Search Results (2,078)

Algae-derived proteins and peptides have gained increasing interest as sustainable bioresources with valuable nutritional and functional properties. This review aims to synthesize current knowledge on their characteristics and applications while highlighting the emerging role of computational tools in peptide research. Key findings show that algae provide diverse proteins and bioactive peptides with advantageous amino acid profiles and notable antioxidant, antihypertensive, antidiabetic, anti-inflammatory, and skin-protective activities. Their applications span food formulation, pharmaceuticals, and cosmetics, although large-scale utilization remains constrained by production, stability, and bioavailability challenges. Computational strategies, including virtual enzymatic hydrolysis, machine-learning prediction, QSAR modeling, molecular docking, molecular dynamics, and toxicity/allergenicity assessment, offer promising avenues for efficient peptide discovery, though their use in algae is still limited. Overall, this review underscores the potential of algae-derived proteins and peptides as multifunctional ingredients and emphasizes the need to integrate in silico pipelines with improved processing and delivery systems to accelerate future translational applications. Full article

Spinal cord injury (SCI) is a devastating neurological disorder that can result in permanent disability and reduced quality of life, characterized by heterogeneous injury mechanisms and limited tools for accurate early diagnosis and prognostic stratification. The clinical course of SCI is driven not only by the initial mechanical insult but also by complex secondary injury cascades involving neuroinflammation, axonal degeneration, demyelination, and maladaptive repair responses. Current diagnostic and prognostic approaches, which rely largely on neurological examination and imaging, provide limited insight into these dynamic molecular processes. In this context, extracellular vesicles (EVs) have emerged as a biologically compelling source of biomarkers for SCI. EVs are released by neurons, glial cells, endothelial cells, and immune cells and carry molecular cargo that reflects cellular stress, injury severity, and endogenous repair activity. Increasing evidence indicates that EV-associated proteins and regulatory microRNAs (miRNAs) encode injury-specific signatures related to neuronal and glial damage, inflammatory signaling, metabolic stress, and functional recovery potential. In this review, we summarize the current knowledge on EV biology in SCI and discuss emerging evidence supporting EV-derived proteins and miRNAs as promising tools for refining diagnosis and prognosis. Our aim is not only to consolidate established findings but also to highlight EV-based molecular signatures as a developing framework for precision biomarker discovery in SCI. Full article

Graph Neural Networks (GNNs) have become a central methodology for modelling biological systems where entities and their interactions form inherently non-Euclidean structures. From protein interaction networks and gene regulatory circuits to molecular graphs and multi-omics integration, the relational nature of biological data makes GNNs particularly well-suited for capturing complex dependencies that traditional deep learning methods fail to represent. Despite their rapid adoption, the effectiveness of GNNs in bioinformatics depends not only on model design but also on how biological graphs are constructed, parameterised and trained. In this review, we provide a structured framework for understanding and applying GNNs in bioinformatics, organised around three key dimensions: (1) graph construction and representation, including strategies for deriving biological networks from heterogeneous sources and selecting biologically meaningful node and edge features; (2) GNN architectures, covering spectral and spatial formulations, representative models such as Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Sample and AggregatE (GraphSAGE) and Graph Isomorphism Network (GIN), and recent advances including transformer-based and self-supervised paradigms; and (3) applications in biomedical domains, spanning disease–gene association prediction, drug discovery, protein structure and function analysis, multi-omics integration and biomedical knowledge graphs. We further examine training considerations, including optimisation techniques, regularisation strategies and challenges posed by data sparsity and noise in biological settings. By synthesising methodological foundations with domain-specific applications, this review clarifies how graph quality, architectural choice and training dynamics jointly influence model performance. We also highlight emerging challenges such as modelling temporal biological processes, improving interpretability, and enabling robust multimodal fusion that will shape the next generation of GNNs in computational biology. Full article

The analysis of knowledge derived from the relection of De magia distinguishes between science, magic, and religion. The first is the result of an investigation based on natural causes. The mystery and solitude that surround the scientist in pursuit of their objectives not only constitute a context of discovery in which not everyone is capable of operating but also allow Vitoria to intuit that people of very different backgrounds are present within it. However, what is important here is not so much the intention that guides them, but rather that the resources employed depend on nature and are available to anyone and, therefore, do not imply any reprehensible action. Full article

