Gene-metabolite linkage marks stored red blood cell quality.

Red blood cell (RBC) transfusion has long been the cornerstone of treatment for multiple diseases, but there is a knowledge gap between biological and genetic factors impacting RBC storage quality and transfusion efficacy. In this issue of Cell Metabolism, Nemkov et al. present a multiomics approach to identify gene-metabolite associations in fresh and stored RBCs.

A BEST classification system of large to giant congenital melanocytic nevi based on expert consensus and distribution characteristics.

Background: Large to giant congenital melanocytic nevi (LGCMN) significantly decrease patients' quality of life, but the inaccuracy of current classification system makes their clinical management challenging.

Objectives: To improve and extend the existing LGCMN 6B/7B classification systems by developing a novel LGCMN classification system based on a new phenotypic approach to clinical tool development.

Methods: Three hundred and sixty-one LGCMN cases were categorized into four subtypes based on anatomic site: bonce (25.

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge.

No Correlation between Plasma GPNMB Levels and Multiple System Atrophy in Chinese Cohorts.

Background: Glycoprotein nonmetastatic melanoma protein B (GPNMB) has been demonstrated to mediate pathogenicity in Parkinson's disease (PD) through interactions with α-synuclein, and plasma GPNMB tended to be a novel biomarker for PD.

Objective: The goal of this study was to investigate whether plasma GPNMB could act as a potential biomarker for the clinical diagnosis and severity monitoring of multiple system atrophy (MSA), another typical synucleinopathy.

Methods: Plasma GPNMB levels in patients with MSA, patients with PD, and healthy control subjects (HCs) were quantified using enzyme-linked immunosorbent assays.

Integration of metabolomics and network pharmacology for enhancing mechanism understanding and medication combination recommendation for diabetes mellitus and diabetic nephropathy.

With the increasing prevalence of diabetes mellitus (DM) and diabetic nephropathy (DN), effective treatment is particularly important for the recovery of patients. However, the currently approved drugs are usually tailored to clinical symptoms and no mechanism-targeted drugs are available. In this study, the combination of metabolomics and network pharmacology was applied to provide reasonable medication combination regimens to meet the different clinical needs for the targeted treatment of DM and DN.

Accurate Classification and Prediction of Acute Myocardial Infarction through an ARMD Procedure.

The risk stratification of acute myocardial infarction (AMI) patients is of prime importance for clinical management and prognosis assessment. Thus, we propose an ensemble machine learning analysis procedure named ADASYN-RFECV-MDA-DNN (ARMD) to address sample-unbalanced problems and enable stratification and prediction of AMI outcomes. The ARMD analysis procedure was applied to the NMR data of sera from 534 AMI-related subjects in four categories with an extremely imbalanced sample proportion.

Profiling of Homocysteine Metabolic Pathway Related Metabolites in Plasma of Diabetic Mellitus Based on LC-QTOF-MS.

Background: Homocysteine (Hcy) has been found to be closely related to the occurrence of diabetes mellitus (DM) and is considered as one of the risk factors of DM. However, Hcy alone is not enough as a factor to predict DM, and our study analyzed and determined the relationship between the main metabolites involved in the Hcy metabolic pathway and DM. Methods: A total of 48 clinical samples were collected, including 18 health control samples and 30 DM samples.

Metabolomic analyses redefine the biological classification of pancreatic cancer and correlate with clinical outcomes.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by high heterogeneity, and the postoperative prognosis of different patients often varies greatly. Therefore, the classification of pancreatic cancer patients and precise treatment becomes particularly important. In our study, H NMR spectroscopy was used to analyze the 76 PDAC serum samples and identify the potential metabolic subtypes.

Differentiation and prognostic stratification of acute myeloid leukemia by serum-based spectroscopy coupling with metabolic fingerprints.

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by complex molecular and cytogenetic abnormalities. New approaches to predict the prognosis of AML have increasingly attracted attention. There were 98 non-M3 AML cases and 48 healthy controls were enrolled in the current work.

Cross-comparative metabolomics reveal sex-age specific metabolic fingerprints and metabolic interactions in acute myocardial infarction.

The elucidation of metabolic perturbations and gender-age-specific metabolic characteristics associated with acute myocardial infarction (AMI) is essential for clinical risk stratification and disease management. A comprehensive cross-comparative metabolomics analysis was performed on the sera from 445 healthy controls, 347 AMI patients without cardiovascular disease (CVD), 79 AMI with CVD (AMICVD) patients including 27 deaths. Machine-learning-based integrated biomarker profiling and global network analysis were used to create a multi-biomarker for distinguishing the different AMI outcomes.

Recoupled-STOCSY-based co-expression network analysis to extract phenotype-driven metabolite modules in NMR-based metabolomics dataset.

Nuclear magnetic resonance (NMR)-based metabolomics study usually involves spectral preprocessing, identification of biomarkers and interpretation of biological processes and pathogenesis, however, the traditional procedure is bound to inborn defects. In this study, a new analytical frame was proposed to assist spectral alignment and dimensionality reduction, screen the differential metabolites and get biological explanation of the metabolic network by combing weighted gene co-expression network analysis (WGCNA) and recoupled statistical total correlation spectroscopy (RSTOCSY). The performance of RSTOCSY-based WGCNA method was evaluated by the NMR dataset of serum from coronary heart disease with diabetes mellitus (CHDDM) patients.

