Search Results (141)

Background: Stable isotope-based analytical methods have brought about a significant transformation in the study of energy nutrient metabolism, enabling precise in vivo measurement of metabolic fluxes at systemic, tissue, and organ-specific levels in both healthy and diseased states. The regulation of these metabolic fluxes is governed by dynamic interactions between proteins, lipids, carbohydrates, and their precursors—such as glucose, fatty acids, and amino acids—as well as final metabolic products. Discussion: Advanced analytical technologies, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), which can offer enhanced precision, have been developed for investigating nutrient metabolism and fluxes in humans, providing precise information on metabolic pathways. These techniques have primarily utilized stable isotopes, such as ²H, ¹³C, ¹⁵N, and ¹⁸O, which have largely replaced radioactive isotopes and are now central to metabolic research. These isotopes have been used to label glucose, fatty acids, or amino acids—the main biomolecular precursors—enabling detailed investigation at systemic, tissue, and organ-specific levels of carbohydrate, lipid, and protein metabolism, and revealing pathway alterations associated with diseases conditions, such as diabetes, non-alcoholic fatty liver disease, cardiovascular disorders, and cancer. The use of deuterium oxide (D₂O) has allowed for long-term metabolic studies, providing a cost-effective and less invasive means to monitor metabolic changes over days to months. Total daily energy expenditure can be measured in free living conditions by the doubly stable isotopes ²H- and ¹⁸O-labeled water method. Stable isotope tracing, combined with advanced imaging and modeling, has also been instrumental in assessing body composition, energy expenditure, and nutrient bioavailability. Collectively, these methods have expanded our understanding of human physiology and disease, supporting the development of novel diagnostic tools, the identification of new biomarkers, and the tailoring of nutritional and therapeutic interventions. Conclusions: This review aimed to provide an overview of the applications of stable isotopes for the study of energy nutrient metabolic pathways. The ongoing integration of stable isotope approaches with artificial intelligence, omics technologies, and miniaturized detection techniques could promise to further refine our understanding of human metabolism and drive advances in personalized medicine. Full article

The reconstruction of biological profiles from aged or degraded human skeletal remains represents a major challenge in both forensic and bioarcheological contexts, particularly when conventional identification approaches fail. Recent advances in molecular genetics, biochemical and histological analyses, and biomolecular anthropology have substantially expanded the range of information that can be recovered from compromised remains. This review synthesizes current integrative approaches combining genomic analyses, stable isotope investigations, epigenetic age estimation, proteomic sex determination, and complementary histological techniques to infer sex, ancestry, kinship, age, diet, mobility, and geographic origin. Genetic methods, including next-generation sequencing (NGS), enable increasingly robust inference even from highly degraded samples. Stable isotope analyses provide insights into dietary patterns and mobility, while DNA methylation markers improve age estimation accuracy. Tooth cementum annulation (TCA), although a histological rather than molecular method, contributes an additional chronological indicator within an integrative analytical framework. Rather than treating these approaches independently, this review proposes a multidisciplinary perspective in which complementary datasets collectively support biological profile reconstruction. Integrative interpretation enhances identification potential and provides more nuanced life-history reconstructions, demonstrating the value of combining molecular, biochemical, and histological evidence in forensic and archaeological investigations. Full article

