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ObjectiveTo characterise the oral microbiome, gut microbiome and serum lipid profiles in patients with active COVID-19 and recovered patients; evaluate the potential of the microbiome as a non-invasive biomarker for COVID-19; and explore correlations between the microbiome and lipid profile.DesignWe collected and sequenced 392 tongue-coating samples, 172 faecal samples and 155 serum samples from Central China and East China. We characterised microbiome and lipid molecules, constructed microbial classifiers in discovery cohort and verified their diagnostic potential in 74 confirmed patients (CPs) from East China and 37 suspected patients (SPs) with IgG positivity.ResultsOral and faecal microbial diversity was significantly decreased in CPs versus healthy controls (HCs). Compared with HCs, butyric acid-producing bacteria were decreased and lipopolysaccharide-producing bacteria were increased in CPs in oral cavity. The classifiers based on 8 optimal oral microbial markers (7 faecal microbial markers) achieved good diagnostic efficiency in different cohorts. Importantly, diagnostic efficacy reached 87.24% in the cross-regional cohort. Moreover, the classifiers successfully diagnosed SPs with IgG antibody positivity as CPs, and diagnostic efficacy reached 92.11% (98.01% of faecal microbiome). Compared with CPs, 47 lipid molecules, including sphingomyelin (SM)(d40:4), SM(d38:5) and monoglyceride(33:5), were depleted, and 122 lipid molecules, including phosphatidylcholine(36:4p), phosphatidylethanolamine (PE)(16:0p/20:5) and diglyceride(20:1/18:2), were enriched in confirmed patients recovery.ConclusionThis study is the first to characterise the oral microbiome in COVID-19, and oral microbiomes and lipid alterations in recovered patients, to explore their correlations and to report the successful establishment and validation of a diagnostic model for COVID-19.

Olfactomedin 4 (OLFM4) is an olfactomedin domain-containing glycoprotein. Multiple signaling pathways and factors, including NF-κB, Wnt, Notch, PU.1, retinoic acids, estrogen receptor, and miR-486, regulate its expression. OLFM4 interacts with several other proteins, such as gene associated with retinoic-interferon-induced mortality 19 (GRIM-19), cadherins, lectins, nucleotide oligomerization domain-1 (NOD1) and nucleotide oligomerization domain-2 (NOD2), and cathepsins C and D, known to regulate important cellular functions. Recent investigations using Olfm4 -deficient mouse models have provided important clues about its in vivo biological functions. Olfm4 inhibited Helicobacter pylori -induced NF-κB pathway activity and inflammation and facilitated H. pylori colonization in the mouse stomach. Olfm4 -deficient mice exhibited enhanced immunity against Escherichia coli and Staphylococcus aureus infection. Olfm4 deletion in a chronic granulomatous disease mouse model rescued them from S. aureus infection. Olfm4 deletion in mice treated with azoxymethane/dextran sodium sulfate led to robust intestinal inflammation and intestinal crypt hyperplasia. Olfm4 deletion in Apc Min/+ mice promoted intestinal polyp formation as well as adenocarcinoma development in the distal colon. Further, Olfm4 -deficient mice spontaneously developed prostatic epithelial lesions as they age. OLFM4 expression is correlated with cancer differentiation, stage, metastasis, and prognosis in a variety of cancers, suggesting its potential clinical value as an early-stage cancer marker or a therapeutic target. Collectively, these data suggest that OLFM4 plays important roles in innate immunity against bacterial infection, gastrointestinal inflammation, and cancer. In this review, we have summarized OLFM4’s initial characterization, expression, regulation, protein interactions, and biological functions.

Clonal haematopoiesis, which is highly prevalent in older individuals, arises from somatic mutations that endow a proliferative advantage to haematopoietic cells. Clonal haematopoiesis increases the risk of myocardial infarction and stroke independently of traditional risk factors 1 . Among the common genetic variants that give rise to clonal haematopoiesis, the JAK2 V617F ( JAK2 VF ) mutation, which increases JAK–STAT signalling, occurs at a younger age and imparts the strongest risk of premature coronary heart disease 1 , 2 . Here we show increased proliferation of macrophages and prominent formation of necrotic cores in atherosclerotic lesions in mice that express Jak2 VF selectively in macrophages, and in chimeric mice that model clonal haematopoiesis. Deletion of the essential inflammasome components caspase 1 and 11, or of the pyroptosis executioner gasdermin D, reversed these adverse changes. Jak2 VF lesions showed increased expression of AIM2, oxidative DNA damage and DNA replication stress, and Aim2 deficiency reduced atherosclerosis. Single-cell RNA sequencing analysis of Jak2 VF lesions revealed a landscape that was enriched for inflammatory myeloid cells, which were suppressed by deletion of Gsdmd . Inhibition of the inflammasome product interleukin-1β reduced macrophage proliferation and necrotic formation while increasing the thickness of fibrous caps, indicating that it stabilized plaques. Our findings suggest that increased proliferation and glycolytic metabolism in Jak2 VF macrophages lead to DNA replication stress and activation of the AIM2 inflammasome, thereby aggravating atherosclerosis. Precise application of therapies that target interleukin-1β or specific inflammasomes according to clonal haematopoiesis status could substantially reduce cardiovascular risk. Accelerated atherosclerosis in a mouse model of clonal haematopoiesis is prevented by genetic interruption of AIM2 inflammasome activation or by inhibition of interleukin-1β.

