Variable Importance In The Projection Research Articles

#2227 Exploring the interplay between gut microbiome, fecal metabolomics and ambulatory blood pressure in white-coat hypertensive patients

Abstract Background and Aims Recent evidence showed the relationship between gut microbiota with hypertension. Few studies also demonstrated that ambulatory blood pressure monitoring variables such as dipping status and linear variability are associated with fecal microbiota and metabolomics. However, the relationship between gut microbiota and metabolomic composition of white-coat hypertensive patients has not been studied yet. In addition, although the link between gut microbiota, blood pressure variability and high salt intake has been shown, none of the studies addressed these changes simultaneously in the same study population. Here, we aimed to investigate the interplay between the gut microbiome, fecal metabolites and 24-hour urine sodium levels between hypertensive, white-coat hypertensive (WCH) and healthy participants diagnosed with ambulatory blood pressure monitoring. Method The study included 18 newly diagnosed hypertensive, 8 WCH, and 21 healthy participants from our tertiary center. Ambulatory blood pressure (BP) monitoring was performed in all participants and nighttime dipping, morning BP surge (MBPS), pre-wake MBPS and linear BP variability were calculated. 16S rRNA sequencing was performed using Illumina MiSeq sequencer. DeSeq2 and linear discriminant analysis effect size (LEfSe) were performed with R. Fecal metabolite levels were analyzed by GC-MS. A partial least squares-discriminant analysis (PLS-DA) model was constructed for the feature selector and classifier. The variable importance in the projection (VIP) coefficient was calculated based on the PLS-DA model. Results ANOVA and post-hoc Tukey test showed WHC and hypertensive groups had higher blood pressure variability and morning-thorough MBPS than normotensive patients (p = 0.02 and 0.04, respectively), whereas 24-h urine Na levels were statistically similar between WCH and hypertensive groups (p = 0.11). There were no significant differences in α- and β-diversity metrics of gut microbiome between study groups. Select taxa were specific to WHC patients, notably Rikenellaceae family and Mitsuokella were higher in WHC against normotensive patients, whereas Ruminococcus callidus, Roseburia, Clostridium, Lactobacillus ruminis, Sutterall, Lachospiraceae and Muribaculaceae were decreased in WHC against normotensive patients in DeSeq2 analysis. Dialister was increased, whereas Bacteroides genus and Bacteroides fragilis were decreased in WHC against hypertensive patients. LEfSe analysis showed positive enrichment in Proteobacteria phylum and negative enrichment in Mitsuokella genus in WHC against hypertensive group. Enterococcus, Lachnobacterium, Odoribacter and Pseudomonas genera had positive; whereas Lactobacillus and Clostridium had negative correlations with 24-h urine Na levels. Positive correlations were shown between linear BP variability and Bacteroides and Paraprevotella genera. Several genera and species were enriched in Q3-4 against Q1-2 quartiles of pre-wake and sleep-thorough MBPS in LEfSe analyzes (Fig. 1). Normotensive and essential hypertensive groups could also be differentiated based on fecal metabolomic composition. Specifically, metabolites with the highest VIP values discriminating WCH from normotensive and hypertensive groups were methyl-palmitoleate, oxalic acid and phenyl β-D-glucopyranoside, respectively (Fig. 2). Conclusion In conclusion, we performed the first study focusing on the fecal microbiome and metabolomic characteristics of white-coat hypertensive patients. Urine Na levels and ambulatory blood pressure variables of the WCH group are different than those of the normotensive and similar to the hypertensive group. Comparative and correlation analyses revealed novel relationships between ambulatory blood pressure variables and gut microbiome of both WCH and hypertensive patients. Fecal metabolomic differences between study groups imply possible mechanisms underlying the effects of gut microbiome on blood pressure. Gut microbiota and metabolites may become valuable targets for interventions of both white-coat and essential hypertension in the near future.

