Search Results (2,095)

Umbilical cord mesenchymal stromal cells (UC-MSCs) are a key element of regenerative medicine due to their ability to secrete growth factors that stimulate proliferation and angiogenesis, and modulate the inflammatory response. Despite their widespread use, the influence of the perinatal microenvironment on their biological properties remains poorly understood. The aim of this study was to assess the influence of pH and blood gas parameters in umbilical cord blood on the global transcriptomic profile of UC-MSCs and to analyze the correlation between the metabolic status of the newborn and the expression of key trophic factors: EGF, FGF2, FGFR1, FGFR3, GDNF, HGF, IGF1, NES, NGF, and PGF. Methods: The study was conducted in two stages. In the first phase, transcriptomic screening was performed using Affymetrix HuGene 2.0 ST microarray on cells isolated from three environmental groups defined by cord blood pH: acidic (pH < 7.35), physiological (7.35–7.39), and alkaline (pH ≥ 7.4). In the second phase, the results were validated using qPCR on an expanded study group (N = 50). Gene expression levels (RQ) were related to blood gas parameters (pH, pCO₂, pO₂, cHCO₃) and the presence of clinical features of threatened neonatal asphyxia. Results: Microarray analysis revealed that environmental pH acts as a molecular phenotypic switch. Under low pH conditions (<7.35), a shift in cell profile from proliferative to structural–migratory was observed. Significant overexpression of genes responsible for extracellular matrix (ECM) organization and adhesion (e.g., COMP, DCN, LUM, FMOD) was observed, while pathways related to cell cycle and cell division (↓CDK1, AURKA, TOP2A) were downregulated. qPCR validation confirmed these observations, demonstrating a strong positive correlation between blood pH and the expression of regenerative mediators: FGFR1 (r = 0.28), EGF (r = 0.30), NGF (r = 0.39), and IGF1 (r = 0.30). A negative correlation was also found between carbon dioxide pressure (pCO₂) and the expression of NGF, FGFR1, and EGF. A significant clinical finding was that in newborns diagnosed with threatened asphyxia, EGF, FGFR1, and NGF gene expression was significantly reduced, indicating impaired trophic potential of the cells in response to metabolic stress. Conclusions: These results indicate that cord blood gas parameters are critical regulators of the genetic activity of UC-MSCs. Metabolic and respiratory acidosis not only inhibit the cells’ proliferative potential but also force them into a matrix remodeling mode, permanently modifying their transcriptomic profile. This suggests that the neonatal acid–base status may serve as an objective indicator of the “biological quality” of isolated stromal cells, which has significant implications for their future applications in cell therapies. Full article

In this paper, a multi-objective optimisation method based on the Non-dominated sorting genetic algorithm II (NSGA-II) is proposed, which proves to be effective in solving the computer-aided molecular design (CAMD) problem in the design of solvents for cooling crystallisation. A multi-objective optimisation model has been developed for the CAMD problem of solvents in the crystallisation process with the toxicity, solubility parameters, and potential recovery of the solvents as objective functions and the feasibility of the molecular structure as constraints. The properties involved are to be calculated by the group contribution method, and the solubility parameters of the solute in the solvent are calculated based on the Universal Quasichemical Functional-group Activity Coefficients (UNIFAC) model. Based on this method, cooling crystallisation solvents for 2-mercaptobenzothiazole (MBT) and sebacic acid were designed. The results indicate that the proposed multi-objective CAMD framework exhibits a certain degree of generality. Even when the optimisation parameters and methods differ from those of other existing frameworks, it does not overlook the optimal solutions under specific design conditions. Furthermore, clustering of the Pareto front for MBT revealed that, since multi-objective optimisation does not aim to obtain a single optimal solution, it can identify multiple candidate solvents that balance potential yield and toxicity. This approach avoids the issue of single-objective optimisation, which tends to overemphasise potential yield at the expense of toxicity. Full article

