Search Results (107)

Mycoplasma bovis is an important pathogen that is associated with respiratory diseases, mastitis, and arthritis in cattle, leading to significant economic losses in the global cattle industry. Most notably in this study, we pioneer the discovery that its secreted effector ENO1 (α-enolase) directly targets host cytoskeletal proteins for metabolic–immune regulation. Using an innovative GST pull-down/mass spectrometry approach, we made the seminal discovery of β-actin (ACTB) as the primary host target of ENO1—the first reported bacterial effector–cytoskeleton interaction mediating metabolic reprogramming. ENO1–ACTB binding depends on a hydrogen bond network involving ACTB’s 117Glu and 372Arg residues. This interaction triggers (1) glycolytic activation via Glut1 upregulation, establishing Warburg effect characteristics (lactic acid accumulation/ATP inhibition), and (2) ROS-mediated activation of dual inflammatory axes (HIF-1α/IL-1β and IL-6/TNF-α). This work establishes three groundbreaking concepts: (1) the first evidence of a pathogen effector hijacking host ACTB for metabolic manipulation, (2) a novel ‘glycolysis–ACTB–ROS-inflammation’ axis, and (3) the first demonstration of bacterial proteins coordinating a Warburg effect with cytokine storms. These findings provide new targets for anti-infection therapies against Mycoplasma bovis. Full article

Background: Liquid chromatography-mass spectrometry (LC-MS), widely used in metabolomics, is often limited by low ionization efficiency and ion suppression, which reduce overall metabolite detectability and quantification accuracy. To address these challenges, chemical isotope labeling (CIL) LC-MS has emerged as a powerful approach, offering high sensitivity, accurate quantification, and broad metabolome coverage. This method enables comprehensive profiling by targeting multiple submetabolomes. Specifically, amine-/phenol- and hydroxyl-containing metabolites are labeled using dansyl chloride under distinct reaction conditions. While this strategy provides extensive coverage, the sequential analysis of each submetabolome reduces throughput. To overcome this limitation, we propose a two-channel mixing strategy to improve analytical efficiency. Methods: In this approach, samples labeled separately for the amine/phenol and hydroxyl submetabolomes are combined prior to LC-MS analysis, leveraging the common use of dansyl chloride as the labeling reagent. This integration effectively doubles throughput by reducing LC-MS runtime and associated costs. The method was evaluated using human urine and serum samples, focusing on peak pair detectability and metabolite identification. A proof-of-concept study was also conducted to assess the approach’s applicability in putative biomarker discovery. Results: Results demonstrate that the two-channel mixing strategy enhances throughput while maintaining analytical robustness. Conclusions: This method is particularly suitable for large-scale studies that require rapid sample processing, where high efficiency is essential. Full article

Although it is more than a century since it was first marketed, acetaminophen remains one of the most popular analgesic agents. In addition, acetaminophen has recently been applied to multimodal analgesia in combination with non-steroidal anti-inflammatory drugs, and its consumption significantly increased during the pandemic of coronavirus disease 2019 as well as diclofenac and ibuprofen. However, the detailed mode of analgesic action of acetaminophen is still unclear. In the present study, we comprehensively discuss conventional, recognized, and postulated mechanisms of analgesic acetaminophen and highlight the current mechanistic concepts while comparing with diclofenac and ibuprofen. Acetaminophen inhibits cyclooxygenase with selectivity for cyclooxygenase-2, which is higher than that of ibuprofen but lower than that of diclofenac. In contrast to diclofenac and ibuprofen, however, anti-inflammatory effects of acetaminophen depend on the extracellular conditions of inflamed tissues. Since the discovery of cyclooxygenase-3 in the canine brain, acetaminophen had been hypothesized to inhibit such a cyclooxygenase-1 variant selectively. However, this hypothesis was abandoned because cyclooxygenase-3 was revealed not to be physiologically and clinically relevant to humans. Recent studies suggest that acetaminophen is deacetylated to 4-aminophenol in the liver and after crossing the blood–brain barrier, it is metabolically converted into N-(4-hydroxyphenyl)arachidonoylamide. This metabolite exhibits bioactivities by targeting transient receptor potential vanilloid 1 channel, cannabinoid receptor 1, Cav3.2 calcium channel, anandamide, and cyclooxygenase, mediating acetaminophen analgesia. These targets may be partly associated with diclofenac and ibuprofen. The perspective of acetaminophen as a prodrug will be crucial for a future strategy to develop analgesics with higher tolerability and activity. Full article

