Empagliflozin alleviates neuroinflammation by inhibiting astrocyte activation in the brain and regulating gut microbiota of high-fat diet mice.

A high-fat diet can modify the composition of gut microbiota, resulting in dysbiosis. Changes in gut microbiota composition can lead to increased permeability of the gut barrier, allowing bacterial products like lipopolysaccharides (LPS) to enter circulation. This process can initiate systemic inflammation and contribute to neuroinflammation. Empagliflozin (EF), an SGLT2 inhibitor-type hypoglycemic drug, has been reported to treat neuroinflammation. However, there is a lack of evidence showing that EF regulates the gut microbiota axis to control neuroinflammation in HFD models. In this study, we explored whether EF could improve neuroinflammation caused by an HFD via regulation of the gut microbiota and the mechanism underlying this phenomenon. Our data revealed that EF alleviates pathological brain injury, reduces the reactive proliferation of astrocytes, and increases the expression of synaptophysin. In addition, the levels of inflammatory factors in hippocampal tissue were significantly decreased after EF intervention. Subsequently, the results of 16S rRNA gene sequencing showed that EF could change the microbial community structure of mice, indicating that the abundance of Lactococcus, Ligilactobacillus and other microbial populations decreased dramatically. Therefore, EF alleviates neuroinflammation by inhibiting gut microbiota-mediated astrocyte activation in the brains of high-fat diet-fed mice. Our study focused on the gut-brain axis, and broader research on neuroinflammation can provide a more holistic understanding of the mechanisms driving neurodegenerative diseases and inform the development of effective strategies to mitigate their impact on brain health. The results provide strong evidence supporting the larger clinical application of EF.

Lactate enhances NMNAT1 lactylation to sustain nuclear NAD+ salvage pathway and promote survival of pancreatic adenocarcinoma cells under glucose-deprived conditions.

The aim of this study was to investigate the underlying molecular mechanism behind the promotion of cell survival under conditions of glucose deprivation by l-lactate. To accomplish this, we performed tissue microarray and immunohistochemistry staining to analyze the correlation between the abundance of pan-Lysine lactylation and prognosis. In vivo evaluations of tumor growth were conducted using the KPC and nude mice xenograft tumor model. For mechanistic studies, multi-omics analysis, RNA interference, and site-directed mutagenesis techniques were utilized. Our findings robustly confirmed that l-lactate promotes cell survival under glucose deprivation conditions, primarily by relying on GLS1-mediated glutaminolysis to support mitochondrial respiration. Mechanistically, we discovered that l-lactate enhances the NMNAT1-mediated NAD+ salvage pathway while concurrently inactivating p-38 MAPK signaling and suppressing DDIT3 transcription. Notably, Pan-Kla abundance was significantly upregulated in patients with Pancreatic adenocarcinoma (PAAD) and associated with poor prognosis. We identified the 128th Lysine residue of NMNAT1 as a critical site for lactylation and revealed EP300 as a key lactyltransferase responsible for catalyzing lactylation. Importantly, we elucidated that lactylation of NMNAT1 enhances its nuclear localization and maintains enzymatic activity, thereby supporting the nuclear NAD+ salvage pathway and facilitating cancer growth. Finally, we demonstrated that the NMNAT1-dependent NAD+ salvage pathway promotes cell survival under glucose deprivation conditions and is reliant on the activity of Sirt1. Collectively, our study has unraveled a novel molecular mechanism by which l-lactate promotes cell survival under glucose deprivation conditions, presenting a promising strategy for targeting lactate and NAD+ metabolism in the treatment of PAAD.

Developing a detection strategy for ten paralytic shellfish poisonings in urine, combining high-throughput DESI-MS screening and accurate UPLC-QqQ/MS quantification.