Evaluating innovation and optimising its role in the inventions is fundamental for applied research, that requires planning the use of available resources. Traditional assessment approaches often miss to capture how innovation stagnates between the ideation and prototyping phases (the Valley of Death), and to learn how innovation emerges from intermediate-steps contributed by individuals. This paper focuses on tracing innovation as an approach enabling mapping of pathways of intermediate-steps and opportunities for valorising unplanned outcomes. We adopt a qualitative case study to explore how innovation pathways can be conceptualised through technological readiness levels. The operational settings of an EU-funded project defined the boundaries of the study. A network analysis explored relationships among themes that emerged from respondents involved in the activities, following an inductive approach to derive themes from data. Findings indicate that intermediate innovation steps, including failures, are viewed as cumulative contributions to novelty. Their documentation is seen as an investment for unlocking latent value embedded in distributed knowledge. Within this scope, we outline a blockchain-based knowledge graph as a proof-of-concept for tracing cumulative contributions, identifying breakthroughs leading to technological maturity and supporting generation of hypothesis grounded on experimental trials. As a result, we suggest that paths recombining prior knowledge into novelty encode latent value that can be interpreted as a function of the network topology, and propose a conceptual framework for analysing value by means of information theory metrics applicable to innovation graphs. Full article

In elite sports, discovering interdisciplinary causal relationships from public data is critical for gaining a competitive edge. However, the causal knowledge required for these practices is difficult to obtain through either existing intervention-based sports science methods or computational techniques focused on statistical association. This paper formalizes a multi-domain collaborative framework, which involves three roles: (1) the elite sports team; (2) the sport science expert; and (3) the causal inference expert. Our nine-step workflow, which processes three core elements of problem, data, and computing, guides these experts through a cycle that systematically transforms practical problems into computational models and, crucially, translates complex analytical outputs back into actionable strategies. The framework also introduces a dual-dimensional “field evaluation” method, encompassing both process and outcome, to quantify the trustworthiness of knowledge in practical settings where a “gold standard” is absent. This framework was applied in an illustrative case study prior to the Paris 2024 Olympics, providing one additional evidence-informed input for the national team. The success was observed and interpreted as contextual consistency rather than causal validation. This framework ensures the practical application of causal discovery in elite sports, offering a repeatable and explainable pathway for generating credible, evidence-based insights from public data for elite sports decision-making. Full article

Termite mounds are rich, underexplored reservoirs of bioactive Streptomyces. This study focuses on the isolation and metabolic characterization of pigment-producing Streptomyces from Macrotermes gilvus mounds in Thailand. Four pigment-producing strains related to S. violarus, S. aureofaciens, S. roseoverticillatus, and S. flavofungini were analyzed. These strains exhibited robust antibacterial properties, primarily against Gram-positive bacteria, and significant antioxidant capacity. Structural elucidation using HRMS and NMR identified a stable pink pigment from strain A2 as a novel cyclized prodigiosin derivative (C₃₆H₄₆N₄O₅). To our knowledge, this is the first report of a novel prodiginine sourced from termite-associated actinobacteria. Feasibility trials in cosmetic formulations confirmed the pigment’s stability, suggesting significant potential for industrial use. These results underscore the value of exploring termite-associated microbes for the discovery of unique, functional natural products. Full article