Analysis of VOCs in Lueyang Black Chicken Breast Meat during the Steaming Process with GC-IMS and Stoichiometry

Steamed chicken breast meat attracts people for its unique flavor and nutritional benefits. In this study, the sensory evaluation of Lueyang black chicken breast meat during steaming was first performed, and their volatile organic compounds (VOCs) were further analyzed by gas chromatography-ion mobility spectroscopy (GC-IMS) combined with stoichiometry. The sensory results demonstrated that the Lueyang black chicken breast meat steamed for 15–30 min was more acceptable in taste, flavor, and chewiness. A total of 60 volatile flavor signal peaks were obtained, and 46 VOCs were recognized from qualitative analysis, containing 24 aldehydes (51.19–72.57%), 8 ketones (10.15–16.97%), 9 alcohols (7.98-13.16%), 2 furans (2.24–10.85%), 2 esters (0.54–1.56%), and 1 ether (0.05–2.47%). A stable and reliable prediction model was set up by orthogonal partial least squares-discriminant analysis (OPLS-DA), and 18 characteristic VOCs (including 10 aldehydes, 3 alcohols, 3 ketones, 1 furan, and 1 ether) were picked out through variable importance in the projection (VIP>1.0 and p<0.05). Principal component analysis (PCA) results indicated that the cumulative contribution ratio was 92% with PC1 68.7% and PC2 23.3%, respectively, indicating that these characteristic VOCs could well discriminate the steaming time of Lueyang black chicken breast meat. Heatmap clustering analysis also demonstrated a similar distinguishing effect. These results could provide references for the research, development, and quality control of ready-to-eat steamed products for Lueyang black chicken breast meat in the future.

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes

PurposeThe duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR.MethodsA total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi’an JiaoTong University were selected as the discovery set. One-to-one case–control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography–mass spectrometry (LC–MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry.ResultsThe discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients.ConclusionDisturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

Correlation between climatic environment and characteristic components of 14 kinds of huajiao by thermal analysis techniques, GC‐MS and HS‐IMS

AbstractHuajiao (Zanthoxylum bungeanum Maxim.) is extensively cultivated in various countries, including China, Korea, and India, owing to its adaptability to diverse environments. This study comprehensively analyzed the volatile substance composition of 14 varieties of red huajiao with distinct geographical origins. Thermal analysis methods, gas chromatography‐mass spectrometry (GC‐MS), and headspace‐ion mobility chromatography (HS‐IMS) were employed to evaluate the total volatile substance composition and content. The study revealed minor variations in water content, volatile matter content, and fractions among the geographically sourced huajiao samples. Utilizing correlation analysis based on GC‐MS and orthogonal partial least squares discriminant analysis (OPLS‐DA) with HS‐IMS, a robust classification method for the 14 types of huajiao was developed. Applying the variable importance in the projection (VIP) method, seven distinctive components were identified as potential markers for distinguishing the geographical origins of red huajiao. By integrating climatic and topographical factors of the 14 huajiao varieties, the correlation analysis of GC‐MS, and OPLS‐DA classification outcomes from HS‐IMS elucidated the influence of geo‐environmental factors on huajiao components and contents. This research provides insights into the impact of diverse geographic environments on the constituents and characteristics of huajiao. It offers valuable guidance for selecting optimal cultivation locations to enhance huajiao quality, aiding consumers in making informed choices.

Machine learning model for deep exploration: Utilizing short wavelength infrared (SWIR) of hydrothermal alteration minerals in the Qianchen gold deposit, Jiaodong Peninsula, Eastern China

The Jiaodong Peninsula in the eastern North China Craton is renowned globally as a major gold province. Notably, the Jiaojia orefield is an important gold-producing district in this region, and its depths are inferred to offer substantial prospecting potential, underscoring the need for innovative exploration tools to uncover concealed orebodies. The Qianchen gold deposit in the southern part of the Jiaojia orefield is characterized by the disseminated-stockwork-style orebodies at depths of ca. –1000 to –1800 m, with three mineralization stages, namely (I) pre-ore K-feldspar-quartz stage, (II) quartz-sericite-gold-polymetallic sulfide stage (main Au mineralization stage), and (III) post-ore quartz-calcite stage. Systematic short-wavelength infrared (SWIR) spectral analysis reveals that sericite group minerals developing in Stage II are the predominant and wide-distributed alteration minerals at Qianchen. Their spectral characteristics demonstrate a shift in the Al-OH absorption feature wavelength positions (Pos2200) to longer wavelengths (>2205 nm) within the orebodies. This shift is likely due to strong water–rock interactions leading to Fe and Mg substituting for Al in octahedral sites. Orthogonal partial least squares discriminant analysis (OPLS-DA) indicates that the H2O, OH, and FeOH spectral bands (Variable Importance in the Projection (VIP) ≥ 1) of sericite are crucial for distinguishing between gold ores and barren wall rocks. Additionally, Long Short-Term Memory (LSTM) models demonstrate that spectral data of sericite can be used to predict ore samples with an accuracy of 82 %, validated by receiver operating characteristic analysis (84 %), and blind test results demonstrate that the predicted grade closely aligns with the actual grade. Therefore, it is concluded that SWIR spectral characteristics of sericite group minerals can serve as an effective indicator for prospecting the Qianchen concealed gold mineralization. This study highlights the potential of coupling between machine learning and SWIR spectral data of alteration minerals to uncover the concealed lode gold mineralization.

