Search Results (122)

The most persistent biomedical challenges of the 21st century are neurodegenerative disorders (NDs), where molecular alterations lead to devastating clinical consequences and progressive neuronal loss. The prevalence of neurodegeneration is continuously rising and becoming the main contributor to chronic disability and mortality. Despite their clinical differences, many conditions share pathogenic processes, including oxidative stress, protein misfolding and aggregation, mitochondrial dysfunction, and neuroinflammation. Instead of functioning independently, these processes cooperate to form a self-reinforcing network that gradually weakens synapses and ultimately leads to neuronal death. This study redefines neurodegeneration as a disorder of system-level failure by emphasizing poor cellular stress integration. In addition to demonstrating how gut microbiome gene networks impact inflammation and amyloid production, new research highlights the relationships between mitochondrial–lysosomal interactions, endoplasmic reticulum stress responses, and transcriptionally driven synaptic vulnerability. A key molecular topic is the interaction and pathogenic convergence of the JAK/STAT, HIF-1α, and Notch signaling pathways. Under ongoing metabolic stress, prolonged stimulation of this triad increases inflammation, hinders the regenerative processes, and maintains pseudo-hypoxic conditions, explaining why single-target treatments have mostly been unsuccessful. This review also explores progress in fluid, digital, and imaging biomarkers that facilitate early diagnosis and patient stratification, and assesses new disease-modifying approaches such as antisense oligonucleotides, immunomodulators, gene therapies, and small-molecular agents. Artificial intelligence is emphasized as an essential tool for integrating multimodal data, drug discovery and predictive modeling. Full article

Natural products have historically been integral to pharmacotherapy, attributed to their remarkable structural diversity and evolutionary refinement for biological interactions. Nonetheless, traditional natural product-based drug discovery has faced challenges such as mechanistic ambiguity, scalability limitations, and inadequate translational predictability. Concurrently, reductionist single-target approaches have been insufficient for addressing complex diseases characterized by network-level dysregulations. Recent advancements in analytical chemistry, genomics, and data-driven methodologies have rejuvenated natural product research by facilitating rapid structural elucidation, systematic exploration of biosynthetic diversity, and rational prioritization of bioactive compounds. Notably, many natural products exhibit multitarget effects that necessitate interpretation beyond isolated molecular interactions. Systems pharmacology offers a quantitative framework to analyze such network-level perturbations by integrating omics data, computational modeling, and experimental validation. However, molecular and computational predictions alone do not suffice to establish therapeutic relevance. Experimental pathology, encompassing histopathology, immunohistochemistry, spatial analysis, and ultrastructural evaluation, remains essential for validating efficacy and safety at tissue and organ levels. This review synthesizes technological innovation, systems pharmacology, and pathological validation to reposition natural products as mechanistically grounded and translationally robust resources for contemporary drug discovery. Full article

Background/Objectives: The rapid expansion of targeted therapies has reshaped oncology by exploiting ligand-receptor interactions (LRI) to improve treatment specificity and patient outcomes. However, the data describing these ligands remain fragmented across multiple sources, limiting accessibility for researchers and clinicians. To address this gap, we developed the OncoSolidDB, the first curated and oncology-focused bioinformatics database dedicated to ligands associated with solid malignancies. Methods: OncoSolidDB integrates and harmonizes data from reliable repositories, including ChEMBL, DrugBank and the Anti-Cancer Fund, consolidating curated structural, chemical, pharmacological, and clinical annotations along with standardized identifiers. Results: The database currently encompasses 243 ligands across 15 major solid tumor types including breast, lung, colorectal, melanoma, prostate, gastric, ovarian, cervical, bladder, esophageal, head and neck, thyroid, pancreatic, renal and liver cancer (Hepatocellular Carcinoma, HCC). Each entry is annotated by standardized identifiers (DrugBank, ChEMBL), approval year, chemical structures (SMILES strings, 2D images), and downloadable protein structure files (PDB format). Temporal coverage spans 1953–2025, enabling exploration of historical trends in oncology drug approvals. The database content is suitable for bioinformatics analysis, molecular docking, virtual screening, ligand-based modeling, and drug repurposing studies. Outputs are available through a freely accessible web interface that supports search browsing by cancer type. Conclusions: By consolidating oncology-specific ligand data into a single, structured platform, OncoSolidDB offers a valuable resource for advancing drug discovery, repurposing strategies, and the rational design of next-generation targeted therapies for solid tumors. OncoSolidDB is accessible via our Bioinformatics Research PortalEinstein. Full article

