Search Results (82)

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care. Full article

Kernicterus spectrum disorder is the permanent and highly disabling neurologic sequel of neonatal exposure to hyperbilirubinemia, presenting, among other symptoms, variable and untreatable motor disabilities. To search for potential biomolecular explanations, we used a Gunn rat colony exhibiting spontaneous hyperbilirubinemia and a large variability of motor deficits on a beam-walking test. Histological and microscopic analyses confirmed worsening damage in the cerebellum (Cll; hypoplasia, increased death of neurons, and disrupted astroglial structures) and parietal motor cortex (hCtx; increased cell sufferance and astrogliosis). Clustering and network analyses of transcriptomic data reveal rearrangement of the physiological expression patterns and signaling pathways associated with bilirubin neurotoxicity. Bilirubin content among hyperbilirubinemic (jj) animals is overlapped, which suggests that the amount of bilirubin challenge does not fully explain the tissue, transcriptomic, proteomic, and neurobehavioral alterations. The expression of nine genes involved in key postnatal brain development processes is permanently altered in a phenotype-dependent manner. Among them, Grm1, a metabotropic glutamatergic receptor involved in glutamate neurotoxicity, is consistently downregulated in both brain regions both at the transcriptomic and proteomic levels. Our results support the role of Grm1 and glutamate as biomolecular markers of ongoing bilirubin neurotoxicity, suggesting the possibility to improve diagnosis by ¹H-MR spectroscopy. Full article

Concentration gradient generation plays a pivotal role in advancing applications across drug screening, chemical synthesis, and biomolecular studies, yet conventional methods remain constrained by labor-intensive workflows, limited throughput, and inflexible gradient control. This study presents a novel multilayer microfluidic chip leveraging shear flow-driven partitioning–recombination mechanisms to enable the flexible and high-throughput generation of concentration gradient droplets. The chip integrates interactive upper and lower polydimethylsiloxane (PDMS) layers, where sequential fluid distribution and recombination are achieved through circular and radial channels while shear forces from the oil phase induce droplet formation. Numerical simulations validated the dynamic pressure-driven concentration gradient formation, demonstrating linear gradient profiles across multiple outlets under varied flow conditions. The experimental results revealed that the shear flow mode significantly enhances mixing uniformity and droplet generation efficiency compared to continuous flow operations, attributed to intensified interfacial interactions within contraction–expansion serpentine channels. By modulating hydrodynamic parameters such as aqueous- and oil-phase flow rates, this system achieved tunable gradient slopes and droplet sizes, underscoring the intrinsic relationship between flow dynamics and gradient formation. The proposed device eliminates reliance on complex channel networks, offering a compact and scalable platform for parallelized gradient generation. This work provides a robust framework for optimizing microfluidic-based concentration gradient systems, with broad implications for high-throughput screening, combinatorial chemistry, and precision biomolecular assays. Full article

Peptide molecules, as fundamental structural units in biological systems, play pivotal roles in diverse biological processes and have garnered substantial attention in biomolecular self-assembly research. Their structural simplicity and high design flexibility make peptides key players in the development of novel biomaterials. High-resolution imaging techniques have provided profound insights into peptide assembly. Recently, the development of cutting-edge technologies, such as super-resolution microscopy (SRM) with unparalleled spatiotemporal resolution, has further advanced peptide assembly research. These advancements enable both the mechanistic exploration of peptide assembly pathways and the rational design of peptide-based functional materials. In this mini review, we systematically examine the structural diversity of peptide assemblies, including micelles, tubes, particles, fibers and hydrogel, as investigated by various high-resolution imaging techniques, with a focus on their assembly characterization and dynamic process. We also summarize the interaction networks of peptide assemblies with proteins, polymers and microbes, providing further insight into the interactions between peptide assemblies and other molecules. Furthermore, we emphasize the transformative role of high-resolution imaging techniques in addressing long-standing challenges in peptide nanotechnology. We anticipate that this review will accelerate the advancement of peptide assembly characterization, thereby fostering the creation of next-generation functional biomaterials. Full article

