Critical reviews in analytical chemistry最新文献

The clinical and laboratory diagnosis of diseases caused by parasites - helminths and protozoa - presents several limitations. To solve these gaps, new technologies have been developed. Metabolomics is in the spotlight due to its potential for discovering biomarkers that can be useful in the clinical laboratory. In this systematic review, we evaluate the main biomarkers identified in parasitic infections by metabolomics and the perspectives for their use in the clinical laboratory. The search was conducted on PubMed, SciELO Brasil and LILACS-Bireme platforms with the combination of descriptors "metabolomics" and "parasites" or "helminth" or "protozoan". A total of 65 studies met our eligibility criteria. Plasmodium spp., Toxoplasma gondii and Schistosoma spp. were the most studied parasites. Experimental infections were more commonly performed, indicating that metabolomics is in the process of being standardized for its application in laboratory routine. Among all metabolites, amino acids were the most commonly observed in parasitic infections. In the context of metabolite detection, the majority of studies employed mass spectrometry (MS), whereas only a limited number utilized nuclear magnetic resonance (NMR) spectroscopy. The main advantage of employing metabolites in diagnostics is their early detectability, overcoming limitations imposed by the parasite's life cycle and excretion dynamics. We demonstrate the potential of metabolomics tools as alternatives to complement the conventional parasitological diagnosis.

Benzene (Bz), phenol (Phen), hydroquinone (HQ), and catechol (CAT) are volatile organic compounds (VOCs) (monoaromatic compounds) considered environmental contaminants as a consequence of human and industrial activities. Prolonged exposure affects human health, causing cancer and death. Researchers have developed and applied analytical methodologies in pretreatment samples and detection techniques to analyze these monoaromatic compounds at trace and ultra-trace concentration levels. The present study is focused on an in-depth review and comparative analysis of removal and extraction techniques, summarizing the sources (analytical matrix), interaction modes (analyte-sorbent) in extraction techniques, solid phase extraction (SPE), dispersive solid phase extraction (DSPE), magnetic solid phase extraction (MSPE), and microextraction techniques), and the determination methods (chromatographic and non-chromatographic) applied in the analysis of these monoaromatic compounds in complex matrices.

An essential excitatory neurotransmitter and metabolic intermediary, L-glutamate is vital for multiple physiological functions, such as memory, learning, and synaptic transmission. A variety of neurological and neurodegenerative conditions have been attributed to aberrant glutamate levels, emphasizing the significance of reliable and continual monitoring in biological systems. Despite their remarkable sensitivity, classical analytical techniques are sometimes compromised by intricate procedures, outrageous expenses, and constrained applicability for point-of-care applications. However, enzymatic electrochemical sensors exhibit higher selectivity; their high production costs and inconsistent functioning make them impractical for long-term use. Nonenzymatic electrochemical sensors, on the other hand, have become a viable alternative due to their superior stability, cost-effectiveness, and ease of manufacture. Recent advancements in nonenzymatic glutamate sensors are thoroughly investigated in this review, with a focus on innovative material strategies that enable enhanced sensitivity, selectivity, and detection limits over a wide concentration range. The aforementioned strategies comprise metal and metal oxide nanostructures, carbon-based platforms, and hybrid composites. It also explores substantial breakthroughs in sensor architecture, operation, and practical applications in intricate biological matrices. These enzyme-free systems' expanding prominence in contemporary biosensing technologies is illustrated by their promise in therapeutic diagnostics, neurochemical research, and point-of-care testing.

Lysergic acid diethylamide (LSD) remains a significant forensic and public health concern due to its widespread abuse and association with drug-facilitated crimes. Detecting LSD and its analogs in biological specimens, particularly postmortem matrices, presents analytical challenges stemming from its low dosage, rapid metabolism, and structural similarities to novel lysergamides. This review critically examines trends in validated analytical methods for LSD detection in forensic toxicology. A systematic review of literature from 1978 to 2025 was conducted using databases, such as PubMed, ScienceDirect, and Google Scholar. The analysis focused on reported methodologies for LSD and its metabolites across various matrices, including blood, urine, hair, oral fluid, and vitreous humor. Extraction techniques (LLE, SPE, DLLME) and analytical platforms (GC-MS/MS, LC-MS/MS, CE-MS) were compared, with emphasis on validation parameters, such as sensitivity, specificity, recovery, LOD, LOQ, matrix effects, and stability. The review identifies LC-MS/MS as the most sensitive and widely validated technique; however, discrepancies remain in matrix-specific validations and stability assessments. Challenges include the lack of certified reference materials for LSD analogs, matrix-dependent degradation, and limited methods for emerging sample types, such as dried blood spots (DBS). Few studies fully comply with modern forensic validation guidelines, limiting the reproducibility and admissibility of results in legal settings. This review highlights critical gaps in current forensic LSD detection protocols and underscores the need for standardized, validated methods applicable to diverse matrices. Future research should prioritize the development of rapid, eco-friendly, high-throughput methods capable of detecting LSD and its analogs at ultra-trace levels.