Ecosystem services dynamics and their influencing factors: Synergies/tradeoffs interactions and implications, the case of upper Blue Nile basin, Ethiopia

Understanding the relationships among ecosystem services (ESs) and their interactions with influencing factors is essential for spatially targeted ecosystem governance. However, classifying the spatial distribution of these diverse interactions still needs improvement. Furthermore, existing studies have insufficiently addressed the specific impacts of bidirectional land cover transitions on ESs. Taking the upper Blue Nile basin as a study area, we estimated the spatiotemporal distribution of annual water yield (AWY), carbon storage (CS), habitat quality (HQ), and soil retention (SR) from 2000 to 2020, using InVEST models and associated formulas. Changes in ESs per inward-outward land cover transition were quantified based on the Cross-Tabulation Matrix. An improved pairwise method was employed to assess the spatially diverse interactions between ESs pairs and their relationship with influencing factors. The statistical significance of influencing factors was evaluated using partial least square regression. The findings indicated that high HQ values were prevalent in the west, while they were in the east for SR. The central and southern areas experienced higher CS and AWY values. During the study period, variations were observed in the mean values of SR (ranging from 22.89 to 23.88 × 102 t/ha/y), AWY (32.13–42.2 × 102 mm/ha/y), CS (90.5–102.9 × 103gC/ha/y) and HQ (0.62–0.64). Synergies were predominant in AWY-CS, AWY-SR, and CS-SR pairs. HQ revealed more of a no-effect and tradeoff relationship with other ESs. The interactions between ESs and influencing factors were dominated by synergies, followed by tradeoffs and no-effect. The influence of landscape structure (gyrate and landscape shape index) and land surface temperature on all ESs and precipitation on AWY and SR was significant (1.049 ≤ Variable Importance in the Projection ≤ 1.371). Overall, the spatiotemporal dynamics of key ESs and the modeling of their drivers are essential policy information for taking spatially explicit conservation measures. This study will also serve as a valuable methodological reference for future research.

Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System.

Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.

Interactions between Epichloë endophyte and the plant microbiome impact nitrogen responses in host Achnatherum inebrians plants.

The clavicipitaceous fungus Epichloë gansuensis forms symbiotic associations with drunken horse grass (Achnatherum inebrians), providing biotic and abiotic stress protection to its host. However, it is unclear how E. gansuensis affects the assembly of host plant-associated bacterial communities after ammonium nitrogen (NH4+-N) treatment. We examined the shoot- and root-associated bacterial microbiota and root metabolites of A. inebrians when infected (I) or uninfected (F) with E. gansuensis endophyte. The results showed more pronounced NH4+-N-induced microbial and metabolic changes in the endophyte-infected plants compared to the endophyte-free plants. E. gansuensis significantly altered bacterial community composition and β-diversity in shoots and roots and increased bacterial α-diversity under NH4+-N treatment. The relative abundance of 117 and 157 root metabolites significantly changed with E. gansuensis infection under water and NH4+-N treatment compared to endophyte-free plants. Root bacterial community composition was significantly related to the abundance of the top 30 metabolites [variable importance in the projection (VIP) > 2 and VIP > 3] contributing to differences between I and F plants, especially alkaloids. The correlation network between root microbiome and metabolites was complex. Microorganisms in the Proteobacteria and Firmicutes phyla were significantly associated with the R00693 metabolic reaction of cysteine and methionine metabolism. Co-metabolism network analysis revealed common metabolites between host plants and microorganisms.IMPORTANCEOur results suggest that the effect of endophyte infection is sensitive to nitrogen availability. Endophyte symbiosis altered the composition of shoot and root bacterial communities, increasing bacterial diversity. There was also a change in the class and relative abundance of metabolites. We found a complex co-occurrence network between root microorganisms and metabolites, with some metabolites shared between the host plant and its microbiome. The precise ecological function of the metabolites produced in response to endophyte infection remains unknown. However, some of these compounds may facilitate plant-microbe symbiosis by increasing the uptake of beneficial soil bacteria into plant tissues. Overall, these findings advance our understanding of the interactions between the microbiome, metabolome, and endophyte symbiosis in grasses. The results provide critical insight into the mechanisms by which the plant microbiome responds to nutrient stress in the presence of fungal endophytes.

