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 (25.48%), extremity (17.73%), shawl (19.67%) and trunks (37.12%) LGCMN. A 'BEST' classification system of LGCMN was established and validated by a support vector machine classifier combined with the 7B system. RESULTS: The most common LGCMN distributions were on bonce and trunks (bathing trunk), whereas breast/belly and body LGCMN were exceptionally rare. Sexual dimorphism characterized distribution, with females showing a wider range of lesions in the genital area. Nearly half of the patients with bathing trunk LGCMN exhibited a butterfly-like distribution. Approximately half of the LGCMN with chest involvement did not have nipple-areola complex involvement. Abdomen, back and buttock involvement was associated with the presence of satellite nevi (r = 0.558), and back and buttock involvement was associated with the presence of nodules (r = 0.364). CONCLUSIONS: The effective quantification of a standardized anatomical site provides data support for the accuracy of the 6B/7B classification systems. The simplified BEST classification system can help establish a LGCMN clinical database for exploration of LGCMN aetiology, disease management and prognosis prediction.

Metabolic Characteristics and Discriminative Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature in Preadolescents and Adolescents.

Growth hormone deficiency (GHD) and idiopathic short stature (ISS) are the most common types of short stature (SS), but little is known about their pathogenesis, and even less is known about the study of adolescent SS. In this study, nuclear magnetic resonance (NMR)-based metabolomic analysis combined with least absolute shrinkage and selection operator (LASSO) were performed to identify the biomarkers of different types of SS (including 94 preadolescent GHD (PAG), 61 preadolescent ISS (PAI), 43 adolescent GHD (ADG), and 19 adolescent ISS (ADI)), and the receiver operating characteristic curve (ROC) was further used to evaluate the predictive power of potential biomarkers. The results showed that fourteen, eleven, nine, and fifteen metabolites were identified as the potential biomarkers of PAG, PAI, ADG, and ADI compared with their corresponding controls, respectively. The disturbed metabolic pathways in preadolescent SS were mainly carbohydrate metabolism and lipid metabolism, while disorders of amino acid metabolism played an important role in adolescent SS. The combination of aspartate, ethanolamine, phosphocholine, and trimethylamine was screened out to identify PAI from PAG, and alanine, histidine, isobutyrate, methanol, and phosphocholine gave a high classification accuracy for ADI and ADC. The differences in metabolic characteristics between GHD and ISS in preadolescents and adolescents will contribute to the development of individualized clinical treatments in short stature.

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. Firstly, the adaptive synthetic sampling (ADASYN) algorithm was used to address the issue of the original sample imbalance. Secondly, the recursive feature elimination with cross-validation (RFECV) processing and random forest mean decrease accuracy (RF-MDA) algorithm was performed to identify the differential metabolites corresponding to each AMI outcome. Finally, the deep neural network (DNN) was employed to classify and predict AMI events, and its performance was evaluated by comparing the four traditional machine learning methods. Compared with the other four machine learning models, DNN presented consistent superiority in almost all of the model parameters including precision, f1-score, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and classification accuracy, highlighting the potential of deep learning in classification and stratification of clinical diseases. The ARMD analysis procedure was a practical analysis tool for supervised classification and regression modeling of clinical diseases.

Metabolic characteristics and pathogenesis of precocious puberty in girls: the role of perfluorinated compounds.

BACKGROUND: Precocious puberty (PP) in girls is traditionally defined as the onset of breast development before the age of 8 years. The specific biomarkers of premature thelarche (PT) and central precocious puberty (CPP) girls are uncertain, and little is known about their metabolic characteristics driven by perfluorinated compounds (PFCs) and clinical phenotype. This study aimed to screen specific biomarkers of PT and CPP and elucidate their underlying pathogenesis. The relationships of clinical phenotype-serum PFCs-metabolic characteristics were also explored to reveal the relationship between PFCs and the occurrence and development of PT and CPP. METHODS: Nuclear magnetic resonance (NMR)-based cross-metabolomics strategy was performed on serum from 146 PP (including 30 CPP, 40 PT, and 76 unspecified PP) girls and 64 healthy girls (including 36 prepubertal and 28 adolescent). Specific biomarkers were screened by the uni- and multivariate statistical analyses. The relationships between serum PFCs and clinical phenotype were performed by correlation analysis and weighted gene co-expression network analysis to explore the link of clinical phenotype-PFCs-metabolic characteristics in PT and CPP. RESULTS: The disordered trend of pyruvate and butyrate metabolisms (metabolites mapped as formate, ethanol, and 3-hydroxybutyrate) were shared and kept almost consistent in PT and CPP. Eight and eleven specific biomarkers were screened for PT and CPP, respectively. The area under curve of specific biomarker combination was 0.721 in CPP vs. prepubertal, 0.972 in PT vs. prepubertal, 0.646 in CPP vs. prepubertal integrated adolescent, and 0.822 in PT vs. prepubertal integrated adolescent, respectively. Perfluoro-n-heptanoic acid and perfluoro-n-hexanoic acid were statistically different between PT and CPP. Estradiol and prolactin were significantly correlated with PFCs in CPP and PT. Clinical phenotypes and PFCs drive the metabolic characteristics and cause metabolic disturbances in CPP and PT. CONCLUSIONS: The elevation of formate, ethanol, and 3-hydroxybutyrate may serve as the early diagnostic indicator for PP in girls. But the stratification of PP still needs to be further determined based on the specific biomarkers. Specific biomarkers of CPP and PT exhibited good sensitivity and can facilitate the classification diagnosis of CPP and PT. PFC exposure is associated with endocrine homeostasis imbalance. PFC exposure and/or endocrine disturbance directly or indirectly drive metabolic changes and form overall metabolic network perturbations in CPP and PT.

