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Abstract The dominant bacteria in the hindgut of calves play an important role in their growth and health, which could even lead to lifelong consequences. However, the identification of core probiotics in the hindgut and its mechanism regulating host growth remain unclear. Here, a total of 1045 fecal samples were analyzed by 16S rRNA gene sequencing from the 408 Holstein dairy calves at the age of 0, 14, 28, 42, 56, and 70 days to characterize the dynamic changes of core taxa. Moreover, the mechanisms of nutrient metabolism of calf growth regulated by core bacteria were investigated using multi-omics analyses. Finally, fecal microbiota transplantation (FMT) in mice were conducted to illustrate the potential beneficial effects of core bacteria. Four calf enterotypes were identified and enterotypes dominated by Bifidobacterium and Oscillospiraceae_UCG-005 were representative. The frequency of enterotype conversion shifted from variable to stable. The close relationship observed between phenotype and enterotype, revealing a potential pro-growth effect of Bifidobacterium, might be implemented by promoting the use of carbohydrate, activating the synthesis of volatile fatty acids, amino acids and vitamin B6, and inhibiting methane production in the hindgut. The FMT results indicated the beneficial effect of Bifidobacterium on host growth and hindgut development. These results support the notion that the Bifidobacterium-dominated fecal microbiome would be an important driving force for promoting the host growth in the early life. Our findings provide new insights into the potential probiotic mining and application strategies to promote the growth of young animals or improve their growth retardation.

Introduction The rapid growth of e-cigarette usage among youth and young people has emerged as a significant public health concern. It is imperative to initiate effective vaping prevention campaigns and undertake relevant research to address this pressing issue. This research seeks to identify effective video advertisements to deter young people from starting to use e-cigarettes. It aims to offer evidencebased insights and recommendations for creating communication materials and designing messages for youth e-cigarette prevention efforts. Methods College students aged 18–24 years (n=40) participated in focus groups within this qualitative study. After viewing four stimulus videos, participants discussed what they perceived as effective and ineffective video characteristics, as well as suggestions for future videos. Results Effective video characteristics included the use of real-life testimonials, displaying specific health hazards, revealing harmful chemical ingredients and the deceptive nature of flavors, and positively perceived effectiveness. Participants generally found that videos with strong visual impact and graphics were more engaging and that approaches using fear and emotion were more effective. Ineffective characteristics included complex and exaggerated information, lack of empathy and irrelevance, insufficiently specific information, extreme and death-themed content, industry messages, as well as preachy tones, animations, metaphors, dull formats, excessive length, and scenes of e-cigarette use. Conclusions Developing anti-e-cigarette campaign materials for youth necessitates target audience-focused qualitative research. This helps in deeply exploring and identifying effective themes and messages, as well as video characteristics and details while avoiding ineffective or even misleading messages and themes from young people’s perspectives outside the United States. Future development of e-cigarette prevention videos for Chinese college students may consider incorporating localized real-life testimonial cases to convey specific harms, including self-efficacy information, and utilizing fear and emotional appeals.

This study aims to elucidate the anti-hypoxia mechanism of sesamoside, an active component of Phlomis younghusbandii Mukerjee , through a network pharmacology approach. Sesamoside has demonstrated potential anti-oxidant and antiglycation activities. The hypoxia-related disease targets were collected from databases like GeneCards and OMIM. Protein-protein interaction (PPI) networks were constructed using the STRING database. GO/KEGG enrichment analysis was performed using the Metascape database to identify biological processes and signaling pathways. Our results indicate that sesamoside interacts with multiple targets related to glucose and lipid metabolism, nucleotide metabolism, and inflammatory, and we find that AKR1B1 (AR) plays a crucial role in sesamoside responses to hypoxia. Molecular docking studies were performed using Autodock software, revealing good binding activity between sesamoside and AR. We then use CCK-8 assay, qPCR, WB, and ELISA analysis to validate the role of sesamoside in regulating AR and participating in anti-hypoxia through cell experiments. The results show that compared with the hypoxia group, sesamoside treatment significantly improves the expression of AR and inflammation cytokines. In summary, this study sheds light on the anti-hypoxia mechanism of sesamoside using a network pharmacology approach, providing a theoretical basis and experimental foundation for its application in the prevention and treatment of hypoxic diseases.

Abstract Background Myocardial ischemia–reperfusion injury (MIRI) is widespread in the treatment of ischemic heart disease, and its treatment options are currently limited. Adiponectin (APN) is an adipocytokine with cardioprotective properties; however, the mechanisms of APN in MIRI are unclear. Therefore, based on preclinical (animal model) evidence, the cardioprotective effects of APN and the underlying mechanisms were explored. Methods The literature was searched for the protective effect of APN on MIRI in six databases until 16 November 2023, and data were extracted according to selection criteria. The outcomes were the size of the myocardial necrosis area and hemodynamics. Markers of oxidation, apoptosis, and inflammation were secondary outcome indicators. The quality evaluation was performed using the animal study evaluation scale recommended by the Systematic Review Center for Laboratory animal Experimentation statement. Stata/MP 14.0 software was used for the summary analysis. Results In total, 20 papers with 426 animals were included in this study. The pooled analysis revealed that APN significantly reduced myocardial infarct size [weighted mean difference (WMD) = 16.67 (95% confidence interval (CI) = 13.18 to 20.16, P

