The electrochemical inertness of conventional β-Co(OH)2 limits its application in non-enzymatic glucose sensing. Here, we demonstrate that MoO42− intercalation substantially activates Co(OH)2 by simultaneously triggering a crystallographic phase transformation from β to α phase and modulating the local electronic microenvironment. The intercalated anions expand the interlayer galleries, yielding a 1.7-fold enlargement of the electrochemically active surface area, withdraw electrons from Co centers to facilitate the generation of high-valent Co(Ⅲ)/Co(Ⅳ) active species at lowered potentials, and markedly strengthen glucose adsorption. Consequently, the MoO42−-intercalated Co(OH)₂ achieves an ultrahigh sensitivity of 2743.9 µA mM−1 cm−2, which is 7.3 times that of pristine Co(OH)2, together with a wide linear range of 4.0 µM to 0.2 mM and a low detection limit of 0.36 µM. This work establishes an intercalation-driven dual-regulation strategy that integrates phase engineering and microenvironment modulation, offering a generalizable approach for the design of advanced sensing materials.
This paper proposes an enhanced electrochemiluminescence (ECL) nanostructure sensor for midazolam (Mdz) determination via one-step electropolymerization of the conductive molecularly imprinted polymer (CMIP) based on polypyrrole (PPy) on a carbon paste electrode (CPE) by the cyclic voltammetric (CV) technique. The FESEM technique showed that the surface of the sensor is made of nano-sized particles. In the absence of hydrogen peroxide, ECL signals of luminol were investigated and an enhanced response was observed by adding Mdz to the solution. Optimal experimental conditions were obtained for the proposed sensor. Its ECL behavior was investigated and the reaction mechanism was discussed. The calibration curve of the CMIP sensor was obtained in the dynamic linear range from 10 to 400 nM with a limit of detection (LOD) of 6 nM for the Mdz determination in solution. The good selectivity of the proposed CMIP sensor was shown by investigating the effect of some interferences on the CMIP and non-imprinted polymer electrode. This sensor was able to determine the amount of Mdz in urine samples with acceptable sensitivity.
Currently, wearable health monitors face challenges such as complex manufacturing processes, trade-offs between flexibility and performance, limited multimodal integration, and issues like low sensitivity, high detection limits, and susceptibility to interference. To address these issues, we present a high-performance, antibacterial wearable sweat sensor based on a flexible Ag/PET electrode fabricated via a silver mirror reaction. Surface pretreatment with carboxyl groups optimizes silver nanoparticle distribution, resulting in excellent conductivity, mechanical stability, and efficient 3D sweat collection. The electrode is further functionalized with Cu-BTC through electrochemical cathodic deposition. The constructed sensors demonstrate high sensitivity for uric acid (32.4 μA mM−1 cm2) detection in artificial sweat (pH 5.5), with a wide linear range (10–300 μM), a low detection limit (1 μM), and strong anti-interference capability against common metabolites (the inhibition rate of over 62.3
Breast cancer is a leading cause of cancer death in women, requiring non-invasive early diagnostic tools. Exosomes are promising liquid biopsy biomarkers that reflect tumor-derived molecular information. Here, we developed an “off-on” fluorescent sensor using nitrogen-doped carbon nanodots (N-CNDs) for sensitive detection of breast cancer–derived exosomes. The sensor uses a magnetic bead–based aptamer complex (MBs-Apt-P1-P2) for target capture. P1 and P2 probes contain both EpCAM-recognition and Poly-T sequences. In the absence of targets, Ag+ quenches N-CNDs fluorescence. Upon target introduction, aptamers bind exosomal EpCAM, competitively releasing P1/P2 probes. After magnetic separation, the released Poly-T sequences capture Ag+ via T-Ag+-T coordination, restoring N-CNDs fluorescence. Each exosome can release two probes, providing dual signal amplification. Under optimal conditions, the sensor responded linearly to 1 × 109–4 × 1011 particles/mL with a detection limit of 35 particles/mL, showing excellent sensitivity for EpCAM-positive breast cancer exosomes. Clinical testing on 11 serum samples (6 patients, 5 healthy donors) distinguished patients with an AUC of 0.94, compared with ELISA (AUC = 0.88). This sensor offers a promising platform for breast cancer detection with high sensitivity and accuracy, warranting further validation in large-scale clinical studies.