The distinct hepatic metabolic profile and relation with impaired liver function in congenital isolated growth hormone-deficient rats.

Patients with growth hormone deficiency (GHD) with inadequate growth hormone levels are often correlated with nonalcoholic fatty liver disease (NAFLD). However, the potential mechanism of how GHD influences liver function remains obscure. In the present study, we aim to perform hepatic metabolomics in Lewis dwarf rats, which were the standard congenital isolated GH-deficient rat, to evaluate the characterizations of hepatic metabolic profiles and explore their relations with liver functions. Lewis dwarf homozygous (dw/dw) rats at 37 weeks (five females and five males), and Lewis dwarf heterozygous (dw/+) rats at 37 weeks (five females and five males) were analyzed in our study. Body lengths and weights, liver weights, serum alanine transaminase (ALT), and serum aspartate transaminase (AST) were measured. ELISA and RT-qPCR were used to assess IGF-1 levels in serum and liver, respectively. The non-targeted metabolomics was performed in the livers of dw/+ and dw/dw rats. Differential metabolites were selected according to the coefficient of variation (CV), variable importance in the projection (VIP) > 1, and P < 0.05. Hierarchical clustering of differential metabolites was conducted, and the KEGG database was used for metabolic pathway analysis. The body weights, body lengths, liver weights, and IGF-1 levels in the serum and liver of dw/dw rats were significantly decreased compared with dw/+ rats. Dw/dw rats exhibited more obvious hepatic steatosis accompanied by higher serum ALT and AST levels. Hepatic metabolomics showed that a total of 88 differential metabolites in positive ion mode, and 51 metabolites in negative ion mode were identified. Among them, lysophosphatidylcholine (LPC) 16:2, LPC 18:3, LPC 22:6, fatty acid esters of hydroxy fatty acids (FAHFA)18:1 were significantly decreased, while palmitoyl acid, dehydrocholic acid, and 7-ketolithocholic acid were significantly increased in dw/dw rats compared with dw/+ rats. These seven differential metabolites were significantly associated with phenotypes of rats. Finally, KEGG pathway analysis showed that the arginine and proline metabolism pathway and bile secretion pathway were mainly clustered. Lewis dw/dw rats with congenital isolated growth hormone deficiency (IGHD) showed liver steatosis and abnormal liver function, which could be potentially associated with the distinctive hepatic metabolic profiles.

Metabolic signatures and potential biomarkers of sarcopenia in suburb-dwelling older Chinese: based on untargeted GC–MS and LC–MS

BackgroundUntargeted metabolomics can be used to expand our understanding of the pathogenesis of sarcopenia. However, the metabolic signatures of sarcopenia patients have not been thoroughly investigated. Herein, we explored metabolites associated with sarcopenia by untargeted gas chromatography (GC)/liquid chromatography (LC)–mass spectrometry (MS) and identified possible diagnostic markers.MethodsForty-eight elderly subjects with sarcopenia were age and sex matched with 48 elderly subjects without sarcopenia. We first used untargeted GC/LC–MS to analyze the plasma of these participants and then combined it with a large number of multivariate statistical analyses to analyze the data. Finally, based on a multidimensional analysis of the metabolites, the most critical metabolites were considered to be biomarkers of sarcopenia.ResultsAccording to variable importance in the project (VIP > 1) and the p-value of t-test (p < 0.05), a total of 55 metabolites by GC–MS and 85 metabolites by LC–MS were identified between sarcopenia subjects and normal controls, and these were mostly lipids and lipid-like molecules. Among the top 20 metabolites, seven phosphatidylcholines, seven lysophosphatidylcholines (LysoPCs), phosphatidylinositol, sphingomyelin, palmitamide, L-2-amino-3-oxobutanoic acid, and palmitic acid were downregulated in the sarcopenia group; only ethylamine was upregulated. Among that, three metabolites of LysoPC(17:0), L-2-amino-3-oxobutanoic acid, and palmitic acid showed very good prediction capacity with AUCs of 0.887 (95% CI = 0.817–0.957), 0.836 (95% CI = 0.751–0.921), and 0.805 (95% CI = 0.717–0.893), respectively.ConclusionsThese findings show that metabonomic analysis has great potential to be applied to sarcopenia. The identified metabolites could be potential biomarkers and could be used to study sarcopenia pathomechanisms.