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Abstract Background Plant secondary metabolites are highly valued for their applications in pharmaceuticals, nutrition, flavors, and aesthetics. It is of great importance to elucidate plant secondary metabolic pathways due to their crucial roles in biological processes during plant growth and development. However, understanding plant biosynthesis and degradation pathways remains a challenge due to the lack of sufficient information in current databases. To address this issue, we proposed a transfer learning approach using a pre-trained hybrid deep learning architecture that combines Graph Transformer and convolutional neural network (GTC) to predict plant metabolic pathways. Results GTC provides comprehensive molecular representation by extracting both structural features from the molecular graph and textual information from the SMILES string. GTC is pre-trained on the KEGG datasets to acquire general features, followed by fine-tuning on plant-derived datasets. Four metrics were chosen for model performance evaluation. The results show that GTC outperforms six other models, including three previously reported machine learning models, on the KEGG dataset. GTC yields an accuracy of 96.75%, precision of 85.14%, recall of 83.03%, and F1_score of 84.06%. Furthermore, an ablation study confirms the indispensability of all the components of the hybrid GTC model. Transfer learning is then employed to leverage the shared knowledge acquired from the KEGG metabolic pathways. As a result, the transferred GTC exhibits outstanding accuracy in predicting plant secondary metabolic pathways with an average accuracy of 98.30% in fivefold cross-validation and 97.82% on the final test. In addition, GTC is employed to classify natural products. It achieves a perfect accuracy score of 100.00% for alkaloids, while the lowest accuracy score of 98.42% for shikimates and phenylpropanoids. Conclusions The proposed GTC effectively captures molecular features, and achieves high performance in classifying KEGG metabolic pathways and predicting plant secondary metabolic pathways via transfer learning. Furthermore, GTC demonstrates its generalization ability by accurately classifying natural products. A user-friendly executable program has been developed, which only requires the input of the SMILES string of the query compound in a graphical interface.

Background This study was performed to identify metabolites associated with incident acute coronary syndrome (ACS) and explore causality of the associations. Methods and Results We performed nontargeted metabolomics in a nested case‐control study in the Dongfeng‐Tongji cohort, including 500 incident ACS cases and 500 age‐ and sex‐matched controls. Three metabolites, including a novel one (aspartylphenylalanine), and 1,5‐anhydro‐d‐glucitol (1,5‐AG) and tetracosanoic acid, were identified as associated with ACS risk, among which aspartylphenylalanine is a degradation product of the gut‐brain peptide cholecystokinin‐8 rather than angiotensin by the angiotensin‐converting enzyme (odds ratio [OR] per SD increase [95% CI], 1.29 [1.13–1.48]; false discovery rate–adjusted P=0.025), 1,5‐AG is a marker of short‐term glycemic excursions (OR per SD increase [95% CI], 0.75 [0.64–to 0.87]; false discovery rate–adjusted P=0.025), and tetracosanoic acid is a very‐long‐chain saturated fatty acid (OR per SD increase [95% CI], 1.26 [1.10–1.45]; false discovery rate–adjusted P=0.091). Similar associations of 1,5‐AG (OR per SD increase [95% CI], 0.77 [0.61–0.97]) and tetracosanoic acid (OR per SD increase [95% CI], 1.32 [1.06–1.67]) with coronary artery disease risk were observed in a subsample from an independent cohort (152 and 96 incident cases, respectively). Associations of aspartylphenylalanine and tetracosanoic acid were independent of traditional cardiovascular risk factors (P‐trend=0.015 and 0.034, respectively). Furthermore, the association of aspartylphenylalanine was mediated by 13.92% from hypertension and 27.39% from dyslipidemia (P

Although mitochondrial gene rearrangement has been observed in many insect lineages, little is known about how it affects mitochondrial gene transcription. To address this question, we first constructed a quantitative transcription map for Aphidius gifuensis, a species of parasitoid wasp known to have a highly rearranged mitochondrial genome (mitogenome) and two potential control regions (CRs). Based on this transcription map, we assessed the models of the mitochondrial transcription and post-transcription cleavage. We found that the J and N strand of this mitogenome differ significantly in transcriptional regulation. On the J strand, we found two transcription initiation sites (TISs), five transcription termination sites (TTSs), and six polycistronic primary transcripts whereas only one TIS, one TTS and one polycistronic primary transcript can be found on the N strand. Most of the non-coding regions of both strands were transcribed into primary transcripts and cleaved after transcription. The proposed mode of transcription of A. gifuensis was similar to that of Drosophila, a model organism with no gene rearrangement. And two rearranged gene clusters (trnI-CR1-trnM-CR2-trnQ and trnW-trnY-trnC) seemed to have little effects on the mode of transcription. In addition, our results revealed the presence of TISs in CR1 and CR2, implying that both CRs maybe required for transcriptional regulation. Analysis of the post-transcriptional cleavage process showed that there were both “forward cleavage” and “reverse cleavage” models in A. gifuensis, and more than one way of cleavages were found in three regions. The incomplete transcripts suggested that the direction of mitochondrial RNA degradation was from 5′ to 3′ end and supported the view of polyadenylation-dependent RNA degradation. Our study provides insights into the transcriptional and post-transcriptional regulation processes of highly rearranged insect mitogenomes.

Reliability, robustness, and interlaboratory comparability of quantitative measurements is critical for clinical lipidomics studies. Lipids’ different ex vivo stability in blood bears the risk of misinterpretation of data. Clear recommendations for the process of blood sample collection are required. We studied by UHPLC-high resolution mass spectrometry, as part of the “Preanalytics interest group” of the International Lipidomics Society, the stability of 417 lipid species in EDTA whole blood after exposure to either 4°C, 21°C, or 30°C at six different time points (0.5 h–24 h) to cover common daily routine conditions in clinical settings. In total, >800 samples were analyzed. 325 and 288 robust lipid species resisted 24 h exposure of EDTA whole blood to 21°C or 30°C, respectively. Most significant instabilities were detected for FA, LPE, and LPC. Based on our data, we recommend cooling whole blood at once and permanent. Plasma should be separated within 4 h, unless the focus is solely on robust lipids. Lists are provided to check the ex vivo (in)stability of distinct lipids and potential biomarkers of interest in whole blood. To conclude, our results contribute to the international efforts towards reliable and comparable clinical lipidomics data paving the way to the proper diagnostic application of distinct lipid patterns or lipid profiles in the future.

