Search Results (300)

Atriplex hortensis var. rubra (red orache, RO) is a halotolerant species rich in nutraceutical compounds, which makes it a valuable crop for human nutrition. This plant could also be exploited for phytoremediation of contaminated soil and wastewater, and for saline aquaponics. A root rot disease was observed on hydroponically grown RO plants, caused by Pythium deliense and Pythium Cluster B2a sp. Identification was based on morphology, molecular analysis (ITS and COI), and phylogenetic analysis. We assessed disease severity in plants grown in a growth chamber with nutrient solutions containing different NaCl concentrations (0, 7, and 14 g L⁻¹ NaCl). In vitro growth at different salinity levels and temperatures was also evaluated. Both Pythium species were pathogenic but showed different responses. Pythium deliense was significantly more virulent than Pythium Cluster B2a sp., causing a steady reduction in root dry weight (RDW) of 70% across all salinity levels. Pythium Cluster B2a sp. reduced RDW by 50% at 0 and 7 g L⁻¹ NaCl while no symptoms were observed at 14 g L⁻¹ NaCl. Pythium deliense grew best at 7 and 14 g L⁻¹ NaCl, while Pythium Cluster B2a sp. growth was reduced at 14 g L⁻¹ NaCl. Both pathogens had an optimum temperature of 30 °C. This is the first report of Pythium spp. causing root rot on RO grown hydroponically. The effective use of halophytic crops must consider pathogen occurrence and fitness in saline conditions. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a major global health challenge characterized by complex adipose–liver interactions mediated by adipokines and hepatokines. Despite rapid field evolution, a comprehensive understanding of research trends and translational advances remains fragmented. This study systematically maps the scientific landscape through bibliometric analysis, identifying emerging domains and future clinical translation directions. Methods: A comprehensive bibliometric analysis of 1002 publications from 2004 to 2025 was performed using thematic mapping, temporal trend evaluation, and network analysis. Analysis included geographical and institutional distributions, thematic cluster identification, and research paradigm evolution assessment, focusing specifically on adipokine–hepatokine signaling mechanisms and clinical implications. Results: The United States and China are at the forefront of research output, whereas European institutions significantly contribute to mechanistic discoveries. The thematic map analysis reveals the motor/basic themes residing at the heart of the field, such as insulin resistance, fatty liver, metabolic syndrome, steatosis, fetuin-A, and other related factors that drive innovation. Basic clusters include metabolic foundations (obesity, adipose tissue, FGF21) and adipokine-centered subjects (adiponectin, leptin, NASH). New themes focus on inflammation, oxidative stress, gut microbiota, lipid metabolism, and hepatic stellate cells. Niche areas show targeted fronts such as exercise therapies, pediatric/novel adipokines (chemerin, vaspin, omentin-1), and advanced molecular processes that focus on AMPK and endoplasmic-reticulum stress. Temporal analysis shows a shift from single liver studies to whole models that include the gut microbiota, mitochondrial dysfunction, and interactions between other metabolic systems. The network analysis identifies nine major clusters: cardiovascular–metabolic links, adipokine–inflammatory pathways, hepatokine control, and new therapeutic domains such as microbiome interventions and cellular stress responses. Conclusions: In summary, this study delineates current trends and emerging areas within the field and elucidates connections between mechanistic research and clinical translation to provide guidance for future research and development in this rapidly evolving area. Full article

Background: Metabolic syndrome (MetS) is a multifactorial condition involving central obesity, dyslipidemia, hypertension, and impaired glucose metabolism, significantly increasing the risk of type 2 diabetes and cardiovascular disease. Objectives: Given the clinical heterogeneity of MetS, this study aimed to identify distinct metabolic phenotypes, referred to as metabotypes, using validated biomarkers and to examine their association with MetS. Materials and Methods: A total of 1245 Korean adults aged 19–79 years were selected from the 2016–2023 Korea National Health and Nutrition Examination Survey. Metabotype risk clusters were derived using k-means clustering based on five biomarkers: body mass index (BMI), uric acid, fasting blood glucose (FBG), high-density lipoprotein cholesterol (HDLc), and non-HDL cholesterol (non-HDLc). Multivariable logistic regression was used to assess associations with MetS. Results: Three distinct metabotype risk clusters (low, intermediate, and high risk) were identified. The high-risk cluster exhibited significantly worse metabolic profiles, including elevated BMI, FBG, HbA1c, triglyceride, and reduced HDLc. The prevalence of MetS increased progressively across metabotype risk clusters (OR: 5.46, 95% CI: 2.89–10.30, p < 0.001). In sex-stratified analyses, the high-risk cluster was strongly associated with MetS in both men (OR: 9.22, 95% CI: 3.49–24.36, p < 0.001) and women (OR: 3.70, 95% CI: 1.56–8.75, p = 0.003), with notable sex-specific differences in lipid profiles, particularly in HDLc. Conclusion: These findings support the utility of metabotyping using routine biomarkers as a tool for early identification of high-risk individuals and the development of personalized prevention strategies in clinical and public health settings. Full article