While standard anti- vascular endothelial growth factor (VEGF) therapy, with or without photodynamic therapy (PDT), is effective for patients with polypoidal choroidal vasculopathy (PCV), not all achieve optimal visual outcomes. This study aimed to compare fortified (double the dose and the volume of the standard one) anti-VEGF combined with PDT versus fortified anti-VEGF monotherapy and to investigate biomolecular profiles and disease relationships among PCV, neovascular age-related macular degeneration (nvAMD), and central serous chorioretinopathy (CSCR). The goal was to identify novel pathways to inform future therapeutic strategies, including hypoxia-inducible factors (HIF)-1α inhibitors. This retrospective cohort study included 23 eyes with indocyanine green-confirmed type C PCV. One eye treated with transpupillary thermotherapy was not included in the following two groups. Patients received either combined therapy (PDT + fortified-dose anti-VEGF; n = 12) or fortified-dose anti-VEGF monotherapy (n = 10). Primary outcomes were changes in best-corrected visual acuity (BCVA) and central retinal thickness (CRT). Secondary outcomes included injection burden and recurrence. Exploratory analyses examined aqueous biomarkers, including VEGF, placental growth factor (PlGF), β-catenin, HIF-1α, and Wnt1 across PCV, CSCR, and nvAMD to identify novel therapeutic targets. Significant (p = 0.003/p = 0.005) median CRT reduction was similar (p = 0.468) between groups (combined/monotherapy: 137.5 µm/106.5 µm). BCVA (median [Q1, Q3]) change in logarithm of the minimum angle of resolution (LogMAR) was not statistically significant (p = 0.279), with 0.25 [0.00, 0.98] in the combined group versus 0.00 [−0.03, 0.28] in the monotherapy group. Treatment burden of anti-VEGFs per person per year was lower with combined therapy (1.16 ± 0.47# PDT + 2.81 ± 0.92# anti-VEGF injections) compared with monotherapy (4.61 ± 1.49# injections). Six eyes demonstrated recurrence at a mean of 15.5 months. Incomplete regression of polyps and branching vascular networks was observed in all eyes. Exploratory biomarker analysis revealed significantly (p < 0.05) higher VEGF and PlGF levels in nvAMD compared with PCV. nvAMD also demonstrated significantly (p < 0.05) higher β-catenin and lower HIF-1α levels relative to PCV and CSCR, while no significant biomarker differences were observed between PCV and CSCR. Combined therapy or monotherapy with fortified anti-VEGFs reduced treatment burden and achieved significant anatomical improvement but did not yield superior functional outcomes, highlighting the therapeutic difficulty of type C PCV. Biomarker profiling revealed shared hypoxia-related mechanisms between PCV and CSCR, with elevated HIF-1α compared to nvAMD indicating a “preliminary” possible role for HIF-1α inhibitors. Differential expression of these biomarkers highlights additional molecular pathways that may inform future targeted interventions. Full article

Head and neck cancer (HNC) diagnostics are undergoing a transformative shift. Recent research published in Cancers highlights a paradigm shift in the comprehensive management of HNC, driven by precision oncology and disruptive technologies. AI-enhanced imaging and non-invasive biomolecular fingerprinting are redefining early detection, with tools like infrared spectroscopy and hyperspectral imaging delivering near-perfect accuracy and real-time surgical guidance. Liquid biopsy is emerging as a powerful surveillance modality, capable of detecting recurrence months before conventional imaging and offering prognostic insights via cell-free DNA analysis. Theranostic agents in nuclear medicine show promise for rare HNC subtypes, though broader molecular targets remain a challenge. These technologies may have utility for complex presentations such as proliferative verrucous leukoplakia (PVL)-associated oral squamous cell carcinoma (OSCC), which disproportionately affects women, and peri-implant OSCC, which is often misdiagnosed and requires aggressive intervention. Collectively, these innovations directly address long-standing challenges: early detection, accurate staging, treatment personalization, monitoring of minimal residual disease and timely cancer care—where diagnostics not only inform treatment but actively shape outcomes. This editorial underscores the urgency of integrating such tools into clinical pathways to improve survival and quality of life for HNC patients globally. Full article

Blind docking predicts binding interactions between two molecular entities without prior knowledge of the binding site. This approach is essential because it explores the entire surface of the receptor to identify potential interaction sites. Blind docking widely works for both protein–protein and ligand–protein interaction studies. In protein–protein blind docking, the method aims to predict the correct orientation and interface of two proteins forming a complex. Protein blind docking is particularly valuable in studying transient interactions, protein–protein recognition, signaling pathways, tentative and significant biomolecular assemblies where structural data is limited. Ligand–protein blind docking discovers potential binding pockets across the entire protein surface. It is frequently applied in early-stage drug discovery, especially for novel or poorly characterized targets. The method helps identify allosteric sites or novel binding regions that are not evident from known structures. Overall, blind docking provides a versatile and powerful tool for studying molecular interactions, enabling discovery even in the absence of detailed structural information. In this scenario, we reported a timeline of attempts to improve this kind of computational approach with ML and hybrid approaches to obtain more reliable predictions. We dedicate two main sections to protein–protein and protein-ligand blind docking, presenting the reliability and caveats for each approach and outlining potential future directions. Full article

