Search Results (119)

Cardiovascular diseases (CVDs) are increasingly being defined not only in terms of metabolic or purely vascular disorders, but also as complex immunometabolic disorders. One of the most groundbreaking discoveries in recent years is the role of neutrophil extracellular networks (NETs/NENs) as a key link between chronic vascular wall inflammation and thrombotic processes. In this article, we present a synthetic overview of the latest data on the biology of NETs/NENs and their impact on the development of atherosclerosis, endothelial dysfunction, and the mechanisms of immunothrombosis. We highlight how these structures contribute to the weakening of atherosclerotic plaque stability, impaired endothelial barrier integrity, platelet activation, and the initiation of the coagulation cascade. We also discuss the modulating role of classic risk factors such as hypertension, dyslipidemia, and exposure to tobacco smoke, which may increase the formation or hinder the elimination of NETs/NENs. We also focus on the practical application of this knowledge: we present biomarkers associated with the presence of NETs/NENs (cfDNA, MPO–DNA complexes, CitH3, NE), which may be useful in diagnostics and risk stratification, and we discuss innovative therapeutic strategies. In addition to classic methods for indirectly inhibiting NET/NEN formation (antiplatelet, anti-inflammatory, and immunometabolic agents), we present experimental approaches aimed at their neutralization and removal (e.g., DNase I, elastase, and myeloperoxidase inhibitors). We pay particular attention to the context of cardiac and cardiac surgical procedures (Percutaneous Coronary Intervention-PCI, coronary artery bypass grafting-CABG), where rapid NET/NEN bursts can increase the risk of acute thrombotic complications. The overall evidence indicates that NETs/NENs represent an innovative and promising research and therapeutic target, allowing us to view cardiovascular diseases in a new light—as a dynamic interaction of inflammatory, atherosclerotic, and thrombotic processes. This opens up new possibilities in diagnostics, combination treatment and personalisation of therapy, although further research and standardization of detection methods remain necessary. Full article

This study aimed to examine reading interest profiles among preservice Chinese language teachers and related factors making them begin to like or dislike reading. In total, 321 college students majoring in Chinese language education in elementary and secondary schools participated in this study and completed a reading interest questionnaire. The questionnaire contains one close-ended question asking about their reading interest levels across seven periods (from preschool to college) and three open-ended questions asking about the reasons influencing their reading interest levels. Latent profile analysis (LPA) was used to identify reading interest profiles, and qualitative analysis was used to examine factors influencing their reading interests. The LPA results revealed three profiles, namely, mountain (up-down), valley (up-down-up), and upslope (up). The qualitative analysis revealed that motivators encouraging students to read included literacy sponsors, improved reading ability, reading time, extrinsic motivators, curiosity and desire for knowledge, access to reading, discovery of preferred texts, and relief from academic stress and relaxation. By contrast, barriers associated with the decline in reading interest included academic burdens and pressure, the availability of alternatives, a lack of reading ability, a loss of reading autonomy, a lack of literacy sponsors, limited access to reading, and inappropriate texts. Literacy researchers and educators should listen to students’ voices, understand their reading experiences, and consider developing appropriate intervention programs for literacy at different periods. Full article

Biodegradable polyesters have gained attention due to their sustainability benefits, considering the escalating environmental challenges posed by synthetic polymers. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), are expected to significantly accelerate research in polymer science. This review article explores “bio” polymer informatics by harnessing insights from the AI techniques used to predict structure–property relationships and to optimize the synthesis of bioplastics. This review also discusses PolyID, a machine learning-based tool that employs message-passing graph neural networks to provide a framework capable of accelerating the discovery of bioplastics. An extensive literature review is conducted on explainable AI (XAI) and generative AI techniques, as well as on benchmarking data repositories in polymer science. The current state-of-the art in ML methods for ring-opening polymerizations and the synthesizability of biodegradable polyesters is also presented. This review offers an in-depth insight and comprehensive knowledge of current AI-based models for polymerizations, molecular descriptors, structure–property relationships, predictive modeling, and open-source benchmarked datasets for sustainable polymers. This study serves as a reference and provides critical insights into the capabilities of AI for the accelerated design and discovery of green polymers aimed at achieving a sustainable future. Full article