Paralytic shellfish poisoning (PSP) is the most widespread and harmful form of shellfish poisoning with high mortality rate. In this study, a combined desorption electrospray ionization mass spectrometry (DESI-MS) and ultra-performance liquid chromatography triple quadrupole mass spectrometry (UPLC-QqQ/MS) method was established for the detection of PSPs in urine. The method was optimized using a spray solution of methanol and water (1:1, v/v) containing 0.1 % FA, at a flow rate of 2.5 µL·min-1 and an applied voltage of 3 kV. The limit of detection (LOD) for PSPs detection by DESI-MS was in the range of 87-265 µg·L-1, which basically meets the requirements for the rapid screening of PSPs. The LOD for UPLC-QqQ/MS was in the range of 2.2-14.9 µg·L-1, with a limit of quantification (LOQ) of 7.3-49.7 µg·L-1, thus fulfilling the quantitative demand for PSPs in urine. Finally, after spiking the urine samples of six volunteers with PSPs to a concentration of 100 µg·L-1, DESI-MS successfully and efficiently detected the positive samples. Subsequently, UPLC-QqQ/MS was employed for precise quantification, yielding results in the range of 84.6-95.1 µg·L-1. The experimental findings demonstrated that the combination of DESI-MS and UPLC-QqQ/MS enables high-throughput, rapid screening of samples and accurate quantification of positive samples, providing assurance for food safety and human health.

MVDINET: A Novel Multi-Level Enzyme Function Predictor With Multi-View Deep Interactive Learning.

As a class of extremely significant of biocatalysts, enzymes play an important role in the process of biological reproduction and metabolism. Therefore, the prediction of enzyme function is of great significance in biomedicine fields. Recently, computational methods for predicting enzyme function have been proposed, and they effectively reduce the cost of enzyme function prediction. However, there are still deficiencies for effectively mining the discriminant information for enzyme function recognition in existing methods. In this study, we present MVDINET, a novel method for multi-level enzyme function prediction. First, the initial multi-view feature data is extracted by the enzyme sequence. Then, the above initial views are fed into various deep specific network modules to learn the depth-specificity information. Further, a deep view interaction network is designed to extract the interaction information. Finally, the specificity information and interaction information are fed into a multi-view adaptively weighted classification. We compressively evaluate MVDINET on benchmark datasets and demonstrate that MVDINET is superior to existing methods.

Niobium Oxide Anode with Lattice Structure Self-Optimization for High-Power and Nearly Zero-Degeneration Battery Operation.

Li+ insertion-induced structure transformation in crystalline electrodes vitally influence the energy density and cycle life of secondary lithium-ion battery. However, the influence mechanism of structure transformation-induced Li+ migration on the electrochemical performance of micro-crystal materials is still unclear and the strategy to profit from such structure transformation remains exploited. Here, an interesting self-optimization of structure evolution during electrochemical cycling in Nb2 O5 micro-crystal with rich domain boundaries is demonstrated, which greatly improves the charge transfer property and mechanical strength. The lattice rearrangement activates the Li+ diffusion kinetics and hinders the particle crack, thus enabling a nearly zero-degeneration operation after 8000 cycles. Full cell paired with lithium cobalt oxides displays an exceptionally high capacity of 176 mA h g-1 at 8000 mA g-1 and excellent long-term durability at 6000 mA g-1 with 63% capacity retention over 2000 cycles. Interestingly, a unique fingerprint based on the intensity ratio of two X-ray diffraction peaks is successfully extracted as a measure of Nb2 O5 electrochemical performance. The structure self-optimization for fast charge transfer and high mechanical strength exemplifies a new battery electrode design concept and opens up a vast space of strategy to develop high-performance lithium-ion batteries with high energy density and ultra-long cycle life.

Evaluation of left ventricular function in patients with coronary slow flow: A systematic review and meta-analysis.

BACKGROUND: Coronary slow flow (CSF) is an angiographic finding defined as delayed distal vessel perfusion without severe stenosis of the epicardial coronary arteries. However, definite alterations in left ventricular (LV) function in patients with CSF remains inconsistent. This study aimed to clarify the changes in LV function in patients with CSF and explore the factors that may influence LV function. METHODS: PubMed, Embase, and Cochrane Library databases were systematically searched. Standardized mean differences and 95% confidence intervals (CI) for the LV function parameters were calculated. Subgroup analysis, meta-regression analysis, and correlation analysis were performed to explore the factors influencing LV function. RESULTS: Twenty-two studies (1101 patients with CSF) were included after searching three databases. In patients with CSF, LV ejection function in patients with CSF was marginally lower (61.8%; 95% CI: 61.0%, 62.7%), global longitudinal strain was decreased (-18.2%; 95% CI: -16.7%, -19.7%). Furthermore, left atrial diameter, left atrial volume index, and E/e' were significantly increased, while E/A and e' were significantly decreased. The mean thrombolysis in myocardial infarction frame count (TFC) was linearly associated with LV function; the larger the mean TFC, the greater the impairment of LV function. CONCLUSIONS: Left ventricular systolic and diastolic functions were impaired in patients with CSF, and this impairment was aggravated with increasing mean TFC.

