Search Results (8,572)

Intracranial aneurysms are common vascular lesions with a highly variable natural history. While most unruptured intracranial aneurysms remain stable throughout life, a biologically aggressive subset progresses to growth and rupture, resulting in aneurysmal subarachnoid hemorrhage with substantial morbidity and mortality. Contemporary evidence demonstrates that aneurysm behavior is dynamic rather than static and reflects the interaction of hemodynamic forces, inflammatory vascular remodeling, genetic susceptibility, and environmental risk factors. Rupture risk is not constant over time and may be highest early after aneurysm formation, followed by a period of relative quiescence in selected lesions. Traditional population-based risk estimates have therefore evolved toward individualized risk stratification incorporating aneurysm size, location, morphology, growth, patient-specific factors, and emerging imaging and computational biomarkers. This chapter reviews the epidemiology, pathobiology, growth patterns, and rupture risk of intracranial aneurysms, integrating foundational observational studies with recent advances in genetics, vessel wall imaging, and predictive modeling. Understanding the natural history of brain aneurysms is essential for balancing the risks of observation against intervention and for guiding future innovations in aneurysm management. Full article

Background: High infectious disease burden and uncontrolled antibiotic usage across human, animal, and environmental contaminants make antimicrobial resistance (AMR) a growing public health problem in Africa. Mobile genetic elements (MGEs) such plasmids, transposons, integrons, conjugative elements, and phages help spread AMR via horizontal gene transfer (HGT) across human, animal, food, and environmental sources. Despite growing evidence for antibiotic resistance genes (ARGs), Africa lacks a one-health-focused synthesis of mobile genetic element-mediated AMR. Objective: This systematic review and meta-analysis aimed to consolidate information on MGEs and ARGs in AMR dissemination throughout Africa’s one health interface. Methods: The literature was searched using PubMed, Scopus, and ScienceDirect. Observational. molecular epidemiology, whole genome sequencing (WGS), and metagenomic investigations of MGE-associated AMR in Africa were eligible. The study selection, data extraction, and quality assessment were performed by two independent reviewer and quality was graded using ROBVIS 2 utilizing Rayyan software. Narrative synthesis, random-effect meta-analysis, subgroup analysis, and meta-regression were utilized. Results: A total of 109 studies were included, with 91 studies contributing to the meta-analysis. MGEs reported were plasmids (71.7%) and integrons (54.8%). ARGs carried by MGEs were blaCTMX-M-15 (78.6%), Sul2 (69.6%), blaTEM (59.1%), and tetA (49.9%). Horizontal gene transfer was seen in 259 instances; however, transmission was unclear. In 442 observations, transmission pathways across human, animal, and environmental interfaces showed AMR prevalence of 75.1% in human, 98.0% in human–animal, and 61.3% in one health interface. Whole-genome sequencing was the most frequently used method for detecting MGEsThe pooled pathogen and AMR prevalence rates were 73.3% (95% CI: 60.5–83.7%) and 94% (95% CI: 85–98%), with significant heterogeneity (I² = 97.8% and 97.4%, respectively). The prevalence of Escherichia coli was 93% and Salmonella enterica 85% in subgroup analysis. Fluoroquinolones, aminoglycosides, and beta-lactams were prevalent in humans (89.7%) and human–animal interactions (98.0%) according to AMR Class. Conclusions: Horizontal gene transfer has propagated MGE-mediated antimicrobial resistance across human, animal, and environmental interfaces in Africa. To combat AMR in Africa, coordinated, genomics-informed One Health surveillance and antibiotic stewardship are needed. Due to variability and publication bias, these data should be considered cautiously. Pooled data may only show descriptive patterns, and not necessarily precise continent-wide prevalence estimates. Full article

