Accurate postmortem interval (PMI) estimation remains a major challenge in forensic science due to the influence of biological, environmental, and circumstantial factors on traditional methods. Mass spectrometry-based proteomics has emerged as a promising approach by enabling the analysis of time-dependent postmortem protein degradation. This systematic review (2020-2025) aims to synthesize recent advances in MS-based proteomic techniques for PMI estimation, identify reproducible protein biomarkers, and evaluate their forensic applicability. A systematic literature search was conducted in PubMed, Scopus, and ScienceDirect for studies published between January 2020 and August 2025, following PRISMA guidelines. Search terms included "postmortem interval," "time since death," "protein degradation," "forensic proteomics," and "mass spectrometry." Original research studies using human or animal tissues and MS-based proteomic methods for PMI estimation were included. Reviews, non-English publications, and duplicates were excluded. Study selection and data extraction were performed independently by two reviewers. Twenty-one studies met the inclusion criteria: ten on human samples, ten on animal models, and one on combined datasets. Skeletal muscle was the most frequently analyzed tissue, followed by bone, liver, heart, and gingiva. Several proteins, including tropomyosin, GAPDH, desmin, tubulin, and eEF1A2, demonstrated reproducible, time-dependent degradation patterns. Analytical approaches such as GC-MS, LC-MS/MS, and MALDI-MS enabled sensitive detection of postmortem proteomic changes. However, substantial variability was observed across studies in tissue selection, postmortem conditions, and analytical protocols, highlighting the need for standardization. Mass spectrometry-based proteomics offers a sensitive and objective approach for PMI estimation and identifies several candidate protein biomarkers with potential forensic utility. Broader application requires standardized protocols, larger validation studies in human samples, and the development of multi-protein marker panels. With further refinement, proteomic profiling could serve as a robust complement to conventional PMI estimation methods in routine forensic practice.
Conventional culture-based and microscopic approaches yield limited information about the diversity, content, and real-time behaviour of biological aerosols. In recent years, mass spectrometry (MS) and molecular biotechnology have evolved as powerful and complementary analytical methods for detecting, identifying, and characterising air biological particles. This study critically reviews recent improvements in MS-based techniques for analysing bioaerosol chemical markers, proteins, metabolites, and toxins, including MALDI-TOF MS, GC-MS, LC-MS/MS, and real-time aerosol mass spectrometry. In parallel, contemporary advances in molecular biotechnology, including as PCR-based assays, metagenomics, and MS-driven proteomics and metabolomics, are described, with a focus on atmospheric applications. Special emphasis is placed on integrated analytical workflows that combine MS with molecular techniques to improve specificity, sensitivity, and source attribution. The current issues of low biomass concentrations, sampling artefacts, data interpretation, and standardisation are discussed, and future perspectives on portable MS systems, multi-omics integration, and AI-assisted data processing are presented. This study offers a thorough analytical chemistry viewpoint on next-generation methodologies for monitoring bioaerosols and promotes the development of enhanced instruments for assessing air quality and protecting human health.
