Dietary pectin and inulin: a promising adjuvant supplement for collagen-induced arthritis through gut microbiome restoration and CD4+ T cell reconstitution.

Dietary strategies rich in fiber have been demonstrated to offer benefits to individuals afflicted with rheumatoid arthritis (RA). However, the specific mechanisms through which a high-fiber diet (HFD) mitigates RA's autoimmunity remain elusive. Herein, we investigate the influence of pectin- and inulin-rich HFD on collagen-induced arthritis (CIA). We establish that HFD significantly alleviates arthritis in CIA mice by regulating the Th17/Treg balance. The rectification of aberrant T cell differentiation by the HFD is linked to the modulation of gut microbiota, augmenting the abundance of butyrate in feces. Concurrently, adding butyrate to the drinking water mirrors the HFD's impact on ameliorating CIA, encompassing arthritis mitigation, regulating intestinal barrier integrity, and restoring the Th17/Treg equilibrium. Butyrate reshapes the metabolic profile of CD4+ T cells in an AMPK-dependent manner. Our research underscores the importance of dietary interventions in rectifying gut microbiota for RA management and offers an explanation of how diet-derived microbial metabolites influence RA's immune-inflammatory-reaction.

Microbiome and antibiotic resistome in bioelectrochemical toilets for onsite treatment of fecal sludge.

Effective management of fecal sludge (FS) is essential for preventing environmental and public health risks. Developing safe and efficient FS treatment technology is crucial for reducing the health risks of onsite sanitation systems. In this study, bioelectrochemical toilets (BETs) were developed to treat FS onsite. Compared with the open-circuit BETs (OC-BETs), BETs exhibited higher removal efficiencies for total organic carbon, total nitrogen, and total phosphorus. Specifically, the enhancements in removal efficiencies were 18.82 ± 1.73 %, 7.28 ± 0.32 %, and 11.41 ± 0.05 % for urine, and 19.28 ± 4.08 %, 21.65 ± 1.23 %, and 24.68 ± 0.95 % for feces, respectively. Microbiome analysis indicated that the dominant populations were affiliated with electroactive bacteria (Desulfuromonas and Pseudomonas) in the electrode biofilm of BETs. The species co-occurrence network showed that the electrode biofilm microbiome in BETs had more complex correlations than that in OC-BETs, suggesting that a weak electrical current enhanced the microbiome stability. The relative abundance of antibiotic resistance genes in BETs and OC-BETs reduced by 59.85 ± 1.32 % and 53.01 ± 2.81 % compared with the initial FS, respectively. These findings indicate that BETs are an alternative system for enhancing onsite treatment of fecal sludge and provide a theoretical foundation for the implementation of BETs.

Understanding the Ecological Robustness and Adaptability of the Gut Microbiome in Plastic-Degrading Superworms (Zophobas atratus) in Response to Microplastics and Antibiotics.

Recent discoveries indicate that several insect larvae are capable of ingesting and biodegrading plastics rapidly and symbiotically, but the ecological adaptability of the larval gut microbiome to microplastics (MPs) remains unclear. Here, we described the gut microbiome assemblage and MP biodegradation of superworms (Zophobas atratus larvae) fed MPs of five major petroleum-based polymers (polyethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate) and antibiotics. The shift of molecular weight distribution, characteristic peaks of C═O, and metabolic intermediates of residual polymers in egested frass proved depolymerization and biodegradation of all MPs tested in the larval intestines, even under antibiotic suppression. Superworms showed a wide adaptation to the digestion of the five polymer MPs. Antibiotic suppression negatively influenced the survival rate and plastic depolymerization patterns. The larval gut microbiomes differed from those fed MPs and antibiotics, indicating that antibiotic supplementation substantially shaped the gut microbiome composition. The larval gut microbiomes fed MPs had higher network complexity and stability than those fed MPs and antibiotics, suggesting that the ecological robustness of the gut microbiomes ensured the functional adaptability of larvae to different MPs. In addition, Mantel's test indicated that the gut microbiome assemblage was obviously related to the polymer type, the plastic degradability, antibiotic stress, and larval survival rate. This finding provided novel insights into the self-adaptation of the gut microbiome of superworms in response to different MPs.

Exploring the mechanisms of glycolytic genes involvement in pulmonary arterial hypertension through integrative bioinformatics analysis.

