Search Results (1,082)

Tacrolimus (TAC) nephropathy is a major complication of immunosuppressive therapy and contributes to chronic kidney disease (CKD) progression through ischemia, metabolic dysfunction, and oxidative stress; however, its protein-level basis remains unclear. This study sought to identify characteristic molecular alterations in renal cortices of TAC-treated mice, so as to clarify the link between replication stress responses and metabolic dysfunction. A previously generated proteomic dataset from a TAC-induced chronic nephrotoxicity mouse model was analyzed using a protein-centered analytical strategy, including statistical, Gene Ontology, pathway, upstream regulator, and disease-enrichment analyses. A total of 7466 proteins were quantified. Upregulated proteins included KAT6A and NCKAP1, whereas downregulated proteins included NDUFC2, HSD17B12, and TECR. Coordinated impairment of CoQ₁₀-dependent and glutathione-dependent antioxidant defenses was identified, reflected by reductions in AIFM2 (FSP1) and GSTA4/GSTT2. Enrichment analyses indicated activation of MCM- and ATR-associated replication stress responses in the upregulated group, and impaired lipid metabolism, CoA biosynthesis, mitochondrial function, and redox regulation in the downregulated group. TAC nephropathy is characterized by two major molecular signatures: central disruption of antioxidant defense systems, spanning FSP1-mediated CoQ₁₀ regeneration and GST- associated antioxidant systems, together with suppression of lipid and energy metabolism and activation of replication stress responses. These findings provide a protein-level molecular framework linking coordinated impairment of antioxidant defense systems, suppression of lipid and energy metabolism, and activation of replication stress responses in TAC-induced chronic nephrotoxicity. These findings also suggest the FSP1 pathway, GST-associated antioxidant systems, and CoA-dependent metabolism as potential therapeutic targets for CKD progression. Full article

Extracellular vesicles (EVs) from ovarian cancer cells are valuable sources for candidate biomarker studies, but conventional static flask culture yields limited material and is difficult to scale reproducibly. We evaluated a serum-free CELLine AD 1000 bioreactor workflow for producing EVs from four ovarian cancer-related (OC-related) cell lines (OVCAR4, CaOV3, PA1, SW626) and human dermal fibroblasts (HDFa) as a non-cancer control. Cells were adapted to CDM-HD serum-free medium and maintained for eight weeks with twice-weekly conditioned-medium collection. EVs were isolated by differential ultracentrifugation followed by size-exclusion chromatography and characterized by nanoparticle tracking analysis, imaging flow cytometry, Western blotting, and transmission and scanning electron microscopy. Across longitudinal harvests, OC-related cultures generally produced higher EV particle concentrations and A280-based bulk protein estimates than HDFa, while individual cell lines showed distinct production profiles and membrane-associated growth patterns. A parallel OVCAR4 T-175 flask, maintained in its original serum-containing medium, provided a contextual reference indicating higher per-collection EV particle recovery with the bioreactor, although this was not a matched culture-format comparison. EV-enriched preparations contained vesicle-like particles, with modal diameters of approximately 96–128 nm. Using imaging flow cytometry, the CD9 signal was higher in OC-related EVs and CD63 was most prominent in HDFa; CD9 and CD63 were also detected in OC-related EV lysates by Western blotting. Because one bioreactor was operated per cell line, these findings should be interpreted as preliminary and descriptive rather than statistically comparative. Overall, this study provides a practical serum-free CELLine AD 1000 workflow for generating characterized OC-related EV material for downstream analytical studies. Full article

Standard Retrieval–Augmented Generation (RAF) often fails in safety-critical manufacturing and reveals the inherent problem of additional and inappropriate retrieved context topics. In engine manufacturing, where topic-based documentation is widely variant-driven, semantic similarity no longer correlates with technical relevance, assembly procedures for engine variants are linguistically often almost identical yet operationally incorrect. Recent RAG systems, relying on the LLM’s ability to discern this context noise during the generation phase, might fail to account for this extreme content-wise overlap. This paper proposes a dual-optimization strategy: ensemble sizing and semantic level enhancement. Instead of relying solely on vector similarity, we partition the retrieval space into sub-ensembles and apply a metadata scoring function. Our findings demonstrate that this hybrid approach might help to transform RAG-based systems into more variant-appropriate delivery systems essential for safety-critical environments. Full article