Drugs play an indispensable role in the fields of medicine， agriculture， and animal husbandry. However， their long-term and improper use may lead to drug residues in food， the environment and organisms， posing a potentially serious threat to human health and the ecological environment. For instance， antibiotic residues may induce bacterial resistance， pesticide residues may cause neurotoxicity， and hormone drugs may interfere with the endocrine system. Therefore， developing sensitive and accurate detection methods for drug residues has become an important prerequisite and current hot topic in drug research. Meanwhile， the complicated matrices and low contents of the residues make it necessary for the widely used chromatography/mass spectrometry （MS） determination technologies to be coupled with efficient sample pretreatment procedures. Molecularly imprinted solid-phase microextraction （MI-SPME） technology combines the rapidity， high efficiency and solvent-free characteristics of SPME， and the specific recognition and selective adsorption capabilities of molecularly imprinted polymers （MIPs）， and shows significant advantages in the highly selective separation and enrichment of drug residues in complex samples. In recent years， the MI-SPME technology has become a research hotspot in the field of drug residue detection.This work systematically reviews the research progress since 2019 on the application of MI-SPME coupled with chromatography/MS in drug residue detection across food safety， environmental monitoring and biomedical fields. First， this work introduces in detail on the working principle and operation process of SPME technology. SPME achieves efficient enrichment of target analytes through the selective adsorption of the stationary phase-coated fibers， offering simplicity， speed， minimal solvent use， and compatibility with analytical instruments such as chromatography/MS.Next， the review focuses on elaborating the preparation methods and new technologies and strategies of MIPs. The traditional methods for preparing MIPs mainly include free radical polymerization， in-situ polymerization and sol-gel methods. However， traditional MIPs have defects such as template leakage risk， limited binding ability， and irregular material morphology， which restrict the application range. To this end， researchers have developed a series of novel preparation technologies and strategies， such as surface imprinting， nanoimprinting， dummy template imprinting， multi-template imprinting， multifunctional monomer imprinting and stimulus-response imprinting. These technologies and strategies have significantly enhanced the recognition and enrichment ability of MIPs for trace drug residues in complex samples by optimizing their structures and performances.To meet the requirements of different sample types and analytical instruments， MI-SPME media need to be designed into specific technical configurations through chemical or physical methods. This review summarizes six different MI-SPME device modes： MIPs-coated fiber SPME， MIPs in-tube SPME （IT-SPME）， MIPs stir bar sorptive extraction （SBSE）， MIPs dispersive SPME （DSPME）， MIPs thin-film SPME （TFME）， and MIPs in-tip SPME. Each mode offers unique advantages for the separation， enrichment and determination of drug residues in real samples. For example， the coated fiber SPME is simple to operate and suitable for direct immersion or headspace extraction of liquid samples； IT-SPME features miniaturization and automation， with excellent compatibility with chromatographic and mass spectrometric systems； DSPME achieves efficient separation and enrichment by dispersing adsorbents directly into sample solutions.Then， the applications of MI-SPME in the fields of food safety， environmental monitoring and biological medicine are summarized， highlighting typical research examples. In the field of food safety， MI-SPME can be used to detect pesticide residues， veterinary drug residues， and drugs for human use in fruits， vegetables， animal meats and dairy products. In environmental monitoring， it can be used for the detection of drug residues in aqueous environments and soil. In the field of biological medicine， it can be used for the analysis of drug residues in biological samples such as plasma， urine， and serum.Although the MI-SPME technology has shown great potential in drug residue detection， it still faces some challenges. For example， the preparation process of MIPs needs to be further optimized to improve their selectivity and stability； the development and application of new materials （such as graphene， metal-organic frameworks） for composite MIPs still need to solve problems such as high cost and complex processes； the integration of MI-SPME technology and automated equipment is also a bottleneck and important direction for future development. Looking ahead， with the advancement of green chemistry principles and point-of-care testing technologies， MI-SPME is expected to play an even greater role in drug residue detection. It will provide more efficient and precise technical support for food safety， environmental monitoring， and biomedical research. 药物广泛应用于医疗、农业及畜牧业领域，然而长期和不规范使用致使其在食品、生物及环境中残留，对人类健康和生态环境构成潜在严重威胁。发展灵敏、精准的药物残留检测方法已成为药物研究的重要前提和当前热点。然而，待测样品基质复杂且药物残留水平极低，因此即便是色谱/质谱这类高效技术，也仍需依赖高效的样品前处理环节。分子印迹固相微萃取（MI-SPME）兼顾固相微萃取快速、高效、无溶剂的优点，以及分子印迹聚合物（MIPs）的特异性识别与选择性吸附能力，在复杂样品药物残留的高选择性分离和萃取方面展现出显著优势。本综述聚焦2019年以来MI-SPME结合色谱/质谱用于药物残留测定的研究进展。首先，介绍了SPME的原理与操作流程，以及用于SPME的MIPs的制备方法和新技术/策略，包括自由基聚合、原位聚合、溶胶-凝胶聚合、表面印迹、纳米印迹、虚拟模板印迹、多模板印迹、多功能单体印迹和刺激响应型印迹。然后，概述了6种不同的MI-SPME装置模式，即基于MIPs的涂层纤维式、管内式、搅拌棒式、分散式、薄膜式和尖端式SPME。随后，重点总结了MI-SPME结合色谱/质谱在食品安全、环境监测、生物医药领域的典型应用。最后，讨论了MI-SPME在药物残留测定中面临的挑战，如MIPs的制备与优化、MI-SPME模式创新、新材料开发与成本控制、自动化集成等，并对MI-SPME制备和药物残留检测应用的前景进行了展望。
This review traces the first 20 years of Orbitrap mass spectrometry as a mainstream high‑resolution and accurate‑mass (HR/AM) technology. It outlines the historical development of the Orbitrap analyzer, the evolution of major instrument families, and the key technological innovations that enabled its widespread adoption. Particular emphasis is placed on hybrid and Tribrid architectures, democratization of HR/AM through benchtop platforms, extension to high‑mass and native analysis, and integration with ion mobility, advanced fragmentation methods, and emerging applications such as structural biology, isotope ratio measurements, and space research. The review concludes with a perspective on future directions and the anticipated role of Orbitrap instrumentation as the core workhorse in analytical laboratories worldwide.
