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ObjectivePre-eclampsia (PE) is one of the malignant metabolic diseases that complicate pregnancy. Gut dysbiosis has been identified for causing metabolic diseases, but the role of gut microbiome in the pathogenesis of PE remains unknown.DesignWe performed a case–control study to compare the faecal microbiome of PE and normotensive pregnant women by 16S ribosomal RNA (rRNA) sequencing. To address the causative relationship between gut dysbiosis and PE, we used faecal microbiota transplantation (FMT) in an antibiotic-treated mouse model. Finally, we determined the microbiome translocation and immune responses in human and mouse placental samples by 16S rRNA sequencing, quantitative PCR and in situ hybridisation.ResultsPatients with PE showed reduced bacterial diversity with obvious dysbiosis. Opportunistic pathogens, particularly Fusobacterium and Veillonella, were enriched, whereas beneficial bacteria, including Faecalibacterium and Akkermansia, were markedly depleted in the PE group. The abundances of these discriminative bacteria were correlated with blood pressure (BP), proteinuria, aminotransferase and creatinine levels. On successful colonisation, the gut microbiome from patients with PE triggered a dramatic, increased pregestational BP of recipient mice, which further increased after gestation. In addition, the PE-transplanted group showed increased proteinuria, embryonic resorption and lower fetal and placental weights. Their T regulatory/helper-17 balance in the small intestine and spleen was disturbed with more severe intestinal leakage. In the placenta of both patients with PE and PE-FMT mice, the total bacteria, Fusobacterium, and inflammatory cytokine levels were significantly increased.ConclusionsThis study suggests that the gut microbiome of patients with PE is dysbiotic and contributes to disease pathogenesis.

Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression 1 – 3 . Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis 4 , colorectal cancer prescreening 5 and therapeutic choices in melanoma 6 . Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic 7 and cardiovascular diseases 8 . To understand the generalizability of microbiota-based diagnostic models of metabolic disease, we characterized the gut microbiota of 7,009 individuals from 14 districts within 1 province in China. Among phenotypes, host location showed the strongest associations with microbiota variations. Microbiota-based metabolic disease models developed in one location failed when used elsewhere, suggesting that such models cannot be extrapolated. Interpolated models performed much better, especially in diseases with obvious microbiota-related characteristics. Interpolation efficiency decreased as geographic scale increased, indicating a need to build localized baseline and disease models to predict metabolic risks. The definition of a 'healthy' microbiome is impacted by geographic regional variations.

Chronic kidney disease (CKD) patients have an increased risk of cardiovascular diseases (CVDs). The present study aimed to investigate the gut microbiota and blood trimethylamine-N-oxide concentration (TMAO) in Chinese CKD patients and explore the underlying explanations through the animal experiment. The median plasma TMAO level was 30.33 μmol/L in the CKD patients, which was significantly higher than the 2.08 μmol/L concentration measured in the healthy controls. Next-generation sequence revealed obvious dysbiosis of the gut microbiome in CKD patients, with reduced bacterial diversity and biased community constitutions. CKD patients had higher percentages of opportunistic pathogens from gamma-Proteobacteria and reduced percentages of beneficial microbes, such as Roseburia , Coprococcus , and Ruminococcaceae. The PICRUSt analysis demonstrated that eight genes involved in choline, betaine, L-carnitine and trimethylamine (TMA) metabolism were changed in the CKD patients. Moreover, we transferred faecal samples from CKD patients and healthy controls into antibiotic-treated C57BL/6 mice and found that the mice that received gut microbes from the CKD patients had significantly higher plasma TMAO levels and different composition of gut microbiota than did the comparative mouse group. Our present study demonstrated that CKD patients had increased plasma TMAO levels due to contributions from both impaired renal functions and dysbiosis of the gut microbiota.