Current Salmonella detection methods are time-consuming or incapable of effectively discriminating viable bacteria from dead cells, restricting their practical applications in food safety monitoring. Thus, in this study, an antigen-binding-enhanced Salmonella divalent nanobody (diVHH) was optimally expressed and chemically conjugated with magnetic beads (MBs). Combined with ATP bioluminescence, a novel strategy was developed for the rapid detection of viable Salmonella. Optimization of prokaryotic expression significantly improved the diVHH yield, with an approximately 2.96-fold enhancement. Then, the diVHH-functionalized MBs achieved capture efficiencies exceeding 90
Synthetic cathinones are one of the most prevalent new psychoactive substances on the drug market over the last 20 years. Their structural diversity, rapid turnover and variable stability in biological matrices pose significant challenges for laboratories. This study applied a comprehensive, multi-analytical workflow to characterise, profile the metabolism, and assess the stability of the synthetic cathinone N-isopropylbutylone, with a particular focus on dried matrix spot approaches. Structural elucidation was achieved through the combined use of GC–MS, LC–HRMS and NMR spectroscopy. This provided complementary information, enabling the compound to be identified with confidence. Investigating the metabolic profile in an animal model led to the identification of multiple phase I and phase II metabolites, including products of N-dealkylation, demethylation followed by O-methylation and glucuronidation. Differences in metabolite detection windows were noted between blood and urine. The applicability of dried blood spots (DBS) and dried urine spots (DUS) for metabolite detection and analyte stability was evaluated over a 90-day period. Dried matrix spots proved to be a valuable technique for analysing cathinones and their metabolites, and their enhanced stability in this medium stored at room temperature was confirmed, with most of the analytes being detectable throughout the entire study period. This study highlights the potential of dried matrix spot approaches as practical and effective tools for analysing synthetic cathinones and their metabolites. It also provides new analytical and metabolic data on N-isopropylbutylone. The proposed workflow could be used in future applications in forensic and clinical toxicology.
Non-targeted analysis (NTA) lacks consistent processes for comparing method performance across different analytical conditions. As a result, method comparison between NTA methods is often dependent on the chemicals detected in a particular study, making it difficult to objectively evaluate how instrumental performance influences the overall detectable space. Here, we describe a two-stage cascade classification approach that treats detectability as a binary outcome defined by experimental retention behavior and signal-to-noise criteria and characterizes the boundary between detectable and non-detectable chemical space using receiver operating characteristic (ROC) analysis. In Stage 1, compounds are filtered by estimated retention index to establish chromatographic accessibility. In Stage 2, detectability is classified using the electron impact cross-section (Q), with the classification threshold selected to satisfy a minimum recall constraint (recall ≥ 0.80). Using this treatment for the detectable space, the area under the ROC curve (AUC) emerges as a method-level figure of merit. Applied to two GC-MS systems operating under identical chromatographic conditions but with different mass selective detectors (Agilent 6890/5975B and 8890/5977C), the approach yielded Stage 2 AUC values of 0.659 (95
Estimating the time since deposition (TSD) of bloodstains at crime scenes is critical in forensic science. Although various approaches have demonstrated feasibility and accuracy for TSD estimation, analysis of volatile organic compounds (VOCs) offers a promising alternative because it is noninvasive, rapid, and potentially cost-effective. In this study, human blood was chemically profiled using solid-phase microextraction coupled with gas chromatography-mass spectrometry (SPME-GC-MS) to investigate changes in VOC profiles over time and under different storage conditions. A total of 39 metabolites were associated with TSD. This study compared the types, abundances, and temporal patterns of VOCs among three substrates: gauze, wood, and glass. Based on 13 characteristic VOCs commonly detected in dried bloodstains across these three substrates within a 7-day period, a machine learning regression model was developed and evaluated. The optimal model was a random forest (RF) model, achieving root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) values of 0.8217, 0.9401, and 0.5103, respectively, for the validation set, and 0.7742, 0.8533, and 0.5437, respectively, for the external validation set. These findings demonstrated that volatolomics analysis has strong potential for estimating bloodstain TSD, offering reliable biomarkers, high model accuracy, and robustness to substrate effects. This study provides a new methodological framework for TSD estimation and lays the foundation for further research in this field.
