Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts.

BACKGROUND: Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways. METHODS: We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed. RESULTS: We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism. CONCLUSION: Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

Solvent-Free Heterogeneous Catalytic Hydrogenation of Polyesters to Diols.

The utilization of carbon resources stored in plastic polymers through chemical recycling and upcycling is a promising approach for mitigating plastic waste. However, most current methods for upcycling suffer from limited selectivity towards a specific valuable product, particularly when attempting full conversion of the plastic. We present a highly selective reaction route for transforming polylactic acid (PLA) into 1,2-propanediol utilizing a Zn-modified Cu catalyst. This reaction exhibits excellent reactivity (0.65 g gcat -1 h-1 ) and selectivity (99.5 %) towards 1,2-propanediol, and most importantly, can be performed in a solvent-free mode. Significantly, the overall solvent-free reaction is an atom-economical reaction with all the atoms in reactants (PLA and H2 ) fixed into the final product (1,2-propanediol), eliminating the need for a separation process. This method provides an innovative and economically viable solution for upgrading polyesters to produce high-purity products under mild conditions with optimal atom utilization.

Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens.

The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.

Highly Efficient Aligned Ion-Conducting Network and Interface Chemistries for Depolarized All-Solid-State Lithium Metal Batteries.

Improving the long-term cycling stability and energy density of all-solid-state lithium (Li)-metal batteries (ASSLMBs) at room temperature is a severe challenge because of the notorious solid-solid interfacial contact loss and sluggish ion transport. Solid electrolytes are generally studied as two-dimensional (2D) structures with planar interfaces, showing limited interfacial contact and further resulting in unstable Li/electrolyte and cathode/electrolyte interfaces. Herein, three-dimensional (3D) architecturally designed composite solid electrolytes are developed with independently controlled structural factors using 3D printing processing and post-curing treatment. Multiple-type electrolyte films with vertical-aligned micro-pillar (p-3DSE) and spiral (s-3DSE) structures are rationally designed and developed, which can be employed for both Li metal anode and cathode in terms of accelerating the Li+ transport within electrodes and reinforcing the interfacial adhesion. The printed p-3DSE delivers robust long-term cycle life of up to 2600 cycles and a high critical current density of 1.92 mA cm-2. The optimized electrolyte structure could lead to ASSLMBs with a superior full-cell areal capacity of 2.75 mAh cm-2 (LFP) and 3.92 mAh cm-2 (NCM811). This unique design provides enhancements for both anode and cathode electrodes, thereby alleviating interfacial degradation induced by dendrite growth and contact loss. The approach in this study opens a new design strategy for advanced composite solid polymer electrolytes in ASSLMBs operating under high rates/capacities and room temperature.

Metabolic effects of SGLT2i and metformin on 3-hydroxybutyric acid and lactate in db/db mice.

Combining a Sodium-Glucose-Cotransporter-2-inhibitor (SGLT2i) with metformin is recommended for managing hyperglycemia in patients with type 2 diabetes (T2D) who have cardio-renal complications. Our study aimed to investigate the metabolic effects of SGLT2i and metformin, both individually and synergistically. We treated leptin receptor-deficient (db/db) mice with these drugs for two weeks and conducted metabolite profiling, identifying 861 metabolites across kidney, liver, muscle, fat, and plasma. Using linear regression and mixed-effects models, we identified two SGLT2i-specific metabolites, X-12465 and 3-hydroxybutyric acid (3HBA), a ketone body, across all examined tissues. The levels of 3HBA were significantly higher under SGLT2i monotherapy compared to controls and were attenuated when combined with metformin. We observed similar modulatory effects on metabolites involved in protein catabolism (e.g., branched-chain amino acids) and gluconeogenesis. Moreover, combination therapy significantly raised pipecolate levels, which may enhance mTOR1 activity, while modulating GSK3, a common target of SGLT2i and 3HBA inhibition. The combination therapy also led to significant reductions in body weight and lactate levels, contrasted with monotherapies. Our findings advocate for the combined approach to better manage muscle loss, and the risks of DKA and lactic acidosis, presenting a more effective strategy for T2D treatment.

Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function.

Accurate risk prediction for myocardial infarction (MI) is crucial for preventive strategies, given its significant impact on global mortality and morbidity. Here, we propose a novel deep-learning approach to enhance the prediction of incident MI cases by incorporating metabolomics alongside clinical risk factors. We utilized data from the KORA cohort, including the baseline S4 and follow-up F4 studies, consisting of 1454 participants without prior history of MI. The dataset comprised 19 clinical variables and 363 metabolites. Due to the imbalanced nature of the dataset (78 observed MI cases and 1376 non-MI individuals), we employed a generative adversarial network (GAN) model to generate new incident cases, augmenting the dataset and improving feature representation. To predict MI, we further utilized multi-layer perceptron (MLP) models in conjunction with the synthetic minority oversampling technique (SMOTE) and edited nearest neighbor (ENN) methods to address overfitting and underfitting issues, particularly when dealing with imbalanced datasets. To enhance prediction accuracy, we propose a novel GAN for feature-enhanced (GFE) loss function. The GFE loss function resulted in an approximate 2% improvement in prediction accuracy, yielding a final accuracy of 70%. Furthermore, we evaluated the contribution of each clinical variable and metabolite to the predictive model and identified the 10 most significant variables, including glucose tolerance, sex, and physical activity. This is the first study to construct a deep-learning approach for producing 7-year MI predictions using the newly proposed loss function. Our findings demonstrate the promising potential of our technique in identifying novel biomarkers for MI prediction.

3D Printing of Multiscale Ti64-Based Lattice Electrocatalysts for Robust Oxygen Evolution Reaction.

Electrically assisted water splitting is an endurable strategy for hydrogen production, but the sluggish kinetics of oxygen evolution reaction (OER) extremely restrict the large-scale production of hydrogen. Developing highly efficient and non-precious catalytic materials is essential to accelerate the sluggish kinetics of OER. However, currently used catalyst supports, such as copper foam, suffer from inferior corrosion resistance and structural stability, resulting in the disabled functionality of 3D conductive networks. To this end, a novel 3D freestanding electrode with corrosion-resistant and robust Ti-6Al-4V titanium alloy lattice as the catalyst support is designed via a 3D printing technology of selective laser melting. After the coating of core-shell Cu(OH)2@CoNi carbonate hydroxides (CoNiCH) on the designed lattice, a unique micro/nano-sized hierarchical porous structure is formed, which endows the electrocatalyst with a promising electrocatalytic activity (a low overpotential of 355 mV at 30 mA cm-2 and Tafel slope of 125.3 mV dec-1 ). Computational results indicate that the CoNiCH exhibits optimized electron transfer and the catalytic activity of the Ni site is higher than that of the Co site in the CoNiCH. Therefore, the integration of robust catalyst supports and highly active materials opens up an avenue for reliable and high-performance OER electrocatalysts.

Classification of pathogens by Raman spectroscopy combined with generative adversarial networks.

Rapid identification of marine pathogens is very important in marine ecology. Artificial intelligence combined with Raman spectroscopy is a promising choice for identifying marine pathogens due to its rapidity and efficiency. However, considering the cost of sample collection and the challenging nature of the experimental environment, only limited spectra are typically available to build a classification model, which hinders qualitative analysis. In this paper, we propose a novel method to classify marine pathogens by means of Raman spectroscopy combined with generative adversarial networks (GANs). Three marine strains, namely, Staphylococcus hominis, Vibrio alginolyticus, and Bacillus licheniformis, were cultured. Using Raman spectroscopy, we acquired 100 spectra of each strain, and we fitted them into GAN models for training. After 30,000 training iterations, the spectra generated by G were similar to the actual spectra, and D was used to test the accuracy of the spectra. Our results demonstrate that our method not only improves the accuracy of machine learning classification but also solves the problem of requiring a large amount of training data. Moreover, we have attempted to find potential identifying regions in the Raman spectra that can be used for reference in subsequent related work in this field. Therefore, this method has tremendous potential to be developed as a tool for pathogen identification.