The purpose of this study was to identify the mechanisms underlying the involvement of glycolytic genes in pulmonary arterial hypertension (PAH). This study involved downloading 3 datasets from the GEO database at the National Center for Biotechnology Information. The datasets were processed to obtain expression matrices for analysis. Genes involved in glycolysis-related pathways were obtained, and genes related to glycolysis were selected based on significant differences in expression. Gene Ontology functional annotation analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and GSEA enrichment analysis were performed on the DEGs. Combining LASSO regression with SVM-RFE machine learning technology, a PAH risk prediction model based on glycolysis related gene expression was constructed, and CIBERSORTx technology was used to analyse the immune cell composition of PAH patients. Gene enrichment analysis revealed that the DEGs work synergistically across multiple biological pathways. A total of 6 key glycolysis-related genes were selected using LASSO regression and SVM. A bar plot was constructed to evaluate the weights of the key genes and predict the risk of PAH. The clinical application value and predictive accuracy of the model were assessed. Immunological feature analysis revealed significant correlations between key glycolysis-related genes and the abundances of different immune cell types. The glycolysis genes (ACSS2, ALAS2, ALDH3A1, ADOC3, NT5E, and TALDO1) identified in this study play important roles in the development of pulmonary arterial hypertension, providing new evidence for the involvement of glycolysis in PAH.

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation. METHODS: Between 2010 and 2021, 77,047 patients with echocardiography and 12-lead ECG performed within 7 days were identified from an academic medical center to provide DLM development (122,728 ECGs), and internal validation (7,637 ECGs). Additional 11,800 patients from a community hospital were identified to external validation. The ECGs were classified as with or without moderate-to-severe VHDs according to transthoracic echocardiography (TTE) records, and we also collected the other echocardiographic data and follow-up TTE records to identify new-onset valvular heart diseases. RESULTS: AI-ECG adjusted for age and sex achieved areas under the curves (AUCs) of >0.84, >0.80, >0.77, >0.83, and >0.81 for detecting aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation, respectively. Since predictions of each DLM shared similar components of ECG rhythms, the positive findings of each DLM were highly correlated with other valvular heart diseases. Of note, a total of 37.5-51.7% of false-positive predictions had at least one significant echocardiographic finding, which may lead to a significantly higher risk of future moderate-to-severe VHDs in patients with initially minimal-to-mild VHDs. CONCLUSION: AI-ECG may be used as a large-scale screening tool for detecting VHDs and a basis to undergo an echocardiography.

A hepatic sparganosis misdiagnosed as intrahepatic mass-forming cholangiocarcinoma: a case report and literature review.

Hepatic sparganosis (HS) is extremely rare and has not been previously reported in Eastern China. We report the diagnosis and treatment of a patient with HS from Xuzhou City, Jiangsu Province, China. The patient was admitted due to an acute biliary tract infection, and the symptoms improved after treatment at the Gastroenterology Department. During an ultrasound examination on admission, an abnormal echo was incidentally discovered at the junction of the left and right lobes of the liver. Thereafter, upper abdominal computed tomography (CT) and magnetic resonance imaging (MRI) non-contrast and contrast-enhanced examinations, and serum tumor biomarker examination were completed. After a multidisciplinary treatment (MDT) discussion at the Department of Hepatobiliary Surgery, the patient was diagnosed with intrahepatic mass-type cholangiocarcinoma (IMCC) and surgery was recommended. The patient underwent surgical treatment, and postoperative pathology revealed HS. No signs of intrahepatic recurrence were observed during the 1-year follow-up period.

AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.

The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality. The trial met its primary outcome, finding that implementation of the AI-ECG alert was associated with a significant reduction in all-cause mortality within 90 days: 3.6% patients in the intervention group died within 90 days, compared to 4.3% in the control group (4.3%) (hazard ratio (HR) = 0.83, 95% confidence interval (CI) = 0.70-0.99). A prespecified analysis showed that reduction in all-cause mortality associated with the AI-ECG alert was observed primarily in patients with high-risk ECGs (HR = 0.69, 95% CI = 0.53-0.90). In analyses of secondary outcomes, patients in the intervention group with high-risk ECGs received increased levels of intensive care compared to the control group; for the high-risk ECG group of patients, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death (0.2% in the intervention arm versus 2.4% in the control arm, HR = 0.07, 95% CI = 0.01-0.56). While the precise means by which implementation of the AI-ECG alert led to decreased mortality are to be fully elucidated, these results indicate that such implementation assists in the detection of high-risk patients, prompting timely clinical care and reducing mortality. ClinicalTrials.gov registration: NCT05118035 .

Evaluation of stress and displacement of maxillary canine during the single canine retraction in the maxillary first premolar extraction cases- A finite element study.