The gut microbiome is of tremendous relevance to human health and disease, so it is a hot topic of omics-driven biomedical research. However, a valid identification of gut microbiota-associated molecules in human blood or urine is difficult to achieve. We hypothesize that bowel evacuation is an easy-to-use approach to reveal such metabolites. A non-targeted and modifying group-assisted metabolomics approach (covering 40 types of modifications) was applied to investigate urine samples collected in two independent experiments at various time points before and after laxative use. Fasting over the same time period served as the control condition. As a result, depletion of the fecal microbiome significantly affected the levels of 331 metabolite ions in urine, including 100 modified metabolites. Dominating modifications were glucuronidations, carboxylations, sulfations, adenine conjugations, butyrylations, malonylations, and acetylations. A total of 32 compounds, including common, but also unexpected fecal microbiota-associated metabolites, were annotated. The applied strategy has potential to generate a microbiome-associated metabolite map (M3) of urine from healthy humans, and presumably also other body fluids. Comparative analyses of M3 vs. disease-related metabolite profiles, or therapy-dependent changes may open promising perspectives for human gut microbiome research and diagnostics beyond analyzing feces.

In 2018, Zhang et al. introduced the Persistent Fault Analysis (PFA) for the first time, which uses statistical features of ciphertexts caused by faulty Sbox to recover the key of block ciphers. However, for most of the variants of PFA, the prior knowledge of the fault (location and value) is required, where the corresponding analysis will get more difficult under the scenario of multiple faults. To bypass such perquisite and improve the analysis efficiency for multiple faults, we propose Chosen-Plaintext based Persistent Fault Analysis (CPPFA). CPPFA introduces chosen-plaintext to facilitate PFA and can reduce the key search space of AES-128 to extremely small. Our proposal requires 256 ciphertexts, while previous state-of-the-art work still requires 1509 and 1448 ciphertexts under 8 and 16 faults, respectively, at the only cost of requiring 256 chosen plaintexts. In particular, CPPFA can be applied to the multiple faults scenarios where all fault locations, values and quantity are unknown, and the worst time complexity of CPPFA is O(28+nf ) for AES-128, where nf represents the number of faults. The experimental results show that when nf > 4, 256 pairs of plaintext-ciphertext can recover the master key of AES-128. As for LED-64, only 16 pairs of plaintext-ciphertext reduce the remaining key search space to 210.

Non-fasting lipidemia (nFL), mainly contributed by postprandial lipidemia (PL), has recently been recognized as an important cardiovascular disease (CVD) risk as fasting lipidemia (FL). PL serves as a common feature of dyslipidemia in Type 2 Diabetes (T2D), albeit effective therapies targeting on PL were limited. In this study, we aimed to evaluate whether the therapy combining probiotics (Prob) and berberine (BBR), a proven antidiabetic and hypolipidemic regimen via altering gut microbiome, could effectively reduce PL in T2D and to explore the underlying mechanism. Blood PL (120 min after taking 100 g standard carbohydrate meal) was examined in 365 participants with T2D from the Probiotics and BBR on the Efficacy and Change of Gut Microbiota in Patients with Newly Diagnosed Type 2 Diabetes (PREMOTE study), a random, placebo-controlled, and multicenter clinical trial. Prob+BBR was superior to BBR or Prob alone in improving postprandial total cholesterol (pTC) and low-density lipoprotein cholesterol (pLDLc) levels with decrement of multiple species of postprandial lipidomic metabolites after 3 months follow-up. This effect was linked to the changes of fecal Bifidobacterium breve level responding to BBR alone or Prob+BBR treatment. Four fadD genes encoding long-chain acyl-CoA synthetase were identified in the genome of this B. breve strain, and transcriptionally activated by BBR. In vitro BBR treatment further decreased the concentration of FFA in the culture medium of B. breve compared to vehicle. Thus, the activation of fadD by BBR could enhance FFA import and mobilization in B. breve and diliminish the intraluminal lipids for absorption to mediate the effect of Prob+BBR on PL. Our study confirmed that BBR and Prob (B. breve) could exert a synergistic hypolipidemic effect on PL, acting as a gut lipid sink to achieve better lipidemia and CVD risk control in T2D.

ObjectiveDyslipidemia is a hallmark of diabetes mellitus (DM). However, specific lipid molecules closely associated with the initiation and progression of diabetes remain unclear. We used a pseudotargeted lipidomics approach to evaluate the complex lipid changes that occurred long before the diagnosis of type 2 diabetes mellitus (T2DM) and to identify novel lipid markers for screening prediabetes mellitus (PreDM) and T2DM in patients from multiple communities.MethodsFour hundred and eighty-one subjects consisting of T2DM, three subtypes of PreDM, and normal controls (NC) were enrolled as discovery cohort. Serum lipidomic profiles of 481 subjects were analyzed using an ultrahigh performance liquid chromatography-triple quadrupole mass spectrometry (UHPLC-QqQ-MS)-based pseudotargeted lipidomics method. The differential lipid molecules were further validated in an independent case-control study consisting of 150 PreDM, 234 T2DM and 94 NC.ResultsMultivariate discriminative analyses show that lipidomics data have considerable potential for identifying lipidome differences among T2DM, subtypes of PreDM and NC. Statistical associations of lipid (sub)species display significant variations in 11 lipid (sub)species levels for T2DM and distinctive differences in 8 lipid (sub)species levels between prediabetic and normoglycemic individuals, with further differences in 8 lipid (sub)species levels among subtypes of PreDM. Adjusted for sex, age and BMI, only two lipid (sub)species of fatty acid (FA) and phosphatidylcholine (PC) were associated at p< 0.05 for PreDM (all) and subtypes of PreDM. The defined lipid markers not only significantly improve the diagnostic accuracy of PreDM and T2DM but also effectively evaluating the risk of developing into each subtype of PreDM and T2DM when addition of age, sex, BMI, and FPG, respectively.ConclusionsOur findings improve insights into the lipid metabolic complexity and interindividual variations among subtypes of PreDM and T2DM, beyond the well-known differences in dyslipidemia in clinic.