Optical Coherence Tomography (OCT) has evolved from a breakthrough ophthalmologic imaging tool into a cornerstone technology in interventional cardiology. After its initial applications in retinal imaging in the early 1990s, OCT was subsequently envisioned for cardiovascular use. In 1995, its ability to visualize atherosclerotic plaques was demonstrated in an in vitro study, and the following year marked the acquisition of the first in vivo OCT image of a human coronary artery. A major milestone followed in 2000, with the first intracoronary imaging in a living patient using time-domain OCT. However, the real inflection point came in 2006 with the advent of frequency-domain OCT, which dramatically improved acquisition speed and image quality, enabling safe and routine imaging in the catheterization lab. With the advent of high-resolution, second-generation frequency-domain systems, OCT has become clinically practical and widely adopted in catheterization laboratories. OCT progressively entered interventional cardiology, first proving its safety and feasibility, then demonstrating superiority over angiography alone in guiding percutaneous coronary interventions and improving outcomes. Today, it plays a central role not only in clinical practice but also in cardiovascular research, enabling precise assessment of plaque biology and response to therapy. With the advent of artificial intelligence and hybrid imaging systems, OCT is now evolving into a true precision-medicine tool—one that not only guides today’s therapies but also opens new frontiers for discovery, with vast potential still waiting to be explored. Tracing its historical evolution from ophthalmology to cardiology, this narrative review highlights the key technological milestones, clinical insights, and future perspectives that position OCT as an indispensable modality in contemporary interventional cardiology. As a guiding thread, the myth of Prometheus is used to symbolize the evolution of OCT—from its illuminating beginnings in ophthalmology to its transformative role in cardiology—as a metaphor for how light, innovation, and knowledge can reveal what was once hidden and redefine clinical practice. Full article

Circular RNAs (circRNAs) are covalently closed RNA molecules generated through a non-canonical splicing event known as back-splicing. This particular class of non-coding RNAs has attracted growing interest due to its evolutionary conservation across eukaryotes, high expression in the central nervous system, and frequent dysregulation in various pathological conditions, including cancer. Traditionally, circRNAs have been characterised by their ability to function as microRNA (miRNA) and protein sponges. However, recent discoveries from multiple research groups have uncovered a novel and potentially transformative mechanism of action: the direct interaction of circRNAs with messenger RNAs (mRNAs) to regulate their fate. These interactions can influence mRNA stability and translation, revealing a new layer of post-transcriptional gene regulation. In this review, we present and analyse the latest evidence supporting the emerging role of circRNAs in diverse biological contexts. We highlight the growing body of research demonstrating circRNA-mRNA interactions as a functional regulatory mechanism and explore their involvement in key physiological and pathophysiological processes. Understanding this novel mechanism expands our knowledge of RNA-based regulation and opens new opportunities for therapeutic strategies targeting circRNA-mRNA networks in human disease. Full article

Surface plasmon resonance (SPR) is a powerful tool for analyzing biomolecular interactions and is widely used in basic biomedical research and drug discovery. Heparan sulfate (HS) is a linear complex polysaccharide and a key component of the extracellular matrix and cell surfaces. HS plays a pivotal role in maintaining cellular functions and tissue homeostasis by interacting with numerous proteins, making it essential for normal physiological processes and disease states. Deciphering the interactome of HS unlocks the mechanisms underlying its biological functions and the potential for novel HS-related therapeutics. This review presents an overview of the recent advances in the application of SPR technology to HS interactome research. We discuss methodological developments, emerging trends, and key findings that illustrate how SPR is expanding our knowledge of HS-mediated molecular interactions. Additionally, we highlight the potential of SPR-based approaches in identifying novel therapeutic targets and developing HS-mimetic drugs, thereby opening new avenues for intervention in HS-related diseases. Full article

