Search Results (1,014)

Enterococcus alishanensis JNUCC 77 (=BLH10) was isolated from the flowers of Zinnia elegans collected at Ilchul Land, Jeju Island, Republic of Korea. Whole-genome sequencing was conducted to clarify its taxonomic position, genomic composition, and adaptive metabolic potential. The assembled genome comprised five contigs totaling 3.86 Mb, with a G + C content of 35.6% and 100% completeness. Genome-based phylogenomic analyses using the Type Strain Genome Server (TYGS) and digital DNA–DNA hybridization (dDDH) confirmed that strain JNUCC 77 belongs to E. alishanensis. Functional annotation revealed enrichment of genes related to transcriptional regulation, carbohydrate metabolism, replication, and DNA repair, suggesting a lifestyle adapted to oxidative and UV-exposed floral habitats rather than pathogenic competitiveness. Genome mining with antiSMASH identified two putative biosynthetic regions associated with terpenoid and isoprenoid metabolism, which are commonly linked to redox regulation and cellular protection. These genomic features indicate that E. alishanensis JNUCC 77 has evolved a metal-assisted, redox-regulated survival strategy suitable for floral microenvironments. Given its origin from vibrant flowers and its genomic potential for redox-protective metabolism, this strain represents an attractive microbial resource for future development of nature-inspired postbiotic and cosmeceutical ingredients that align with the clean and eco-friendly image of flower-derived biotechnologies. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article

Background: Human papillomavirus (HPV) plays a crucial role in the pathogenesis of oropharyngeal squamous cell carcinomas (OPSCC). Accurate HPV status classification is essential for therapeutic stratification. While p16 immunohistochemistry (IHC) is the clinical surrogate marker, it has limited specificity. Methods: In this study, we implemented a weakly supervised deep learning approach using the Clustering-constrained Attention Multiple-Instance Learning (CLAM) framework to directly predict HPV status from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) of OPSCC. A total of 123 WSIs from two cohorts (The Cancer Genome Atlas (TCGA) cohort and OPSCC cohort from the University of Naples Federico II (OPSCC-UNINA)) were used. Results: Attention heatmaps revealed that the model predominantly focused on tumor-rich regions. Errors were primarily observed in slides with conflicting p16/in situ hybridization (ISH) status or suboptimal quality. Morphological analysis of high-attention patches confirmed that cellular features extracted from correctly classified slides align with HPV status, with a Random Forest classifier achieving 83% accuracy at the cell level. Conclusions: This work supports the feasibility of deep learning-based HPV prediction from routine H&E slides, with potential clinical implications for streamlined, cost-effective diagnostics. Full article

Callicarpa americana L. is a member of the Lamiaceae family with important ornamental and medicinal value. Although the chloroplast genome of Lamiaceae has been extensively studied, its mitochondrial genome remains unreported, limiting a comprehensive understanding of the phylogeny and genome evolution of Lamiaceae. In this study, the complete mitochondrial genome of C. americana was successfully assembled for the first time. The genome is 499,565 bp in length, showing a complex multi-branched closed-loop structure that contains 37 protein-coding genes, 23 tRNA genes, and 4 rRNA genes. The difference in mitochondrial genome size is relatively large compared to Orobanchaceae species, but the difference in GC content is not obvious. The expansion of genome size was mainly due to the accumulation of non-coding regions and repetitive sequences. Meanwhile, two pairs of long repetitive sequences (LR3 and LR5) mediated homologous recombination. The mitogenome was also identified; there were a total of 494 C-to-U RNA editing sites in protein-coding genes. In addition, 42 mitochondrial plastid DNA fragments (MTPTs) were detected, with a total length of 21,464 bp, accounting for 4.30% of the genome. Repeat sequence analysis showed that tetranucleotide SSR was the most abundant repeat type in the mitochondria of Lamiaceae. Phylogenetic analysis based on the alignment of 32 protein-coding gene sequences showed that Callicarpa is sister to the other eight species of Lamiaceae. This work fills an important gap by presenting the first complete mitochondrial genome of C. americana, providing an important data resource for further understanding the structural evolution, dynamic recombination mechanism, and phylogeny of the mitochondrial genome of Lamiaceae. Full article

