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Infrared (IR) technologies have been universally acknowledged as a valuable pedagogical tool for exploring novel and abstract scientific subjects in science education. This study explores the roles of IR images played in middle school students' Evidence-based Reasoning (EBR) process in support of the understanding of the heat radiation process. Specifically, we implement image processing algorithms explicitly for the visual artifacts mentioned in students' descriptions of the radiation phenomenon to obtain the numeric representations of their corresponding features. Meanwhile, the quality of those descriptions is further coded with the guidance of the EBR framework for indicating students' understanding levels of the phenomenon. Finally, the associations between the numerical image features and the quality of descriptions are analyzed to examine the effectiveness of the IR visual artifacts in helping students understand the heat radiation process. The analytical results found that the image features are further positively correlated with the quality of the descriptions generated by students for the heat radiation. The results further suggest the IR images have the potential of driving students to think proactively and explore detailed procedural changes in learning the heat radiation process. Finally, our study calls for the integration of interdisciplinary instructional approaches in science education to reduce students' cognitive load and guide learning attention, for example, incorporating visualization and relevant processing approaches to present and analyze the otherwise invisible abstract process to help students make sense related knowledge more easily.

ABSTRACT Redundant carbapenemase-producing (RCP) bacteria, which carry double or multiple carbapenemases, represent a new and concerning phenomenon. The objective of this study is to conduct a comprehensive analysis of the epidemiology and genetic mechanisms of RCP strains to support targeted surveillance and control measures. A retrospective analysis was conducted using surveillance data from 277 articles. Statistical analysis was performed to determine and evaluate species prevalence, proportions of carbapenemases, antibiotic susceptibility profiles, sample information, and patient outcomes. Complete plasmid sequencing data were utilized to investigate potential antimicrobial resistance or virulence advantages that strains may gain from acquiring redundant carbapenemases. RCP bacteria are widely distributed globally, and their prevalence is increasing over time. Several countries, including China, India, Iran, Turkey, and South Korea, have reported more than 100 RCP strains. The most commonly reported RCP species are Klebsiella pneumoniae and Acinetobacter baumannii, which exhibit varying proportions of carbapenemase combinations. Certain species-carbapenemase combinations, such as K. pneumoniae carrying New Delhi metallo-β-lactamase (NDM) + oxacillinase (OXA) (56.76%) and K. pneumoniae carbapenemase (KPC) + Verona integron-encoded metallo-β-lactamase (VIM) (50.00%) carbapenemases, are associated with high mortality rates. In patients with RCP strains isolated from the bloodstream and respiratory system, the mortality rates are 58.70% and 69.23%, respectively. Analysis of plasmids from RCP strains suggests that they may acquire additional antibiotic resistance phenotypes and virulence factors. Carbapenem-resistant bacteria carrying redundant carbapenemases pose a significant global health threat. This study provides valuable insights into the epidemiology and genetic mechanisms of these bacteria, supporting the development of effective control and prevention strategies to mitigate their transmission.IMPORTANCEThis study examined the global distribution patterns of 1,780 bacteria with double or multiple carbapenemases from 277 articles and assessed their clinical impact. The presence of multiple carbapenemases increases the chances of co-resistance to other classes of antibiotics and more virulence factors, further complicating the clinical management of infections.

Naoxintong Capsule (NXT), a renowned traditional Chinese medicine (TCM) formulation, has been broadly applied in China for more than 30 years. Over decades, accumulating evidences have proven satisfactory efficacy and safety of NXT in treating cardiovascular and cerebrovascular diseases (CCVD). Studies have been conducted unceasingly, while this growing latest knowledge of NXT has not yet been interpreted properly and summarized comprehensively. Hence, we systematically review the advancements in NXT research, from its chemical constituents, quality control, pharmacokinetics, to its profound pharmacological activities as well as its clinical applications in CCVD. Moreover, we further propose specific challenges for its future perspectives: 1) to precisely clarify bioactivities of single compound in complicated mixtures; 2) to evaluate the pharmacokinetic behaviors of NXT feature components in clinical studies, especially drug-drug interactions in CCVD patients; 3) to explore and validate its multi-target mechanisms by integrating multi-omics technologies; 4) to re-evaluate the safety and efficacy of NXT by carrying out large-scale, multicenter randomized controlled trials. In brief, this review aims to straighten out a paradigm for TCM modernization, which help to contribute NXT as a piece of Chinese Wisdom into the advanced intervention strategy for CCVD therapy.

Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows instructors to offer in-time intervention and support for these at-risk students. Specifically, we develop a task model to characterize the engineering design process so that the data features can be associated with the abstract engineering design phases. A two-stage feature selection method is proposed to address the data sparsity and high dimensionality problems. Then, instead of relying on probability-based algorithms such as Bayesian Networks to represent the task model, this study used the Radial Basis Function based Support Vector Machines for prediction to identify the struggling students. Next, we employ an extra-tree classification method to rank the importance of these features. Teachers can integrate the feature importance ranking with the abstract task model to diagnose students' problems for scaffolding design. The results show that the proposed approach can outperform the baseline models as well as providing actionable insights for teachers to provide personalized and timely feedback to students. Implications of this study for research and practice are then discussed.

Multimodal Learning Analytics (MMLA) has huge potential for extending the work beyond traditional learning analytics for the capabilities of leveraging multiple data modalities (e.g. physiological data, digital tracing data). To shed a light on its applications and academic development, a systematic bibliometric analysis was conducted in this paper. Specifically, we examine the following aspects: (1) Analyzing the yearly publication and citation trends since the year 2010; (2) Recognizing the most prolific countries, institutions, and authors in this field; (3) Identifying the collaborative patterns among countries, institutions, and authors, respectively; (4) Tracing the evolving procedure of the applied keywords and development of the research topics during the last decade. These analytic tasks were conducted on 194 carefully selected articles published since 2010. The analytical results revealed an increasing trend in the number of publications and citations, identified the prominent institutions and scholars with significant academic contributions to the area, and detected the topics (e.g. characterizing learning processes using multimodal data, implementing ubiquitous learning platforms) that received the most attention. Finally, we also highlighted the current research hotspots attempting to initiate potential interdisciplinary collaborations to promote further progress in the area of MMLA.

This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for maintaining the consecutiveness among learning activities. To demonstrate the efficacy of our model in identifying at-risk students, we compare the weekly AUCs and averaged performance (i.e., accuracy, precision, recall, and f1-score) over each course with the baseline models (i.e., Random Forest, Support Vector Machine, and Decision Tree), respectively. Furthermore, we adopt a Top-"K" metric to examine the number of at-risk students that the model is able to identify with high precision during each week. Finally, the model output is interpreted through a model-agnostic interpretation approach to support instructors to make informed recommendations for students' learning. The experimental results demonstrate the capability and interpretability of our model in identifying at-risk students in online learning settings. In addition to that our work also provides significant implications in building accountable machine learning pipelines that can be used to automatically generated individualized learning interventions while considering fairness between different learning groups.

Porous polymer show great potential for CO2 capture owing to its high specific surface area, adjustable porous structure and chemical structure. Herein, a novel CO2 adsorption materials ALP−M (M: Cu2+, Mg2+, Ni2+) were prepared through coordination between azo‐linked porous polymer (ALP) synthesized by oxidative coupling polymerization of tris(4‐aminophenyl)amine and metal ions. The structures and morphology of as‐prepared materials were demonstrated by FT‐IR, XRD, XPS, BET and SEM, the CO2 capture performance and mechanism of ALP−M were also investigated through static adsorption and column adsorption experments, the results show that metal ions can enhance the CO2 adsorption and desorption performance of ALPs by coordination adsorption. ALP−M showed a highest static CO2 adsorption capacity of 2.67 mmol g−1 at 273 K (1 bar), which is 35.5 % higher than that of ALP. The metal ions served as Lewis acid sites can enhance the affinity of adsorbent for CO2. In column adsorption experiments, the ALP−M showed a higher saturated adsorption capacity 4.99 mmol g−1 at 273 K (1 bar) than that of static adsorption, and excellent cycling stability after 500 adsorption and desorption cycles, the adsorption capacity retention rate of ALP−M for CO2 can reach 98 %. [ABSTRACT FROM AUTHOR]

