Search Results (2,618)

Natural product research has experienced substantial growth over the past two decades, driven by a renewed appreciation for the structural complexity and biological relevance of compounds derived from nature. Technological advances in separation science, spectroscopic characterization, and high-sensitivity bioassays have collectively restored natural products to a position of prominence in modern drug discovery efforts. Nature remains the most prolific source of bioactive molecular diversity, drawing from microorganisms, plants, and marine life to offer a vast reservoir of structurally novel scaffolds whose pharmacological potential remains largely unexplored. Effective extraction and isolation remain foundational to natural product research, as the quality and purity of isolated compounds directly govern the reliability of downstream biological evaluation. Recent years have witnessed remarkable innovation in this space, spanning green and designer solvent systems, pressurized and ultrasound-assisted extraction platforms, supercritical fluid techniques, and integrated purification workflows that dramatically reduce processing time while improving compound recovery and analytical throughput. Particularly noteworthy is the growing application of artificial intelligence and machine learning tools for solvent selection, extraction optimization, and metabolite dereplication, which in combination with advanced phase-separation strategies and informatic platforms have substantially expanded the scope of detectable and characterizable metabolites within complex biological matrices. This review summarizes recent progress in extraction and isolation methodologies supporting natural product research, with particular emphasis on combinatorial extraction strategies, next-generation solvent systems, and AI-driven applications that have collectively improved operational efficiency, selectivity, and analytical output over the past five years. Full article

The growing demand for gold in various high-technology applications necessitates the development of efficient and selective methods for its recovery and analysis, which can be achieved using such macrocyclic ligands as crown esters and their aza-substituted derivatives. The present paper reports on the equilibrium constants for the formation of gold(III) complexes with 18-crown-6, 1-aza-18-crown-6, 1,10-diaza-18-crown-6, and the cryptand 4,7,13,16,21,24-hexaoxa-1,10-diazabicyclo[8.8.8]hexacosane (Kryptofix 222) in aqueous solution at T = 298.2 K, p = 0.1 MPa, I → 0. The equilibrium constants (log β) for the substitution of chloride ions by macrocycles were determined to be 4.52 ± 0.04, 9.15 ± 0.03, 9.08 ± 0.07, and 11.51 ± 0.08, respectively. Equilibrium constants for protonated and polyligand species are also provided. The complexation mechanism was elucidated using a combination of spectroscopic techniques. UV-Vis and IR spectroscopy confirm the substitution of chloride ligands by the nitrogen donor atoms of the aza-macrocycles within the tetrachloroaurate(III) ion. Furthermore, ¹H NMR analysis reveals that the diaza-substituted ligands can form both inclusion complexes, where the gold cation is encapsulated within the macrocyclic cavity, and exclusion complexes. These findings provide a quantitative foundation for the design of novel macrocycle-based extractants and sensors for gold(III). Full article

Alzheimer’s Disease (AD) is the leading cause of dementia worldwide, and early diagnosis is crucial to minimize neurological damage and loss of quality of life. Here, we report an electrochemical biosensor for detecting miRNAs 29a and 34a, potential non-invasive biomarkers associated with AD. The biosensor consisted of a glassy carbon electrode (GCE) modified with a novel nanohybrid of gold nanoparticles stabilized by 3-n-propyl(4-dimethylaminopyridinium) silsesquioxane chloride (AuNPs–Si4DMAP⁺Cl⁻). Thiolated anti-miRNA probes were immobilized separately on the GCE/AuNPs-Si4DMAP⁺Cl⁻, followed by BSA blocking. Target miRNAs were detected via hybridization with complementary probes using electrochemical impedance spectroscopy. The nanohybrid, characterized by spectroscopic and morphological techniques, significantly enhanced the electrochemical response and was effective detecting both miRNAs, showing suspension stability over 600 days. LOD and LOQ were 1.79 pM and 5.87 pM for miRNA-29a, and 2.21 pM and 11.01 pM for miRNA-34a. These results highlight the platform’s potential for electrochemical detection of these miRNAs in blood, supporting earlier detection of AD and other neurodegenerative diseases. Full article