Nitrooxidative stress, driven by excess reactive nitrogen species like peroxynitrite, contributes to the pathogenesis of many chronic diseases. Among its molecular footprints, 3-nitrotyrosine (3NT) has emerged as a biologically relevant marker of protein nitration. Its accumulation reflects oxidative damage and altered protein function, positioning it as a promising biomarker. Proteomics has advanced our understanding of nitrooxidative stress and its clinical implications. The integration of high-resolution MS with immunoaffinity and structural modeling enables precise mapping of nitration sites and functional interpretation. However, limitations such as low stoichiometry, ion suppression, and antibody cross-reactivity still constrain the field. Emerging computational predictors and miniaturized platforms offer promising avenues for expanding the clinical utility of 3NT. Future efforts should focus on standardizing workflows, validating site-specific modifications, and translating proteomic insights into diagnostic and therapeutic strategies. This review outlines the biochemical mechanisms of 3NT formation, emphasizing peroxynitrite-dependent and heme peroxidase-mediated pathways. Proteomic strategies for detecting and quantifying nitrated proteins are discussed, including mass spectrometry workflows, enrichment techniques, and immunodetection. Challenges in site-specific identification, antibody specificity, and ionization-induced fragmentation are addressed. Disease-specific patterns of 3NT accumulation in neurodegenerative, cardiovascular, and oncologic contexts are highlighted, along with in silico prediction of nitration sites. Despite significant methodological advances, key limitations such as low nitration stoichiometry, antibody cross-reactivity, and ionization-dependent artifacts continue to challenge confident site-specific analysis of 3-nitrotyrosine. Future progress will depend on improved enrichment strategies, standardized mass spectrometry workflows, and the integration of computational prediction tools with experimental validation. Addressing these gaps will be essential for translating nitrotyrosine profiling into robust mechanistic and clinical applications.
Desorption electrospray ionization-mass spectrometry imaging (DESI-MSI), as an ambient ionization technique, enables label-free, matrix-free, and minimal sample pretreatment molecular mapping of biological surfaces under atmospheric pressure. Since its inception in 2004, DESI-MSI has evolved through innovations in ionization sources, sprayer design, and data processing, allowing high-throughput visualization of spatial distributions for diverse molecules (e.g., lipids, metabolites, drugs) in tissues. This review systematically reviews the fundamental principles, instrumental configurations, and critical technical parameters of DESI-MSI. Recent technological advancements, specifically the development of nanoscale DESI achieving spatial resolutions under 50 μm and the implementation of additive-enhanced solvents such as metal ions and reactive anions to enhance sensitivity and specificity, are critically evaluated. Furthermore, the multidisciplinary applications of DESI-MSI are comprehensively examined: (1) in biomedicine, it facilitates cancer margin delineation (e.g., breast, prostate tumors) and neuropathological biomarker discovery; (2) in pharmaceutical research, it enables in situ drug distribution analysis and tissue metabolism profiling; (3) in plant sciences, it supports spatial mapping of phytochemicals, pesticide residues, and host-microbe interactions. Nevertheless, despite its advantages in real-time analysis and nondestructive sampling, inherent challenges persist, including limited spatial resolution (~200 μm) and complexities in interpreting complex datasets. Future work should emphasize the integration of multimodal imaging approaches, machine learning-driven data processing pipelines, and clinical translation strategies for intraoperative diagnostics. Collectively, this study positions DESI-MSI as a transformative spatial metabolomics tool with expanding potential in clinical diagnostics and high-throughput drug screening.
