Search Results (503)

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

Cancer disparities in low- and middle-income countries (LMICs) arise from multifaceted interactions between environmental exposures, infectious agents, and systemic inequities, such as limited access to care. The exposome, a framework encompassing the totality of non-genetic exposures throughout life, offers a powerful lens for understanding these disparities. In LMICs, populations are disproportionately affected by air and water pollution, occupational hazards, and oncogenic infections, including human papillomavirus (HPV), hepatitis B virus (HBV), Helicobacter pylori (H. pylori), human immunodeficiency virus (HIV), and neglected tropical diseases, such as schistosomiasis. These infectious agents contribute to increased cancer susceptibility and poor outcomes, particularly in immunocompromised individuals. Moreover, climate change, food insecurity, and barriers to healthcare access exacerbate these risks. This review adopts a population-level exposome approach to explore how environmental and infectious exposures intersect with genetic, epigenetic, and immune mechanisms to influence cancer incidence and progression in LMICs. We highlight the critical pathways linking chronic exposure and inflammation to tumor development and evaluate strategies such as HPV and HBV vaccination, antiretroviral therapy, and environmental regulation. Special attention is given to tools such as exposome-wide association studies (ExWASs), which offer promise for exposure surveillance, early detection, and public health policy. By integrating exposomic insights into national health systems, especially in regions such as sub-Saharan Africa (SSA) and South Asia, LMICs can advance equitable cancer prevention and control strategies. A holistic, exposome-informed strategy is essential for reducing global cancer disparities and improving outcomes in vulnerable populations. Full article

Conventional methods for generating knock-out or knock-in mammalian cell models using CRISPR-Cas9 genome editing often require tedious single-cell clone selection and expansion. In this study, we develop and optimise rapid and robust strategies to engineer homozygous fluorescent reporter knock-in cell pools with precise genome editing, circumventing clonal variability inherent to traditional approaches. To reduce false-positive cells associated with random integration, we optimise the design of donor DNA by removing the start codon of the fluorescent reporter and incorporating a self-cleaving T2A peptide system. Using fluorescence-assisted cell sorting (FACS), we efficiently identify and isolate the desired homozygous fluorescent knock-in clones, establishing stable cell pools that preserve parental cell line heterogeneity and faithfully reflect endogenous transcriptional regulation of the target gene. We evaluate the knock-in efficiency and rate of undesired random integration in the electroporation method with either a dual-plasmid system (sgRNA and donor DNA in two separate vectors) or a single-plasmid system (sgRNA and donor DNA combined in one vector). We further demonstrate that coupling our single-plasmid construct with an integrase-deficient lentivirus vector (IDLV) packaging system efficiently generates fluorescent knock-in reporter cell pools, offering flexibility between electroporation and lentivirus transduction methods. Notably, compared to the electroporation methods, the IDLV system significantly minimises random integration. Moreover, the resulting reporter cell lines are compatible with most of the available genome-wide sgRNA libraries, enabling unbiased CRISPR screens to identify key transcriptional regulators of a gene of interest. Overall, our methodologies provide a powerful genetic tool for rapid and robust generation of fluorescent reporter knock-in cell pools with precise genome editing by CRISPR-Cas9 for various research purposes. Full article

Background/Objectives: SIRT1 is a NAD⁺-dependent protein deacetylase regulating metabolic and stress response pathways. Genetic variations in the SIRT1 gene may contribute to the pathogenesis of type 2 diabetes mellitus (T2DM). This case–control study investigates the associations of two SIRT1 promoter polymorphisms, rs12778366 and rs3758391, in patients with type 2 diabetes mellitus (T2DM), gestational diabetes mellitus (GDM), preeclampsia, and healthy controls. Methods: This case–control study compared the genotypes between T2DM and pregnant and non-pregnant controls. We also compared genotypes between pregnant women with T2DM, GDM, preeclampsia, and healthy pregnant controls. Genomic DNA was extracted and analyzed using PCR-RFLP for the detection of rs12778366 and rs3758391 polymorphisms. Genotype frequencies were compared using chi-square tests, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results: The study included 66 patients with T2DM, 36 with GDM, 12 with preeclampsia, and 81 pregnant and non-pregnant controls (33 pregnant controls). Although rs3758391 was more frequent in T2DM, the difference was not statistically significant between SIRT1 polymorphisms and T2DM. The CT genotype was more prevalent in T2DM (54.5%) compared to controls (33.4%); however, this difference was not significant. We finally found no significant association of the investigated SIRT1 polymorphisms with any of the conditions studied. In addition, the small sample size, especially for preeclampsia cases, limits the statistical power to detect significant associations. Conclusions: Although no significant association was observed between SIRT1 polymorphisms and diabetes, the findings of our study underscore the need for further studies examining SIRT1 polymorphisms in various ethnic groups, with a focus on leveraging these genetic variations in diabetes pathophysiology. Larger studies in the Greek population could also provide additional meaningful findings. Full article