Soybean rust caused by the fungus Phakopsora pachyrhizi threatens global soybean production, causing yield losses of up to 80%. Race-specific Rpp genes provide short-term resistance due to pathogen variability, whereas partial resistance (PR) offers durable, broad-spectrum protection, though its genetic basis remains unclear. This study aimed to identify genomic regions and candidate genes underlying PR using the Fixed and Random Model Circulating Probability Unification (FarmCPU) genome-wide association study (GWAS) and machine learning (ML) methods, Random Forest (RF) and Support Vector Regression (SVR). A panel of 312 soybean accessions was evaluated under natural infection across six Ugandan environments. Rust index (RI), derived from rust severity and sporulation level, was used to estimate heritability (H²) and rank genotypes through Best Linear Unbiased Predictions (BLUPs), while Best Linear Unbiased Estimators (BLUEs) supported GWAS input. After quality control, 8272 SNPs were analyzed within a ±60 kb linkage disequilibrium (LD) window. Multi-environmental Analysis (MEA) of RI showed significant genetic effects (p < 0.01); H² = 0.57–0.68. Sixty-one loci were detected: six by FarmCPU, 15 by RF, and 41 by SVR. Key genes included Glyma.01G128100 (a WRKY transcription factor) and Glyma. 13G228000, receptor-like kinase) and Glyma.20G173100 (WD40-domain regulator). Integrating ML with GWAS improved locus detection, confirming the polygenic nature of PR and supporting the use of genomic selection and locus pyramiding for durable rust resistance. Full article

Wild relatives of cultivated legumes represent valuable genetic resources for crop improvement, ecosystem resilience, and sustainable agriculture. This study presents a comparative ecological and metagenomic assessment of three biogeographically distinct regions in Bulgaria—Kaliakra, Strandzha, and the Eastern Rhodopes—where populations of wild legumes, including Pisum elatius, Cicer montbrettii, Vicia incisa, and Lupinus spp., occur. Field expeditions were conducted during flowering and seed maturation stages, followed by rhizosphere soil sampling and high-throughput sequencing targeting bacterial 16S rRNA and fungal ITS regions. Soil physicochemical properties, microbial diversity indices, and taxonomic composition were analysed and compared among regions. Distinct microbial community structures were identified. Kaliakra soils were dominated by Firmicutes (26–58%) and Proteobacteria (20–25%), while Strandzha soils showed higher abundance of Actinobacteriota (12–68%) and Proteobacteria (10–35%). The Eastern Rhodopes exhibited more balanced communities, with Proteobacteria (30–45%), Firmicutes (7–43%), and Actinobacteriota (3–11%). Fungal communities also differed significantly, with Nectriaceae dominating in Kaliakra, higher evenness in Strandzha, and intermediate diversity in the Eastern Rhodopes. Alpha diversity revealed the highest bacterial richness in Kaliakra, whereas the Eastern Rhodopes showed the greatest community evenness. Beta diversity analysis demonstrated clear regional separation driven by environmental filtering. These findings highlight the ecological and microbiological differentiation of wild legume habitats and support their conservation and utilisation in sustainable agriculture and breeding programs. Full article

Bolting height is a key genetic trait that affects the stress tolerance, environmental adaptation, and winter survival of Brassica napus winter rapeseed. It is particularly important for enhancing winter survival in the arid–frigid regions. This study aimed to elucidate the genetic relationship between bolting height and cold stress tolerance, thereby supporting breeding for enhanced cold tolerance. Ninety-five winter rapeseed accessions were used in this study. Through both spring and autumn sowing trials, the dynamic changes in bolting height under different environments were systematically analyzed, and the genetic stability of bolting height as well as its correlation with cold tolerance were clarified. Bolting height showed consistent variation trends between spring and autumn sowing trials, exhibiting high genetic stability. It displayed an extremely significant negative correlation with cold tolerance: genotypes with lower bolting height possessed stronger cold tolerance. The regulatory mechanism underlying low bolting and cold tolerance was revealed at cellular and molecular levels. Low bolting accessions exhibited flat and broad shoot apical meristems, with small and compact cells, a high nucleoplasmic ratio, and indistinct vacuoles. The gibberellin synthesis gene BnaA06g24070D was downregulated, while the key cold-tolerant gene BnCBF5 was upregulated. Exogenous hormone treatment preliminarily verified the causal regulatory effect of bolting height on cold tolerance. In both spring and autumn sowing trials, bolting height at the initial flowering stage showed an extremely significant positive correlation with vernalization index, with correlation coefficients of 0.80 and 0.78, respectively. Lower bolting height corresponded to a smaller vernalization index and stronger temperature sensitivity. Moreover, bolting height at the initial flowering stage showed an extremely significant negative correlation with comprehensive cold tolerance scores, with correlation coefficients of −0.77 and −0.80, respectively. Low-bolt materials had significantly higher overwintering rates and comprehensive cold tolerance scores, as well as a markedly lower semi-lethal temperature (LT₅₀), compared with high-bolt accessions. Low-bolt accessions presented significantly prolonged bolting stage, bud stage, initial flowering stage, and whole growth durations, and their agronomic trait stability across years was significantly superior to that of high-bolt accessions. This study confirmed that low bolting height is a crucial breeding trait for the cold tolerance of winter rapeseed, and thus an important selection indicator for the cold tolerance improvement of winter rapeseed in arid–frigid regions in northern China. Full article