The exponential growth of astronomical data necessitates the adoption of artificial intelligence (AI) and machine learning for timely and efficient scientific discovery. While AI techniques have achieved significant successes across diverse astronomical domains, their inherent complexity often obscures the reasoning behind their predictions, hindering scientific trust and verification. This review addresses the crucial need for interpretability in AI-powered astronomy. We survey key applications where AI is making significant impacts and review the foundational concepts of transparency, interpretability, and explainability. A comprehensive overview of various interpretable machine learning methods is presented, detailing their mechanisms, applications in astronomy, and associated challenges. Given that no single method offers a complete understanding, we emphasize the importance of employing a suite of techniques to build robust interpretations. We argue that prioritizing interpretability is essential for validating results, guarding against biases, understanding model limitations, and ultimately enhancing the scientific value of AI in astronomy. Building trustworthy AI through explainable methods is fundamental to advancing our understanding of the universe. Full article

Discovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was developed to quantify objects’ similarity attenuation. Considering the topological accessibility and spatial feature similarity of network nodes, an approach named the Weighted Similarity measure method considering Topological Accessibility (WSTA) is proposed to measure object association. The WSTA can capture both spatial interaction patterns and topological relationships in complex urban environments, thereby improving the accuracy of spatially associated object discovery. The proposed approach is validated using real-world point-of-interest (POI) datasets from Beijing city. The results suggest that integrating topological relationship approaches yields significant accuracy improvements in existing baseline methods, thereby enriching geospatial data retrieval in the era of big geospatial data. Full article

Traditional food technologies, while essential, often face limitations in sensitivity, real-time detection, and adaptability to complex biological systems such as microbial biofilms. These constraints have created a growing demand for more advanced, precise, and non-invasive tools to ensure food safety and quality. In response to these challenges, cross-disciplinary technological integration has opened new opportunities for the food industry and public health, leveraging methods originally developed in other scientific fields. Although their industrial-scale implementation is still evolving, their application in research and pilot settings has already significantly improved our ability to detect and control biofilms, thereby strengthening food safety protocols. Advanced analytical techniques, the identification of pathogenic species and their virulence markers, and the screening of “natural” antimicrobial compounds can now be conceptualized as interconnected elements within a virtual framework centered on “food” and “biofilm”. In this short review, starting from the basic concepts of biofilm and associated microorganisms, we highlight a selection of emerging analytical approaches—from optical methods, microfluidics, Atomic Force Microscopy (AFM), and biospeckle techniques to molecular strategies like CRISPR, qPCR, and NGS, and the use of organoids. Initially conceived for biomedical and biotechnological applications, these tools have recently demonstrated their value in food science by enhancing our understanding of biofilm behavior and supporting the discovery of novel anti-biofilm strategies. Full article

Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication difficulties and repetitive behaviors. Emerging evidence suggests a potential link between ASD and celiac disease (CD), possibly mediated by immune dysregulation and nutrient deficiencies. This study explores the shared biological pathways between ASD and CD using an in silico approach. Methods: Gene–disease associations for ASD and CD were retrieved from DisGeNET using MedGen Concept IDs (C1510586 and C0007570, respectively). An over-representation analysis (ORA) was conducted using GeneTrail 3.2 to identify significantly enriched biological pathways, which were then compared for overlap. A false discovery rate (FDR) < 0.05 was considered statistically significant. Results: The gene–disease association analysis identified 536 ASD-related genes and 52 CD-related genes. The ORA revealed several shared biological pathways, including immune pathways, cellular metabolism, and micronutrient processing (e.g., folate, selenium, vitamin A). These findings suggest immune dysfunction and nutrient malabsorption as potential mechanistic links between ASD and CD. Conclusions: The observed pathway overlap supports the hypothesis that immune dysregulation and metabolic disturbances contribute to both ASD and CD. Nutrient deficiencies, driven by CD-associated malabsorption, may exacerbate ASD symptoms. Additionally, sensory processing abnormalities in ASD could impact dietary choices, complicating gluten-free diet adherence. Future studies should validate these findings in clinical cohorts and explore dietary interventions, such as targeted supplementation, to mitigate ASD symptoms in individuals with CD. Full article