Five modifications of polytetrafluoroethylene (PTFE) are considered as a modern alternative to PTFE as sliding layers of bridge bearing parts. Radiation-modified PTFE without additives and with nano-additives as well as composites based on PTFE with bronze inclusions and nanomodified carbon fiber fillers were investigated. Ultra-high-molecular-weight polyethylene (UHMWPE) and classic pure PTFE were considered as control samples. The thermomechanical properties of the materials were studied within the framework of dynamic mechanical analysis in the operating temperature range of bridge structures [−40; +80] °C. The exit zones from the linear theory of viscoelasticity were established for all the materials considered. Temperature dependencies of the storage modulus and the loss modulus were determined. Thermoviscoelastic models of material behavior were constructed using a numerical identification procedure, experimental data, and simulation models. The thermomechanics of materials during the deformation of the spherical support part of the bridge were analyzed. Temperature dependencies of the parameters of the contact stress-strain state were determined with an average coefficient of determination R² = 0.97 and an average error size RMSE = 0.092. Full article

Ready-to-eat sea cucumbers (RSC) cannot be preserved at room temperature due to autolysis, which is closely related to the instability of collagen resulting from the disruption of hydrogen bonds. To investigate the protective effect of N-(3-Dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) cross-linking against disruption of hydrogen bonds and its role in stabilizing RSC quality at room temperature, this study designed comparative experiments involving EDC/NHS cross-linking treatments with varying sequences of hydrogen bonds disruption. The results indicated that EDC/NHS positively affects the stabilization of the collagen structure in RSC. The various quality parameters of both groups of RSC that underwent cross-linking treatment before and after hydrogen bonds disruption were significantly better than those of the control group, which only experienced the breaking of hydrogen bonds. Notably, the Eb group, which underwent EDC/NHS cross-linking treatment prior to the disruption of the hydrogen bonds network, yielded even more favorable results. Preliminary analyses of textural properties and moisture content suggested that EDC/NHS helps delay the deterioration of RSC quality. The levels of soluble components and carbonyl groups indicated that prior cross-linking treatment is more effective in mitigating collagen degradation and oxidation. Differential scanning calorimetry revealed that the reduction in ΔH for the Eb group was only 2.4%. Furthermore, fluorescence spectroscopy, Fourier transform infrared spectroscopy, and circular dichroism spectroscopy, examined from the perspectives of secondary and tertiary structures respectively, indicated that the cross-linking mechanism of EDC/NHS involves the formation of a more robust network of amide bonds, thereby preventing the disruption of hydrogen bonds and enhancing collagen stability, enabling it to better resist the cleavage of hydrogen bonds due to urea. The scanning electron microscope and Van Gieson’s staining techniques offer a clearer illustration of this point from a microscopic perspective. Moreover, molecular docking simulations have indicated the cross-linking mechanism of EDC/NHS at the atomic level, thereby establishing a scientific foundation for the potential application and development of EDC/NHS in room-temperature storage technologies for RSC. Full article

An optimized method of triclosan MIPs using a Design of Experiments (DOE) strategy was developed. The concentrations of methacrylic acid (MAA, monomer), 2-hydroxyethyl methacrylate (HEMA, co-monomer), and acetonitrile (ACN, solvent) were chosen as the critical parameters for the preparation process since they affect imprinting efficacy, morphological structure, and release profile of the material. A Box–Behnken design was utilized for the evaluation of how these factors influence the imprinting factor (IF). The optimized formulation revealed proper IF value indicating efficient molecular recognition. FTIR analysis validated the presence of acrylate-based bonds in the network structure. In addition, SEM images indicated a porous and aggregated structure of MIPs, which facilitated the accessibility of imprinted cavities. Release kinetics revealed two-phase profiles characterized by a moderate initial stage followed by sustained release up to 48 h. The Korsmeyer–Peppas model represented a better correlation (R² = 0.9754) compared to other kinetic models, implying complex diffusion-controlled release processes. Finally, MD simulations confirmed the experimental findings since MAA exhibited higher binding frequencies with triclosan than HEMA, proving its dominant role in molecular recognition. Full article

Allium mongolicum Regel is a valuable desert plant, yet its functional genomic research is hindered by low genetic transformation efficiency due to monocot regeneration recalcitrance and dense tissue structures. This study established a dual genetic transformation platform for A. mongolicum, encompassing in vivo transient expression and stable hairy root induction. By evaluating various infiltration methods, vacuum impregnation was identified as the optimal transient strategy and was successfully applied to localize the candidate proteins AmJAZ2 and AmSWEET14 to the nucleus and plasma membrane, respectively. For stable transformation, shortened stems were utilized as the optimal target explants. An orthogonal experimental design was employed to optimize key parameters, including Agrobacterium density, acetosyringone concentration, vacuum parameters, and co-cultivation duration. GFP fluorescence and PCR analysis confirmed the stable integration and expression of the transgene in the induced hairy roots. In conclusion, this study establishes preliminary, species-specific genetic transformation protocols for A. mongolicum, providing a baseline technical reference that may support subsequent exploratory research on the molecular biology and secondary metabolism of this species. Full article