Jacalin-related lectins play crucial roles in plant adaptation to abiotic and biotic stresses. The rice genome encodes four putative jacalin-related lectin kinase genes (OsJacLKs), but their functions toward environmental stresses remain largely uncharacterized. This study demonstrates that a putative jacalin-related lectin kinase, OsJacLK1, conferred resistance to the rice blast fungus Magnaporthe oryzae rather than salt stress. OsJacLK1 protein exhibited agglutination activities and affinity toward chitin, fungal cell wall, and mannose. OsJacLK1 was transcriptionally activated by stress-related phytohormones salicylic acid (SA), methyl jasmonate (MeJA), abscisic acid (ABA), and indoleacetic acid (IAA), as well as salinity, chitin, and M. oryzae inoculation, suggesting its involvement in broad stress-responsive signaling pathways. Overexpression of OsJacLK1 in rice led to reduced susceptibility to rice blast disease, whereas loss-of-function osjaclk1 lines showed no significant phenotypic difference from wild-type plants upon infection. Enhanced resistance in OsJacLK1-overexpressing lines was associated with a stronger reactive oxygen species (ROS) burst and elevated hydrogen peroxide accumulation, accompanied by the up-regulation of defense-related genes (OsRac1, OsSGT1, OsMAPK6, OsPAL1, OsNAC4, OsPBZ1, OsAOS2, and OsJAZ8). Collectively, our findings establish that OsJacLK1 acts as a positive regulator of rice immunity against M. oryzae, modulating the cellular redox state, highlighting its potential as a candidate for genetic improvement of disease resistance in rice. Full article

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS) that impacts nearly 3 million people worldwide. While the etiology and pathogenesis of MS are not yet fully understood, current evidence suggests that it results from complex interactions between genetic and environmental conditions. Clarifying the autoimmune mechanisms underlying MS remains a central objective in the development of effective therapeutic strategies. Interferon-beta (IFN-$\beta$) is one of the most frequently prescribed disease-modifying treatments for individuals with MS. However, despite its established efficacy, recent studies report that approximately 30–50% of patients exhibit inadequate response to IFN-$\beta$, largely due to genetic variability. Machine learning (ML), a branch of artificial intelligence (AI), employs data-driven computational models to enhance predictive accuracy and classification. In recent MS research, unsupervised learning techniques such as hierarchical clustering and K-means have been applied for classification purposes. However, these methods often fail to yield optimal solutions because they require numerous arbitrary decisions and perform adequately only when datasets contain clusters of similar sizes and lack significant outliers. Fuzzy systems (FSs) are designed to model complex, ambiguous real-world phenomena. In this study, an AI algorithm incorporating a fuzzy system, informed by expert neurologist input, is proposed to enhance the assignment of unknown class labels related to IFN-$\beta$ response in MS patients. Additionally, a genetic algorithm (GA) is introduced to identify optimal solutions within the search space, facilitating hyperparameter optimization of a deep learning (DL) model trained with genetic biomarkers to identify patients likely to benefit from this therapy. Experimental results demonstrate that the fuzzy system achieved $80\%$ classification efficiency, in contrast to $64\%$ with conventional hierarchical clustering. Furthermore, an artificial neural network (ANN) model, with hyperparameters optimized by the GA, achieved an accuracy of 0.8–1.0, surpassing the multi-layer perceptron (MLP), which achieved 0.6–0.8 accuracy using conventional tuning methods. Full article

