3 results about "Plasma Metabolism" patented technology

This invention belongs to the field of diagnostic testing technology. Addressing the problem that existing gestational diabetes screening methods may miss the optimal time for early intervention, leading to the failure to promptly detect and manage some potentially hyperglycemic pregnant women, increasing the risk of adverse maternal and fetal outcomes, this invention proposes a plasma metabolic biomarker for the early diagnosis or prediction of gestational diabetes and its application. This plasma metabolic biomarker is at least one of L-lysine, propargylglycine, N6-acetyl-L-lysine, cyclic leucine, and glycine-deoxycholic acid. The five plasma metabolic biomarkers provided by this invention can be applied to the early diagnosis or prediction of gestational diabetes, and are of great significance for the prevention or treatment of gestational diabetes.

The present application relates to the field of medical informatics, and provides a paliperidone efficacy prediction method and system based on plasma metabolomics. The method comprises the following steps: collecting basic data of a target patient; performing feature selection on metabolites through the basic data to obtain relevant metabolites corresponding to paliperidone efficacy; training a neural network based on the relevant metabolites and baseline data in the basic data to obtain a prediction model; and performing paliperidone efficacy prediction on a patient to be predicted through the prediction model to obtain a prediction result. The present application can predict the probability of patient response to paliperidone, thereby more accurately evaluating the efficacy of paliperidone before medication, and providing an auxiliary decision for individualized medication.

The application discloses a method for differentiating Late-Life Depression (LLD) and amnestic Mild Cognitive Impairment (aMCI) based on plasma metabolomics combined with neural cognitive evaluation. The application detects metabolite spectrum in the plasma of the subject by hydrogen proton magnetic resonance spectrum metabolomics technology, screens out Scyllo-inositol as a key differential marker, and combines with neural cognitive test scores (especially memory scale scores) to construct a joint diagnosis model. The method can effectively distinguish LLD, aMCI and healthy control groups, and has high diagnostic accuracy and specificity. In addition, the method further introduces two-way double-sample Mendelian Randomization (MR) analysis to verify the causal relationship between Scyllo-inositol and the risk of dementia from the genetic point of view, and to strengthen the reliability of Scyllo-inositol as a biomarker. The application provides a diagnosis method with both metabolomics and genetic evidence support for early identification, accurate typing and intervention of high-risk groups of senile dementia, and has important clinical application value.