A comprehensive theoretical investigation of the C₃H₂O potential energy surface (PES) was conducted, revealing 30 equilibrium structures (EQs), 128 transition state structures (TSs), and 35 direct dissociation channels (DCs), establishing a global reaction network comprising 101 isomerization pathways and dissociation channels. Particular focus was placed on the five most stable isomers, H₂CCCO (EQ3), OC(H)CCH (EQ7), H-c-CC(O)C-H (EQ0), HCC(H)CO (EQ1), and HO-c-CCC-H (EQ12), and their reactions with water molecules. Multicomponent artificial force-induced reaction (MC-AFIR) calculations were employed to study bimolecular collisions between H₂O and these stable isomers. The product distributions revealed isomer-specific reactivity patterns: EQ3 and EQ7 predominantly formed neutral species at high collision energies, EQ0 produced both ionic and neutral species, while EQ1 and EQ12 exhibited more accessible reaction pathways at lower collision energies with a propensity for spontaneous isomerization. Born–Oppenheimer Molecular Dynamics (BOMD) simulations complemented these findings, suggesting several viable products emerge from reactions with water molecules, including HCCC(OH)₂H (EQ7 + H₂O), OCCHCH₂OH (EQ1 + H₂O), and HO-c-CC(H)C(OH)-H (EQ12 + H₂O). This investigation elucidates the intrinsic relationships between isomers and their potential products, formed through biomolecular collisions with water molecules, establishing a fundamental framework for future conformational and reactivity studies of the C₃H₂O family. Full article

Background: Exosomes are a group of extracellular vesicles that are released by almost all mammalian cell types and engage in intracellular communication. Studies conducted in recent years have shown that exosomes are involved in a variety of diseases, where they may act as “vehicles” for the transmission of biomolecules and biomolecular information. Amyloidoses constitute a critical subgroup of these diseases, caused by extracellular deposition or intracellular inclusions of insoluble protein fibrils in cells and tissues. However, how exosomes are involved in these diseases remains largely unexplored. Methods: To detect possible links between amyloid proteins and exosomes, protein data from amyloidosis-isolated exosomes were collected and visualized using biological networks. Results: This biomedical informatics approach for the analysis of interaction networks, in combination with the existing literature, highlighted the involvement of exosomes in amyloidosis while strengthening existing hypotheses regarding their mechanism of action. Conclusion: This work is focused on exosomes from patients with Alzheimer’s disease and identifies important amyloidogenic proteins found in exosomes. These proteins can be used for future research in the field of exosome-based biomarkers of amyloidosis and potential prognostic or preventive approaches. Full article

Recent advances in graph neural networks have transformed structural pattern learning in domains ranging from social network analysis to biomolecular modeling. Nevertheless, practical deployments in mission-critical scenarios such as binary code similarity detection face two fundamental obstacles: first, the inherent noise in graph construction processes exemplified by incomplete control flow edges during binary function recovery; second, the substantial distribution discrepancies caused by cross-architecture instruction set variations. Conventional GNN architectures demonstrate severe performance degradation under such low signal-to-noise ratio conditions and cross-domain operational environments, particularly in security-sensitive vulnerability identification tasks where feature instability or domain shifts could trigger critical false judgments. To address these challenges, we propose GBsim, a novel approach that combines graph neural networks with natural language processing. GBsim employs a cross-architecture language model to transform binary functions into semantic graphs, leverages a multilayer GCN for structural feature extraction, and employs a Transformer layer to integrate semantic information, generates robust cross-architecture embeddings that maintain high performance despite significant distribution shifts. Extensive experiments on a large-scale cross-architecture dataset show that GBsim achieves an MRR of 0.901 and a Recall@1 of 0.831, outperforming state-of-the-art methods. In real-world vulnerability detection tasks, GBsim achieves an average recall rate of 81.3% on a 1-day vulnerability dataset, demonstrating its practical effectiveness in identifying security threats and outperforming existing methods by 2.1%. This performance advantage stems from GBsim’s ability to maximize information preservation across architectural boundaries, enhancing model robustness in the presence of noise and distribution shifts. Full article

Plants face an array of environmental stresses, including both abiotic and biotic stresses. These stresses significantly impact plant lifespan and reduce agricultural crop productivity. Abiotic stresses, such as ultraviolet (UV) radiation, high and low temperatures, salinity, drought, floods, heavy metal toxicity, etc., contribute to widespread crop losses globally. On the other hand, biotic stresses, such as those caused by insects, fungi, and weeds, further exacerbate these challenges. These stressors can hinder plant systems at various levels, including molecular, cellular, and development processes. To overcome these challenges, multi-omics computational approaches offer a significant tool for characterizing the plant’s biomolecular pool, which is crucial for maintaining homeostasis and signaling response to environmental changes. Integrating multiple layers of omics data, such as proteomics, metabolomics, ionomics, interactomics, and phenomics, simplifies the study of plant resistance mechanisms. This comprehensive approach enables the development of regulatory networks and pathway maps, identifying potential targets for improving resistance through genetic engineering or breeding strategies. This review highlights the valuable insights from integrating multi-omics approaches to unravel plant stress responses to both biotic and abiotic factors. By decoding gene regulation and transcriptional networks, these techniques reveal critical mechanisms underlying stress tolerance. Furthermore, the role of secondary metabolites in bio-based products in enhancing plant stress mitigation is discussed. Genome editing tools offer promising strategies for improving plant resilience, as evidenced by successful case studies combating various stressors. On the whole, this review extensively discusses an advanced multi-omics approach that aids in understanding the molecular basis of resistance and developing novel strategies to improve crops’ or organisms’ resilience to abiotic and biotic stresses. Full article