Diagnostic and prognostic value of echocardiography in pulmonary hypertension: an umbrella review of systematic reviews and meta-analyses.

BACKGROUND: The role of echocardiography in the diagnostic and prognostic assessment of pulmonary hypertension (PH) has been widely studied recently. However, these findings have not undergone normative evaluation and may provide confusing evidence for clinicians. To evaluate and summarize existing evidence, we performed an umbrella review. METHODS: Systematic reviews and meta-analyses were searched in PubMed, Embase, Web of Science, and Cochrane Library from inception to September 4, 2022. The methodological quality of the included studies was assessed using Assessment of Multiple Systematic Reviews (AMSTAR), and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system was used to evaluate the quality of evidence. RESULTS: Thirteen meta-analyses (nine diagnostic and four prognostic studies) were included after searching four databases. The methodological quality of the included studies was rated as high (62%) or moderate (38%) by AMSTAR. The thirteen included meta-analyses involved a total of 28 outcome measures. The quality of evidence for these outcomes were high (7%), moderate (29%), low (39%), and very low (25%) using GRADE methodology. In the detection of PH, the sensitivity of systolic pulmonary arterial pressure is 0.85-0.88, and the sensitivity and specificity of right ventricular outflow tract acceleration time are 0.84. Pericardial effusion, right atrial area, and tricuspid annulus systolic displacement provide prognostic value in patients with pulmonary arterial hypertension with hazard ratios between 1.45 and 1.70. Meanwhile, right ventricular longitudinal strain has independent prognostic value in patients with PH, with a hazard ratio of 2.96-3.67. CONCLUSION: The umbrella review recommends echocardiography for PH detection and prognosis. Systolic pulmonary arterial pressure and right ventricular outflow tract acceleration time can be utilized for detection, while several factors including pericardial effusion, right atrial area, tricuspid annular systolic displacement, and right ventricular longitudinal strain have demonstrated prognostic significance. TRIAL REGISTRATION: PROSPERO (CRD42022356091), https://www.crd.york.ac.uk/prospero/ .

One-Step Multiview Fuzzy Clustering With Collaborative Learning Between Common and Specific Hidden Space Information.

Multiview data are widespread in real-world applications, and multiview clustering is a commonly used technique to effectively mine the data. Most of the existing algorithms perform multiview clustering by mining the commonly hidden space between views. Although this strategy is effective, there are two challenges that still need to be addressed to further improve the performance. First, how to design an efficient hidden space learning method so that the learned hidden spaces contain both shared and specific information of multiview data. Second, how to design an efficient mechanism to make the learned hidden space more suitable for the clustering task. In this study, a novel one-step multiview fuzzy clustering (OMFC-CS) method is proposed to address the two challenges by collaborative learning between the common and specific space information. To tackle the first challenge, we propose a mechanism to extract the common and specific information simultaneously based on matrix factorization. For the second challenge, we design a one-step learning framework to integrate the learning of common and specific spaces and the learning of fuzzy partitions. The integration is achieved in the framework by performing the two learning processes alternately and thereby yielding mutual benefit. Furthermore, the Shannon entropy strategy is introduced to obtain the optimal views weight assignment during clustering. The experimental results based on benchmark multiview datasets demonstrate that the proposed OMFC-CS outperforms many existing methods.

Residual Sketch Learning for a Feature-Importance-Based and Linguistically Interpretable Ensemble Classifier.