Using liquid chromatography and mass spectrometry to predict potential biomarkers for missed miscarriage and its metabolic pathways in a tertiary center: A cross-sectional analytic study.

To determine and compare the serum metabolites in missed abortion versus normal early pregnancy using ultra-high-performance liquid chromatography and tandem time-of-flight mass spectrometry, and to determine how these metabolites can be used to predict the potential biomarkers and possible metabolic pathways of a missed abortion. The serum of patients with a missed abortion was used as the experimental group and the serum of patients with an induced abortion during normal early pregnancy was used as the control group. Principal component analysis and orthogonal partial least square discriminant analysis were additionally used to observe the difference in metabolite distribution between the two groups. A variable weight value (variable importance in the projection; VIP) obtained from the orthogonal partial least squares discriminant analysis model more than 1 and P less than 0.05 were taken to indicate significant differences in metabolite screening. After this, enrichment analysis of the metabolic pathways of these metabolites was conducted using Fisher precise test in order to find the metabolic pathway with the highest correlation with the differential metabolites. In total, 30 patients were included in the experimental group, with 30 patients in the control group. Fifty-five metabolites (VIP > 1, P < 0.05) with significant differences related to missed abortion were selected, among which 35 metabolites increased and 20 decreased in patients with a missed abortion. KEGG pathway enrichment analysis showed that the four metabolic pathways with the highest correlation were cholesterol metabolism, arginine biosynthesis, cell apoptosis, and the FoxO signaling pathway. The missed abortion serum metabolites and changes in related metabolic pathways reported in this study provide a basis for the early prediction and diagnosis of a missed abortion.

Candidate urinary biomarkers show promise for distinguishing between calcium oxalate versus struvite urolithiasis in dogs.

To identify metabolites and metabolic pathways affected in dogs with struvite and calcium oxalate urolithiasis compared to healthy dogs. To explore the candidate urinary biomarkers to distinguish dogs with struvite and calcium oxalate urolithiasis. 13 dogs with calcium oxalate urolithiasis, 7 dogs with struvite urolithiasis, and 13 healthy dogs were recruited between September 2021 and January 2023. Metabolomic profiles were analyzed from urine samples using UPLC-MS MS. According to the variable importance in the projection (> 1) and correlation coefficient (P < .05) obtained by orthogonal partial least squares discriminant analysis, the differential metabolites were screened. The Kyoto Encyclopedia of Genes and Genomes database was used to identify the metabolic pathways involved. Compared to healthy dogs, those with calcium oxalate urolithiasis exhibited distinct metabolites primarily associated with phenylalanine metabolism, nicotinic acid, and nicotinamide metabolic pathways. Conversely, dogs with struvite urolithiasis demonstrated variations in metabolites mainly linked to tryptophan metabolism and glycerophospholipid metabolic pathways. Between calcium oxalate and struvite groups, pyocyanin, glycylprolylarginine, traumatin, cysteinyl-leucine, and 8-hydroxydodecylcarnitine are candidate urinary biomarkers. Our findings provide an in-depth analysis of metabolic perturbations associated with calcium oxalate and struvite urolithiasis in dogs. We also identified candidate urinary biomarkers distinguishing between dogs with calcium oxalate and struvite urolithiasis, which can be subsequently validated to assist in stone diagnosis and guide treatment choices.

Metabolomic analysis of seminal fluids in infertile individuals.

