搜索结果：Analytical Chemistry

Landfill gas (LFG) is primarily composed of CH4 and CO2, together with a wide range of trace compounds generated during the decomposition of domestic waste in landfills. During energy production from LFG, trace compounds such as sulfur-containing compounds and siloxanes cause the formation of metal oxide-based deposits. However, studies integrating gas composition with deposit chemistry, phase identification, and multi-technique validation on the same samples remain limited. This study aims to establish the linkage between LFG composition and deposit formation, focusing on the transformation of organometallic compounds into oxide phases. A multi-analytical approach including scanning electron microscopy with energy-dispersive spectroscopy, X-ray diffraction, inductively coupled plasma optical emission spectroscopy, wavelength-dispersive and energy- dispersive X-ray fluorescence was applied to characterize deposits collected from engine components. In addition to the organometallic compounds identified in standard LFG analyses at the study site, other compounds reported in the literature and detected through gas analysis were also considered. The results demonstrate that Si and S are directly associated with LFG constituents, while Ca is linked to lubricant oil additives, and metal(loid)s (Sb, Sn, As) are related to organometallic compounds present in LFG. A broad spectrum of trace elements was identified, providing comprehensive elemental coverage and highlighting potential occupational health risks associated with elements such as As, Cr, Ni, Ba, Zn, and Zr. By integrating gas composition, and deposit chemistry, this study provides new insights into deposit formation and supports the development of improved gas quality control strategies and mitigation approaches for both engine performance and health protection.

The extracellular matrix (ECM) is being investigated as an innovative artificial ovary 3D scaffold for fertility preservation in humans and animals who are unable to utilise traditional reproductive biotechnologies. This review provides an overview of the ovarian matrisome in native and decellularized ECM (dECM) of pigs, mice and humans. A substantial proportion of components identified in porcine ovarian dECM were also reported in native human ECM, particularly collagens and proteoglycans, while comparisons with murine ovarian ECM (native and decellularized) reveal shared components in these groups as well as in glycoproteins. This indicates a considerable degree of shared core ECM proteins across species and positions porcine ECM as a promising matrix for interspecies applications, including those aimed at humans. The ovarian ECM may provide a favourable environment for the development of oocytes, follicles, or cells from different species during follicle culture or in vitro gametogenesis. However, its composition can vary depending on the ovarian region, the age of the organ, and the decellularization protocol applied. Despite these variations, studies using porcine ovarian dECM have demonstrated follicular growth, oestradiol secretion, embryo formation, and live births in murine models, supporting its potential role in ovarian function recovery. Nevertheless, optimisation of decellularization protocols and rigorous analytical evaluation remain necessary to assess process efficiency and ECM preservation, improving the estimation of its functional efficacy in fertility preservation. In conclusion, porcine ovarian dECM represents a promising natural alternative to synthetic support matrices for fertility preservation techniques in patients and animals.

Genomic profiling of patients for genetic variants that modify the effect of specific medications has many benefits, including the possibility of avoiding toxicities and ensuring an adequate effect of the medication. Our intention was to develop a comprehensive, high-quality pharmacogenetic test panel for clinical use with a less expensive technique than high-coverage whole-genome sequencing. We designed a targeted pan-pharmacogenomics (pan-PGx) panel based on Twist probe capture by applying Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines and aggregated data from PharmVar, PharmGKB and IPD-IMGT/HLA. Sequencing was performed using Illumina short-read sequencing. In-house computer scripts combined with freely available software, particularly PharmCAT, were used for the analysis. Validation was largely performed with Genetic Testing Reference Materials Coordination Program (GeT-RM) DNA from Coriell when applicable, otherwise with DNA from clinically well-documented material. The validation showed that the method is both accurate and well-suited for large-scale clinical testing of pharmacogenes. Calls of single-nucleotide variants, InDels, and structural/hybrid genes and copy number variants in all major pharmacogenes could be translated into dose recommendations, making the test appropriate for clinical use. This assay is suitable for clinical use and pharmacogenomic-guided drug treatments.

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.

