Search Results (6,348)

To address compacted soils with high power consumption and waterlogging risks in rice–rapeseed rotation areas of the Yangtze River, this study designed a ditching machine combining a stepped cutter head and trapezoidal cleaning blade, where the mechanical synergy between components minimizes energy loss during soil-cutting and -throwing processes. We mathematically modeled soil cutting–throwing dynamics and blade traction forces, integrating soil rheological properties to refine parameter interactions. Discrete Element Method (DEM) simulations and single-factor experiments analyzed impacts of the inner/outer blade widths, blade group distance, and blade opening on power consumption. Results indicated that increasing the inner/outer blade widths (200–300 mm) by expanding the direct cutting area significantly reduced the cutter torque by 32% and traction resistance by 48.6% from reduced soil-blockage drag; larger blade group distance (0–300 mm) initially decreased but later increased power consumption due to soil backflow interference, with peak efficiency at 200 mm spacing; the optimal blade opening (586 mm) minimized the soil accumulation-induced power loss, validated by DEM trajectory analysis showing continuous soil flow. Box–Behnken experiments and genetic algorithm optimization determined the optimal parameters: inner blade width: 200 mm; outer blade width: 300 mm; blade group distance: 200 mm; and blade opening: 586 mm, yielding a simulated power consumption of 27.07 kW. Field tests under typical 18.7% soil moisture conditions confirmed a <10% error between simulated and actual power consumption (28.73 kW), with a 17.3 ± 0.5% reduction versus controls. Stability coefficients for the ditch depth, top/bottom widths exceeded 90%, and the backfill rate was 4.5 ± 0.3%, ensuring effective drainage for rapeseed cultivation. This provides practical theoretical and technical support for efficient ditching equipment in rice–rapeseed rotations, enabling resource-saving design for clay loam soils. Full article

A one-pot synthetic methodology that combines an Ugi-Zhu three-component reaction (UZ-3CR) with a cascade sequence (intermolecular aza Diels–Alder cycloaddition/intramolecular N-acylation/decarboxylation/dehydration) using microwave-heating conditions, ytterbium (III) triflate (Yb(OTf)₃) as the catalyst, and chlorobenzene (for the first time in a multi-component reaction (MCR)) as the solvent, was developed to synthesize twelve new fluorinated-pyrrolo[3,4-b]pyridin-5-ones containing a 4-amino-7-chloroquinoline moiety, yielding 50–77% in 95 min per product, with associated atom economies around 88%, also per product. Additionally, by in vitro tests, compounds 19d and 19i were found to effectively stop early SARS-CoV-2 replication, IC₅₀ = 6.74 µM and 5.29 µM, at 0 h and 1 h respectively, while cell viability remained above 90% relative to the control vehicle at 10 µM. Additional computer-based studies revealed that the most active compounds formed strong favorable interactions with important viral proteins (M_pro, NTDα and NTDo) of coronavirus, supporting a two-pronged approach that affects both how the virus infects the cells and how it replicates its genetic material. Finally, quantum chemistry analyses of non-covalent interactions were performed from Density-Functional Theory (DFT) to better understand how the active compounds hit the virus. Full article

To address the complex pile–soil interaction mechanisms in predicting the compressive bearing capacity of HSCM piles (Helix Stiffened Cement Mixing piles) in marine soft soil regions, this study proposes an intelligent prediction method based on a GA-BPNN (Genetic Algorithm-Optimized Back Propagation Neural Network). A high-quality database comprising 1243 data points was established through finite element numerical simulations. By integrating data preprocessing techniques and the GA-BPNN model, the study systematically investigated the influence of helical blade spacing H₁ and H₂, strength ratio C_ref/S_u, and diameter ratio D_sc/D_H on bearing capacity. The results demonstrate that the GA-BPNN model achieves a prediction accuracy of 99.07%, with a mean squared error (MSE) of 7.20 × 10⁻³ and a coefficient of determination R² of 0.990. SHAP value analysis reveals that the strength ratio and diameter ratio are the dominant factors, exhibiting nonlinear relationships with bearing capacity characterized by saturation effects and threshold-dependent behavior. Laboratory tests further confirm strong correlations between cement–soil strength C_ref, formed pile diameter D_sc, and bearing capacity. The findings indicate that the GA-BPNN model provides an efficient and accurate approach for predicting the bearing capacity of HSCM piles, offering a reliable basis for engineering parameter optimization. Full article

