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Flavonoids represent a class of natural plant secondary metabolites with multiple activities including antioxidant, antitumor, anti-inflammatory, and antimicrobial properties. However, due to their structural characteristics, they often exhibit low bioavailability in vivo. In this review, we focus on the in vivo study of flavonoids, particularly the effects of gut microbiome on flavonoids, including common modifications such as methylation, acetylation, and dehydroxylation, etc. These modifications aim to change the structural characteristics of the original substances to enhance absorption and bioavailability. In order to improve the bioavailability of flavonoids, we discuss two feasible methods, namely dosage form modification and chemical modification, and hope that these approaches will offer new insights into the application of flavonoids for human health. In this article, we also introduce the types, plant sources, and efficacy of flavonoids. In conclusion, this is a comprehensive review on how to improve the bioavailability of flavonoids.

Considering that intravascular reactive oxygen species (ROS) and inflammation are two characteristic features of the atherosclerotic microenvironment, developing an appropriate strategy to treat atherosclerosis by synergistically regulating ROS and inflammation has attracted widespread attention. Herein, a special molecule, zoledronic acid, containing imidazole and bisphosphonate groups, was selected for the first time to assist the assembly of cerium ions and produce functionalized ceria-zoledronic acid nanocomposites (CZ NCs). It not only serves as a new carrier for different kinds of drugs (e.g. probucol, PB) but also exerts an efficient multienzyme activity to achieve collaborative therapy. More importantly, platelet membrane-coated biomimetic nanoplatform (PCZ@PB NCs) specifically accumulate at inflammatory atherosclerotic lesions, synergistically regulate ROS levels and inflammation, and efficiently inhibit foam cell formation. This novel assembly method can also be applied in the treatment of many other diseases associated with oxidative stress and inflammation. Regulating reactive oxygen species and inflammation are important for treating atherosclerosis. Here, the authors create a cell membrane coated zoledronic acid ceria nanocomposite loaded with probucol for synergistic drug and scavenging nanozyme treatment of atherosclerosis demonstrating application in vivo.

Social groups in various social species are organized with hierarchical structures that shape group dynamics and the nature of within-group interactions. In-group social bonding, exemplified by grooming behaviors among animals and collective rituals and team-building activities in human societies, is recognized as a practical adaptive strategy to foster group harmony and stabilize hierarchical structures in both human and nonhuman animal groups. However, the neurocognitive mechanisms underlying the effects of social bonding on hierarchical groups remain largely unexplored. Here, we conducted simultaneous neural recordings on human participants engaged in-group communications within small hierarchical groups ( n = 528, organized into 176 three-person groups) to investigate how social bonding influenced hierarchical interactions and neural synchronizations. We differentiated interpersonal interactions between individuals of different (inter-status) or same (intra-status) social status and observed distinct effects of social bonding on inter-status and intra-status interactions. Specifically, social bonding selectively increased frequent and rapid information exchange and prefrontal neural synchronization for inter-status dyads but not intra-status dyads. Furthermore, social bonding facilitated unidirectional neural alignment from group leader to followers, enabling group leaders to predictively align their prefrontal activity with that of followers. These findings provide insights into how social bonding influences hierarchical dynamics and neural synchronization while highlighting the role of social status in shaping the strength and nature of social bonding experiences in human groups.

In vitro and some animal models have shown that quercetin, a polyphenol derived from plants, has a wide range of biological actions including anti-carcinogenic, anti-inflammatory and antiviral activities; as well as attenuating lipid peroxidation, platelet aggregation and capillary permeability. This review focuses on the physicochemical properties, dietary sources, absorption, bioavailability and metabolism of quercetin, especially main effects of quercetin on inflammation and immune function. According to the results obtained both in vitro and in vivo, good perspectives have been opened for quercetin. Nevertheless, further studies are needed to better characterize the mechanisms of action underlying the beneficial effects of quercetin on inflammation and immunity.

Background Handeliodendron Rehder and Eurycorymbus Hand.-Mazz. are the monotypic genera in the Sapindaceae family. The phylogenetic relationship of these endangered species Handeliodendron bodinieri (Lévl.) Rehd. and Eurycorymbus cavaleriei (Lévl.) Rehd. et Hand.-Mazz. with other members of Sapindaceae s.l. is not well resolved. A previous study concluded that the genus Aesculus might be paraphyletic because Handeliodendron was nested within it based on small DNA fragments. Thus, their chloroplast genomic information and comparative genomic analysis with other Sapindaceae species are necessary and crucial to understand the circumscription and plastome evolution of this family. Results The chloroplast genome sizes of Handeliodendron bodinieri and Eurycorymbus cavaleriei are 151,271 and 158,690 bp, respectively. Results showed that a total of 114 unique genes were annotated in H. bodinieri and E. cavaleriei , and the ycf1 gene contained abundant SSRs in both genomes. Comparative analysis revealed that gene content, PCGs, and total GC content were remarkably similar or identical within 13 genera from Sapindaceae, and the chloroplast genome size of four genera was generally smaller within the family, including Acer , Dipteronia , Aesculus , and Handeliodendron . IR boundaries of the H. bodinieri showed a significant contraction, whereas it presented a notable expansion in E. cavaleriei cp genome. Ycf1 , ndhC-trnV-UAC , and rpl32-trnL-UAG-ccsA were remarkably divergent regions in the Sapindaceae species. Analysis of selection pressure showed that there are a few positively selected genes. Phylogenetic analysis based on different datasets, including whole chloroplast genome sequences, coding sequences, large single-copy, small single-copy, and inverted repeat regions, consistently demonstrated that H. bodinieri was sister to the clade consisting of Aesculus chinensis and A. wangii and strongly support Eurycorymbus cavaleriei as sister to Dodonaea viscosa . Conclusion This study revealed that the cp genome size of the Hippocastanoideae was generally smaller compared to the other subfamilies within Sapindaceae, and three highly divergent regions could be used as the specific DNA barcodes within Sapindaceae. Phylogenetic results strongly support that the subdivision of four subfamilies within Sapindaceae, and Handeliodendron is not nested within the genus Aesculus .