To address three major industry pain points—the lack of quantified profit-sharing standards in the two-stage pricing under the separation model of military research and production, the absence of stable Nash equilibrium in single-layer synchronous optimization, and insufficient incentives for full-cycle process optimization in design units—this paper constructs a two-level Stackelberg leader–follower game model with the general contracting unit as the leader and design and general contracting units as followers. This aligns with current prototype incentives and phased pricing policies for production rewards and penalties. At the theoretical level, it improves the complete proof system for the two-stage concave profit two-level Stackelberg Nash equilibrium, distinguishes the mathematical differences in equilibrium existence between sequential decision-making and synchronous optimization, and extracts general rules for phased differentiated profit sharing: high-innovation segments should be allocated more profit weight; simply maximizing total alliance profit may cause imbalanced interests, while introducing a minimum net profit-weighted objective can achieve Pareto improvements without profit loss. This conclusion can be applied to multi-stage general contracting scenarios across industries, such as EPC and military–civil collaborative innovation, enriching the basic theory of profit sharing and hierarchical games. Theoretically, the existence of the lower-level Nash equilibrium is proven using Brouwer’s fixed-point theorem, and combining it with the strictly monotonically decreasing feature of the best response function, uniqueness of the equilibrium is derived. Multiple sets of differentiated initial values are simulated to rule out multi-equilibrium bifurcation risk. The model incorporates the military’s reward and penalty policies as rigid exogenous constraints, sets dual individual rationality constraints of ‘cooperative profit greater than baseline profit with no allocation, and both parties’ net profit non-negative,’ and introduces differentiated cost-reduction efficiency and quadratic increasing effort costs to characterize the heterogeneous input of the two types of development entities. For models with piecewise nonlinearity and multi-constraint nonconvex structures, this paper modifies the standard PSO into a Bi-PSO solving framework through hierarchical temporal adaptation. It does not innovate the underlying particle update mechanism and is only used to match the sequential decision order of the leader–follower game. By comparing five algorithms—IPM, GA, SA, DE, and adaptive PSO—through 20 repeated simulations: gradient-based interior point methods easily get stuck in locally invalid solutions that violate cooperation thresholds; differential evolution has the best numerical global search performance, but all general evolutionary algorithms optimize allocation and effort variables simultaneously, disrupting the Stackelberg hierarchical timing. Only Bi-PSO maintains consistent game logic. Using a pricing case for a certain type of equipment and jointly calibrating all parameters with policy documents, three simulation scenarios were set up: no allocation, equal 50/50 split, and single-layer profit maximization. Under the no-allocation mode, R&D investment from the design unit drops to zero and alliance benefits plummet; a blanket equal split ignores differences in technical contributions across two stages, leading to clear efficiency losses; single-layer optimization only pursues total profit maximization, causing a severe imbalance in profit distribution. The two-layer basic framework can achieve the upper limit of alliance benefits, and by adding a weighted optimization goal that considers both total profit and cooperation fairness, it can achieve equal net profits for both parties without reducing overall profit. Through single-parameter sweeps and two-factor heatmap simulations, the study further revealed the coupled effects of main party efficiency and mass production rewards and penalties on equilibrium input and optimal sharing ranges. A robust check was performed by replacing the logarithmic concave output function, producing a standardized allocation range resilient to parameter perturbations: optimal split for the prototype stage is 0.4–0.6 for the design unit, and for mass production stage 0.7–0.9. The findings suggest that high-contribution stages in multi-phase collaboration contracts should receive more benefits, and a weighted fairness objective can achieve Pareto improvements. These conclusions can extend to multi-stage collaboration scenarios such as EPC and military–civilian cooperation. Theoretically, this research further completes the equilibrium proof system for two-party concave payoff two-layer games, providing a new reference for the theory of phased differentiated benefit-sharing contracts in the military sector. Methodologically, it proposes a two-layer intelligent solving tool adapted to leader–follower sequential decisions, effectively mitigating issues where single-layer model equilibria fail or analytical algorithms struggle with multi-constraint nonconvex games. The results can provide quantitative support for the military, general contracting unit, and design unit in drafting equipment incentive pricing contracts and managing full-cycle cost collaboration. Full article

