Associations of Diet and Lifestyle with Mortality and Stroke: The China Cardiometabolic Disease and Cancer Cohort (4C) Study.

OBJECTIVE: This study aimed to examine the individual and combined associations between dietary habits and lifestyle factors concerning all-cause mortality and stroke in Chinese adults. METHOD: We conducted a nationwide, multicenter, prospective cohort study involving 10,008 participants, gathering baseline data on lifestyle, metabolic status, dietary habits, and living behaviors. Subsequently, a 10-year follow-up was performed, resulting in the inclusion of 7,612 participants in this study. We employed Spearman correlation analysis, restricted cubic spline regression, and Cox regression analysis to evaluate the connections between outcome events, dietary habits, and lifestyle. RESULT: For each additional serving of pulses consumed per week, there was a slight decrease in the risk of all-cause mortality (HR: 0.91, 95% CI: 0.83-0.99). The hazard ratios for stroke were 2.24 (1.48, 3.37) for current smokers, in comparison to individuals who had never smoked. Appropriate intake of specific dietary factors and certain lifestyle habits were associated with reduced stroke: fruit drinks at 0.51 (0.34, 0.87), and animal viscera at 0.58 (0.32, 1.04). Weekly consumption of at least 21 servings of vegetables (0.72, 0.53-0.98), 0-1 serving of fried food (0.58, 0.38-0.90), and at least 1 serving of carbonated beverages (0.51, 0.28-0.92) was associated with a reduced risk of stroke. CONCLUSION: Smoking was found to be linked to an increased risk of stroke. A higher intake of fruit drinks and animal viscera was associated with a reduced risk of stroke. In contrast, a higher intake of beans was associated with a decreased risk of overall mortality. Consuming an appropriate amount of vegetables, fried foods, and carbonated drinks was found to potentially lower the risk of stroke. Collectively, these findings underscore the importance of developing tailored dietary interventions conducive to the Chinese populace's health.

The causal relationship between plasma protein-to-protein ratios and type 2 diabetes and its complications: Proteomics mendelian randomization study.

AIM: In recent years, proteomics research has surged, with numerous observational studies identifying associations between plasma proteins and type 2 diabetes. However, research specifically focusing on the ratios of plasma proteins in type 2 diabetes remains relatively scarce. METHODS: This study primarily employed a two-sample, two-step Mendelian randomization (MR) approach, leveraging genetic data from several large, publicly accessible genome-wide association studies, wherein single nucleotide polymorphisms served as proxies for exposures and diseases. Within this framework, we applied two-sample MR to assess the associations between the 2821 plasma protein-to-protein ratios and type 2 diabetes along with its complications and utilized reverse MR to confirm the unidirectionality of these causal relationships. In addition, we employed two-step MR to investigate the potential mediating role of body mass index in these associations. To augment the robustness of our findings, we systematically implemented a series of sensitivity analyses. RESULTS: The results gleaned from the inverse-variance weighted method elucidated that a cumulative sum of 23 protein-to-protein ratios bore a causal nexus with type 2 diabetes across both sample cohorts. With each incremental elevation of 1 standard deviation in the genetically anticipated protein-to-protein ratio, the susceptibility to type 2 diabetes oscillated from 0.93 (95% confidence interval: 0.87, 1.00) for the CNTN3/NCSS1 protein level ratio to 1.13 (1.06, 1.22) for the DBNL/NCK2 protein level ratio. Moreover, a tally of eight protein-to-protein ratios correlated with a minimum of one complication linked to type 2 diabetes. Diverse sensitivity analyses corroborated the robustness of these observations. CONCLUSIONS: The outcomes of our investigation unveiled correlations between 23 plasma protein-to-protein ratios and type 2 diabetes, with eight of these ratios entwined with complications of type 2 diabetes. These discoveries offer novel perspectives on the diagnosis and management of type 2 diabetes and its associated complications.

Association between gut microbiota and adrenal disease: a two-sample Mendelian randomized study.