The purpose of this research study was to examine the overall effect of adaptive learning systems deployed using artificial intelligence technology across a range of relevant variables (e.g., duration, student level, etc.). Following a systematic procedure, this meta-analysis examined literature from 18 academic databases and identified N = 45 independent studies utilizing AI-enabled adaptive learning. This meta-analysis examined the overall effect of AI-enabled adaptive learning systems on students' cognitive learning outcomes when compared with non-adaptive learning interventions and found that they have a medium to large positive effect size (g = 0.70). The effect was significantly moderated by publication type, origin of study, student classification level, student discipline, duration, and research design. In addition, all three adaptive sources (cognitive, affective, and behavioral) and adaptive targets (navigation and assessment) were significant moderators. The type of AI used in the adaptive engine did not moderate the effects. Implications for both practice and research are provided. [ABSTRACT FROM AUTHOR]

AbstractUnderstanding the various roles and their corresponding evolving patterns is essential for evaluating the health of learning communities, such as in Asynchronous Online Discussion (AOD) forums. Existing research primarily focuses on identifying specific roles without considering the dynamics of role evolution, which are significant to ongoing community development. Guided by the Reader-to-Leader Framework (RtLF), this study employs multiple machine learning approaches to identify and predict the evolution of roles over time in a learning community. Our approach was applied to a K-12 Math online learning community, using data from 51,821 students to evaluate the community’s health over two years. First, a Gaussian Mixture Model was used to cluster students into five groups that represent their roles roughly in RtLF using the first nine months of learning activities. We then trained an XGBoost model to classify learner roles for subsequent periods based on meta-data. Third, we evaluate the health of the community by comparing the distribution of different roles over time and argue it is a healthy community. This study demonstrates how machine learning approaches can identify learner roles and support the health and development of learning communities. [ABSTRACT FROM AUTHOR]

The Focal Article (Ackerman et al., 2024) provides a comprehensive framework and detailed guidelines to faculty, students, and measurement professionals. We used a data‐driven approach that examines recent job posts (including full‐time positions at academic institutions, nonprofit organizations, and for‐profit companies, as well as internship positions) to evaluate the match between the skills required by the jobs and the Foundational Competencies and provide summaries and recommendations thereof. [ABSTRACT FROM AUTHOR]

Science classes should support students' development of scientific argumentation. While previous studies have analyzed argumentative texts, they have overlooked the ways in which other types of representations, including images, affect the production of such texts. In addition, studies into the use of visual images in science education have offered mostly qualitative analyses. To fill these gaps in the research, this study used techniques of automated image processing to extract relevant information from student-generated visual artifacts. Specifically, it used a series of image-processing algorithms to automatically extract and quantify features of images created by students to serve as evidence in support of scientific arguments. Using various statistical analyses, we identified the relationships between the extracted features and the students' performance levels in constructing scientific arguments. The results revealed that the presence of water in a student's image correlated significantly with that student's claim and explanation scores and that the amount of water present in a student's image correlated significantly with that student's claim score, but not with their explanation score. These results indicate that automatic image processing can successfully identify image features that affect students' performance in scientific argumentation. Using this analysis as an example, we discuss implications for incorporating automated image processing into further research into scientific argumentation and the development of automated feedback.

Learning and participation are inseparable in online environments. To improve online learning, much effort has been devoted to encouraging online participation. However, previous research has investigated participation from a variable-based perspective, looking only for relationships between participation and other variables. Time can change and even reverse this relationship, however. Online learning is a cumulative process, but participation at several critical moments is more significant for learning than is participation at other points. To fully understand how learning unfolds over time, it is necessary to shift to a new perspective on learning. Adopting an event-based view on which the units of analysis are separate but interrelated learning events, this study investigates longitudinal patterns in online participation. Using data mining techniques for education, the study validated longitudinal patterns of participation as an accurate measure for differentiating learner performance. In addition, the first segment of this online learning experience was identified as the most critical moment in which educators should provide efficient interventions to help their learners maintain active participation. Additional design, pedagogical, and methodological implications for online teaching and facilitation practices are discussed at the end.