Antibody-drug conjugates (ADCs) are innovative drugs composed of cytotoxic molecules (payload) linked to antibodies, that selectively target and kill cancer cells upon internalization. In vivo, ADCs exist as intact molecules, naked antibodies, or released, unconjugated (linker-)payload. Accurate quantification of these entities is crucial for understanding ADCs pharmacokinetics. Ligand-binding assays are commonly used to measure ADC concentrations and total antibody concentrations, whereas LC-MS/MS is used to analyze the payload. Due to limitations in ligand-binding assays, this review focuses on quantitative LC-MS methods for the different ADC entities. Quantitative LC-MS assays were described for all ADC entities, available from full manuscripts and regulatory reviews of 12 ADCs evaluated by the European Medicine Agency, by January 2025. The review summarized sample pre-treatment, chromatography, mass spectrometry, validation, and stability data for each LC-MS method. Overall, critical details were often missing, particularly concerning sample pre-treatment, validation criteria, and sample stability. In conclusion, LC-MS quantification of ADC entities is feasible but current methods lack sufficient detail. Our review highlights the need for further research to develop reliable LC-MS assays for ADCs. This review may serve as a starting point and outlines key factors to consider in future LC-MS method development.
Summary: Mass spectrometry (MS) is a cornerstone technology in modern molecular biology, powering diverse applications across proteomics, metabolomics, lipidomics, glycomics, and beyond. As the field continues to evolve, rapid advancements in instrumentation, acquisition strategies, machine learning, and scalable computing have reshaped the landscape of computational MS. This perspective reviews recent developments and highlights key challenges, including data harmonization, statistical confidence estimation, repository-scale analysis, multi-omics integration, and privacy in clinical MS. We also discuss the increasing importance of machine learning and the need to build corresponding literacy within the community. Finally, we reflect on the role of the Computational Mass Spectrometry (CompMS) Community of Special Interest of the International Society for Computational Biology in supporting collaboration, innovation, and knowledge exchange. With MS-based technologies now central to both basic and translational research, continued investment in robust and reproducible computational methods will be essential to realize their full potential.
Florasulam is a triazolopyrimidine herbicide that controls broadleaf and grass weeds in cereal crops and turf. It acts as an acetolactase synthase (ALS) inhibitor, blocking the synthesis of essential amino acids required for plant growth. Due to its potency, florasulam is effective at very low application rates making it cost-effective with reduced environmental off-target effects. As is common practice, florasulam has been subject to periodic reviews by regulatory agencies during re-registration requirements since its introduction into the market in 1998. The goal of these reviews is to ensure that the herbicide carries out its intended use without creating adverse side effects to humans and the environment. Since scientific methods are continually evolving and being developed, global regulatory agencies can require additional studies to address data gaps for pesticide renewals. During this re-registration process for florasulam, new environmental fate studies were conducted to meet new European Food Safety Authority (EFSA) guidelines. Consequently, florasulam-[triazole(13C,15N2)] and florasulam-[phenyl(13C6)] stable isotopes were synthesized to support the re-registration process.
Top-down proteomics (TDP) characterizes proteoforms in cells, tissues, and biofluids, in discovery mode and on a global scale, requiring analytical tools with high peak capacity for proteoform separation and high sensitivity for proteoform detection, given the extremely high proteoform complexity and wide proteoform concentration dynamic range. Capillary electrophoresis-mass spectrometry (CE-MS) has become an essential tool for TDP in various biomedical applications due to its excellent separation efficiency and high measurement sensitivity for proteoforms, especially with continued improvements in reproducibility and robustness over the last several years. This review summarizes the technological advancements and biomedical applications of CE-MS-based TDP in the last 3 years (2023-2025) and offers brief perspectives at the end.