Background: Some observational studies and clinical experiments suggest a close association between gut microbiota and metabolic diseases. However, the causal effects of gut microbiota on adrenal diseases, including Adrenocortical insufficiency, Cushing syndrome, and Hyperaldosteronism, remain unclear. Methods: This study conducted a two-sample Mendelian randomization analysis using summary statistics data of gut microbiota from a large-scale genome-wide association study conducted by the MiBioGen Consortium. Summary statistics data for the three adrenal diseases were obtained from the FinnGen study. The study employed Inverse variance weighting, MR-Egger, and MR-PRESSO methods to assess the causal relationship between gut microbiota and these three adrenal diseases. Additionally, a reverse Mendelian randomization analysis was performed for bacteria found to have a causal relationship with these three adrenal diseases in the forward Mendelian randomization analysis. Cochran's Q statistic was used to test for heterogeneity of instrumental variables. Results: The IVW test results demonstrate that class Deltaproteobacteria, Family Desulfovibrionaceae, and Order Desulfovibrionales exhibit protective effects against adrenocortical insufficiency. Conversely, Family Porphyromonadaceae, Genus Lachnoclostridium, and Order MollicutesRF9 are associated with an increased risk of adrenocortical insufficiency. Additionally, Family Acidaminococcaceae confers a certain level of protection against Cushing syndrome. In contrast, Class Methanobacteria, Family Lactobacillaceae, Family Methanobacteriaceae, Genus. Lactobacillus and Order Methanobacteriales are protective against Hyperaldosteronism. Conversely, Genus Parasutterella, Genus Peptococcus, and Genus Veillonella are identified as risk factors for Hyperaldosteronism. Conclusions: This two-sample Mendelian randomization analysis revealed a causal relationship between microbial taxa such as Deltaproteobacteria and Desulfovibrionaceae and Adrenocortical insufficiency, Cushing syndrome, and Hyperaldosteronism. These findings offer new avenues for comprehending the development of adrenal diseases mediated by gut microbiota.

The potential value of dual-energy CT radiomics in evaluating CD8+, CD163+ and αSMA+ cells in the tumor microenvironment of clear cell renal cell carcinoma.

PURPOSE: This study aims to develop radiomics models and a nomogram based on machine learning techniques, preoperative dual-energy computed tomography (DECT) images, clinical and pathological characteristics, to explore the tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC). METHODS: We retrospectively recruited of 87 patients diagnosed with ccRCC through pathological confirmation from Center I (training set, n = 69; validation set, n = 18), and collected their DECT images and clinical information. Feature selection was conducted using variance threshold, SelectKBest, and the least absolute shrinkage and selection operator (LASSO). Radiomics models were then established using 14 classifiers to predict TME cells. Subsequently, we selected the most predictive radiomics features to calculate the radiomics score (Radscore). A combined model was constructed through multivariate logistic regression analysis combining the Radscore and relevant clinical characteristics, and presented in the form of a nomogram. Additionally, 17 patients were recruited from Center II as an external validation cohort for the nomogram. The performance of the models was assessed using methods such as the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: The validation set AUC values for the radiomics models assessing CD8+, CD163+, and αSMA+ cells were 0.875, 0.889, and 0.864, respectively. Additionally, the external validation cohort AUC value for the nomogram reaches 0.849 and shows good calibration. CONCLUSION: Radiomics models could allow for non-invasive assessment of TME cells from DECT images in ccRCC patients, promising to enhance our understanding and management of the tumor.

Nitrogenous fertilizer plays a more important role than cultivars in shaping sorghum-associated microbiomes.

The plant microbiome plays a crucial role in facilitating plant growth through enhancing nutrient cycling, acquisition and transport, as well as alleviating stresses induced by nutrient limitations. Despite its significance, the relative importance of common agronomic practices, such as nitrogenous fertilizer, in shaping the plant microbiome across different cultivars remains unclear. This study investigated the dynamics of bacterial and fungal communities in leaf, root, rhizosphere, and bulk soil in response to nitrogenous fertilizer across ten sorghum varieties, using 16S rRNA and ITS gene amplicon sequencing, respectively. Our results revealed that nitrogen addition had a greater impact on sorghum-associated microbial communities compared to cultivar. Nitrogen addition significantly reduced bacterial diversity in all compartments except for the root endophytes. However, N addition significantly increased fungal diversity in both rhizosphere and bulk soils, while significantly reducing fungal diversity in the root endophytes. Furthermore, N addition significantly altered the community composition of bacteria and fungi in all four compartments, while cultivars only affected the community composition of root endosphere bacteria and fungi. Network analysis revealed that fertilization significantly reduced microbial network complexity and increased fungal-related network complexity. Collectively, this study provides empirical evidence that sorghum-associated microbiomes are predominantly shaped by nitrogenous fertilizer rather than by cultivars, suggesting that consistent application of nitrogenous fertilizer will ultimately alter plant-associated microbiomes regardless of cultivar selection.