An integrating strategy for serum metabolomics and microarray analysis to expand the understanding of diet-induced obesity.

Obesity is a key component of metabolic syndrome and is precipitated by complex interactions between multiple environmental and genetic factors. The integration of multi-level bioinformation is needed to understand the altered endogenous molecule and metabolic mechanisms. In this study, an integrated analytical strategy was proposed by combining microarray data from a gene expression omnibus database and in vitro serum metabolomic data to unearth bioinformation associated with cafeteria diet induced obesity.

Metabolic Interactions and Differences between Coronary Heart Disease and Diabetes Mellitus: A Pilot Study on Biomarker Determination and Pathogenesis.

Comprehensive understanding of plasma metabotype of diabetes mellitus (DM), coronary heart disease (CHD), and especially diabetes mellitus with coronary heart disease (CHDDM) is still lacking. In this work, the plasma metabolic differences and links of DM, CHD, and CHDDM patients were investigated by the strategy of comparative metabolomics based on H NMR spectroscopy combined with network analysis for revealing their metabolic differences. A total of 17 metabolites are related to three diseases, among which valine, alanine, leucine, isoleucine, and -acetyl-glycoprotein are positively correlated with CHD and CHDDM (odds ratios (OR) > 1).

A metabonomics and lipidomics based network pharmacology study of qi-tonifying effects of honey-processed Astragalus on spleen qi deficiency rats.

Honey-processed Astragalus is a dosage form of radix Astragali processed with honey, which is deemed to contain better qi-tonifying effects in traditional Chinese medicine theroy. Our previous study has demonstrated that honey-processed Astragalus exhibited a better effect on reinforcing qi (vital energy) and immune improvement toward spleen qi deficiency compared with radix Astragali. However, the detailed mechanisms related to qi-tonifying effects of honey-processed Astragalus is still unclear.

Multi-fingerprint profiling combined with chemometric methods for investigating the quality of Astragalus polysaccharides.

It is a challenge to ascertain the quality of polysaccharides due to their complex chemical structure; therefore, multi-fingerprint profiling was used to investigate the quality of Astragalus polysaccharides (APS) harvested from Inner Mongolia (NM) and Gansu (GS) with the help of chemometric analysis. Additionally, FT-IR and H NMR were applied to characterize the chemical structure of the harvested APS. The spectral fingerprinting results indicated that APS had reduced similarity when they were from different origins.

Screening and Identification of the Metabolites of Water Extracts of Raw and Honey-Processed Astragalus in Rat Urine Based on UHPLC/ESI-Q-TOF-MS and Multivariate Statistical Analysis.

Radix Astragali is a famous traditional Chinese medicine and honey-processed Astragalus is a product of Radix Astragali acquired by honey-processing. These two products are widely utilized to treat various diseases. In this study, we screened bioactive components and metabolites of raw and honey-processed Astragalus in rat urine by ultra-performance liquid chromatography equipped with electrospray ionization/quadrupole time-of-flight mass spectrometry (UHPLC/ESI-Q-TOF-MS) combined with multivariate statistical analysis.

Application of pathways activity profiling to urine metabolomics for screening Qi-tonifying biomarkers and metabolic pathways of honey-processed Astragalus.

Honey-processed Astragalus, a widely used Qi-tonifying and immunomodulating herb in traditional Chinese medicine, has strengthened the tonic effects and achieved fewer side effects compared with astragali radix in clinical application. Here, we focus on Qi-tonifying biomarkers and pathways of honey-processed Astragalus using urine metabolomics that provide the basis for building the linkage between metabolites in rat urine and its symptoms. The spleen Qi deficiency model group, normal group, astragali radix group, and honey-processed Astragalus group were implemented to evaluate Qi-tonifying effects.

[Identification of biomarkers in urine of rats with spleen Qi deficiency and biological significance].

To identify biomarkers for spleen Qi deficiency by analyzing small molecule metabolites in urine, in order to expound the relationship between biomarkers and metabolic pathways. The spleen Qi deficiency model was established through dietary restriction and overstrain. All of the rats received D-xylose absorption experiment and blood routine test.

Structure Characterization of Honey-Processed Astragalus Polysaccharides and Its Anti-Inflammatory Activity In Vitro.

Honey-processed is a dosage form of Radix mixed with honey by a traditional Chinese medicine processing method which strengthens the tonic effect. polysaccharide (APS), perform its immunomodulatory effects by relying on the tonic effect of Radix , therefore, the improved pharmacological activity of honey-processed polysaccharide (HAPS) might be due to structural changes during processing. The molecular weights of HAPS and APS were 1,695,788 Da, 2,047,756 Da, respectively, as determined by high performance gel filtration chromatography combined with evaporative light scattering detection (HPGFC-ELSD).

Microbial community characterization, activity analysis and purifying efficiency in a biofilter process.

The growth and metabolism of microbial communities on biologically activated carbon (BAC) play a crucial role in the purification of drinking water. To gain insight into the growth and metabolic characteristics of microbial communities and the efficiency of drinking water treatment in a BAC filter, we analyzed the heterotrophic plate count (HPC), phospholipid, dehydrogenase, metabolic function and water quality parameters during start-up and steady-state periods. In the start-up process of the filter with natural biofilm colonization, the variation in heterotrophic plate count levels was S-curved.