OBJECTIVES: This finite element study aimed to simulate maxillary canine movement during anterior teeth retraction. MATERIALS AND METHODS: Three methods of maxillary canine movement including miniscrew sliding with high hooks (MSH), miniscrew sliding with low hooks (MSL), and the traditional sliding method (TS) without using miniscrews were simulated using three-dimensional finite element analysis. The initial displacement of the maxillary canine, the maximum principal stress of the periodontal ligament and the Von Mises stress were calculated. RESULTS: The distolingual tipping movements of the canine were shown in three movement modes. MSH showed a small tendency to lingual tipping movement and a extrusion movement while MSL had the largest lingual inclination. TS demonstrated a tendency toward distolingual torsion displacement. Compressive stress values were mainly concentrated in the range - 0.003 to -0.006 MPa. For tensile stress, the distribution of MSH and MSL was concentrated in the range 0.005 to 0.009 MPa, TS was mainly distributed about 0.003 MPa. Von Mises equivalent stress distribution showed no significant difference. CONCLUSIONS: The loss of tooth torque was inevitable, irrespective of which method was used to close the extraction space. However, miniscrew application and higher hooks reduced the loss of torque and avoided lingual rotation. CLINICAL RELEVANCE: This study shows that miniscrew implants with different hooks can better control the movement of the maxillary canines. The non-invasive nature of the finite element analysis and its good simulation of dental stress and instantaneous motion trend have a clinical advantage in the analysis of tooth movement.

Assessing the causal relationships of gut microbial genera with hyperuricemia and gout using two-sample Mendelian randomization.

BACKGROUND AND AIMS: The causal relationship between gut microbiota and gout and hyperuricemia (HUA) has not been clarified. The objective of this research was to evaluate the potential causal effects of gut microbiota on HUA and gout using a two-sample Mendelian randomization (MR) approach. METHODS AND RESULTS: Genetic instruments were selected using summary statistics from genome-wide association studies (GWASs) comprising a substantial number of individuals, including 18,473 participants for gut microbiome, 288,649 for serum urate (SU), and 763,813 for gout. Two-sample MR analyses were performed to determine the possible causal associations of gut microbial genera with the risk of HUA and gout using the inverse-variance weighted (IVW) method, and robustness of the results was confirmed by several sensitivity analyses. A reverse MR analysis was conducted on the bacterial taxa that were identified in forward MR analysis. Based on the results of MR analyses, Escherichia-Shigella (OR = 1.05; 95% CI, 1.01-1.08; P = 0.009) exhibited a positive association with SU levels, while Lachnospiraceae NC2004 group (OR = 0.95; 95% CI, 0.92-0.98; P = 0.001) and Family XIII AD3011 group (OR = 0.94; 95% CI, 0.90-0.99; P = 0.015) were associated with a reduced HUA risk. Moreover, Coprococcus 3 (OR = 1.17, 95% CI: 1.01-1.34, P = 0.031) was causally associated with a higher gout risk. In reverse MR analysis, no causal relationships were identified between these bacterial genera and HUA or gout. CONCLUSION: This study provides evidence for a causal association between gut microbial genera and HUA or gout, and further investigations of the underlying mechanism are warranted.

Dynamic asymmetric spillovers and connectedness between Chinese sectoral commodities and industry stock markets.

This study investigates the dynamic and asymmetric propagation of return spillovers between sectoral commodities and industry stock markets in China. Using a daily dataset from February 2007 to July 2022, we employ a time-varying vector autoregressive (TVP-VAR) model to examine the asymmetric return spillovers and dynamic connectedness across sectors. The results reveal significant time-varying spillovers among these sectors, with the industry stocks acting as the primary transmitter of information to the commodity market. Materials, energy, and industrials stock sectors contribute significantly to these spillovers due to their close ties to commodity production and processing. The study also identifies significant asymmetric spillovers with bad returns dominating, influenced by major economic and political events such as the 2008 global financial crisis, the 2015 Chinese stock market crisis, the COVID-19 pandemic, and the Russia-Ukraine war. Furthermore, our study highlights the unique dynamics within the Chinese market, where net information spillovers from the stock market to commodities drive the financialization process, which differs from the bidirectional commodity financialization observed in other markets. Finally, portfolio analysis reveals that the minimum connectedness portfolio outperforms other approaches and effectively reflects asymmetries. Understanding these dynamics and sectoral heterogeneities has important implications for risk management, policy development, and trading practices.