Trapped ion mobility spectrometry (TIMS) is a highly versatile alternative to the more conventional drift tube ion mobility spectrometer (DTIMS). In TIMS, ions are analyzed using an electric field that holds ions stationary against moving gas. In the basic TIMS, ions are accumulated and trapped in the electric field and then eluted over time according to their collision cross section (CCS) as the strength of the electric field is scanned down. The resultant small size and low operating voltage of TIMS compared to prior approaches make it ideal for hybridization with mass spectrometry. Since its introduction and coupling with Time-of-Flight Mass Spectrometry (TOFMS) in 2011, TIMS has been widely and successfully applied in various bioanalytical fields, including proteomics, glycomics, metabolomics, lipidomics, and native mass spectrometry. In particular, the first commercial TIMS-MS instrument introduced by Bruker Daltonics Inc. (timsTOF), launched in 2016, quickly shined as one of the main reference instruments in bottom-up proteomics. The increased peak capacity, resulting from the additional dimension of separation-that is, mobility-leads to mass spectra of reduced complexity and a greater depth of peptide identification. In this retrospective, different designs and operational modes of TIMS will be presented with a focus on the advantages, potentials and challenges of this technology within the fields of the omics sciences, spanning from metabolomics to structural biology, including single cell analysis. Additionally, the newest platforms utilizing TIMS technology will be introduced, with a focus on future applications and direction of the technology.
Normalization is a critical step in metabolomics studies to ensure the quality of metabolomics data, reduce quantitative variability, and enable confident and robust statistical analyses. From an analytical perspective, metabolomics normalization encompasses multiple distinct processes. Broadly, normalization can refer to (1) sample normalization, which mitigates variation due to differences in total metabolite amounts; (2) signal correction, which reduces batch effects, instrumental fluctuations, and retention time drifts during data collection; and (3) statistical transformation and scaling, which prepare data for statistical analyses. Each of these normalization processes addresses unique analytical and bioinformatic needs, but the term "normalization" is often used broadly, leading to confusion in method development, selection, and implementation. Moreover, many well-established normalization algorithms in genomics and proteomics are not always transferable to metabolomics due to differences in analytical workflows and data characteristics. To address these issues, we believe it is crucial to gain a clear understanding of the purpose of each normalization type, its appropriate implementation, and the evaluation criteria. This perspective outlines the key normalization tasks in metabolomics, reviews existing tools, and provides recommendations for their appropriate applications. We also highlight two critical considerations: (1) selecting an appropriate missing value imputation method and (2) establishing strategies to evaluate and compare normalization outcomes. The goal of this work is to provide recommendations for the rigorous development and implementation of normalization techniques in metabolomics, thereby enhancing analytical accuracy and precision, improving data interpretability, and ultimately advancing the biological insights gained from metabolomics studies.
Nitazenes are highly potent synthetic opioids increasingly detected in illicit drug markets and associated with significant public-health and forensic challenges. Rapid identification tools, such as lateral-flow immunoassay (LFA) test strips, are widely used in harm-reduction and forensic settings; however, their analytical performance for detecting nitazene-class opioids remains insufficiently characterized. This systematic review aims to evaluate the analytical performance of nitazene LFA test strips when used on seized drug materials and laboratory-prepared solutions, compared with mass spectrometry (MS) reference methods (liquid chromatography-tandem mass spectrometry (LC-MS/MS), liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS), and gas chromatography-mass spectrometry (GC-MS)). Outcomes included sensitivity, specificity, cross-reactivity, limits of detection, and operational interferences. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Diagnostic-Test Accuracy (PRISMA-DTA) guidelines and applying Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) framework, five eligible studies were synthesized, including laboratory evaluations and field-paired analyses. Only studies evaluating LFAs on drug materials - not biological samples - were included. Our results showed that two studies provided paired LFA-mass-spectrometry datasets. Under concentrated preparation conditions (10 mg/1 mL), LFAs demonstrated complete agreement with MS. Sensitivity declined at higher dilution (10 mg/5 mL), while specificity remained high. Analytical detectability varied substantially across nitazene analogues, with N-desethyl-metonitazene exhibiting the greatest sensitivity. Matrix effects, caffeine adulteration (~300 µg/mL), solvent concentrations >10% acetonitrile, and elevated temperatures all reduced line intensity or hindered wicking.  In conclusion, Nitazene LFA test strips show potential value as preliminary material-based screening tools in forensic and harm-reduction applications, but should not be interpreted as confirmatory. Their use requires standardized protocols, conservative interpretation rules, and mandatory mass-spectrometric confirmation. Large, independent, multi-site analytical validation studies are needed to establish reliability, optimize field use, and support integration into drug-checking programs.