Laribacter hongkongensis is a new emerging foodborne pathogen that causes community-acquired gastroenteritis and traveler’s diarrhea. However, the genetic features of L. hongkongensis have not yet been properly understood. A total of 45 aquatic animal-associated L. hongkongensis strains isolated from intestinal specimens of frogs and grass carps were subjected to whole-genome sequencing (WGS), along with the genome data of 4 reported human clinical strains, the analysis of virulence genes, carbohydrate-active enzymes, and antimicrobial resistance (AMR) determinants were carried out for comprehensively understanding of this new foodborne pathogen. Human clinical strains were genetically more related to some strains from frogs inferred from phylogenetic trees. The distribution of virulence genes and carbohydrate-active enzymes exhibited different patterns among strains of different sources, reflecting their adaption to different host environments and indicating different potentials to infect humans. Thirty-two AMR genes were detected, susceptibility to 18 clinical used antibiotics including aminoglycoside, chloramphenicol, trimethoprim, and sulfa was checked to evaluate the availability of clinical medicines. Resistance to Rifampicin, Cefazolin, ceftazidime, Ampicillin, and ceftriaxone is prevalent in most strains, resistance to tetracycline, trimethoprim-sulfamethoxazole, ciprofloxacin, and levofloxacin are aggregated in nearly half of frog-derived strains, suggesting that drug resistance of frog-derived strains is more serious, and clinical treatment for L. hongkongensis infection should be more cautious.

Context Naoxintong Capsule (NXT), a Chinese medicine, has been widely used for the treatment of coronary heart disease (CHD) in clinics. Objective This study evaluated the cardioprotective effects of NXT alone and in combination with ticagrelor (TIC) and atorvastatin (ATO). Materials and methods Qi deficiency and blood stasis rats were established by 8 weeks high fat diet feeding and 16 days exhaustive swimming and randomly divided into seven groups, that is, NXT (250, 500 and 1000 mg/kg/d), TIC (20 mg/kg/d), ATO (8 mg/kg/d), NXT (500 mg/kg/d)+TIC (20 mg/kg/d) and NXT (500 mg/kg/d)+ATO (8 mg/kg/d) group, with oral administration for 12 weeks. The contents of TC, TG, LDL-C, HDL-C, IL-1β, IL-6, IL-8, TNF-α, AST, ALT, SOD, MDA, CK-MB, LDH, TXA2, PGI2, IgA, IgG, IgM and C3 in serum were measured. Results NXT + TIC group was significantly superior to the TIC group in decreasing the levels of TC (4.34 vs. 5.54), TG (3.37 vs. 4.66), LDL-C (1.21 vs. 1.35), LDH (4919.71vs. 5367.19) and elevating SOD level (248.54 vs. 192.04). NXT + ATO group was significantly superior to the ATO group in decreasing the levels of AST (195.931 vs. 241.63), ALT (71.26 vs. 83.16), LDH (4690.05 vs. 5285.82), TXA2 (133.73 vs. 158.67), IgG (8.08 vs. 9.80), C3 (2.03 vs. 2.35) and elevating the levels of HDL-C (1.19 vs. 0.91), SOD (241.91vs. 209.49). Conclusions The present findings demonstrate that the combined use of NXT with TIC and ATO had better integrated regulating effects than TIC and ATO, respectively. The mechanism of action requires further research.

Discussion forums are increasingly central to massive open online courses (MOOCs), and it is vital for learners to participate in associated forum activities. Active forum participation positively relates to learner achievement in that more posts yield better learner performance. However, this numerically aggregated measure overlooks the fact that longitudinal trajectories of forum participation temporally vary among learners with different motivations in taking MOOCs. To provide timely support for learners to stay engaged, it is important to understand the temporal variation of longitudinal forum participation and how different motivations account for the variance. Using educational data mining techniques, this research identified three clusters with different longitudinal participation trajectories and also indicated that intrinsically motivated learners outperformed others in their longitudinal forum engagement. Also, examining longitudinal forum participation more accurately differentiated learner performance than the numerically aggregated measure. Last, learners persistently engaging in forums were more likely to perform better in MOOCs.