The inverted U-shaped relationship between epinephrine and pancreatic ductal adenocarcinoma patients' survival with compensation of lymphocyte.

BACKGROUND: The relationship between epinephrine and cancer can be dose-dependent in in vivo study. Whether it is the same in human body still needs verification. METHOD: We used frozen human pancreatic ductal adenocarcinoma (PDAC) tissues to detect epinephrine content and analyzed its relationship with survival using the K-M method and Cox regression. Disturbance of blood cell count and C-reactive protein and identification of related potent intermediary factors were also analyzed. RESULTS: K-M plot and Cox regression all showed the inverted U-shaped relationship between epinephrine and PDAC survival. Lymphocyte adjustment can increase the HRs of epinephrine for PDAC death by >10%. CONCLUSION: Epinephrine played an anti-tumor or pro-tumor effect depending on the specific concentration. Circulating lymphocyte count was elevated and might acted as a compensation pathway to reduce the pro-tumor effect of epinephrine to PDAC.

Xinyang tablet alleviated cardiac dysfunction in a cardiac pressure overload model by regulating the receptor-interacting serum/three-protein kinase 3/FUN14 domain containing 1-mediated mitochondrial unfolded protein response and mitophagy.

ETHNOPHARMACOLOGICAL RELEVANCE: Xinyang tablet (XYT) has been used for heart failure (HF) for over twenty years in clinical practice, but the underlying molecular mechanism remains poorly understood. AIMS OF THE STUDY: In the present study, we aimed to explore the protective effects of XYT in HF in vivo and in vitro. MATERIALS AND METHODS: Transverse aortic constriction was performed in vivo to establish a mouse model of cardiac pressure overload. Echocardiography, tissue staining, and real-time quantitative PCR (qPCR) were examined to evaluate the protective effects of XYT on cardiac function and structure. Adenosine 5'-triphosphate production, reactive oxygen species staining, and measurement of malondialdehyde and superoxide dismutase was used to detect mitochondrial damage. Mitochondrial ultrastructure was observed by transmission electron microscope. Immunofluorescence staining, qPCR, and Western blotting were performed to evaluate the effect of XYT on the mitochondrial unfolded protein response and mitophagy, and to identify its potential pharmacological mechanism. In vitro, HL-1 cells and neonatal mouse cardiomyocytes were stimulated with Angiotensin II to establish the cell model. Western blotting, qPCR, immunofluorescence staining, and flow cytometry were utilized to determine the effects of XYT on cardiomyocytes. HL-1 cells overexpressing receptor-interacting serum/three-protein kinase 3 (RIPK3) were generated by transfection of RIPK3-overexpressing lentiviral vectors. Cells were then co-treated with XYT to determine the molecular mechanisms. RESULTS: In the present study, XYT was found to exerta protective effect on cardiac function and structure in the pressure overload mice. And it was also found XYT reduced mitochondrial damage by enhancing mitochondrial unfolded protein response and restoring mitophagy. Further studies showed that XYT achieved its cardioprotective role through regulating the RIPK3/FUN14 domain containing 1 (FUNDC1) signaling. Moreover, the overexpression of RIPK3 successfully reversed the XYT-induced protective effects and significantly attenuated the positive effects on the mitochondrial unfolded protein response and mitophagy. CONCLUSIONS: Our findings indicated that XYT prevented pressure overload-induced HF through regulating the RIPK3/FUNDC1-mediated mitochondrial unfolded protein response and mitophagy. The information gained from this study provides a potential strategy for attenuating mitochondrial damage in the context of pressure overload-induced heart failure using XYT.