Integrative analysis identified two subtypes and a taurine-related signature to predict the prognosis and efficacy of immunotherapy in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most prevalent subtypes of primary liver cancer, with high mortality and poor prognosis. Immunotherapy has revolutionized treatment strategies for many cancers. However, only a subset of patients with HCC achieve satisfactory benefits from immunotherapy. Therefore, a reliable biomarker that could predict the prognosis and immunotherapy response in patients with HCC is urgently needed. Taurine plays an important role in many physiological processes. However, its participation in the occurrence and progression of liver cancer and regulation of the composition and function of various components of the immune microenvironment remains elusive. In this study, we identified and validated two heterogeneous subtypes of HCC with different taurine metabolic profiles, presenting distinct genomic features, clinicopathological characteristics, and immune landscapes, using multiple bulk transcriptome datasets. Subsequently, we constructed a risk model based on genes related to taurine metabolism to assess the prognosis, immune cell infiltration, immunotherapy response, and drug sensitivity of patients with HCC. The risk model was validated using several independent external cohorts and showed a robust predictive performance. In addition, we evaluated the expression patterns of taurine metabolism-related genes in the tumor microenvironment and the heterogeneity of taurine metabolism among cancer cells using a single-cell transcriptome. In conclusion, our study provides insights into the important role played by taurine metabolism in tumor progression and immune regulation. Furthermore, the risk model can serve as a biomarker to assess patient prognosis and immunotherapy response, potentially helping clinicians make more precise and personalized clinical decisions.

A Deep-Learning Algorithm-Enhanced Electrocardiogram Interpretation for Detecting Pulmonary Embolism.

Background: The early diagnosis of pulmonary embolism (PE) remains a challenge. Electrocardiograms (ECGs) and D-dimer levels are used to screen potential cases. Objective: To develop a deep learning model (DLM) to detect PE using ECGs and investigate the clinical value of false detections in patients without PE. Methods: Among patients who visited the emergency department between 2011 and 2019, PE cases were identified through a review of medical records. Non-PE ECGs were collected from patients without a diagnostic code for PE. There were 113 PE and 51,456 non-PE ECGs in the training and validation sets for developing the DLM, respectively, and 27 PE and 13,105 non-PE cases in an independent testing set for performance validation. A human-machine competition was conducted from the testing set to compare the performance of the DLM with that of physicians. Receiver operating characteristic (ROC) curves, sensitivity, and specificity were used to determine the diagnostic value. Survival analysis was used to assess the prognosis of the patients without PE, stratified by DLM prediction. Results: The DLM was as effective as physicians in diagnosing PE, with 70.8% sensitivity and 69.7% specificity. The area under the ROC curve of DLM was 0.778 in the testing set and up to 0.9 with D-dimer and demographic data. The non-PE patients whose ECG was misclassified as PE by DLM had higher all-cause mortality [hazard ratio (HR) 2.13 (1.51-3.02)] and risk of non-cardiovascular hospitalization [HR 1.55 (1.42-1.68)] than those correctly classified. Conclusions: A DLM-enhanced ECG system may prompt PE recognition and provide prognostic outcomes in patients with false-positive predictions.

Blame others but hurt yourself: blaming or sympathetic attitudes toward victims of COVID-19 and how it alters one's health status.

OBJECTIVE: This study explored the relationship between blame/sympathy and blamer's/sympathizer's perceived health status. DESIGN: We recruited participants via an online survey platform. Study 1 was a cross-sectional study using data (N = 3304, Mage = 28.22, SDage = 7.92, and 39.3% female) collected from 30 provinces, municipalities, and autonomous regions of China on February 3, 2020. Study 2 used the daily diary method collecting data from February 4 to 9, 2020. Sample (N = 2456, Mage = 28.49, SDage = 7.49, and 39.4% were female) was obtained by inviting participants in Study 1 on the same platform. MAIN OUTCOME MEASURES: Self-reported health status and life satisfaction. RESULTS: In Study 1, blame was negatively associated with perceived health status, while sympathy was positively associated with it. Negative emotions and risk perception are the underlying mechanisms, but neither of them has effects on the relationship between sympathy and perceived health status. Study 2 replicated these results using multilevel analysis. CONCLUSION: The results highlight the importance of people's attitudes on perceived health status. While sympathy is positively related to perceived health status, blaming has a negative association with perceived health status. Negative emotions and risk perceptions are the underlying mechanisms.

Optimization of biogas production from straw wastes by different pretreatments: Progress, challenges, and prospects.