Food contaminants, including plasticizers, pesticide residues, heavy metals, and biotoxins, pose persistent risks to food quality and human health. Their diverse sources, complex migration pathways, and potential long-term toxicity make removal difficult. Conventional removal technologies, such as physical treatment, chemical degradation, adsorption, membrane separation, and biological methods, can reduce contaminant levels to varying degrees. However, they often show limited selectivity, matrix interference, harsh operating requirements, or losses of nutritional and functional components. Molecularly imprinted polymers (MIPs) are synthetic recognition materials with binding sites tailored to a target contaminant. Their template-induced cavities provide complementarity in size, shape, and functional-group arrangement, enabling selective adsorption in complex matrices. Recent studies apply MIPs to the enrichment, detection, and removal of plasticizers, pesticide residues, heavy metals, and biotoxins. Unlike recent surveys centered on MIP-assisted analysis and sensing, this review uses contaminant removal as the organizing problem and compares MIP-based strategies with conventional decontamination across four hazard classes. MIPs offer tunable selectivity, chemical stability, and reusability, but practical food applications still face template leakage, slow mass transfer, incomplete safety evaluation, matrix dependence, and scale-up limitations. Future work should prioritize green synthesis, surface imprinting, magnetic recovery, and systematic validation in real food matrices. To prevent analytical extraction from being conflated with remediation, the evidence is classified from proof-of-binding and analytical cleanup to edible-matrix treatment and process validation, and representative studies are compared using capacity, removal or recovery, equilibration time, selectivity, reuse, and matrix validation. Recent evidence also reveals substantial gaps for PFAS, microplastics, and nanoplastics: selective recognition is advancing, but food-safe removal remains largely unvalidated. Full article

Echelon utilization of power batteries serves as a crucial pathway to achieving resource recycling and reducing environmental impacts. However, its large-scale development is constrained by the cognitive disparities between formal enterprises and new energy vehicle (NEV) owners. Previous studies have largely proceeded from single technological, economic, or policy dimensions, relying on quantitative data or a single-stakeholder perspective, while neglecting the fuzziness and subjective uncertainty inherent in the qualitative judgments of corporate experts, and failing to effectively integrate the opinions of vehicle owners. To address these shortcomings, this study constructs a hybrid evaluation framework that integrates Fuzzy DEMATEL with Importance-Performance Analysis (IPA). Through content analysis and Exploratory Factor Analysis (EFA), we distill key criteria and underlying dimensions. We collect both importance ratings and performance scores from corporate experts and NEV owners respectively, and compare the IPA results of the two groups. This study contributes in the following aspects: (1) It establishes a multi-dimensional analytical framework covering both technical and social dimensions, thereby enriching the application of Socio-Technical Systems Theory in the field of echelon utilization; (2) A hybrid method integrating Fuzzy DEMATEL and IPA is proposed, in which the centrality of each criterion calculated by DEMATEL serves as the importance indicator in IPA. This approach effectively handles the fuzziness inherent in experts’ linguistic evaluations and the interdependencies among criteria, and reveals the cognitive differences between the two parties through comparative analysis; (3) Based on the dual-perspective comparison, the analytical results show that while both parties share consensus on certain technical capabilities, significant cognitive divergences exist regarding policy incentives and backend technology investment, providing a scientific basis for enterprises to optimize resource allocation. Full article

Wind turbines are critical infrastructure whose economical deployment benefits from blade monitoring. Tower-radar remote sensing generates radargrams from a mast-bound active sensor for this and related purposes. Initial machine learning classifiers are publicly available, yet they have not been stress-tested. Suitable test imagery remains scarce. This paper addresses these gaps through interconnected investigations, supported by two data contributions: a synthetic surrogate benchmark (spanning eight image dimensions in a full-factorial design) and an enrichment of the empirical WiRoRa dataset augmented with human annotations and machine-generated ones where the latter can also serve as an on-the-fly labeling solution in the field. The studies report: multi-class anomaly filtering; a sensitivity analysis revealing that the end-to-end-trained (E2E) classifier is poorly calibrated, while its pretrained counterpart is substantially more stable; adversarial vulnerability evaluation showing that the E2E model is also more easily fooled; an analytical derivation of when/why bottleneck training on auxiliary imagery improves representations; a surrogate test confirming the bottleneck hypothesis; and a preliminary Mixture-of-Experts pilot for enhanced traceability as well as scalability that performs environmental/operational metaparameter regression as an archetypal example. Together, the results expose failure modes of existing classifiers and chart a path toward intrinsically interpretable systems for structural health monitoring and beyond. Full article