Identification of potential biomarkers in malnutrition children with severity by 1H-NMR-based metabolomics: a preliminary study in the Chinese population.

PURPOSE: Child malnutrition is a global public health problem, but the underlying pathophysiologic mechanisms with severity remain poorly understood, and the potential biomarkers served to the clinical diagnosis are still not available. This study aimed to identify the serum metabolic characteristics of malnourished children with severity. METHODS: Fasted overnight serum samples were collected following clinical standard procedures among 275 malnourished and 199 healthy children from the Women and Children's Hospital, Xiamen University Child Health Department from July 2020 to May 2022. Nuclear magnetic resonance (NMR)-based metabolomics strategy was applied to identify the potential serum biomarkers of malnutrition from 275 malnourished children aged 4 to 84 months with mild (Mil, 199 cases), moderate (Mod, 101 cases), and severe (Sev, 7 cases) malnutrition. RESULTS: Ten, fifteen, and fifteen differential metabolites were identified from the Mil, Mod, and Sev malnutrition groups, respectively. Eight common metabolites, including increased acetoacetate, acetone, ethanol, succinate, 3-hydroxybutyrate, and decreased alanine, methionine, and N-acetyl-glycoprotein, could be the potential biomarkers for malnourished children. The altered metabolic pathways were mainly related to energy metabolism and amino acid metabolism via the network-based pathway enrichment. CONCLUSION: Eight potential biomarkers, including acetoacetate, acetone, ethanol, succinate, 3-hydroxybutyrate, alanine, methionine, and N-acetyl-glycoprotein, could characterize the child malnutrition. Child malnutrition-induced abnormal energy metabolism, impaired nutrition utilization and the reduced nutrient availability, and more metabolic disturbance will appear with the severity. Our results are valuable for further studies on the etiology and pathogenesis of malnutrition for clinical intervention and improvement.

Quantitative comparison and rapid discrimination of Panax notoginseng powder and Caulis clematidis armandii using NMR combined with pattern recognition.

BACKGROUND: The market demand for Panax notoginseng (P. notoginseng) is growing rapidly because of its useful properties in food and medicine. However, the frequent adulteration of P. notoginseng seriously affects the health of consumers and is a great challenge to food safety. In this study, low- and high-field nuclear magnetic resonance (LF/HF-NMR) were applied to detect the transverse relaxation distribution of P. notoginseng contaminated with different ratios of Caulis clematidis armandii (CCA) and the components in P. notoginseng and CCA, respectively. RESULTS: Fifty-seven kinds of major and minor components in P. notoginseng and CCA were identified and quantified from their high-resolution NMR spectra, and there were significant differences in ginsenosides, sucrose, and glucose between P. notoginseng and CCA. Furthermore, the partial least squares regression analysis results indicated that LF-NMR parameters (T21 and S21 ) changed linearly as the ratio of CCA increased, and these changes were attributed to the variations in polysaccharide and sucrose in adulterated P. notoginseng. CONCLUSION: In the relaxation time-based pattern recognition models, the authentic P. notoginseng powder could be classified with 100% accuracy from adulterated P. notoginseng when the adulteration ratio was greater than 30%, demonstrating the possibility of LF-NMR, in combination with pattern recognition, for rapid discrimination of food authenticity. © 2022 Society of Chemical Industry.

Metabolomic characteristics of hand-foot-mouth disease facilitate discovery and diagnosis of pathogeny.