Study of radiomics based on dual-energy CT for nuclear grading and T-staging in renal clear cell carcinoma.

INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) is the most lethal subtype of renal cell carcinoma with a high invasive potential. Radiomics has attracted much attention in predicting the preoperative T-staging and nuclear grade of ccRCC. OBJECTIVE: The objective was to evaluate the efficacy of dual-energy computed tomography (DECT) radiomics in predicting ccRCC grade and T-stage while optimizing the models. METHODS: 200 ccRCC patients underwent preoperative DECT scanning and were randomized into training and validation cohorts. Radiomics models based on 70 KeV, 100 KeV, 150 KeV, iodine-based material decomposition images (IMDI), virtual noncontrasted images (VNC), mixed energy images (MEI) and MEI + IMDI were established for grading and T-staging. Receiver operating characteristic analysis and decision curve analysis (DCA) were performed. The area under the curve (AUC) values were compared using Delong test. RESULTS: For grading, the AUC values of these models ranged from 0.64 to 0.97 during training and from 0.54 to 0.72 during validation. In the validation cohort, the performance of MEI + IMDI model was optimal, with an AUC of 0.72, sensitivity of 0.71, and specificity of 0.70. The AUC value for the 70 KeV model was higher than those for the 100 KeV, 150 KeV, and MEI models. For T-staging, these models achieved AUC values of 0.83 to 1.00 in training and 0.59 to 0.82 in validation. The validation cohort demonstrated AUCs of 0.82 and 0.70, sensitivities of 0.71 and 0.71, and specificities of 0.80 and 0.60 for the MEI + IMDI and IMDI models, respectively. In terms of grading and T-staging, the MEI + IMDI model had the highest AUC in validation, with IMDI coming in second. There were statistically significant differences between the MEI + IMDI model and the 70 KeV, 100 KeV, 150 KeV, MEI, and VNC models in terms of grading (P < .05) and staging (P ≤ .001). DCA showed that both MEI + IDMI and IDMI models outperformed other models in predicting grade and stage of ccRCC. CONCLUSIONS: DECT radiomics models were helpful in grading and T-staging of ccRCC. The combined model of MEI + IMDI achieved favorable results.

The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma.

OBJECTIVE: We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC). METHODS: A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed. RESULTS: The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency. CONCLUSION: DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.

DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction.

The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-drug associations (MDAs) before drug administration. However, conventional biological experiments to predict MDAs are plagued by drawbacks such as time-consuming, high costs, and potential risks. On the contrary, computational approaches can speed up the screening of MDAs at a low cost. Most computational models usually use a drug similarity matrix as the initial feature representation of drugs and stack the graph neural network layers to extract the features of network nodes. However, different calculation methods result in distinct similarity matrices, and message passing in graph neural networks (GNNs) induces phenomena of over-smoothing and over-squashing, thereby impacting the performance of the model. To address these issues, we proposed a novel graph representation learning model, dual-modal graph learning for microbe-drug association prediction (DMGL-MDA). It comprises a dual-modal embedding module, a bipartite graph network embedding module, and a predictor module. To assess the performance of DMGL-MDA, we compared it against state-of-the-art methods using two benchmark datasets. Through cross-validation, we illustrated the superiority of DMGL-MDA. Furthermore, we conducted ablation experiments and case studies to validate the effective performance of the model.

Test of the ability of HPV L1 conserved sequence polypeptide to antisera to degrade HPV6 infection / 中国免疫学杂志

