Clinical metabolomics has emerged as a powerful systems biology approach for characterizing metabolic alterations associated with human health and disease. By comprehensively profiling endogenous metabolites in clinical biospecimens, clinical metabolomics provides valuable insights into disease mechanisms, biomarker discovery, therapeutic monitoring, and personalized medicine. Recent advances in analytical technologies, including nuclear magnetic resonance (NMR) spectroscopy, gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), have substantially improved metabolite coverage, analytical sensitivity, and quantitative reliability. In parallel, developments in computational and bioinformatics tools have facilitated high-dimensional data interpretation and pathway-level biological understanding. Despite these advances, several analytical and translational challenges remain in clinical metabolomics, including biospecimen variability, lack of standardized sample preparation protocols, inter-laboratory reproducibility, batch effects, and difficulties in translating metabolomics-derived biomarkers into clinical practice. Therefore, robust workflows encompassing biospecimen handling, sample preparation, analytical validation, quality control, statistical analysis, and biological interpretation are essential for generating reliable and clinically meaningful metabolomics data. This review summarizes current analytical and computational workflows in clinical metabolomics, with particular emphasis on biospecimen handling, sample preparation strategies, analytical measurement platforms, data processing methodologies, and translational considerations. In addition, emerging trends including artificial intelligence-driven data analysis, multi-omics integration, and precision medicine applications are discussed. Collectively, this review highlights the critical role of standardized and translationally oriented workflows in advancing the clinical implementation of metabolomics.
Sesame oil is widely consumed across Asia, yet reliable analytical approaches for geographical origin authentication remain limited, particularly after roasting-induced chemical transformation. In this study, untargeted volatile fingerprinting using headspace solid-phase microextraction coupled with comprehensive two-dimensional gas chromatography/high resolution mass spectrometry (HS-SPME–GC×GC/HRMS) was applied to discriminate roasted sesame oils from Korea, China, and India. A total of 205 volatile compounds were identified, and roasting markedly increased volatile chemical complexity through Maillard reaction pathways. Multivariate statistical analyses, including principal component analysis (PCA) and hierarchical clustering analysis (HCA), revealed clear origin-dependent clustering patterns despite extensive thermal transformation. ClassyFire-based chemical ontology analysis further demonstrated significant differences in volatile chemical superclass distributions among origins, particularly in hydrocarbons and organic acid-related compounds. In addition, an integrated marker prioritization strategy combining FDR-corrected ANOVA significance and PLS-DA variable importance in projection (VIP), supported by PCA loading magnitudes, identified multiple candidate volatile compounds associated with geographical origin discrimination. Several compounds showed strong association with Korean or Chinese sesame oils, whereas discrimination of Indian-origin oils appeared to rely more on multivariate compositional patterns than on individual marker compounds. These findings highlight the potential of untargeted GC×GC/HRMS volatile fingerprinting for sesame oil authentication and geographical origin traceability.