Background: Patients with neurodevelopmental and neuromuscular disorders often show overlapping clinical phenotypes. Pathogenic variants in KMT5B, a histone lysine methyltransferase, have been linked to neurodevelopmental disorders, yet their effects on human skeletal muscle remain unexplored. We report on a patient with KMT5B-linked disease who presented to a neuromuscular specialty clinic with significant involvement of skeletal muscle, where a multi-omics approach established the genetic diagnosis and revealed neuromuscular findings relevant for diagnosis, care and rehabilitation. Methods: Whole-exome sequencing was performed from blood and data was analyzed using the RD-Connect Genome Phenome Analysis Platform. Histological analysis and proteomic profiling were performed on muscle tissue. Results: Whole-exome sequencing revealed a pathogenic heterozygous variant (c.554_557del, p.Tyr185Cysfs*27) in KMT5B. Histological examination revealed fiber-type grouping, angular fibers, increased fast-twitch fiber proportion, and lipid droplet accumulation, indicative of muscle denervation. Proteomic profiling identified 77 dysregulated proteins, including upregulation of sarcomeric proteins, mitochondrial and glycolytic enzymes, acute-phase and complement factors, and extracellular matrix components, reflecting structural remodeling, metabolic adaptation, and inflammatory activation. These findings align with the role types observed in Kmt5b mouse models, supporting a role of KMT5B in neuromuscular function. Conclusions: We present the first combined histological and proteomic analysis of quadriceps muscle from a patient carrying a pathogenic KMT5B variant with a neuromuscular phenotype. The convergence of histological and proteomic alterations suggests that KMT5B haploinsufficiency may be associated with fiber-type shifts, denervation, and metabolic stress in human skeletal muscle. Understanding these processes provides mechanistic insight into motor deficits and informs targeted therapeutic strategies, including physiotherapeutic interventions, and early compensatory measures. Full article

The convergence of artificial intelligence and synthetic biology is innovating and accelerating the design of novel viral genomes, expanding both therapeutic opportunities and dual-use risk. This review articulates a countermeasure strategy for emerging and engineered viruses leveraging the programmable CRISPR modality. Building on mounting in vitro and in vivo evidence that Cas9 degrades DNA viruses (e.g., Orthopoxviruses, HSV-1, ASFV), while Cas13 targets RNA viral genomes (e.g., Influenza A, Dengue, RSV), both leading to reduced viremia, diminished disease burden, and alleviated symptoms. Here, we outline a rapid-response pipeline to position CRISPR-based countermeasures in translational and pandemic-response frameworks, linking real-time sequencing to AI-assisted gRNA selection and multiplexed cassette design to achieve viral targeting efficacy. To minimize resistance and off-target risk, we emphasize multi-gRNA cocktails, continuous genomic surveillance, and adaptive gRNA rotation. We also propose governance mechanisms, such as pre-cleared gRNA repositories, transparent design logs, standardized off-target/safety screening, and alignment with evolving nucleic-acid-synthesis screening frameworks to enable emergency deployment while preserving security. Furthermore, compressing the time from sequence to treatment and complementary to vaccines and small-molecule antivirals, CRISPR represents a technologically agile and strategically essential capability to combat both natural outbreaks and AI-enabled biothreats. Collectively, programmable CRISPR antivirals represent an auditable, rapidly adaptable foundation for next-generation biodefense preparedness. Full article