Exosomes are nanoscale extracellular vesicles that carry valuable biomolecular information. However, their characterization still depends on complex and costly techniques such as flow cytometry. In this study, a paper-based Vertical Flow Assay (VFA) specifically designed for the detection and profiling of exosomes derived from metastatic breast cancer cell lines is presented. The assay operates in an ELISA-like format, targeting exosomal surface proteins (CD9, CD63, CD81, and EGFR1) with specific antibodies and a secondary antibody conjugated to alkaline phosphatase. Upon reaction with the NBT/BCIP substrate, an insoluble indigo precipitate forms on the nitrocellulose membrane, generating a visual signal that can be further quantified by smartphone imaging. The VFA was optimized for membrane type, pore size, and blocking agents, reaching a detection limit of ~6 × 10⁷ exosomes µL⁻¹ in less than 20 min. Comparative studies with bead-based flow cytometry confirmed consistent biomarker expression profiles, demonstrating the reliability of the method. By enabling exosome biomarker profiling in a simplified and low-cost format, this approach provides a promising alternative to flow cytometry and other applications required for exosome characterization. Full article

Accurate prediction of the impact of genetic variants on human health is of paramount importance to clinical genetics and precision medicine. Recent machine learning (ML) studies have tried to predict variant pathogenicity with different levels of success. However, most missense variants identified on a clinical basis are still classified as variants of uncertain significance (VUS). Our approach allows for the interpretation of a variant for a specific disease and, thus, for the integration of disease-specific domain knowledge. We utilize a comprehensive knowledge graph, with 11 types of interconnected biomedical entities at diverse biomolecular and clinical levels, to classify missense variants from ClinVar. We use BioBERT to generate embeddings of biomedical features for each node in the graph, as well as DNA language models to embed variant features directly from genomic sequence. Next, we train a two-stage architecture consisting of a graph convolutional neural network to encode biological relationships. A neural network is then used as the classifier to predict disease-specific pathogenicity of variants, essentially predicting edges between variant and disease nodes. We compare performance across different versions of our model, obtaining prediction-balanced accuracies as high as 85.6% (sensitivity: 90.5%; NPV: 89.8%) and discuss how our work can inform future studies in this area. Full article

The mammalian epidermis forms a critical barrier against environmental insults and water loss. The formation of its outermost layer, the stratum corneum, involves a rapid terminal differentiation process that has traditionally been explained by the “bricks and mortar” model. Recent advances reveal a more dynamic mechanism governed by intracellular liquid–liquid phase separation (LLPS). This review proposes that the lifecycle of the granular layer is orchestrated by LLPS. Evidence is synthesized showing that keratohyalin granules (KGs) are biomolecular condensates formed by the phase separation of the intrinsically disordered protein filaggrin (FLG). The assembly, maturation, and pH-triggered dissolution of these condensates are essential for cytoplasmic remodeling and the programmed flattening of keratinocytes, a process known as corneoptosis. In parallel, an LLPS-based signaling pathway is described in which the kinase RIPK4 forms condensates that activate the Hippo pathway, promoting transcriptional reprogramming and differentiation. Together, these structural and signaling condensates drive skin barrier formation. This review further reinterprets atopic dermatitis, ichthyosis vulgaris, and Bartsocas-Papas syndrome as diseases of aberrant phase behavior, in which pathogenic mutations alter condensate formation or material properties. This integrative framework offers new insight into skin biology and suggests novel opportunities for therapeutic intervention through biophysics-informed biomaterial and regenerative design. Full article

Background/Objectives: The potential of DNA as an information-dense storage medium has inspired a broad spectrum of creative systems. In particular, hybrid biomolecular systems that integrate new materials and chemistries with DNA could drive novel functions. In this work, we explore the potential for proteins to serve as molecular file addresses. We stored DNA-encoded data in yeast and leveraged yeast surface display to readily produce the protein addresses and make them easy to access on the cell surface. Methods: We generated yeast populations that each displayed a distinct protein on their cell surfaces. These proteins included binding partners for cognate antibodies as well as chromatin-associated proteins that bind post-translationally modified histone peptides. For each specific yeast population, we transformed a library of hundreds of DNA sequences collectively encoding a specific image file. Results: We first demonstrated that the yeast retained file-encoded DNA through multiple cell divisions without a noticeable skew in their distribution or a loss in file integrity. Second, we showed that the physical act of sorting yeast displaying a specific file address was able to recover the desired data without a loss in file fidelity. Finally, we showed that analog addresses can be achieved by using addresses that have overlapping binding specificities for target peptides. Conclusions: These results motivate further exploration into the advantages proteins may confer in molecular information storage. Full article