Motivated by both the commonly used "from wholly coarse to locally fine" cognitive behavior and the recent finding that simple yet interpretable linear regression model should be a basic component of a classifier, a novel hybrid ensemble classifier called hybrid Takagi-Sugeno-Kang fuzzy classifier (H-TSK-FC) and its residual sketch learning (RSL) method are proposed. H-TSK-FC essentially shares the virtues of both deep and wide interpretable fuzzy classifiers and simultaneously has both feature-importance-based and linguistic-based interpretabilities. RSL method is featured as follows: 1) a global linear regression subclassifier on all original features of all training samples is generated quickly by the sparse representation-based linear regression subclassifier training procedure to identify/understand the importance of each feature and partition the output residuals of the incorrectly classified training samples into several residual sketches; 2) by using both the enhanced soft subspace clustering method (ESSC) for the linguistically interpretable antecedents of fuzzy rules and the least learning machine (LLM) for the consequents of fuzzy rules on residual sketches, several interpretable Takagi-Sugeno-Kang (TSK) fuzzy subclassifiers are stacked in parallel through residual sketches and accordingly generated to achieve local refinements; and 3) the final predictions are made to further enhance H-TSK-FC's generalization capability and decide which interpretable prediction route should be used by taking the minimal-distance-based priority for all the constructed subclassifiers. In contrast to existing deep or wide interpretable TSK fuzzy classifiers, benefiting from the use of feature-importance-based interpretability, H-TSK-FC has been experimentally witnessed to have faster running speed and better linguistic interpretability (i.e., fewer rules and/or TSK fuzzy subclassifiers and smaller model complexities) yet keep at least comparable generalization capability.

Transcriptomics integrated with metabolomics reveals the ameliorating effect of mussel-derived plasmalogens on high-fat diet-induced hyperlipidemia in zebrafish.

Plasmalogens (Pls), a special group of phospholipids, are effective in ameliorating neurodegenerative disease. In the present study, the metabolic effects of seafood-derived Pls on high fat diet (HFD)-induced hyperlipidemia in zebrafish were evaluated, and the underlying mechanisms of dietary Pls against hyperlipidemia were explored through integrated analyses of hepatic transcriptomics and metabolomics. The results demonstrated that Pls supplementation could effectively alleviate HFD-induced obesity symptoms, such as body weight gain, and decrease total hepatic cholesterol and triglyceride levels. Integrated hepatic transcriptome and metabolome data suggested that Pls mainly altered lipid metabolism pathways (FA metabolism, primary bile acid biosynthesis, steroid hormone biosynthesis, and glycerolipid and glycerophospholipid metabolism) and the TCA cycle, induced the overexpression of anti-oxidation enzymes (Cat, Gpx4, Sod3a and Xdh), reduced disease biomarkers (such as glutarylcarnitine, gamma-glutamyltyrosine, and 11-prostaglandin f2) and gut microbiota-derived metabolites, and increased (±)12(13)-diHOME, EPA, lysoPC and PC levels. Moreover, 5 abnormally regulated metabolites were identified as potential biomarkers associated with hyperlipidemia according to the metabolomics results and suggested the involvement of gut microbiota in the anti-hyperlipidemic effects of Pls. Collectively, these findings suggest that the protective role of Pls is mainly associated with the promotion of unsaturated fatty acid biosynthesis and cholesterol efflux, lipid and phospholipid PUFA remodeling, and anti-oxidation and anti-inflammatory capabilities. This study provides valuable information for reasonably explaining the beneficial effects of seafood-derived Pls in alleviating hyperlipidemia and thus may contribute to the development and application of Pls as functional foods or dietary supplements to protect against obesity and hyperlipidemia.

Construction of a metabolism-related gene prognostic model to predict survival of pancreatic cancer patients.

Pancreatic cancer (PC) is one of the most fatal malignant tumors, and is commonly diagnosed at an advanced stage with no effective therapy. Metabolism-related genes (MRGs) and immune-related genes (IRGs) play considerable roles in the tumor microenvironment. Therefore, an effective prediction model based on MRGs and IRGs could aid in the prognosis of PC. In this study, differential expression analysis was performed to gain 25 intersectional genes from 857 differentially expressed MRGs (DEMRGs), and 1353 differentially expressed IRGs, from The Cancer Genome Atlas database of PC. Cox and Lasso regression were applied and a five-DEMRGs prognostic model constructed. Survival analysis, ROC values, risk curve and validation analysis showed that the model could independently predict PC prognosis. In addition, the correlation analysis suggested that the five-DEMRGs prognostic model could reflect the status of the immune microenvironment, including Tregs, M1 macrophages and Mast cell resting. Therefore, our study provides new underlying predictive biomarkers and associated immunotherapy targets.