The stability of valsartan (Val) and its products (the presence of genotoxic impurities, such as N-nitrosodimethylamine as NDMA and N-nitrosodiethylamine as NDEA) is very crucial as it directly impacts safety and efficacy. Before, its product was recalled by various pharma industries due to the presence of the genotoxic impurities. It is always challenging to estimate the impurity profiling of the impurities during its shelf-life using a conventional methodology. The study addressed the HSPiP and QbD (quality by design)-driven the optimized mobile phase to improve system suitability, reliability, and sensitivity. The conventional analytical methods are less sensitive and selective in accelerated stress study for detecting the degradants and impurities. Therefore, the study addressed the combined effort of HSPiP, QbD, and in-silico tools for predicting the degradation profiling, optimizing the method, and assessing toxicity to circumvent the above limitations. Moreover, the AGREEprep and AGREE tools assessed the greenness of the method. HSPiP screened the right combination of solvents based on Hansen parameters. Zeneth evaluated the impact of the impurities on the stability. Furthermore, in-silico toxicity assessment predicted degradants to evaluate valsartan safety. The analytical method was optimized with high desirability ( 0.98). For each stress condition, the degradants with their pathways, were predicted by employing the Zeneth software. Then, the stress study evaluated the precision of the degradants. The number of acidic, basic, oxidative, and photolysis degradants were found as 4, 3, 2, and 1, respectively, with the optimized method. The newly developed HSPiP, QbD, and Zeneth enabled stability-indicating UFLC method was environmentally sustainable, sensitive, accurate, reproducible, simple, rapid, economic, and reliable to detect even the trace degradants of VAL. x
Predicting peptide retention time (RT) remains a significant challenge, particularly when training data is limited. In this study, we present MetaRT, a stacked-ensemble machine learning framework designed to predict the RTs from small dataset of hydrophobic peptides. Peptides composed of hydrophobic amino acids—phenylalanine (F), isoleucine (I), methionine (M), and tryptophan (W) were synthesized, and their experimental RTs were measured from the mixture entities. MetaRT utilizes a graph convolutional network (GCN) to extract structural features from the peptide sequences. The MetaRT model architecture employed multiple base learners, integrating the outputs through a meta-learner optimized via hyperparameter tuning and 3-fold cross-validation. Besides, the performance of MetaRT was compared to three ensemble methods - weight averaging, bagging, and boosting. The results demonstrated that structure-based MetaRT outperformed both base learners and the ensemble models, achieving a lower root mean square error (RMSE) of 0.08 and a maximum RT deviation of approximately 1.4 min. Compared to the prediction performance on molecular descriptors inclusion, the structure-guided model consistently performed well in terms of RMSE. Notably, MetaRT accurately predicted the RTs of sequence isomers by leveraging the structural features, with deviations ranging from 0.2 to 1.3 min. In contrast, descriptor-based model showed increased prediction error for the isomeric sequences. For peptides with lower hydrophobicity that were not included in the training data, the structure-based predictions led to the maximum deviation of 4.9 min from the experimental RTs. The entire predicted RTs were subsequently validated by linear regression analyses with the corresponding experimental values. These findings highlight the potential of MetaRT as a structure-based predictive tool for improving RT prediction accuracy, especially in data-limited scenarios. Future work will focus on enhancing the robustness of MetaRT by incorporating a wider variety of peptide classes to further refine its predictive capabilities.
Kidneys are highly susceptible to metabolic changes associated with diabetes, which contribute to the progression of chronic kidney disease (CKD). Among the various cell types in the kidney, proximal tubular epithelial cells (PTECs) are particularly affected by diabetes. However, accurate detection and quantification of essential energy coenzymes such as nicotinamide adenine dinucleotide hydrogen (NADH) and flavin adenine dinucleotide (FAD) in PTECs have been challenging. In this study, we employed fluorescence lifetime imaging (FLIM) with a phasor analysis approach to quantitatively assess metabolic activity specific to PTECs in diabetic kidneys. We analyzed NADH and FAD lifetime in PTEC cells through FLIM analysis and also analyzed metabolic changes in human kidney tissue according to the CKD stage. Furthermore, we compared metabolic changes following glucagon-like peptide-1 receptor agonist (GLP-1RA) treatment using db/db mice. Our results demonstrated a significant reduction in NADH lifetime in both the mitochondria and cytoplasm of PTECs under high glucose conditions. Phasor analysis in db/db mice revealed shortened NADH and FAD lifetimes, which were quantitatively validated by increased NADH and decreased FAD production in db/db kidneys, indicating a high redox ratio in diabetic kidneys. Additionally, the phasor plot and lifetime measurements effectively reflected metabolic alterations in db/db kidneys in response to GLP-1RA treatment. In the kidneys of patients with type 2 diabetes, the peak region of the phasor plot shifted counterclockwise toward longer lifetimes as CKD progressed compared to normal kidneys. Quantitative imaging using FLIM in diabetic kidneys enables the detection and measurement of NADH and FAD with spatial information in PTECs, thereby providing insights into the progression of diabetic kidney disease and the response to treatment.