Background/Objectives: Type 1 diabetes (T1D) and multiple sclerosis (MS) are autoimmune, multifactorial, organ-specific disorders mediated by immune cells. Their co-occurrence has been partially attributed to shared genetics and environmental factors. We aimed to dissect the shared genetic architecture between T1D and MS using large-scale genome-wide association studies (GWASs) and colocalization analyses. Methods: We applied a Bayesian colocalization framework to two large-scale GWAS data sets: a T1D study comprising 18,942 cases and 501,638 controls, and an MS GWAS including 14,802 cases and 26,703 controls. Results: We identified 26 shared colocalizing association signals between T1D and MS. Among them, seven loci (EOMES, RGS14, DLL1, ZNF438/ZEB1, SESN3, WARS1/SLC25A47, and IRF8) were novel for T1D and two (UBAC2 and LAT) for MS. Several signals showed supportive evidence in additional datasets and demonstrated functional annotation characteristics consistent with disease involvement. Conclusions: Colocalization can be a powerful discovery tool for disorders with co-divided genetic architecture, as prioritizing shared rather than individual causal variants may enhance the detection of novel loci. Our findings indicate that T1D and MS predominantly share general autoimmune susceptibility signals (17/26), rather than disease-specific (private), often with opposite direction of effect (9/26), underscoring their immunological heterogeneity. Full article

Purpose: Obesity, characterized by abnormal fat accumulation with comorbidities, continues to increase dramatically, particularly in the pediatric population. Identifying the environmental and genetic causes underlying the development of obesity during early childhood is crucial for establishing preventive and protective treatments for this complex disease. We aimed to investigate genetic variants related to non-syndromic early-onset childhood obesity. Methods: Whole-exome sequencing was performed in three independent consanguineous families with obesity, including three index cases and two additional affected siblings. Non-synonymous variants with minor allele frequency < 0.01 in all normal populations were filtered using the Genomize-SEQ Platform. Variant confirmations and familial segregations were analyzed by Sanger sequencing. Results: WES revealed a shared ATXN3 gene variant and two known variants of the SH2B1 and ADIPOQ genes, which were reported to be associated with obesity. Additionally, five heterozygous novel gene variants of the ANKK1, NEGR1, OGDH, ABCB1, and GSK3B genes were identified, which are predicted to cause excessive fat accumulation and disruption of energy balance in individuals. Conclusions: We suggest that the cumulative effects of all obesity-associated detected variants lead to the early-onset obesity phenotype observed in individuals. Hence, periodic follow-up and treatment opportunities are recommended for index cases, alongside the adoption of a more active lifestyle and healthy nutrition practices. Full article

Neurodegenerative diseases, characterized by progressive neuronal loss and functional decline, impose a substantial global health burden. Autophagy, the principal intracellular degradative pathway for clearing misfolded proteins and damaged organelles, is vital for neuronal homeostasis, whereas maladaptive neuroinflammation is increasingly being recognized as a central driver of disease progression. A growing body of evidence indicates a bidirectional, tightly coupled relationship between autophagy and neuroinflammation: impaired autophagic flux promotes accumulation of damage-associated molecules that activate innate immune responses, while sustained inflammatory signaling further disrupts autophagy, together forming a self-reinforcing cycle that accelerates neurodegeneration. This interplay is regulated by diverse genetic, molecular, cellular, and environmental factors and manifests in cell-type-specific ways across microglia, astrocytes. Therapeutic strategies emerging from these insights include modulation of autophagic pathways (e.g., mTOR, AMPK, TFEB), targeted inhibition of inflammasome and pro-inflammatory mediators (notably NLRP3-related signaling), and delivery platforms for small molecules or nucleic acids, with increasing interest in multi-target and stage-specific interventions. This review integrates mechanistic evidence and translational advances, highlights gaps in cell-type and stage-specific understanding, and outlines priorities for developing safe, effective therapies that target the autophagy–neuroinflammation axis in neurodegenerative disorders. Full article