Cysteine (Cys) acts as a key upstream regulator of cellular redox homeostasis and ferroptosis. Distinct ferroptosis inducers exert different effects on Cys metabolism. Erastin disrupts Xc−-dependent cystine uptake to deplete cellular Cys and trigger ferroptosis, while RSL3 induces ferroptosis by impairing downstream lipid metabolism without disturbing Cys homeostasis. Thus, real-time monitoring of Cys fluctuations is vital for distinguishing early ferroptosis initiation from downstream cascades. Conventional PET-type turn-on fluorescent probes suffer from high background and poor sensitivity. Herein, we report an excitation-customized strategy to develop ICT-type turn-on probe for Cys sensing. Based on a series of chalcogen-regulated near-infrared hemicyanine dyes, a selenium-substituted fluorophore HDXZ was screened out to construct the Cys-specific probe BXXZ. Under the customized excitation, BXXZ exhibited high fluorescence enhancement (66-fold), a low detection limit (0.23 μM), fast response, and excellent selectivity for Cys over Hcy and GSH. Benefiting from these merits, BXXZ enabled real-time visualization of differential Cys fluctuation in ferroptosis induced by erastin and RSL3, and achieved in situ tracking of intratumoral Cys fluctuation in HepG2 xenograft models. This work provides a feasible design strategy for high-quality ICT-type turn-on probes and a powerful imaging tool to explore the relationship between Cys homeostasis and ferroptosis.
In this study, we developed a cold solvent washing–enhanced AFADESI-MSI spatial lipidomics approach that improves detection sensitivity of tissue lipid metabolites while preserving molecular spatial distribution fidelity. Using brain tissue homogenate sections as benchmark samples, the solvent composition, washing temperature, and washing time were systematically optimized, with the optimal condition determined as methanol/water (8:2, v/v) treatment at −80 °C for 1 h. The optimized method yielded an average of more than 150 enhanced mass spectral peaks in mouse brain, kidney, and liver tissues, with significant signal enhancement. Molecular imaging of fine cerebellar structures (fiber tracts, 60 μm) confirmed that lipid spatial fidelity was largely maintained. Clinical application to pancreatic cancer tissues revealed that PC and PE species were significantly upregulated in cancer tissues, with longer-chain PC species (e.g., PC 38:5, PC 40:7) showing preferential upregulation. Notably, PC 36:3 exhibited robust discriminatory power between cancer and distal margin tissues, demonstrating the broad tissue applicability of this method in revealing previously undetectable lipid spatial distributions and providing a novel analytical tool for lipid biomarker discovery in pancreatic cancer.
The human immunodeficiency virus type 1 (HIV-1) continues to pose a major global public health burden. However, progress in curbing the HIV pandemic is slowing, with the annual number of new infections remaining persistently high globally. HIV-1 p24 antigen-specific antibodies serve as the most consistent and robust markers for early HIV-1 seroconversion. Thus, the development of ultra-sensitive p24 IgG detection methods is of clinical and epidemiological significance. In this study, an assay for the detection of HIV p24-specific IgG in serum was established using the luciferase immunomagnetic system (LIMS), with the incorporation of a humanized Gaussia luciferase (hGLuc)-p24 chimeric antigen. Under optimized conditions, the assay enables semi-automated processing of 32 clinical serum samples within 15 min, with a minimal sample volume of only 2.5 μL per test. Compared to conventional enzyme-linked immunosorbent assay (ELISA), the HIV-1 p24 LIMS assay not only streamlines the experimental workflow but also exhibits superior sensitivity, a wider dynamic range for signal detection, and enhanced discrimination between positive and negative specimens. These integrated advantages render the assay particularly valuable for scenarios demanding rapid, accurate and high-throughput diagnostic responses to HIV-1 infection.