This study investigates the epistemological potentials of parametric analysis for digitally modeling ordinary, existing buildings, addressing a gap in architectural research. While traditional digital modeling prioritizes geometric accuracy, it often limits the ability to generate new architectural insights, treating models as static representations rather than as tools for knowledge production. This research challenges the assumption that geometric accuracy is necessary for epistemological validity, proposing parametric analysis as a hypothesis-generating tool capable of uncovering latent spatial and morphological properties that conventional methods overlook. Using Suárez’s inferential conception of scientific representation as a theoretical framework, this research employs a comparative case study methodology, contrasting direct and parametric digital models of the Federal Archive Building in New York City, analyzing their respective contributions to architectural knowledge. Existing documentation of the Federal Archive Building provides the primary data. The findings reveal that parametric modeling can enable the discovery of latent design properties by facilitating the systematic exploration of geometric variations while maintaining other logics, specifically by demonstrating how certain architectural features accommodate site irregularities while preserving visual coherence. This research advances theoretical discourse by repositioning parametric models from descriptive artifacts to instruments of architectural reasoning, challenging conventional associations between representational accuracy and epistemological validity. Practical applications are suggested in heritage documentation, comparative architectural analysis, and educational contexts where the interpretive exploration of buildings can generate new insights beyond what geometrically accurate models alone can provide. Full article

Cerebral plasticity is the ability of the brain to change and adapt in response to experience or learning. Its hallmarks are developmental flexibility, complex interactions between genetic and environmental influences, and structural–functional changes comprising neurogenesis, axonal sprouting, and synaptic remodeling. Studies on brain plasticity have important practical implications. The molecular characteristics of changes in brain plasticity may reveal disease course and the rehabilitative potential of the patient. Neurological disorders are linked with numerous cerebral non-coding RNAs (ncRNAs), in particular, microRNAs; the discovery of their essential role in gene regulation was recently recognized and awarded a Nobel Prize in Physiology or Medicine in 2024. Herein, we review the association of brain plasticity and its homeostasis with ncRNAs, which make them putative targets for RNA-based diagnostics and therapeutics. New insight into the concept of brain plasticity may provide additional perspectives on functional recovery following brain damage. Knowledge of this phenomenon will enable physicians to exploit the potential of cerebral plasticity and regulate eloquent networks with timely interventions. Future studies may reveal pathophysiological mechanisms of brain plasticity at macro- and microscopic levels to advance rehabilitation strategies and improve quality of life in patients with neurological diseases. Full article

Background/Objectives: Colorectal cancer (CRC) is one of the most common oncological disorders. Its fundamental treatments include surgery and chemotherapy, predominantly utilizing 5-fluorouracil (5-FU). Despite medical advances, CRC continues to present a high risk of recurrence, metastasis and low survival rates. Consequently, significant emphasis has been directed towards exploring novel types of cell death, particularly ferroptosis. Ferroptosis is characterized by iron imbalance and the accumulation of lipid peroxides and reactive oxygen species (ROS), leading to cellular damage and death. Thus, the discovery of safe inducers of ferroptosis, offering new hope in the struggle against CRC, remains crucial. In this study, we applied the concept of drug repositioning, selecting mesalazine (MES), a non-steroidal anti-inflammatory drug (NSAID), for investigation. Methods: The study was conducted on the colon cancer cell line DLD-1 and normal intestinal epithelial cells from the CCD 841 CoN cell line. Both cell lines were treated with MES solutions at concentrations of 10, 20, 30, 40, and 50 mM. Cytotoxicity was assessed using the MTT assay, while ferroptosis-related gene expression analysis was performed using oligonucleotide microarrays, with RT-qPCR used for validation. Results: MES effectively reduces the viability of DLD-1 cells while minimally affecting normal intestinal cells. Subsequent oligonucleotide microarray analysis revealed that MES significantly alters the expression of 56 genes associated with ferroptosis. Conclusions: Our results suggest that MES may induce ferroptosis in CRC, providing a foundation for further research in this area. Full article

