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.

Metabolomic analysis reveals the biological characteristics of giant congenital melanocytic nevi.

Giant congenital melanocytic nevi (GCMN) is a congenital cutaneous developmental deformity tumor that usually occurs at birth or in the first few weeks after birth, but its pathogenesis is still unclear. In this study, nuclear magnetic resonance-based metabolomics strategy was employed to evaluate the metabolic variations in serum and urine of the GCMN patients in order to understand its underlying biochemical mechanism and provide a potential intervention idea. Twenty-nine metabolites were observed to change significantly in serum and urine metabolomes, which are mainly involved in a variety of metabolic pathways including glyoxylate and dicarboxylate metabolism, TCA cycle and metabolisms of amino acids. The substantial cores of all the disturbed metabolic pathways are related to amino acid metabolism and carbohydrate metabolism and regulate the physiological state of the GCMN patients. Our results provide the physiological basis and physiological responses of GCMN and will be helpful for better understanding the molecular mechanisms of GCMN in future research.

Origin tracing and adulteration identification of bird's nest by high- and low-field NMR combined with pattern recognition.

Edible bird's nest (EBN) is a high-value health food with various nutrients and bioactive components. With increasing demand for EBN, they are often adulterated with cheaper ingredients or falsely labeled by the origin information, thus harming consumer interests. In this study, high- and low-field nuclear magnetic resonance (HF/LF-NMR) technology combined with multivariate statistical analysis was used to identify the geographical marker of EBN from different origins and authenticate the adulterated EBN with various adulterants at different adulteration rates. Authentic EBN samples from Malaysia were used to simulate adulteration using gelatin (GL), agar (AG) and starch (ST) at 10 %, 20 %, 40 %, 60 %, 80 %, and 100 % w/w, respectively. The results showed significant differences in composition among EBN from different origins, with isocaproate and citric acid serving as geographical markers for Malaysia and Vietnam, respectively. Leucine, glutamic acid, and N-acetylglycoprotein serving as geographical markers for Indonesia. In addition, PLS model further verified the accuracy of origin identification of EBN. The LF-NMR results of adulteration EBN showed a linear correlation between the transverse relaxation (T2, S2) and the adulterated ratio. The OPLS-DA based on T2 spectra could accurately identify authentic EBN from adulterated with GL, AG and ST at 40 %, 20 %, and 20 %, respectively. Fisher discrimination model was able to differentiate at 20 %, 20 %, and 40 %, respectively. These results show that the 1H NMR combined with multivariate statistical analysis method could be a potential tool for the detection of origin and adulteration of EBN.

Childhood overweight and obesity: age stratification contributes to the differences in metabolic characteristics.

OBJECTIVE: The aim of this study was to identify the differential metabolic characteristics of children with overweight and obesity and understand their potential mechanism in different age stratifications. METHODS: Four hundred seventy-three children were recruited and divided into two age stratifications: >4 years (older children) and ≤4 years (younger children), and overweight and obesity were defined according to their BMI percentile. A one dimensional proton nuclear magnetic resonance (1 H-NMR)-based metabolomics strategy combined with pattern recognition methods was used to identify the metabolic characteristics of childhood overweight and obesity. RESULTS: Four and sixteen potential biomarkers related to overweight and two and twenty potential biomarkers related to obesity were identified from younger and older children, respectively. Fluctuations in phenylalanine, tyrosine, glutamine, leucine, histidine, and ascorbate co-occurred in children with obesity at two age stratifications. The disturbances in biosynthesis and metabolism of amino acids, lipid metabolism, and galactose metabolism disturbance were mainly involved in children with overweight and obesity. CONCLUSIONS: The metabolic disturbances show a significant progression from overweight to obesity in children, and different metabolic characteristics were demonstrated in age stratifications. The changes in the levels of phenylalanine, tyrosine, glutamine, leucine, histidine, and ascorbate were tracked with the persistence of childhood obesity. These findings will promote the mechanistic understanding of childhood overweight and obesity.

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.

Untargeted Metabolomics Analysis Revealed the Difference of Component and Geographical Indication Markers of Panax notoginseng in Different Production Areas.

Panax notoginseng (P. notoginseng) has excellent medicinal and food dual-use characteristics. However, P. notoginseng with a unique origin label has become the target of fraud because of people confusing or hiding its origin. In this study, an untargeted nuclear magnetic resonance (NMR)-based metabolomics approach was used to discriminate the geographical origins of P. notoginseng from four major producing areas in China. Fifty-two components, including various saccharides, amino acids, saponins, organic acids, and alcohols, were identified and quantified through the NMR spectrum, and the area-specific geographical identification components were further screened. P. notoginseng from Yunnan had strong hypoglycemic and cardiovascular protective effects due to its high acetic acid, dopamine, and serine content, while P. notoginseng from Sichuan was more beneficial for diseases of the nervous system because of its high content of fumarate. P. notoginseng from Guizhou and Tibet had high contents of malic acid, notoginsenoside R1, and amino acids. Our results can help to distinguish the geographical origin of P. notoginseng and are readily available for nutritional recommendations in human consumption.