Protein fucosylation is a biologically significant post-translational modification that modulates protein stability, receptor signaling, and immune function. However, the structural heterogeneity of N-glycopeptides complicates reliable discrimination of fucosylation topology, particularly between core and outer modifications. In this study, we developed and systematically evaluated a machine learning–based framework for automated classification of N-glycopeptide fucosylation into four categories (none, core, outer, and dual) directly from tandem mass spectrometry (MS/MS) data. A total of 1320 glycopeptide spectra derived from immunoglobulin G (IgG) and alpha-1-acid glycoprotein (AGP) were used for supervised training and testing. Thirteen diagnostic fragment ions were extracted as quantitative features and applied to deep neural network (DNN) and support vector machine (SVM) classifiers. Model robustness was enhanced through architectural refinement and imbalance-aware optimization strategies, including focal loss and synthetic minority over-sampling. The optimized DNN model using collision-induced dissociation (CID) spectra achieved 96.5
Two-dimensional (2D) platinum diselenide (PtSe2) has emerged as a promising noble-metal-based transition metal chalcogenide (TMD) for next-generation nanoelectronics due to its excellent chemical stability and favorable carrier mobility. Although defect-mediated electronic tuning is important for advancing PtSe2-based device platforms, existing post-growth approaches, i.e., plasma and ion-based treatments, tend to cause structural damage which are not compatible with wafer-scale processing. Thermal annealing offers a more scalable and less destructive route; however, the structural, chemical, and electronic evolution of wafer-scale CVD-grown PtSe2 under thermal treatment remains insufficiently understood. In this work, we systematically investigate wafer-scale CVD-grown PtSe2 layers before and after post-annealing process to clarify the thermal-driven modification of their structural, optical, chemical, and electronic properties. As-prepared PtSe2 layers exhibit uniform- and wafer scale-layered crystallinity and intrinsic p-type semiconducting behavior. Upon moderate annealing at 400 ℃, the PtSe2 layers undergo a distinct transition toward metallic-like transport, revealed by reduced gate modulation, suppressed temperature-dependent conductance, and the pronounced decrease in activation energy. X-ray photoelectron spectroscopy identifies the shift toward Pt-rich, Se-deficient stoichiometry accompanied by the emergence of metallic Pt⁰ states, indicating thermally induced chalcogen-vacancy formation. Complementary optical absorbance and Tauc-plot analyses confirm bandgap narrowing and a downward shift of the Fermi level, consistent with defect-induced restructuring of the electronic band landscape. This study establishes thermal annealing as a scalable and effective route for defect-engineered modulation of PtSe2 layers, enabling a controllable semiconductor-to-metal-like transition suitable for next-generation electronic, sensing, and contact-engineered device architectures.
This work presents a deep learning-based pipeline for reliable quantitative analysis of Scanning Transmission Electron Microscopy (STEM) images acquired under practical low-dose conditions, in which shot noise and scan distortion can degrade the accuracy of subsequent measurements. The proposed workflow integrates four tasks in a unified analysis sequence: denoising, atomic position localization, atomic classification, and segmentation. Supervised models are trained on the physics-based TEMImageNet dataset generated by forward modeling. We employ UNet3+ for image denoising and for predicting a Gaussian map that encodes atomic center likelihoods. Atomic center positions are extracted from either the denoised image or the corresponding map, and the atoms are then grouped using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). For instance segmentation, we introduce a two-stage strategy in which the predicted Gaussian map provides point prompts to Segment Anything Model 2 (SAM2), and the Gaussian map input is used to improve the separation of closely spaced atoms. Experiments show that the denoising model improves image fidelity while preserving atomic structure information, and that the Gaussian map, rather than the denoised image, leads to more accurate separation of adjacent atoms in segmentation. Overall, the proposed pipeline is robust to low-dose noise and acquisition-related artifacts, and produces baseline outputs that can support atomic-scale deformation mapping, defect quantification, and microstructure analysis.