The adulteration of weight-loss dietary supplements with illegal additives poses significant risks to public health, it is necessary to develop sensitive and reliable analytical methods for their monitoring. A scoping review was conducted using PubMed, ScienceDirect, and Web of Science databases (between 2012 and 2025). This review summarizes the public health risks posed by illegal additives in weight-loss dietary supplements and evaluates advanced analytical solutions. It introduces in detail the typical enrichment techniques, such as solid-phase extraction (SPE), molecularly imprinted solid-phase extraction (MISPE), dispersive solid-phase extraction (d-SPE), magnetic solid-phase extraction (MSPE) and solid-phase microextraction (SPME), which use porous materials to efficiently enrich targets from complex matrices. Correspondingly, we compare the principles, advantages (e.g., high sensitivity of chromatography-mass spectrometry, rapid screening capability of immunoassays, and portability of electrochemical sensors) and limitations (e.g., interference, cost and portability) of major detection technologies. Future development should focus on intelligent platform, multi-technology integration and artificial intelligence to improve monitoring and safeguard public health. Notably, the advantageous features of the enrichment and detection techniques summarized in this review also provide significant guidance for research and applications in related fields such as chemistry, food and pharmacy.

Migraine is recognized as a major neurological disorder characterized by substantial global prevalence, affecting more than one billion individuals worldwide. In 2025, the FDA approved a novel fixed-dose mixture of rizatriptan (RIZ) and meloxicam (MEL) for managing acute migraine. In response, an affordable and sensitive TLC-densitometric strategy was developed for determining MEL and RIZ concurrently in their pure powders and recently approved formulation. The separation process was achieved on a 60F254 silica gel stationary phase employing a moving phase of methanol, ethyl acetate, and 25% aqueous ammonia (9:1:0.05, by volume). The medications were UV-scanned at 254.0 nm. Key chromatographic parameters influencing strategy performance were systematically investigated and optimized. Our strategy was fully validated, consistent with ICH standards, and demonstrated satisfactory linearity over concentration ranges of 2-12 μg/band for MEL and 1-6 μg/band for RIZ, along with good accuracy, robustness, precision, and selectivity, enabling reliable quantification without intrusion from common excipients. Furthermore, the environmental sustainability and overall analytical performance of our strategy were comprehensively assessed utilizing multiple appraisal tools, confirming its favorable environmental profile, balanced analytical performance, and practical applicability compared with previously documented methods. Compared with previously published LC methods, particularly UPLC, our strategy requires lower solvent consumption and reduced energy demand and generates less analytical waste owing to the simultaneous analysis of multiple samples on a single plate, while also offering greater operational simplicity. The ability to simultaneously estimate all analytes in a single TLC run cheaply renders our strategy efficient and well-suited for regular QC testing.

The increasing appearance of drug-adulterated edible products has created new analytical demands for forensic and regulatory laboratories, particularly in high-sugar confectionery matrices where matrix effects can compromise multi-residue detection. Here, we report an optimized liquid-liquid extraction (LLE) coupled with LC-MS/MS enabling the simultaneous screening and quantification of 223 drugs of abuse in candy and jelly products. During method optimization, LLE was directly compared with dispersive solid-phase extraction (d-SPE). Despite the common assumption that additional cleanup improves performance, LLE provided more consistent recovery and precision across chemically diverse analytes without loss of sensitivity. The method was validated using matrix-matched calibration and evaluated for specificity, matrix effects, limits of quantification (LOQs), linearity, accuracy, and precision. Most analytes achieved R2 ≥ 0.98, accuracies within 70-120%, and precision ≤30%, with LOQs generally in the low ng/g range. Under inter-day conditions, a substantial proportion of analytes were quantitatively validated, while additional compounds remained suitable for qualitative confirmation, resulting in broad analytical coverage across both candy and jelly matrices. These findings demonstrate that a simplified LLE-based approach can provide a robust and high-throughput platform for multi-class drug screening in high-sugar confectionery products, supporting practical implementation in forensic and regulatory surveillance.