Background and Objectives: The cytokine IL-6, methyltransferase NSD2, pro-protein convertase Furin, and growth factor receptor IGF-1R are essential factors in the proliferation of cancer cells. These proteins are involved in the tumor process by generating several cell-signaling pathways. However, the interactions of these oncogenic biomarkers, Furin, IL-6, and NSD2, and their links with the inhibitor SERPINA-1 remain largely unknown. Materials and Methods: Cell proliferation is measured by colorimetric and enzymatic methods. The genetic expressions of SERPINA-1, Furin, IL-6, and NSD2 are measured by qRT-PCR, while the expression of IGF-1R on the cell surface is measured by flow cytometry. Results: The proliferation of cells overexpressing SERPINA-1 (JP7pSer+) is decreased by more than 90% compared to control cells (JP7pSer-). The kinetics of the gene expression ratios of Furin, IL-6, and NSD2 show an increase for 48 h, followed by a decrease after 72 h for the three biomarkers in JP7pSer+ cells compared to JP7pSer- cells. The expression of IGF-1R on the cell surface in both cell lines is low, with JP7pSer- cells expressing 1.33 times more IGF-1R than JP7pSer+ cells. Conclusions: These results suggest gene correlations of SERPINA-1 overexpression with decreased cell proliferation and modulation of gene expression of Furin, IL-6, and NSD2. This study should be complemented by molecular transcriptomic and proteomic experiments to better understand the interaction of SERPINA-1 with IL-6, Furin, and NSD2, and their effect on tumor progression. Full article

Multiple sclerosis (MS) is a chronic, immune-mediated disorder of the central nervous system (CNS) characterized by inflammation, demyelination, axonal degeneration, and gliosis. Its pathophysiology involves a complex interplay of genetic susceptibility, environmental triggers, and immune dysregulation, ultimately leading to progressive neurodegeneration and functional decline. Although significant advances have been made in disease-modifying therapies (DMTs), many patients continue to experience disease progression and unmet therapeutic needs. Drug repurposing—the identification of new indications for existing drugs—has emerged as a promising strategy in MS research, offering a cost-effective and time-efficient alternative to traditional drug development. Several compounds originally developed for other diseases, including immunomodulatory, anti-inflammatory, and neuroprotective agents, are currently under investigation for their efficacy in MS. Repurposed agents, such as selective sphingosine-1-phosphate (S1P) receptor modulators, kinase inhibitors, and metabolic regulators, have demonstrated potential in promoting neuroprotection, modulating immune responses, and supporting remyelination in both preclinical and clinical settings. Simultaneously, artificial intelligence (AI) is transforming drug discovery and precision medicine in MS. Machine learning and deep learning models are being employed to analyze high-dimensional biomedical data, predict drug–target interactions, streamline drug repurposing workflows, and enhance therapeutic candidate selection. By integrating multiomics and neuroimaging data, AI tools facilitate the identification of novel targets and support patient stratification for individualized treatment. This review highlights recent advances in drug repurposing and discovery for MS, with a particular emphasis on the emerging role of AI in accelerating therapeutic innovation and optimizing treatment strategies. Full article

The relationship between metabolic dysfunction and mental health disorders is complex and has received increasing attention. This review integrates current research to explore how stress-related growth differentiation factor 15 (GDF15) signaling, ceramides derived from gut microbiota, and mitochondrial dysfunction in the brain interact to influence both metabolic and psychiatric conditions. Evidence suggests that these pathways converge to regulate brain energy homeostasis through feedback mechanisms involving the autonomic nervous system and the hypothalamic–pituitary–adrenal axis. GDF15 emerges as a key stress-responsive biomarker that links peripheral metabolism with brainstem GDNF family receptor alpha-like (GFRAL)-mediated anxiety circuits. Meanwhile, ceramides impair hippocampal mitochondrial function via membrane incorporation and disruption of the respiratory chain. These disruptions may contribute to sustained pathological states such as depression, anxiety, and cognitive dysfunction. Although direct mechanistic data are limited, integrating these pathways provides a conceptual framework for understanding metabolic–psychiatric comorbidities. Furthermore, differences in age, sex, and genetics may influence these systems, highlighting the need for personalized interventions. Targeting mitochondrial function, GDF15-GFRAL signaling, and gut microbiota composition may offer new therapeutic strategies. This integrative perspective helps conceptualize how metabolic and psychiatric mechanisms interact for understanding the pathophysiology of metabolic and psychiatric comorbidities and highlights therapeutic targets for precision medicine. Full article