Tolperisone (Tol) is a centrally acting muscle relaxant prescribed for conditions involving muscle pain. It is typically administered orally as a racemic mixture; however, variations in pharmacological properties between the two enantiomers have been reported, with the S-(+) enantiomer exhibiting greater muscle-relaxant activity. In this study, a capillary electrophoresis (CE) analytical method was developed to quantify Tol enantiomers in pharmaceutical formulations. Additionally, the chiral separation mechanisms were explored through a molecular modeling study utilizing SwissDock, a freely accessible, web-based small-molecule docking service. The effects of key CE parameters (background electrolyte concentration and pH, chiral selector type and concentration, temperature, and separation voltage) were thoroughly investigated. Separation was achieved within 15 min in an internally uncoated fused-silica capillary (50 μm ID, 56 cm effective / 64.5 cm total length) using a phosphate buffer containing 20 mM carboxymethyl-β-cyclodextrin (CM-β-CD) as the background electrolyte. The migration order and identification of the R and S enantiomers were ascertained through chiral liquid chromatography and optical rotation measurements. The proposed CE method was fully validated and employed to quantify each enantiomer in commercial tablets. Molecular docking results indicated the formation of inclusion complexes between the enantiomers and CM-β-CD, accompanied by ionic interactions and hydrogen bonds. However, enantio-resolution was primarily attributed to differences in the electrophoretic mobilities of the complexes rather than to enantiospecific affinities.
Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the removal efficiency of the cationic dye methylene blue from wastewater using activated carbon derived from Calotropis gigantea leaves (CGAC). The adsorbent was characterized by SEM-EDAX, FT-IR, XRD, BET, and XPS analyses. Batch adsorption studies showed that the equilibrium data were best described by the Freundlich isotherm, followed by Langmuir and Temkin models over the investigated temperature range, while the adsorption kinetics followed a pseudo-second-order model. A dataset comprising 128 experimental runs (100 for training and 28 for testing) was employed for model development using initial dye concentration, contact time, temperature, adsorbent dosage, and pH as input variables. Principal component analysis (PCA) was applied, but did not significantly alter model performance compared to the raw data. Among 14 ANN training functions and three transfer functions, the trainbr–tansig combination provided the highest predictive accuracy. In the ANFIS framework, the Gaussmf membership function with four memberships yielded optimal results. Although ANFIS achieved excellent training accuracy, ANN demonstrated more stable and reliable generalization across both training and testing datasets. Sensitivity analysis identified contact time as the most influential parameter governing dye removal. ANN and ANFIS are confirmed as effective modelling tools for predicting and optimizing dye adsorption by CGAC, with ANN showing superior robustness.
Abstract Background Determining the sources and contribution rates of metal contamination through multi-isotope analysis is an urgent and important topic in environmental research. However, acid digestion and extraction methods vary worldwide depending on the environmental media and researcher, potentially resulting in bias in measured concentrations and isotope ratios. Therefore, this study analyzed differences in element concentrations and multi-isotopic compositions (Cu, Zn, Pb, and Sr) between the total digestion and aqua regia extraction methods using six certified reference materials (CRMs) and 11 environmental samples. Finding Although differences depend on the type of CRM, the average recovery rates for metal elements extracted using the aqua regia extraction method ranged from 50.8% for Ti to 97.9% for Mn of the total concentration. For Cu, Zn, and Pb, which are widely used in environmental pollution research using stable isotopes, the average recovery rates obtained using the aqua regia extraction method were 97.5% for Cu, 97.6% for Zn, and 96.0% for Pb of the total contents, sufficiently high to warrant isotopic analysis. The Cu, Zn, and Pb isotopic compositions of CRMs did not differ between the two acid digestion methods and were consistent with previously reported results. For Sr, the average and range of recoveries extracted by the aqua regia extraction were 87.7% and 80.8–98.7%, respectively. The Sr isotopic composition in the aqua regia extraction was slightly lower than that in the total digestion method. However, considering the greater variability of strontium isotopes in environmental samples, the differences in Sr isotopic values across different acid digestion methods are likely to be minimal. Conclusion The concentration and isotopic composition of the CRMs using the aqua regia extraction method presented in this study will significantly contribute to improving the accuracy of analytical data for trace elements in environmental studies.