Objective:To determine whether human papillomavirus(HPV L1)C-terminal conserved sequence antibodies with cross-reactive major capsid proteins of different types of HPV L1 have the ability to degrade HPV6 infection.Methods:Condyloma specimens were collected,HPV6 infection cases were identified from the collected samples,and virus was extracted.Polypeptide anti-sera were diluted in different proportions,and then co-cultured and neutralized with the resulting virus,then removed to contact mono-layer-cultured human immortalized keratinocytes and tested by HPV6 disease using PCR.Content of HPV6 DNA in human immortalized keratinocytes was exposed,and the presence of HPV6 L1 protein in this cells was tested by ELISA.Results:Human immortalized ke-ratinocytes infected with HPV6 virus neutralization at different dilution concentrations,the PCR products of their DNA extracts were electrophoresis and showed positive bands of HPV6 specificity zone at 280 bp of the gel,and the intensity of positive bands gradually decreased with increasing antiserum concentration.Protein extracted from human immortalized keratinocytes exposed to anti-serum neutralizing virus was tested by ELISA,and the amount of HPV L1 protein showed the same gradient trend as the above PCR test results,and the difference were statistically significant.Conclusion:It is preliminarily proved that HPV6 L1 conserved sequence polypeptide antisera can partially degrade the infection ability of the virus,and it has the value of studying more HPV neutralization types.

Radiomic features of the hippocampal based on magnetic resonance imaging in the menopausal mouse model linked to neuronal damage and cognitive deficits.

Estrogen deficiency in the early postmenopausal phase is associated with an increased long-term risk of cognitive decline or dementia. Non-invasive characterization of the pathological features of the pathological hallmarks in the brain associated with postmenopausal women (PMW) could enhance patient management and the development of therapeutic strategies. Radiomics is a means to quantify the radiographic phenotype of a diseased tissue via the high-throughput extraction and mining of quantitative features from images acquired from modalities such as CT and magnetic resonance imaging (MRI). This study set out to explore the correlation between radiomics features based on MRI and pathological features of the hippocampus and cognitive function in the PMW mouse model. Ovariectomized (OVX) mice were used as PWM models. MRI scans were performed two months after surgery. The brain's hippocampal region was manually annotated, and the radiomic features were extracted with PyRadiomics. Chemiluminescence was used to evaluate the peripheral blood estrogen level of mice, and the Morris water maze test was used to evaluate the cognitive ability of mice. Nissl staining and immunofluorescence were used to quantify neuronal damage and COX1 expression in brain sections of mice. The OVX mice exhibited marked cognitive decline, brain neuronal damage, and increased expression of mitochondrial complex IV subunit COX1, which are pathological phenomena commonly observed in the brains of AD patients, and these phenotypes were significantly correlated with radiomics features (p < 0.05, |r|>0.5), including Original_firstorder_Interquartile Range, Original_glcm_Difference Average, Original_glcm_Difference Average and Wavelet-LHH_glszm_Small Area Emphasis. Meanwhile, the above radiomics features were significantly different between the sham-operated and OVX groups (p < 0.01) and were associated with decreased serum estrogen levels (p < 0.05, |r|>0.5). This initial study indicates that the above radiomics features may have a role in the assessment of the pathology of brain damage caused by estrogen deficiency using routinely acquired structural MR images.

ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection.

It is a critical step in lead optimization to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. Classical single-task learning (STL) has effectively predicted individual ADMET endpoints with abundant labels. Conversely, multi-task learning (MTL) can predict multiple ADMET endpoints with fewer labels, but ensuring task synergy and highlighting key molecular substructures remain challenges. To tackle these issues, this work elaborates a multi-task graph learning framework for predicting multiple ADMET properties of drug-like small molecules (MTGL-ADMET) by holding a new paradigm of MTL, "one primary, multiple auxiliaries." It first adeptly combines status theory with maximum flow for auxiliary task selection. The subsequent phase introduces a primary-task-centric MTL model with integrated modules. MTGL-ADMET not only outstrips existing STL and MTL methods but also offers a transparent lens into crucial molecular substructures. It is anticipated that this work can promote lead compound finding and optimization in drug discovery.

The value of a dual-energy CT Iodine map radiomics model for the prediction of collagen fiber content in the ccRCC tumor microenvironment.