Lignocellulosic biomass (LCB) presents a promising feedstock for carbon management due to enormous potential for achieving carbon neutrality and delivering substantial environmental and economic benefit. Bioenergy derived from LCB accounts for about 10.3 % of the global total energy supply. The generation of bioenergy through anaerobic digestion (AD) in combination with carbon capture and storage, particularly for methane production, provides a cost-effective solution to mitigate greenhouse gas emissions, while concurrently facilitating bioenergy production and the recovery of high-value products during LCB conversion. However, the inherent recalcitrant polymer crystal structure of lignocellulose impedes the accessibility of anaerobic bacteria, necessitating lignocellulosic residue pretreatment before AD or microbial chain elongation. This paper seeks to explore recent advances in pretreatment methods for LCB biogas production, including pulsed electric field (PEF), electron beam irradiation (EBI), freezing-thawing pretreatment, microaerobic pretreatment, and nanomaterials-based pretreatment, and provide a comprehensive overview of the performance, benefits, and drawbacks of the traditional and improved treatment methods. In particular, physical-chemical pretreatment emerges as a flexible and effective option for methane production from straw wastes. The burgeoning field of nanomaterials has provoked progress in the development of artificial enzyme mimetics and enzyme immobilization techniques, compensating for the intrinsic defect of natural enzyme. However, various complex factors, such as economic effectiveness, environmental impact, and operational feasibility, influence the implementation of LCB pretreatment processes. Techno-economic analysis (TEA), life cycle assessment (LCA), and artificial intelligence technologies provide efficient means for evaluating and selecting pretreatment methods. This paper addresses current issues and development priorities for the achievement of the appropriate and sustainable utilization of LCB in light of evolving economic and environmentally friendly social development demands, thereby providing theoretical basis and technical guidance for improving LCB biogas production of AD systems.

Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases.

Background: The electrocardiogram (ECG) may be the most popular test in the management of cardiovascular disease (CVD). Although wide applications of artificial intelligence (AI)-enabled ECG have been developed, an integrating indicator for CVD risk stratification was not investigated. Since mortality may be the most important global outcome, this study aimed to develop a survival deep learning model (DLM) to establish a critical ECG value and explore the associations with various CVD events. Methods: We trained a DLM with 451,950 12-lead resting ECGs obtained from 210,552 patients, for whom 23,592 events occurred. The internal validation set included 27,808 patients with one ECG for each patient. The external validations were performed in a community hospital with 33,047 patients and two transnational data sets with 233,647 and 1631 ECGs. We distinguished the cause of mortality and additionally investigated CVD-related outcomes, including new-onset acute myocardial infarction (AMI), stroke (STK), and heart failure (HF). Results: The DLM achieved C-indices of 0.858/0.836 in internal/external validation sets by using ECG over a 10-year period. The high-mortality-risk group identified by the proposed DLM presented a hazard ratio (HR) of 14.16 (95% confidence interval (CI): 11.33-17.70) compared to the low-risk group in the internal validation and presented a higher risk of cardiovascular (CV) mortality (HR: 18.50, 95% CI: 9.82-34.84), non-CV mortality (HR: 13.68, 95% CI: 10.76-17.38), AMI (HR: 4.01, 95% CI: 2.24-7.17), STK (HR: 2.15, 95% CI: 1.70-2.72), and HF (HR: 6.66, 95% CI: 4.54-9.77), which was consistent in an independent community hospital. The transnational validation also revealed HRs of 4.91 (95% CI: 2.63-9.16) and 2.29 (95% CI: 2.15-2.44) for all-cause mortality in the SaMi-Trop and Clinical Outcomes in Digital Electrocardiography 15% (CODE15) cohorts. Conclusions: The mortality risk by AI-enabled ECG may be applied in passive electronic-health-record-based CVD risk screening, which may identify more asymptomatic and unaware high-risk patients.

Surface Structure Analysis and Formaldehyde Removal Mechanism of Lotus Shell Biochar: An Experimental and Theoretical Perspective.