Hindered amine light stabilizers (HALS) are polymer additives extensively used to improve the durability of plastic materials by inhibiting degradation induced by ultraviolet radiation. Due to their effectiveness, HALS are incorporated into a wide variety of polymeric products intended for both indoor and outdoor applications. However, because these compounds are not chemically bound to the polymer, they can be released into the environment through processes such as volatilization, abrasion, and dissolution. As a result, HALS may accumulate in dust and other environmental matrices. Their occurrence in indoor and outdoor dust raises concerns regarding environmental persistence and potential human exposure, underscoring the need for robust and sensitive analytical methods for their determination. In this study, a new analytical methodology for the determination of HALS in dust samples was developed and optimized. Different sample preparation techniques, including matrix solid-phase dispersion (MSPD), pressurized liquid extraction (PLE), and ultrasound-assisted extraction (UAE), were evaluated with the aim of improving extraction efficiency while minimizing matrix effects. Quantitative analysis was performed using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) equipped with a triple quadrupole (QqQ) mass analyzer, providing high sensitivity and selectivity. The optimized procedure was subsequently applied to dust samples collected from various indoor environments to investigate the occurrence and distribution of HALS. These findings contribute to a better understanding of HALS contamination in dust and associated human exposure pathways.

Toxic amide herbicide residues pose significant risks to food safety and environmental health. In this study, an ultrasound-assisted dispersive liquid‒liquid extraction method utilizing a hydrophobic menthol-based deep eutectic solvent (DES) was developed for the efficient separation and determination of four amide herbicides in complex matrices, including vegetables, soil, and water. DES composed of decanoic acid (hydrogen bond acceptor) and menthol (hydrogen bond donor) at a molar ratio of 2:3 demonstrated superior extraction performance compared to those using lauric or oleic acid as a hydrogen bond acceptor. The extraction was equilibrated within 2 min under neutral pH conditions. Molecular docking simulations revealed that the N─H groups in the amide moieties served as the primary active sites for hydrogen bonding interactions with the DES. By integrating this green extraction technique with high-performance liquid chromatography, a robust analytical method was established, exhibiting excellent linearity (R2 > 0.9991) over a wide range (1-200 ng mL-1), with low limits of detection (0.3-0.5 ng mL-1) and high precision (intraday RSDs ≤ 3.6%, inter-day RSDs ≤ 8.2%). The method achieved high recoveries (89.8%-116%) across various samples. Greenness assessments using the GAREEprep and Analytical Ecological Scale showed scores of 0.57 and 85, respectively, underscoring the sustainability of the method. This study presents a rapid, sensitive, and sustainable alternative for monitoring amide herbicide residues, contributing to food safety and environmental protection.

This paper reports on the development of an analytical method for the simultaneous determination of 4-methyl phenol, 4-hydroxybenzoic acid and five bile acids, namely cholic acid, glycochenodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, and hyodeoxycholic acid (logP 1.6-4.9) in urine, exploring porous acrylic polymers as unconventional solid-phase extraction sorbents before HPLC-MS/MS analysis. These materials, obtained by polymerization of high internal phase emulsions with different composition, were used in a miniaturized setting (µSPE), preliminarily in synthetic urine samples enriched with 200 ng mL-1 of each selected compound. It was found that the polyethylene glycol (PEG)-modified polymer combines good analytical performance, in terms of extraction efficiency (76-100% for most analytes), from 1 mL samples and single-fraction elution in pure ethanol. Recovery was assessed in untreated real urine (1 mL, pH ~ 3.5) at three quality control levels (50, 200, 500 ng mL-1) observing inter-day RSD below 20% (n = 3). The polymer exhibited a service life of at least ten sorption/desorption cycles, a further strength point of this sample treatment ascertained by greenness metrics. This streamlined procedure also provided satisfactory sample clean-up, with a negligible matrix-effect, as observed in HPLC-MS/MS. An ad hoc chromatographic method was developed to enable the simultaneous separation and quantification of all targets in a single run. Good linearity was observed in the range 10-600 ng mL-1 (R2 > 0.9919) with suitable sensitivity (a few ng mL-1) to future application for monitoring these endogenous compounds.