Background/Objectives: Maternal exposures are known to influence the risk of isolated cleft lip with or without cleft palate (CL/P)—a common and highly heritable birth defect with a multifactorial etiology. Methods: To identify new risk loci, we conducted a genome-wide gene–environment interaction (GEI) analysis of CL/P with maternal smoking and vitamin use in Filipinos (N_cases = 540, N_controls = 260). Since GEI analyses are typically low in power and the results can be difficult to interpret, we applied multiple testing frameworks to evaluate potential GEI effects: a one degree-of-freedom (1df) GxE test, the 3df joint test, and the two-step EDGE approach. Results: While no genome-wide significant interactions were detected, we identified 11 suggestive GEIs with smoking and 24 with vitamin use. Several implicated loci contain biologically plausible genes. Notable interactions with smoking include loci near FEZF1, TWIST2, and NET1. While FEZF1 is involved in early neuronal development, TWIST2 and NET1 regulate epithelial–mesenchymal transition, which is required for proper lip and palate fusion. Interactions with vitamins encompass CECR2—a chromatin remodeling protein required for neural tube closure—and FURIN, a critical protease during early embryogenesis that activates various growth factors and extracellular matrix proteins. The activity of both proteins is influenced by folic acid. Conclusions: Our findings highlight the critical role of maternal exposures in identifying genes associated with structural birth defects such as CL/P and provide new paths to explore for CL/P genetics. Full article

Neurodegenerative diseases (NDs) pose a major challenge to global healthcare systems owing to their devastating effects and limited treatment options. These disorders are characterized by progressive loss of neuronal structure and function, resulting in cognitive and motor impairments. Current therapies primarily focus on symptom management rather than on targeting the underlying causes. However, clustered regularly interspaced short palindromic repeat (CRISPR) technology offers a promising alternative by enabling precise genetic modifications that could halt or even reverse ND progression. CRISPR-Cas9, the most widely used CRISPR system, acts as a molecular scissor targeting specific DNA sequences for editing. By designing guide RNAs (gRNAs) to match sequences in genes associated with NDs, researchers can leverage CRISPR to knockout harmful genes, correct mutations, or insert protective genes. This review explores the potential of CRISPR-based therapies in comparison with traditional treatments for NDs. As research advances, CRISPR has the potential to revolutionize ND treatment by addressing its genetic underpinnings. Ongoing clinical trials and preclinical studies continue to expand our understanding and application of this powerful tool to fight debilitating conditions. Full article

Background/Objectives: Obesity, a major risk factor for cardiometabolic traits, is influenced by both genetic and environmental factors. Genetic studies have identified multiple single-nucleotide polymorphisms (SNPs) associated with obesity and related traits. This study aimed to examine the association between genetic risk score (GRS) and obesity-associated traits, while incorporating SNPs with established gene–diet interactions to explore their potential role in precision nutrition (PN) strategies. Methods: A total of 4279 participants were stratified into low- and intermediate-/high-GRS groups based on 18 SNPs linked to obesity and cardiometabolic traits. This study followed a case–control design, where cases included individuals with overweight/obesity, T2DM-positive (+), or CVD-positive (+) individuals and controls, which comprised individuals free of these traits. Logistic regression area under the curve (AUC) models were used to assess the predictive power of the GRS and traditional risk factors on BMI, T2DM and CVD. Results: Individuals in the intermediate-/high-GRS group had higher odds of being overweight or obese (OR = 1.23, CI: 1.03–1.48, p = 0.02), presenting as T2DM+ (OR = 1.56, CI: 1.03–2.49, p = 0.03) and exhibiting CVD-related traits (OR = 1.56, CI: 1.25–1.95, p < 0.0001), compared to the low-GRS group. The GRS was the second most predictive factor after age for BMI (AUC = 0.515; 95% CI: 0.462–0.538). The GRS also demonstrated a predictive power of 0.528 (95% CI: 0.508–0.564) for CVD and 0.548 (95% CI: 0.440–0.605) for T2DM. Conclusions: This study supports the potential utility of the GRS in assessing obesity and cardiometabolic risk, while emphasizing the potential of PN approaches in modulating genetic susceptibility. Incorporating gene–diet interactions provides actionable insights for personalized dietary strategies. Future research should integrate multiple gene–diet and gene–gene interactions to enhance risk prediction and targeted interventions. Full article