Despite the widespread adoption of magnetic resonance imaging (MRI), safety incidents during MRI examinations continue to be reported. Building upon prior FDA-led analyses of MAUDE data, this study extends the temporal scope and analytical depth to systematically examine MRI-related adverse events reported between 2018 and 2024. Using a semi-automated classification framework that combined keyword-based text analysis with expert review, MRI-related incidents were categorized into granular safety domains, and inter-category correlations and associations were analyzed to characterize incident patterns at the level of individual events. The results demonstrate that MRI-related incidents persist annually, with no substantial reduction observed in Operation- and RF/Gradient-related events, which together account for a large proportion of reported cases. In parallel, implant-related incidents have become increasingly prominent and are associated with the majority of reported MRI events, extending beyond any single failure category. Although further subclassification of implant-related incidents was attempted, substantial semantic overlap among incident descriptions highlighted the complex and intertwined nature of implant-associated MRI safety challenges. Overall, these findings indicate that contemporary MRI safety risks are increasingly shaped by interactions among implants, software-based systems, and operational factors, rather than isolated hardware failures alone. Furthermore, the developed analytical pipeline establishes a reproducible measurement framework for large-scale medical device surveillance. This study provides data-driven insights that may inform future medical device design, more conservative MRI safety guidelines, and expanded education for both medical professionals and patients.
Abstract To improve the reliability of the liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis of biological samples, it is necessary to evaluate the measurement uncertainty (MU). Although the Guide to the Expression of Uncertainty in Measurement (GUM) is widely used as an international standard, it does not sufficiently reflect potential correlations between input variables and nonlinear factors, resulting in an overestimated uncertainty of an analytical method. In this study, GUM and the Monte Carlo method (MCM) were used to estimate the MU of urinary N-desethyl blonanserin (DBNS) concentration. LC–MS/MS was used to quantify urine DBNS levels based on a calibration curve. GUM overestimates the MU in sample preparation and calibration curve construction compared to MCM. In the case of sample preparation, GUM obtained the combined relative standard uncertainty (RSU) for each sample by taking the square root of the sum of RSU values, assuming a uniform error distribution. By contrast, MCM generated random numbers within the actual error range of each sample and added them to form a trapezoidal distribution as the sum of two uniform distributions. In the case of calibration curve construction, GUM used approximate values to estimate the uncertainty for the calibration curve fitted using least squares regression. However, the correlation coefficients between inputs have a considerable effect on the estimated MU. Therefore, the correlation coefficients between input variables in MCM mitigated the contribution of the input variables to the uncertainty budget, ultimately reducing uncertainty by 9.9%, and thereby improving the reliability of quantifying low‑concentration metabolites in biological samples. Despite its mathematical advantages, routine adoption of MCM in bioanalytical laboratories is often limited by programming barriers. To address this, we provide an adaptable, open–source R–script framework that simplifies MCM implementation in LC–MS/MS workflows. By facilitating robust uncertainty estimation, this toolkit enhances the reliability of trace–level bioanalytical measurements and extends the methodological utility of MCM to routine laboratory practice without requiring advanced computational expertise.