Water samples from seven Matagorda Bay locations were collected in spring, summer, and fall to assess micro- and nano-plastics contamination. Samples were oxidatively digested (30% H2O2), filtered, dried, and analyzed by Fourier Transform Infrared spectroscopy FT-IR ATR, Raman spectroscopy, Differential Scanning Calorimetry DSC, and Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy SEM-EDS. The identification of micro- and nano-plastics in estuarine waters is complicated by high salinity and inorganic matrices that can obscure or mask polymeric signals. To address this challenge, an integrated analytical approach was employed, combining vibrational spectroscopy, thermal analysis, and electron microscopy to compensate for the limitations of individual techniques. While surface-sensitive and element-specific methods were influenced by salt encapsulation, differential scanning calorimetry provided complementary bulk thermal evidence of polymeric materials embedded within the inorganic matrix. The spectra consistently showed peaks at ∼3300, 1630, and 1100 cm-1, indicating amine and hydrocarbon groups associated with synthetic polymers. The thermograms of DSC for fall and summer revealed three melting transitions (∼105-110 °C, ∼150 °C, ∼220 °C), consistent with polyethylene PE, polypropylene PP, polyvinyl chloride PVC, and high-melting polyamides PA or polystyrene PS. By contrast, spring samples showed only two transitions (∼85-95 °C, 140-150 °C), suggesting absence of some micro/nano-plastic materials. The outcomes of SEM-EDS demonstrated that the dried residues were dominated by inorganic salts (sodium Na, magnesium Mg, calcium Ca, sulfur S, chlorine Cl) with little detectable carbon, implying micro- and nano-plastic particles were embedded in a salt matrix. Overall, the data suggests the presence of common plastics (PE, PP, PVC, PA) in Matagorda Bay waters, with possible seasonal variation. The prevalent salt background highlights analytical challenges in detecting plastics in estuarine samples. Combining DSC technique alongside SEM-EDS and FT-IR show micro/nano-plastic particles encapsulation within inorganic salt. These findings underscore the plastic pollution in this coastal system and the need for rigorous monitoring and improved isolation of microplastics from saline matrices.

Intensive insecticide use has been repeatedly employed to prevent the spread of mosquito-borne Chikungunya fever, but poses potential exposure risks to ecosystems and environments. Herein, we developed a dual mass spectrometric (MS) strategy for on-site monitoring of typical insecticides in different environmental media and for assessing associated exposure risks during the Chikungunya epidemics. The dual MS strategy demonstrated good analytical performance for on-site analysis, including sensitivity (at parts per billion level), reproducibility (<20%, n = 7), and analytical speed (within 10 min). It was found that the main active compounds, such as permethrin and allethrin, persist in water and soil, whereas volatile auxiliaries such as toluene and isooctyl alcohol rapidly volatilize into ambient air immediately after spraying. Particularly, sawtooth-shaped accumulation effects of non-volatile insecticides were found after repeated applications. Furthermore, a successive five-day monitoring revealed that health exposure risks would be significantly increased due to the cumulative effects of repeated insecticide spraying. Overall, this study not only provides a promising strategy for the on-site monitoring of insecticides but also offers valuable insights into their cumulative effects and associated exposure risks across different environmental media.

Conducting multi-mode detection of multiple targets simultaneously on a single platform can significantly enhance the accuracy and flexibility of the detection process. Herein, a fluorescence-colorimetric dual-mode sensing platform was constructed by in-situ coating polydopamine (PDA) on Cu/Ce multivalent metal-organic frameworks (Cu/Ce-MOFs). The obtained Cu/Ce-MOFs@PDA exhibits excellent fluorescent properties and multienzyme-like nanozymes activity (including mimics oxidase, peroxidase, phosphatase, and laccase), facilitating dual-mode detection of pesticides, phenolic pollutants, and biomolecules. Particularly, the excellent detection performance is attributed to the synergistic Ce4+/Ce3+-Cu2+/Cu+ redox cycling and the interfacial electron modulation of polydopamine. In addition, the sensing platform demonstrates excellent signal reproducibility, remarkable anti-interference capabilities and strong applicability to real samples, with a recovery ranging from 84.3% to 118.8%. This work demonstrates a generalizable approach for constructing multifunctional bimetallic MOFs-based nanozymes, enabling the development of integrated analytical platforms with multi-mode signal outputs for environmental and biosensing applications.