The control of oxygen stoichiometry via topochemical reduction offers a powerful route to manipulate the functional properties of complex oxides. Here, we investigate the chemical and structural evolution of epitaxial BiFeO₃ (BFO) thin films under CaH₂ treatment in a sealed tube, using a representative reduction condition of 365 °C for 2 h and a temperature window of 345 to 380 °C to probe the reduction dependent evolution. The inherent sensitivity of BFO’s multiferroic properties to oxygen vacancy formation and cation valence states makes it an ideal platform to probe reduction pathways. The aim of this work is to elucidate the detailed reduction pathway, including phase stability, valence changes in Bi and Fe, and the morphological consequences of oxygen extraction. Using a combination of spectroscopic, diffraction, and microscopic techniques, it was demonstrated that CaH₂ annealing does not yield a homogeneous oxygen-deficient perovskite. Instead, it triggers a decomposition into Bi₂O₃, metallic Bi, and FeO_x secondary phases, accompanied by severe surface roughening. This chemical reconstruction leads to a strong suppression of the ferromagnetic-like response and a redshift in the optical absorption edge. Full article

Reducing measurement uncertainty is crucial to enable the adoption of rapid point-of-use techniques for clinical and industrial applications. Diagnosis of neonatal respiratory distress syndrome and liposome formulation quality control are two applications for which measuring the ratio of the lecithin to sphingomyelin composition of liposomes is important, for which no rapid measurement currently exists. Raman and infrared spectroscopies are two complementary approaches to examine characteristic molecular vibrations that can spectroscopically measure liposomes and, when combined with machine learning, predict their composition. We show that employing a data-fusion approach the uncertainty in the predicted compositions compared to the individual modalities (IR R²: 0.902 and Raman R²: 0.951) can be reduced to obtain more accurate and precise measurements (low-level fused model R²: 0.973, mean squared error: 0.024, prediction interval width: 0.303, high-level weighted fusion model R²: 0.970, mean squared error: 0.027, prediction interval width: 0.268). Full article

Background/Objectives: Nanostructured biomaterials based on natural polymers have gained increasing attention in pharmaceutics due to their biocompatibility, multifunctionality, and diverse biomedical applications. This novel study aimed to biofabricate chitosan-doped zinc oxide nanocomposites (CS-ZnONCs) using Leucas aspera leaf extract and to evaluate their physicochemical properties and in vitro biomedical performance. Methods: CS-ZnONCs were synthesized using L. aspera leaf extract through a green precipitation approach, and the resulting nanocomposites were characterized by various spectroscopic techniques. The in vitro antioxidant, antibacterial, and anti-inflammatory activities were evaluated, while wound-healing potential was assessed using L929 fibroblast cell migration assays. Results: UV–visible analysis confirmed the formation of CS-ZnONCs, with a characteristic absorption peak at 362 nm, and FTIR spectra indicated the presence of various important functional groups. XRD results demonstrated the crystalline nature of ZnO within the chitosan matrix. Well-dispersed, quasi-spherical nanoparticles with an average size of 44 ± 3.1 nm were identified by HR-TEM, and a positive zeta potential (+9 mV) suggested considerable colloidal stability. CS-ZnONCs showed a high swelling capacity (88 ± 2.75% for 2%) and significant phytocompound release (65.38 ± 2.79% at pH 7.4). The CS-ZnONCs showed significant antioxidant activity (ABTS of 88.19 ± 1.59%), notable antibacterial efficacy against Staphylococcus aureus (18.78 ± 0.98 mm) and Escherichia coli (17.14 ± 0.96 mm), and significant anti-inflammatory activity (82.12 ± 1.47% membrane stabilization). In vitro biocompatibility and wound-healing assays revealed significant cytocompatibility in Vero cells, with 98.75 ± 1.17% cell viability observed, whereas the fibroblast migration assay demonstrated near-complete wound closure (96.55 ± 6.46%). Conclusions: The green-synthesized CS-ZnONCs exhibit favorable physicochemical properties, biocompatibility, and multifunctional biological activities, supporting their potential as a promising sustainable biomaterial nanomedicine for pharmaceutical formulations, wound healing, and regenerative medicine applications. Full article