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article

Grass carp in aquaculture exhibited symptoms of bacterial infection leading to mortality. To investigate the cause of the disease and control grass carp infections, samples from diseased grass carp were collected, and a bacterial strain named XH-1 was isolated from the internal organs of the infected fish. Artificial infection experiments were conducted to determine whether the isolated strain XH-1 was the pathogenic bacterium. The biological characteristics of the isolated strain were studied through a 16S rRNA sequence analysis, physiological and biochemical identification, and phylogenetic tree construction. Extracts from 14 traditional Chinese herbs were tested to evaluate their bacteriostatic and bactericidal effects on the isolated strain. The regression infection experiment confirmed that the isolated strain XH-1 was the pathogenic bacterium causing the grass carp disease. Biological characterization studies identified the bacterium as Aeromonas veronii, which is clustered with A. veronii MW116767.1 on the phylogenetic tree. Among the 14 Chinese herbal extracts, Lignum sappa, Pericarpium granna, Artemisia argyi, Scutellaria baicalensis Georgi, Coptis chinensis, and Artemisiacapillaris thunb exhibited significant bacteriostatic effects on XH-1. Lignum sappa showed the highest sensitivity to A. veronii, with the largest inhibition zone diameter, and its minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were 7.813 mg/mL and 15.625 mg/mL, respectively. As the concentration of Lignum sappa extract increased, its bacteriostatic and bactericidal effects strengthened. When the concentration exceeded 14 mg/mL, it maintained strong bactericidal activity over 32 h. This study on A. veronii XH-1 provides theoretical insights for the prevention of grass carp aquaculture diseases and the use of traditional Chinese herbs for treatment. Full article

Downy mildew is a highly destructive fungal disease that significantly reduces both the yield and quality of kimchi cabbage. Conventional detection methods rely on manual scouting, which is labor-intensive and prone to subjectivity. This study proposes an automated detection approach using RGB imagery acquired by an unmanned aerial vehicle (UAV), integrated with lightweight deep learning models for leaf-level identification of downy mildew. To improve disease feature extraction, Simple Linear Iterative Clustering (SLIC) segmentation was applied to the images. Among the evaluated models, Vision Transformer (ViT)-based architectures outperformed Convolutional Neural Network (CNN)-based models in terms of classification accuracy and generalization capability. For late-stage disease detection, DeiT-Tiny recorded the highest test accuracy (0.948) and macro F1-score (0.913), while MobileViT-S achieved the highest diseased recall (0.931). In early-stage detection, TinyViT-5M achieved the highest test accuracy (0.970) and macro F1-score (0.918); however, all models demonstrated reduced diseased recall under early-stage conditions, with DeiT-Tiny achieving the highest recall at 0.774. These findings underscore the challenges of identifying early symptoms using RGB imagery. Based on the classification results, prescription maps were generated to facilitate variable-rate pesticide application. Overall, this study demonstrates the potential of UAV-based RGB imaging for precision agriculture, while highlighting the importance of integrating multispectral data and utilizing domain adaptation techniques to enhance early-stage disease detection. Full article

Sleep posture detection is a potentially important component of sleep quality assessment and health monitoring. Accurate identification of sleep postures can offer valuable insights into an individual’s sleep patterns, comfort levels, and potential health risks. For example, improper sleep postures may lead to musculoskeletal issues, respiratory disturbances, and even worsen conditions like sleep apnea. Additionally, for long-term bedridden patients, continuous monitoring of sleep postures is essential to prevent pressure ulcers and other complications. Traditional methods for sleep posture detection have several limitations: wearable sensors can disrupt natural sleep and cause discomfort, camera-based systems raise privacy concerns and are sensitive to environmental conditions, and pressure-sensing mats are often complex and costly. To address these issues, we have developed a low-cost non-contact sleeping posture detection system. Our system features eight optimally distributed triaxial accelerometers, providing a comfortable and non-contact front-end data acquisition unit. For sleep posture classification, we employ an improved density peak clustering algorithm that incorporates the K-nearest neighbor mechanism. Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. Experimental results demonstrate that the PCSN can accurately distinguish six sleep postures: prone, supine, left log, left fetus, right log, and right fetus. The average accuracy is 98.42%, outperforming most state-of-the-art deep learning models. The PCSN achieves the highest scores across all metrics: 98.64% precision, 98.18% recall, and 98.10% F1 score. The proposed system shows considerable promise in various applications, including sleep studies and the prevention of diseases like pressure ulcers and sleep apnea. Full article

