such as type of systems, total cooling capacity, and hydraulic arrangement, need to be considered. On the one hand, at present, different technical criteria for selecting the most suitable design variables are available. Studies that corroborate the influence of the design variables over the operational variables are missing. In order to fill this knowledge gap, this work proposes a statistical analysis of design variables in chiller plants operating in medium- and large-scale applications and evaluates their influence on energy consumption and life cycle cost (LCC) under the same thermal demand conditions. A case study involving 138 chiller plant combinations featuring different arrangements and a Cuban hotel was selected. The results suggested that the total chiller design and cooling capacity distribution among chillers have a significant influence on the energy consumption of the chiller plant with a Spearman’s Rho and Kendall Tau (τ) correlation index value of −0.625 and 0.559, respectively. However, with LCC, only the cooling capacity distribution among the chillers had a certain influence with a Kendall Tau correlation index value of 0.289. As for the considered total cooling capacity, the applied statistical test showed that this design variable does not have any influence on performing the chiller plant.

As part of the design process of a chiller plant, one of the final stages is the energy testing of the system in relation to future operating conditions. Recent studies have suggested establishing robust solutions, but a conservative approach still prevails at this stage. However, the results of some recent studies suggest the application of a new co-design (control–design) approach. The present research involves a comparative analysis between the use of conventional staging and the co-design approach in the design phase of a chiller plant. This paper analyzes the energy consumption estimations of six different chiller plant combinations for a Cuban hotel. For the conservative approach using on/off traditional staging, the results suggest that the best option would be the adoption of a chiller plant featuring a symmetrical configuration. However, the outcomes related to the co-design approach suggest that the best option would be an asymmetrical configuration. The energy savings results were equal to 24.8% and the resulting coefficient of performance (COP) was 59.7% greater than that of the symmetrical configuration. This research lays firm foundations for the correct choice and design of a suitable chiller plant configuration for a selected hotel, allowing for significant energy savings in the tourism sector.

The apolipoprotein E (APOE) ε4 allele represents the strongest genetic risk factor for Alzheimer's disease (AD), but its role in genetically diverse Latin American and Caribbean (LAC) populations is underexplored. We conducted a meta‐analysis of 35 studies from 11 LAC countries, encompassing 3206 patients with AD and 5515 controls. The ε4 allele demonstrated significant association with increased AD risk (odds ratio [OR] = 3.25, 95% confidence interval [2.82–3.76]), while ε3 showed lower odds (0.42, [0.37–0.48]). Homozygous ε4/ε4 carriers had elevated risk (6.84, [5.09–9.19]), and heterozygous ε3/ε4 carriers showed moderate risk (2.59, [2.31–2.91]). Country‐level analyses revealed variability, with Ecuador showing the highest OR for ε4/ε4 (13.29, [1.56–113.4]). These results confirm APOE ε4 as a major AD risk factor in LAC populations and highlight regional differences relevant to precision medicine. Highlights This study is the largest regional apolipoprotein E meta‐analysis on Alzheimer's disease (AD) risk across Latin America. The ε4 allele is associated with AD risk, but with regional variability across Latin American and Caribbean (LAC) countries. The ε3 allele showed a protective effect in LAC populations (pooled odds ratio 0.42). The ε2 allele us not associated with protection in LAC, diverging from findings in other regions. Findings underscore the need for region‐specific dementia risk estimates.

Chronic pain (CP) is a global public health issue and a critical factor in the aging process. Chile, as one of the most aged countries in Latin America, presents a unique context for exploring CP and its associated factors. Despite its significance in aging, previous studies in the region often fail to comprehensively address key variables such as age, income, mood, mobility, diet, and cognitive skills, nor do they systematically investigate the relationship between CP and cognitive impairment. This study presents a comprehensive analysis of CP prevalence, related sociodemographic and health variables, and its link to cognitive impairment, using representative data of the Chilean population 15 years and older from the 2009-2010 and 2016

In a centralized wholesale power system market scenario, the transmission and distribution constraints limit dispatch and require additional generation to support an integrated area known as Security Generation. This one must be covered in each area, and its cost transferred to the final users. The security generation cost gets higher when operation implicates the use of thermal power plants whose price equation depends on international fuels costs. This paper examines the consequences of a high reliance on thermal power plants. It focuses on scheduled reserves affecting electricity unit costs and the potential for constraints to lead to long-term consequences. The paper analyzes the security generation behavior based on reports and uses a real scenario to support simulations and decisions, evidencing monthly cost and an estimated CO2 emission. Results show the direct cost-saving potential of investing in renewable projects and technology. The paper can allow to replicate the analysis in comparable areas and regions with similar challenges.

Latin American populations may present patterns of sociodemographic, ethnic and cultural diversity that can defy current universal models of healthy aging. The potential combination of risk factors that influence aging across populations in Latin American and Caribbean (LAC) countries is unknown. Compared to other regions where classical factors such as age and sex drive healthy aging, higher disparity-related factors and between-country variability could influence healthy aging in LAC countries. We investigated the combined impact of social determinants of health (SDH), lifestyle factors, cardiometabolic factors, mental health symptoms and demographics (age, sex) on healthy aging (cognition and functional ability) across LAC countries with different levels of socioeconomic development using cross-sectional and longitudinal machine learning models ( n  = 44,394 participants). Risk factors associated with social and health disparities, including SDH ( β  > 0.3), mental health ( β  > 0.6) and cardiometabolic risks ( β  > 0.22), significantly influenced healthy aging more than age and sex (with null or smaller effects: β  < 0.2). These heterogeneous patterns were more pronounced in low-income to middle-income LAC countries compared to high-income LAC countries (cross-sectional comparisons), and in an upper-income to middle-income LAC country, Costa Rica, compared to China, a non-upper-income to middle-income LAC country (longitudinal comparisons). These inequity-associated and region-specific patterns inform national risk assessments of healthy aging in LAC countries and regionally tailored public health interventions. Machine learning models showed that social disparities, cardiometabolic disease and mental health were the main predictors of aging in Latin American populations, with these factors being more pronounced in low- and middle-income compared to high-income Latin American countries.