The clinical diagnosis of Parkinson’s disease (PD) primarily relies on clinician-administered observational assessment tools, such as the Unified Parkinson’s Disease Rating Scale (UPDRS). However, these approaches are significantly influenced by subjectivity and exhibit insufficient sensitivity for early-stage symptom detection. The introduction of deep learning techniques has opened new avenues for the early diagnosis of PD. In contrast to traditional methods, deep learning models are capable of processing large-scale, high-dimensional, and complex datasets to automatically learn latent feature relationships, making them particularly suitable for scenarios involving multimodal data fusion. The multimodal diagnosis of PD is confronted with two enduring challenges: (1) the dependence on pre-existing knowledge of established genetic risk loci, and (2) the low efficiency and limited interpretability in handling interactions among cross-modal features. To address these challenges, this study introduces an innovative multimodal deep learning framework with two primary contributions: (1) a Genome-Wide Association Study (GWAS)-Transformer architecture that autonomously selects single nucleotide polymorphism (SNP) features through GWAS and utilizes a multi-head attention mechanism to model potential associations between non-risk loci, thereby eliminating the reliance on known susceptibility genes; (2) a Residual Attention Contrastive Fusion (RACF) module that tackles the heterogeneity of cross-modal features by dynamically allocating attention weights and applying contrastive loss constraints. Evaluation results on the Parkinson’s Progression Markers Initiative (PPMI) dataset demonstrate that our model achieves a classification accuracy of 91.2% and an AUC of 0.94, and predicts nine potential novel risk loci. This work presents a novel paradigm for the discovery of new risk loci based on deep learning and offers valuable insights from a multi-omics perspective for advancing PD research. Full article

This paper introduces a neuro-symbolic approach for relational exploration in cultural heritage knowledge graphs, exploiting Large Language Models (LLMs) for explanation generation and a mathematically grounded model to quantify the interestingness of relationships. We demonstrate the importance of the proposed interestingness measure through a quantitative analysis, highlighting its significant impact on system performance, particularly in terms of precision, recall, and F1-score. Utilizing the Wikidata Cultural Heritage Linked Open Data (WCH-LOD) dataset, our approach achieves a precision of 0.70, recall of 0.68, and an F1-score of 0.69, outperforming both graph-based (precision: 0.28, recall: 0.25, F1-score: 0.26) and knowledge-based (precision: 0.45, recall: 0.42, F1-score: 0.43) baselines. Furthermore, the proposed LLM-powered explanations exhibit better quality, as evidenced by higher BLEU (0.52), ROUGE-L (0.58), and METEOR (0.63) scores compared to baseline approaches. We further demonstrate a strong correlation (0.65) between the interestingness measure and the quality of generated explanations, validating its ability to guide the system towards more relevant discoveries. This system offers more effective exploration by achieving more diverse and human-interpretable relationship explanations compared to purely knowledge-based and graph-based methods, contributing to the knowledge-based systems field by providing a personalized and adaptable relational exploration framework. Full article

The penile tubular urethra forms by canalization of the urethral plate without forming an obvious urethral groove in mice, while the urethral epithelium forms a fully open urethral groove before urethra closure through the distal-opening-proximal-closing process in humans and guinea pigs. Our knowledge of the mechanism of penile development is mainly based on studies in mice. To reveal how the fully opened urethral groove forms in humans and guinea pigs, we compared the expression patterns and levels of key developmental genes using in situ hybridization and quantitative PCR during glans and preputial development between guinea pigs and mice. Our results revealed that, compared with mouse preputial development, which started before sexual differentiation, preputial development in guinea pigs was delayed and initiated at the same time that sexual differentiation began. Fgf10 was mainly expressed in the urethral epithelium in developing genital tubercle (GT) of guinea pigs. The relative expression of Shh, Fgf8, Fgf10, Fgfr2, and Hoxd13 was reduced more than 4-fold in the GT of guinea pigs compared to that of mice. Hedgehog and Fgf inhibitors induced urethral groove formation and restrained preputial development in cultured mouse GT, while Shh and Fgf10 proteins induced preputial development in cultured guinea pig GT. Our discovery suggests that the differential expression of Shh and Fgf10/Fgfr2 may be the main reason a fully opened urethral groove forms in guinea pigs, and it may be similar in humans as well. Full article