An efficient variational method is presented for estimating the diffusion coefficients and free-energy profiles along selected collective variables from projected molecular dynamics trajectories under both equilibrium and non-equilibrium conditions. This method is based on the assumption that the short-time transition probability density of the coordinate moves can be approximated by a Gaussian form. Defining a loss function as the sum of Kullback-Leibler divergences between the analytical short-time propagators of an overdamped Langevin model and those estimated directly from the projected trajectories maximizes the agreement between the two and allows for its analytic evaluation. For cases where the Gaussian approximation is insufficient, we present a robust alternative. To efficiently minimize this loss function by varying diffusion and free-energy profiles along collective variables, we use an adaptive Monte Carlo scheme. The method is applied to two model systems exhibiting diffusive dynamics, as well as to water diffusion across the interface of a biomolecular condensate, demonstrating its robustness and accuracy.

This study reports the first confirmation of Caribbean ciguatoxins (C-CTXs) in fish samples from the South Atlantic region, specifically the Fernando de Noronha archipelago (Brazil). The work was based on fish samples opportunistically collected by local health authorities in connection with suspected ciguatera poisoning cases and from related commercial settings, rather than on a prospective field survey. Samples were analyzed using methodologies developed and validated during the two phases of the EuroCigua project, and EuroCigua reference materials were used to support toxin characterization. C-CTXs were confirmed in fish species commonly associated with ciguatera poisoning, particularly Seriola dumerili and Sphyraena sp., with toxin profiles resembling those previously reported in Caribbean and East Atlantic areas. These findings provide the first analytical evidence of C-CTX occurrence in Brazil and support the need for strengthened surveillance and future targeted investigations in tropical and subtropical areas where ciguatera risk remains insufficiently characterized.

Pentachlorophenol (PCP), a highly toxic and bioaccumulative chlorinated phenol, remains a persistent threat in aquatic ecosystems due to its widespread use and environmental stability. In this study, a novel, eco-friendly, and label-free fluorescence-based sensing platform utilizing unmodified C-phycocyanin (CPC) - a naturally fluorescent, water-soluble protein - for the selective and sensitive detection of PCP in aqueous media was developed. The method exploits direct fluorescence quenching of CPC, exhibiting a clear concentration-dependent emission decrease at 644&#xa0;nm with excellent linearity and reproducibility. Unlike conventional approaches relying on nanomaterials or chemically modified probes, this method employs a natural biopolymer without derivatization or signal amplification. The developed method achieved a linear detection range of 0.9-31.1&#xa0;&#x3bc;g.mL-1, with a limit of detection (LOD) of 0.0780&#xa0;&#x3bc;g.mL-1 and a limit of quantification (LOQ) of 0.2599&#xa0;&#x3bc;g.mL-1. Among 28 tested analytes, only PCP caused significant quenching, highlighting the exceptional selectivity of the system. Molecular docking analysis supported these findings, revealing that PCP interacts with key residues in the &#x3b2;-subunit chromophore-binding pocket of CPC, consistent with the observed fluorescence quenching. Validation through spike-recovery experiments and GC-MS comparisons confirmed the method's analytical accuracy and practical applicability. This sustainable sensing approach not only broadens the scope of natural protein-based probes in analytical science but also provides an environmentally responsible platform for real-time monitoring of persistent pollutants in aquatic environments.

Sea salt increasingly harbors organic contaminants from personal care products, yet current monitoring methods lack spatial resolution and require destructive sampling. This study introduces an innovative analytical framework integrating Laser-Induced Fluorescence (LIF) Hyperspectral Imaging (HSI) with machine learning for the rapid, non-destructive detection of sunscreen residues on salt crystals. To simulate contamination, seawater from the Mediterranean coast (Alexandria, Egypt) was spiked to achieve a 10&#xa0;mg/L sunscreen concentration within the seawater matrix prior to crystallization; this formulation contained Ethylhexyl Methoxycinnamate, Homosalate, and Ethylhexyl Salicylate. A SOC710 HS camera (128 bands) acquired fluorescence data under 450&#xa0;nm laser excitation. Raw data underwent preprocessing and dimensionality reduction via Sparse Principal Component Analysis (Sparse PCA, &#x3bb;&#x2009;=&#x2009;0.5, k&#x2009;=&#x2009;4 components, 73.4% sparsity). A Support Vector Machine (SVM) with an RBF kernel was trained on these sparse features. Performance evaluation employed tenfold stratified cross-validation, an 80-20 holdout test on ROI-based spectra, and independent sample validation against manually annotated pixel-wise ground-truth masks. While ROI-based tests yielded near-perfect accuracy under ideal conditions, full-image evaluation achieved &#x2248;96% pixel-wise accuracy (precision&#x2009;&#x2248;&#x2009;0.99, recall&#x2009;&#x2248;&#x2009;0.95, F1&#x2009;&#x2248;&#x2009;0.97), providing a realistic estimate under heterogeneous conditions. Full-image classification mapped widespread contamination (57.8% of pixels), whereas an independently prepared clean salt sample produced zero false positives. The integrated Sparse PCA-SVM framework transforms fluorescence-imaging data into spatio-chemical maps, simultaneously revealing contaminant presence and spatial distribution on salt surfaces, thereby offering a powerful paradigm for the interpretable monitoring of organic pollutants in food materials.

