Arginine Vasopressin (AVP) is a nonapeptide synthesized in the hypothalamus and released by the neurohypophysis. It plays a central role in water and electrolyte homeostasis, urine concentration and blood pressure regulation. Dysregulation of AVP secretion is associated with several pathological conditions, including central diabetes insipidus and syndrome of inappropriate antidiuretic hormone secretion. Currently, AVP quantification mainly relies on immunoassays which suffers from limitations such as cross reactivity, limited sensitivity and complex sample handling. Given the very low endogenous concentrations of AVP in human plasma (low ng/L range), a highly sensitive LC-MS/MS method was developed and validated for plasma AVP quantification. AVP was extracted using Oasis WCX solid-phase extraction prior to LC-MS/MS analysis. PBS containing 0.1% BSA was used as surrogate matrix for calibration and validation samples to avoid endogenous interferences. Method validation was performed according to the Clinical Laboratory Standards Institute guidelines. Precision, accuracy, and measurement uncertainty were evaluated using single-nested analysis of variation and e.noval software. Stability in whole blood and plasma samples was also investigated. The method was validated over a concentration range from 0.2 ng/L to 60 ng/L, with precision (coefficient of variation) below 10%, accuracy ranging from 96% and 109%, measurement uncertainty below 15%, and a lower limit of quantification of 0.2 ng/L. EDTA blood samples remained stable up to 3 h at room temperature before centrifugation. In conclusion, we have developed and validated a highly sensitive LC-MS/MS method for the quantification of AVP in plasma. Our novel methodology is well-suited for clinical applications.

GNE myopathy (GNEM) is a rare neuromuscular disorder caused by pathogenic variants in the GNE gene and traditionally associated with impaired sialic acid biosynthesis. In a previous untargeted lipidomic study, we identified a lipid feature significantly enriched in the serum of patients with GNE myopathy compared with healthy controls. In the present work, we aimed to determine the structural identity of this disease-associated feature and to establish a robust analytical strategy for its quantitative assessment. Analysis of the mass spectrometric data pointed to bis(monoacylglycero)phosphate 18:1_18:1 or BMP (18:1_18:1) as a plausible candidate, based on the consistency of the nominal mass and fragmentation pattern with this lipid class. In the present work, we therefore aimed to determine the structural identity of this disease-associated feature and to establish a robust analytical strategy for quantitative assessment. We therefore performed the chemical synthesis of BMP (18:1_18:1) and, based on its MS/MS fragmentation pattern, developed a selective tandem mass spectrometry method for its quantification. Levels of BMP (18:1_18:1) were measured in serum samples from patients with GNE myopathy, healthy controls, and patients with myotonic dystrophy type 2 (DM2) as a disease comparison cohort. BMP levels were significantly increased in the serum of patients with GNE myopathy compared with both healthy controls and DM2 patients. Together, these findings define a previously unrecognized circulating lipid alteration in GNE myopathy and provide new insight into disease-associated lipid dysregulation.

For much of its history, forensic toxicology was built on a core analytical question: what poison or drug is present, and at what concentration? That question still matters, but modern casework increasingly shows that it is no longer sufficient on its own. The field grew from the nineteenth-century chemical toxicology of Mathieu Orfila and the arsenic-detecting Marsh test into a discipline central to medicolegal death investigation, overdose certification, and exposure reconstruction. Yet contemporary practice must now contend with novel psychoactive substances, highly potent synthetic opioids, complex polysubstance deaths, delayed reporting, postmortem redistribution, matrix degradation, and incomplete toxicological context. These pressures are pushing forensic toxicology beyond simple compound detection toward mechanism-aware, data-rich interpretation. This review develops the idea of precision forensic toxicology as a proposed, forward-looking conceptual framework for that transition rather than a description of established or routinely implemented forensic practice. In this model, classical analytical chemistry remains essential, but it is connected to toxicogenomics, transcriptomics, epigenomics, proteomics, metabolomics, exposomics, microbiomics, and spatial multi-omics, then integrated through systems toxicology and artificial intelligence. Recent literature shows that toxicogenomics can reveal early molecular perturbations before overt phenotype, that postmortem metabolomics can assist cause-of-death screening, that RNA- and miRNA-based markers may improve postmortem interval estimation, and that machine learning can strengthen high-resolution mass-spectrometry workflows and multi-omics interpretation. At the same time, routine implementation remains constrained by standardization, validation, explainability, privacy governance, and regulatory uncertainty. None of these components is yet part of routine forensic casework; each remains at a research or early-validation stage, and their translation into daily practice will require dedicated feasibility, validation, and regulatory work, which this review discusses explicitly. Taken together, the evidence suggests that the future of poisoning investigation will depend less on any single assay and more on interoperable workflows that connect toxicant detection with biological response, individual susceptibility, and transparent decision support, and that these workflows are intended to complement, not replace, classical toxicological and medicolegal interpretation.

Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE) that requiring early screening. This study preliminarily explored urinary metabolomic profiles to identify potential, non-invasive candidate biomarkers for the early diagnosis of LN. Urine samples were collected from 55 participants: 20 SLE patients with LN, 20 SLE patients without LN, and 15 healthy controls. Untargeted liquid chromatography-mass spectrometry (LC-MS) was utilized to determine metabolic profiles, and support vector regression was applied for data normalization. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select optimal features, while receiver operating characteristic (ROC) curves and the area under the curve (AUC) evaluated diagnostic performance. Direct comparison between SLE patients with and without LN identified 234 differential metabolites uniquely associated with renal involvement. In the comparison between SLE patients with and without active LN, the 7-metabolite signature achieved an AUC of 0.962, indicating its potential utility as a screening tool to identify high-risk patients requiring further diagnostic workup. In conclusion, this urine-based metabolomics study reveals distinct metabolic alterations associated with renal involvement in SLE. The identified 7-metabolite signature serves as a proof-of-concept, non-invasive adjunctive biomarker toolkit, offering preliminary insights into the metabolic pathogenesis of LN while warranting future prospective validation.