Association Rule Mining (ARM) relies on concept lattices as an effective knowledge representation structure. However, classical ARM methods face significant limitations, including the generation of misleading rules during data-to-formal-context mapping and poor handling of heterogeneous data types such as linguistic, continuous, and imprecise data. This study aims to address these limitations by introducing a novel fuzzy data structure called the “fuzzy iceberg lattice” and its corresponding construction algorithm. The primary objectives of this study are to enhance the efficiency of extracting and visualizing frequent fuzzy closed item sets and to optimize both execution time and storage requirements. The necessity of this research stems from the high computational cost and redundancy associated with traditional fuzzy approaches, which, while capable of managing quantitative and imprecise data, are often impractical for large-scale applications in real scenarios. The proposed approach incorporates a ‘fuzzy min-max basis algorithm’ to derive exact and approximate rule bases from the extracted fuzzy closed item sets, eliminating redundancy while preserving valuable insights. Experimental results on benchmark datasets demonstrate that the proposed fuzzy iceberg lattice outperforms traditional fuzzy concept lattices, achieving an average reduction of 74.75% in execution time and 70.53% in memory usage. This efficiency gain, coupled with the lattice’s ability to handle crisp, quantitative, fuzzy, and heterogeneous data types, underscores its potential to advance ARM by yielding a manageable number of high-quality fuzzy concepts and rules. Full article

Understanding the systemic biochemistry of early pregnancy in the mare is essential for developing new diagnostics and identifying causes for pregnancy loss. This study aimed to elucidate the dynamic lipidomic changes occurring during the initial stages of equine pregnancy, with a specific focus on days 7 and 14 post-ovulation. By analysing and comparing the plasma lipid profiles of pregnant and non-pregnant mares, the objective of this study was to identify potential biomarkers for pregnancy and gain insights into the biochemical adaptations essential for supporting maternal recognition of pregnancy and early embryonic development. Employing discovery lipidomics, we analysed plasma samples from pregnant and non-pregnant mares on days 7 and 14 post-conception using the SCIEX ZenoTOF 7600 system. This high-resolution mass spectrometry approach enabled us to comprehensively profile and compare the lipidomes across these critical early gestational timepoints. Our analysis revealed significant lipidomic alterations between pregnant and non-pregnant mares and between days 7 and 14 of pregnancy. Key findings include the upregulation of bile acids, sphingomyelins, phosphatidylinositols, and triglycerides in pregnant mares. These changes suggest enhanced lipid synthesis and mobilization, likely associated with the embryo’s nutritional requirements and the establishment of embryo–maternal interactions. There were significant differences in lipid metabolism between pregnant and non-pregnant mares, with a notable increase in the sterol lipid BA 24:1;O5 in pregnant mares as early as day 7 of gestation, suggesting it as a sensitive biomarker for early pregnancy detection. Notably, the transition from day 7 to day 14 in pregnant mares is characterized by a shift towards lipids indicative of membrane biosynthesis, signalling activity, and preparation for implantation. The study demonstrates the profound lipidomic shifts that occur in early equine pregnancy, highlighting the critical role of lipid metabolism in supporting embryonic development. These findings provide valuable insights into the metabolic adaptations during these period and potential biomarkers for early pregnancy detection in mares. Full article