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. An NMR-based metabolomic strategy was applied to identify the potential urinary biomarkers of DM or/and DN, while network pharmacology was used to identify the therapy targets of DM and DN by intersecting the targets of diseases and currently approved drugs. According to the enriched signaling pathways using the potential biomarkers and the therapy targets, the specific medication combinations were recommended for the specific clinical demands in terms of hypoglycemic, hypertensive, and/or lipid-lowering. For DM, 17 potential urinary biomarkers and 12 disease-related signaling pathways were identified, and 34 combined medication regimens related to hypoglycemia, hypoglycemia, and hypertension, and hypoglycemia, hypertension, and lipid-lowering were administered. For DN, 22 potential urinary biomarkers and 12 disease-related signaling pathways were identified, and 21 combined medication regimens related to hypoglycemia, hypoglycemia, and hypertension were proposed. Molecular docking was used to verify the binding ability, docking sites, and structure of the drug molecules to target proteins. Moreover, an integrated biological information network of the drug-target-metabolite-signaling pathways was constructed to provide insights into the underlined mechanism of DM and DN as well as clinical combination therapy.

Delayed CO2 postconditioning promotes neurological recovery after cryogenic traumatic brain injury by downregulating IRF7 expression.

AIMS: Few treatments are available in the subacute phase of traumatic brain injury (TBI) except rehabilitation training. We previously reported that transient CO2 inhalation applied within minutes after reperfusion has neuroprotective effects against cerebral ischemia/reperfusion injury. In this study, it was hypothesized that delayed CO2 postconditioning (DCPC) starting at the subacute phase may promote neurological recovery of TBI. METHODS: Using a cryogenic TBI (cTBI) model, mice received DCPC daily by inhaling 5%/10%/20% CO2 for various time-courses (one/two/three cycles of 10-min inhalation/10-min break) at Days 3-7, 3-14 or 7-18 after cTBI. Beam walking and gait tests were used to assess the effect of DCPC. Lesion size, expression of GAP-43 and synaptophysin, amoeboid microglia number and glia scar area were detected. Transcriptome and recombinant interferon regulatory factor 7 (Irf7) adeno-associated virus were applied to investigate the molecular mechanisms. RESULTS: DCPC significantly promoted recovery of motor function in a concentration and time-course dependent manner with a wide therapeutic time window of at least 7 days after cTBI. The beneficial effects of DCPC were blocked by intracerebroventricular injection of NaHCO3 . DCPC also increased puncta density of GAP-43 and synaptophysin, and reduced amoeboid microglia number and glial scar formation in the cortex surrounding the lesion. Transcriptome analysis showed many inflammation-related genes and pathways were altered by DCPC, and Irf7 was a hub gene, while overexpression of IRF7 blocked the motor function improvement of DCPC. CONCLUSIONS: We first showed that DCPC promoted functional recovery and brain tissue repair, which opens a new therapeutic time window of postconditioning for TBI. Inhibition of IRF7 is a key molecular mechanism for the beneficial effects of DCPC, and IRF7 may be a potential therapeutic target for rehabilitation after TBI.

Discover boy specific-biomarkers and reveal gender-related metabolic differences in central precocious puberty.

The incidence of central precocious puberty (CPP) in boys is rising, but lack of effective molecular biomarkers often leads to delayed treatment and thus the terrible clinical complications in adulthood. This study aims to identify the specific-biomarkers of CPP boys and understand the gender-related differences in metabolic characteristics of CPP. The specific-biomarkers of CPP boys were identified from serum by cross-metabolomics combined with linear discriminant analysis effect size analysis after age correction, and union receiver operating characteristic curve analyses were perform to optimize the combination of specific-biomarkers. The differences in metabolic characteristics between boys and girls with CPP were explored by cross-metabolomics and weighted gene co-expression network analysis. Results show that CPP activated in advance the HPG axis and induced gender-related clinical phenotypes. Seven serum metabolites were identified as specific-biomarkers of CPP boys, including acetoacetate, aspartate, choline, creatinine, myo-inositol, N,N-dimethylglycine and N-Acetyl-glycoprotein. The combination of aspartate, choline, myo-inositol and creatinine achieved an optimized diagnosis, where AUC is 0.949, prediction accuracy for CPP boys is 91.1%, and the average accuracy is 0.865. The metabolic disorders of CPP boys mainly involve in glycerophospholipid metabolism, and synthesis and degradation of ketone bodies. Betaine, glutamine, isoleucine, lactate, leucine, lysine, pyruvate, α-&ß-glucose were identified as gender-related biomarkers for CPP, and they are mainly involved in glycolysis/gluconeogenesis, pyruvate metabolism, and alanine, aspartate and glutamate metabolism. Biomarkers combination provides a promising diagnostic potential for CPP boy with a favorite sensitivity and specificity. In addition, the differences of metabolic characteristics between boys and girls with CPP will contribute to the development of individualized clinical treatments in CPP.

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.

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.