Aims/HypothesisLarge-scale prediabetes screening is still a challenge since fasting blood glucose and HbA1c as the long-standing, recommended analytes have only moderate diagnostic sensitivity, and the practicability of the oral glucose tolerance test for population-based strategies is limited. To tackle this issue and to identify reliable diagnostic patterns, we developed an innovative metabolomics-based strategy deviating from common concepts by employing urine instead of blood samples, searching for sex-specific biomarkers, and focusing on modified metabolites.MethodsNon-targeted, modification group-assisted metabolomics by liquid chromatography–mass spectrometry (LC-MS) was applied to second morning urine samples of 340 individuals from a prediabetes cohort. Normal (n = 208) and impaired glucose-tolerant (IGT; n = 132) individuals, matched for age and BMI, were randomly divided in discovery and validation cohorts. ReliefF, a feature selection algorithm, was used to extract sex-specific diagnostic patterns of modified metabolites for the detection of IGT. The diagnostic performance was compared with conventional screening parameters fasting plasma glucose (FPG), HbA1c, and fasting insulin.ResultsFemale- and male-specific diagnostic patterns were identified in urine. Only three biomarkers were identical in both. The patterns showed better AUC and diagnostic sensitivity for prediabetes screening of IGT than FPG, HbA1c, insulin, or a combination of FPG and HbA1c. The AUC of the male-specific pattern in the validation cohort was 0.889 with a diagnostic sensitivity of 92.6% and increased to an AUC of 0.977 in combination with HbA1c. In comparison, the AUCs of FPG, HbA1c, and insulin alone reached 0.573, 0.668, and 0.571, respectively. Validation of the diagnostic pattern of female subjects showed an AUC of 0.722, which still exceeded the AUCs of FPG, HbA1c, and insulin (0.595, 0.604, and 0.634, respectively). Modified metabolites in the urinary patterns include advanced glycation end products (pentosidine-glucuronide and glutamyl-lysine-sulfate) and microbiota-associated compounds (indoxyl sulfate and dihydroxyphenyl-gamma-valerolactone-glucuronide).Conclusions/InterpretationOur results demonstrate that the sex-specific search for diagnostic metabolite biomarkers can be superior to common metabolomics strategies. The diagnostic performance for IGT detection was significantly better than routinely applied blood parameters. Together with recently developed fully automatic LC-MS systems, this opens up future perspectives for the application of sex-specific diagnostic patterns for prediabetes screening in urine.

The online healthcare community (OHC) has attained rapid development in recent years in which users are facilitated to exchange disease information and seek medical treatment. However, users’ motivation of participation in OHCs is still under investigation. Taking the perspective of user perceived value, this paper examined the impacts of different incentive levels including identity incentive, privilege incentive, and material incentive on user perceived value, user engagement, and user loyalty. To test the proposed hypotheses, the study adopted the methods of the between-subjects experiment and questionnaire. Based on the data analysis by ANOVA and structural equation model, the results show there are significant differences in the impacts of different incentive levels on users’ perceived value. Most of the incentive measures exert significant effects on simple user hedonic value and community identity value. Accordingly, the research findings suggest that affective support value and self-health management value demonstrate more importance for user engagement and user loyalty. Therefore, OHCs should try to improve users’ affective support value and self-health management value which are the ultimate aims of the OHCs. Our study sheds some light on profoundly understanding the design of incentive mechanism of OHC and contributes to the research of OHC services.

In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce.

Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.

The gut microbiome affects systemic metabolism and is a therapeutic target for type 2 diabetes. Here the authors demonstrate in a randomized controlled trial that effects of berberine, a plant alkaloid known to lower blood glucose, may be explained by the inhibition of Ruminococcus bromii mediated biotransformation of the bile acid deoxycholic acid.

Studies of key metabolite variations and their biological mechanisms in cerebral infarction (CI) have increased our understanding of the pathophysiology of the disease. However, how metabolite variations in different periods of CI influence these biological processes and whether key metabolites from different periods may better predict disease progression are still unknown. We performed a systematic investigation using the metabonomics method. Various metabolites in different pathways were investigated by serum metabolic profiling of 143 patients diagnosed with CI and 59 healthy controls. Phe-Phe, carnitine C18:1, palmitic acid, cis-8,11,14-eicosatrienoic acid, palmitoleic acid, 1-linoleoyl-rac-glycerol, MAG 18:1, MAG 20:3, phosphoric acid, 5α-dihydrotestosterone, Ca, K, and GGT were the major components in the early period of CI. GCDCA, glycocholate, PC 36:5, LPC 18:2, and PA showed obvious changes in the intermediate time. In contrast, trans-vaccenic acid, linolenic acid, linoleic acid, all-cis-4,7,10,13,16-docosapentaenoic acid, arachidonic acid, DHA, FFA 18:1, FFA 18:2, FFA 18:3, FFA 20:4, FFA 22:6, PC 34:1, PC 36:3, PC 38:4, ALP, and Crea displayed changes in the later time. More importantly, we found that phenylalanine metabolism, medium-chain acylcarnitines, long-chain acylcarnitines, choline, DHEA, LPC 18:0, LPC 18:1, FFA 18:0, FFA 22:4, TG, ALB, IDBIL, and DBIL played vital roles in the development of different periods of CI. Increased phenylacetyl-L-glutamine was detected and may be a biomarker for CI. It was of great significance that we identified key metabolic pathways and risk metabolites in different periods of CI different from those previously reported. Specific data are detailed in the Conclusion section. In addition, we also explored metabolite differences of CI patients complicated with high blood glucose compared with healthy controls. Further work in this area may inform personalized treatment approaches in clinical practice for CI by experimentally elucidating the pathophysiological mechanisms.

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Euglena gracilis paramylon (EGP) is a polymeric polysaccharide composed of linear β-1,3 glucan. The water insolubility of EGP severely limits its application. This work aimed to improve the functional characteristics of EGP by hydrogen peroxide (H2O2) degradation and carboxymethylated modification. The results showed that the crystallinity of EGP degraded by H2O2 and carboxymethylated modification decreased by 14% and 46%, and the thermal degradation temperature was significantly descending in a crystallinity-dependent manner. In addition, the results showed that H2O2 degradation and carboxymethylation significantly improved the adsorption capacity of EGP for oil, dyes, and metal ions, and their water solubility increased by 9% and 85%. This result will provide a valuable theoretical basis for the development and utilization of EGP.