Regulatory CD4+ T cells (Tregs) are essential for immune tolerance, tissue repair, and control of inflammation, but functional human Tregs cannot be identified reliably by a single protein. This targeted narrative review evaluates protein-level and proteomics-supported Treg signatures with explicit attention to species, sample source, analytical platform, validation strategy, and intended use. Primary human LC-MS/MS studies reveal pathway-level differences involving T-cell receptor signaling, metabolism, lysosomal activity, and lineage protection, whereas murine proteomic studies provide mechanistic candidates such as Themis1 but do not establish human biomarkers. CyTOF, functional single-cell protein profiling, spatially resolved protein imaging, and multi-omics further resolve phenotypic and tissue heterogeneity. Established CD25high/CD127low/FOXP3-based panels support enrichment and phenotyping; CTLA-4, ICOS, TIGIT, GITR, PD-1, chemokine receptors, suppressive enzymes, metabolic proteins, and emerging candidates report functional or tissue states but are not Treg-exclusive. Cancer currently provides the strongest tissue-level and prognostic evidence, whereas data in healthy aging, cardiovascular and metabolic disease, osteoarthritis, and neurodegeneration remain heterogeneous and mainly exploratory. Across contexts, validated diagnostic sensitivity, specificity, reference ranges, prospective clinical utility, and inter-laboratory reproducibility are largely absent. Treg immunoproteomics is therefore best regarded as a discovery and stratification framework rather than a standardized clinical diagnostic test. Full article

Dietary patterns increasingly influence research on neurodegenerative diseases, with attention shifting from isolated nutrients to integrative nutritional models. The Amazonian Diet, a biodiversity-based dietary pattern rich in native fruits, seeds, freshwater fish, and cassava-derived foods, is naturally enriched in bioactive compounds including polyphenols, anthocyanins, carotenoids, methylxanthines, selenium, vitamins, and unsaturated fatty acids. This review aimed to investigate the potential relevance of key foods derived from the Amazonian Diet to Parkinson’s disease (PD) by integrating compositional nutritional analysis, multivariate analytical approaches, and mechanistic evidence synthesis. In Stage 1, the composition of 36 Amazonian foods was analyzed using TBCA and FAO data, followed by hierarchical clustering analysis (Ward’s linkage, Euclidean distance). Distinct compositional patterns were identified, highlighting foods with high bioactive diversity, relevant lipid composition, and dietary fiber. In Stage 2, an integrative literature review (PubMed/MEDLINE, SciELO) of in vitro, in vivo, observational, and clinical studies suggested that açaí berry, guaraná, cocoa/cacao, camu-camu, and Brazil nuts contain nutrients and bioactive compounds that intersect with biological pathways implicated in PD, including oxidative stress, mitochondrial dysfunction, and neuroinflammation. However, this review does not evaluate the effects of the Amazonian Diet on PD incidence, progression, symptoms, levodopa response, or biomarkers. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly being evaluated for ophthalmic diagnosis, screening, and triage, yet its role in paediatric eye care remains less established than in adult ophthalmology. This systematic review aimed to synthesise evidence on AI-enabled tools for paediatric ophthalmic diagnosis, screening, triage, surveillance, and referral, with an emphasis on diagnostic performance, safety, workflow integration, equity, and implementation readiness in primary, community, and primary care-relevant settings. Methods: A PRISMA-guided systematic review was conducted using MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore from inception to 30 March 2026. Eligible studies evaluated AI or machine-learning tools for children and adolescents aged 0–18 years in relation to paediatric eye conditions. Study selection and data extraction were undertaken independently by reviewers, with disagreements resolved by consensus or third-reviewer adjudication. Methodological and reporting quality was evaluated using an author-adapted six-domain rubric informed by APPRAISE-AI. Diagnostic-accuracy studies were assessed using an author-adapted QUADAS-2 framework incorporating QUADAS-AI-informed AI-specific considerations, the prediction-model study was assessed using PROBAST+AI, and the non-randomised treatment-effect study was assessed using ROBINS-I. The public dataset descriptor was evaluated separately using an author-developed dataset-quality, representativeness, and applicability framework. Because of clinical and methodological heterogeneity, findings were synthesised thematically. Results: Twelve empirical studies and one public dataset descriptor were included, covering retinopathy of prematurity, retinoblastoma, amblyopia risk, myopia, congenital cataract, and visual-acuity assessment. AI systems frequently demonstrated promising diagnostic or screening performance, including sensitivity-first detection of treatment-requiring retinopathy of prematurity, high discrimination for retinoblastoma activity, and strong myopia prediction using fundus images. Several studies supported feasibility in neonatal, school, and community workflows using smartphone-based imaging, task-shifted operators, tele-referral, and human-in-the-loop review. However, external and temporal validation, calibration, patient-level reporting, subgroup and fairness assessment, and economic evaluation were limited. Conclusions: AI-enabled tools show promise for supporting selected paediatric ophthalmic screening, triage, and surveillance pathways, particularly when combined with image-quality control, explicit escalation, and human oversight. However, confidence in the reported performance is limited by single-centre studies and enriched samples, small numbers of clinically important cases, heterogeneous analytical units, potentially optimistic aggregation procedures, limited external or temporal validation, incomplete calibration, and absent fairness analyses. Routine autonomous implementation remains premature. Full article