Spatial metabolomics enables in situ mapping of metabolites within tissues, which is crucial for understanding physiological and pathological processes, including tumor metabolic reprogramming, neurodegenerative disease mechanisms, and drug distribution in target organs. Mass spectrometry imaging is a powerful tool for spatial metabolomics research. With the advancement of mass spectrometry and nanotechnology, surface-assisted laser desorption/ionization mass spectrometry imaging (SALDI-MSI) that uses nanomaterials instead of organic matrices has emerged, effectively overcoming the inherent limitations of traditional methods in small-molecule metabolite analysis, such as matrix background interference and uneven crystallization. Consequently, SALDI-MSI has become a highly promising analytical technique. This article systematically reviews the latest progress of nanomaterial-enhanced SALDI-MSI in spatial metabolomics. It first introduces various mass spectrometry imaging techniques used in spatial metabolomics, explains their working principles, and compares their advantages and disadvantages. Then, the fundamental mechanisms of SALDI are described to provide a theoretical basis for nanomaterial design, followed by a discussion of common sample preparation methods in SALDI-MSI. The review focuses on the design strategies and application cases of typical nanomaterials for SALDI-MSI, including metal/metal oxide nanoparticles, carbon-based materials, thin-film materials, and nanostructured silicon platforms. Finally, challenges and future directions in standardization, reproducibility, and quantification are discussed. This review aims to provide a reference for the rational design of high-performance SALDI substrates and to promote the development of spatial metabolomics.
This review provides a comprehensive summary of recent advancements in metabolomic analysis within traditional Chinese medicine (TCM) for the treatment of diabetes mellitus. It focuses on the standardized profiling of syndrome-specific metabolites, the identification of bioactive compounds using high-throughput techniques, and the elucidation of mechanisms based on metabolic pathways. The review aims to establish a reproducible analytical framework for TCM metabolomics, which includes identifying active compounds, clarifying mechanisms of action, and assessing therapeutic efficacy. By synthesizing the current body of evidence, this work seeks to provide a scientific foundation for future research, enhance the integration of metabolomics with TCM theory, and support the modernization and global acceptance of TCM in diabetes care. Ultimately, it addresses key challenges, such as the subjective nature of syndrome diagnosis and the complexity of multicomponent interactions. A systematic review of the peer-reviewed literature was conducted using PubMed, Web of Science, CNKI, and Wanfang Data for studies published up to 2025. The review included original research, reviews, and clinical trials that utilized metabolomic techniques (LC-MS/MS, GC-MS, and 600 MHz NMR) and standardized workflows (sample preparation, derivatization, instrument analysis, and data processing) in diabetic models. The qualitative synthesis focused on high-throughput analytics, multivariate statistics (principal component analysis/partial least squares-discriminant analysis [PCA/PLS-DA] with 7-fold cross-validation and CV-ANOVA, p < 0.05), and metabolite identification via the human metabolome database (HMDB)/METLIN metabolite database (mass error < 5 ppm); furthermore, these data were integrated with network pharmacology or multiomics approaches. Metabolomics has revealed distinct metabolic profiles for TCM syndromes. Specifically, the Qi-Yin deficiency (QYD) syndrome is associated with altered levels of L-glutamate (variable importance in projection [VIP] > 1, p < 0.05) and arachidonic acid (limit of detection [LOD] = 0.01-0.1 ng/mL), as well as the accumulation of turbid toxins involving pantothenate and CoA biosynthesis. Key hypoglycemic bioactive compounds, such as epicatechin and berberine, were identified using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS), with a mass error of < 5 ppm. Mechanistic studies have shown that TCM works through multiple pathways, including improving insulin resistance (e.g., mulberry leaves modulating amino acid/lipid metabolism); protecting β-cells (e.g., timosaponin BII restoring phosphatidylserine levels); regulating glycolipid metabolism (e.g., Huanglian decoction influencing energy-related metabolites); modulating gut microbiota (e.g., Gegen Qinlian decoction altering bile acids and short-chain fatty acids); and preventing complications (e.g., Sophora flavescens affecting oxidative stress through glycerophospholipid metabolism). Furthermore, metabolomics enabled efficacy comparisons, highlighting improved outcomes with nanoformulations or exercise-TCM combinations. Metabolomics provides a powerful approach to objectively assess TCM syndromes, clarify multitarget mechanisms, and comprehensively evaluate efficacy in diabetes treatment, which aligns well with TCM's holistic principles. Despite existing challenges, such as the lack of standardized TCM syndrome classification and the complexity of metabolomic data (e.g., overlapping metabolite signals and multipathway crosstalk), future studies should focus on rigorous experimental designs, standardized protocols, and multiomics integration to promote biomarker discovery, personalized TCM, and its global integration into diabetes management.