Age-related macular degeneration (AMD) is one of the leading causes of irreversible vision loss among the elderly, and is influenced by a combination of genetic and environmental risk factors. While genetic associations in AMD are well-established, the molecular mechanisms underlying disease onset and progression remain poorly understood. A growing body of evidence suggests that epigenetic modifications may serve as a potential missing link regulating gene–environment interactions. This review incorporates recent findings on DNA methylation, including both hypermethylation and hypomethylation patterns affecting genes such as silent mating type information regulation 2 homolog 1 (SIRT1), glutathione S-transferase isoform (GSTM), and SKI proto-oncogene (SKI), which may influence key pathophysiological drivers of AMD. We also examine histone modification patterns, chromatin accessibility, the status of long non-coding RNAs (lncRNAs) in AMD pathogenesis and in regulating pathways pertinent to the pathophysiology of the disease. While the field of ocular epigenetics remains in its infancy, accumulating evidence to date points to a burgeoning role for epigenetic regulation in AMD, pre-clinical studies have yielded promising findings for the prospect of epigenetics as a future therapeutic avenue. Full article

The frequency of specific variants associated with the risk of developing cardiovascular diseases has been extensively studied through genome-wide association studies (GWASs). Differences between populations may be caused by the interaction of several factors, such as environmental and genetic backgrounds. Here, we studied 19 SNPs involved in atherosclerosis (AT) and venous thromboembolism (VTE) risk in the Azorean and mainland Portuguese populations and compared their frequencies with other European, Asian, and African populations. Results revealed that, although there was no difference between Azorean and mainland populations, eight SNPs in ADAMTS7, PCSK9, APOE, and LDLR genes showed significant statistical differences (χ², p < 0.05) when compared with the European population. The multilocus genetic profile (MGP) analysis demonstrated that 7.4% of mainlanders and 11.2% of Azoreans have a high-risk of developing atherosclerosis. The opposite tendency was observed for venous thromboembolism risk, where the mainland population presented a higher risk (6.5%) than the Azorean population (4.1%). Significant differences in VTE-MGP distribution were found among the Azorean geographic groups (p < 0.05), with the Eastern group showing the highest VTE risk. Conversely, for the risk AT-MGP, the Central group shows the highest risk (12.9%). Taken together, the data suggest a risk of developing a cardiovascular disease consistent with the European population. However, the Azorean-specific genetic background and socio-cultural habits (dietary and sedentary) may explain the differences observed, validating the need to assess the allelic and genotypic frequencies between different populations, especially in small geographical locations, such as the Azores archipelago. In conclusion, these findings can improve the prevention, diagnosis, and treatment of high-risk individuals, and contribute to reducing the lifelong burden of cardiovascular diseases in the Azorean population. Full article

Background/Objectives: Mycobacterium abscessus is an opportunistic pathogen causing infections mainly in patients with immunosuppression and chronic pulmonary pathologies. Extended treatment periods are needed to tackle this pathogen, bacterial eradication is rare, and recurrence can take place with time. New alternative treatments are being investigated, such as bacteriophage therapy. This work describes the characterization of the mycobacteriophage P3MA, showing its ability to infect clinical and standard M. abscessus strains. Methods: Phylogenetic analysis, electron microscopy, growth curves, biofilm assays, checkerboard, and granuloma-like medium studies were performed. Results: P3MA inhibited the growth of clinical samples in both planktonic and biofilm states as well as in a granuloma-like model. The study of the interaction with antibiotics revealed that P3MA exhibited an antagonistic effect combined with clarithromycin, indifference with amikacin, and synergy with imipenem. Conclusions: All these results suggest that, after genetic engineering, P3MA could be a promising candidate for phage therapy in combination with imipenem, including lung infections. Full article