BACKGROUND AND PURPOSE: Renal cell carcinoma (RCC) is a heterogeneous group of cancers. The collagen fiber content in the tumor microenvironment of renal cancer has an important role in tumor progression and prognosis. A radiomics model was developed from dual-energy CT iodine maps to assess collagen fiber content in the tumor microenvironment of ccRCC. METHODS: A total of 87 patients with ccRCC admitted to our hospital were included in this retrospective study. Among them, 59 cases contained large amounts of collagen fibers and 28 cases contained a small amount of collagen fibers. We established a radiomics model using preoperative dual-energy CT scan Iodine map (IV) imaging to distinguish patients with multiple collagen fibers from those with few collagen fibers in the tumor microenvironment of ccRCC. We extracted features from dual-energy CT Iodine map images to evaluate the effects of six classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT). The effects of the models built based on the dynamic and venous phases are also compared. Model performance was evaluated using quintuple cross-validation and area under the receiver operating characteristic curve (AUC). In addition, a clinical model was developed to assess the clinical factors affecting collagen fiber content. RESULTS: Compared to KNN, SVM, and LR classifiers, RF, DT, and XGBoost classifiers trained with higher AUC values, with training sets of 0.997, 1.0, and 1.0, respectively. In the validation set, the highest AUC was found in the SVM classifier with a size of 0.722. In the comparative test of the active and intravenous phase models, the SVM classifier had the best effect with its validation set AUC of 0.698 and 0.741. In addition, there was a statistically significant effect of patient age and maximum tumor diameter on the collagen fiber content in the tumor microenvironment of kidney cancer. CONCLUSION: Radionics features based on preoperative dual-energy CT IV can be used to predict the amount of collagen fibers in the tumor microenvironment of renal cancer. This study better informs clinical prognosis and patient management. Iodograms may add additional value to dual-energy CTs.

CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning.

MOTIVATION: Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as the optimization of lead compounds. Considering wet-lab experiment is time-consuming, laborious, and expensive, in silico prediction of metabolic stability is an alternative choice. However, few computational methods have been developed to address this task. In addition, it remains a significant challenge to explain key functional groups determining metabolic stability. RESULTS: To address these issues, we develop a novel cross-modality graph contrastive learning model named CMMS-GCL for predicting the metabolic stability of drug candidates. In our framework, we design deep learning methods to extract features for molecules from two modality data, i.e. SMILES sequence and molecule graph. In particular, for the sequence data, we design a multihead attention BiGRU-based encoder to preserve the context of symbols to learn sequence representations of molecules. For the graph data, we propose a graph contrastive learning-based encoder to learn structure representations by effectively capturing the consistencies between local and global structures. We further exploit fully connected neural networks to combine the sequence and structure representations for model training. Extensive experimental results on two datasets demonstrate that our CMMS-GCL consistently outperforms seven state-of-the-art methods. Furthermore, a collection of case studies on sequence data and statistical analyses of the graph structure module strengthens the validation of the interpretability of crucial functional groups recognized by CMMS-GCL. Overall, CMMS-GCL can serve as an effective and interpretable tool for predicting metabolic stability, identifying critical functional groups, and thus facilitating the drug discovery process and lead compound optimization. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are freely available at https://github.com/dubingxue/CMMS-GCL.

A facile alternative strategy of upcycling mixed plastic waste into vitrimers.

Chemical depolymerization has been identified as a promising approach towards recycling of plastic waste. However, complete depolymerization may be energy intensive with complications in purification. In this work, we have demonstrated upcycling of mixed plastic waste comprising a mixture of polyester, polyamide, and polyurethane through a reprocessable vitrimer of the depolymerized oligomers. Using poly(ethylene terephthalate) (PET) as a model polymer, we first demonstrated partial controlled depolymerization, using glycerol as a cleaving agent, to obtain branched PET oligomers. Recovered PET (RPET) oligomer was then used as a feedstock to produce a crosslinked yet reprocessable vitrimer (vRPET) despite having a wide molecular weight distribution using a solventless melt processing approach. Crosslinking and dynamic interactions were observed through rheology and dynamic mechanical analysis (DMA). Tensile mechanical studies showed no noticeable decrease in mechanical strength over multiple repeated melt processing cycles. Consequently, we have clearly demonstrated the applicability of the above method to upcycle mixed plastic wastes into vitrimers and reprocessable composites. This work also afforded insights into a potentially viable alternative route for utilization of depolymerized plastic/mixed plastic waste into crosslinked vitrimer resins manifesting excellent mechanical strength, while remaining reprocessable/ recyclable for cyclical lifetime use.