In this paper, we examine a sequence of uncountable iterated function systems (U.I.F.S.), where each term in the sequence is constructed from an uncountable collection of contraction mappings along with a linear and continuous operator. Each U.I.F.S. within the sequence is associated with an attractor, which represents a set towards which the system evolves over time, a Markov-type operator that governs the probabilistic behavior of the system, and a fractal measure that describes the geometric and measure-theoretic properties of the attractor. Our study is centered on analyzing the convergence properties of these systems. Specifically, we investigate how the attractors and fractal measures of successive U.I.F.S. in the sequence approach their respective limits. By understanding the convergence behavior, we aim to provide insights into the stability and long-term behavior of such complex systems. This study contributes to the broader field of dynamical systems and fractal geometry by offering new perspectives on how uncountable iterated function systems evolve and stabilize. In this paper, we undertake a comprehensive examination of a sequence of uncountable iterated function systems (U.I.F.S.), each constructed from an uncountable collection of contraction mappings in conjunction with a linear and continuous operator. These systems are integral to our study as they encapsulate complex dynamical behaviors through their association with attractors, which represent sets towards which the system evolves over time. Each U.I.F.S. within the sequence is governed by a Markov-type operator that dictates its probabilistic behavior and is described by a fractal measure that captures the geometric and measure-theoretic properties of the attractor. The core contributions of our work are presented in the form of several theorems. These theorems tackle key problems and provide novel insights into the study of measures and their properties in Hilbert spaces. The results contribute to advancing the understanding of convergence behaviors, the interaction of Dirac measures, and the applications of Monge–Kantorovich norms. These theorems hold significant potential applications across various domains of functional analysis and measure theory. By establishing new results and proving critical properties, our work extends existing frameworks and opens new avenues for future research. This paper contributes to the broader field of mathematical analysis by offering new perspectives on how uncountable iterated function systems evolve and stabilize. Our findings provide a foundational understanding that can be applied to a wide range of mathematical and real-world problems. By highlighting the interplay between measure theory and functional analysis, our work paves the way for further exploration and discovery in these areas, thereby enriching the theoretical landscape and practical applications of these mathematical concepts. Full article

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. This study’s goal was to identify the signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via artificial intelligence-enabled literature-based discovery. Cross-domain text mining of 33+ million PubMed articles was performed with SemNet 2.0 to identify and rank multi-scalar and multi-factorial pathophysiological concepts related to DKD. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores. High-ranking genes and proteins intersected three domains—DKD, the immune response, and glomerular endothelial cells. The top 10% of ranked concepts were mapped to the following biological functions: angiogenesis, apoptotic processes, cell adhesion, chemotaxis, growth factor signaling, vascular permeability, the nitric oxide response, oxidative stress, the cytokine response, macrophage signaling, NF$\mathsf{\kappa }$B factor activity, the TLR pathway, glucose metabolism, the inflammatory response, the ERK/MAPK signaling response, the JAK/STAT pathway, the T-cell-mediated response, the WNT/$\mathsf{\beta }$-catenin pathway, the renin–angiotensin system, and NADPH oxidase activity. High-ranking genes and proteins were used to generate a protein–protein interaction network. The study results prioritized interactions or molecules involved in dysregulated signaling in DKD, which can be further assessed through biochemical network models or experiments. Full article

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a significant global health challenge, further compounded by the issue of antimicrobial resistance (AMR). AMR is a result of several system-level molecular rearrangements enabling bacteria to evolve with better survival capacities: metabolic rewiring is one of them. In this review, we present a detailed analysis of the metabolic rewiring of Mtb in response to anti-TB drugs and elucidate the dynamic mechanisms of bacterial metabolism contributing to drug efficacy and resistance. We have discussed the current state of AMR, its role in the prevalence of the disease, and the limitations of current anti-TB drug regimens. Further, the concept of metabolic rewiring is defined, underscoring its relevance in understanding drug resistance and the biotransformation of drugs by Mtb. The review proceeds to discuss the metabolic adaptations of Mtb to drug treatment, and the pleiotropic effects of anti-TB drugs on Mtb metabolism. Next, the association between metabolic changes and antimycobacterial resistance, including intrinsic and acquired drug resistance, is discussed. The review concludes by summarizing the challenges of anti-TB treatment from a metabolic viewpoint, justifying the need for this discussion in the context of novel drug discovery, repositioning, and repurposing to control AMR in TB. Full article