Micelles, formed from surfactants, offer alternative media for separation and detection techniques, addressing challenges such as sample complexity, method sensitivity and selectivity, analysis cost and time, and environmental impact. The critical micelle concentration (CMC) plays a pivotal role in micelle formation, with normal and reverse micelles being the main structures observed. Additionally, niosomes and liposomes contribute to sample preparation methods. Polymeric micelles exhibit a core-shell configuration, allowing for modification of their properties. Micellar systems find application in various techniques, including cloud point extraction (CPE), coacervation extraction, microextraction, and supercritical fluid extraction. CPE offers environmentally friendly and cost-effective extraction, enhancing analyte solubility and detection limits. The review further discusses the applications of micellar systems in CPE, including the analysis of hazardous organic impurities and the purification of biological compounds. Metal-CPE is explored as a method for extracting organically chelated metals. The utilization of micellar systems in sample preparation showcases their potential in improving analytical methodologies.

Alkaloids represent one of the most significant classes of compounds of natural and synthetic origin due to their pronounced biological activity and broad applications in pharmacy, medicine, and toxicology. Their determination in complex matrices such as biological fluids or pharmaceutical formulations necessitates the use of highly sensitive, selective, and reliable analytical techniques. Among these, electrochemical methods, particularly voltammetry, provide rapid, cost-effective, and precise detection. This review systematically evaluates voltammetric approaches for the determination of alkaloids and their metabolites, with a particular focus on studies published over the last two decades (2005-2025). More than 90 publications have been critically analyzed, covering voltammetric methodologies applied to 26 alkaloids and their metabolites, and highlighting key trends in electrode selection, experimental conditions, and strategies to enhance analytical performance. Special emphasis is placed on linking the electrochemical behavior of alkaloids to their chemical structure and functional group affiliation, providing insights into reaction mechanisms and detection sensitivity. The review also incorporates illustrative schemes of representative alkaloids and their electrochemical transformations, demonstrating practical applications for pharmaceutical analysis, food safety, and forensic monitoring. By critically assessing the strengths and limitations of current methodologies, this work offers a valuable resource for researchers and professionals in electroanalytical chemistry, pharmaceutical sciences, biomedicine, and toxicology, supporting the development of more selective and efficient voltammetric techniques for alkaloid investigation.

The integration of Artificial Intelligence (AI) into High-Performance Liquid Chromatography (HPLC) method development marks a paradigm shift from empirical and interpretive frameworks toward adaptive, data-driven optimization. This critical review dissects the technological evolution from traditional Design of Experiments (DoE) and retention modeling to AI-powered platforms employing machine learning (ML), deep learning (DL), and reinforcement learning (RL). While AI offers unmatched capabilities in predicting retention times, optimizing gradient conditions, and enabling real-time control, its adoption remains fragmented due to critical challenges in model interpretability, regulatory validation, and data standardization. A key insight is the persistent mischaracterization of deterministic simulators (e.g., DryLab^®, AutoChrom^™) as AI tools, which obfuscates the conceptual boundaries between mechanistic modeling and data-driven learning. Furthermore, black-box models-though powerful-suffer from poor explainability, limiting their acceptance in GxP-regulated environments. The review emphasizes the need for hybrid frameworks that merge mechanistic transparency with AI adaptability, and highlights gaps in training dataset diversity, feature engineering, and lifecycle-based model validation. Emerging trends such as explainable AI (XAI), closed-loop reinforcement learning, digital twins, and federated learning are discussed as pivotal enablers of next-generation autonomous analytical platforms. Ultimately, this review establishes that AI is not merely a computational enhancement, but a strategic imperative for scalable, reproducible, and intelligent HPLC workflows. However, its transformative potential can only be realized through ethical deployment, domain-aligned design, and interdisciplinary collaboration that aligns innovation with regulatory trust and operational relevance.

Early diagnosis is crucial not only for the treatment of diseases, but also for improving personal life quality. For these reasons, different methods and technologies are being developed for diagnosis of diseases. Sensor technologies have a very important place among these technologies. Electrochemical sensors are widely used in the diagnosis of diseases by biomarker detection due to their advantages such as being cheaper than alternative methods, simple design, sensitive and accurate detection capacity, portable and suitable for point-of-care use. Furthermore, it is perfectly suited to the analysis of pharmaceutical compounds. In recent years, sensitivity and stability of sensors have been increased by modification of sensors with different nano-bio materials. Nanosheets are one of these nanomaterials. This review highlights the structural properties and advantages of different types of nanosheets and their potential as promising tools for biomedical applications in biomarker and drug analysis.

The extensive use of organophosphorus pesticides (OPs) in agriculture has significantly contributed to enhanced crop yield and pest control. However, their persistence and high toxicity have raised serious environmental and public health concerns, including neurological disorders, endocrine disruption, and long-term ecological damage. This necessitates the development of rapid, highly sensitive, and cost-effective detection methods for monitoring OP residues in food, water, and soil. In recent years, gold nanoparticles (AuNPs) have gained considerable attention as smart sensing platforms for OP detection, owing to their remarkable optical properties, tunable surface chemistry, and excellent biocompatibility. This review highlights recent progress in AuNPs-based colorimetric and fluorometric sensors specifically tailored for detecting a broad range of OPs. The fundamental detection mechanisms, such as enzyme inhibition, aptamer binding, and aggregation-induced plasmonic shifts are thoroughly discussed to provide insights into sensor design strategies. By integrating nanotechnology with environmental and food safety frameworks, AuNP-based smart sensors represent a transformative approach for real-time, user-friendly detection of OPs. The innovations summarized in this review aim to support the development of accessible analytical tools that can be used by both professionals and non-specialists, ultimately contributing to safer agricultural practices and improved public health outcomes.