Analytical chemistry has changed profoundly over the last four decades with a tremendous expansion of analytical detection power. However, an increasing gap seems to exist between access to high-quality matrix Reference Materials and Certified Reference Materials (CRMs) and the sheer number of new analytes that can be determined in many matrices. The development of new RMs is moreover often connected with the use and development of novel measurement methods, especially in emerging fields of measurement. A challenge arises when characterising such RMs through interlaboratory comparisons, as it creates an RM/method causality dilemma, namely the production of new RMs require reliable and accurate methods, which in turn need good-quality RMs for method harmonisation and analytical quality assurance purposes. To overcome this dilemma, RM-producers must take iterative steps to gradually improve their candidate materials particularly with respect to homogeneity, which in turn allows improving the performance of the analytical methods. As both materials and methods evolve, they will eventually reach a point where target parameters can be certified in an appropriate matrix material. Provision of shared samples of specific matrices to a community of researchers could be a promising approach for future development of certified reference materials. This Trends article describes the components needed to establish such a resource and its expected outcomes. The core elements necessary for such a resource include (1) a material processing facility, (2) a storage and distribution centre, and (3) an online database for reporting measurement results. We propose the term Reference Matrix Materials (RMMs) for this category of samples. When multiple laboratories analyse RMMs, clusters of results eventually emerge in the database, tentatively providing a network of laboratories capable of performing specific measurements. Those results would subsequently become particularly valuable assets for RM-producers and proficiency testing providers. Method developers and analytical laboratories would also benefit from access to common materials enabling them to harmonise, compare, and evaluate the capabilities of different analytical methods. By providing such an anticipatory and dynamic platform, RM-producers and method developers can break open the closed circle of the RM/method causality dilemma. Such an approach could be a driver for faster, more universal, and efficient progress in the development of new RMs and analytical measurement methods alike.
Steroidal estrogens are a type of compound that disrupts endocrine function and could potentially threaten food safety and human health. So, there is a real need for a sensitive and convenient method to detect estrogens in food samples. Magnetic silica (Fe3O4@SiO2) has been recognized as a versatile adsorbent material with a porous structure that provides numerous imprinting sites while reducing mass transfer resistance, making it a promising candidate for adsorption applications. Fe3O4@SiO2 denotes core-shell nanoparticles with magnetite as core and silica as outer shell. However, to further enhance the adsorption capacity and selectivity, the incorporation of metal-organic frameworks (MOFs) can be highly beneficial. In this work, estrogens were detected using molecularly imprinted polymers (MIPs) based on composites of Fe3O4@SiO2 and aluminum metal-organic framework (Al-MOF). The Al-MOF, specifically MIL-68(Al), was selected for its high surface area, tunable pore size, and strong stability, which contribute to its excellent adsorption properties. The incorporation of MIL-68(Al) into the Fe3O4@SiO2 matrix not only increases the available surface area but also provides additional specific binding sites for estrogens, resulting in enhanced selectivity and sensitivity. The resulting Fe3O4@SiO2@MIL-68(Al)@MIP material proved to be a powerful adsorbent for estrogens with a maximum adsorption capacity of 34.52 mg g−1. Notably, Fe3O4@SiO2@MIL-68(Al)@MIP exhibited high adsorption capacity and selectivity and achieved a limit of detection (LOD) of 1.1–4.6 ng mL−1 (0.04–0.18 µg kg−1) and a linear response range of 40.96–8000 ng mL−1, with recoveries between 87.34
A simple, low-cost, and sensitive electrochemical sensor was developed for the determination of zinc ions by anodic stripping voltammetry (ASV). The sensor (denoted G/W/ARS/MES) consists of a graphite–wax paste modified in situ with Alizarin Red S (ARS). The nature of the Zn–ARS interaction was examined by quantum-chemical calculations together with FT-IR and UV–Vis spectroscopy, which consistently indicated coordination of Zn(II) through the hydroxyl and carbonyl groups of ARS. Under optimised conditions—a universal buffer of pH 4.5–5.5, a 0.1 M H3PO4 + 0.1 M KNO3 supporting electrolyte and an 80 s accumulation time—the modified sensor produced a well-resolved Zn(II) stripping peak at − 1040 mV, shifted by about 280 mV from that of the unmodified electrode and clearly separated from the signals of Cd(II), Pb(II), and Cu(II). The peak current varied linearly with concentration over the range 2.0–35.0 µg L−1 (R2 = 0.9910), with a detection limit of 0.57 µg L−1. The method showed good repeatability and high selectivity and was applied to the determination of trace Zn(II) in waste, ground, and industrial-zone waters and in agricultural soils of the Jizzakh and Tashkent regions. The results agreed well with those obtained by atomic absorption spectrometry, confirming the accuracy of the proposed method.