Infertility impacts a substantial number of couples worldwide, and about 50% of cases are linked to male factors. The analysis of seminal fluid composition can improve diagnostic accuracy and offer deeper insights into the pathophysiology of male factor infertility. This study seeks to identify novel markers for diagnosing and treating male infertility by comparing organic acid profiles in the seminal fluid of individuals with normospermia, oligospermia, and azoospermia. Semen samples were collected from men with normospermia, oligospermia, and azoospermia. The organic acid profile in the seminal fluid was analyzed using liquid chromatography-mass spectrometry/mass spectrometry (LC/MS-MS). Data analysis was performed using SPSS and MetaboAnalyst. The study revealed significant differences in metabolite levels among normospermic, oligospermic, and azoospermic individuals. In groups with oligospermia, there were significant decreases in the levels of 2-OH-Isovaleric Acid, 3-Methyl-2-Oxovaleric Acid, Ethyl-Malonic Acid, Citric Acid, Oxoproline, Malic Acid, N-Acetyl-Aspartic Acid, Suberic Acid, Glutaconic Acid, and Succinic Acid. Similarly, individuals with azoospermia exhibited a notable reduction in the levels of Citric Acid, Malic Acid, and Suberic Acid. Furthermore, according to the Variable Importance in the Projection (VIP) score analysis, Ethyl-Malonic Acid, Glycolic Acid, and 3-Methyl-2-Oxovaleric Acid were identified as crucial factors for diagnosis and potential treatment strategies. The data obtained from the study highlights the significant potential of metabolites in assessing infertility and gaining a more in-depth understanding of the underlying pathological mechanisms.

A Preliminary Study on the Plasma Metabolomic Profiles of Patients Suffering From Acute Diquat Poisoning

To analyze the plasma metabolomic features of patients suffering from acute diquat (DQ) poisoning and to explore the molecular mechanism and potential biomarkers of DQ poisoning. A total of 7 patients suffering from acute DQ poisoning were enrolled in the DQ poisoning group. The poisoning of these patients occurred within a 12-h window at the time of enrollment. Meanwhile, 7 healthy immediate family members of the patients were enrolled as the normal controls. Liquid chromatography-mass spectrometry (LC-MS) was used to perform non-targeted metabolomic profiling of the plasma samples and to screen and identify differential metabolites and metabolic pathways. A total of 104 metabolites were screened and identified (P<0.05 and the variable importance in the projection [VIP]>1). Compared with those of the control group, 61 metabolites, such as sorbitol and galactitol, were up-regulated, and 43 metabolites, such as myo-inositol and gamma-glutamylcysteine, were down-regulated in the DQ poisoning group. Pathway enrichment analysis revealed changes in 11 metabolic pathways, including those for galactose metabolism and linoleic acid metabolism (P<0.05). Metabolomics analysis of plasma samples from DQ poisoning patients shows that DQ mainly interferes with the metabolism of energy, amino acids, and lipids, thus causing metabolic disorders. Some potential biomarkers closely associated with oxidative stress and organ damage of the liver, kidney, and nervous system have been identified.

PSX-25 The Fecal Metabolome Differs Between Nelore Bulls Fed Pre- and Post-Feedlot Diets: Initial Results