Internet of Things (IoT) applications and services have transformed the way people interact with their environment, enhancing comfort and quality of life. Additionally, Machine Learning (ML) approaches show significant promise for detecting intrusions in IoT environments. However, the high dimensionality, class imbalance, and complexity of network traffic—combined with the dynamic nature of sensor networks—pose substantial challenges to the development of efficient and effective detection algorithms. In this study, a multi-objective metaheuristic optimization approach, referred to as MOOIDS-IoT, is integrated with ML techniques to develop an intelligent cybersecurity system for IoT environments. MOOIDS-IoT combines a Genetic Algorithm (GA)-based feature selection technique with a multi-objective Particle Swarm Optimization (PSO) algorithm. PSO optimizes convergence speed, model complexity, and classification accuracy by dynamically adjusting the weights and thresholds of the deployed classifiers. Furthermore, PSO integrates Pareto-based multi-objective optimization directly into the particle swarm framework, extending conventional swarm intelligence while preserving a diverse set of non-dominated solutions. In addition, the GA reduces training time and eliminates redundancy by identifying the most significant input characteristics. The MOOIDS-IoT framework is evaluated using two lightweight models—MOO-PSO-XGBoost and MOO-PSO-RF—across two benchmark datasets, namely the NSL-KDD and CICIoT2023 datasets. On CICIoT2023, MOO-PSO-RF obtains 91.42% accuracy, whereas MOO-PSO-XGBoost obtains 98.38% accuracy. In addition, both models perform well on NSL-KDD (MOO-PSO-RF: 99.66% accuracy, MOO-PSO-XGBoost: 98.46% accuracy). The proposed approach is particularly appropriate for IoT applications with limited resources, where scalability and model efficiency are crucial considerations. Full article

Background: Polycystic ovary syndrome (PCOS) is a complex and multifactorial disorder affecting reproductive, endocrine, and metabolic functions in women of reproductive age. While environmental and lifestyle factors play a role, increasing evidence highlights the contribution of genetic and epigenetic mechanisms to its pathogenesis. Objective: This narrative review aims to provide an updated overview of the current evidence regarding the role of genetic variants, gene expression patterns, and epigenetic modifications in the etiopathogenesis of PCOS, with a focus on their impact on ovarian function, fertility, and systemic alterations. Methods: A comprehensive search was conducted across MEDLINE, EMBASE, PubMed, Web of Science, and the Cochrane Library using MeSH terms including “PCOS”, “Genes involved in PCOS”, and “Etiopathogenesis of PCOS” from January 2015 to June 2025. The selection process followed the SANRA quality criteria for narrative reviews. Seventeen studies published in English were included, focusing on original data regarding gene expression, polymorphisms, and epigenetic changes associated with PCOS. Results: The studies analyzed revealed a wide array of molecular alterations in PCOS, including the dysregulation of SIRT and estrogen receptor genes, altered transcriptome profiles in cumulus cells, and the involvement of long non-coding RNAs and circular RNAs in granulosa cell function and endometrial receptivity. Epigenetic mechanisms such as the DNA methylation of TGF-β1 and inflammation-related signaling pathways (e.g., TLR4/NF-κB/NLRP3) were also implicated. Some genetic variants—particularly in DENND1A, THADA, and MTNR1B—exhibit signs of positive evolutionary selection, suggesting possible ancestral adaptive roles. Conclusions: PCOS is increasingly recognized as a syndrome with a strong genetic and epigenetic background. The identification of specific molecular signatures holds promise for the development of personalized diagnostic markers and therapeutic targets. Future research should focus on large-scale genomic studies and functional validation to better understand gene–environment interactions and their influence on phenotypic variability in PCOS. Full article

Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal tract. Although the aetiology of IBD remains largely unknown, several studies suggest that an individual’s genetic susceptibility, external environmental factors, intestinal microbial flora, and immune responses are all factors involved in and functionally linked to the pathogenesis of IBD. Beyond the gastrointestinal manifestations, IBD patients frequently suffer from psychiatric comorbidities, particularly depression and anxiety. It remains unclear whether these disorders arise solely from reduced quality of life or whether they share overlapping biological mechanisms with IBD. This review aims to explore the bidirectional relationship between IBD and depressive disorders (DDs), with a focus on four key shared mechanisms: immune dysregulation, genetic susceptibility, alterations in gut microbiota composition, and dysfunction of the hypothalamic–pituitary–adrenal (HPA) axis. By examining recent literature, we highlight how these interconnected systems may contribute to both intestinal inflammation and mood disturbances. Furthermore, we discuss the reciprocal pharmacologic interactions between IBD and DDs: treatments for IBD, such as TNF-alpha and integrin inhibitors, have demonstrated effects on mood and anxiety symptoms, while certain antidepressants appear to exert independent anti-inflammatory properties, potentially reducing the risk or severity of IBD. Overall, this review underscores the need for a multidisciplinary approach to the care of IBD patients, integrating psychological and gastroenterological assessment. A better understanding of the shared pathophysiology may help refine therapeutic strategies and support the development of personalized, gut–brain-targeted interventions. Full article