Background : Immunotherapy plays a significant role in the treatment of hepatocellular carcinoma (HCC). Members of the S100 protein family (S100s) have been widely implicated in the pathogenesis and progression of tumors. However, the exact mechanism by which S100s contribute to tumor immunity remains unclear. Methods: To explore the role of S100s in HCC immune cells, we collected and comparatively analyzed single-cell RNA sequencing (scRNA-seq) data of HCC and hepatitis B virus-associated HCC. By mapping cell classification and searching for S100s binding targets and downstream targets. Results: S100A6/S100A11 was differentially expressed in tumor T cells and involved in the nuclear factor (NF) κB pathway. Further investigation of the TCGA dataset revealed that patients with low S100A6/S100A11 expression had a better prognosis. Temporal cell trajectory analysis showed that the activation of the NF-κB pathway is at a critical stage and has an important impact on the tumor microenvironment. Conclusion: Our study revealed that S100A6/S100A11 could be involved in regulating the differentiation and cellular activity of T-cell subpopulations in HCC, and its low expression was positively correlated with prognosis. It may provide a new direction for immunotherapy of HCC and a theoretical basis for future clinical applications. [ABSTRACT FROM AUTHOR]

NaoShuanTong capsule (NSTC) has been commonly used to treat blood circulation disorders and neurological dysfunction against cerebral ischemia in China. In this study, using an acute blood stasis rat model, canonical correlation analysis between chemical high performance liquid chromatography (HPLC) fingerprint and in vivo biotests was conducted to discover and identify the bioactive compounds responsible for NSTC’s protective effects on hemorheological disorders. Four independent effect components were extracted from the 12 originally detected parameters. Three bioactive compounds were found to have significant contribution in two or more effect components against hemorheological disorders. Typhaneoside could influence the erythrocyte aggregation and coagulation function. Isorhamnetin-3-O-neohesperidoside had impacts on the erythrocyte aggregation, erythrocyte deformability and platelet aggregation. Benzoyl-oxypaeoniflorin was associated with erythrocyte aggregation, coagulation function and platelet aggregation. The findings could offer useful references for NSTC’s quality control and clinical practice. Abbreviations Adradrenaline ASPaspirin APTTactivated partial thromboplastin time CCAcanonical correlation analysis EAIerythrocyte aggregation index EDIerythrocyte deformability index EEIerythrocyte electrophoresis index HCThaematokrit HPLChigh performance liquid chromatography MPARmaximum platelet aggregation rate NSTCNaoShuanTong Capsule PCAprincipal component analysis PTPollen Typhae PTprothrombin time PVplasma viscosity RCRadix Curcumae RGRhizome Gastrodiae RPRRadix Paeoniae Rubra RRRadix Rhapontici TCMtraditional Chinese medicine WBVwhole blood viscosit

Background/Aims: Sputum symptoms are commonly seen in the elderly. This study aimed to identify an efficacious expectorant treatment stratagem through evaluating the secretion-promoting activation and cystic fibrosis transmembrane conductance regulator (CFTR) expression of the bioactive herbal monomer naringenin. Methods: Vectorial Cl- transport was determined by measuring short-circuit current (ISC) in rat airway epithelium. cAMP content was measured by ELISA in primary cultured epithelial cells and Calu-3 cells. CFTR expression in Calu-3 cells was determined by qPCR. Results: Addition of naringenin to the basolateral side of the rat airway led to a concentration-dependent sustained increase in ISC. The current was suppressed when exposed to Cl–-free solution or by bumetanide, BaCl2, and DPC but not by DIDS and IBMX. Forskolin-induced ISC increase and CFTRinh-172/MDL-12330A-induced ISC inhibition were not altered by naringenin. Intracellular cAMP content was significantly increased by naringenin. With lipopolysaccharide stimulation, CFTR expression was significantly reduced, and naringenin dose-dependently enhanced CFTR mRNA expression. Conclusion: These results demonstrate that naringenin has the ability to stimulate Cl- secretion, which is mediated by CFTR through a signaling pathway by increasing cAMP content. Moreover, naringenin can increase CFTR expression when organism CFTR expression is seriously hampered. Our data suggest a potentially effective treatment strategy for sputum.