Global Plus Local Jointly Regularized Support Vector Data Description for Novelty Detection.

In many practice application, the cost for acquiring abnormal data is quite expensive, thus the one-class classification (OCC) problem attracts great attention. As one of the solutions, support vector data description (SVDD) gains a continuous focus in outlier detection since it is based on the data description. For the sphere obtained by SVDD, both the center and the volume (or radius) strongly depend on the support vectors, while the support vectors are sensitive to the tradeoff parameter C . Hence, how to select this parameter is a rather challenging problem. In order to address this problem, we define several distance metrics relative to the image region in Gaussian kernel space. With the distance metrics, two probability densities relative to the global region and the local region are designed, respectively. Then, the information quantity and the information entropy are developed for regularizing the tradeoff parameter. This novel SVDD is called global plus local jointly regularized support vector data description (GL-SVDD), in which both the global region information and the local image region information jointly penalize the images as possible outliers. Finally, we use the UCI dataset and the hyperspectral data of cherry fruit to evaluate the performance of several OCC approaches. Experimental results show that GL-SVDD is encouraging.

Fast AUC Maximization Learning Machine With Simultaneous Outlier Detection.

While AUC maximizing support vector machine (AUCSVM) has been developed to solve imbalanced classification tasks, its huge computational burden will make AUCSVM become impracticable and even computationally forbidden for medium or large-scale imbalanced data. In addition, minority class sometimes means extremely important information for users or is corrupted by noises and/or outliers in practical application scenarios such as medical diagnosis, which actually inspires us to generalize the AUC concept to reflect such importance or upper bound of noises or outliers. In order to address these issues, by means of both the generalized AUC metric and the core vector machine (CVM) technique, a fast AUC maximizing learning machine, called ρ -AUCCVM, with simultaneous outlier detection is proposed in this study. ρ -AUCCVM has its notorious merits: 1) it indeed shares the CVM's advantage, that is, asymptotically linear time complexity with respect to the total number of sample pairs, together with space complexity independent on the total number of sample pairs and 2) it can automatically determine the importance of the minority class (assuming no noise) or the upper bound of noises or outliers. Extensive experimental results about benchmarking imbalanced datasets verify the above advantages of ρ -AUCCVM.

Understanding the pathogenesis of coronary slow flow: Recent advances.

Coronary slow flow is taken to be indicative of delayed filling of terminal vessels of the coronary arteries in the absence of coronary stenosis, as detected using coronary angiography. Patients suffering from coronary slow flow typically experience recurrent chest pain, thereby markedly affecting their quality of life. The etiology and pathogenesis of coronary slow flow, which is gradually attracting clinical attention, have yet to be sufficiently established, although it is currently believed that they may be associated with endothelial dysfunction in the coronary arteries, inflammatory response, abnormalities in microvascular reserve function, subclinical atherosclerosis, blood cell and platelet abnormalities, and genetic factors. In this review, we provide a brief overview of recent progress in research on the pathogenesis of coronary slow flow with a view toward elucidating the possible underlying pathogenesis and identify targets and directions for the treatment of this condition.

Study on the physicochemical indexes, nutritional quality, and flavor compounds of Trichiurus lepturus from three representative origins for geographical traceability.