Background/Objectives: Genetic abnormalities are critical for the diagnosis, prognosis, and therapeutic management of myelodysplastic syndromes (MDS). This study aims to evaluate the clinical utility of Multiplex Ligation-dependent Probe Amplification (MLPA) as a rapid and cost-effective method, determining its place alongside Next-Generation Sequencing (NGS) for the initial genetic assessment of patients with MDS. Methods: Bone marrow samples from 68 patients newly diagnosed with MDS were analyzed. Genomic DNA was investigated using the SALSA MLPA P414-C1 MDS probe mix to detect common copy number variations (CNVs). Results: MLPA detected genetic variants in 25 patients (36.8%). The most common finding was a single chromosomal abnormality (26.5%). Multiple pathological findings were observed in only 1.5% of patients, and a JAK2 mutation was observed in 8.8% of the cohort. However, the presence of these aberrations did not show a statistically significant association with overall survival (OS) in the cohort. Patient sex was identified as the only variable that was associated with a marginal level of statistical significance regarding OS, indicating a worse prognosis for males. Conclusions: MLPA is a valuable, rapid, and cost-effective tool for initial genetic screening in low-resource settings. This was highlighted by our finding that sex was the sole significant prognostic factor, while the MLPA-detected variants were not found to be significant. The findings suggest that comprehensive risk stratification aligned with international standards requires more advanced molecular technologies. Full article

Mitochondria play essential roles for animal reproduction, influencing not only cellular energetics but also gamete quality, inheritance and evolutionary patterns. Currently, most research still focuses on chordates or mitochondrial diseases and their impact on the health of germ cells. However, few studies focus on integrative synthesis that connect comparative morphology, inheritance mechanisms and evolutionary theory. In this review, we integrate cross-phyla evidence to explore two interconnected dimensions: the fate of mitochondria during gametogenesis and the strategy shaping their evolution. We compare mitochondrial morphology, distribution, and metabolic strategies in gametogenesis, revealing how these traits align with reproductive modes and ecological adaptations. Then we further discuss how mitochondrial genome evolution, bottleneck effects and mito-nuclear coevolution contribute to germline stability and maternal inheritance. Special attention is given to exceptional systems such as Doubly Uniparental Inheritance (DUI) in bivalves, which challenges conventional mode of strictly maternal transmission and illuminates the flexibility of mito-nuclear evolution. Altogether, these perspectives highlight mitochondria as gatekeepers and evolutionary recorders in the reproductive systems across metazoans, providing a unifying framework for future research across ecology, evolution and molecular biology. Full article

Next-generation sequencing technology was employed to read and assemble the complete plastid genome of the ‘Mejhoul’ date palm cultivar (Phoenix dactylifera L.). The genome consisted of 158,436 base pairs (bp) with a GC content of 37.24%, and it included 95 protein-coding genes, 44 tRNA genes, and eight rRNA genes. The plastome of five ‘Mejhoul’ genotypes from Jordan was compared with three genotypes from the USA, Morocco, and the UAE. It revealed 91 single-nucleotide polymorphisms (SNPs) and 23 insertions–deletions (InDels); the majority of them (62%) were located in intergenic regions, while the remaining variants were located in intragenic regions, including tRNA and rRNA genes. When the plastomes of all eight ‘Mejhoul’ genotypes were aligned, along with major cultivars ‘Barhee’ and ‘Khalas’, 24 SNPs and 23 InDels could be found. This would enable the development of a cultivar-specific fingerprint test for authentication. The phylogenetic tree was constructed using seventeen date palm cultivars. The phylogenetic analysis places ‘Mejhoul’ as a lineage derived within Clade I rather than as an early-diverging cultivar, suggesting it shares a more recent common ancestor with ‘Deglet Noor’ and ‘Barhee’. Full article

Obesity remains a dominant risk factor for cardiovascular disease, yet its classification continues to rely heavily on body mass index (BMI)—a metric that fails to capture individual variability in fat distribution, metabolic health, and cardiometabolic risk. This narrative review analyzes 35 articles published between 2018 and 2025 to explore the limitations of BMI and outlines emerging strategies for obesity redefinition through a precision medicine lens. Drawing from recent advances in imaging, metabolomics, and genomic profiling, we highlight alternative metrics such as visceral adipose tissue (VAT), epicardial adipose tissue (EAT), waist-to-hip ratio (WHR), and multi-omic phenotyping that provide superior predictive value for cardiovascular outcomes. The review synthesizes data on metabolically healthy and unhealthy phenotypes, emphasizes the pathophysiological role of EAT in heart failure and arrhythmogenesis, and discusses the cardioprotective effects of pharmacologic agents such as glucagon-like peptide-1 (GLP-1) receptor agonists. Clinical implications include improved risk stratification, earlier disease detection, and individualized therapeutic targeting. Despite current barriers to widespread implementation—such as imaging cost, access to omics, and lack of guideline integration—this paradigm shift holds promise for refining cardiovascular prevention strategies. Redefining obesity using biologically informed, phenotype-based models is indispensable for aligning clinical practice with the complexities of modern cardiometabolic disease. Full article