Ovarian clear cell carcinoma (OCCC) is resistant to chemotherapy, with limited treatment options for advanced and recurrent disease. The prevalence of OCCC differs by region. Assessing the expression of programmed cell death ligand 1 (PD-L1), PD-1, and CD8 + T cell infiltration in OCCC is crucial, as their correlation with patient survival may provide valuable prognostic insights. We collected data from 36 samples from 18 OCCC patients, including 18 pairs of tumors and adjacent nonneoplastic samples. The optimized multiplex immunofluorescence technique was used to stain paraffin sections for immune factors related to the immune microenvironment of OCCC and clinical prognosis. The expression of PDL1 and PD1 in the tumor cells and tumor stromal cells was not significantly correlated with prognosis. Professional quantitative pathological analysis software was used to count the CD8 + cytotoxic T-cells in tumor regions and adjacent nonneoplastic regions in postoperative specimens. There were more CD8 + cytotoxic T-cells in the adjacent nonneoplastic areas than in the tumor tissue samples ( p  <  0.001) . Further analysis revealed that a difference in cell density between CD8 + non-tumor-infiltrating lymphocytes (NTILs) and CD8 + tumor-infiltrating lymphocytes (TILs) exceeding 70 cells/mm 2 was associated with poorer progression-free survival (PFS) ( p  =  0.042 ). In adjacent nonneoplastic regions, worse PFS was significantly observed in patients with high CD8 + T-cell expression in both total and stromal cells than those with low expression ( p  =  0.012 vs p  =  0.007 ). The presence of CD8 + T-cells had significant potential for predicting the prognosis of patients with OCCC, which lays a foundation for the development of biomarkers for immune checkpoint blockade treatment response in OCCC patients.

Background Polystachya Hook. is a large pantropical orchid genus (c. 240 species) distributed in Africa, southern Asia and the Americas, with the center of diversity in Africa. Previous studies on species of this genus have not obtained the complete chloroplast genomes, structures and variations. Additionally, the phylogenetic position of the genus in the Orchidaceae is still controversial and uncertain. Therefore, in this study, we sequenced the complete plastomes of six Kenya Polystachya species based on genome skimming, subjected them to comparative genomic analysis, and reconstructed the phylogenetic relationships with other Orchidaceae species. Results The results exhibited that the chloroplast genomes had a typical quadripartite structure with conserved genome arrangement and moderate divergence. The plastomes of the six Polystachya species ranged from 145,484 bp to 149,274 bp in length and had an almost similar GC content of 36.9–37.0%. Gene annotation revealed 106–109 single-copy genes. In addition, 19 genes are duplicated in the inverted regions, and 16 genes each possessd one or more introns. Although no large structural variations were observed among the Polystachya plastomes, about 1 kb inversion was found in Polystachya modesta and all 11 ndh genes in the Polystachya plastomes were lost or pseudogenized. Comparative analysis of the overall sequence identity among six complete chloroplast genomes confirmed that for both coding and non-coding regions in Polystachya , SC regions exhibit higher sequence variation than IRs. Furthermore, there were various amplifications in the IR regions among the six Polystachya species. Most of the protein-coding genes of these species had a high degree of codon preference. We screened out SSRs and found seven relatively highly variable loci. Moreover, 13 genes were discovered with significant positive selection. Phylogenetic analysis showed that the six Polystachya species formed a monophyletic clade and were more closely related to the tribe Vandeae. Phylogenetic relationships of the family Orchidaceae inferred from the 85 chloroplast genome sequences were generally consistent with previous studies and robust. Conclusions Our study is the initial report of the complete chloroplast genomes of the six Polystachya species, elucidates the structural characteristics of the chloroplast genome of Polystachya , and filters out highly variable sequences that can contribute to the development of DNA markers for use in the study of genetic variability and evolutionary studies in Polystachya . In addition, the phylogenetic results strongly support that the genus of Polystachya is a part of the tribe Vandeae.