Compound–target interaction (CTI) prediction plays a critical role in drug discovery and the functional study of food-derived bioactive compounds. However, traditional experimental methods for CTI identification are limited by high costs, long cycle times, and high false-positive rates, highlighting an urgent need for more efficient approaches. Machine learning (ML) has become a revolutionary tool to address these challenges. In this review, we focus on recent developments in ML-based CTI prediction. We first systematically outline the commonly used public databases and feature extraction methods for both compounds (molecular fingerprints) and proteins (sequence-derived features), followed by elaborating on four types of ML approaches, including classical supervised learning, matrix factorization, graph topology-based inference, and deep neural network frameworks. In particular, this review explores the emerging application of these computational approaches in identifying targets of food-derived bioactive compounds, underscoring its significant potential to advance functional food research. Moreover, we analyze key challenges, such as limited model interpretability, high data dependency, and insufficient multi-source information integration, and put forth future prospects to improve the prediction of food-derived CTIs, thereby facilitating their application in functional food research. Full article

Acute lymphoblastic leukemia (ALL) is a clinically diverse cancer in which microRNA (miRNA)-mediated post-transcriptional regulation contributes to leukemogenesis and subtype heterogeneity. In this study, miRNA expression profiling by microarray was performed on ALL cases (B-ALL and T-ALL) and healthy controls. Data were normalized and analyzed for differential expression using false discovery rate (FDR)-adjusted p-values. Differentially expressed miRNAs were further examined using unsupervised visualization to assess overall disease-related expression patterns. To explore their biological significance, experimentally validated miRNA–target interactions were obtained using multiMiR, limited to validated databases (miRTarBase, TarBase, and miRecords) and summarized via target-burden ranking, miRNA–target network analysis, and Circos–style interaction mapping. A unique miRNA expression signature was identified in ALL. Upregulated miRNAs included miR-106a-5p, miR-106b-5p, miR-17-5p, miR-20a-5p, miR-20b-5p, miR-181b-5p, and miR-128-3p, while miR-127-3p, miR-139-5p, miR-433-3p, and miR-584-5p were downregulated. Validated targets concentrated on key leukemia-related genes like PTEN, BCL2L11, CDKN1A, CCND1, RB1, E2F1, and TGFBR2. KEGG pathway analysis highlighted pathways associated with leukemic cell survival and growth, including MAPK, cell cycle, autophagy, Hippo, ubiquitin-mediated proteolysis, and mTOR signaling pathways. These findings reveal a concise ALL-associated miRNA panel predominantly comprising the miR-17/20/106 family and provide a prioritized set of candidate regulatory networks for subtype-specific validation and functional follow-up studies. Full article

Solid-state studies evaluating intermolecular geometries in methylated nucleobases are not extensively explored. In the course of the present study, we have solved the crystal structures of 1-, 3- and 7-methylated adenines and guanines, including the monohydrate and sesquihydrate forms of 3-methyladenine and 3-methylguanine, respectively, by means of single-crystal X-ray diffraction and synchrotron/laboratory X-ray powder diffraction (XRPD). In situ high temperature XRPD experiments, coupled with differential thermal analysis/thermogravimetry (DTA/TG) measurements, allowed for monitoring crystallographic changes after water removal of N3-methylated compounds, and the discovery of a high temperature polymorph in the case of 3-methyladenine. Our findings indicate that H-bonding schemes describe ribbon planar motifs of molecules in the majority of cases, or linear double-bonded strands of molecules in a few cases. π-π stacking interactions were compared with existing findings of theoretical calculations and existing crystallographic data, showing how N-methylated purine bases follow the trend predicted by Hunter and Sanders, 1990. The present study provides the first systematic experimental insights into the solid state of the presented compounds. Full article