Fecal microbiota transplantation (FMT) documented transplanting a donor fecal sample to a receipt individual for a desired physiologic effect. However, whether the gut microbiota construction, intestinal maturation, and behavioral plasticity are modulated by FMT during the early life of broilers is waiting for verification. To evaluate the role of transfer of fecal microbiota from aged broilers donor (BD) to another individual, 96 birds were equally divided into a check (CK, control) group and a broiler recipient (BR) group. FMT was conducted daily from 5 to 12 days of age to determine the future impact on body weight, behavior, intestinal development, and gut microbiota. Results indicated that fearfulness in the CK group was higher than the BR group in both the behavioral tests (p < 0.05). The muscularis mucosa, thickness of muscle layer, and thickness of serous membrane layer in the BR group were higher compared with those of the CK group in the jejunum (p < 0.05). In the gut microbiota, Shannon diversity showed no difference, while beta diversity presented a difference in principal coordination analysis (PCoA) between the CK and BR groups. At the phylum level, the relative abundance of Lentisphaerae in the CK group was lower than the BR (p = 0.052) and BD (p = 0.054) groups. The relative abundance of Tenericutes in the BD group was higher than that in the CK and BR groups (p < 0.05). At the genus level, Megamonas in the CK group was higher than the BR (p = 0.06) and BD (p < 0.05) groups. In the BR group, the functional capabilities of microbial communities analyzed by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were increased in the glutamatergic synapse and N-glycan biosynthesis pathways in comparison with the CK and BD groups (p < 0.05). Some characteristics of gut microbiota in the donor chickens could be transferred to recipient chickens by FMT. In conclusion, exogenous FMT as a probiotic-like administration might be an efficient way to improve the physiology and behavior of chickens. Notably, the role of microbiota for various individuals and periods remains undefined, and the mechanism of microbiota on behaviors still needs further investigation.

Physical exercise is a powerful measure to prevent cardiometabolic diseases. However, the individual response to lifestyle interventions is variable and cannot, to date, be predicted. N-Lactoylphenylalanine (Lac-Phe) produced during exercise has recently been shown to mediate weight loss in obese mice. Lac-Phe could also contribute to, and potentially explain differences in, the effectiveness of exercise interventions in humans. Sedentary overweight and obese subjects completed an 8-week supervised endurance exercise intervention (n = 22). Before and after the intervention, plasma levels of Lac-Phe were determined by UHPLC-MS in the resting state and immediately after an acute bout of endurance exercise. Adipose tissue volume was quantified using MRI. Acute exercise caused a pronounced increase in Lac-Phe, both before and after the intervention. Higher levels of Lac-Phe after acute exercise were associated with a greater reduction in abdominal subcutaneous and, to a lower degree, visceral adipose tissue during the intervention. Lac-Phe produced during physical activity could contribute to weight loss by acting as a signaling molecule that regulates food intake, as previously shown in mice. Quantification of Lac-Phe during an exercise test could be employed as a tool to predict and potentially improve the individual response to exercise-based lifestyle interventions in overweight humans and those with obesity.

Paramylon from Euglena gracilis (EGP) is a polymeric polysaccharide composed of linear β-1,3 glucan. EGP has been proved to have antibacterial activity, but its effect is weak due to its water insolubility and high crystallinity. In order to change this deficiency, this experiment carried out carboxymethylated modification of EGP. Three carboxymethylated derivatives, C-EGP1, C-EGP2, and C-EGP3, with a degree of substitution (DS) of 0.14, 0.55, and 0.78, respectively, were synthesized by varying reaction conditions, such as the mass of chloroacetic acid and temperature. Fourier transform infrared spectroscopy (FTIR), gel permeation chromatography (GPC), and nuclear magnetic resonance (NMR) analysis confirmed the success of the carboxymethylated modification. The Congo red (CR) experiment, scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermogravimetry (TG) were used to study the conformation, surface morphology, crystalline nature, and thermostability of the carboxymethylated EGP. The results showed that carboxymethylation did not change the triple helix structure of the EGP, but that the fundamental particles’ surface morphology was destroyed, and the crystallization area and thermal stability decreased obviously. In addition, the water solubility test and antibacterial experiment showed that the water solubility and antibacterial activity of the EGP after carboxymethylation were obviously improved, and that the water solubility of C-EGP1, C-EGP2, and C-EGP3 increased by 53.31%, 75.52%, and 80.96% respectively. The antibacterial test indicated that C-EGP3 had the best effect on Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), with minimum inhibitory concentration (MIC) values of 12.50 mg/mL and 6.25 mg/mL. The diameters of the inhibition zone of C-EGP3 on E. coli and S. aureus were 11.24 ± 0.15 mm and 12.05 ± 0.09 mm, and the antibacterial rate increased by 41.33% and 43.67%.

BackgroundPolycystic ovary syndrome (PCOS) is a complex reproductive endocrine disorder. And metabolic syndrome (MS) is an important bridge for PCOS patients to develop other diseases, such as diabetes and coronary heart disease. Our aim was to study the potential metabolic characteristics of PCOS-MS and identify sensitive biomarkers so as to provide targets for clinical screening, diagnosis, and treatment.MethodsIn this study, 44 PCOS patients with MS, 34 PCOS patients without MS, and 32 healthy controls were studied. Plasma samples of subjects were tested by ultraperformance liquid chromatography (UPLC) system combined with LTQ-orbi-trap mass spectrometry. The changes of metabolic characteristics from PCOS to PCOS-MS were systematically analyzed. Correlations between differential metabolites and clinical characteristics of PCOS-MS were assessed. Differential metabolites with high correlation were further evaluated by the receiver operating characteristic (ROC) curve to identify their sensitivity as screening indicators.ResultsThere were significant differences in general characteristics, reproductive hormone, and metabolic parameters in the PCOS-MS group when compared with the PCOS group and healthy controls. We found 40 differential metabolites which were involved in 23 pathways when compared with the PCOS group. The metabolic network further reflected the metabolic environment, including the interaction between metabolic pathways, modules, enzymes, reactions, and metabolites. In the correlation analysis, there were 11 differential metabolites whose correlation coefficient with clinical parameters was greater than 0.4, which were expected to be taken as biomarkers for clinical diagnosis. Besides, these 11 differential metabolites were assessed by ROC, and the areas under curve (AUCs) were all greater than 0.7, with a good sensitivity. Furthermore, combinational metabolic biomarkers, such as glutamic acid + leucine + phenylalanine and carnitine C 4: 0 + carnitine C18:1 + carnitine C5:0 were expected to be sensitive combinational biomarkers in clinical practice.ConclusionOur study provides a new insight to understand the pathogenesis mechanism, and the discriminating metabolites may help screen high-risk of MS in patients with PCOS and provide sensitive biomarkers for clinical diagnosis.