Background/Objectives: Neuromedin U (NMU) is a highly conserved gene encoding a neuropeptide involved in the regulation of feeding behavior and energy homeostasis. We aimed to analyze the association between NMU genetic and epigenetic variations and cardio-metabolic parameters in an Italian population to identify the role of these variants in cardio-metabolic risk. Methods: A total of 4028 subjects were randomly selected from the Moli-sani study cohort. NMU haplotypes were estimated using seven SNPs located in the gene body and in the promoter region; DNA methylation levels in the promoter region, previously associated with lipid-related variables in the same population, were also used. Results: Among the haplotypes inferred, the haplotype carrying the highest number of minor variants (frequency 16.6%), when compared with the most frequent haplotype, was positively associated with insulin levels, HOMA-IR, and diastolic blood pressure, and negatively with HDL-cholesterol. The multivariable analysis that considered methylation levels along with their interactions with SNPs showed that increased methylation levels in two close CpG sites were associated with higher levels of lipid-related variables. Conclusions: This study supports a role for NMU as a regulator of human metabolism. This finding suggests that NMU could be a potential target for preventive interventions against coronary and cerebrovascular diseases, and that NMU genetic and epigenetic variability may serve as a biomarker for cardio-metabolic risk. Full article

Background/Objectives: Medulloblastoma is the most common malignant brain tumor in children and comprises four molecular subtypes—WNT, SHH, Group 3, and Group 4—each with distinct genetic, epigenetic, and metabolic features. Increasing evidence highlights the critical role of metabolic reprogramming and epigenetic alterations in driving tumor progression, therapy resistance, and clinical outcomes. This review aims to explore the interplay between metabolic and epigenetic mechanisms in medulloblastoma, with a focus on their functional roles and therapeutic implications. Methods: A comprehensive literature review was conducted using PubMed and relevant databases, focusing on recent studies examining metabolic pathways and epigenetic regulation in medulloblastoma subtypes. Particular attention was given to experimental findings from in vitro and in vivo models, as well as emerging preclinical therapeutic strategies targeting these pathways. Results: Medulloblastoma exhibits metabolic adaptations such as increased glycolysis, lipid biosynthesis, and altered amino acid metabolism. These changes support rapid cell proliferation and interact with the tumor microenvironment. Concurrently, epigenetic mechanisms—including DNA methylation, histone modification, chromatin remodeling, and non-coding RNA regulation—contribute to tumor aggressiveness and treatment resistance. Notably, metabolic intermediates often serve as cofactors for epigenetic enzymes, creating feedback loops that reinforce oncogenic states. Preclinical studies suggest that targeting metabolic vulnerabilities or epigenetic regulators—and particularly their combination—can suppress tumor growth and overcome resistance mechanisms. Conclusions: The metabolic–epigenetic crosstalk in medulloblastoma represents a promising area for therapeutic innovation. Understanding subtype-specific dependencies and integrating biomarkers for patient stratification could facilitate the development of precision medicine approaches that improve outcomes and reduce long-term treatment-related toxicity in pediatric patients. Full article

The compound effects of extreme climate change and intensive urban development have led to more frequent urban inundation, highlighting the urgent need for the fine-scale evaluation of stormwater drainage system performance in high-density urban built-up areas. A typical basin, located in Shenzhen, was selected, and a pipe–river coupled SWMM was developed and calibrated via a genetic algorithm to simulate the storm drainage system. Design storm scenario analyses revealed that regional inundation occurred in the central area of the basin and the enclosed culvert sections of the midstream river, even under a 0.5-year recurrence period, while the downstream open river channels maintained a substantial drainage capacity under a 200-year rainfall event. To systematically identify bottleneck zones, two novel metrics, namely, the node cumulative inundation volume and the conduit cumulative inundation length, were proposed to quantify the local inundation severity and spatial interactions across the drainage network. Two critical bottleneck zones were selected, and strategic improvement via the cross-sectional expansion of pipes and river culverts significantly enhanced the drainage efficiency. This study provides a practical case study and transferable technical framework for integrating hydraulic modeling, spatial analytics, and targeted infrastructure upgrades to enhance the resilience of drainage systems in high-density urban environments, offering an actionable framework for sustainable urban stormwater drainage system management. Full article