Analytica, Volume 6, Issue 2 (June 2025) – 7 articles

Early detection of cancer can dramatically improve long-term prognosis and survivability in a variety of different cancer types. However, for many cancer types, the ability to effectively detect early-developing tumors is challenging, especially in physiological locations with limited visibility or access. Previously, we reported a sensing platform and methodology to detect biomarker peptides found in urine from ovarian cancer patients. This sensing platform relies on peptide interactions with gold nanoclusters through thiol-mediated linkages; thus, the sensitivity of the biomarker assay is directly related to appropriate redox states of the biomarkers in question. Here, we report on an expansion of the traditional thiol-reactivity assay originally developed by Ellman to include and evaluate a variety of solution modifications that may be used in conjunction with the biomarker-sensing platform. Because biomarker peptides may be isolated from a variety of biological tissues or fluids, depending on the target condition or disease, we screened numerous solution conditions that may be directly used in sample preparation and peptide extraction. The data demonstrate that the assay maintains linearity under these various conditions. The assay was then applied to a variety of models and biomarker peptides and exhibits the expected linear response. These results demonstrate the applicability of the thiol-reactivity assay to biologically derived samples, and the flexibility to ensure sample preparation and treatment will retain the appropriate sample redox conditions to ensure optimal interactions with the biosensor platform. It also facilitates the ability to perform quality control on clinically derived biological samples to ensure appropriate preparations, and concentrations are available for application to the nanopore biosensor platform. Full article

Over the past decade, more than a thousand new psychoactive substances (NPSs) have emerged worldwide. This rapid proliferation of “designer drugs” poses significant challenges for drug control, forensic analysis, and public health. Artificial intelligence (AI) has increasingly been applied to address these challenges in NPS design and analysis. This review provides a comprehensive overview of AI methodologies—including deep learning, generative models, and quantitative structure–activity relationship (QSAR) modeling—and their applications in the synthesis, prediction, and identification of NPSs. We discuss how AI-driven generative models have been used to design novel psychoactive compounds and predict their pharmacological activity, how QSAR models can forecast potency and toxicological profiles, and how machine learning is enhancing analytical chemistry workflows for NPS identification. Special emphasis is placed on mass spectrometry (MS)-based techniques, where AI algorithms (e.g., for spectral prediction and pattern recognition) are revolutionizing the detection and characterization of unknown NPSs. A dedicated section examines the legal and regulatory implications of AI-generated psychoactive substances in the European Union (EU) and United States (USA), highlighting current policies, potential gaps, and the need for proactive regulatory responses. The review concludes with a discussion of the benefits and limitations of AI in this domain and outlines future directions for research at the intersection of AI, analytical chemistry, and drug policy. Full article

The objective of this work is to study the anti-inflammatory effect in vitro and in vivo of microwave (MW) extracts of Thymus algeriensis. The in vitro study was performed by the human red blood cell protection test, while the in vivo study used the carrageenan-induced rat paw edema model. The experimental results were confirmed by a molecular docking calculation. The results indicated that all the microwave extracts have a moderate anti-inflammatory effect, depending on their richness in phenolic compounds. Among the extracts studied, the one obtained at 100 °C for 15 min exhibited the most pronounced anti-inflammatory effect, with an inhibition of 78.52%, which is attributed to its high flavonoid content. In particular, the flavonoids naringin and catechin showed the best affinity for the target protein, with values of −10.3 kcal/mol and −9.2 kcal/mol, respectively, as well as low inhibition constants of 0.028 μM and 0.18 μM. These results indicate that these flavonoids generate interactions that enhance the stability of the target ligand–protein complex, thus contributing to the observed anti-inflammatory effect. Full article

This research introduces a novel synthetic method for introducing highly luminescent silver nanoclusters (AgNCs). The technique relies on coffee Arabica seed extraction (CSE), which is the focus of this study. Our developed and manufactured ecologically friendly approach has enhanced the selectivity of AgNCs for Hg(II) ions. The coffee extract was employed in the synthesis process to stabilize and enhance the quantity of AgNCs generated. Various advanced techniques were used to characterize the AgNCs precisely in their prepared condition concerning size, surface modification, and composition. The fluorescence quenching of the AgNCs was the mechanism via which the CSE-AgNCs reacted to the principal metal ions in the experiment. Using this sensing methodology, a very accurate and selective sensing method is provided for Hg(II) in the dynamic range of 0.117 µM to 1.4 µM, with a limit of detection (LOD) equal to 35.21 nM. Comparative research was conducted to determine how selective CSE-AgNCs are for Hg(II) ions compared to other ions. Consequently, a notable degree of selectivity of AgNCs towards these Hg(II) metal ions was achieved, allowing the sensitive detection of Hg(II) metal ions, even their interfering metal ions, in the environment. AgNCs can detect Hg(II) at acceptable values within the nanomolar range. Based on their characteristics, Hg(II) ions were detected in real samples using CSE-AgNCs. Full article