Abstract The objective of this study was to evaluate the fecal metabolome of Nelore bulls (Bos taurus indicus) fed pre- and post-feedlot diets. In pre-feedlot, 21 animals belonging to the Nelore Qualitas breeding program aged 666 ± 68 days old, and 534.5 ± 34.2 kg of body weight were allocated to one pen for 15 days. The pre-feedlot diet contained corn silage (80%) and sugarcane bagasse (20%). Next, the bulls (546.0 ± 41.2 kg of body weight) were submitted into a finishing stage in feedlot for 56 days. During the post-feedlot diet contained wet grain corn (44.6%), corn silage (27.6%), citrus pulp (11.9%), peanut bran (8%), sugarcane bagasse (4.9%), premix (1.8%) and urea (1.2%) twice a day with ad libitum access in Intergado electronic troughs (Intergado Ltda., Contagem, Minas Gerais, Brazil). Pre- and post-feedlot fecal samples were collected directly from the rectal ampulla of each bovine and stored in a -80°C freezer for metabolomic assays by Nuclear Magnetic Resonance analysis (1H NMR). The metabolites were identified, quantified and data processing using the Chenomx NMR Suite 8.6 (Chenomx Inc., Edmonton, AB, Canada) and MetaboAnalyst 5.0 (http://www.metaboanalyst.ca). We identified and quantified 57 and 55 metabolites in the fecal samples of the bulls fed pre- and post-feedlot diet, respectively. We observed that 39 metabolites differed (P &amp;lt; 0.05) between pre- and post-feedlot diet. The 2-phenylpropionate, caprylate, fumarate, deaminotyrosine, gamma-glutamyl phenylalanine, NADH, thymidine and pantothenate were observed only in the pre-feedlot diet. However, acetamide, 2-oxoglutarate, uracil, xanthine, trimethylamine and demethyl sulfone were consumed exclusively post-feedlot diet. Our multivariate analyses demonstrated that the fecal metabolite profiles of the Nelore bulls fed pre- and post-feedlot diet were different. The first two components of principal component analysis (PCA) explained 86.1% of the total data variance (PC1 = 77.6%; PC2 = 8.5%). Likewise, the partial least squares discriminant analysis (PLS-DA) explained 85.5% of the data variance with the first two components (component 1 (74.4%) vs. component 2 (8.1%)). Three fecal metabolites (acetate, butyrate, and propionate) with variable importance in the projection (VIP scores over 1.0; P&amp;lt;0.05) were greater in animals fed post-feedlot diet. Additionally, metabolome profile offers information on the main metabolic pathways, and five pathways were identified as different (P&amp;lt;0.05 and impact &amp;gt;2) in the fecal samples that bulls fed pre and post feedlot diets: pyruvate metabolism (bta00620); glycine, serine and threonine metabolism (bta00260); synthesis and degradation of ketone bodies (bta00088); histidine metabolism (bta00340) and biosynthesis of phenylalanine, tyrosine and tryptophan (bta00400). Our results suggest that pre- and post-feedlot diets modify the fecal metabolome profile of Nelore bulls. In addition, further studies about metabolic effects of diets are required for a better understanding of these influence on Nelore bulls.

Multi-omics analysis of microbial communities and metabolites during the fermentation of traditional Midu Juanti

The current study employed a combination of high-throughput sequencing and non-targeted metabolomics techniques to elucidate the changes in the quality, microbial communities, and metabolites throughout the fermentation process of traditional Midu Juanti, a traditional fermented meat product that originates from ethnic minority regions in southwestern China. The findings show that 22 dominant genera (7 bacterial and 15 fungal genera), with a relative abundance >1%, were successfully identified during the fermentation of Midu Juanti. Additionally, 979 metabolites were identified using the Human Metabolome Database (HMDB) annotation. Among these, lipids and lipid-like molecules constituted the largest proportion, followed by organic acids and their derivatives. Moreover, based on the variable importance in the projection (VIP) > 2 and P < 0.05, 60 differentially changed metabolites (DCMs) were identified. Meanwhile, the presence of Lactobacillus was positively correlated with 54 DCMs (P < 0.05, |r| > 0.5), including tryptophyl-threonine and (5R)-5-hydroxyhexanoic acid. Conversely, a negative correlation was observed between the presence of Monascus and these 54 DCMs (P < 0.05, |r| > 0.5). This study has the potential to enhance the processing theory of fermented meat products and establish a solid theoretical foundation for the control and enhancement of the quality of Midu Juanti.

A novel fault detection framework integrated with variable importance analysis for quality-related nonlinear process monitoring

Monitoring the quality-related variables is an essential task in industrial processes. However, large amounts of variables and complex relationship between measured and quality variables pose challenges in improving fault detection performance. This paper proposes a novel quality-related fault detection framework based on variable importance analysis for nonlinear process monitoring. In the proposed framework, a new nonlinear variable division index called improved variable importance in the projection (IVIP) is designed to better identify quality-related variables. First, measured variables are divided into quality-related and independent spaces according to their IVIP values. Then, considering different data characteristics, a hybrid detection model combined multivariate exponentially weighted moving average and kernel principal component analysis (MEKPCA) is constructed in these two spaces to monitor abnormal events. To provide an integrated decision, the Bayesian inference strategy is finally utilized to fuse useful information from different spaces. Three case studies, including a simulated numerical system, the Tennessee Eastman process, and a wastewater treatment process, demonstrate that our proposed IVIP-MEKPCA outperforms other comparison methods.