Diagnostic Value of Susceptibility-Weighted Imaging Combined with Diffusion-Weighted Imaging in Early Intracerebral Hemorrhage.

Objective: The incidence of early intracerebral hemorrhage (ICH) is gradually increasing and has been shown to affect an increasing number of younger people. Conventional imaging modalities might have a low detection rate of early and small ICH lesions. This study aimed to investigate the diagnostic value of susceptibility-weighted imaging (SWI) combined with diffusion-weighted imaging (DWI) in early ICH. Materials and Methods: The data of 61 patients with early ICH diagnosed by computed tomography (CT) scan between January 2019 and February 2020 were assessed. Using CT as the gold standard, we compared the diagnostic sensitivity, accuracy, and imaging characteristics of SWI + DWI versus SWI or DWI alone. Results: A total of 78 lesions were detected by CT in 61 patients with early ICH. The diagnostic sensitivity and accuracy of SWI + DWI were significantly higher than those of SWI or DWI alone. In terms of imaging characteristics, DWI demonstrated very low signal intensity in the hematoma center at different stages of early ICH with high signal intensity in the surrounding tissue, and the signal range gradually increased over time. By contrast, SWI displayed very low signal intensity at different stages, and the signal intensity also gradually increased over time. Conclusion: Compared with SWI or DWI alone, SWI combined with DWI could improve the detection rate of hematoma lesions in patients with early ICH.

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.

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.

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. The changes of most metabolites were dependent on AMI, but gender and age also give additional contributions to the changes of histidine, malonate, O-acetyl-glycoprotein and trimethylamine N-oxide. The altered metabolic pathways included gut dysbiosis, increased amino acid metabolism, glucose metabolism and ketone metabolism, and inactivation of tricarboxylic acid cycle. Enhanced histidine metabolism and microbiota dysbiosis may be one of the key factors during the developing of AMI into AMICVD. For the differential diagnosis of AMI events, three sets of specific multi-biomarkers provided relatively high accuracy with the areas under the curve more than 0.8 and hazard ratio more than 1 in the discovery set, and the results were reproduced and confirmed by the validation set. First use of cross-comparative metabolomics and machine-learning-based integrated biomarker analysis gives great capability to discriminate the different AMI outcomes. Also, the multi-biomarkers seem to be a valid and accurate auxiliary diagnosis biomarker in addition to standard stratification based on clinical parameters.

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. The statistical recoupling of variables (SRV) was successfully used to categorize the whole dataset into a number of superclusters of signals and served to spectral alignment, and its effectiveness was confirmed by the wine dataset with a larger spectral drift. Three phenotype-driven metabolite modules related to CHDDM were identified from the dataset by WGCNA, and 22 metabolites were further identified from the three modules according to the metabolic correlations within or between modules, and 40 significant metabolic correlations were observed from the intra- and inter-metabolites in the 2D pseudospectrum. These modules involve amino acid metabolism, microbial metabolism and glucose metabolism, and their analysis of metabolite network diffusion revealed a new discovery that the ferroptosis pathway is related to CHDDM. This RSTOCSY-based WGCNA approach provides an effective analysis workflow for information recovery and structure identification of metabolites and improving interpretability and understanding of the disease pathogenesis.

Effect of different cooking methods on nutritional intake and different storage treatments on nutritional losses of abalone.

As the most important marine edible shellfish, the nutritional quality of abalone has been paid attention. In this study, the chemical and nutritional compositions of abalones were obtained, and three cooking methods, steaming, boiling and frying, were evaluated by in vitro gastric digestion simulation to understand their nutritional changes by 1H NMR spectroscopy combined with multivariate statistical analyses. The nutritional losses were also monitored under different cold storage conditions. The results indicated that boiling can keep more amino acids and fatty acids than steaming and frying, thus being recommended as the best cooking method of abalone. The abalone could maintain fresh within one day under 4 °C, and the deterioration process occurred subsequently. These results help to understand the digestion of cooked abalone and the changes of nutrients through storage and cooking process, leading to a scientific recommendation of cooking method and storage condition for healthy eating.

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.

Evaluating biometrics by using a hybrid MCDM model.

Biometrics has been developing for decades in diverse industries, such as consumer electronics, internet of things, financial industry, etc. The purpose of this research is to build a decision-making model to evaluate and improve the performances of biometrics for administrators to design and make suitable biometric systems. This paper adopts a hybrid multiple criteria decision making (MCDM) model, comprising decision-making trial and evaluation laboratory (DEMATEL), and DEMATEL-based analytic network process (called DANP) to probe into the interrelationship and influential weights among criteria of biometrics. According to DEMATEL technique, the empirical results indicate that criteria of biometrics have self-effect relationships. The dimension of biometrics that administrators of biometrics should enhance first when improving the performances is usability. The criterion of universality with the highest influencing value to systematically affect all other evaluation factors is what administrators of biometrics should comprehensively consider. In the top three criteria for evaluation by DANP, biometric systems with the most influential weight is the criterion that can be improved to have higher recognition rates for increasing the performances of biometrics, followed by biometric conditions and permanence.