High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are scarce or difficult to obtain. Large Language Models (LLMs) provide powerful capabilities for synthetic text generation, yet the quality of generated data strongly depends on the design of input prompts. Prompt engineering is therefore critical, but it remains largely manual and difficult to scale, particularly in black-box settings where model internals are inaccessible. This work introduces EVOLMD-MO, a multi-objective evolutionary framework for automated prompt learning aimed at generating high-quality synthetic text datasets using black-box LLMs. The proposed approach formulates prompt optimization as a multi-objective search problem in which candidate prompts evolve through genetic operators guided by two complementary objectives: semantic fidelity to reference data and generative diversity of the produced samples. To support scalable optimization, the framework integrates a modular multi-agent architecture that decouples prompt evolution, LLM interaction, and evaluation mechanisms. The evolutionary process is implemented using the NSGA-II algorithm, enabling the discovery of diverse Pareto-optimal prompts that balance semantic preservation and diversity. Experimental evaluation using large-scale disaster-related social media data demonstrates that the proposed approach consistently improves prompt quality across generations while maintaining a stable trade-off between fidelity and diversity. Compared with a single-objective baseline, EVOLMD-MO explores a significantly broader semantic search space and produces more diverse yet semantically coherent synthetic datasets. These results indicate that multi-objective evolutionary prompt learning constitutes a promising strategy for black-box LLM-driven data generation, with potential applicability to adaptive data analytics and real-time decision-support systems in highly dynamic environments, pending broader validation across domains and models. Full article

Background: The ability of synthetic plastics to persist in the environment and the accumulation of microplastics has intensified the need to explore biological mechanisms capable of interacting with, and possibly degrading, polymeric materials. Microbial enzymes that have extensive catalytic flexibility represent promising candidates in this context. Aim: This study set out to examine the molecular interaction patterns and dynamical stability of Pseudomonas aeruginosa elastase (LasB) with representative structural fragments of typical synthetic plastics to assess the suitability of the enzyme to polymer-derived substrates. Methods: The crystallographic structure of LasB (PDB ID: 1EZM) was retrieved from the Protein Data Bank and pre-prepared with the help of AutoDock4.2.6 Tools. Those polymer-derived ligands that were associated with the major industrial plastics such as polyamide (PA), polyvinyl chloride (PVC), polycarbonate (PC), poly-ethylene terephthalate (PET), polymethyl methacrylate (PMMA), and polyurethane (PUR) were retrieved in the PubChem database and geometrically optimized with the help of the MMFF94 force field. AutoDock Vina, with a specific grid box around the catalytic pocket, including Zn²⁺ ion, was used to perform molecular docking simulations. PyMOL and BIOVIA Discovery Studio software were used to analyze binding conformations, interaction residues and types of intermolecular contacts. Phosphoramidon, a known metalloprotease inhibitor, served as a positive control to confirm the docking protocol. Additional assessment of the structural stability and conformational behavior of the enzyme–ligand complexes was conducted by molecular dynamics (MD) simulations with the Desmond engine and explicit solvent model in a 50 ns trajectory using the OPLS4 force field. RMSD, RMSF, radius of gyration, hydrogen bonding analysis and solvent accessibility parameters were used to measure structural stability. Results: The docking experiment showed varying binding affinities with the test polymers. Polycarbonate (−5.774 kcal/mol) and polyurethane (−5.707 kcal/mol) had the highest in-teractions with the LasB catalytic pocket, polyamide (−5.277 kcal/mol) and PET (−4.483 kcal/mol) followed PMMA and PVC, which had weaker affinities. The following were the important residues involved in interaction networks: Glu141, His140, Val137, Arg198, Tyr114, and Trp115 that were implicated in interaction networks with hydrophobic interactions, π-cation interactions and van der Waals forces that were the major stabilization forces. MD simulations had stabilized complexes, and RMSD values were found to be within acceptable ranges of stability, and ligand-specific changes (around 1.0-3.2 A), which is also in line with stable protein-ligand systems. Phosphoramidon used as a positive control had an RMSD of 1.205 A which is within this stability range. PCA determined various ligand-bound conformational states of LasB with PA in com-pact state, PC and PVC in intermediate states and PUR, PMMA and PET in ex-panded conformations, indicating structur-al stability and adaptability of the binding pocket. Conclusion: These findings show that LasB has a structurally flexible catalytic pocket that can accommodate a wide range of polymer-derived ligands. These results offer an insight into the recognition of enzymes with polymers at the molecular level and also indicate that LasB might help in the interaction of microorganisms with synthetic plastics in environmental systems. Full article