Benzene, a well-known carcinogenic airborne pollutant, poses significant health risks, particularly in industries such as petroleum, shoemaking, and painting. Despite strict regulations, chronic occupational exposure persists, contributing to the onset of acute myeloid leukemia (AML) and other malignancies. Benzene’s carcinogenicity stems from its metabolic activation, leading to increased oxidative stress, DNA damage, and cancer transformation. While its toxicity is well-documented, the link between genetic and epigenetic alterations and cancer susceptibility in exposed workers remains underexplored. This study aims to identify early biomarkers of benzene exposure and AML risk by analyzing gene expression and DNA methylation datasets from GEO DataSets, integrated with molecular pathway analyses, as well as miRNA-target and protein-protein network evaluations. This multi-approach led to the identification of nine deregulated genes (CRK, CXCR6, GSPT1, KPNA1, MECP2, MELTF, NFKB1, TBC1D7, ZNF331) in workers exposed to benzene, with NFKB1 showing strong discriminatory potential. Also, dose-dependent DNA methylation changes were observed in CXCR6 and MELTF, while selected miRNAs such as let-7d-5p, miR-126-3p, and miR-361-5p emerged as key post-transcriptional regulators. Furthermore, functional enrichment linked these genes to immune response, inflammation, cell proliferation, and apoptosis pathways. While network analyses highlighted NFKB1, CRK, and CXCR6 as central to benzene-associated leukemogenesis. Altogether, these findings provide novel insights into an early biomarker fingerprint for benzene exposure and AML susceptibility, supporting the future development of biomolecular-based targeted occupational health monitoring and personalized preventive strategies for at-risk workers. Full article

Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency. The integration of a 1D CNN model on FPGA hardware for adaptive, non-linear noise filtering sets this design apart from conventional filtering approaches by achieving high accuracy and low power consumption in a portable format. This integration of SiNW-FET with FPGA-based CNN noise reduction offers a unique approach, as prior noise reduction techniques for biosensors typically rely on linear filtering or digital smoothing, which lack adaptive capabilities for complex, non-linear noise patterns. By introducing the 1D CNN on FPGA, this architecture enables real-time, high-fidelity noise reduction, preserving critical signal characteristics without compromising processing speed. Notably, the findings presented in this work are based exclusively on comprehensive simulations using COMSOL and MATLAB, as no physical prototypes or biomarker detection experiments were conducted. The SiNW-FET biosensor, functionalized with antibodies specific to viral antigens, detects impedance shifts caused by antibody–antigen interactions, providing a highly sensitive platform for viral detection. A high-gain folded-cascade amplifier enhances the Signal-to-Noise Ratio (SNR) to approximately 70 dB, verified through COMSOL and MATLAB simulations. Additionally, a 1D CNN model is employed for adaptive noise reduction, filtering out non-linear noise patterns and achieving an approximate 75% noise reduction across a broad frequency range. The CNN model, implemented on an Altera DE2 FPGA, enables high-throughput, low-latency signal processing, making the system viable for real-time applications. Performance evaluations confirmed the proposed system’s capability to enhance the SNR significantly while maintaining a compact and energy-efficient design suitable for portable diagnostics. This integrated architecture thus provides a powerful solution for high-precision, real-time viral detection, and continuous health monitoring, advancing the role of biosensors in accessible point-of-care diagnostics. Full article