Abstract In a Chinese prospective cohort, 500 patients with new‐onset type 2 diabetes (T2D) within 4.61 years and 500 matched healthy participants are selected as case and control groups, and randomized into discovery and validation sets to discover the metabolite changes before T2D onset and the related diabetogenic loci. A serum metabolomics analysis reveals that 81 metabolites changed significantly before T2D onset. Based on binary logistic regression, eight metabolites are defined as a biomarker panel for T2D prediction. Pipecolinic acid, carnitine C14:0, epinephrine and phosphatidylethanolamine 34:2 are first found associated with future T2D. The addition of the biomarker panel to the clinical markers (BMI, triglycerides, and fasting glucose) significantly improves the predictive ability in the discovery and validation sets, respectively. By associating metabolomics with genomics, a significant correlation (p

Fucoxanthin is a natural marine xanthophyll and exhibits a broad range of biological activities. In the present study, a simple and efficient two-step method was used to purify fucoxanthin from the diatom, Phaeodactylum tricornutum. The crude pigment extract of fucoxanthin was separated by silica gel column chromatography (SGCC). Then, the fucoxanthin-rich fraction was purified using a hydrophile–lipophile balance (HLB) solid-phase extraction column. The identification and quantification of fucoxanthin were determined by high-performance liquid chromatography (HPLC) and electrospray ionization mass spectrometry (ESI-MS). This two-step method can obtain 92.03% pure fucoxanthin and a 76.67% recovery rate. In addition, 1H and 13C NMR spectrums were adopted to confirm the identity of fucoxanthin. Finally, the purified fucoxanthin exhibited strong antioxidant properties in vitro with the effective concentration for 50% of maximal scavenging (EC50) of 1,1-Dihpenyl-2-picrylhydrazyl (DPPH) and 2,2′-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) free radicals being 0.14 mg·mL−1 and 0.05 mg·mL−1, respectively.

This study aims to give an insight into the development trends and patterns of social organizations (SOs) in China from the perspective of network science integrating geography and public policy information embedded in the network structure. Firstly, we constructed a first-of-its-kind database which encompasses almost all social organizations established in China throughout the past decade. Secondly, we proposed four basic structures to represent the homogeneous and heterogeneous networks between social organizations and related social entities, such as government administrations and community members. Then, we pioneered the application of graph models to the field of organizations and embedded the Organizational Geosocial Network (OGN) into a low-dimensional representation of the social entities and relations while preserving their semantic meaning. Finally, we applied advanced graph deep learning methods, such as graph attention networks (GAT) and graph convolutional networks (GCN), to perform exploratory classification tasks by training models with county-level OGNs dataset and make predictions of which geographic region the county-level OGN belongs to. The experiment proves that different regions possess a variety of development patterns and economic structures where local social organizations are embedded, thus forming differential OGN structures, which can be sensed by graph machine learning algorithms and make relatively accurate predictions. To the best of our knowledge, this is the first application of graph deep learning to the construction and representation learning of geosocial network models of social organizations, which has certain reference significance for research in related fields.

This paper presents a tri‐wideband bandpass filter (TWB‐BPF) with compact size, high band‐to‐band isolation, and multiple transmission zeros (TZs). The proposed TWB‐BPF is based on a multiple‐mode resonator (MMR), which is interpreted by the method of the even‐ and odd‐mode analysis technique. The MMR can excite 11 resonant modes, where the first two modes comprise the first passband, the next four modes form the second passband, and the last five modes are used to generate the third passband. In addition, 10 TZs are yielded to obtain high band‐to‐band isolation and wide stopband suppression characteristics up to 14.95fc1 (fc1 is the center frequency of the first passband). To verify the proposed filter, a TWB‐BPF with 3‐dB fractional bandwidths (FBWs) of 37.4%, 43.5%, and 40.4% is designed, fabricated, and measured.

Abstract Diabetic retinopathy (DR) is the main cause of vision loss or blindness in working age adults worldwide. The lack of effective diagnostic biomarkers for DR leads to unsatisfactory curative treatments. To define potential metabolite biomarkers for DR diagnosis, a multiplatform‐based metabolomics study is performed. In this study, a total of 905 subjects with diabetes without DR (NDR) and with DR at different clinical stages are recruited. Multiplatform metabolomics methods are used to characterize the serum metabolic profiles and to screen and validate the DR biomarkers. Based on the criteria p

Grape (Vitis vinifera L.) is a fruit tree with high salt tolerance and high nutritional value, medicinal value, and economic value. Suberin in roots is characterized by long-chain fatty acids and is thought to be related to the salt tolerance of grape. The key enzyme in the fatty acid elongation process is β-ketoacyl-CoA synthase (KCS). The function and the regulatory mechanism of VvKCS in response to salt stress in grape are unclear. In this study, VvKCS was isolated from V. vinifera L. A real-time quantitative polymerase chain reaction analysis showed that salt stress enhanced VvKCS transcription levels in grapes. Overexpression of VvKCS increased the tolerance to salt stress in Arabidopsis during the germination and seedling stages. The improved salt tolerance was the result of the combined contributions of multiple mechanisms including the regulation of expression of ion transporters and channels, accumulation of osmotic regulating substances, and maintenance of membrane stability. The results of this study are valuable information on plant salt tolerance and provide a theoretical basis for the molecular mechanism of grape salt tolerance.

