Search Results (1,299)

Background/Objectives: Myeloid neoplasms (MNs) with SF3B1 mutations define a distinct entity associated with a favorable prognosis. However, not all MN patients harboring SF3B1 mutations meet the diagnostic criteria for this entity, and different mutation types may be associated with distinct clinical outcomes. We aimed to evaluate the impact of variant allele frequency (VAF) and SF3B1 mutation type across hematopoietic lineages to improve patient stratification. Methods: VAF and the distribution of the p.K700E hotspot compared with other SF3B1 variants were evaluated using paired sequencing data from bone marrow (myeloid) and CD3⁺ (non-myeloid) samples from 23 MN patients with SF3B1 mutations to assess their association with clinical outcomes. Results: Overall, 47.8% of SF3B1 mutations detected in myeloid samples (VAF 42.4%) were also identified in the lymphoid lineage (VAF 17.8%). SF3B1 VAF in CD3⁺ samples correlated with worse prognosis markers. No differences were observed in overall co-mutation burden; however, only myeloid-restricted SF3B1 mutations appeared to represent initiating events. p.K700E mutations (n = 12) were restricted to the myeloid lineage, whereas non-p.K700E mutations (n = 11) were predominantly detected in both myeloid and lymphoid lineages, suggesting multilineage involvement. Conclusions: Distinct mutational patterns and clonal progression mechanisms were observed for different SF3B1 mutation types and depending on the affected hematopoietic lineage. Our findings suggest that the SF3B1 VAF across different lineages may refine patient stratification beyond mutation type alone. Full article

Aspergillus fumigatus is a leading cause of invasive fungal disease in humans and is classified as a critical priority threat by the World Health Organization. Triazole antifungals remain the cornerstone of therapy, yet their effectiveness is steadily being eroded by the continuous rise in drug resistance. Most resistance mechanisms trace back to mutations in Cyp51A, spawning well-defined genotypes such as TR₃₄/L98H and TR₄₆/Y121F/T289A. However, the Cyp51A genotype–phenotype landscape in A. fumigatus is far from straightforward. Isolates that share an identical TR genotype can display strikingly divergent susceptibility profiles, and mutational hotspots in Cyp51A, such as G54, M220 and G448, are linked to varying resistances, challenging assumptions about predictable resistance behavior. Complicating matters further, an expanding array of resistance mechanisms, independent of Cyp51A, is now being uncovered. This review summarizes the current state of knowledge on azole resistance in A. fumigatus, dissecting the intricate genotype–phenotype relationships, spotlighting emerging non-Cyp51A pathways and outlining future strategies to enhance the detection and clinical management of antifungal resistance. Full article

Background: Glutaric acidemia type 1 (GA1) is an autosomal recessive neurometabolic disorder caused by pathogenic variants in glutaryl-CoA dehydrogenase (GCDH), with variable clinical severity despite early biochemical detectability. Population-specific mutational spectra and genotype–phenotype correlations remain insufficiently defined in infantile-onset disease. Therefore, this study aimed to define the GCDH variant spectrum in GA1 patients with mild hepatopathy and assess genotype–phenotype correlations. Methods: We performed integrated clinical, biochemical, and molecular characterization of 15 unrelated patients with infantile-onset GA1. Whole-exome sequencing (WES) was performed for all participants, and the resulting data were compared with the reference sequence of the GCDH gene. Results: All patients presented within the first 6 months of life with macrocephaly, seizures, dystonia, and feeding difficulties. Neurological impairment and mild hepatopathy were variably observed, and one patient developed an acute encephalopathic crisis. Six homozygous GCDH variants were identified, predominantly missense. A common variant, c.541G>C (p.Glu181Gln), accounted for 73.3% of cases and defined a consistent phenotype of early macrocephaly and movement disorder with frequent mild hepatic involvement, suggesting regional enrichment and raising the possibility of a founder effect that warrants confirmation in future haplotype studies. A truncating variant, c.382C>T (p.Arg128Ter), was associated with severe early encephalopathy. Exon 6 represented a mutational hotspot. Biochemically, all patients showed elevated urinary glutaric and 3-hydroxyglutaric acids, increased glutarylcarnitine, and low-to-normal free carnitine, with higher metabolite levels in clinically more severe cases. All variants were pathogenic or likely pathogenic and extremely rare in population databases. Conclusions: This cohort reveals a striking predominance of the GCDH c.541G>C variant and establishes a clear biochemical signature with genotype-associated clinical patterns in infantile-onset GA1. These findings support a population-specific mutational spectrum, refine genotype–phenotype correlations, and underscore the importance of early molecular diagnosis to guide targeted neurological and hepatic monitoring as well as regional screening strategies. Full article

Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade’s hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade’s dynamic responses in both flapwise and edgewise directions. A dedicated hardware–software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems. Full article

Antimicrobial resistance (AMR) has become one of the top ten global public health threats. Many countries have recognized the societal and economic burden of AMR. AMR has reduced the effectiveness of antimicrobial therapies, and this results in high mortality, morbidity, and health care expenditure. Like all the other developing countries, South Africa (SA) falls under the same ambiguous management system of antimicrobials. A lot of research focused on the global public health threat “AMR”. However, studies on AMR in wastewater are not yet enough, even though they are beginning to gain momentum. This paper highlights the imperatives of surveying AMR pathogens in wastewater since wastewaters are consecrated as hotspots for the dissemination and propagation of AMR genes. RNA was extracted from the untreated wastewater samples collected from the Tshwane district in Gauteng province, SA. Metatranscriptomics analysis was proposed for the analysis and profiling of AMR genes present in the wastewater. A total of 39 AMR gene families and 39 AMR drug classes were detected across 17 samples. The Metatranscriptomics approach discussed in this paper demonstrates the importance of wastewater surveillance, as it can be used as an early detecting system for communicable diseases and for monitoring wastewater. Full article

Pharmaceuticals are increasingly recognized as emerging contaminants with potential impacts on agroecosystems. Among these, antibiotics such as ciprofloxacin (CPX) persist in wastewater and may enter agricultural soils through irrigation or fertilization practices, yet their effects on crop plants remain poorly understood. This study evaluated the phytotoxic effects of ciprofloxacin on early growth and photosynthetic pigment content in purple maize (Zea mays L.), a variety of nutritional and cultural importance. Seeds were germinated in an agar-based medium (0.5%) and exposed to three concentrations of ciprofloxacin (3, 10, and 30 mg·L⁻¹) for seven days under controlled conditions. Germination percentage, seedling fresh weight, organ length (root, stem, and leaf), and photosynthetic pigment concentrations (chlorophylls a and b, and carotenoids) were determined. Ciprofloxacin exposure resulted in dose-dependent reductions in germination (from 83% at 3 mg·L⁻¹ to 50% at 30 mg·L⁻¹) and root elongation, while stem length remained unaffected. Chlorophyll content decreased with increasing ciprofloxacin concentration, with the lowest values observed at 30 mg·L⁻¹, while carotenoid levels remained stable, with no statistically significant differences observed. Although ciprofloxacin is typically detected in environmental matrices at ng–µg L⁻¹ levels, higher concentrations may occur in localized contamination hotspots; ciprofloxacin affected early developmental and physiological processes in maize under these elevated exposure conditions. These findings highlight the importance of integrating phytotoxicity assessments into agricultural ecopharmacovigilance strategies and contribute to understanding the risks associated with pharmaceutical contamination in crop production systems. Full article

Shoreline change detection from remote sensing imagery remains challenging in environments subject to water level fluctuations, as remotely sensed shoreline positions reflect instantaneous hydrodynamic states rather than true geomorphic change. In the Great Lakes, seasonal and short-term water level variations can produce apparent shoreline shifts unrelated to sediment dynamics. Reliable calibration with bathymetry and water level data can mitigate this effect, but such data are often unavailable or difficult to obtain for many coastal and lacustrine systems worldwide. To address this limitation, we proposed a morphology-based framework that quantifies geometric change between successive shoreline curves using a discrete Fréchet distance, a modified Euclidean distance and a Union distance metric. Rather than relying solely on cross-shore displacements, the approach leverages shape similarity to differentiate water-level-driven shifts from true morphological change. We evaluated the framework across three spatial scales (100 m, 500 m, and 1000 m) along 125 km of southwestern Lake Michigan coastline using 2010 and 2020 aerial imagery, benchmarking against water-level-calibrated DSAS erosion hotspots. The Fréchet distance improved monotonically with scale, achieving strong agreement at 1000 m (F1 = 0.84, Spearman $\rho$ = 0.79) but limited reliability at 100 m. While individual morphology-based metrics appeared competitive with or inferior to uncalibrated DSAS at each scale, the union of both distances substantially outperformed uncalibrated DSAS at management-relevant scales (F1 of 0.64 vs. 0.50 at 500 m and 0.79 vs. 0.42 at 1000 m), reflecting the complementary nature of shape-based and displacement-based detection. The Patient Rule Induction Method (PRIM) further identified gentle nearshore slopes and moderate separation from engineered structures as the geomorphic conditions under which the morphology-based and calibrated erosion indicators converged most closely (in-box F1 = 0.92 at 1000 m and 0.72 at 500 m). These results suggest that the proposed framework, particularly the complementary union of both metrics, provides a practical, calibration-free alternative for multiscale shoreline change screening in lacustrine and microtidal, data-limited environments, while local-scale applications still benefit from explicit water-level correction. Full article