Capillary electrophoresis-sodium dodecyl sulfate (CE-SDS), a high-resolution and high-sensitivity analytical technique, is an essential tool for analyzing the critical quality attributes (CQAs) of monoclonal antibodies (mAbs) and their derivatives, including antibody-drug conjugates (ADCs) and bispecific antibodies (bsAbs). This study systematically reviews the applications of CE-SDS in analyzing the purity and fragments of mAbs, characterizing positional isomers of ADCs, and identifying mismatch impurities in bsAbs. Focusing on the core technical challenge that CE-SDS cannot be directly coupled with mass spectrometry (MS) for fragment structure identification, the study summarizes technical solutions based on indirect identification approaches and offline/online coupling strategies with Capillary Zone Electrophoresis-Mass Spectrometry (CZE-MS). In addition, from a regulatory science perspective, this study details the key considerations for method validation, establishment of quality standards, and preparation of regulatory submissions for CE-SDS. This study aims to provide a systematic reference for the development and quality control of related biopharmaceuticals, highlighting future development directions, including high-throughput analysis, coupling techniques, and degradation prediction.
Novel psychoactive substances (NPS) have rapidly diversified the global drug market, creating escalating challenges for forensic toxicology and mortality surveillance. While regional reports have described fatal NPS involvement, the global prevalence and temporal trends of NPS detection in fatalities are insufficiently quantified. A search following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines was performed in PubMed, Embase, Scopus, and Web of Science (January 2008-May 2025), supplemented by screening a repository of 96 primary studies, to find studies with toxicologically confirmed detection of NPS in fatalities. We included autopsy or surveillance datasets with analytical confirmation by gas chromatography-mass spectrometry, liquid chromatography-tandem mass spectrometry (LC-MS/MS), high-resolution mass spectrometry, or nuclear magnetic resonance spectroscopy. The primary outcome was the proportion of NPS-positive cases among all forensic cases undergoing comprehensive toxicological analysis. Random-effects meta-analysis estimated pooled proportions and subgroup trends. Heterogeneity, I², meta-regression by year, region, and analytical method, and publication bias by Egger's test were assessed. Of 621 records screened (525 from databases and 96 from supplementary repository), 125 studies met the inclusion criteria and 86 contributed to quantitative synthesis. The pooled global proportion of fatalities with NPS detection was 7.8% (95% CI: 6.2%-9.8%), with a marked increase from 2.5% (2008-2014) to 9.3% (2015-2025). Synthetic opioids predominated (3.4%), followed by cathinones (2.1%) and cannabinoids (1.6%). North America showed the highest pooled proportion (9.4%), followed by Europe (6.9%). Meta-regression showed that later study year (β = 0.12, p = 0.004) and LC-MS/MS use (β = 0.17, p = 0.019) independently predicted higher detection proportions. Publication bias was not significant (p = 0.27). The detection of NPS in fatalities has increased globally, driven by potent synthetic opioids and enhanced analytical detection. Enhanced forensic capacity, standardization of toxicological panels, and real-time surveillance need to be developed to mitigate the emerging global risks.