GPR35-mediated kynurenic acid sensing contributes to maintenance of gut microbiota homeostasis in ulcerative colitis.

Ulcerative colitis (UC) is a recurrent inflammatory disease related to gut microbiota disorder. Metabolites and their sensors play an important role in the communication between gut microbes and their host. Our previous study revealed that G protein-coupled receptor 35 (GPR35) is a key guardian of kynurenic acid (KA) and a core element of the defense responses against gut damage. However, the mechanism remains unknown. In this study, a DSS-induced rat colitis model was established and 16S rRNA sequencing was applied to explore the influence of GPR35-mediated KA sensing on gut microbiota homeostasis. Our results demonstrated that GPR35-mediated KA sensing is a necessary component in maintaining gut barrier integrity against DSS-induced damage. Furthermore, we provide compelling evidence suggesting that GPR35-mediated KA sensing plays a crucial role in maintaining gut microbiota homeostasis, which contributes to alleviation of DSS-induced colitis. In addition, five classes (Actinobacteria, Beta-/Gamma-proteobacteria, Erysipelotrichi, and Coriobacteriia) and six genera (Corynebacterium, Allobaculum, Parabacteroides, Sutterella, Shigella, and Xenorhabdus) were identified as the marked bacterial taxa that characterized the progression and outcome of colitis and are regulated by GPR35-mediated KA sensing. Our findings highlight that GPR35-mediated KA sensing is an essential defense mechanism against disorder of gut microbiota in UC. The results provide insights into the key role of specific metabolites and their monitor in maintaining gut homeostasis.

Association between Peripartum Mean Arterial Pressure and Postpartum Readmission for Preeclampsia with Severe Features.

OBJECTIVE: This study aimed to evaluate the relationship between peripartum mean arterial pressure (MAP) and postpartum readmission for preeclampsia with severe features. STUDY DESIGN: This is a retrospective case-control study comparing adult parturients readmitted for preeclampsia with severe features to matched nonreadmitted controls. Our primary objective was to evaluate the association between MAP at three time points during the index hospitalization (admission, 24-hour postpartum, and discharge) and readmission risk. We also evaluated readmission risk by age, race, body mass index, and comorbidities. Our secondary aim was to establish MAP thresholds to identify the population at highest risk of readmission. Multivariate logistic regression and chi-squared tests were used to determine the adjusted odds of readmission based on MAP. Receiver operating characteristic analyses were performed to evaluate risk of readmission relative to MAP; optimal MAP thresholds were established to identify those at highest risk of readmission. Pairwise comparisons were made between subgroups after stratifying for history of hypertension, with a focus on readmitted patients with new-onset postpartum preeclampsia. RESULTS: A total of 348 subjects met inclusion criteria, including 174 controls and 174 cases. We found that elevated MAP at both admission (adjusted odds ratio [OR]: 1.37 per 10 mm Hg, p < 0.0001) and 24-hour postpartum (adjusted OR: 1.61 per 10 mm Hg, p = 0.0018) were associated with increased risk of readmission. African American race and hypertensive disorder of pregnancy were independently associated with increased risk of readmission. Subjects with MAP > 99.5 mm Hg at admission or >91.5 mm Hg at 24-hour postpartum had a risk of at least 46% of requiring postpartum readmission for preeclampsia with severe features. CONCLUSION: Admission and 24-hour postpartum MAP correlate with risk of postpartum readmission for preeclampsia with severe features. Evaluating MAP at these time points may be useful for identifying women at higher risk for postpartum readmission. These women may otherwise be missed based on standard clinical approaches and may benefit from heightened surveillance. KEY POINTS: · Existing literature focuses on management of antenatal hypertensive disorders of pregnancy.. · Elevated peripartum MAP is associated with increased odds of readmission for preeclampsia.. · Peripartum MAP may predict readmission risk for de novo postpartum preeclampsia..

Revision of the subgenus Stegana (Steganina) from China, with assessment of species delimitation using DNA barcodes (Diptera, Drosophilidae).