Abstract The Iron-Sulfur World Hypothesis, a hypothesis for the origin of life on early Earth, posits that hydrogen generation from sulfide minerals such as iron sulfide was the first step in the origin of life. This study investigated whether hydrogen evolution from sulfide minerals is possible at room temperature and atmospheric pressure, without application of any external potential. The spontaneous hydrogen evolution reaction by iron-nickel sulfide Fe x Ni(1−x)S, which is instantaneously generated by injecting a precursor of iron-nickel sulfide into a batch reactor under alkaline anoxic conditions at room temperature and atmospheric pressure, was confirmed by analyzing the composition of the reactor headspace gas. Gas evolution analysis showed that a significant amount of H2 was produced, which was influenced by the Fe content, with the maximum level observed in FeS without the injection of Ni-containing precursors. Structural characterization using X-ray diffraction (XRD) and X-ray absorption near edge structure (XANES) confirmed the presence of amorphous and nanocrystalline phases, highlighting the influence of composition and synthesis conditions on the reactivity of the material. These findings provide insight into the H2 evolution from iron-nickel sulfides under anoxic, alkaline conditions and represent an important experimental demonstration of spontaneous abiotic H2 generation under geochemically relevant ambient conditions.
Abstract Isotopic analysis of uranium particles collected from nuclear facilities provides important safeguards-relevant information on material origin and uranium enrichment status. In this study, uranium particle isotope ratios were measured using a large geometry- secondary ion mass spectrometry (LG-SIMS) installed at Korea Basic Science Institute (KBSI). The accuracy and precision of the analytical protocol were evaluated using the uranium certified reference materials (CRM). In addition, the effect of sample substrates on analytical performance was assessed by comparing conventional carbon planchets with silicon wafer substrates for particle fixation. The results confirm that LG-SIMS provides reliable uranium isotope ratio measurements at the single-particle level. As in the case of 235U with a relatively strong signal, the analytical results demonstrated accuracy within ~ 1% bias, independent of the CRM and substrate type. In addition, a decrease in uranium hydride formation was observed on silicon wafers compared to carbon planchets. These findings confirm the suitability of silicon wafers as a cost-effective alternative substrate for uranium particle analysis.
SARS-CoV-2 exhibits substantial genomic diversity, with emerging variants posing challenges to global public health. In this study, we applied unsupervised clustering to genome sequences using k-mer encoding to explore viral genetic variation. Both 2-mer and 3-mer representations were analyzed with hierarchical clustering and dimensionality reduction techniques (PCA and t-SNE) to identify underlying genomic structure. Internal validation metrics consistently indicated four optimal clusters, with the 3-mer + t-SNE configuration achieving the highest cluster quality (Silhouette Score = 0.648, Calinski-Harabasz Index = 2443.3, Davies-Bouldin Index = 0.325). Cluster analysis revealed distinct lineage-defining mutations, including D614G and P681H in Clusters 1 and 4, and N501Y and E484A in Cluster 4, highlighting their functional relevance in infectivity and immune escape. Sensitivity analysis confirmed that these mutations significantly contributed to k-mer feature vectors and cluster separation. External validation using PANGO lineage annotations demonstrated strong concordance (ARI = 0.83, NMI = 0.86), confirming that the clustering framework effectively recapitulates known viral population structure. These results provide a scalable and biologically meaningful approach for monitoring SARS-CoV-2 genomic diversity, detecting emerging variants, and supporting genomic epidemiology efforts.