Spinal cord stimulation (SCS) is an established therapy for chronic pain, yet treatment response remains highly variable and patient selection largely empirical. The identification of biomarkers with the potential to predict and monitor therapeutic response is therefore critical for advancing toward precision neuromodulation. This study provides a structured narrative synthesis of current evidence on biomarkers in SCS, focusing on their predictive and monitoring roles and their translational potential. Available studies were analysed across electrophysiological, neuroimaging, autonomic, and molecular domains and conceptually organized into predictive biomarkers—reflecting baseline biological states associated with treatment susceptibility—and monitoring biomarkers, capturing physiological and molecular adaptations following stimulation. Among predictive approaches, intraoperative electroencephalography (EEG) and resting-state functional magnetic resonance imaging (rs-fMRI) have shown promising but exploratory discriminative performance. However, EEG findings are derived from intraoperative settings, limiting their applicability to pre-implantation patient selection. In contrast, monitoring biomarkers—including heart rate variability, metabolic imaging, and immunological parameters—provide objective measures of treatment-induced changes but do not currently support predictive use. Molecular and genomic biomarkers, while mechanistically informative, remain exploratory and lack validated clinical utility. A central limitation of the field is the fragmentation of biomarker research, with most studies evaluating single modalities in isolation. To address this gap, we propose a translational framework integrating predictive and monitoring biomarkers through a two-stage model combining baseline stratification with longitudinal response assessment. Although biomarker research in SCS is rapidly evolving, its clinical application remains limited. The development of multimodal, validated biomarker strategies may support improved patient selection and more objective evaluation of treatment response, enabling a transition toward mechanism-based neuromodulation. Full article

This study presents a hybrid additive manufacturing approach to fabricate bioinspired stainless steel 316L-copper (SS316L-Cu) multimaterial structures using laser powder bed fusion (LPBF). The present study incorporates honeycomb lattice structures with varying wall thicknesses (0.25 mm, 0.5 mm, 0.75 mm, and 1.0 mm) to investigate the effect of geometric parameters on mechanical performance. Mechanical testing was conducted according to ISO 6892 standards, and the results revealed a strong dependence of tensile strength and ductility on lattice thickness. Copper (Cu) infiltration into SS316L lattice structures improved ductility by 30% compared to the monolithic SS316L lattice, with minimal compromise in tensile strength. To complement experimental results, molecular dynamics (MD) simulations were performed to study atomic-scale deformation and validate the trend of strength enhancement with increasing wall thickness. The findings demonstrate the potential of combining LPBF and liquid Cu infiltration to develop multifunctional, mechanically robust, and thermally conductive metallic composites. This approach provides valuable insight into structure–property relationships and supports the design of next-generation multifunctional composites for structural and thermal applications. Full article

Selenium (Se) biofortification is a promising approach to improve the nutritional value and functional quality of microgreens, although species-specific responses to Se remain insufficiently understood. This study investigated the effects of Se biofortification on physiological status, antioxidant responses, phenolic composition, and molecular changes in four Brassica microgreens: broccoli, kohlrabi, pak choi, and kale, using biochemical analyses, HPLC, and FTIR spectroscopy. The indicators of nutritional quality and stress-related metabolism in Brassica microgreens showed species-specific responses due to selenium treatment. Kohlrabi showed coordinated osmotic and metabolic adjustment involving osmolyte accumulation and enhanced antioxidant response, although moderate membrane sensitivity was observed at the highest selenium concentration. Pak choi maintained tolerance through balanced metabolic adjustment and enzymatic defense, while broccoli responded predominantly through enzymatic antioxidant mechanisms. Kale exhibited pronounced non-enzymatic responses, including anthocyanin accumulation and enhanced radical scavenging capacity. PCA confirmed species-specific response strategies and differential associations among biochemical parameters. Changes in antioxidant functionality were associated with both metabolite accumulation and structural reorganization of phenolic-related compounds. Overall, Se biofortification improved functional and nutritional traits of the investigated Brassica microgreens, although higher selenium concentrations induced moderate oxidative and membrane-related stress in certain Brassica microgreens. These findings highlight the importance of species-specific optimization of Se application to maximize crop quality while minimizing potential effects of Se toxicity. Full article