The identification/classification of textile fibres is essential in manufacturing, forensic science, cultural heritage preservation, and recycling. Conventional methods, including solubility tests, optical microscopy, and chromatographic techniques, are often destructive, labour-intensive, and limited in scope. Vibrational spectroscopy, particularly near-infrared (NIR), Fourier-transform infrared (FTIR), and Raman spectroscopy, has emerged as a rapid, non-destructive, and accurate alternative for fibre analysis. However, multi-composition textiles, dyes, finishing agents, and ageing effects frequently cause overlapping spectral features, hampering direct interpretation. This review examines the combined use of vibrational spectroscopy and chemometrics for textile fibre discrimination. It critically evaluates the performance of different spectroscopic techniques in classifying natural, synthetic, and blended fibres. The role of multivariate analysis methods, such as PCA, PLS, LDA, SIMCA, and machine learning algorithms, in improving spectral interpretation and classification accuracy is highlighted. Key factors affecting model robustness, including spectral pre-processing, sample heterogeneity, moisture, and colour, are also discussed. The integration of spectroscopy with chemometrics provides a robust, scalable, and sustainable solution for fibre identification, supporting quality control, fraud detection, and circular economy initiatives. This approach demonstrates significant potential for both research and industrial applications. Full article

Carnosine (or β-alanyl-L-histidine) is an endogenous compound playing very important roles in human organisms as antiglycation and antioxidant agents, and, in addition, helping to mitigate illnesses such as cancer and neurodegenerative diseases. Aiming to explore the chelating ability of carnosine, based on its coordinating possibilities, we started to investigate the metal complexes of essential copper(II), zinc(II), and iron(II) ions coordinated to this dipeptide. Different compounds were isolated in the solid state by adding stoichiometric amounts of metal salts to carnosine at controlled pH or under a controlled atmosphere, with the formation of mono-, bi- and polynuclear species. These complexes were subsequently characterized mainly by spectroscopic techniques (UV–Vis, IR, EPR), in addition to elemental analysis. A binuclear species was isolated with copper(II) and had its structure determined by X-ray diffraction, improving previously reported data in the literature. Two insoluble correlated trinuclear species were isolated with zinc(II) ions, using perchlorate or chloride as counter-ions. In the case of iron, a mononuclear species was verified with Fe(II) ions, obtained under an inert atmosphere. Further, the antioxidant properties of free carnosine and the copper–carnosine complex were verified by their scavenging activity toward the ABTS^•+ radical, using Trolox as a reference, showing significant activity. The carnosine–metal complexes were also tested as potential antineoplastic agents, in comparison to the free ligand, after 24 h of incubation at 37 °C, using malignant HeLa, SKMEL 28 and SKMEL 147, and non-tumor fibroblast cells. Results indicated neglected or poor anti-proliferative properties of these metal complexes, when compared to other similar compounds described in the literature. Full article

Prefilled syringes are valuable drug delivery systems, offering convenience and precision dosing. Among the critical factors influencing their performance is the stability of the silicone oil layer, which acts as a lubricant, guaranteeing the gliding properties of the plunger. The silicone oil, if it comes in contact with therapeutic formulations, can be subject to drug–container interactions, potentially leading to silicone oil release into the solution, thereby altering the gliding properties of the syringe and leading to unwanted particle formation, compromising drug efficacy and safety. Different measurement techniques, such as visual inspection, dynamic light scattering and spectroscopic analysis, are used to assess silicone oil layer stability in prefilled syringes. However, a quantitative, rapid and low-volume screening method to rapidly evaluate container compatibility for therapeutic formulations is not available. Here, we present a multi-well-based screening protocol allowing users to quantify, through fluorescence, the silicone oil released into a solution upon contact with liquid formulations. Fluorescently labeled uniform silicone oil layers of the desired thickness are deposited in glass-bottom wells and exposed to typical formulations, containing surfactants and monoclonal antibodies. The release of silicon oil as a function of contact time is quantified using fluorescence calibration. Beyond its use as a screening tool to evaluate drug–container compatibility, our protocol can contribute to the fundamental understanding of the factors and mechanisms influencing silicone oil layer stability and, furthermore, to the optimization of drug delivery systems. Full article