The adsorption of gaseous HCHO by raw lotus shell biochar carbonized at 500, 700, and 900 °C from the perspective of its internal crystal structure and surface functional groups was investigated by an integrated approach of experiments and density functional theory calculations. The results showed that lotus shell biochar carbonized at 700 °C had the best adsorption effect at a HCHO concentration of 10.50 ± 0.30 mg/m3, with an adsorption removal rate of 87.64%. The HCHO removal efficiency by lotus shell biochar carbonized at 500 and 900 °C was determined to be 80.96 and 83.07%, respectively. The HCHO adsorption on lotus shell biochar carbonized at 700 °C conformed to pseudo-second-order kinetics and was predominantly controlled by chemical adsorption. The Langmuir isotherm was the underlying mechanism for the monomolecular layer adsorption with a maximum adsorption capacity of 0.329 mg/g. The density functional theory calculations revealed that the adsorption of HCHO on the surface of CaCO3 and KCl in lotus shell biochar carbonized at 700 °C was a chemical adsorption process, with adsorption energies ranging from -64.375 to -87.554 kJ/mol. The strong interaction between HCHO and the surface was attributed to the electron transfer from HCHO to the surface, facilitated by metal atoms (Ca or K) and the oxygen atoms of HCHO. The carboxyl group on the surface of lotus shell biochar carbonized at 700 °C was identified as the key functional group responsible for HCHO adsorption. This study advanced our understanding of the environmental functions of inorganic crystals and surface functional groups in raw biochar and will enable the further development of biochar materials in environmental applications.

Integrated network pharmacology and gut microbiome analysis to reveal the mechanism of Qu-Zhuo-Tong-Bi decoction against hyperuricemia and gout.

ETHNOPHARMACOLOGICAL RELEVANCE: Qu-zhuo-tong-bi decoction (QZTBD) is a classic Chinese herbal medicine that has shown therapeutic efficacy in clinical practice against hyperuricemia and gout. However, the potential mechanisms of QZTBD remain poorly investigated. AIM OF THE STUDY: To assess the therapeutic effects of QZTBD on hyperuricemia and gout and to reveal its mechanisms of action. MATERIALS AND METHODS: A Uox-KO mouse model of hyperuricemia and gout was established, and QZTBD was administered at a dosage of 18.0 g/kg/d. Throughout the experimental period, the effects of QZTBD on gout symptoms were monitored and analyzed. The integrated network pharmacology and gut microbiota analysis strategy was conducted to explore the mechanism of QZTBD in the treatment of hyperuricemia and gout. Targeted metabolomic analysis was performed to investigate the variation of amino acids and Spearman's rank correlation analysis was conducted to reveal the relationship between the discrepant bacterial genera and the altered amino acid. Flow cytometry was utilized to analysis the proportion of Th17 and Treg cells, and the production of pro-inflammatory cytokines was measured by ELISA. qRT-PCR and Western blot assay were applied to detect the expression of mRNA and protein respectively. Autodock vina 1.1.2 was used to evaluate the docking interactions. RESULTS: QZTBD treatment showed remarkable efficacy against hyperuricemia and gout with respect to attenuation of disease activity metrics through gut microbiome recovery and intestinal immune homeostasis. The administration of QZTBD significantly elevated the abundance of Allobaculum and Candidatus sacchairmonas, corrected the aberrant amino acid patterns, repaired the impaired intestinal barrier, restored the balance of Th17/Treg cells via PI3K-AKT-mTOR pathway, and reduced the levels of inflammatory cytokines such as IL-1ß, IL-6, TNF-α and IL-17. Fecal microbiota transplantation from QZTBD treated mice demonstrated convincing evidence of efficacy and mechanism of QZTBD. CONCLUSION: Taken together, our study explores the therapeutic mechanism of an effective herbal formula, QZTBD, for gout treatment through remodeling gut microbiome and regulating the differentiation of CD4+ T cells via PI3K-AKT-mTOR pathway.

Stepwise changes in flavonoids in spores/pollen contributed to terrestrial adaptation of plants.

Protecting haploid pollen and spores against UV-B light and high temperature, 2 major stresses inherent to the terrestrial environment, is critical for plant reproduction and dispersal. Here, we show flavonoids play an indispensable role in this process. First, we identified the flavanone naringenin, which serves to defend against UV-B damage, in the sporopollenin wall of all vascular plants tested. Second, we found that flavonols are present in the spore/pollen protoplasm of all euphyllophyte plants tested and that these flavonols scavenge reactive oxygen species to protect against environmental stresses, particularly heat. Genetic and biochemical analyses showed that these flavonoids are sequentially synthesized in both the tapetum and microspores during pollen ontogeny in Arabidopsis (Arabidopsis thaliana). We show that stepwise increases in the complexity of flavonoids in spores/pollen during plant evolution mirror their progressive adaptation to terrestrial environments. The close relationship between flavonoid complexity and phylogeny and its strong association with pollen survival phenotypes suggest that flavonoids played a central role in the progression of plants from aquatic environments into progressively dry land habitats.