The high morbidity and mortality of cancer pose a severe challenge to human health. Traditional diagnostic and therapeutic strategies still exhibit obvious limitations in early diagnostic sensitivity, therapeutic precision, and real-time monitoring of treatment efficacy. The development of nanotechnology has provided novel solutions for precision cancer theranostics. Among nanomaterials, gold nanoparticles (AuNPs) have become a research hotspot in tumor nanomedicine due to their tunable size and morphology, excellent localized surface plasmon resonance (LSPR) effect, and favorable biocompatibility. However, despite encouraging preclinical outcomes, several challenges hinder their clinical translation, including an incomplete understanding of long-term toxicity, complex in vivo biological interactions, the lack of standardized evaluation protocols, and regulatory uncertainties and manufacturing reproducibility issues. This paper systematically reviews the physicochemical and biological mechanisms of AuNPs in cancer theranostics, and summarizes the latest research advances of AuNPs in cancer detection and diagnosis (including biomarker detection and multimodal imaging) as well as in therapeutic fields, covering photothermal therapy (PTT), photodynamic therapy (PDT), radiosensitization, targeted drug and nucleic acid delivery, and immunotherapy-assisted strategies. Furthermore, we discuss the development of intelligent and stimuli-responsive theranostic nanoplatforms based on AuNPs, and outline their future prospects in precision medicine and personalized cancer therapy, with particular emphasis on the requirements for clinical translation, including safety evaluation, large-scale production, and regulatory approval pathways. Full article

The plant mitochondrial genome has become a current research hotspot as an independent genetic model. Nevertheless, mitochondrial genome information for most Dendrobium species remains unknown. In this study, the assembly of mitochondrial genome of Dendrobium nobile Lindl.,1830 and Dendrobium denneanum Kerr., 1933 was conducted through the application of second- and third-generation sequencing technologies, with the mitochondrial genome of D. denneanum Kerr. being reported first. The results revealed that the mitochondrial genomes of the two species possessed a multi-chromosome circular structure. Their total lengths were 641,414 bp and 558,760 bp, consisting of 21 and 19 contigs, respectively. A total of 67 and 72 genes, 993 and 1491 repeat sequences, and 549 and 553 RNA editing sites were identified. Gene loss was observed. A total of 26 and 36 homologous fragments were detected between the mitochondrial and the chloroplast genome, accounting for 5.09% and 4.93% of the total lengths, respectively, indicating intracellular gene transfer. Synteny and phylogenetic analyses revealed that the two species shared extensive collinear regions and clustered together in a distinct clade of the phylogenetic tree, indicating a close sister relationship. These findings enrich the mitochondrial genome database and provide valuable insights to guide future research on species identification and molecular evolution of the genus Dendrobium. Full article

To characterize the ecological and genomic architecture of temperate bacteriophages in Escherichia coli isolated from Brazilian broiler chickens, we analyzed 63 femur-derived genomes, most fulfilling molecular avian pathogenic E. coli (APEC) criteria, and tested whether temperate phage regions are enriched for antimicrobial resistance genes (ARGs), virulence factors, plasmid markers, and other mobilome components. Diversity was summarized using incidence-based richness estimators and bootstrap confidence intervals, and positional enrichment was assessed using permutation-based statistical analysis. We detected 1164 phage-like elements, including 188 medium- and high-quality phages, of which 93.6% were temperate. Median temperate diversity per genome was three phage genera and three temperate regions. At the population level, 19 temperate genera were observed, with a Chao2 estimate of 21.2, indicating near-saturated genus-level diversity. Positional mobilome analysis showed significant enrichment of insertion sequences within temperate regions (p < 0.05), while ARGs, virulence factors, and plasmid markers were not significantly enriched inside temperate phage coordinates (p > 0.05). The surrounding genomic neighborhood (±20 kb) accumulated mobile elements but showed no significant enrichment. CRISPR spacer matches further supported ongoing host–phage interactions. Overall, temperate phages are widespread and ecologically structured in Brazilian broiler-associated E. coli, but they are not preferential hotspots for ARG, virulence, or plasmid gene enrichment; instead, they are chiefly associated with insertion-sequence enrichment. Full article