Trichiurus lepturus (hairtail) is an important economic component of China's marine fishing industry. However, due to the difficulty in identifying the appearance of hairtail from different geographical distributions, hairtails with geographical indication trademarks were imitated by general varieties. In this study, the texture characteristics, color, basic nutrients, amino acids, mineral, fatty acids, and volatile flavor substances were used as indicators for multivariate statistical analysis to determine whether three origins of hairtails from the habitats of Zhoushan (East China Sea, T.Z), Hainan (South China Sea, T.N), and Qingdao (Yellow Sea, T.Q) in the market could be distinguished. The findings revealed that there were significant differences in amino acids composition, mineral composition, fatty acid composition in lipids, and volatile flavor substances among the hairtails of three origins (P < 0.05), but no differences in color, texture, protein content. T.Z had moisture, crude fat, essential amino acids (EAA), flavor amino acids (FAA), unsaturated fatty acids (UFA), and docosahexaenoic acids and dicosapentaenoic acids (ΣEPA + DHA) contents of 74.33, 5.4%, 58.25 mg⋅g-1, 46.20 mg⋅g-1, 66.84 and 19.38%, respectively, and the contents of volatile alcohols, aldehydes and ketones were 7.44, 5.30, and 5.38%, respectively. T.N contains moisture, crude fat, EAA, FAA, UFA and ΣEPA + DHA as 77.69, 2.38%, 64.76 mg⋅g-1, 52.44 mg⋅g-1, 65.52 and 29.45%, respectively, and the contents of volatile alcohols, aldehydes and ketones as 3.21, 8.92, and 10.98%, respectively. T.Q had the contents of moisture, crude fat, EAA, FAA, UFA, and ΣEPA + DHA 79.69, 1.43%, 60.9 mg⋅g-1, and 49.42 mg⋅g-1, respectively. The contents of unsaturated fatty acid and ΣEPA + DHA were 63.75 and 26.12%, respectively, while the volatile alcohols, aldehydes, and ketones were 5.14, 5.99, and 7.85%, respectively. Partial least squares-discriminant analysis (PLS-DA) multivariate statistical analysis showed that volatile flavor compounds could be used as the most ideal indicators for tracing the source of hairtail. In conclusion, the findings of this study can distinguish the three hairtail origins using some basic indicators, providing ideas for hairtail geographical identification.

Fabrication and characterization of tea polyphenol W/O microemulsion-based bioactive edible film for sustained release in fish floss preservation.

A coated nanoemulsion (CNE)-based edible film was fabricated on the surface of fish floss (FF) to extend its shelf life during storage. The antioxidant tea polyphenol (TPP) was embedded into W/O microemulsion, which was further encapsulated into multiple emulsion (Multi-E) together with functional soluble dietary fiber (SDF). The physicochemical properties indicated that the nanoemulsion-based edible film (NEF) improved the morphology of FF and reduced the crystallinity of the film by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The water vapor permeability increased gradually and rose to only 0.99% after 5 h, resulting in the water activity of FF at a low level (≤0.51) during the storage period. The TPP inside was released at a constant rate (≤18.10%) on the surface, and such a rate was accelerated in the simulated gastrointestinal environment, especially in intestine reaching 60.12% after 5 h of digestion. Besides, the effect of NEF on the flavor was also evaluated and the contents of ketones, phenols, and pyrazines increased, which displayed a regulating effect on the overall flavor of FF by blocking the external moisture and suppressing the microorganism activity. In summary, the NEF effectively enhanced the flavor and taste of FF, controlled the release of TPP, and reduced the water activity during the storage, thereby extending the shelf life.

MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction.

Circular RNA (circRNA) is closely involved in physiological and pathological processes of many diseases. Discovering the associations between circRNAs and diseases is of great significance. Due to the high-cost to verify the circRNA-disease associations by wet-lab experiments, computational approaches for predicting the associations become a promising research direction. In this paper, we propose a method, MDGF-MCEC, based on multi-view dual attention graph convolution network (GCN) with cooperative ensemble learning to predict circRNA-disease associations. First, MDGF-MCEC constructs two disease relation graphs and two circRNA relation graphs based on different similarities. Then, the relation graphs are fed into a multi-view GCN for representation learning. In order to learn high discriminative features, a dual-attention mechanism is introduced to adjust the contribution weights, at both channel level and spatial level, of different features. Based on the learned embedding features of diseases and circRNAs, nine different feature combinations between diseases and circRNAs are treated as new multi-view data. Finally, we construct a multi-view cooperative ensemble classifier to predict the associations between circRNAs and diseases. Experiments conducted on the CircR2Disease database demonstrate that the proposed MDGF-MCEC model achieves a high area under curve of 0.9744 and outperforms the state-of-the-art methods. Promising results are also obtained from experiments on the circ2Disease and circRNADisease databases. Furthermore, the predicted associated circRNAs for hepatocellular carcinoma and gastric cancer are supported by the literature. The code and dataset of this study are available at https://github.com/ABard0/MDGF-MCEC.