Biologists often set out to find relevant data in an ever-changing landscape of interesting databases. While leading journals publish descriptions of databases, they are usually not recent and do not frequently update the list that discards defunct or poor-quality databases. These indices usually include databases that are proactively requested to be included by their authors. The challenge for individual biologists, then, is to discover, explore, and select databases of interest from a large unorganized collection and effectively use them in their analysis without too large of an investment. The advocation of the FAIR data principle to improve searching, finding, accessing, and inter-operating among these diverse information sources in order to increase usability is proving to be a difficult proposition and consequently, a large number of data sources are not FAIR-compliant. Since linked open data do not guarantee FAIRness, biologists are now left to individually search for information in open networks. In this paper, we propose SoDa, for intelligent data foraging on the internet by biologists. SoDa helps biologists to discover resources based on analysis requirements and generate resource access plans, as well as storing cleaned data and knowledge for community use. SoDa includes a natural language-powered resource discovery tool, a tool to retrieve data from remote databases, organize and store collected data, query stored data, and seek help from the community when things do not work as anticipated. A secondary search index is also supported for community members to find archived information in a convenient way to enable its reuse. The features supported in SoDa endows biologists with data integration capabilities over arbitrary linked open databases and construct powerful computational pipelines using them, capabilities that are not supported in most contemporary biological workflow systems, such as Taverna or Galaxy. Full article

The reduction in the supply of fossil fuel available, combined with global warming’s effects on the atmosphere, has led to the discovery of employing sustainable energy for everyday activities. Road energy harvesting is one example of sustainable energy that can be used, as the majority of people spend a substantial amount of their daily activities commuting from one location to another, and numerous types of transportation generate heat that can be converted into energy. This alternative energy source can be implemented on the road, considering that roads are critical infrastructure that has a significant effect on a country’s economy. Furthermore, road infrastructure has been contributing towards the affordability of urbanization and migration, whether locally or internationally. Currently, researchers are working towards integrating road energy harvesting around the world by incorporating various types of materials and technology connected via a sensing system. Many materials have been attempted, including ceramics, polymers, lead-free, nanomaterials, single crystals, and composites. Other possible sources to generate energy from roadways, such as solar power, thermal energy, and kinetic energy, have been investigated as well. However, many studies available only focused on the disclosure of novel materials or the review of technologies produced for road energy harvesting. There have been limited studies that focused on a comprehensive review of various materials and technologies and their implications for the performance of road energy harvesting. Hence, the main objective of this research is to undertake a thorough and in-depth review in order to identify the best materials and technologies for certain types of application in road energy harvesting. The paper discusses energy-harvesting technology, sensing systems, and the potential network based on them. Comprehensive analyses were conducted to evaluate in-depth comparisons between different materials and technologies used for road energy harvesting. The novelty of this study is related to the appropriate efficient, durable, and sustainable materials and technologies for their relevant potential application. The results of this review paper are original since it is the first of its kind, and, to the best knowledge of the authors’ knowledge, a similar study is not available in the open literature. Full article

The exponential growth of biomedical literature necessitates advanced methods for Literature-Based Discovery (LBD) to uncover hidden, meaningful relationships and generate novel hypotheses. This research integrates Large Language Models (LLMs), particularly transformer-based models, to enhance LBD processes. Leveraging LLMs’ capabilities in natural language understanding, information extraction, and hypothesis generation, we propose a framework that improves the scalability and precision of traditional LBD methods. Our approach integrates LLMs with semantic enhancement tools, continuous learning, domain-specific fine-tuning, and robust data cleansing processes, enabling automated analysis of vast text and identification of subtle patterns. Empirical validations, including scenarios on the effects of garlic on blood pressure and nutritional supplements on health outcomes, demonstrate the effectiveness of our LLM-based LBD framework in generating testable hypotheses. This research advances LBD methodologies, fosters interdisciplinary research, and accelerates discovery in the biomedical domain. Additionally, we discuss the potential of LLMs in drug discovery, highlighting their ability to extract and present key information from the literature. Detailed comparisons with traditional methods, including Swanson’s ABC model, highlight our approach’s advantages. This comprehensive approach opens new avenues for knowledge discovery and has the potential to revolutionize research practices. Future work will refine LLM techniques, explore Retrieval-Augmented Generation (RAG), and expand the framework to other domains, with a focus on dehallucination. Full article