Common carp (Cyprinus carpio) is one of the most significant fish species worldwide, with its natural distribution spanning Europe and Asia. To conduct a genome-wide association study (GWAS) and compare the prediction accuracy of genomic selection (GS) models for the growth traits of common carp in spring and autumn at 2 years of age, a total of 325 carp individuals were re-sequenced and phenotypic measurements were taken. Three GWAS methods (FarmCPU, GEMMA, and GLM) were applied and their performance was evaluated in conjunction with various GS models, using significance levels based on p-values. GWAS analyses were performed on eight traits (including the body length, body weight, fat content of fillet, and condition factor) for both spring and autumn seasons. Eleven different GS models (such as Bayes A, Bayes B, and SVR-linear) were combined to evaluate their performance in genomic selection. The results demonstrate that the FarmCPU method consistently exhibits superior stability and predictive accuracy across most traits, particularly under higher SNP densities (e.g., 5K), where prediction accuracies frequently exceed 0.8. Notably, when integrated with Bayesian approaches, FarmCPU achieves a substantial performance boost, with the prediction accuracy reaching as high as 0.95 for the autumn body weight, highlighting its potential for high-resolution genomic prediction. In contrast, GEMMA and GLM exhibited a more variable performance at lower SNP densities. Overall, the integration of FarmCPU with genomic selection (GS) models offers one of the most reliable and efficient frameworks for trait prediction, particularly for complex traits with substantial genetic variation. This approach proves especially powerful when coupled with Bayesian methodologies, further enhancing its applicability in advanced breeding programs. Full article

Background/Objetives: Sea lice (Caligus rogercresseyi) pose a major threat to Atlantic salmon (Salmo salar) aquaculture by compromising fish health and reducing production efficiency. While genetic variation in parasite load has been reported, the molecular mechanisms underlying this variation remain unclear. Methods: two sea lice challenge trials were conducted, achieving high infestation rates (47.5% and 43.5%). A total of 85 fish, selected based on extreme phenotypes for lice burden (42 low, 43 high), were subjected to transcriptomic analysis. Differential gene expression was integrated with allele-specific expression (ASE) analysis to uncover cis-regulatory variation influencing host response. Results: Sixty genes showed significant ASE (p < 0.05), including 33 overexpressed and 27 underexpressed. Overexpressed ASE genes included Keratin 15, Collagen IV/V, TRIM16, and Angiopoietin-1-like, which are associated with epithelial integrity, immune response, and tissue remodeling. Underexpressed ASE genes such as SOCS3, CSF3R, and Neutrophil cytosolic factor suggest individual variation in cytokine signaling and oxidative stress pathways. Conclusions: several ASE genes co-localized with previously identified QTLs for sea lice resistance, indicating that cis-regulatory variants contribute to phenotypic differences in parasite susceptibility. These results highlight ASE analysis as a powerful tool to identify functional regulatory elements and provide valuable candidates for selective breeding and genomic improvement strategies in aquaculture. Full article