The increasing scale and imbalance of modern network traffic pose significant challenges for multi-class intrusion detection systems (IDSs), particularly in identifying rare attack types. Traditional intrusion detection approaches based on supervised classification or unsupervised anomaly detection often suffer from limited generalization under severe class imbalance, high-dimensional feature spaces, and noisy traffic, resulting in poor detection of minority attack classes. To address these limitations, this study presents a hybrid intrusion detection framework that integrates unsupervised feature learning, anomaly scoring, and supervised classification within a unified pipeline. A denoising autoencoder trained exclusively on normal traffic is employed to learn compact and noise-resistant feature representations, while an isolation forest independently generates statistical anomaly scores. These complementary features are then fused and classified using a Light Gradient Boosting Machine (LightGBM). The main contribution of this work lies in the effective integration of these components, combined with a balanced training strategy based on the Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTE-ENN), as well as robust validation procedures. The framework is evaluated on the Network Security Laboratory Knowledge Discovery and Data Mining dataset (NSL-KDD) and the UNSW-NB15 intrusion detection dataset using stratified cross-validation and multiple independent runs. Experimental results demonstrate consistently high classification accuracy (~99%) and strong macro-F1 performance (>97%) across all attack categories on both NSL-KDD and UNSW-NB15 datasets. The framework achieves exceptional detection of rare classes (R2L: 99% F1, U2R: 100% F1), significantly outperforming prior approaches (AE-SAC: 83.97% F1, RL-NIDS: poor U2R recall), while maintaining low inference latency (~2–3 ms per sample, 415 samples/second) suitable for real-time network security deployment. Full article

Background: Diabetes mellitus is characterized by elevated blood sugar due to absolute or relative insulin deficiency. Diabetes is classified as type 1 (T1D) or type 2 diabetes (T2D), gestational diabetes, and other types, such as monogenic diabetes, exocrine pancreatic disorders, and medication-induced diabetes. Objectives: This review article provides an overview of diabetes genetics, covering polygenic, monogenic, and syndromic forms of the disorder with emphasis on aspects to help clinicians in diagnosis, management, and counseling, but also to foster valuable knowledge for diabetic researchers in identifying phenotypes that will help inform gene discovery. Key Findings: Most cases of T1D and T2D are polygenic with environmental triggers. T1D results from autoimmune destruction of pancreatic beta cells leading to absolute insulin deficiency. Genetic studies of T1D have focused on the identification of loci associated with increased susceptibility to T1D. Early studies showed a linkage between T1D and several human leukocyte antigen (HLA) susceptibility loci on chromosome 6. Genome-wide association studies (GWAS) have identified more than 100 HLA- and non-HLA loci that increase susceptibility to T1D. It has been well established that a substantial portion of the genetic risk for T1D is encoded in the HLA locus. The non-HLA loci INS, CTLA4, IL2RA, IFIH1, and PTPN22 make moderate contributions to T1D risk. Many other non-HLA loci have small effects to the phenotype and are relevant to autoimmunity, but they are yet to be identified. T2D, on the other hand, is associated with obesity and insulin resistance with relative insulin deficiency. Thousands of gene variants that are common and contribute small effects have also been identified through GWAS to contribute to T2D risk, but the rarer variants may confer significant risk to an individual’s risk. Common variants in the TCF7L2 locus consistently carry one of the largest risks associated with T2D with a reported 1.7-fold disease odds for homozygous carriers. The usefulness of individual variants for genetic counseling in the common forms of diabetes has been limited in clinical settings in the past. The development of polygenic risk scores (PRS) and partitioned polygenic risk scores (PPRS), statistics derived from GWAS, are being used to predict and classify diabetes. The performance of PRS and PPRS varies by ancestry and type of diabetes. The PRS performs better with T1D, with an area under the curve and receiver operating characteristics (AUC-ROC) ranging from 0.87 to 0.93, compared to 0.72–0.75 for T2D. The genetic architecture of T2D is markedly more polygenic than T1D, and the PPRS has been useful in assessing risk in that setting. Monogenic diabetes comprises several dysglycemic disorders that include neonatal diabetes, maturity-onset diabetes of the young (MODY), and other genetic syndromes that have diabetes either as an associated finding and/or as a complication. Some of the monogenic diabetes gene variants have incomplete penetrance and variable expressivity leading to different ages of onset and variable presentation even within the same family. Hence some patients with these conditions have been previously diagnosed as having T1D or T2D. Many monogenic disorders follow Mendelian inheritance patterns, so genetic counseling is relatively straightforward if pathogenic variants are found to be inherited from a parent. Counseling for forms of diabetes due to maternally inherited mitochondrial cytopathies, such as MELAS and Kearns–Sayres syndrome, is not straightforward due to the occurrence of two or more populations of genetically distinct mitochondrial DNAs in the cells (heteroplasmy); the higher the percent of pathogenic variants in a cell or tissue, the greater the chance for affectation of disorder. Implications: Early stages of diabetes may be asymptomatic, and improvement in methodologies to identify individuals at high risk is important so prevention strategies can be targeted to susceptible individuals to slow or obviate the onset of disease and to minimize complications. Conclusions: Diabetes is a heterogeneous disorder, and accurate definition of phenotypes in the setting of non-syndromic and syndromic forms, development of powerful statistical methodologies, use of next-generation sequencing applications to interrogate the genome, incorporation of epigenetic mechanisms in statistical modeling and accurate curation of gene variants, will help us to realize application of genomic medicine and to inform diabetes care. Full article