Legionella pneumophila, an environmental bacterium that parasitizes protozoa, is the causative pathogen of Legionnaires' disease. L. pneumophila adopts a distinct biphasic life cycle that allows it to adapt to environmental conditions for survival, replication, and transmission. This cycle consists of a non-virulent replicative phase (RP) and a virulent transmissive phase (TP). Timely and fine-tuned expression of growth and virulence factors in a life cycle-dependent manner is crucial. Herein, we report evidence that CsrA, a key regulator of the switch between the RP and the TP, is dually regulated in a ClpP-dependent manner during the biphasic life cycle of L. pneumophila. First, we show that the protein level of CsrA is temporal during the life cycle and is degraded by ClpP during the TP. The ectopic expression of CsrA in a ΔclpP mutant, but not in the wild type, inhibits both the initiation of the RP in vitro and the invasiveness to Acanthamoeba castellanii, indicating that the ClpP-mediated proteolytic pathway regulates the CsrA protein level. We further show that the temporally expressed IHFB is the transcriptional inhibitor of csrA and is degraded via a ClpP-dependent manner during the RP. During the RP, the level of CsrA is increased by promoting the degradation of IHFB and reducing the degradation of the accumulated CsrA via a ClpP-dependent manner. During the TP, the level of CsrA is decreased by inhibiting the degradation of IHFB and promoting the degradation of the accumulated CsrA via a ClpP-dependent manner as well. In conclusion, our results show that the growth-stage-specific expression level of CsrA is dually regulated by ClpP-dependent proteolysis at both the transcription and protein levels during the biphasic life cycle of L. pneumophila.

Dan-hong injection (DHI) is extracted from Salvia miltiorrhiza (SM) and Carthamus tinctorius (CT) and is widely used for the treatment of cardiovascular diseases. Our previous results showed DHI could improve hemorheology in rats. Since complex cellular interactions such as inflammation and oxidative stress are believed to be implicated in the pathogenesis of cardiovascular events, investigation of such pathological factors will contribute substantially to the understanding of the features and mechanisms of DHI. Therefore, in this study we used a rat model of blood stasis to explore the overall effects of DHI by detecting twenty three indexes, which were related to inflammation, immune response, vascular endothelial function, myocardial energy metabolism, oxidative stress, platelet aggregation, liver and renal function. Meanwhile, the interaction between SM and CT was discussed by comparing the effects of each single herb. DHI could significantly decrease the serum contents of IL-1β, TNF-α, IL-6, IL-8, IgM, IgG, IgA, MPO, hs-CRP, MDA, LDH, CK-MB, PAF, ALP and Cr, while elevate NO, SOD, TP and UA levels, indicating that DHI could inhibit inflammation and platelet aggregation, thereby relieving immune response and peroxidation, protecting vascular endothelial and organ function, and then prevent and treat cardiovascular diseases. In terms of compatibility, SM and CT showed complementary effects on markers of inflammatory and oxidative status, vascular endothelial damage and myocardial energy metabolism. On the other hand, they counteracted each other and SM reduced the side effects of creatinine caused by CT. This study contributes to comprehensively understand the pharmacodynamics effects and mechanism of DHI.

Naoxintong capsule (NXT), a Chinese medicine, has performed excellent effects on the prevention and treatment against cardiovascular diseases. NXT is a fine powder mixture without any herb extraction, and there must be lots of ingredients hard to be absorbed. However, little is known about the correlation between the NXT’s cardioprotective effects and gut microbiota. Herein, we report the effect of NXT on the development of cardiovascular diseases and clarify the correlation between NXT’s cardioprotective effects and gut microbiota. In the current study, minipigs were selected and fed with high-fat diet and NXT daily for successive 8 months. During the process, up to 18 biomedical parameters were monthly determined to observe the dynamic changes after NXT treatment. At the end of experimental process, pathological examinations of heart, coronary artery, carotid artery, thoracic aorta, and abdominal aorta were conducted by HE staining and 16SrDNA sequencing, and analyzing of gut microbiota were conducted. Our results showed that NXT’s effects against cardiovascular diseases were through regulating blood lipid profiles, inhibiting vascular inflammation, enhancing antioxidant capacity, and alleviating myocardial injury, without damages on liver and kidney particularly. Concurrently, we also found that long-term administration of NXT increased the diversity of gut microbiota, influenced the microbiome structure and composition stably, and revered the increase of the ratio of the Firmicutes to Bacteroidetes (F/B ratio) in relative abundance. Specifically, our results revealed some key bacterium of Caproiciproducens (enhanced), Sutterella (enhanced), Erysipelotrichaceae (enhanced), and Romboutsia (decreased) that were closely involved in NXT’s effects. Taken together, our study demonstrates that NXT can inhibit the development of cardiovascular diseases by ameliorating high-fat diet–induced metabolic disorders and partly through improving gut microbiota.