This study presents an innovative approach for optimizing microwave-assisted arsenic extraction in rice samples by integrating multiple machine learning (ML) models with grey wolf optimization (GWO). Four critical extraction parameters were systematically evaluated: microwave power (300–600 W), temperature (50–80 °C), extraction time (30–60 min), and nitric acid concentration (0.1–0.5 M). A comprehensive hybrid framework was developed, incorporating six distinct machine learning (ML) models: The following boosting methods are employed: XG Boosting, LS Boosting, KRR_RBF (Kernel Ridge Regression with RBF kernel), SVR_Poly (Support Vector Regression with Polynomial kernel), a weighted hybrid, and a stacked ensemble. The stacked ensemble model achieved excellent performance (R²=0.986, CCC = 0.993, EF = 0.986) and outperformed individual models. GWO optimization identified three optimal operating points, with the highest yield (1.21 µg/g) achieved at 599.52 W and 50.03 °C, exhibiting only 4.20
This paper examines the erosion of Public Key Cryptography (PKC) security under adaptive adversarial optimisation driven by artificial intelligence. The problem addressed is the growing mismatch between algorithm-centric cryptographic security models and operational attack realities, where adversaries exploit implementation-level observability rather than breaking cryptographic primitives. The methodology integrates a reproducible bibliometric analysis of Web of Science records, qualitative evidence from twenty expert interviews and three industry workshops, and a technical synthesis of AI-enabled attack mechanisms across the cryptographic lifecycle. Results show that existing research is structurally concentrated on algorithmic robustness, with no significant focus on AI-driven attack vectors, while 82% of practitioners attribute private key compromise to AI-augmented optimisation and side-channel inference. The paper's contribution is fourfold: (1) identification of a systemic research gap in AI-enabled cryptographic attacks; (2) development of an adaptive adversarial threat model spanning key generation to validation; (3) empirical validation of implementation-layer compromise mechanisms; and (4) formulation of AI-aware cryptographic resilience requirements extending beyond post-quantum approaches. The findings demonstrate that cryptographic security must be reconceptualised as an adaptive, system-level property rather than a function of algorithm strength alone.
Abstract Polymer electrolyte membranes (PEMs), such as Nafion, have been widely employed as separators between the anode and cathode electrodes in various fuel cells and electrolyzers due to their excellent ionic conductivity and durability. However, several critical aspects of PEMs, including the mechanisms of proton conduction, remain poorly understood. Moreover, the native, degraded, and regenerated states of PEMs require further investigation to enhance their performance. Consequently, comprehensive integrated analyses, particularly those integrating multiscale and multiple time-scale techniques, are essential not only for understanding the behavior of PEMs but also for improving their functional properties. In this context, we briefly overview various analytical methods used for structural and dynamical characterization of PEMs, including the self-diffusion and translational dynamics of ions. The application of solid-state nuclear magnetic resonance spectroscopy to the study of PEMs is also reviewed. In addition, in-situ/operando analyses and the integration of artificial intelligence (AI) or machine learning with accumulated analytical data are discussed as emerging strategies for developing new design concepts for PEMs.
Abstract The stratum corneum (SC)—the outermost layer of the skin—serves as a barrier that protects the body from environmental stress and prevents transdermal moisture loss. Skin ceramides comprise a sphingoid base backbone linked via an amide bond to a fatty acid. Fatty acids in ceramides are classified into four types: non-hydroxy (N), α-hydroxy (A), ω-hydroxy (O), and esterified ω-hydroxy (EO). Among these, O-type ceramides are ester-linked via their ω-hydroxyl group to the cornified envelope protein. While O-type ceramides containing four distinct sphingoid bases have been reported, bound-type acylceramides featuring an additional acyl group esterified to the sphingoid hydroxyl remain unreported. Therefore, this study aims to identify and characterize 1-O-esterified O-type phytoceramide (1-O-EOP) as a novel class of bound ceramides in human SC and murine epidermis. Using high-resolution mass spectrometry and direct infusion-ion trap mass spectrometry, we show that these ceramides are esterified with an additional fatty acid at the 1-hydroxy group of the phytosphingosine backbone within ω-hydroxy ceramides. Based on the tandem mass spectrometry fragmentation patterns of 1-O-EOP ceramides, comprehensive fragmentation schemes are proposed. In addition, we have developed a method to identify and profile 1-O-EOP ceramides in human SC and murine epidermis, providing a foundation for detecting bound 1-O-acylceramides in mammalian skin.