Continuous and noninvasive body fluid monitoring has attracted increasing attention in personalized healthcare, chronic disease management, and wearable point-of-care testing. Fiber-optic surface plasmon resonance (SPR) biosensors are particularly promising for this purpose because they combine label-free and real-time with miniaturization and low sample volume requirements. However, current body fluid sensing technologies and conventional bare metal SPR interfaces still face critical challenges, including insufficient analytical accuracy in complex biofluids, broad resonance linewidths, weak signal readability for trace biomarkers, and mechanical perturbations during wearable operation. These limitations highlight the need for nanomaterial-engineered fiber-optic SPR platforms that can convert interfacial molecular events into stable and sensitive signals. The review summarizes recent progress in nanomaterial-enabled fiber-optic SPR biosensors for continuous body fluid monitoring. Emphasis is first placed on nanomaterial mediated local electromagnetic field enhancement and plasmonic mode regulation. Subsequent discussion focuses on their functions in interfacial recognition, analyte enrichment, rapid mass transport, antifouling protection, and flexible integration for continuous operation. On this basis, representative sensing targets, material strategies, and device architectures for tears, urine, exhaled breath condensate, saliva and sweat are systematically analyzed. Finally, current challenges and future opportunities are discussed from the perspective of sensing reliability, wearable integration, and real sample validation. Full article

This article links Mathematics and the Arts through the STEAM approach to highlight the potential of this interdisciplinary approach in early childhood education. From this perspective, the aim is to present the design and implementation of the STEAM activity, We Build Trees with Cuisenaire Rods, and to analyse how soft skills are promoted within a group of 18 five-year-old pupils. Using the Thick Description approach and an analytical model, it has been identified that: (1) Mathematics and the Arts maintained an ongoing dialogue throughout the sequence of seven tasks included in the STEAM activity; (2) the STEAM activity contributed to the development of soft skills such as critical and creative thinking, problem-solving, collaboration and teamwork, technological and digital literacy, curiosity and autonomy, and design and design thinking. We conclude that, from the interdisciplinary STEAM perspective, the Arts are not an instrumental discipline serving Mathematics, nor is Mathematics merely a setting for working on art; rather, both disciplines enrich one another to promote soft skills in early years education. Full article

A chitosan-based edible biofilm enriched with pomegranate peel extract and essential oils was developed as a sustainable active packaging material that exploits agro-industrial by-products, in agreement with circular economy principles. Targeted HPLC–MS/MS analysis quantified twelve polyphenols, with total polyphenol content ranging from 88.20 to 137.38 μg/g dry weight. Ellagic acid was the predominant compound (90.78 μg/g dry weight), followed by gallic acid (28.80 μg/g dry weight). Untargeted HPLC–HRMS analysis revealed additional flavonoid and ellagitannin-related derivatives, confirming the chemical complexity of the incorporated phenolic fraction. ICP-OES analysis showed heavy metal concentrations below the analytical quantification limits, supporting compliance with food-contact safety requirements. During 45 days of refrigerated storage, coated Pecorino Romano PDO cheese exhibited lower yeast and mold counts than uncoated controls, with a maximum reduction of approximately 0.87 log₁₀ CFU/g. Enterobacteriaceae remained below the detection limit in coated samples throughout storage, whereas they were detected in untreated cheese at the end of storage. These findings demonstrate the potential of the developed edible biofilm as a safe and sustainable active packaging material for refrigerated dairy products while promoting the valorization of pomegranate processing by-products. Full article