The evolution of metabolomics, a field aimed at comprehensively measuring the small organic molecule composition of a biological matrix, fundamentally reshaped the landscape of analytical measurement reliability. The 2011 release of Standard Reference Material (SRM) 1950 Metabolites in Frozen Human Plasma by the National Institute of Standards and Technology (NIST) marked the first reference material providing certification for 45 analytes to address the measurement precision of complex biological matrices. Assigning high-order values to many compounds is highly resource-intensive for a National Metrology Institute. Furthermore, certified values for individual metabolites cannot resolve the broad challenges associated with sample processing, analytical measurement, and data processing in untargeted workflows. The metabolomics community recognized the potential for SRM 1950 to serve as a benchmark material for method development and technical quality control (QC) rather than strictly for quantification. Recognizing this consumer-driven shift, NIST launched a novel reference material (RM) development strategy to provide accessible, fit-for-purpose materials evaluated by a new statistical framework for production homogeneity, the Coefficient of Disagreement. Here, we introduce the transition toward matrix-specific QC Suites featuring phenotypically distinct metabolite profiles, the first generation of which includes a human plasma suite, urine suite, liver suite, and fecal material. By prioritizing efficient production and embracing a community engagement model for consensus-based deep characterization, these suites offer a reliable tool for laboratory comparisons, instrument assessment, software development, and training. This new class of RMs underpins the next phase of measurement reliability, complementing traditional measurand certification while supporting the diverse needs of comprehensive metabolic profiling. Photo credit: Amanda Bayless
Untargeted urinary metabolomics represents a promising approach for investigating metabolic alterations associated with oncogenic processes such as breast cancer (BC). However, the stable selection of informative m/z features remains a central challenge in biomarker-oriented studies, particularly in the context of early BC detection and population-level screening. Urine samples from two independent cohorts (n = 50 and n = 75) and analyzed using distinct UHPLC–QTOF–ESI⁺ mass spectrometry workflows, yielded 224 and 129 aligned m/z features, respectively. Classification and embedded feature selection were implemented within a leakage-controlled Random Forest (RF) framework using Gini index-based importance ranking. Model evaluation incorporated repeated train-test splits and cross-validation to ensure methodological rigor and minimize overfitting. By consistently applying the same RF-based analytical framework to two analytically distinct cohorts generated under different chromatographic separation conditions, we demonstrate that a unified supervised strategy can achieve comparably high classification performance despite differences in feature dimensionality. Further, controlled dimensionality reduction identified compact panels of 25 m/z features per cohort while preserving classification performance. Importantly, stability was maintained after feature reduction, with strong accuracy, F1 scores, and receiver operating characteristic and precision–recall characteristics observed in both full and reduced models. This cross-cohort consistency indicates that the discriminative signal captured by the RF approach is not cohort-specific nor dependent on a particular separation workflow, but rather reflects reproducible metabolic patterns associated with BC. The stability of feature selection was further supported by substantial overlap between RF-derived Gini importance rankings and variable importance in projection (VIP) scores obtained from partial least squares discriminant analysis (PLS-DA) in MetaboAnalyst 5.0, indicating concordance across distinct supervised multivariate frameworks. Collectively, these findings highlight the advantage of a unified, supervised tree-based strategy capable of delivering stable classification and interpretable dimensionality reduction across independent untargeted metabolomics platforms, providing a structured and transferable framework for metabolomics-driven biomarker discovery and future clinical validation.
Light activation is a promising approach for lowering the power consumption and increasing the selectivity of chemoresistive gas sensors based on semiconducting metal oxides. In this study, different aspects of commonly used materials are investigated, compared, and evaluated for their application as gas sensors. Their ability to detect CO and NO 2 at low temperature (70 ∘ C) highly depends on the wavelength used for activation. The reaction with atmospheric oxygen is also highly affected by illumination, as adsorption, desorption, and free charge carrier concentration are influenced by light. For this reaction, the relationship between band gap and photon energy becomes apparent, which is not the case for the detection of analyte molecules. Investigations on the long-term stability indicate that only certain combinations of wavelengths and materials are suitable for prolonged and stable operation.