Improving phosphorus use efficiency (PUE) in crops is critical for sustainable agriculture. Although the transcriptional regulation of phosphate starvation responses, centered on regulators such as the PHR1 and SPX proteins, is well established, the post-transcriptional mechanisms remain incompletely understood. This gap hinders a comprehensive view of how plants adapt to low-P conditions. This review synthesizes current knowledge on the gene regulatory networks involved in low-P adaptation in barley, with a specific focus on the emerging role of RNA processing. It highlights the limited knowledge of how alternative splicing contributes to this response. AS provides a rapid and energy-efficient means of fine-tuning gene expression, expanding proteome diversity and enabling more sophisticated adaptation mechanisms than the relatively binary “on/off” mode of transcriptional control. Several core regulators of AS, including serine–arginine-rich proteins and hnRNPs, have been identified, with the former discussed in detail and illustrated with key examples. Building on the advanced mechanistic insights into AS gained from model crops such as rice, this review proposes a predictive framework to prioritize research targets and experimental strategies. Such an approach can accelerate the discovery of analogous mechanisms in barley, thereby bridging a critical knowledge gap and advancing strategies to improve PUE in this important cereal crop. Full article

Rhizobia are a polyphyletic group of Proteobacteria comprising approximately 700 different species. Despite significant advancements in their taxonomy, evolutionary history, and ecological importance, substantial knowledge gaps remain regarding a detailed understanding of rhizobial biodiversity in a geographical context and the interest in studying and valorizing native rhizobial strains. This bibliometric study used data from the last four decades (1985–2025) to review the taxonomic and functional diversity of the documented novel taxa of rhizobia described from African ecosystems, as well as their valorization status as biofertilizers. It aims to discuss the interest in knowing, preserving, and valorizing native rhizobial resources in the global context of climate change and biodiversity erosion. The study revealed that the first African indigenous novel species of rhizobia was published in 1988, although research on rhizobia dates back to the 1950s in Africa. To date, ~63 species (approximately 9% of the total in the world) and two genera of rhizobia have been described using native isolates from 11 African countries, with substantial discoveries from the Succulent Karoo hotspot of biodiversity in South Africa. Approximately 51% of species are affiliated with Bradyrhizobium and Mesorhizobium, with Vachellia karroo and Senegalia spp. (formerly Acacia spp.) as their primary hosts. Most species-type strains (~89%) were found to be infective on legumes and are good candidates for biofertilizer development. However, there is a limited level of commercial valorization of indigenous isolates as inoculants, mainly because the production of biological intrants is still at the experimental stage in Africa. Interestingly, important breaking point discoveries have been made using native rhizobial strains from Africa, including the pioneering demonstration in 2001 that Burkholderia (beta-rhizobia) is a symbiotic genus with legumes. It also includes the discovery of stem-nodulating rhizobia and Nod factor-independent symbiotic processes in some rhizobia. Together, this review highlights the importance of native African rhizobial strains. This underscores the need to accelerate their agronomic valorization to better support the transition to more resilient and sustainable legume-based farming systems in African countries. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is a growing health concern, yet the role of non-coding RNAs (ncRNAs), including microRNAs (miRNAs), in its pathogenesis remains poorly understood. In this pilot study, we aimed to identify significantly expressed miRNAs and ncRNAs and correlate transcriptomic patterns of the findings with previously identified coding gene expression profiles to explore potential regulatory mechanisms in MASLD. Participants were selected from an existing study population. We conducted transcriptomic profiling of miRNAs and other ncRNAs in whole-blood samples from African American (AA) individuals with MASLD and matched controls (n = 4 per group) as a discovery cohort. A subsequent qRT-PCR validation study was performed in 30 participants, including 14 individuals with MASLD and 16 controls. miRNA sequencing was performed by Zymo, USA, followed by miRNA extraction using the Zymo-Seq™ miRNA Library Kit. Differentially expressed miRNAs and ncRNAs were analyzed using Ingenuity Pathway Analysis (IPA) to identify associated biological pathways. A total of 1412 miRNAs and 5423 other ncRNAs were identified in this study. Among them, 35 miRNAs and 28 other ncRNAs exhibited significant differential expressions (fold-change cutoff 1.5, p < 0.05). miR-206 was consistently upregulated, whereas miR-1343-5p, miR-1299, miR-224-5p, and miR-193a-5p were downregulated across all samples. miR-206 upregulation and miR-185-3p/miR-224-5p/miR-218-5p downregulation were validated, associating with lipid metabolism impairment and hepatic fibrosis via the AMPK/TGF-β pathway, implicating ncRNA-mediated regulation. To our knowledge, this is the first whole-blood non-coding RNA transcriptomic study in AA MASLD, an under-represented population. This small-scale pilot study requires validation in large multi-ethnic cohorts to confirm generalizability. Full article