While the catalytic role of ubiquitin-conjugating (UBE2/E2) enzymes within the ubiquitin-proteasome system is well established, their substrate-specifying functions in cancer, particularly at the tumor-immune interface, remain comparatively underexplored. In this review, we provide an integrative re-evaluation of the UBE2 family organized around three concepts that, we argue, distinguish recent UBE2 research from earlier descriptive accounts: (i) tumor-context-dependent functional duality, in which the same UBE2 enzyme exerts opposing effects across tissue types and substrate landscapes; (ii) convergence of mechanistically distinct UBE2 members on a small set of shared signaling and immunometabolic nodes; and (iii) the emerging role of UBE2 enzymes at the tumor-immune interface, directly linking UBE2 biology to contemporary immunotherapy paradigms. Building on this framework, we synthesize recent evidence showing how dysregulated UBE2 members shape major cancer hallmarks, including uncontrolled proliferation, oncogenic signaling, metabolic reprogramming, immune evasion, and therapeutic resistance, while emphasizing that most reported phenotypes are derived from a limited set of tumor lineages and should not be uncritically extrapolated to all malignancies. Across tumor settings, multiple UBE2 members converge on PI3K/AKT, MAPK/ERK, NF-κB, HIF-1α, antigen-presentation pathways, checkpoint-ligand regulation, and macrophage polarization. We further discuss the potential of UBE2-based signatures as candidate prognostic and immune-stratification biomarkers. While these associations are biologically compelling, most derive from retrospective cohorts and require prospective, multi-center validation before clinical consideration. We also evaluate current pharmacological strategies targeting UBE2-driven vulnerabilities, including small-molecule inhibitors, natural compounds, and combination approaches with chemotherapy and immune checkpoint blockade. Collectively, this synthesis positions UBE2 enzymes as context-resolved regulators of tumor progression and the immune microenvironment, with growing potential for biomarker-guided therapeutic exploitation in precision cancer immunopharmacology, subject to rigorous prospective validation.

Internet of Things (IoT) technology is now widely used in energy, healthcare, services, transportation, and other fields. With the increase in industrial equipment (e.g., smart mobile terminals, sensors, and other embedded devices) in the Internet of Things and the advent of Industry 4.0, there has been an explosion of data generated that is characterized by a high volume but small size. How to manage and protect sensitive private data in data sharing has become an urgent issue for enterprises. Traditional data sharing and storage relies on trusted third-party platforms or distributed cloud storage, but these approaches run the risk of single-node failure, and third parties and cloud storage providers can be vulnerable to attacks that can lead to data theft. To solve these problems, this paper proposes a Hyperledger Fabric blockchain-based secure data transfer scheme for enterprises in the Industrial Internet of Things (IIOT). We store raw data in the IIoT in the InterPlanetary File System (IPFS) network after encryption and store the Keyword-index table we designed in Hyperledger Fabric blockchain, and enterprises share the data by querying the Keyword-index table. We use Fabric’s channel mechanism combined with our designed Chaincode to achieve privacy protection and efficient data transmission while using the Elliptic Curve Digital Signature Algorithm (ECDSA) to ensure data integrity. Finally, we performed security analysis and experiments on the proposed scheme, and the results show that overall the data transfer performance in the IPFS network is generally better than the traditional network, In the case of transferring 5 MB file size data, the transmission speed and latency of IPFS are 19.23 mb/s and 0.26 s, respectively, and the IPFS network is almost 4 times faster than the TCP/IP network while taking only a quarter of the time, which is more advantageous when transferring small files, such as data in the IIOT. In addition, our scheme outperforms the blockchain systems mainly used today in terms of both throughput, latency, and system overhead. The average throughput of our solution can reach 110 tps (transactions are executed per second), and the minimum throughput in experimental tests can reach 101 tps.

Gingival inflammation grade serves as a well-established index in periodontitis. The aim of this study was to develop a deep learning network utilizing a novel feature extraction method for the automatic assessment of gingival inflammation. T-distributed Stochastic Neighbor Embedding (t-SNE) was utilized for dimensionality reduction. A convolutional neural network (CNN) model based on DenseNet was developed for the identification and evaluation of gingival inflammation. To enhance the performance of the deep learning (DL) model, a novel teeth removal algorithm was implemented. Additionally, a Grad-CAM +  + encoder was applied to generate heatmaps for computer visual attention analysis. The mean Intersection over Union (MIoU) for the identification of gingivitis was 0.727 ± 0.117. The accuracy rates for the five inflammatory degrees were 77.09%, 77.25%, 74.38%, 73.68% and 79.22%. The Area Under the Receiver Operating Characteristic (AUROC) values were 0.83, 0.80, 0.81, 0.81 and 0.84, respectively. The attention ratio towards gingival tissue increased from 37.73% to 62.20%, and within 8 mm of the gingival margin, it rose from 21.11% to 38.23%. On the gingiva, the overall attention ratio increased from 51.82% to 78.21%. The proposed DL model with novel feature extraction method provides high accuracy and sensitivity for identifying and grading gingival inflammation.