Urine metabolomics study of intervention of Xihuang Pills in rats with hyperplasia of mammary gland

This study aimed to investigate the mechanism of Xihuang Pills in improving hyperplasia of mammary gland(HMG) in rats based on urine metabolomics using ultra-performance liquid chromatography-quadrupole-Orbitrap mass spectrometry(UPLC-Q-Orbitrap-MS). The HMG rat model was established by intramuscular injection of estradiol benzoate solution(0.5 mg·kg~(-1), 25 days) followed by progesterone injection(5 mg·kg~(-1), 5 days). UPLC-Q-Orbitrap-MS technology was used to establish the endogenous small-molecule metabolic profiles in urine samples of rats in the blank group, the HMG model group, and Xihuang Pills group. Multivariate statistical analysis was performed for pattern recognition, t test and variable importance in the projection(VIP) were used to screen potential biomarkers. The significantly changed differential metabolites were identified using the online database Human Metabolome Database(HMDB). Metabolic pathway enrichment analysis was conducted using the MetaboAnalyst 5.0 database. The results showed that 90 differential metabolites with significant changes(P&lt;0.05) were identified between the blank group and the HMG model group using the HMDB. Among them, 48 metabolites significantly reverted(P&lt;0.05) after administration of Xihuang Pills, which may be related to the regulatory effect of Xihuang Pills. Thirteen metabolic pathways significantly associated with HMG were identified when the differential metabolites were imported into the MetaboAnalyst 5.0 database, and Xihuang Pills could modulate seven of these pathways. These metabolic pathways mainly involved histidine metabolism, arginine and proline metabolism, β-alanine metabolism, glycine, serine and threonine metabolism, tryptophan metabolism, pyrimidine metabolism, and amino sugar and nucleotide sugar metabolism. This study utilized UPLC-Q-Orbitrap-MS and urine metabolomics technology to analyze the mechanism of Xihuang Pills in improving HMG, laying the foundation for further in-depth research.

서리피해를 받은 잎으로 만든 홍차의 GC×GC-TOFMS 휘발성 화합물 분석

Korea suffers from unusual droughts, heavy rain, or high temperatures due to the abnormal weather phenomenon worldwide. This climate change directly impacts the occurrence of frost in spring, which is causing serious damage to the buds and leaves of tea plants that mainly grow in spring. Frost damage delays the harvest time and decreases production, leading to a decline in the income of tea-producing farmers. Therefore, it is essential to develop ways of using discarded tea leaves. This study analyzed the volatile compounds in black tea prepared from the discarded tea leaves by two-dimensional gas chromatography-time of flight mass spectrometry (GC×GC-TOFMS). The total relative content of 481 compounds was approximately 1.8 times higher in frost-damaged black tea than in regular black tea. The ketone and ester levels in frost-damaged black tea were approximately twice as high as that in normal black tea. Two types of black tea samples were well distinguished by a dependable PLS-DA model, which presented satisfied variance and cross-validated predictive capability (R2Y=0.997, Q2=0.956) without overfitting phenomena (R2=0.953, Q2=-0.266). One hundred eighty compounds with variable importance in the projection (VIP) values &gt;1.0 were applied. The result of HCA analysis, esters, and alkenes were concentrated in normal black tea, and the ketones and heterocyclic compounds were concentrated in frost-damaged black tea. These results provide basic data for improving the process of producing good products using discarded leaves caused by frost damage.

Extraction of Phosphorus from Sewage Sludge Ash—Influence of Process Variables on the Electrodialytic Process

Phosphorus is a critical, irreplaceable raw material, and developing methods to recover P from secondary sources such as sewage sludge ash (SSA) is crucial. Two-compartment electrodialytic extraction (2C-ED) is one method where an electric DC field is applied to extract P and separate heavy metals simultaneously. Several process parameters influence 2C-ED, and they influence each other mutually. This paper explores chemometrics modeling to give insight into the 2C-ED process and, specifically, optimization of the experimental parameters towards 80% P extraction. A projections-to-latent-structures model was constructed based on new 2C-ED experiments conducted with one SSA type. The model was stable (high correlation factor and predictive power). Variable importance in the projection (VIP) plots showed that the influence of the variables was in the order: current &gt; duration &gt; L:S ratio &gt; stirring velocity &gt; dispersion solution (weak acid or distilled water). Contour plots were used for exploring different P extraction strategies. For example, more P mass per unit current was extracted at an L:S ratio of 7 compared to L:S 14. This shows that treating a thicker SSA suspension is preferable to optimize the current efficiency. The chemometric model proved valuable for optimizing the 2C-ED process and future scale-up.