Background The metabolic syndrome (MetS) epidemic is associated with economic development, lifestyle transition and dysbiosis of gut microbiota, but these associations are rarely studied at the population scale. Here, we utilised the Guangdong Gut Microbiome Project (GGMP), the largest Eastern population-based gut microbiome dataset covering individuals with different economic statuses, to investigate the relationships between the gut microbiome and host physiology, diet, geography, physical activity and socioeconomic status. Results At the population level, 529 OTUs were significantly associated with MetS. OTUs from Proteobacteria and Firmicutes (other than Ruminococcaceae) were mainly positively associated with MetS, whereas those from Bacteroidetes and Ruminococcaceae were negatively associated with MetS. Two hundred fourteen OTUs were significantly associated with host economic status (140 positive and 74 negative associations), and 157 of these OTUs were also MetS associated. A microbial MetS index was formulated to represent the overall gut dysbiosis of MetS. The values of this index were significantly higher in MetS subjects regardless of their economic status or geographical location. The index values did not increase with increasing personal economic status, although the prevalence of MetS was significantly higher in people of higher economic status. With increased economic status, the study population tended to consume more fruits and vegetables and fewer grains, whereas meat consumption was unchanged. Sedentary time was significantly and positively associated with higher economic status. The MetS index showed an additive effect with sedentary lifestyle, as the prevalence of MetS in individuals with high MetS index values and unhealthy lifestyles was significantly higher than that in the rest of the population. Conclusions The gut microbiome is associated with MetS and economic status. A prolonged sedentary lifestyle, rather than Westernised dietary patterns, was the most notable lifestyle change in our Eastern population along with economic development. Moreover, gut dysbiosis and a Western lifestyle had an additive effect on increasing MetS prevalence.

In the version of this article originally published, in the sentence “Applying the same approach to obesity (Fig. 2b), MetS (Fig. 2c) and fatty liver (Fig. 2d) yielded similar results,” two figure panels were cited incorrectly. The data for obesity are in Fig. 2c, and the data for MetS are in Fig. 2b. The sentence has been updated with the correct citations in the print, PDF and HTML versions of the article.

Xanthine oxidase (XO) is a key enzyme in the generation and development of hyperuricemia. Thiazolidine-2-thione, a typical heterocyclic compound, have been widely used in the field of drug synthesis. In this study, a series of novel thiazolidine-2-thione derivatives were synthesized as XO inhibitors, and the XO inhibitory potencies of obtained compounds were evaluated by in vitro enzyme catalysis. The result shown that compound 6k behaved the strongest XO inhibitory activity with an IC 50 value of 3.56 μmol/L, which was approximately 2.5-fold more potent than allopurinol. The structure-activity relationship revealed that the phenyl-sulfonamide group was indispensable for thiazolidine-2-thione derivatives to produce XO inhibitory activity. The enzyme inhibition kinetics analyses confirmed that compound 6k exerted a mixed-type XO inhibition. Additionally, the molecular docking results suggested that the 4-fluorophenyl-sulfonyl moiety could interact with Gly260 and Ile264 in the innermost part of the active pocket through 2 hydrogen bonds, while the thiazolidinethione moiety could form two hydrogen bonds with Glu263 and Ser347 in hydrophobic pockets. In summary, the results described above suggested that compound 6k could be a valuable lead compound for the treatment of hyperuricemia as a novel XO inhibitor.

Accurate estimation of plantation aboveground biomass (AGB) is critical for quantifying carbon cycles and informing sustainable forest resource management, but enhancing estimation accuracy remains a key challenge. Although tree height and stand age are recognized as critical predictors for enhancing AGB models in addition to spectral vegetation indices, their individual and combined contributions in regional plantation forests remain insufficiently quantified, especially concerning the potential for leveraging the distinct characteristics of fast-growing plantations to facilitate AGB estimation. This study developed multi-source remote sensing-based Eucalyptus AGB estimation models for Nanning, Guangxi, integrating stand age and tree height to assess their impacts. Stand age was mapped from Landsat time-series imagery, and tree height was derived from UAV-LiDAR data. Plot-level reference AGB was obtained using fused UAV and terrestrial LiDAR point clouds. A random forest model, incorporating these variables with Sentinel-2 spectral information and topography, then achieved regional AGB estimation. The findings demonstrate that (1) tree height serves as the most influential predictor for AGB estimation at the regional scale, yielding a robust model performance (R2 = 0.84). (2) Tree height captures the majority of the explanatory power associated with stand age. Once tree height was included as a predictor, the subsequent addition of stand age offered no significant improvement in model accuracy (R2 = 0.85). (3) Given the challenges in obtaining precise tree height data and the robust correlation between stand age and tree height in fast-growing plantations, the integration of stand age substantially improved the accuracy of AGB estimations (from the spectral model of R2 = 0.54 to R2 = 0.74), with performance approaching that of tree height-based models (ΔR2 = 0.10). Consequently, in fast-growing plantations, which are often characterized by high stand homogeneity, a hybrid model incorporating stand age can offer a reliable and cost-effective solution for AGB estimation.