BACKGROUND: Hand-foot-mouth disease (HFMD) is a significant public health concern, especially in Asia-Pacific countries. Its diagnosis mainly depends on clinical symptoms. It is easy to miss the source of infection and best treatment period. This research aims to provide a tool for its early clinical diagnosis and for predicting the possibility of complications. METHODS: The serum samples of 39 HFMD children and 36 healthy children were collected for clinical testing and 1 H-NMR spectroscopy. Metabolomic analyses were performed to obtain the metabolic differences between the HFMD and healthy children and to speculate on the pathogenesis of HFMD. RESULTS: Thirty-nine children were divided into severe cases and mild cases. Severe cases demonstrated more obvious inflammatory responses, but no metabolic difference was observed between the severe and mild cases. The metabolic differences between HFMD and healthy children were noticeable. Ten differential metabolites were screened out as the potential biomarkers for HFMD, and seven disturbed metabolic pathways responsible for HFMD were affected by inflammation, impaired intestinal absorptive function, and immune response. CONCLUSIONS: Our results will provide a complementary tool for the early diagnosis of HFMD and potential ideas for later treatment.

Compositional Analysis of Four Kinds of Citrus Fruits with an NMR-Based Method for Understanding Nutritional Value and Rational Utilization: From Pericarp to Juice.

Citrus is one of the most important economic crops and is widely distributed across the monsoon region. Citrus fruits are deeply loved by consumers because of their special color, fragrance and high nutritional value. However, their health benefits have not been fully understood, especially the pericarps of citrus fruits which have barely been utilized due to their unknown chemical composition. In the present study, the pericarp and juices of four typical varieties of citrus fruits (lemon, dekopon, sweet orange and pomelo) were analyzed by NMR spectroscopy combined with pattern recognition. A total of 62 components from the citrus juices and 87 components from the citrus pericarps were identified and quantified, respectively. The different varieties of the citrus fruits could be distinguished from the others, and the chemical markers in each citrus juice and pericarp were identified by a combination of univariate and multivariate statistical analyses. The nutritional analysis of citrus juices offers favorable diet recommendations for human consumption and data guidance for their potential medical use, and the nutritional analysis of citrus pericarps provides a data reference for the subsequent comprehensive utilization of citrus fruits. Our results not only provide an important reference for the potential nutritional and medical values of citrus fruits but also provide a feasible platform for the traceability analysis, adulteration identification and chemical composition analysis of other fruits.

Discriminant Analysis of the Nutritional Components between Organic Eggs and Conventional Eggs: A 1H NMR-Based Metabolomics Study.

The difference of nutrient composition between organic eggs and conventional eggs has always been a concern of people. In this study, 1H nuclear magnetic resonance (NMR) technique combined with multivariate statistical analyses was conducted to identify the metabolite different in egg yolk and egg white in order to reveal the nutritional components information between organic and conventional eggs. The results showed that the nutrient content and composition characteristics were different between organic and conventional eggs, among which the content of glucose, putrescine, amino acids and their derivatives were found higher in the organic eggs yolk, while phospholipids were demonstrated higher in conventional eggs yolk. Organic acid, alcohol, amine, choline and amino acids were higher in conventional eggs white, but glucose and lactate in organic egg were higher. Our study demonstrated that there are more nutritive components and higher nutritional value in organic eggs than conventional eggs, especially for the growth and development of infants and young children, and conventional eggs have more advantages in promoting lipid metabolism, preventing fatty liver, and reducing serum cholesterol. Eggs have important nutritional value to human body, and these two kinds of eggs can be selected according to the actual nutrient needs.

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 1H 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 N-acetyl-glycoprotein are positively correlated with CHD and CHDDM (odds ratios (OR) > 1). The trimethylamine oxide, glycerol, lactose, indoleacetate, and scyllo-inositol are closely related to the development of DM to CHDDM (OR > 1), and indoleactate (OR: 1.06, 95% confidence interval (CI): 1.01-1.12) and lactose (OR: 2.46, 95% CI: 1.67-3.25) are particularly prominent in CHDDM. We identified three multi-biomarkers types that were significantly associated with glycosylated hemoglobin (HbA1C) at baseline. All diseases demonstrated dysregulated glycolysis/gluconeogenesis and amino acid biosynthesis pathway. In addition, enrichment in tryptophan metabolism observed in CHDDM, enrichment in inositol phosphate metabolism observed in DM, and the metabolites related to microbiota metabolism were dysregulated in both DM and CHDDM. The comparative metabolomics strategy of multi-diseases offers a new perspective in disease-specific markers and pathogenic pathways.