Background/Objectives: The phytosulfokine receptor (PSKR) gene family plays a crucial role in regulating plant growth, development, and stress response. Here, the PSKR gene family was characterized in Triticum aestivum L. The study aimed to bridge knowledge gaps and clarify the functional roles of TaPSKRs to create a solid foundation for examining the structure, functions, and regulatory aspects. Methods: The investigation involved genome-wide identification of PSKRs through collection and chromosomal assignment, followed by phylogenetic analysis and gene expression profiling. Additionally, interactions with their interactors were stimulated and analyzed to elucidate their function. Results: The wide-genome inspection of all TaPSKRs led to 25 genes with various homeologs, resulting in 57 TaPSKR members distributed among the A, B, and D subgenomes. Investigating the expression of 61 TaPSKR cDNAs in RNA-seq datasets generated from different growth stages at 14, 21, and 60 days old and diverse tissues such as leaves, shoots, and roots provided further insight into their functional purposes. The expression profile of the TaPSKRs resulted in three key clusters. Gene cluster 1 (GC 1) is partially associated with root growth, suggesting that specific TaPSKRs control root development. The GC 2 cluster targeted genes that show high levels of expression in all tested leaf growth stages and the early developmental stage of the shoots and roots. Furthermore, the GC 3 cluster was composed of genes that are constantly expressed, highlighting their crucial role in regulating various processes during the entire life cycle of wheat. Molecular docking simulations showed that phytosulfokine type α (PSK-α) interacted with all TaPSKRs and had a strong binding affinity with certain TaPSKR proteins, encompassing TaPSKR1A, TaPSKR3B, and TaPSKR13A, that support their involvement in PSK signaling pathways. The crucial arbitration of the affinity may depend on interactions between wheat PSK-α and PSKRs, especially in the LRR domain region. Conclusions: These discoveries deepened our knowledge of the role of the TaPSKR gene family in wheat growth and development, opening up possibilities for further studies to enhance wheat durability and yield via focused innovation approaches. Full article

Objectives: Pharmacokinetic issues were the leading cause of drug attrition, accounting for approximately 40% of all cases before the turn of the century. To this end, several high-throughput in vitro assays like microsomal stability have been developed to evaluate the pharmacokinetic profiles of compounds in the early stages of drug discovery. At NCATS, a single-point rat liver microsomal (RLM) stability assay is used as a Tier I assay, while human liver microsomal (HLM) stability is employed as a Tier II assay. We experimentally screened and collected data on over 30,000 compounds for RLM stability and over 7000 compounds for HLM stability. Although HLM stability screening provides valuable insights, the increasing number of hits generated, along with the time- and resource-intensive nature of the assay, highlights the need for alternative strategies. One promising approach is leveraging in silico models trained on these experimental datasets. Methods: We describe the development of an HLM stability prediction model using our in-house HLM stability dataset. Results: Employing both classical machine learning methods and advanced techniques, such as neural networks, we achieved model accuracies exceeding 80%. Moreover, we validated our model using external test sets and found that our models are comparable to some of the best models in literature. Additionally, the strong correlation observed between our RLM and HLM data was further reinforced by the fact that our HLM model performance improved when using RLM stability predictions as an input descriptor. Conclusions: The best model along with a subset of our dataset (PubChem AID: 1963597) has been made publicly accessible on the ADME@NCATS website for the benefit of the greater drug discovery community. To the best of our knowledge, it is the largest open-source model of its kind and the first to leverage cross-species data. Full article

A century after the first scientific research on the chemical structures of pyrethrins was published (in 1923), this paper aims to provide an exhaustive review of the historical research pathways and relative turning points that led to the discovery and mass production of pyrethroids, which have become among the most commercially successful insecticides. These compounds, which are not specific to any particular pest, are used globally and offer cost-effective advantages against a broad spectrum of pests in both agricultural and non-agricultural situations. They are utilized in the context of both harvest and post-harvest applications, as well as in the implementation of public health programs and veterinary applications. Currently, the research for new pyrethroids has essentially reached a standstill due to the increasingly widespread occurrence of insecticide resistance in pests. Nevertheless, several research paths remain open regarding these pesticides. This paper represents the current state of knowledge regarding pyrethroids, exposing both their advantages and disadvantages. Moreover, further investigation, at the molecular level, on their mode of action (MoA) could be very useful to improve their specificity. The results of this review may stimulate additional research for the development of novel pyrethroids having enhanced efficacy, low cost and reduced environmental impact. Full article