Background/Objectives: Enterococcus spp. are important indicators of AMR and potential opportunistic pathogens. Urban green spaces, frequented by dogs and humans, may serve as reservoirs for resistant bacteria. This study assessed the occurrence, AMR profiles, and virulence traits of Enterococcus spp. in dog feces from urban green spaces in Porto (Portugal). Methods: In December 2023 and May 2024, 240 dog fecal samples were collected from 12 urban green spaces across Porto. Enterococcus spp. were isolated using selective culture, identified to species level, and tested for antimicrobial susceptibility following CLSI guidelines. PCR screening was performed for resistance genes (vanA, vanB, erm(A/B/C), vatD/E, tet(M/O/L/K)) and virulence genes (gelE, ace). Environmental and socioeconomic features, including vegetation density (NDVI), presence of water features, and neighborhood deprivation (EDI), were recorded to explore associations with bacterial occurrence and traits. Results: Thirty-two isolates were recovered, mainly E. faecium (n = 9) and E. faecalis (n = 7). High resistance rates were observed to tetracycline (56.3%) and quinupristin/dalfopristin (37.5%), with lower rates for vancomycin, teicoplanin, and ciprofloxacin (3.1%), and imipenem (6.3%). Tet(M) was the most prevalent resistance gene (40.6%), and gelE and ace were frequently detected, often co-occurring with resistance determinants. Distribution of resistance and virulence genes varied across green spaces, with widely used parks showing more isolates. Vegetation density and water features were not directly associated with bacterial recovery. Conclusions: Dog feces in urban green spaces contribute to localized AMR hotspots, acting as potential reservoirs of resistant and potentially pathogenic Enterococcus spp. These findings highlight the importance of One Health strategies for urban sanitation and AMR surveillance. Full article

Terrestrial environments function as major sinks and dynamic sources of microplastics. Land use strongly influences inputs, accumulation, and transport pathways of these contaminants in the environment. Despite the extensive literature, few reviews have compared contamination levels and the potential impacting factors across land uses. To fill this gap, this review synthesizes current knowledge on the origins, occurrence, pathways, and ecological effects of microplastics across diverse land uses. The review revealed multiple interconnected pathways that drive microplastic contamination in terrestrial systems. Abundances are consistently higher in intensively managed croplands, urban areas and industrial vicinities. However, their detection in remote environments underscores the critical role of diffuse inputs and long-range atmospheric transport. Vertically, microplastics are enriched in topsoils, and their concentrations declines with depth. Horizontally, concentration declines with increasing distance from major hotspots like agricultural fields, industrial facilities, and road networks. Ecologically, microplastics alter soil physical properties, modify chemical conditions, and shift microbial community composition and enzyme activities. Furthermore, they stress soil fauna and plants through ingestion, toxicity, and physical blockage, with impacts contingent on polymer type, particle morphology, and concentration. Collectively, this review reveals consistent spatial patterns and widespread adverse ecological impacts, highlighting the clear need for integrated management strategies to mitigate terrestrial microplastic pollution. Full article

Road safety studies commonly use machine learning to predict crashes or to estimate crash-based treatment effects. This study instead audits the modelled fatal-and-serious-injury (FSI) risk produced by the iRAP ViDA risk engine. We analyse 147,466 segments (100 m each) from 12 surveys grouped into four European reporting groups. In Stage 1, gradient-boosted trees reproduce the engine’s risk surface under road-grouped cross-validation(R² ≈ 0.92 with flows and survey identifiers), and Shapley-based attributions identify which coded attributes drive modelled risk at 396 hotspots (top-three segments per road). In Stage 2, a causal-forest double machine learning estimator adjusts for 38 covariates to estimate segment-level conditional contrasts between modelled risk and six retrofittable treatments across all eligible segments. Simple absolute and relative reduction thresholds translate these associations into 1170 association-based candidate upgrades. On 321 over-lapping hotspots, the candidate upgrades show moderate agreement with iRAP’s Safer Roads Investment Plan (Recall = 0.77; Precision = 0.66; Cohen’s κ = 0.40). All results are conditional associations on a calibrated risk engine whose totals are anchored to project- or network-level fatality totals or fatality estimates used in calibration, not causal effects on observed crashes. Full article