This study investigated the epidemiology, antimicrobial resistance, and transmission risks of β-lactamase, cefotaxime-hydrolyzing, Munich (bla_CTX-M)-positive Escherichia coli (CTX-M-EC) in large-scale pig farms in Jiangxi Province (China). In total, 278 samples (manure, wastewater, drinking water, and flies) were collected. CTX-M-EC strains were isolated and analyzed using antimicrobial susceptibility testing, resistance gene profiling, multilocus sequence typing, and genetic environment analysis with gene transfer assessed by transduction experiments. Twenty-seven CTX-M-EC strains (9.71%) were isolated, all exhibiting multi-drug resistance with 100% resistance to cefotaxime, ciprofloxacin, and tetracycline, and >90% resistance to ceftazidime, florfenicol, and trimethoprim-sulfamethoxazole. Four bla_CTX-M subtypes were identified. bla_CTX-M-55 was the predominant subtype (70.37%) and was distributed across diverse sequence types and serotypes. Each strain harbored multiple antibiotic resistance genes, plasmids, and virulence genes. Mobile elements such as ISEcp1 and IS26 were detected surrounding the bla_CTX-M gene, and 96.29% of strains successfully transferred the bla_CTX-M gene via transduction. Clones highly homologous to pig manure strains were detected in flies and sewage, suggesting that this resistance gene can spread between animals, the environment, and vectors. These findings highlight the high transmission risk of bla_CTX-M and underscore the need for rational antibiotic use, waste management, and vector control within a One Health framework. Full article

Postoperative endophthalmitis is a rare but severe complication of cataract surgery that may lead to significant visual impairment and can occasionally present as clusters or outbreaks linked to lapses in infection control. On 29 October 2025, three cases of postoperative endophthalmitis following cataract surgery at an ophthalmology clinic (Clinic A) in Yeongju, Republic of Korea, were reported to public health authorities. All cases were confirmed as Pseudomonas aeruginosa (P. aeruginosa). An epidemiological investigation was conducted to identify the source and transmission route; all 54 patients who underwent cataract surgery at Clinic A in October 2025 (62 procedures, including eight bilateral cases) were included. Infection control practices were assessed through on-site inspection, staff interviews, medical record review, and telephone surveys. Environmental samples were collected and cultured selectively for P. aeruginosa. Isolates were analyzed using pulsed-field gel electrophoresis (PFGE) to assess genetic relatedness. Among 54 surgical patients, three developed endophthalmitis (attack rate: 5.6%). All cases occurred in patients operated on 23 October by the same surgeon; no additional cases were identified. P. aeruginosa was isolated from all three patients. Of 45 environmental samples, three were positive: the laundry room drain, the sink drain in the local anesthesia room, and the interior of cassette No. 2 and instruments within the operating room steam sterilizer. PFGE showed 95.7% band pattern similarity between patient isolates and those from the sterilizer. This outbreak was attributed to P. aeruginosa, with the steam sterilizer identified as the most probable source. Inadequate infection control and sterilization practices likely contributed. These findings highlight the critical importance of strict sterilization monitoring and adherence to infection prevention and control practices to prevent recurrence. Full article