Tropaeolum majus L. is an edible plant known for its therapeutic and medicinal effects, as it possesses bioactive compounds (polyphenols, glucosinolates, fatty acids) and has various biological activities, which makes it interesting and makes it the research objective of this work. The aim of this study was to extract the phenolic compounds present in the T. majus plant by maceration and ultrasound-assisted extraction techniques using two solvents: 80% ethanol and water. In vitro digestion was performed to see how stable the phenolic components are after digestion. An LC-MS/MS instrument was used to identify and quantify the phenolic components. The highest extraction yield for the T. majus extract was obtained when 80% ethanol was used as the solvent after ultrasound-assisted extraction (32.63 ± 2.28 mg/0.5 g fresh material), while the opposite was true for the T. majus extract when water was used as the solvent and maceration as the technique (21.00 ± 3.26 mg/0.5 g fresh material). However, water extracted more phenolic components for identification. In general, the major compounds before in vitro digestion with commercial enzymes and with 80% ethanol and water as the solvents were p-hydroxybenzoic acid, protocatechuic acid, p-coumaric acid, caffeic acid and chlorogenic acid. After in vitro digestion using both solvents and extraction procedures, the stable phenolic compounds were p-hydroxybenzoic acid (>67%) and p-coumaric acid (>35%). Caffeic acid and quinic acid were not detected after digestion. The stability of certain phenolic components could influence the use of such extracts as dietary supplements with beneficial effects on human health, making them interesting for the general population. Full article

With the rise in global temperatures, it is of great significance to achieve rapid and accurate detection of greenhouse gases, such as carbon dioxide and methane. Raman spectroscopy not only overcomes the weakness of absorption spectroscopy in simultaneously measuring homonuclear diatomic molecules but also enables the simultaneous detection of multiple gases using a single-wavelength laser. However, due to the small Raman scattering cross-section and weak intensity of molecules, its application in gas detection is limited. To enhance the intensity of Raman scattering, this paper designs and constructs a multi-pass enhanced Raman spectroscopy setup. This study focuses on the effects of Raman scattering collection geometry, laser multi-pass patterns, and laser polarization relative to the Raman collection direction on signal intensity. Investigations into Raman scattering collection angles of 30°, 60°, and 90° reveal that the Raman scattering signal intensity increases as the collection angle decreases. Different laser multi-pass patterns also impact the signal, with the near-concentric linear multi-pass pattern found to collect more signals. To minimize the influence of excitation light on the signal, a side collection system is employed. Experiments show that the Raman scattering signal is stronger when the laser polarization is perpendicular to the collection direction. This study achieves overall system performance enhancement through coordinated optimization of multiple physical mechanisms, including Raman scattering collection geometry, laser multi-pass patterns, and laser polarization characteristics. The optimized setup was employed to characterize the laser power dependence for nitrogen, oxygen, and carbon dioxide detection. The results showed that the Raman scattering intensity varied linearly with the laser power of the gases, with linear fitting goodness R² values of 0.9902, 0.9848, and 0.9969, respectively. Finally, by configuring different concentrations of carbon dioxide gas using nitrogen, it was found that the Raman scattering intensity varied linearly with the concentration of carbon dioxide, with a linear fitting goodness R² of 0.9812. The system achieves a CO₂ detection limit of 500 ppm at 200 s integration time, meeting the requirements for greenhouse gas emission monitoring applications. Full article

Electroanalysis has emerged as a critical tool in the pharmaceutical industry, offering versatile and sensitive methods for drug analysis. This review explores the principles, techniques, and applications of electroanalysis in pharmaceuticals, emphasizing its role in drug development, quality assurance, pharmacokinetics, and environmental monitoring. Key electroanalytical methods, including voltammetry, potentiometry, and amperometry, are detailed along with their practical applications, such as detecting active pharmaceutical ingredients, monitoring drug metabolites, and ensuring product stability. Innovations in electrode materials and biosensors have enhanced their sensitivity and specificity, paving the way for advanced drug screening and therapeutic monitoring. Challenges like electrode fouling, selectivity issues, and regulatory constraints are discussed, along with strategies to overcome them. Future trends highlight the integration of nanotechnology, AI, and portable sensors to facilitate real-time analysis and personalized medicine. These advancements position electroanalysis as an indispensable component of modern pharmaceutical research and healthcare. Future perspectives emphasize the integration of nanotechnology and artificial intelligence (AI) to optimize experimental processes and data interpretation. This study also predicts the increased adoption of lab-on-a-chip systems and bioelectrochemical sensors to meet the growing demand for precision medicine and sustainable pharmaceutical practices. These advancements position electroanalysis as a cornerstone of pharmaceutical research, paving the way for more efficient drug development, improved patient outcomes and better environmental management. This comprehensive review underscores the transformative potential of electroanalysis in addressing the evolving challenges of the pharmaceutical industry and provides a foundation for future innovations. This review does not explicitly define the timeframe for the considered advancements. However, it discusses recent technological developments, including innovations in nanostructured electrodes, microfluidic integration, and AI-driven data analysis, indicating a focus on advancements primarily from the last few years, i.e., from 2020 to 2025. Full article