Arginine plays a critical role in biomolecular interactions due to its guanidinium side chain, which enables multivalent electrostatic and hydrogen bonding contacts. In this study, atomistic molecular dynamics simulations were conducted across a broad concentration range (26–605 mM) to investigate the thermodynamic and structural features of arginine self-assembly in aqueous solution. Key observables—including hydrogen bond count, radius of gyration, contact number, and isobaric heat capacity—were analyzed to characterize emergent behavior. A three-regime aggregation pattern (dilute, cooperative, and saturated) was identified and quantitatively modeled using the Hill equation, revealing a non-linear transition in clustering behavior. Spatial analyses were supplemented with trajectory-based clustering and radial distribution functions. The heat capacity peak observed near 360 mM was interpreted as a thermodynamic signature of hydration rearrangement. Trajectory analyses utilized both GROMACS tools and the MDAnalysis library. While force field limitations and single-replica sampling are acknowledged, the results offer mechanistic insight into how arginine concentration modulates molecular organization—informing the understanding of biomolecular condensates, protein–nucleic acid complexes, and the design of functional supramolecular systems. The findings are in strong agreement with experimental observations from small-angle X-ray scattering and differential scanning calorimetry. Overall, this work establishes a cohesive framework for understanding amino acid condensation and reveals arginine’s concentration-dependent behavior as a model for weak, reversible molecular association. Full article

Aging affects all tissues in an organism, including the tracheobronchial tree, with structural and functional changes driven by mechanisms such as oxidative stress, cellular senescence, epigenetic modifications, mitochondrial dysfunction, and telomere shortening. Airway aging can be accelerated by intrinsic or extrinsic factors. This review brings together information from the literature on the molecular changes occurring in all layers of the tracheobronchial airway wall. It examines the biomolecular changes associated with aging in the mucosa, submucosa, cartilage, and smooth muscle of the airways. At the mucosal level, aging reduces ciliary function and disrupts mucin homeostasis, impairing mucociliary clearance and contributing to chronic respiratory diseases such as COPD (Chronic Obstructive Pulmonary Disease). Cellular senescence and oxidative stress drive extracellular matrix remodeling and chronic inflammation. Airway cartilage undergoes age-related changes in collagen and fibronectin composition, leading to increased stiffness, while heightened MMP (Matrix Metalloproteinases) activity exacerbates ECM (extracellular matrix) degradation. In airway smooth muscle, aging induces changes in calcium signaling, hypertrophy, and the secretion of pro-inflammatory mediators, further perpetuating airway remodeling. These changes impair respiratory function and increase susceptibility to chronic respiratory conditions in the elderly. By consolidating current knowledge, this review aims to provide a comprehensive overview of the molecular changes occurring in the respiratory tract with aging and to highlight new molecular perspectives for future research on this topic. Full article

Estimating confidence intervals in small or noisy datasets is a recurring challenge in biomolecular research, particularly when data contain outliers or exhibit high variability. This study introduces a robust statistical method that combines a hybrid bootstrap procedure with Steiner’s most frequent value (MFV) approach to estimate confidence intervals without removing outliers or altering the original dataset. The MFV technique identifies the most representative value while minimizing information loss, making it well suited for datasets with limited sample sizes or non-Gaussian distributions. To demonstrate the method’s robustness, we intentionally selected a dataset from outside the biomolecular domain: a fast-neutron activation cross-section of the ¹⁰⁹Ag(n, 2n)^108mAg reaction from nuclear physics. This dataset presents large uncertainties, inconsistencies, and known evaluation difficulties. Confidence intervals for the cross-section were determined using a method called the MFV–hybrid parametric bootstrapping (MFV-HPB) framework. In this approach, the original data points were repeatedly resampled, and new values were simulated based on their uncertainties before the MFV was calculated. Despite the dataset’s complexity, the method yielded a stable MFV estimate of 709 mb with a 68.27% confidence interval of [691, 744] mb, illustrating the method’s ability to provide interpretable results in challenging scenarios. Although the example is from nuclear science, the same statistical issues commonly arise in biomolecular fields, such as enzymatic kinetics, molecular assays, and diagnostic biomarker studies. The MFV-HPB framework provides a reliable and generalizable approach for extracting central estimates and confidence intervals in situations where data are difficult to collect, replicate, or interpret. Its resilience to outliers, independence from distributional assumptions, and compatibility with small-sample scenarios make it particularly valuable in molecular medicine, bioengineering, and biophysics. Full article