The cell cycle, essential for growth, reproduction, and genetic stability, is regulated by a complex network of cyclins, Cyclin-Dependent Kinases (CDKs), phosphatases, and checkpoints that ensure accurate cell division. CDKs and phosphatases are crucial for controlling cell cycle progression, with CDKs promoting it and phosphatases counteracting their activity to maintain balance. The nucleolus, as a biomolecular condensate, plays a key regulatory role by serving as a hub for ribosome biogenesis and the sequestration and release of various cell cycle regulators. This phase separation characteristic of the nucleolus is vital for the specific and timely release of Cdc14, required for most essential functions of phosphatase in the cell cycle. While mitosis distributes chromosomes to daughter cells, meiosis is a specialized division process that produces gametes and introduces genetic diversity. Central to meiosis is meiotic recombination, which enhances genetic diversity by generating crossover and non-crossover products. This process begins with the introduction of double-strand breaks, which are then processed by numerous repair enzymes. Meiotic recombination and progression are regulated by proteins and feedback mechanisms. CDKs and polo-like kinase Cdc5 drive recombination through positive feedback, while phosphatases like Cdc14 are crucial for activating Yen1, a Holliday junction resolvase involved in repairing unresolved recombination intermediates in both mitosis and meiosis. Cdc14 is released from the nucleolus in a regulated manner, especially during the transition between meiosis I and II, where it helps inactivate CDK activity and promote proper chromosome segregation. This review integrates current knowledge, providing a synthesis of these interconnected processes and an overview of the mechanisms governing cell cycle regulation and meiotic recombination. Full article

Divalent metal cations are of vital importance in biochemistry and materials science, and their structural and thermodynamic properties in aqueous solution have often been used as targets for the development of ion models. This study presented a strategy for designing nonbonded point charge models of divalent metal cations (Mg²⁺ and Ca²⁺) and Cl⁻ by targeting quantum mechanics (QM)-based ion–water dimer interactions. The designed models offered an accurate representation of ion–water interactions in the gas phase and showed reasonable performance for non-targeted properties in aqueous solutions, such as the ion–water oxygen distance (IOD), coordination number (CN), and density and viscosity of MgCl₂ and CaCl₂ solutions at low concentrations. Our metal cation models yielded considerable overestimates of the hydration free energies (HFEs) of the ions, whereas the Cl⁻ model displayed good performance. Together with the overestimated density and viscosity of the salt solutions, these results indicated the necessity of re-optimizing ion–ion interactions and/or including polarization effects in the design of ion models. The designed Mg²⁺ model was capable of maintaining the crystal metal-binding networks during MD simulation of a metalloprotein, indicating great potential for biomolecular simulations. This work highlighted the potential of QM-based ion models to advance the study of metal ion interactions in biological and material systems. Full article

In this study, ¹H-NMR spectroscopy coupled with chemometrics was applied to study the wine metabolome and to classify wines according to different grape varieties and different terroirs. By obtaining the metabolomic fingerprinting and profiling of the wines, it was possible to assess the metabolic biomarkers leading the classification (i.e., phenolic compounds, aroma compounds, amino acids, and organic acids). Moreover, information about the influence of the soil in shaping wine metabolome was obtained. For instance, the relationship between the soil texture and the content of amino acids and organic acids in wines was highlighted. The analysis conducted in this study allowed extraction of relevant spectral information not only from the most populated and concentrated spectral areas (e.g., aliphatic and carbinolic areas), but also from crowded spectral areas held by lowly concentrated compounds (i.e., polyphenols). This may be due to a successful combination between the parameters used for data reduction, preprocessing and elaboration. The metabolomic fingerprinting also allowed exploration of the H-bonds network inside the wines, which affects both gustatory and olfactory perceptions, by modulating the way how solutes interact with the human sensory receptors. These findings may have important implications in the context of food traceability and quality control, providing information about the chemical composition and biomolecular markers from a holistic point of view. Full article

The escalating prevalence of metabolic disorders, notably type 2 diabetes (T2D) and obesity, presents a critical global health challenge, necessitating deeper insights into their molecular underpinnings. Our study integrates proteomics and metabolomics analyses to delineate the complex molecular landscapes associated with T2D and obesity. Leveraging data from 130 subjects, including individuals with T2D and obesity as well as healthy controls, we elucidate distinct molecular signatures and identify novel biomarkers indicative of disease progression. Our comprehensive characterization of cardiometabolic proteins and serum metabolites unveils intricate networks of biomolecular interactions and highlights differential protein expression patterns between T2D and obesity cohorts. Pathway enrichment analyses reveal unique mechanisms underlying disease development and progression, while correlation analyses elucidate the interplay between proteomics, metabolomics, and clinical parameters. Furthermore, network analyses underscore the interconnectedness of cardiometabolic proteins and provide insights into their roles in disease pathogenesis. Our findings may help to refine diagnostic strategies and inform the development of personalized interventions, heralding a new era in precision medicine and healthcare innovation. Through the integration of multi-omics approaches and advanced analytics, our study offers a crucial framework for deciphering the intricate molecular underpinnings of metabolic disorders and paving the way for transformative therapeutic strategies. Full article