Yeasts, especially the conventional species Saccharomyces cerevisiae and Schizosaccharomyces pombe, as well as some unconventional species such as Pichia pastoris, Kluyveromyces marxianus and Yarrowia lipolytica, have become fundamental model organisms for understanding the molecular mechanisms underlying human diseases. Their eukaryotic cell organization, genetic simplicity, and strong conservation of essential biological pathways make them indispensable in biomedical research. This review provides a comprehensive overview of the role of different yeast species in modeling human disorders, highlighting historical milestones and groundbreaking discoveries that have shaped current knowledge. The article discusses the applications of yeast models in studying neurodegenerative diseases such as Alzheimer’s and Huntington’s, as well as metabolic diseases, infectious diseases and mitochondrial disorders, and their growing importance in cancer research and drug discovery. Special attention is given to humanized yeast models, which enable the expression and functional analysis of human genes and the heterologous synthesis of human proteins within yeast cells. Finally, the paper addresses the limitations and challenges of yeast as a model system while outlining future directions and emphasizing the organism’s continued relevance in personalized medicine and functional genomics. Full article

RNA viruses pose a persistent global threat due to their high mutation rates, zoonotic potential, and rapid adaptability. Emergence events have risen steadily, as demonstrated by major outbreaks caused by Influenza A, Ebola, Zika, and Chikungunya viruses, followed by the coronavirus epidemics of Severe Acute Respiratory Syndrome coronavirus (SARS-CoV-1) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and culminating in the COVID-19 pandemic. These characteristics frequently compromise the durability of existing vaccines and antiviral therapies, highlighting the urgent need for new antiviral agents. Alkaloids, a structurally diverse class of nitrogen-containing natural compounds, have gained attention for their ability to interfere with multiple stages of the viral life cycle, including entry, replication, protein synthesis, and host immune modulation. To our knowledge, this review compiles all currently reported alkaloids with antiviral activity against RNA viruses and summarizes their proposed mechanisms of action, distinguishing evidence from in vitro, in vivo, and in silico studies. Quaternary alkaloids are discussed separately because their permanent ionic charge enables distinctive interactions with membranes and host pathways. Although many findings are promising, clinical translation remains limited by incomplete mechanistic validation, scarce in vivo data, suboptimal bioavailability, narrow therapeutic windows, and inconsistent experimental methodologies. To advance the field, future research should prioritize RT-qPCR–based antiviral evaluation to accurately quantify viral replication, incorporate mechanistic assays to clarify modes of action, apply structure–activity relationship (SAR) approaches for rational optimization, and expand in vivo pharmacokinetic and efficacy studies to assess therapeutic feasibility. Overall, alkaloids represent a promising yet underdeveloped reservoir for next-generation antiviral discovery against rapidly evolving RNA viruses. Full article