Background/Objectives: Obesity is associated with cardiovascular disease worldwide. An increased lipoprotein A (LpA) level is an independent risk factor for cardiovascular disease in children. Genetic polymorphisms of the LPA gene may play an important role in susceptibility to obesity. The aim of this study was to investigate the association of LPA rs10455872 polymorphism with the risk and clinical phenotypes of childhood obesity. Methods: This study included 103 children with obesity and 77 healthy controls. Genotyping of the LPA rs10455872 polymorphism was performed using real-time PCR. Results: The genotype distributions of the LPA rs10455872 polymorphism did not differ significantly between children with obesity and healthy children (p = 0.563). A marked difference in insulin levels was observed between children with obesity carrying the AG (16.90 IU/mL) and AA (25.57 IU/mL) genotypes. A marked difference was also observed in CRP levels between children with obesity with the AG (2.31 mg/L) and AA (4.25 mg/L) genotypes. After correcting for multiple comparisons using the false discovery rate (FDR), significant differences were found between AG and AA genotypes in vitamin B12 (adjusted p = 0.024). Serum iron showed a borderline association (adjusted p = 0.072). A statistically significant correlation was found between the metabolic syndrome index and body fat ratio among children with obesity with the AA genotype (p = 0.028). Conclusions: Although limited by the small number of children with obesity with the AG genotype, some differences were noted between the AG and AA genotypes. These exploratory findings require further investigation in adequately powered studies. In children with obesity with the AA genotype, the metabolic syndrome index increases as the body fat ratio increases. Full article

Type 2 diabetes (T2D) represents a public health problem globally, with the highest prevalence reported among older adults. While an interplay of various determinants including genetic, epigenetic, environmental factors and unhealthy lifestyle, particularly diet, has been established to contribute to T2D development, emerging evidence supports the role of interactions between nutrients or dietary patterns and genes in the pathogenesis of this metabolic disorder. The amount, and especially the type of carbohydrates, in particular, have been correlated with the risk of non-communicable chronic disease and mortality. This narrative review aims to discuss the updated data on the complex and not fully elucidated relationship between carbohydrate–gene interactions and incidence of T2D, identifying the most susceptible genes able to modulate the dual association between carbohydrate intake and risk of developing T2D. The identification of genetic polymorphisms in response to this macronutrient represents a potentially powerful target to estimate individual risk and prevent the development of T2D in the context of personalized medicine. The postulation around novel foods potentially tailored to minimize the risks of developing T2D will pave the way for a new era into food research in relation to the safeguarding of well-being status in patients affected by, or at risk for, T2D. Full article

Background/Objectives: Wolf–Hirschhorn syndrome (WHS; OMIM #194190) is a rare neurodevelopmental disorder, caused by deletions in the distal short arm of chromosome 4. It is characterized by developmental delay, epilepsy, intellectual disability, and distinctive facial dysmorphism. Clinical presentation varies widely, complicating prognosis and individualized care. Methods: We assembled a cohort of 140 individuals with genetically confirmed WHS from Spain and Latin-America, and developed and validated a multidimensional, Clinician-Reported Outcome Assessment (ClinRO) based on the Global Functional Assessment of the Patient (GFAP), derived from standardized clinical questionnaires and weighted by HPO (Human Phenotype Ontology) term frequencies. The GFAP score quantitatively captures key functional domains in WHS, including neurodevelopment, epilepsy, comorbidities, and age-corrected developmental milestones (selected based on clinical experience and disease burden). Results: Higher GFAP scores are associated with worse clinical outcomes. GFAP showed strong correlations with deletion size, presence of additional genomic rearrangements, sex, and epilepsy severity. Ward’s clustering and discriminant analyses confirmed GFAP’s discriminative power, classifying over 90% of patients into clinically meaningful groups with different prognoses. Conclusions: Our findings support GFAP as a robust, WHS-specific ClinRO that may aid in stratification, prognosis, and clinical management. This tool may also serve future interventional studies as a standardized outcome measure. Beyond its clinical utility, GFAP also revealed substantial social implications. This underscores the broader socioeconomic burden of WHS and the potential value of GFAP in identifying high-support families that may benefit from targeted resources and services. Full article