In this study, a methodology that combines ultrasound-enhanced extraction with the use of hydrophobic deep eutectic solvents (DESs) and three-phase partitioning (TPP) was presented for the green isolation of polysaccharides from longan shells (LSP). The extraction system was a DES composed of an equal molar ratio of dodecanoic acid and octanoic acid, which was used as the separation medium. Generally, the main phase separation mechanisms involved in the purification of the polysaccharide were investigated. The ideal operational parameters were found through systematic optimization by the single-variable experiment with the response surface methodology, i.e., the extraction temperature of 63.8 °C, the phase volume ratio of 1:1.04 (v/v), and the ammonium sulfate concentration of 26.3%. The extraction efficiency is 2.42 ± 0.03% for LSP when the above operational parameters are used. The structural characterization showed that the isolated LSP is an acidic heteropolysaccharide rich in galacturonic acid and arabinose. It was also shown that the molecular architecture of LSP includes both types of glycosidic bonds, which are also of importance for its physicochemical properties. The polysaccharide exhibits an open fibrous network structure. Notably, the DES maintained stable performance over five successive reuses without significant degradation. Concerning the antioxidant capacity, LSP at 0.4 mg/mL showed 96.6 ± 2.0% inhibition of ABTS radical, and showed an iron-reducing capacity of 68.67 ± 2.02 micromol Trolox per gram (concentration-dependent effect). These results are present a new method for the sustainable extraction of bioactive macromolecules. Full article

Symmetry plays a fundamental role in the structural analysis of lattice-based systems, particularly in graphene-like molecular structures. In chemical graph theory, counting independent sets is equivalent to computing the Merrifield–Simmons (M–S) index, a key descriptor of molecular stability in conjugated systems. Most existing exact counting methods rely on regular lattice symmetry, where structural uniformity simplifies computation; however, these approaches are difficult to extend to irregular graphs, where symmetry breaking introduces non-local dependencies and increases computational complexity. This paper proposes an asymmetry-aware algorithmic framework based on Hamiltonian traversals and a traversal-induced pathwidth parameter $w\left(G\right)$, defined through backward dependencies. Our method organizes non-local adjacencies into a bounded set of structured constraints, enabling a dynamic programming scheme over a reduced state space. The resulting algorithm runs in time $\mathcal{O}\left(2^{w\left(G\right)}·\mathrm{poly}\left(n\right)\right)$ and is fixed-parameter tractable with respect to $w\left(G\right)$. The results demonstrate that asymmetry-aware traversal strategies enable efficient exact enumeration in irregular mesh graph families, providing a robust computational framework for analyzing molecular descriptors in graphene-based structures with topological defects such as Stone–Wales transformations. Full article

Molecular property prediction is a fundamental task in drug discovery and materials design. While graph neural networks (GNNs) and SMILES-based Transformers have made significant strides, the former are often limited by local message-passing bottlenecks such as over-squashing, while the latter frequently lack explicit topological constraints and suffer from severe vocabulary imbalance. In this work, we revisit the granularity of molecular modeling and propose a representation learning framework built upon bond-level sequences. Our framework models molecules as sequences of directed bond tokens and introduces a structure-aware hybrid attention mechanism. By imposing hard topological constraints on a subset of attention heads to reinforce local connectivity while preserving global receptive fields in the remaining heads, the design is intended to separate short-range chemical bonding from long-range contextual dependencies. For pre-training, we implemented a multi-scale consistency learning paradigm, which utilizes an atom-centric group masking strategy to induce a hierarchical loss of local structural information and employs contrastive and triplet losses to ensure identity consistency across varying scales of structural degradation. Furthermore, by incorporating macro-scale physicochemical descriptors (e.g., LogP, TPSA) as global anchors, we examined how the inclusion of global attribute bias can provide weak physicochemical priors during pre-training, while its effect during downstream fine-tuning remains task-dependent. Experimental results demonstrate that our lightweight model, with approximately 3.5 million parameters, exhibits a dataset-dependent performance profile across MoleculeNet benchmarks and shows promising behavior on selected topology-sensitive tasks, particularly MUV. Ablation studies further analyze the contribution of bond-level connectivity, the stage-dependent dynamics of global attribute bias, structured masking, and pre-training configurations. Ultimately, this work provides an alternative representation design for molecular modeling, offering a parameter-efficient option for future molecular learning systems alongside traditional SMILES-based and graph-based formulations. Full article