This study evaluates the impact of four drying methods—open sun drying, solar drying, infrared drying, and microwave drying—on the quality attributes and elemental retention of apricots (Prunus armeniaca L.). Experimental trials were conducted in June 2024 at the Tashkent Institute of Chemical-Technology using equal quantities of fresh apricots. Drying was continued until the moisture content, measured gravimetrically, dropped below 20% (wet basis), followed by spectroscopic analysis to determine macro- and microelement concentrations. Solar-dried apricots showed higher retention of essential nutrients in this experimental trial: potassium (2.37%), silicon (0.538%), magnesium (0.145%), calcium (0.176%), and sulfur (0.152%). In contrast, open sun drying led to significant nutrient degradation and poor visual quality. Microwave drying preserved some micronutrients but resulted in surface scorching due to uneven heating. Infrared drying yielded acceptable results but required substantial energy input. Among all methods, solar drying provided the optimal balance of high product quality and energy efficiency. The drying process required negligible electrical energy owing to exclusive reliance on solar radiation. This method supports sustainable food processing by reducing energy demand and greenhouse gas emissions while preserving nutritional quality. The results highlight solar drying as a promising, eco-friendly technique for preserving the nutritional integrity of agricultural products. These findings offer valuable scientific guidance for selecting appropriate drying technologies in the food processing industry, especially in regions with high solar potential. However, the study is limited to a single fruit variety and seasonal conditions. Full article

Background: Endometriosis is a major cause of female infertility. It significantly impacts oocyte quality and embryonic development. The condition’s pathophysiological mechanisms are multifactorial. However, they are believed to be reflected in the biochemical composition of follicular fluid (FF). FF is the immediate microenvironment of the developing oocyte hence its relevance. Conventional analytical methods provide only a limited view of this complex biofluid. This underlies the need for holistic profiling techniques. Objective: This narrative review synthesizes current knowledge on the potential of Fourier-Transform Infrared (FTIR) and Raman spectroscopy. The two are scrutinized as label-free, non-destructive tools for analyzing FF in the context of endometriosis. As such, the aim is to bridge the understanding of the disease’s impact on the follicular niche with the analytical power of these spectroscopic techniques, ultimately highlighting a critical research gap, while critically evaluating the translational pathway required to bring these techniques from research laboratories into routine clinical IVF practice. This includes assessment of practical feasibility, cost-effectiveness, turnaround time, standardization requirements, and comparison with existing clinical biomarkers. Methods: We outline the fundamental principles of FTIR and Raman spectroscopy and their complementary strengths. The review then consolidates evidence from proteomic and metabolomic studies demonstrating FF alterations in endometriosis. We also showcase the successful application of vibrational spectroscopy in other reproductive diagnostics. This synthesis is vital to identifying a specific unmet need in the field. Conclusions: Despite the known importance of FF and the proven capability of FTIR and Raman spectroscopy in related areas, there is a striking lack of studies applying these techniques directly to the FF of women with endometriosis. This review concludes by framing this void as a pivotal research opportunity. In doing so, it presents a direct rationale and methodological framework for a future study designed to characterize the unique spectral fingerprints of endometriosis in FF, with the goal of uncovering novel biomarkers and pathophysiological insights. Full article

Antibody–drug conjugates (ADCs) comprise a monoclonal antibody covalently bound to a cytotoxic payload by a linker. ADCs minimize off-target effects on healthy tissues, leveraging the specificity of monoclonal antibodies to deliver cytotoxic drugs to the intended tumor site. ADCs can be prone to poor behavior, including aggregation and misfolding, leading to poor efficacy, impaired pharmacokinetics, and immunogenicity. It is advantageous to understand the developability and potential liabilities of a protein candidate prior to costly in vivo studies or clinical trials. This review summarizes biophysical and structural techniques used to characterize ADCs and introduces emerging techniques aimed at accurately assessing the developability of protein candidates. Stability is commonly assayed using techniques like differential scanning calorimetry (DSC), differential scanning fluorimetry (DSF), or spectroscopic probes such as circular dichroism and intrinsic fluorescence. Drug-to-antibody ratio (DAR) is a critical parameter that can be measured using absorbance spectroscopy or chromatographic analysis. Aggregation and self-association can be probed using scattering techniques such as dynamic light scattering (DLS), static light scattering (SLS), and size exclusion chromatography–multi-angle light scattering (SEC-MALS), as well as more specialized approaches such as fluorescence correlation spectroscopy (FCS) and analytical ultracentrifugation (AUC). Mass spectrometry (MS) provides extremely valuable insight into stability, covalent modifications, and, through approaches like hydrogen–deuterium exchange (HDX-MS), structural dynamics of ADCs. Looking forward, the use of biophysical assays in ex vivo matrices and strategic use of artificial intelligence/machine learning (AI/ML) approaches are likely to advance the efficient and rapid development of ADCs and other next-generation protein therapeutics. Full article