This study aimed to investigate population genetic differences related to reproductive traits between Duroc and Yorkshire (Dutch Large White) pigs using two approaches: linear mixed models that dissect additive and dominant effects, and selective sweep analysis. (1) Methods: Genome-wide single-nucleotide polymorphism (SNP) data of 3917 Duroc and 3217 Yorkshire pigs were analyzed. The first principal component (PC1) was used as a simulated phenotype to capture population-level variance. Additive and dominant genetic effects were partitioned and evaluated by using the combination of the linear mixed models (LMM) and ADDO’s algorithm (LMM + ADDO). In parallel, selective sweep signals were detected using fixation index (F_ST) and nucleotide diversity (θ_π) analyses. A comparative assessment was then conducted between the LMM + ADDO and the selective sweep analysis results. Significant loci were annotated using quantitative trait loci (QTL) databases and the Ensembl genome browser. (2) Results: There are 39040 SNPs retained after quality control. Using the LMM + ADDO framework with PC1 as a simulated phenotype, a total of 632 significant SNPs and 184 candidate genes were identified. Notably, 587 SNPs and 171 genes were uniquely detected by the LMM + ADDO method and not among loci detected by the top 5% of F_ST and θ_π values. Key candidate genes associated with litter size included HSPG2, KAT6B, SAMD8, and LRMDA, while DLGAP1, MYOM1, and VTI1A were associated with teat number traits. (3) Conclusions: This study demonstrates the power of integrating additive and dominant effect modeling with population genetics approaches for the detection of genomic regions under selection. The findings provide novel insights into the genetic architecture of reproductive traits in pigs and have practical implications for understanding the inheritance of complex traits. Full article

Staphylococcus aureus (S. aureus) is a major cause of opportunistic infections in humans and animals, leading to severe systemic diseases. The rise of MDR strains associated with animal carriage poses significant health challenges, underscoring the need to investigate animal-derived S. aureus. Objectives: This study examined the genotypic relatedness and phenotypic profiles of antimicrobial resistance in S. aureus, previously sampled from nostril swabs of healthy horses from two geographically distant Brazilian states (Northeast and South), separated by over 3700 km. The study also sought to confirm the presence of methicillin-resistant (MRSA) and borderline oxacillin-resistant (BORSA) strains and to characterize the isolates through molecular typing using PCR. Methods: Among 123 screened staphylococci, 21 isolates were confirmed as S. aureus via biochemical tests and PCR targeting species-specific genes (femA, nuc, coa). Results: REP-PCR analysis generated genotypic profiles, revealing four antimicrobial resistance patterns, with MDR observed in ten isolates. Six isolates exhibited cefoxitin resistance, suggesting methicillin resistance, despite the absence of the mecA gene. REP-PCR demonstrated high discriminatory power, grouping the isolates into five major clusters. Conclusions: The genotyping indicated no clustering by geographical origin, highlighting significant genetic diversity among S. aureus strains colonizing horses’ nostrils in Brazil. These findings highlight the widespread and varied nature of S. aureus among horses, contributing to a deeper understanding of its epidemiology and resistance profiles in animals across diverse regions. Ultimately, this genetic diversity can pose a public health risk that the epidemiological surveillance services must investigate. Full article

Genome-wide association studies (GWASs) play a central role in precision medicine, powering a range of clinical applications from pharmacogenomics to disease risk prediction. A critical component of GWASs is genotype imputation, a computational method used to infer untyped genetic variants. While imputation increases variant coverage by estimating genotypes at untyped loci, this expanded coverage can enhance the ability to detect genetic associations in some cases. However, imputation also introduces biases, particularly for rare variants and underrepresented populations, which may compromise clinical accuracy. This review examines the challenges and clinical implications of genotype imputation errors, including their impact on therapeutic decisions and predictive models, like polygenic risk scores (PRSs). In particular, the sources of imputation errors have been deeply explored, emphasizing the disparities in performance across ancestral populations and downstream effects on healthcare equity and addressing ethical considerations surrounding the access to equitable genomic resources. Based on the above, we propose evidence-based best practices for clinical GWAS implementation, including the direct genotyping of clinically actionable variants, the cross-population validation of imputation models, the transparent reporting of imputation quality metrics, and the use of ancestry-matched reference panels. As genomic data becomes increasingly adopted in healthcare systems worldwide, ensuring the accuracy and inclusivity of GWAS-derived insights is paramount. Here, we suggest a framework for the responsible clinical integration of imputed genetic data, paving the way for more reliable and equitable personalized medicine. Full article