Endometriosis is a prevalent chronic inflammatory disease characterized by a considerable delay between initial symptoms and diagnosis through surgery. The pressing need for a timely, non-invasive diagnostic solution underscores the focus of current research efforts. This study examines the diagnostic potential of the menstrual blood lipidome. The lipid profile of 39 samples (23 women with endometriosis and 16 patient of control group) was acquired using reverse-phase high-performance liquid chromatography-mass spectrometry with LipidMatch processing and identification. Profiles were normalized based on total ion counts. Significant differences in lipids were determined using the Mann-Whitney test. Lipids for the diagnostic model, based on logistic regression, were selected using a combination of variance importance projection filters and Akaike information criteria. Levels of ceramides, sphingomyelins, cardiolipins, triacylglycerols, acyl- and alkenyl-phosphatidylethanolamines, and alkenyl-phosphatidylcholines increased, while acyl- and alkyl-phosphatidylcholines decreased in cases of endometriosis. PE P-16:0/18:1 and CL 16:0_18:0_22:5_22:6 serve as marker lipids in the diagnostic model, exhibiting a sensitivity of 81% and specificity of 85%. The diagnostic approach based on dried spots of menstrual blood holds promise as an alternative to traditional non-invasive methods for endometriosis screening.

Hypoxic-ischemic encephalopathy (HIE) is one of the most common causes of childhood disability. Hypothermic therapy is currently the only approved neuroprotective approach. However, early diagnosis of HIE can be challenging, especially in the first hours after birth when the decision to treat with hypothermic therapy is critical. Differentiating HIE from other neonatal conditions, such as sepsis, further complicates the diagnosis. This study investigated the utility of a metabolomic-based approach using the NeoBase 2 MSMS kit to diagnose HIE using dry blood stains in a Rice-Vannucci model of HIE in rats. We evaluated the diagnostic accuracy of this method between 3 and 6 hours after the onset of HIE, including in the context of systemic inflammation and concomitant hypothermic therapy. Discriminant analysis revealed several metabolite patterns associated with HIE. A logistic regression model using glycine levels achieved high diagnostic accuracy with areas under the curve (AUC) of 0.94 at 3 hours and 0.96 at 6 hours after the onset of HIE. In addition, orthogonal partial least squares discriminant analysis, which included five metabolites, achieved 100% sensitivity and 80% specificity within 3 hours of HIE. These results highlight the significant potential of the NeoBase 2 MSMS kit for the early diagnosis of HIE and could improve patient management and outcomes in this serious illness.