Plant architecture-related traits are key agronomic traits affecting crop growth and yield. To unravel the genetic architecture of plant height (PH), ear height (EH), tassel length (TL), and tassel primary branch number (TPBN), 379 DH lines derived from 21 maize hybrids were used for genome-wide association study (GWAS) and genomic selection (GS) analyses. Although plant architecture-related traits were significantly influenced by genotype and genotype-by-environment interactions, moderate to high broad-sense heritability was observed for PH (81.3%), EH (79.6%), TL (86.4%), and TPBN (82.5%). Using six different models for GWAS, seven unique SNPs on chromosomes 1, 2, and 3 were identified for PH, 92 unique SNPs located on chromosomes 1 to 9 were identified for EH, three unique SNPs on chromosome 6 were detected for TL, and 18 unique SNPs located on chromosomes 1, 4, 5, 8, and 10 were identified for TPBN at the p-value threshold of 7.42 × 10⁻⁶. A few hotspot genomic regions conferring plant architecture-related traits were identified, located in bins 2.07, 4.07, 8.03, 6.01, and 10.00. A total of 144 putative candidate genes were identified, which were enriched in endocytosis and lipid biosynthetic process, electron carrier activity, chloroplast stroma, and plastid stroma. The prediction accuracy evaluated through 5-fold cross-validation was 0.44 for PH, 0.43 for EH, 0.31 for TL, and 0.30 for TPBN. When the training population size (TPS) reached 60–70% or marker density (MD) reached 3000, the prediction accuracy tends to stabilize, indicating that the optimum size of TPS and MD were 60–70% and 3000 for GS, respectively. The highest prediction accuracy evaluated by using 30–5000 significant SNPs corresponding to the lowest p-value was 0.70 for PH, 0.85 for EH, 0.58 for TL, and 0.75 for TPBN, with an increase in accuracy of 59.1% to 150.0%. These results demonstrate that integrating GS with a subset of highly significant SNPs can substantially enhance prediction efficiency, thereby facilitating the selection of superior genotypes and accelerating the breeding of maize varieties with optimized plant architecture. This study has further elucidated the genetic basis of maize architecture-related traits and provided valuable information on how to implement GS to breed novel maize varieties with optimized plant types. Full article

Distracted driving is one of the primary causes of road traffic accidents. Behavior recognition technology based on machine vision has emerged as a research hotspot due to its non-contact and high-efficiency nature. To address the challenges of complex lighting conditions in the driver’s cabin, low detection accuracy for small-scale keypoints, and the difficulty in effectively characterizing behavioral features, this paper proposes a distracted driving behavior recognition method based on an improved YOLOv8n-Pose model and multi-feature fusion. First, the original YOLOv8n-Pose model is optimized. A P2 detection layer is added to enhance the feature extraction capabilities for small-scale human keypoints, and the SE attention module is incorporated to improve the model’s robustness under complex lighting conditions. In addition, the loss function is replaced with focal loss to tackle the class imbalance problem, thus forming the YOLOv8n-PSF-Pose keypoint detection network. Subsequently, based on the coordinates of 12 human keypoints extracted by this network, a multi-dimensional feature vector is constructed, which takes joint angles as the core and integrates the relative distances between keypoints and the number of valid keypoints. Finally, a BP neural network is adopted to classify the constructed feature vectors, enabling the accurate recognition of six typical distracted driving behaviors (normal driving, drinking or eating, making phone calls, using mobile phones, operating vehicle infotainment systems, and turning around to fetch items). The experimental results show that the improved YOLOv8n-PSF-Pose model achieves an mAP50 of 93.8% in keypoint detection, which is 6.7 percentage points higher than the original model; the BP classification model based on multi-feature fusion achieves an F1-score of 97.7% in the behavior recognition task, which is significantly better than traditional classifiers such as SVM and random forest, and the image processing speed on the NVIDIA RTX 3090TI reaches a high throughput of 45 FPS. This proves that the proposed method achieves an excellent balance between accuracy and speed. This study provides an effective solution for the real-time and accurate recognition of distracted driving behaviors. Full article