Human saliva is a heterogeneous bodily fluid with a complex composition, which contains antibodies, proteins, and viruses, making it applicable in clinical diagnosis. There are several advantages of the analysis of saliva samples over other biofluids, including a non-invasive and simple collection procedure for extraoral detection. Biomarker or pathogen detection in saliva can be performed with various methods: mass spectrometry, PCR, ELISA, electrochemical, and optical methods such as fluorescence, SPR, and SERS. The early detection of cancer and other disease biomarkers, as well as infectious agents, can be crucial for effective treatment and minimization of mortality from those diseases. The following paper reviews extraoral detection techniques to identify the most sensitive methods for diagnosing early and asymptomatic patients. The LODs collected and tabulated from 149 analytical papers, alongside the sensitivity, specificity, and sometimes the area under the curve (AUC) tabulated from 118 clinical studies, have all become parameters for the comparative quantitative analysis. Based on the limited but substantial number of analytical studies on the detection of cortisol in saliva (29), the electrochemical platforms demonstrated the highest sensitivity, with a geometric mean LOD of 11 pM. Within these methods, voltametric ones showed the best performance with 6 pM geometric mean LOD. Electrochemical techniques are then followed by immunoassay- and mass spectrometry-based platforms, with corresponding geometric average LOD values of 39.1 and 171 pM, respectively. However, clinical outcomes are at least as meaningful as LOD values. In terms of clinical analysis, ELISA and direct-SERS outperformed other methods, achieving balanced accuracy of approximately 87% and AUC values of 0.96 for direct SERS and 0.86 for ELISA. MS and PCR followed closely, with balanced accuracies around 84%. While the direct SERS is not yet widespread in clinical applications, its potential can be forged if the standardization issue is addressed.
Emodin, rhein, aloe-emodin, physcion, and chrysophanol are five representative anthraquinones (AQs) in rhubarb. They exhibit diverse pharmacological activities, including antitumor, anti-inflammatory, antibacterial, and antioxidant effects, and are widely used in traditional Chinese medicines (TCMs), dietary supplements, and functional foods. However, with their increasing application, the potential toxicity of AQs has become increasingly prominent. Moreover, their toxic mechanisms and metabolism-related toxicities remain incompletely elucidated, which has limited their clinical development and safe application to a certain extent. This paper systematically reviews the toxicological characteristics, toxic mechanisms, and metabolism-related toxicity of the five major AQs in rhubarb, and explores research strategies for AQs toxicity based on advanced technologies, aiming to provide new insights and references for their safe application and further studies. A comprehensive search was conducted in PubMed, Google Scholar, Web of Science, and CNKI for peer-reviewed research articles and reviews published in the past 15 years. The research progress of the five major rhubarb AQs and the applications of cutting-edge technologies in their toxicity studies were summarized. The toxicities of AQs mainly target the liver, kidney, heart, reproductive system, and nervous system. Their toxic mechanisms involve multiple pathways, including mitochondrial apoptosis, oxidative stress, death receptor pathway, endoplasmic reticulum (ER)-related apoptosis, caspase-dependent apoptosis, autophagy, inflammation, DNA damage, and bilirubin metabolism. The in vivo metabolism of AQs is complicated, and both Phase I and Phase II metabolites are linked to toxicity. The diverse metabolites can interconvert and undergo various reactions in vivo, posing great challenges for toxicity research. In the future, the application of advanced technologies, such as multi-omics and single-cell sequencing, artificial intelligence (AI) and computer simulation, microfluidics, and mass spectrometry imaging (MSI) will provide strong support for accurately elucidating toxic mechanisms, enabling toxicity prediction, and optimizing detoxification strategies, and will become effective approaches to advance toxicity studies of rhubarb AQs. The intrinsic toxicity and complex metabolic processes of rhubarb AQs restrict their clinical application. Current studies on the toxic mechanisms, metabolism-toxicity relationships, and multifactorial regulation of AQs still require further in-depth investigation. Future research should focus on the metabolism-toxicity relationship, key toxic targets, and detoxification strategies of AQs, and establish an integrated toxicity research system combined with advanced technologies, so as to provide robust support for the safe application and clinical development of rhubarb AQs.