Satellite remote sensing data is essential for large-scale, timely, and repeatable monitoring of forest species diversity. While various methods have been applied to satellite-based diversity estimation at regional scales, selecting suitable sensor and monitoring period remains challenging, especially in tropical forests. This study aims to identify the optimal time window, spatial resolution, and metrics for species diversity estimation in the Jianfengling tropical forest in southern China. We constructed stepwise linear regression models for estimating Richness, Simpson, and Shannon-Wiener indices using in-situ species diversity and heterogeneity metrics of spectra and structure. For analyzing phenology influence, we utilized six Sentinel-2 images acquired bimonthly from January to November. For evaluating scale dependency, we resampled the GF2 image to five spatial resolutions ranging from 0.8 to 10 m. The results indicated that the suitable phenological periods for species diversity estimation were at the beginning and end of the growing season, especially September performing the best for all diversity indices. Among four types of heterogeneity metrics, spectral information consistently explained most variance in species diversity indices across all periods. The optimal spatial resolution for estimating Richness and Shannon-Wiener index was 4–5 m, which corresponded to the average tree crown size. The texture features made a significant contribution compared to other metrics. Our study highlights that species diversity monitoring is highly dependent on the spatiotemporal scales of remote sensing data. It may offer practical guidance for selecting appropriate data and methods for species diversity monitoring in tropical forests.

Millet bran, rich in bioactive phenolic compounds, holds potential for both nutritional and therapeutic applications. In this study, bound phenolic compounds were isolated from millet bran, yielding a potent fraction named BPS‐2. UPLC‐MS/MS detected 16 major phenolic compounds in BPS‐2. In vitro assays revealed that BPS‐2 exerted a significant anti‐inflammatory activity in lipopolysaccharide (LPS)‐induced RAW 264.7 macrophage, as manifested by reduced production of inflammatory mediators (IL‐1β, IL‐6, and TNF‐α) and downregulation of the expression levels of the pro‐inflammatory enzymes Cyclooxygenase‐2 (COX‐2) and nitric oxide synthase (iNOS). Network pharmacological analysis identified the suppression of the TLR4/NF‐κB pathway as the primary mechanism mediating the anti‐inflammatory activity of BPS‐2, which was validated using the LPS‐induced RAW 264.7 macrophage model and liver injury mice model. Western blot analysis revealed that BPS‐2 significantly decreased the phosphorylation of IκBα and p65 to regulate the TLR4/NF‐κB signaling pathway, thereby exerting anti‐inflammatory activity. Molecular docking studies revealed strong interactions between the active compounds of BPS‐2 and TLR4 through key amino acid residues, including Pro116, Thr114, and Arg105. These results underscore the potential application of millet bran bound phenolic compounds as naturally occurring anti‐inflammatory substances. BPS‐2 alleviates liver injury mice and RAW264.7 cell inflammation via TLR4/NF‐κB pathway.

Tree trunk volume is a key parameter in forest inventory. Traditional forest surveys typically rely on sample trees and trunk volume equations to estimate tree trunk volume; however, the collection of sample trees is destructive, and trunk volume equations often involve considerable estimation errors. As an emerging technology, terrestrial laser scanning (TLS) has been regarded as an efficient and high-precision alternative for tree trunk volume estimation. Nevertheless, the accuracy of TLS in tree-level trunk volume estimation still lacks systematic evaluation. To this end, this study used TLS to scan disc samples cut from standard trees, and evaluated the reliability of TLS-based tree trunk volume estimation by comparing point cloud-derived disc volumes with those obtained using the water displacement method. Utilizing the Leica RTC360 scanner, 123 disc samples from four tree species ( and ) were collected. A novel bottom surface filling algorithm based on point cloud projection was developed to mitigate data loss at disc bases, followed by Poisson surface reconstruction and trunk volume calculation via the Divergence Theorem. The results demonstrated high accuracy (R² = 0.940, CCC = 0.9745, rRMSE = 14.92%), with a slight underestimation bias (-5.31 cm³). Species-specific analyses indicated significant differences in estimation accuracy (Kruskal-Wallis, H = 21.1606, p = 0.0001), with exhibiting the highest accuracy (rRMSE = 4.37%) due to its smooth bark and uniform wood structure, while showed the largest errors (rRMSE = 7.10%) attributed to its rough, blocky bark. Bark characteristics and wood structure were identified as key factors influencing TLS accuracy. The analysis revealed that smoother scanned surfaces-comprising both bark surfaces and cross-sections-resulted in higher estimation accuracy. These surface characteristics are closely linked to species-specific external texture and internal wood structure. This study elucidates the influence mechanisms of species-specific physical characteristics on the accuracy of TLS-based trunk volume estimation and proposes targeted strategies for optimizing scanning parameters and point cloud processing. The study provides a robust theoretical and technical foundation for high-precision, non-destructive tree trunk volume measurement in forestry applications.