The COVID-19 outbreak highlighted the critical demand for rapid, ultra-sensitive, and selective biosensing platforms to support early diagnosis, point-of-care testing, and large-scale surveillance. Among the various sensing strategies being explored, electrochemical biosensors have attracted considerable interest due to their inherent sensitivity, simplicity, and potential for miniaturization. In this context, hybrid materials combining molecularly imprinted polymers (MIPs) with two-dimensional MXenes have recently emerged as promising platforms for biosensing applications. MXenes offer excellent electrical conductivity, hydrophilic surfaces, and abundant functional groups. At the same time, MIPs provide binding sites that recognize targets, similar to those of natural receptors, yet exhibit enhanced thermal and chemical stability. When these materials are integrated, they can provide a sensing interface that benefits from both efficient electron transfer and highly selective molecular recognition. This review highlights recent advances in MXene-MIP composite materials applied in electrochemical biosensing, with particular emphasis on their potential for pandemic diagnostics. Various fabrication approaches are discussed, including in situ polymerization on MXene sheets, electropolymerization-based surface imprinting, and layer-by-layer (LbL) or covalent grafting strategies. Such strategies allow better control of the sensing interface. Additionally, the influence of various electrochemical transduction techniques and device configurations on sensor performance is also examined. Recent reports on the detection of pandemic-associated biomarkers, such as C-reactive protein, interleukin-6, ferritin, D-dimer, and cardiac troponins, are reviewed to highlight the analytical capabilities of these hybrid systems. Finally, the main challenges that still limit practical applications, such as MXene oxidation, reproducibility of the imprinting process, and device integration, are discussed, along with possible future research directions. Overall, MXene-MIP hybrid materials appear to offer a versatile and promising route toward next-generation electrochemical biosensors for rapid and sensitive diagnostic applications.
Untargeted metabolomics of clinical toxicology samples is often constrained by limited sample volume, incomplete metabolome coverage, and technical variability introduced by multiple LC-MS injections. Here, we applied and evaluated a valve-assisted 4-in-1 polarity-partitioned LC-QTOF-MS workflow for single-injection plasma metabolomic profiling. The term “4-in-1” refers to four complementary LC-ionization data channels acquired from a single injection: HILIC-ESI(+), HILIC-ESI(−), C8-ESI(+), and C8-ESI(−). The workflow combines valve-controlled collection and transfer of weakly retained HILIC effluent with sequential HILIC and C8 analyses and dual-polarity MS acquisition. Quality-control analyses demonstrated stable retention behavior and reproducible feature detection, and the single-injection design reduced the need for multiple separate LC-MS injections. As a clinical toxicology application, the workflow was applied to plasma samples from patients with chlorfenapyr poisoning and healthy controls, with poisoned patients further stratified according to plasma tralopyril concentration. PCA and OPLS-DA were used as exploratory tools to visualize global metabolic differences. Differential LC-MS features were screened using multivariate and univariate statistical criteria, followed by metabolite annotation and pathway enrichment analysis based on annotated differential metabolites. Prominent perturbations were observed in amino acid metabolism, the urea cycle, and energy-related pathways. Several annotated amino acids, including glutamine, asparagine, alanine, and threonine, differed among the exposure groups. These exploratory findings support the feasibility of the workflow for limited-volume clinical plasma metabolomics and identify candidate metabolic alterations consistent with mitochondrial metabolic stress in chlorfenapyr poisoning. Experimental design and metabolomic workflow of the study. The workflow includes plasma sample collection, preparation, LC-QTOF-MS analysis using a valve-assisted 4-in-1 polarity-partitioned workflow, data processing, metabolite annotation, statistical analysis, and pathway-level interpretation.
Mass spectrometry imaging (MSI) has emerged as a powerful tool in dermatological research due to its high sensitivity, label-free detection, and ability to simultaneously analyze multiple molecular species. This review systematically summarizes recent advancements in the application of MSI for visualizing both endogenous metabolites/biomacromolecules and exogenous compounds in the skin. Firstly, the basics of MSI are introduced, including the different ionization sources and the general workflow of MSI experiments. Then, the applications of MSI in skin physiology and metabolism analysis and identification of molecular characteristics or potential biomarkers of skin pathological status are summarized. Furthermore, MSI is also widely used as a complementary method for skin penetration studies of topical pharmaceutical and cosmetic ingredients. Examples of MSI applications are presented to indicate the diverse fields MSI contributes to and emphasize the advantages of MSI compared to conventional techniques. Nevertheless, the limitations and challenges are also discussed. Overall, MSI is expected to further advance both dermatological research and clinical applications.