This study aims to identify depressive risks in elderly individuals with subjective cognitive decline (SCD) and develop a predictive model using machine learning algorithms to enable timely interventions.Data from the 2015 and 2018 waves of the China Health and Retirement Longitudinal Study (CHARLS) were used, including 1,921 elderly individuals. Depression was assessed with the CESD-10 scale. Three machine learning models—Gradient Boosting, Random Forest, and Boosted XGBoost—were used to predict depression risk over three years, incorporating 10 demographic, 5 health, 13 chronic disease, 3 lifestyle, and 2 physical function factors. Lasso feature selection identified 10 key variables for model training. Model performance was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, calibration, and decision curve analysis. Among all evaluated models, Boosted XGBoost demonstrated the highest predictive accuracy in the test set (AUC = 0.893), outperforming both Gradient Boosting (AUC = 0.887) and Random Forest (AUC = 0.861). However, Random Forest (RF) achieved superior sensitivity. Consequently, we performed feature importance analysis using both Boosted XGBoost and RF models. The results identified five significant predictors of depression in older adults with subjective cognitive decline (SCD): educational attainment, digestive health status, arthritis diagnosis, sleep duration, and residential location.The machine learning model developed in our study demonstrates strong predictive performance for depression risk among older adults with subjective cognitive decline (SCD), enabling early identification of high-risk individuals. These findings provide a scientific foundation for understanding depression progression mechanisms and developing personalized intervention strategies.

To study the effects of different oxygen functional groups on the quality of flotation clean low-rank coal, two kinds of collectors with different oxygen-containing functional groups, methyl laurate, and dodecanol, were selected and their flotation behaviors were investigated. The Bulianta coal was the typical sub-bituminous coal in China, and the coal molecular model of which was constructed based on proximate analysis, ultimate analysis, 13C-NMR, and XPS. The chemical structure model of the coal molecule was optimized, and the periodic boundary condition was added via the method of molecular dynamics methods. The different combined systems formed by collectors, water, and a model surface of Bulianta coal have been studied using molecular dynamics simulation. The simulation results of dodecanol and methyl laurate on the surface of Bulianta coal show that dodecanol molecules are not evenly adsorbed on the surface of coal, and have higher adsorption capacity near carboxyl and hydroxyl groups, but less adsorption capacity near carbonyl and ether bonds. Methyl laurate can completely cover the oxygen-containing functional groups on the coal surface. Compared with dodecanol, methyl laurate can effectively improve the hydrophobicity of the Bulianta coal surface, which is consistent with the results of the XPS test and the flotation test.

Elevated hematocrit is associated with cardiovascular risk; however, the causality and mechanisms are unclear. The JAK2V617F (Jak2VF) mutation increases cardiovascular risk in myeloproliferative disorders and in clonal hematopoiesis (CH). Jak2VF mice with elevated white blood cells, platelets and red blood cells (RBCs) display accelerated atherosclerosis and macrophage erythrophagocytosis. To investigate whether selective erythroid Jak2VF expression promotes atherosclerosis, we developed hyperlipidemic Erythropoietin Receptor Cre mice that express Jak2VF in the erythroid lineage (VFEpoR mice). VFEpoR mice without elevated blood cell counts showed increased atherosclerotic plaque necrosis, erythrophagocytosis and ferroptosis. Selective induction of erythrocytosis with low dose erythropoietin further exacerbated atherosclerosis with prominent ferroptosis, lipid peroxidation and endothelial damage. VFEpoR RBCs had reduced antioxidant defenses and increased lipid hydroperoxides. Phagocytosis of human or murine WT or JAK2VF RBCs by WT macrophages induced ferroptosis, which was prevented by the ferroptosis inhibitor Liproxstatin-1. Liproxstatin-1 reversed increased atherosclerosis, lipid peroxidation, ferroptosis and endothelial damage in VFEpoR mice and in Jak2VF chimeric mice simulating CH, but had no impact in controls. Erythroid lineage Jak2VF expression leads to qualitative and quantitative defects in RBCs that exacerbate atherosclerosis. Phagocytosis of RBCs by plaque macrophages promotes ferroptosis, suggesting a new therapeutic target to reduce RBC-mediated cardiovascular risk.