This investigation presents an overview of various optical biosensors utilized for the detection of cancer cells. It covers a comprehensive range of technologies, including surface plasmon resonance $\left(SPR\right)$ sensors, which exploit changes in refractive index $\left(RI\right)$ at the sensor surface to detect biomolecular interactions. Localized surface plasmon resonance $\left(LSPR\right)$ sensors offer high sensitivity and versatility in detecting cancer biomarkers. Colorimetric sensors, based on color changes induced via specific biochemical reactions, provide a cost-effective and simple approach to cancer detection. Sensors based on fluorescence work using the light emitted from fluorescent molecules detect cancer-specific targets with specificity and high sensitivity. Photonics and waveguide sensors utilize optical waveguides to detect changes in light propagation, offering real-time and label-free detection of cancer biomarkers. Raman spectroscopy-based sensors utilize surface-enhanced Raman scattering $\left(SERS\right)$to provide molecular fingerprint information for cancer diagnosis. Lastly, fiber optic sensors offer flexibility and miniaturization, making them suitable for in vivo and point-of-care applications in cancer detection. This study provides insights into the principles, applications, and advancements of these optical biosensors in cancer diagnostics, highlighting their potential in improving early detection and patient outcomes. Full article

Penetrating brain injuries (PBI) constitute a significant subset of traumatic brain injuries, characterized by high morbidity and mortality due to their unique pathophysiological mechanisms. Despite its clinical prevalence in civilian and military settings, progress in translational research remains limited due to a lack of well-characterized pre-clinical models that accurately replicate human PBI. Existing models often fail to adequately simulate critical aspects such as ballistic dynamics, tissue cavitation, and secondary injury cascades, limiting their translational relevance and hindering therapeutic advancements. This scoping review aims to systematically evaluate existing pre-clinical models, including animal, computational, ballistic, and hybrid simulations, to assess their methodological rigor, translational applicability and reported outcome measures. Using PRISMA-ScR guidelines, we will conduct a comprehensive literature search across multiple databases, extracting data on model characteristics, injury induction techniques, histopathological findings, biomolecular markers, and functional assessments. Additionally, bibliometric analyses will provide insights into research trends and gaps in PBI modeling, particularly concerning replicating real-world injury mechanisms and long-term functional outcomes. Through this evaluation, we aim to identify optimal experimental frameworks for studying PBI pathophysiology and recovery mechanisms while informing future model development for therapeutic advancements. The findings from this review will serve as a foundation for advancing pre-clinical PBI research, guiding future model development and therapeutic innovations, and ultimately enhancing treatment strategies and patient outcomes. Full article

Orthohantavirus infection is a rodent-to-human zoonotic disease with a worldwide distribution, resulting in more than 200,000 cases per year. Human infection leads to two diseases, haemorrhagic fever with renal syndrome and hantavirus cardiopulmonary syndrome, with mortality rates ranging from 1% to 38%. Apart from the data on cases presenting obvious clinical symptoms, the true prevalence is poorly understood, especially in the occupational groups considered to be at risk of exposure. As there is currently no approved therapy or vaccine, surveillance is essential to locate the presumed site of infection following orthohantavirus outbreaks in order to control the spread of infection. To this end, the use of rapid diagnostic tools is essential to rapidly provide data on viral circulation. This review focuses mainly on the available diagnostic methods, both serological and biomolecular, and the surveillance systems used for orthohantaviruses. The information gathered could provide a valid basis for the implementation of further surveillance systems in a country lacking up-to-date data. Full article