Background and Objectives: Human Papillomavirus (HPV) is a leading cause of cervical and other anogenital cancers, with over 200 known genotypes classified into high-risk, probable high-risk, and low-risk groups. While conventional diagnostic and classification approaches often rely on sequence alignment, phylogenetic relationships, or protein structure analyses, these methods are limited in scalability, cost efficiency, and generalizability to emerging HPV types. This study aims to develop a novel, machine learning-based framework for classifying HPV genotypes by oncogenic risk using genome-derived numerical features. A key objective is to introduce TATA-box, CAAT-box, and CpG-island-based features to HPV risk prediction for the first time. Methods: We constructed a comprehensive feature set that integrates regulatory sequence motifs (TATA-box, CAAT-box, CpG islands) with dinucleotide and trinucleotide (k-mer) composition derived from full HPV genomes. Multiple machine learning algorithms were implemented to evaluate classification performance across all risk categories. Model accuracy, precision, recall, and F1-score were calculated to assess the effectiveness and robustness of the proposed feature set. Results: The proposed method achieves an average precision of 0.95, a recall of 0.95, an F1-score of 0.95, and an accuracy of 97.47%. The experimental findings indicate that the proposed method not only attains high classification accuracy across all HPV risk groups but also surpasses existing models in generalizability by utilizing genomic data and novel biologically informed features. Conclusions: This study introduces regulatory motif-based numerical features to HPV classification for the first time and demonstrates that integrating these with k-mer descriptors yields a highly accurate and scalable machine learning model. Unlike previous studies, which often focus on specific HPV genes or a limited subset of types, our method is scalable, robust, and capable of classifying known and emerging HPV types with high reliability. This highlights its potential for real-world deployment in large-scale epidemiological screening and vaccine development programs. Full article

Climate change poses significant challenges to livestock, particularly in tropical regions. The Amazon biome, which hosts one of the world’s largest cattle populations, faces growing risks of nutritional, metabolic, and infectious diseases driven by heat stress (HS) and environmental instability. This systematic review synthesizes evidence from primary studies, international reports (IPCC, FAO), and peer-reviewed literature on cattle physiology, disease dynamics, and climate adaptation. HS reduces feed intake, disrupts endocrine–metabolic homeostasis, and suppresses immunity, increasing susceptibility to metabolic, deficiency and infectious diseases. Breed-specific immune responses offer opportunities for genetic and management-based adaptation. Socio-economic impacts disproportionately affect smallholders, linking livestock health to food security and poverty. Ensuring sustainable cattle production in the Amazon will require climate-smart strategies integrating nutrition, genetics, reproduction, and health management, supported by policies that align adaptation and mitigation. Future research should prioritize immune-metabolic biomarkers, periparturient disease monitoring, and genomic tools for thermotolerance. Full article

The exponential growth of viral metagenomic data has created an urgent need for accurate and scalable tools for virus discovery, yet the extreme diversity, rapid evolution, and limited reference databases for viruses pose unique computational challenges that traditional sequence comparison methods struggle to address. This systematic review, conducted in accordance with PRISMA 2020, examines current trends and methodological advances in virus discovery tools from 1990 to 2025. As virus discovery is a broad and multi-dimensional topic, this review focuses on the first-line tools used to analyze the results of high-throughput sequencing. The review was conducted using the PubMed database with a snowballing approach, with over 54 key studies selected for the analysis. These studies encompass the following approaches: alignment-based methods, rapid similarity estimation techniques, profile hidden Markov model methods, combination pipelines, k-mer-based approaches, and machine learning-based methods. The transition from alignment-based to machine learning methods has dramatically improved the detection of divergent viruses, yet challenges remain in interpreting model decisions and handling incomplete viral genomes. This review summarizes current knowledge and potential future directions for the development of virus detection capabilities. Full article