Fruit softening is a key component of the irreversible ripening program, contributing to the palatability necessary for frugivore-mediated seed dispersal. The underlying textural changes are complex and result from cell wall remodeling and changes in both cell adhesion and turgor. While a number of transcription factors (TFs) that regulate ripening have been identified, these affect most canonical ripening-related physiological processes. Here, we show that a tomato fruit ripening–specific LATERAL ORGAN BOUNDRIES (LOB) TF, SlLOB1, up-regulates a suite of cell wall–associated genes during late maturation and ripening of locule and pericarp tissues. SlLOB1 repression in transgenic fruit impedes softening, while overexpression throughout the plant under the direction of the 35s promoter confers precocious induction of cell wall gene expression and premature softening. Transcript and protein levels of the wall-loosening protein EXPANSIN1 (EXP1) are strongly suppressed in SlLOB1 RNA interference lines, while EXP1 is induced in SlLOB1-overexpressing transgenic leaves and fruit. In contrast to the role of ethylene and previously characterized ripening TFs, which are comprehensive facilitators of ripening phenomena including softening, SlLOB1 participates in a regulatory subcircuit predominant to cell wall dynamics and softening.

An acid modified approach to enhance the adsorption capacity of low-rank coal (lignite) is proposed to reduce the risk of heavy metal ions within the wastewater. Adsorption kinetics, adsorption thermodynamics, adsorption coefficient and density functional theory DFT calculations were studied in this paper, respectively. The results indicate that the adsorption capacity of lignite was enlarged after HNO3 modification, and pseudo-second order kinetics model and Langmuir isothermal adsorption model can be used to describe the adsorption process. The surface chemical properties of lignite play a dominant role rather than the specific surface area and total pore volume in the Pb(II) cation adsorption process, and it is suggested that the adsorption of Pb(II) cation by raw lignite (RL) and modified lignite (ML) is mainly completed by chemical adsorption. The Fourier transform infrared spectroscopy (FTIR) characterization showed that the surface oxygen functional groups of lignite increased after modification. The results of interaction energies between the model molecule and Pb(II) cation show that Pb(II) cation and -C-O-C are most easily combined, followed by -COOH, and -C = O is the weakest.

Gut microbiota make up the largest microecosystem in the human body and are closely related to chronic metabolic diseases. Herein, 520 fecal samples are collected from different regions of China, the gut microbiome in chronic kidney disease (CKD) is characterized, and CKD classifiers based on microbial markers are constructed. Compared with healthy controls (HC, n = 210), gut microbial diversity is significantly decreased in CKD (n = 110), and the microbial community is remarkably distinguished from HC. Genera Klebsiella and Enterobacteriaceae are enriched, while Blautia and Roseburia are reduced in CKD. Fifty predicted microbial functions including tryptophan and phenylalanine metabolisms increase, while 36 functions including arginine and proline metabolisms decrease in CKD. Notably, five optimal microbial markers are identified using the random forest model. The area under the curve (AUC) reaches 0.9887 in the discovery cohort and 0.9512 in the validation cohort (49 CKD vs 63 HC). Importantly, the AUC reaches 0.8986 in the extra diagnosis cohort from Hangzhou. Moreover, Thalassospira and Akkermansia are increased with CKD progression. Thirteen operational taxonomy units are correlated with six clinical indicators of CKD. In conclusion, this study comprehensively characterizes gut microbiome in non‐dialysis CKD and demonstrates the potential of microbial markers as non‐invasive diagnostic tools for CKD in different regions of China. Compared with healthy controls, gut microbial diversity in CKD is significantly reduced, Klebsiella and Akkermansia are significantly increased, Roseburia and Faecalibacterium are significantly reduced, and the predictive function of gut microbiota such as ascorbate metabolism and lipopolysaccharide biosynthesis is significantly enhanced. Akkermansia increases along with the progression of CKD, which is positively correlated with serum creatinine and blood urea nitrogen, and negatively correlated with estimated glomerular filtration rate, and could be used as a therapeutic target to improve the prognosis of CKD. Importantly, gut microbial markers have strong diagnostic potential for CKD and achieve cross‐regional validation, which can be used as a non‐invasive diagnostic tool for CKD.

We present a user-friendly and transferable genome-wide DNA G-quadruplex (G4) profiling method that identifies G4 structures from ordinary whole-genome resequencing data by seizing the slight fluctuation of sequencing quality. In the human genome, 736,689 G4 structures were identified, of which 45.9% of all predicted canonical G4-forming sequences were characterized. Over 89% of the detected canonical G4s were also identified by combining polymerase stop assays with next-generation sequencing. Testing using public datasets of 6 species demonstrated that the present method is widely applicable. The detection rates of predicted canonical quadruplexes ranged from 32% to 58%. Because single nucleotide variations (SNVs) influence the formation of G4 structures and have individual differences, the given method is available to identify and characterize G4s genome-wide for specific individuals. Current methods to identify G-quadruplex structures in DNA require specialized protocols and multiple rounds of sequencing. Here, the authors develop a method to detect G-quadruplex structures in DNA based on fluctuations in sequencing quality in a standard sequencing experiment.