Group A rotaviruses (RVAs) are a major cause of acute dehydrating diarrhea in infants and young animals worldwide. In rabbits, RVAs are associated with enteric disease, likely in combination with other pathogens. We report the identification and characterization of a lapine RVA strain in an Italian rabbit breeding farm. Increased mortality rates associated with enteric symptoms were reported in the facility in post-weaning rabbits around 40 days of age. By quantitative RT-PCR, an RVA strain was identified in the intestinal contents of deceased rabbits. A PCR-based enrichment protocol coupled with Nanopore sequencing allowed the reconstruction of the nearly complete genome of a rabbit RVA strain, Rabbit-wt/ITA/36-9/2022/G3P[14], with a genotype constellation (G3-P[14]-I2-R2-C2-M3-A9-N2-T6-E5-H3) conserved among lapine RVAs. Each of the 11 gene segments displayed high nucleotide identity and phylogenetic clustering with lapine rotavirus strains, as well as two Belgian human G3P[14] strains, which had been shown to have a zoonotic (lapine) origin. However, the NSP2 gene of strain 36-9 clustered closer with a group of rare human G3P[9] strains, suggesting a common path during their evolution. Gathering sequence data on animal RVAs is pivotal to reconstructing the history of homologous and heterologous RVAs in various mammals, including humans. Full article

Streptococcus suis is a significant zoonotic pathogen of public health importance. In this study, whole-genome sequencing of 177 isolates of Streptococcus suis, isolated from diseased swine across 15 provinces in China between 2017 and 2019, was performed. A total of 23 serotypes and 28 ST types were identified, with serotypes 2 and 3 comprising 50.8% of the isolates, and sequence types ST353 and ST117 accounting for 23.7%. Clustering analysis based on known virulence-associated factors (VAFs) resulted in the identification of four distinct clusters, and virulence was assessed using animal models, including a unique, highly virulent cluster designated as cluster I. Drug susceptibility testing indicated that 97.7% of the isolates were multidrug-resistant. A total of 26 resistance-associated genes were identified within the genome, 18 of which were associated with integrative and conjugative elements (ICEs) and/or integrative mobilizable elements (IMEs). Nevertheless, our understanding of suis virulence in terms of phylogeny remains incomplete. This study contributes to the understanding of the population structure and genetic characteristics of suis, provides a framework and novel partitioning approach for future investigations into its virulence and pathogenicity, and complements the data on antibiotic resistance. Full article

Tilapia (Oreochromis spp.) is a globally important farmed fish. Analyses of genetic variation across different types of tilapia are essential for the development of superior breeding populations. We investigated the genetic structures of breeding populations of blue tilapia (Oreochromis aureus) (OA), Nile tilapia (Oreochromis niloticus) (ON), and red tilapia (Oreochromis spp.) (OS) by whole-genome resequencing. The results showed that the OS population had maintained high genetic diversity but significant genetic differentiation from the OA population. Principal component analysis, phylogenetic analysis, and genetic clustering analysis revealed a clear pattern of genetic differentiation among the three populations. The genetic structure of the ON population differed from that of the OA population but was similar to that of the OS population. Population kinship analysis revealed a close relationship between the ON and OS populations. Selective scanning analyses of three comparison groups (OA vs. ON, OA vs. OS, and ON vs. OS) revealed population-selected regions related to metabolism, endocrine, and immune systems, harboring key genes (qrsl1, pde4d, hras, ikbkb, prkag1, prkaa2, prkacb, irs2, and eif4e2). These key genes were related to growth, reproduction, and disease resistance, indicating that breeding programs have selected for these traits. Due to the lack of stable morphological characteristics of juvenile fish and the changes in external environmental conditions that lead to changes in individual morphological characteristics, SNP fingerprints were successfully constructed for the identification of the three populations based on the differences in SNPs. Based on the five core SNP markers, two combinations of SNP markers were developed to accurately identify the three populations of tilapia at the genomic level. These results provide new information about tilapia genetic resources and reference data for identification and breeding purposes. Full article