The rapid aging of the agricultural workforce underscores the need for technologies that ensure both productivity and usability. This study introduces an AIoT-enabled elderly-friendly greenhouse that integrates ergonomic and agronomic parameters into a unified optimization framework. Experiments with ten elderly participants (60–75 years) combined anthropometric assessments, environmental monitoring, and machine learning–based irrigation modeling. Results showed that an optimal planting table height of 75 cm maximized comfort (4.44 ± 0.34) and minimized fatigue (1.89 ± 0.66). Work–rest scheduling identified early morning (06:00–09:00) and late afternoon (15:00–18:00) as periods with reduced heat strain. Ventilation at 60% fan speed-maintained comfort ranges while stabilizing microclimate conditions. For irrigation, Random Forest Regression achieved the best accuracy (R² ≈ 0.75), with soil moisture as the dominant predictor. A Genetic Algorithm (GA) further improved outcomes, increasing comfort scores by 30% and reducing water use by 20%. By embedding ergonomic (X_opt, T_comfort, V_comfort) and agronomic (W, I, θ_opt) variables as objectives, the system creates greenhouses that are both “user-aware” and “plant-aware.” This dual approach enhances productivity, sustainability, and usability, offering practical insights for AIoT-enabled smart greenhouses in aging societies. Full article

Wireless sensor networks (WSNs) are the backbone of IoT-enabled smart manufacturing, environmental monitoring, and industrial automation. However, their broadcast nature makes communication links vulnerable to eavesdropping, routing manipulation, and denial-of-service attacks. Strategically placing monitor nodes to check each link is an effective approach to protect against attacks, but energy, connectivity, and capacity constraints should be considered while picking monitor nodes. In this paper, we tackle the Minimum-Weighted Connected Capacitated Vertex Cover (MWCCVC) problem, which minimizes monitoring costs, ensures backbone connectivity, and adheres to per-node capacity constraints. Unlike prior works that consider weighted vertex cover, connectivity constraints, or capacitated variants separately, the proposed MWCCVC model jointly integrates all three dimensions within a single vertex cover-based monitoring framework. We first provide a Branch-and-Bound (B&B) solver with linear programming relaxation bounds and constraint-based pruning strategies that produces optimum solutions. Three constructive greedy heuristics (GD, GR, GW) and two hybrid genetic algorithms (HGA, HGA-v2) that combine parameterized greedy decoders with evolutionary search are proposed; all methods guarantee full edge coverage, induced-subgraph connectivity, and max-flow-validated capacity feasibility. Tests on 130 small, 160 medium, and 19 large benchmark instances show that HGA matches B&B optima on every small instance, beats the time-limited B&B by 6.6% on medium instances, where the percentage is computed based on the relative difference in average total weight with respect to B&B, and stays the best on large graphs with up to 1000 nodes. The HGA-v2 tries to balance the quality and speed, with only a 3.1% difference at $10\times$ faster execution. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a global threat to human, animal, and environmental health. Among bacteria, E. coli is frequently used as a key indicator of AMR. Despite their genetic proximity to humans, studies on AMR in Non-Human Primates (NHPs) remain limited, particularly in semi-anthropized environments. This study aims to characterize the antibiotic resistance profiles and phylo-groups of E. coli isolated from NHPs and humans at a primatology center. Methods: A total of 143 stool samples were collected, including 125 from NHPs and 18 from humans. Isolates were cultured on Eosin Methylene Blue agar and then identified by MALDI-TOF mass spectrometry. Antibiotic susceptibility was assessed using the Kirby–Bauer disk diffusion method, with 30 antibiotics following CASFM-EUCAST recommendations. E. coli phylo-groups were characterized by quadruplex PCR according to the Clermont method, targeting the genes. Results: A total of 122 E. coli isolates (85.31%) were recovered, with comparable prevalence observed across NHPs and human staff. More than half of the isolates (55.74%) were resistant to at least one antibiotic tested, and 12.3% were classified as multi-drug resistant (MDR). Resistance rates of isolates in Mandrillus sphinx, Pan troglodytes, and humans were 50.6%, 57.7%, and 80.0%, respectively, with no significant statistical difference (p = 0.11). A single Extended-Spectrum Beta-Lactamase (ESBL) producing isolate was identified in the mandrill. Phylo-group analysis revealed the dominance of group A (50%), followed by groups B1, D, and C. Conclusions: Resistance profiles and phylo-group distribution among NHPs could suggest bacterial exchange and potential for cross-transmission of AMR within the shared environment. Full article