Background: Spatial heterogeneity in tumor tissue has been linked to patient prognosis. To exploit both structural and semantic cues in whole slide images (WSIs), we propose Dual eXplanatory Framework (DuXplore), a dual-branch deep learning framework that integrates tissue architecture and cellular morphology for hepatocellular carcinoma (HCC) prognosis. Method: At the macroscopic level, DuXplore constructs a multi-channel tissue organization probability map (MTOP) to represent the spatial layout of eight tissue categories within the WSIs. At the microscopic level, a feature-guided Fused Structure Tensor (FST) based on tissue composition is employed to extract representative cell morphology patches. Accordingly, MTOP representations are modeled by Macro-Net, while FST-guided patches are modeled by Micro-Net. Each branch produces a 32-dimensional prognostic embedding, which are fused and passed through a multi-layer perceptron with a Cox proportional hazards head to generate patient-level risk predictions. To further elucidate the distinct contributions of the two branches, we conducted model-agnostic interpretability analyses, including occlusion sensitivity mapping (OSM) on MTOP and nuclear morphometrics from CellProfiler on high- versus low-risk tiles. Result: DuXplore achieves promising performance with C-indices of 0.764 on the public Cancer Genome Atlas (TCGA) dataset and 0.713 on the Eastern Hepatobiliary HCC (EHBH) cohort from our clinical center, along with significant patient risk stratification (log-rank p < 0.001). OSM highlighted necrosis and central fibrosis as high-risk and marginal fibrosis as protective; these patterns were corroborated by multivariable Cox using reproducible structural parameters (N-ratio, FIB-center, FIB-edge). Micro-level analysis revealed that higher nuclear staining intensity, increased texture irregularity (GLCM features), and greater morphological heterogeneity characterize high-risk tiles, aligning with pathological understanding. Conclusions: DuXplore advances prognostic modeling by coupling structure-aware micro-sampling with macro architectural encoding, delivering robust, generalizable survival prediction and biologically plausible explanations. While validated on HCC WSIs, broader multi-center, multi-omics studies are warranted to refine sampling scales and enhance clinical translation. Full article

Bloodstream infections and sepsis necessitate rapid, sensitive, and clinically relevant diagnostics to minimize treatment delays and improve clinical outcomes. Next-generation sequencing enables culture-independent pathogen detection, antimicrobial resistance profiling, and genome-informed epidemiology. This narrative review integrates clinical evidence with practical workflows across three complementary approaches. We describe the use of plasma microbial cell-free DNA for broad organism detection and burden monitoring, as well as metagenomic next-generation sequencing of blood or plasma for unbiased pathogen discovery, including culture-negative and polymicrobial infections. Same-day Oxford Nanopore Technologies sequencing of positive blood culture broth is also discussed as a way to accelerate species identification, targeted resistance reporting, and infection-prevention decisions. We outline the sample-to-result steps, typical turnaround time (TAT), and stewardship-aligned decision points. Analytical reliability depends on effective reduction in human DNA background, stringent control of background and reagent-derived nucleic acids in low-biomass samples, and documented and validated bioinformatics workflows that are supported by curated taxonomic and resistance databases. Quantitative reports should adhere to validated thresholds and should be interpreted in the context of internal controls and clinical pretest probability. Ongoing challenges include variable correlation between genotype and phenotype for specific pathogen and antibiotic pairs, interpretation of low-level signals, and inconsistent regulatory and reimbursement environments. Advances in portable sequencing, faster laboratory and analytical workflows, and scaled liquid biopsy strategies may further reduce the TAT and expand access. Integrating these tools within One Health frameworks and global genomic surveillance programs could support early resistance detection and coordinated public health action, which could help to advance sepsis care toward more precise treatment and real-time infection control insights. Full article