Persistence fault analysis (PFA) is a novel fault analysis technique proposed in CHES 2018 and demonstrated with rowhammer-based fault injections. However, whether such analysis can be applied to traditional fault attack scenario, together with its difficulty in practice, has not been carefully investigated. For the first time, a persistent fault attack is conducted on an unprotected AES implemented on ATmega163L microcontroller in this paper. Several critical challenges are solved with our new improvements, including (1) how to decide whether the fault is injected in SBox; (2) how to use the maximum likelihood estimation to pursue the minimum number of ciphertexts; (3) how to utilize the unknown fault in SBox to extract the key. Our experiments show that: to break AES with physical laser injections despite all these challenges, the minimum and average number of required ciphertexts are 926 and 1641, respectively. It is about 38% and 28% reductions of the ciphertexts required in comparison to 1493 and 2273 in previous work where both fault value and location have to be known. Furthermore, our analysis is extended to the PRESENT cipher. By applying the persistent fault analysis to the penultimate round, the full PRESENT key of 80 bits can be recovered. Eventually, an experimental validation is performed to confirm the accuracy of our attack with more insights. This paper solves the challenges in most aspects of practice and also demonstrates the feasibility and universality of PFA on SPN block ciphers.

The authors examine the effects of antidiabetic medication on the gut microbiome and bile acid composition and show that these data can be used to stratify treatment regimens for type 2 diabetes.

Cherries are rich in bioactive phenolic compounds and are often fermented into cherry wines. The degradation of anthocyanins during storage will cause color deterioration. The study aimed to utilize sinapic acid and grape tannins in cherry wine to maintain a high fraction in the colored forms of anthocyanins, in order to maximize the color intensity, the latter being associated with good product quality. The effects on the anthocyanin profile and on color parameters of copigments, utilizing spectral measurement combined with UPLC-MS quantitative analysis, have been evaluated in sweet cherry wines. The copigmentation effect of sinapic acid and grape tannin was accompanied by the bathochromic shift and the hyperchromic effect, which lead to an increase in color intensity (lower L*, higher a* and b*). During the aging process, sinapic and grape tannin increased the content of pyranoanthocyanins in cherry wine, especially the addition of sinapic acid makes the cherry wine generate 10-syringyl-pyranocyanidin-3-rutinoside. These results demonstrate that sinapic acid is suitable for adding before alcohol fermentation, while grape tannins can be added before aging.

Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the above mentioned some drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food proteins, this work proposed to introduce deep learning model—transformer with self-attention mechanism, ensemble learning models (representative as Light Gradient Boosting Machine (LightGBM) eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation showed that the area under the receiver operating characteristic curve (AUC) of the deep model was the highest (0.9578), which was better than the ensemble learning and baseline algorithms. But the deep model need to be pre-trained, and the training time is the longest. By comparing the characteristics of the transformer model and boosting models, it can be analyzed that, each model has its own advantage, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.

Gliomas are the most aggressive phenotypes of brain tumors and are classified into four grades according to the malignancy degree by the World Health Organization. Metabolic profiling can provide an overview of metabolic reprogramming at a specific stage of tumor initiation and development. Studies about metabolic alterations related to different grades of gliomas are helpful to understand the molecular mechanism for progression of glioma. In the current study, metabolomics and lipidomics analyses based on chromatography-mass spectrometry were performed on different grades of glioma tissues. Differential metabolites between glioma and para-tumor tissues were studied and used as the basis to explore metabolic alterations related to glioma grading. It was found that short-chain acylcarnitines were elevated, whereas lysophosphatidylethanolamines (LPEs) were decreased in high-grade gliomas. Furthermore, the gene expression of short/branched-chain acyl-coenzyme dehydrogenase (ACADSB), which is involved in fatty acid oxidation, was found down-regulated with glioma progression by analyzing related genes and pathways. In addition, LPE metabolism showed a significant difference among different grades of gliomas. These important metabolic pathways related to glioma progression may provide potential clues for further study on the mechanisms and treatment of glioma.

Despite it being the most common incident of cancer among men, the pathophysiological mechanisms contributing to prostate cancer (PCa) are still poorly understood. Altered mitochondrial metabolism is postulated to play a role in the development of PCa. To determine the key metabolites (which included mitochondrial oncometabolites), benign prostatic and cancer tissues of patients with PCa were analyzed using capillary electrophoresis and liquid chromatography coupled with mass spectrometry. Gene expression was studied using real-time PCR. In PCa tissues, we found reduced levels of early tricarboxylic acid cycle metabolites, whereas the contents of urea cycle metabolites including aspartate, argininosuccinate, arginine, proline, and the oncometabolite fumarate were higher than that in benign controls. Fumarate content correlated positively with the gene expression of oncogenic HIF1α and NFκB pathways, which were significantly higher in the PCa samples than in the benign controls. Furthermore, data from the TCGA database demonstrated that prostate cancer patients with activated NFκB pathway had a lower survival rate. In summary, our data showed that fumarate content was positively associated with carcinogenic genes.

Persistence is an intrinsic nature for many errors yet has not been caught enough attractions for years. In this paper, the feature of persistence is applied to fault attacks, and the persistent fault attack is proposed. Different from traditional fault attacks, adversaries can prepare the fault injection stage before the encryption stage, which relaxes the constraint of the tight-coupled time synchronization. The persistent fault analysis (PFA) is elaborated on different implementations of AES-128, specially fault hardened implementations based on Dual Modular Redundancy (DMR). Our experimental results show that PFA is quite simple and efficient in breaking these typical implementations. To show the feasibility and practicability of our attack, a case study is illustrated on the shared library Libgcrypt with rowhammer technique. Approximately 8200 ciphertexts are enough to extract the master key of AES-128 when PFA is applied to Libgcrypt1.6.3 with redundant encryption based DMR. This work puts forward a new direction of fault attacks and can be extended to attack other implementations under more interesting scenarios.

In the title compound, C22H27ClN6O22+·2ClO4−, the molecule adopts an E conformation about the C=N double bond. The quinazoline ring is approximately planar, with an r.m.s. deviation of 0.0432 Å, and forms a dihedral angle of 5.77 (4)° with the chlorophenyl ring. The crystal packing features N—H...O hydrogen bonds.