Simultaneous quantitative analysis of multiple biogenic amines (BAs) in food is challenging due to the high cost of reference standards, weak ultraviolet absorption, and complex matrix interference. To address these limitations, this study developed a strategy integrating dansyl chloride (DNS-Cl) derivatization with the quantitative analysis of multi-components by single marker (QAMS) approach to enable simultaneous quantification of nine BAs in fermented wine using HPLC-UV. A key methodological innovation of this work is the first-time adoption of a weighted scoring system for the systematic selection of the optimal internal standard (IS) for QAMS. This system comprehensively evaluates the stability of the relative correction factor (RCF), retention time suitability, and response intensity, moving beyond traditional empirical choices. Through this rigorous evaluation, tyramine (Tyr) was selected as the optimal internal standard. The established RCFs proved reproducible and stable across different instruments and operational conditions (RSD&#xa0;<&#xa0;10%). Method validation demonstrated good linearity (R2&#xa0;>&#xa0;0.9985), precision (RSD&#xa0;<&#xa0;2.28%), and accuracy (recovery: 93.92%-107.43%). Bland-Altman analysis showed that 100% of data points lay within the 95% confidence interval, demonstrating strong agreement with conventional external standard methods. In conclusion, the developed derivatization-based QAMS method is accurate, reliable, and cost-effective for the simultaneous quantification of nine biogenic amines in fermented wine, offering a practical alternative to traditional approaches. This method provides a low-cost analytical tool for quality control of BAs in fermented foods and expands the applicability of QAMS to compounds with weak UV absorption.

To address the limitations of existing approaches&#x2500;targeted next-generation sequencing (NGS) and simplex droplet digital PCR (ddPCR) assays&#x2500;for circulating tumor DNA (ctDNA) detection in uveal melanoma (UM), we developed and validated mutation-agnostic multiplex drop-off ddPCR assays at the targeted loci as a novel strategy. Two multiplex drop-off ddPCR assays covering hotspot mutations in GNAQ p.Q209, GNA11 p.Q209, p.R183, SF3B1 p.R625, PLCB4 p.D630, and CYSLTR2 p.L129Q were developed. Analytical sensitivity and specificity were determined. Clinical validation was performed using DNA from tumor tissue and plasma samples from a prospective cohort of Metastatic UM (MUM) (ALCINA, NCT02866149) or archived clinical samples. The multiplex assays demonstrated high sensitivity, with detection limits ranging from 0.06% to 0.13%, comparable to those of simplex ddPCR assays. Somatic mutations in tumor DNA (N = 12) were successfully identified using the multiplex assays, and all were confirmed by NGS. ctDNA was detected in 62.8% of 43 plasma samples, with a median mutant allele frequency (MAF) of 2.9% (interquartile range [IQR] 0.6-7.4%). Strong concordance with simplex ddPCR results was observed (r = 0.98, p < 0.0001 for multiplex 1, N = 32; r = 0.97, p < 0.0001 for multiplex 2, N = 11), further supporting the accuracy of the multiplex assays. Our mutation-agnostic multiplex drop-off ddPCR assays provide a sensitive, specific, and cost-effective alternative to targeted NGS and simplex ddPCR for ctDNA monitoring in UM. By minimizing reliance on prior knowledge of tumor genotype with NGS, these assays enable broader clinical applicability for real-time treatment monitoring in UM.