Metastatic breast cancer (MBC) remains an overwhelming clinical challenge due to its inherent clonal evolution and the frequent development of drug resistance. A significant hurdle in therapeutic discovery is the reliance on traditional 2D cell cultures and animal models, which often fail to accurately replicate human tumor pathophysiology or predict clinical responses. Consequently, the field of oncology is increasingly exploring a transition towards human-centric research that prioritizes biological data derived directly from patients. Considering the FDA Modernization Act 2.0 and the 2025 FDA Roadmap, frameworks are being established to explore the integration of new approach methodologies (NAMs)—including patient-derived organoids (PDOs) and organ-on-a-chip (OoC) systems—into the drug development pipeline. This review examines how these platforms aim to better simulate the human physiological environment by capturing the complex architecture and microenvironment of the tumor. We further discuss how the integration of these models with Artificial Intelligence (AI), spatial multi-omics, and real-time liquid biopsies is being investigated to enhance the speed and precision of therapeutic testing. While still in the translational phase, emerging evidence suggests that human-centric platforms may eventually support rapid functional drug screening, potentially informing patient treatment responses within clinically relevant timeframes. Strengthening the biological link between the patient and their longitudinal data represents a promising strategy to address the complexities of MBC and improve clinical outcomes. These human-centric platforms preserve patient-specific tumor heterogeneity, recapitulate microenvironmental interactions, and enable functional drug testing under physiologically relevant conditions, thereby improving translational accuracy compared to conventional models. Full article

Background: Point mutations to the androgen receptor (AR) ligand-binding domain (LBD) are becoming increasingly recognized as a mechanism of therapeutic resistance in castration resistant prostate cancer (CRPC). The present review explores how point mutations induce molecular changes that contribute to the eventual treatment failure of androgen receptor pathway inhibitors (ARPIs) in CRPC. Methods: The PubMed database was searched for structural studies on the AR LBD. Eligible articles included molecular docking analysis and emphasized changes in ligand–receptor interactions after point mutation. Structural data were obtained from the Protein Data Bank (PDB) using the search parameters “Androgen receptor ligand binding domain”, “Homo sapiens”, and “X-ray diffraction”. PDB files of wild-type and point mutant AR LBDs were accumulated for analysis. Results: A functional shift from inhibiting to activating AR has been documented for multiple ARPIs. Crystallography data and in silico evaluation have deciphered how changes in steric hindrance of the AF-2 domain contribute to ARPI loss of function. To combat therapeutic resistance, discovery efforts have begun to consider combination approaches of orthosteric and allosteric inhibitors, as well as compounds that target other AR domains. Although lead compounds have been identified, none have progressed into the clinic. Conclusions: Questions remain regarding the best approach for rationally designing new AR targeting therapeutics. Understanding how structural changes to the AR LBD lead to the failure of clinical therapeutics is a necessary step that should precede drug discovery campaigns. Moreover, computational modeling is a powerful tool that should be leveraged to streamline therapeutic development. Full article

The rapid digitization of cultural heritage materials has led to the substantial growth of digital library collections, particularly large and heterogeneous archives of periodicals. This expansion has intensified challenges related to content discovery, accessibility, and user engagement. Users increasingly struggle to navigate large periodical collections and identify relevant materials. In this context, intelligent interaction with cultural content has become an essential aspect of effectively accessing and utilizing resources in modern digital libraries, highlighting the need for adaptive and user-oriented mechanisms that support navigation and discovery. Artificial intelligence-driven personalization offers promising solutions. However, digital library environments often contain sparse interaction data, evolving user interests, and continuously growing collections. These characteristics limit the effectiveness of standalone content-based or collaborative approaches. This work proposes an integrated personalization approach that combines behavioral interaction data with semantic relationships between documents to support adaptive content delivery in digital libraries. The approach facilitates the discovery of both established and newly digitized or rarely accessed materials, supporting more effective access, exploration, and reuse of large and diverse digital library collections. Full article

Precise drug target discovery is pivotal to mitigating the escalating costs and high attrition rates that characterize pharmaceutical research and development. Given that traditional single-omics methods often fail to elucidate the systemic complexity of human diseases, deep learning (DL)-enabled multi-omics integration has emerged as a transformative frontier. This review systematically summarizes the advancements in DL-driven multi-omics integration for drug target discovery. First, the multi-omics data foundation and integration strategies are delineated, followed by an exploration of the DL architectures utilized for processing such data. Subsequently, the efficacy of DL-driven multi-omics integration is examined regarding the identification of novel disease drivers, prediction of synthetic lethality interactions, and prioritization of therapeutic targets. Finally, addressing persistent challenges related to data sparsity, model interpretability, and target druggability and validation hurdles, emerging opportunities driven by Generative AI, Large Multimodal Models (LMMs), Explainable AI (XAI), and multidimensional feasibility assessment frameworks are discussed in the context of advancing precision medicine. Full article