Ultra-high performance liquid chromatography/ quadrupole time of flight mass spectrometry-based metabonomics platform was employed to profile the plasma metabolites of patients with metabolic syndrome and the healthy controls. Data analysis revealed lots of differential metabolites between the two groups, and most of them were identified as lipids. Several fatty acids and lysophosphatidylcholines were of higher plasma levels in the patient group, indicating the occurrence of insulin resistance and inflammation. The identified ether phospholipids were decreased in the patient group, reflecting the oxidative stress and some metabolic disorders. These identified metabolites can also be used to aid diagnosis of patients with metabolic syndrome. These results showed that metabonomics was a promising and powerful method to study metabolic syndrome.

BACKGROUND: A decline in body insulin sensitivity in apparently healthy individuals indicates a high risk to develop type 2 diabetes. Investigating the metabolic fingerprints of individuals with different whole body insulin sensitivity according to the formula of Matsuda, et al. (ISI(Matsuda)) by a non-targeted metabolomics approach we aimed a) to figure out an unsuspicious and altered metabolic pattern, b) to estimate a threshold related to these changes based on the ISI, and c) to identify the metabolic pathways responsible for the discrimination of the two patterns. METHODOLOGY AND PRINCIPAL FINDINGS: By applying infusion ion cyclotron resonance Fourier transform mass spectrometry, we analyzed plasma of 46 non-diabetic subjects exhibiting high to low insulin sensitivities. The orthogonal partial least square model revealed a cluster of 28 individuals with alterations in their metabolic fingerprints associated with a decline in insulin sensitivity. This group could be separated from 18 subjects with an unsuspicious metabolite pattern. The orthogonal signal correction score scatter plot suggests a threshold of an ISI(Matsuda) of 15 for the discrimination of these two groups. Of note, a potential subgroup represented by eight individuals (ISI(Matsuda) value between 8.5 and 15) was identified in different models. This subgroup may indicate a metabolic transition state, since it is already located within the cluster of individuals with declined insulin sensitivity but the metabolic fingerprints still show some similarities with unaffected individuals (ISI >15). Moreover, the highest number of metabolite intensity differences between unsuspicious and altered metabolic fingerprints was detected in lipid metabolic pathways (arachidonic acid metabolism, metabolism of essential fatty acids and biosynthesis of unsaturated fatty acids), steroid hormone biosyntheses and bile acid metabolism, based on data evaluation using the metabolic annotation interface MassTRIX. CONCLUSIONS: Our results suggest that altered metabolite patterns that reflect changes in insulin sensitivity respectively the ISI(Matsuda) are dominated by lipid-related pathways. Furthermore, a metabolic transition state reflected by heterogeneous metabolite fingerprints may precede severe alterations of metabolism. Our findings offer future prospects for novel insights in the pathogenesis of the pre-diabetic phase.

BACKGROUND: Exercise is an extreme physiological challenge for skeletal muscle energy metabolism and has notable health benefits. We aimed to identify and characterize metabolites, which are components of the regulatory network mediating the beneficial metabolic adaptation to exercise. METHODOLOGY AND PRINCIPAL FINDINGS: First, we investigated plasma from healthy human subjects who completed two independent running studies under moderate, predominantly aerobic conditions. Samples obtained prior to and immediately after running and then 3 and 24 h into the recovery phase were analyzed by a non-targeted (NT-) metabolomics approach applying liquid chromatography-qTOF-mass spectrometry. Under these conditions medium and long chain acylcarnitines were found to be the most discriminant plasma biomarkers of moderately intense exercise. Immediately after a 60 min (at 93% V(IAT)) or a 120 min run (at 70% V(IAT)) a pronounced, transient increase dominated by octanoyl-, decanoyl-, and dodecanoyl-carnitine was observed. The release of acylcarnitines as intermediates of partial beta-oxidation was verified in skeletal muscle cell culture experiments by probing (13)C-palmitate metabolism. Further investigations in primary human myotubes and mouse muscle tissue revealed that octanoyl-, decanoyl-, and dodecanoyl-carnitine were able to support the oxidation of palmitate, proving more effective than L-carnitine. CONCLUSIONS: Medium chain acylcarnitines were identified and characterized by a functional metabolomics approach as the dominating biomarkers during a moderately intense exercise bout possessing the power to support fat oxidation. This physiological production and efflux of acylcarnitines might exert beneficial biological functions in muscle tissue.

BACKGROUND: Endurance exercise induces lipolysis, increases circulating concentrations of free fatty acids (FFA) and the uptake and oxidation of fatty acids in the working muscle. Less is known about the regulation of lipid metabolism in the liver during and post-exercise. METHODOLOGY/PRINCIPAL FINDINGS: We performed an ultra fast liquid chromatography-mass spectrometry (UFLC-MS) based lipidomics analysis of liver tissue samples obtained from C57Bl/6J mice immediately after a 60 min treadmill run of moderate intensity, and after 3 h of recovery. The PLS-DA scores plot for 115 quantified lipid molecular species revealed a clear separation of the hepatic lipid profile of sedentary from recovering mice, but not from mice immediately after running. 21 lipid species were considered to be most responsible for the difference in the hepatic lipid profiles, including 17 triacylglycerides (TG), one lysophosphatidylcholine (LPC) and three phosphatidylcholines (PC). TG species were found to be more abundant in the recovery phase, while PC species were decreased. The degree of accumulation of individual TG species correlated well with the amount of theoretical energy stored whereas no increase was found for TG species containing only saturated or one monounsaturated fatty acid. Total liver TG content as assayed by an enzymatic method was increased to 163% in the recovery phase, while it was significantly decreased in skeletal muscle by the exercise bout and remained less in the recovery phase. Results from fasted and refed mice indicate that fasting-induced lipolysis was associated with a pronounced accumulation of hepatic TG, which is reversed by refeeding for 5 h. Thus food intake per se did not elevate hepatic TG. CONCLUSION: These data indicate that high availability of FFA induced by endurance exercise or fasting resulted in a transient hepatic TG accumulation, while muscle TG content was decreased during exercise presumably due to increased muscle fatty acid oxidation.