Early disease detection using biosensors is a significant challenge in modern medicine. This study aimed to investigate Lactobacillus rhamnosus GG (LGG) as a bacterial biosensor for biomarkers indicative of oral and systemic health conditions. For this purpose, LGG was cultured and then exposed to phosphate buffer, 10 wt.% sucrose solution, pH = 4.0, lactic acid, and filter-sterile saliva from five subjects. A total of 10 groups consisted of filter-sterile, freshly pooled saliva of subject 1 (SANT), subject 2 (SLAN), subject 3 (SLFU), subject 4 (SLPA), subject 5 (SLPO), phosphate buffer solution (pH = 7.4, BUF), and PBS with resin. Subsequently, the proteomic profiling of the samples was done by high-resolution mass spectrometry, focusing on the expression of bacterial proteins. The samples were evaluated for the biosensing capacity of LGG through its proteomic expression. Statistical comparisons were performed to outline proteomic changes, clustering upregulated and downregulated proteins relevant to stress response, metabolism, and environmental adaptation. The identification of key proteins associated with metabolic regulation, response to oxidative stress, and bacterial adaptation was possible using heatmaps and volcano plots. Each subject’s salivary composition also presented its unique, characteristic proteomic signature. Results showed a massive downregulation of proteins linked with stress under nutrient-rich conditions. In conclusion, the early detection of protein expression modifications related to environmental niche changes has shown that LGG can serve as a promising novel diagnostic tool, potentially overcoming many drawbacks of current physicochemical transducer-based biosensors. Full article

Background: Acyl-CoA oxidase (ACX), a ubiquitous eukaryotic enzyme, catalyzes the initial steps of fatty acid β oxidation and plays an important role in the biosynthesis of jasmonic acid (JA). At present, no studies have been reported on ACX family members of maize and their function in disease resistance. Objectives: This study aims to lay a foundation for clarifying the functions of ACX family genes in maize growth, development, and stress response by conducting a genome-wide identification of ACX family genes in maize, analyzing the expression characteristics of these genes in maize growth and development, hormone treatment and response to biotic and abiotic stresses, and exploring the functions of key genes in the maize disease resistance process through the use of mutants. Methods: ProtParam, TBtools, MEME, MEGA, and IBS tools were used to identify maize ACX family genes and analyze the physicochemical properties of their proteins, chromosome location, phylogenetic relationships among family members, conserved domains, conserved motifs, and cis-acting elements. Meanwhile, the expression patterns of maize ACX family genes in different tissues and their expression patterns under abiotic and biotic stresses were studied by using the data from the maize GDB database and qRT-PCR technology. Moreover, the mutants of ZmACX1, ZmACX3, ZmACX4, and ZmACX5 genes were obtained, and the disease resistance of the mutants was detected to further determine the functions of ACX genes in the maize disease resistance process. This study identified maize ACX family genes using bioinformatics methods. Results: We discovered that six ACX genes in the maize genome are distributed across four different chromosomes. Cluster analysis further classified these genes into three subfamilies. All maize ACX genes possess a conserved ACOX domain, and their promoter regions are enriched with cis-acting elements associated with heat stress and the plant hormone response. Under various tissue, biotic, and abiotic stress conditions, as well as treatments with methyl jasmonate (MeJA) and salicylic acid (SA), the expression levels of maize ACX family genes exhibited significant differences. Notably, the expression levels of ZmACX1, ZmACX3, ZmACX4, and ZmACX5 were significantly up-regulated following stress and pathogen infection, suggesting their involvement in maize growth, development, and disease resistance. To elucidate the function of these genes in maize disease resistance, the resistance of ZmACX1, ZmACX3, ZmACX4, and ZmACX5 mutants to Cochliobolus heterostrophus, Curvularia lunata, and Fusarium graminearum were further examined. The results revealed that compared to the wild-type B73, the lesion area of the mutants was significantly increased after inoculation with pathogens. This directly demonstrated the crucial role of these genes in maize resistance to C. heterostrophus, C. lunata, and F. graminearum. Conclusions: In summary, this study systematically identified maize ACX family genes, and thoroughly investigated their expression patterns and functions in maize disease resistance. Our findings provide valuable insights into the comprehensive understanding of the function and mechanism of maize ACX family genes. Full article