The usage of magnetic nanoparticles (MNPs), particularly iron oxide-based systems such as magnetite (${\mathrm{Fe}}_3$${\mathrm{O}}_4$) and maghemite ($\gamma$-${\mathrm{Fe}}_2$${\mathrm{O}}_3$), has significantly advanced the field of theranostics. These nanoparticles unite therapeutic and diagnostic capabilities due to their favorable magnetic properties and surface engineering potential. However, the path from synthesis to clinical application poses substantial challenges, including optimization of structure–property–function relationships, biocompatibility issues, and effective surface functionalization. Various synthesis methods, such as co-precipitation and thermal decomposition, aim to achieve specific nanoparticle characteristics, although they encounter obstacles related to scalability and reproducibility. Furthermore, characterizing these systems through structural, microstructural and spectroscopic techniques is vital to determine their functional efficacy and ensure their safe biomedical usage. This review comprehensively examines recent advancements and identifies existing challenges in the clinical translation of MNPs, highlighting the need for refined methods and standardized protocols to effectively exploit their theranostic potential. It outlines future directions, emphasizing the importance of green synthesis and robust characterization frameworks to enhance the integration of MNPs in personalized medicine. Full article

Metal additive manufacturing (AM) facilitates the production of geometrically complex components, yet its broader industrial use remains limited by the risk of defect formation and uncertainties in their detection, originating from the highly dynamic and high-temperature process environment. To make additive manufacturing more reliable and establish high-quality parts, it is important to understand how these defects form and how their characteristics appear during the process. This review explains the main causes of common defects, such as cracking, porosity, lack of fusion, and inclusions in metal AM processes, including Powder Bed Fusion and Directed Energy Deposition. It also connects main defect formation mechanisms to the optical, thermal, acoustic, and spectroscopic signals that can be measured during the process. Moreover, it is described how commonly used in situ monitoring systems work and how their signals correspond to melt pool dynamics, vapor plume, particle movement, and the solidification process for each kind of defect. An overview is provided of how data from these systems are analyzed, including the extraction of features from images, the evaluation of temperature fields, and the use of time and frequency domain techniques for various signals. By linking the physics of defect formation to measurable process signals, the interpretation of sensor data is enabled, and potential strategies for monitoring specific problems are outlined. Finally, recent developments are examined, including the integration of multiple sensors, advanced feature-representation approaches, and real-time data interpretation coupled with adaptive control. Together, these directions represent promising advances towards more intelligent and reliable monitoring systems for the future of metal AM. Full article

To establish a rapid traceability method for tea varieties and address the limitations of traditional identification techniques, this study focused on four types of tea—Longjing, Maofeng, Zhuyeqing, and Biluochun—using near-infrared (NIR) spectroscopy. A total of 84 sets of NIR spectra were collected and preprocessed using Savitzky–Golay smoothing (S-G), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), and first derivative (1stDer) methods. Dimensionality reduction and feature selection were then performed using principal component analysis (PCA), linear discriminant analysis (LDA), their combination (PCA-LDA), and the successive projections algorithm (SPA). Classification models based on multiple linear regression (MLR) and support vector machine (SVM) were constructed and evaluated via five-fold cross-validation to assess generalization ability and stability. The results indicated that the SVM model significantly outperformed the MLR model in overall classification and generalization. The PCA-LDA combined approach proved to be the most effective feature selection method. The optimal classification model for tea variety traceability was achieved using MSC or SNV preprocessing combined with PCA-LDA-SVM, yielding a mean five-fold cross-validation accuracy of 96.67%. The confusion matrix revealed that misclassifications mainly occurred between Longjing and Biluochun and between Maofeng and Zhuyeqing, which can be attributed to similarities in processing techniques and chemical composition among these tea varieties. This study provides a rapid, non-destructive, and accurate spectroscopic detection method for tea quality control and traceability, offering a valuable reference for the rapid identification of agricultural products. Full article