Background: Gynecological cancers include collections of cancers with diverse cellular and molecular characteristics that often develop drug resistance, making them treatment-resistant. Biomolecule–drug conjugates (BDCs), especially antibody–drug conjugates (ADCs), have revolutionized the targeted therapy of cancer; however, the creation of these entities has so far been achieved by empirical, resource-intensive design methods. Objective: The aim of this review is to critically analyze how AI can be used for the rational design and optimization of high-affinity BDCs for gynecological cancer treatment. Methods and discussion: Recent advances in machine learning (ML)- and deep learning (DL)-based methods to predict biomolecule-target binding affinity, structural compatibility, linker stability, payload selection, trafficking in the cell, and biomolecule resistance mechanisms are summarized. The review also explores the possibilities for incorporation of structural, chemical, biological, and multi-omics data to enhance specificity, efficacy, and safety of conjugates. Besides antibody-based systems, AI-assisted design approaches with peptides, aptamers, and hybrid biomolecular systems are also included. This review also highlights parameters and experimental/numerical validation restrictions related to data quality, interpretability of models, regulatory aspects, etc. Conclusions: AI-based conjugate engineering is increasingly moving BDC development from a largely ‘trial and error’ approach to a more predictive and data-driven approach. While there are still challenges to be addressed in terms of translations and validations, the potential of AI approaches in the field of precision oncology and the development of more personalized treatment is promising in the context of gynecological cancers. Full article

Synthesis of bifunctional cytisine–squaramide derivatives bearing a single amino acid moiety has revealed an unexpected and intriguing chemical challenge. During modification of cytisine squaramates with α-amino acids, base-sensitive amido esters readily underwent hydrolysis, forming poorly soluble amido-acid side products that resisted standard purification and initially obscured their identity. Persistent observation of these elusive precipitates prompted a deliberate co-crystallization approach, which unambiguously revealed their supramolecular nature using single-crystal X-ray diffraction. With this insight, optimized purification strategies allowed isolation of analytically pure Cyt-SQ-OH and its derivatives, which were characterized by complementary spectroscopic techniques, X-ray crystallography and computational studies. Furthermore, the DFT-optimized parameters of all compounds were determined, providing additional insight into their structural and electronic properties. This work highlights the interplay between reactivity, solubility, and supramolecular assembly in cytisine–squaramide-amino acid hybrids, providing a robust platform for future exploration of multifunctional conjugates with potential applications in medicinal chemistry, molecular recognition, and materials science. Full article

Research on oxazolones, particularly 4-alkynyloxazolones, has garnered increasing interest due to the presence of an alkynyl group, which facilitates molecular conjugation and enables diverse chemical modifications. In this study, three representative 4-alkynyloxazolone derivatives (L1–L3) were synthesized and structurally characterized through single-crystal X-ray diffraction and computational analysis to obtain a reliable structure of L1–L3 to subsequently predict in silico interactions with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. The crystallographic results revealed high molecular planarity and multifurcated hydrogen bonding. Considering the obtained crystallographic structure, theoretical descriptors such as HOMO–LUMO energy gaps and electrostatic potential maps indicated that these compounds exhibit favorable electronic reactivity, particularly for L3, with favorable drug-like predictions. The lack of methoxy groups in L2 and L3 makes these compounds have lower predicted toxicity parameters than L1. Molecular docking calculations targeting SARS-CoV-2 spike glycoprotein in three different feasible conformations in a biological matrix, i.e., three receptor-binding domains (RBD) in down conformation, two RBD in down and one in up conformation, as well as RBD bound to the human receptor angiotensin-converting enzyme 2 (ACE2), suggested strong binding affinities and specific interactions with the RBD moiety, mainly in the up conformation. Overall, this work integrates crystallographic and computational approaches to establish the structural and in silico evaluation of spike-binding properties of early substituted 4-alkynyloxazolones, suggesting L3 as a candidate for future in vitro antiviral assays. Full article