Background/Objectives: Metabolic syndrome (MetSy) comprises a cluster of risk factors—including obesity, dyslipidaemia, hypertension, and impaired glucose metabolism—that increase the risk of cardiovascular disease, type 2 diabetes, and other conditions. There are close ties between the complications and outcomes of obesity and MetSy. The practical value of MetSy as a distinct diagnosis in primary care remains uncertain. This study aimed to explore general practitioners’ (GPs) perspectives on the utility of MetSy for diagnosing and managing patients in light of evolving concepts in the field of metabolic health. Methods: A qualitative study design was employed, with semi-structured interviews conducted among 15 GPs in rural Western Australia. Participants were recruited via GP networks with convenience and snowball sampling. Transcribed interviews were thematically analysed using Braun and Clarke’s reflexive approach, with iterative coding, theme identification, and member checking to ensure trustworthiness. Results: GPs generally recognized MetSy as a constellation of risk factors that heighten the risk of cardiovascular disease. Insulin resistance was frequently identified as a unifying pathophysiological driver. Nevertheless, most participants did not routinely diagnose MetSy in clinical practice, rather focusing on treating individual component conditions (e.g., obesity, hypertension, dyslipidaemia) due to readily available disease-specific guidelines. Only a minority of GPs actively used the MetSy concept for patient education to highlight interconnected risks and the potential benefits of holistic lifestyle interventions. Paucity of dedicated MetSy management guidelines and uncertainty around diagnostic criteria further hindered routine application of the syndrome in practice. Conclusions: While GPs are aware of MetSy and its broad implications, few use it as a distinct clinical tool. Development of tailored guidelines and expanded educational resources would empower GPs to integrate a more holistic, MetSy-focused approach to patient care. Full article

Currently, understanding the immune response, its abnormalities, and its diagnostic possibilities is a key point in the management of patients with various diseases, from infectious to oncological ones. The aim of this review was to analyze the data presented in the current literature on immune disorders and the possibility of their laboratory diagnostics in combination with clinical manifestations. We have performed a systematic analysis of the literature presented in international databases over the last ten years. We have presented data on the possibility of diagnosing immunopathological processes due to changes in immune cells and soluble molecules involved in the pathogenesis of a wide range of diseases, as well as the determination of antibodies to detect autoimmune processes. By applying laboratory techniques such as hematology, flow cytometry, ELISA, etc., available to most clinical laboratories worldwide, clinical data on immune system dysfunction in a wide range of diseases are being collected. This process is unfortunately still very far from being completed. However, with all the diversity of accumulated knowledge, we can currently state that the pathogenesis of the vast majority of immune-mediated diseases is not yet known. At the same time, the current success in dividing immune-mediated diseases into distinct clusters based on different types of inflammatory responses that are based on the involvement of different populations of T helper cells and cytokine molecules represents significant progress. Further research in this direction seems very promising, as it allows the identification of new target cells and target molecules for both improved diagnostics and targeted therapies. Full article

Patients affected by the rare disease pseudoxanthoma elasticum (PXE) exhibit the calcification of elastic fibers in ocular, dermal, and vascular tissues. These symptoms are triggered by mutations in the ATP-binding cassette transporter subfamily C member 6 (ABCC6), whose substrate remains unknown. Interestingly, ABCC6 is predominantly expressed in the liver tissue, leading to the hypothesis that PXE is a metabolic disorder. We developed a genome-editing system targeting ABCC6 in human immortalized hepatocytes (HepIms) for further investigations. The HepIms were transfected with an ABCC6-specific clustered regulatory interspaced short palindromic repeat (CRISPR-Cas9) genome-editing plasmid, resulting in the identification of a heterozygous (ht^ABCC6HepIm) and a compound heterozygous (cht^ABCC6HepIm) clone. These clones were analyzed for key markers associated with the PXE pathobiochemistry. Hints of impaired lipid trafficking, defects in the extracellular matrix remodeling, the induction of calcification inhibitor expression, and the down regulation of senescence and inflammatory markers in ABCC6-deficienct HepIms were found. Our ABCC6 knock-out model of HepIms provides a valuable tool for studying the metabolic characteristics of PXE in vitro. The initial analysis of the clones mirrors various features of the PXE pathobiochemistry and provides an outlook on future research approaches. Full article