A total of 58 (eight known and 50 new) species of the subgenus Stegana (Steganina) from China were surveyed and (re)described: S. (S.) bacilla Chen & Aotsuka, 2004, S. (S.) belokobylskiji Sidorenko, 1997, S. (S.) hirticeps Wang, Gao, & Chen, 2013, S. (S.) izu Sidorenko, 1997, S. (S.) kanmiyai Okada & Sidorenko, 1992, S. (S.) masanoritodai Okada & Sidorenko, 1992, S. (S.) maymyo Sidorenko, 1997, stat. rev., S. (S.) nigripes Zhang & Chen, 2015, S. (S.) alafoliacea Zhang & Chen, sp. nov., S. (S.) baoxing Li & Chen, sp. nov., S. (S.) bibarbata Li & Chen, sp. nov., S. (S.) bimai Cui & Chen, sp. nov., S. (S.) cinereipecta Zhang & Chen, sp. nov., S. (S.) cardua Cui & Chen, sp. nov., S. (S.) cordhirsuta Wang & Chen, sp. nov., S. (S.) cornuta Li & Chen, sp. nov., S. (S.) cucullata Li & Chen, sp. nov., S. (S.) cultella Cui & Chen, sp. nov., S. (S.) curvitabulata Cui & Chen, sp. nov., S. (S.) daiya Cui & Chen, sp. nov., S. (S.) dendrophila Zhang & Chen, sp. nov., S. (S.) flabella Li & Chen, sp. nov., S. (S.) flavipes Li & Chen, sp. nov., S. (S.) formosa Zhang & Chen, sp. nov., S. (S.) fusca Li & Chen, sp. nov., S. (S.) fuscipes Li & Chen, sp. nov., S. (S.) glaucopalpula Cui & Chen, sp. nov., S. (S.) haba Zhang & Chen, sp. nov., S. (S.) hirticlavata Cui & Chen, sp. nov., S. (S.) iaspidea Zhang & Chen, sp. nov., S. (S.) idiasta Cui & Chen, sp. nov., S. (S.) kanda Cui & Chen, sp. nov., S. (S.) labao Li & Chen, sp. nov., S. (S.) lancang Li & Chen, sp. nov., S. (S.) latifoliacea Wang & Chen, sp. nov., S. (S.) liusanjieae Li & Chen, sp. nov., S. (S.) magniflava Cui & Chen, sp. nov., S. (S.) mailangang Li & Chen, sp. nov., S. (S.) marenubila Cui & Chen, sp. nov., S. (S.) menghai Zhang & Chen, sp. nov., S. (S.) menglian Li & Chen, sp. nov., S. (S.) minutiflava Li & Chen, sp. nov., S. (S.) multiprocera Li & Chen, sp. nov., S. (S.) nayun Li & Chen, sp. nov., S. (S.) nigridentata Wang & Chen, sp. nov., S. (S.) nigripalpula Cui & Chen, sp. nov., S. (S.) otphylla Cui & Chen, sp. nov., S. (S.) radiciflava Zhang & Chen, sp. nov., S. (S.) rava Cui & Chen, sp. nov., S. (S.) sciophila Li & Chen, sp. nov., S. (S.) septencolorata Li & Chen, sp. nov., S. (S.) serrata Zhang & Chen, sp. nov., S. (S.) silvestrella Zhang & Chen, sp. nov., S. (S.) simola Cui & Chen, sp. nov., S. (S.) yani Li & Chen, sp. nov., S. (S.) yixiang Zhang & Chen, sp. nov., S. (S.) zaduo Cui & Chen, sp. nov., and S. (S.) zhuoma Cui & Chen, sp. nov. We also provided a complete list of Chinese Steganina species together with their geographical distributions. In addition, the majority of currently available DNA barcode (partial sequence of the mitochondrial cytochrome c oxidase subunit I (COI) gene) sequences of this subgenus (435 sequences of 102 spp.) were employed in a molecular analysis for species delimitation. Taken together, morphology- and molecular-based species delimitation results reached a consensus for an overwhelming majority of these Steganina species (98 of 102 spp.).