This paper exposes the latent but potent role of seemingly hidden spatial autocorrelation (SA) in all geographic theories, highlighting that it is everywhere, matters, and is a fundamental property of geotagged phenomena. This narrative examines and extends the literature about the inescapable nature of the SA paradigm and the near-universal mixing of positive and negative SA. This study summary transcends the widespread but often implicit treatment of SA within geographic theories that their assumptions help achieve when they embed spatial processes, shape geospatial expectations, and define independent areal units so that these theory-delineating constraints largely absorb SA, reducing residual spatial dependence/correlation and improving conjectural validity, masking its presence for decades if not centuries. This paper explores selected prominent human geography theories (spatial optimization, agricultural location, gravity-model-based spatial interaction, central place systems), cultural and humanistic geography, geohumanities abstractions, physical geography theories (plate tectonics, climatology, uniformitarianism, soil formation), cartographic theories (geometric projections, semiotic/communication, cognitive/perceptual, geographic information systems anchored spatial analysis), and basic geospatial data gathering methodologies (qualitative and quantitative spatial sampling). It demonstrates that across the discipline of geography, exposing masquerading SA deepens theoretical coherence and strengthens methodological integrity, encouraging integrated spatial reasoning that bridges interpretive and analytical traditions. This article concludes by providing exemplifications of bringing scholastically unrealized SA in geographic theories out of obscurity, together with certain salient benefits from doing so, affirming the magnitude of fulfilling its major objective: SA is poised for discovery in all geospatial theories, from those for human and humanistic geography, through physical geography, to those for cartography as well as methodologies concerning all georeferenced data collection missions. Full article

Cancer health disparities represent profound inequalities in incidence, outcomes, and survivorship across populations. While traditionally examined through distinct lenses of either molecular biology or social epidemiology, these disparities arise from the complex interplay of genetic susceptibility, epigenetic dysregulation, and social determinants of health (SDoH). This review proposes that DNA damage and genomic instability serve as a critical mechanistic bridge, integrating exposures from the societal level to cellular dysfunction. We synthesize evidence demonstrating how SDoH—such as systemic inequities, environmental exposures, and chronic stress—converge with genetic and epigenetic factors to disproportionately increase DNA damage burden, impair repair mechanisms, and accelerate tumorigenesis in marginalized communities. Using the elevated gastrointestinal cancer rates among Native Hawaiians and Pacific Islanders (NH/PI) as a case study, we illustrate how historical, environmental, and socioeconomic factors interact with biological pathways to drive disparities. The review highlights key advances in DNA damage research—from somatic mutation theory to the modern understanding of chronic genomic stress—and explores how innovations in single-cell genomics, biomarker discovery, and computational modeling can unravel disparity etiologies. We argue that a translational framework linking social exposure data to molecular biomarkers of DNA damage is essential for moving beyond descriptive disparities to mechanistic understanding. Ultimately, addressing cancer equity requires interdisciplinary strategies that bridge molecular oncology, public health, and community-engaged research, targeting the root causes where social inequities become biologically embedded as genomic instability. Full article

Cancer and Alzheimer’s disease (AD) display an inverse relationship, and there is a need to further explore this interplay. One key genetic contributor to AD is SORL1, the loss of which is thought to be causally related to AD development. SORL1 also appears to be implicated in cancer. To examine SORL1 and its network, this article simulated SORL1 and its interactions via signal-flow Boolean modelling, including in silico knockouts (mirroring in vivo loss-of-function mutations). This model (SKM034) predicted a total of 29 key changes in molecular relationships following the loss of SORL1 or another highly connected protein (ERBB2). Literature validation demonstrated that 2 of these predictions were at least partially validated experimentally, whilst 27 were Potentially Novel Predictions (PNPs). Complementing the in-depth relationship analyses was signal flow analysis through the network’s structure, validated using cell line and cancer patient RNA-seq data. Correct prediction rates for these analyses reached 60% (statistically significant relative to a random model). This article demonstrates the clinical relevance of this Alzheimer’s-related network in a cancer context and, through the PNPs, provides a strong starting point for in vitro experimental validation. As with previously published models using similar methods, the model may be reanalysed in different contexts for further discoveries. Full article