Enhancing Li-Ion Transport in Solid Electrolytes by Confined Water.

Developing new oxide solid electrolytes with fast Li-ion transport and high stability is an important step to realize high-performance solid-state Li-ion batteries. Hydrate materials containing confined water widely exist in nature or can be easily synthesized. However, they have seldom been explored as Li-ion solid electrolytes due to the stereotype that the presence of water limits the electrochemical stability window of a solid electrolyte. In this work, it is demonstrated that confined water can enhance Li-ion transport while not compromising the stability window of solid electrolytes using Li-H-Ti-O quaternary compounds as an example system. Three Li-H-Ti-O quaternary compounds containing different amounts of confined water are synthesized, and their ionic conductivity and electrochemical stability are compared. The compound containing structural pseudo-water is demonstrated to have an ionic conductivity that is 2-3 order of magnitude higher than the water-free Li4 Ti5 O12 and similar stability window. A solid-state battery is made with this new compound as the solid electrolyte, and good rate and cycling performance are achieved, which demonstrates the promise of using such confined-water-containing compounds as Li-ion solid electrolytes. The knowledge and insights gained in this work open a new direction for designing solid electrolytes for future solid-state Li-ion batteries. Broadly, by confining water into solid crystal structures, new design freedoms for tailing the properties of ceramic materials are introduced, which creates new opportunities in designing novel materials to address critical problems in various engineering fields.

Real-Time Profiling and Distinction of Lipids from Different Mammalian Milks Using Rapid Evaporative Ionization Mass Spectrometry Combined with Chemometric Analysis.

The price of mammalian milk from different animal species varies greatly due to differences in their yield and nutritional value. Therefore, the authenticity of dairy products has become a hotspot issue in the market due to the replacement or partial admixture of high-cost milk with its low-cost analog. Herein, four common commercial varieties of milk, including goat milk, buffalo milk, Holstein cow milk, and Jersey cow milk, were successfully profiled and differentiated from each other by rapid evaporative ionization mass spectrometry (REIMS) combined with chemometric analysis. This method was developed as a real-time lipid fingerprinting technique. Moreover, the established chemometric algorithms based on multivariate statistical methods mainly involved principal component analysis, orthogonal partial least squares-discriminant analysis, and linear discriminant analysis as the screening and verifying tools to provide insights into the distinctive molecules constituting the four varieties of milk. The ions with m/z 229.1800, 243.1976, 257.2112, 285.2443, 299.2596, 313.2746, 341.3057, 355.2863, 383.3174, 411.3488, 439.3822, 551.5051, 577.5200, 628.5547, 656.5884, 661.5455, 682.6015, and 684.6146 were selected as potential classified markers. The results of the present work suggest that the proposed method could serve as a reference for recognizing dairy fraudulence related to animal species and expand the application field of REIMS technology.

Effects of Onion Extract and Onion Peel Extract on the Formation of Polycyclic Aromatic Hydrocarbons in Charcoal-Grilled Pork Patties.

ABSTRACT: This study investigated the effects of different concentrations (0.0005, 0.005, 0.05, 0.25, and 0.5%) of onion extract (OE) and onion peel extract (OPE) on the formation of four polycyclic aromatic hydrocarbons (PAHs) in charcoal-grilled pork patties. Both OE and OPE inhibited the formation of four PAHs in charcoal-grilled pork patties, with the highest inhibition rate reaching 88.33% on 0.50% OE addition and 98.79% on 0.05 and 0.25% OPE addition. OPE has greater inhibitory effect on the formation of four PAHs than OE does; this may be related to OPE's higher concentrations of flavonoids and higher free-radical scavenging activities. Both OE and OPE worked to lower thiobarbituric acid reactive substance values of charcoal-grilled pork patties. The inhibitory effect of OE and OPE on four PAHs showed the same trend as their antilipid oxidation effects, but the correlation was not strong. In addition to antilipid oxidation, other pathways are also involved in the inhibition of PAH formation.