Objectives: A high-performance liquid chromatography (HPLC) with mass spectrometry (MS) detection method was developed to quantify several tyrosine kinase inhibitors (TKIs) and their relevant metabolites. This method is suitable for therapeutic drug monitoring (TDM) of alectinib, brigatinib, dabrafenib, lenvatinib, lorlatinib, osimertinib and trametinib in patients with solid tumors using volumetric absorptive microsampling (VAMS^®). Methods: The HPLC-MS system contained four pumps, a Turboflow HTLC CycloneTM 1.0 × 50 mm solid phase extraction column for analyte enrichment, and a Kinetex 2.6 µm C18 100Å, 100 × 3.0 mm column for analyte separation. An acetonitrile–water gradient was used for the separation, and the ions generated by ESI (+)-ionization were detected in single-ion mode. This method was validated according to recent European Medicines Agency (EMA) and Food and Drug Administration (FDA) guidelines. Results: The accuracy and precision shown during method validation were within the acceptable limits for all analytes in plasma and whole blood. All analytes showed acceptable stability in both matrices for at least 28 days when stored at −21 °C. So far, 100 venous plasma and 94 capillary blood samples have been collected and analyzed. Conclusions: We developed a reliable method to quantify several TKIs from plasma and capillary blood, which is intended for TDM purposes in clinical practice. Full article

Vitamin D comprises a group of fat-soluble secosteroids traditionally associated with animal physiology, calcium-phosphate homeostasis, and skeletal metabolism. However, vitamin D and related compounds have also been reported in taxonomically distinct non-animal systems, including fungi, microalgae, other algae, phytoplankton, and higher plants, although the strength of evidence differs substantially among these groups. This review synthesizes current knowledge on the occurrence, structural chemistry, UV-B-driven photochemical mechanisms, environmental determinants, analytical challenges, and biofortification potential of vitamin D formation in photosynthetic organisms and fungi. Vitamin D synthesis is initiated by UV-B radiation, primarily within the 290–315 nm range, which converts sterol precursors such as 7-dehydrocholesterol and ergosterol into previtamin D intermediates and is followed by thermal isomerization to the corresponding vitamin D forms. Continued irradiation may additionally generate lumisterol, tachysterol, and other photoproducts, thereby limiting net vitamin D accumulation. This non-enzymatic mechanism supports the interpretation that vitamin D formation can occur outside vertebrates when an appropriate 5,7-diene sterol precursor is accessible to a sufficient UV-B dose. In photosynthetic organisms and fungal matrices, net vitamin D accumulation is constrained by the spectral dose of UV-B, environmental exposure, tissue architecture, sterol localization, oxygen availability, antioxidant capacity, and ROS-mediated degradation. Studies of microalgae and phytoplankton, including reports concerning Emiliania huxleyi, suggest the occurrence or UV-B-dependent formation of both vitamin D₂ and vitamin D₃. However, these findings require evaluation according to the analytical method, use of authentic standards, experimental conditions, and confidence of compound identification. In fungi, the UV-B-induced conversion of abundant ergosterol to vitamin D₂ is well established. Microalgae represent a developing source of vitamin D₂ and vitamin D₃, whereas evidence for nutritionally relevant vitamin D accumulation in higher plants remains limited and heterogeneous. Although higher plants contain diverse phytosterols, the formation of vitamin D₄, vitamin D₅, or related analogues requires appropriate photoreactive 5,7-diene precursors and should not be inferred directly from the presence of common phytosterols such as β-sitosterol. Analytical detection remains challenging because of low concentrations, complex lipophilic matrices, and structural similarity among secosteroids and photoproducts; therefore, reliable identification requires validated analytical procedures. LC-MS/MS provides high sensitivity and selectivity but should be supported by authentic standards, preferably isotope-labelled internal standards, retention-time agreement, quantitative and qualifying ions, matrix-recovery assessment, limits of detection and quantification, and evaluation of ion suppression. Structurally similar analogues and photoproducts may additionally require orthogonal confirmation. Nutritionally, post-harvest UV-B enrichment of edible mushrooms is currently the best-validated strategy for increasing non-animal vitamin D₂ content. Microalgae constitute a developing platform for vitamin D₂ and vitamin D₃ production, whereas biofortification of higher plants remains experimental. Full article