Full-scale polymer relaxation induced by single-chain confinement enhances mechanical stability of nanocomposites.

Polymer nanocomposites with tuning functions are exciting candidates for various applications, and most current research has focused on static mechanical reinforcement. Actually, under service conditions of complex dynamic interference, stable dynamic mechanical properties with high energy dissipation become more critical. However, nanocomposites often exhibit a trade-off between static and dynamic mechanics, because of their contradictory underlying physics between chain crosslinking and chain relaxation. Here, we report a general strategy for constructing ultra-stable dynamic mechanical complex fluid nanocomposites with high energy dissipation by infusing complex fluids into the nanoconfined space. The key is to tailor full-scale polymer dynamics across an exceptionally broad timescale by single-chain confinement. These materials exhibit stable storage modulus (100 ~ 102 MPa) with high energy dissipation (loss factor > 0.4) over a broad frequency range (10-1 ~ 107 Hz)/temperature range (-35 ~ 85°C). In the loss factor > 0.4 region, their dynamic mechanical stability (rate of modulus change versus temperature (k)) is 10 times higher than that of conventional polymer nanocomposites.

Large-Area Layered Membranes with Precisely Controlled Nano-Confined Channels.

Two-dimensional (2D) nanosheets-based membranes, which have controlled 2D nano-confined channels, are highly desirable for molecular/ionic sieving and confined reactions. However, it is still difficult to develop an efficient method to prepare large-area membranes with high stability, high orientation, and accurately adjustable interlayer spacing. Here, we present a strategy to produce metal ion cross-linked membranes with precisely controlled 2D nano-confined channels and high stability in different solutions using superspreading shear-flow-induced assembly strategy. For example, membranes based on graphene oxide (GO) exhibit interlayer spacing ranging from 8.0 ± 0.1 Å to 10.3 ± 0.2 Å, with a precision of down to 1 Å. At the same time, the value of the orientation order parameter (f) of GO membranes is up to 0.95 and GO membranes exhibit superb stability in different solutions. The strategy we present, which can be generalized to the preparation of 2D nano-confined channels based on a variety of 2D materials, will expand the application scope and provide better performances of membranes.

Recent Progress in Polyion Complex Nanoparticles with Enhanced Stability for Drug Delivery.

Polyion complex (PIC) nanoparticles, including PIC micelles and PICsomes, are typically composed of poly(ethylene glycol) block copolymers coupled with oppositely charged polyelectrolytes or therapeutic agents via electrostatic interaction. Due to a simple and rapid preparation process with high drug-loading efficiency, PIC nanoparticles are beneficial to maintaining the chemical integrity and high biological activity of the loaded drugs. However, the stability of PIC nanoparticles can be disrupted in high-ionic-strength solutions because electrostatic interaction is the DRIVING force; these disruptions can thus impair drug delivery. Herein, we summarize the advances in the use of PIC nanoparticles for delivery of charged drugs, focusing on the different chemical and physical strategies employed to enhance their stability, including enhancing the charge density, crosslinking, increasing hydrophobic interactions, forming hydrogen bonds, and the development of PIC-based gels. In particular, we describe the use of PIC nanoparticles to load peptide antibiotics targeting antibiotic-resistant and biofilm-related diseases and the use of nanoparticles that load chemotherapeutics and gaseous donors for cancer treatment. Furthermore, the application of PIC nanoparticles as magnetic resonance imaging contrast agents is summarized for the first time. Therefore, this review is of great significance for advances in the use of polymeric nanoparticles for functional drug delivery.

Development and applications of lipid hydrophilic headgroups for nucleic acid therapy.

Nucleic acid therapy is currently the most promising method for treating tumors and genetic diseases and for preventing infectious diseases. However, the biggest obstacle to this therapy is delivery of the nucleic acids to the target site, which requires overcoming problems such as capture by the immune system, the need to penetrate biofilms, and degradation of nucleic acid performance. Designing suitable delivery vectors is key to solving these problems. Lipids-which consist of a hydrophilic headgroup, a linker, and a hydrophobic tail-are crucial components for the construction of vectors. The headgroup is particularly important because it affects the drug encapsulation rate, the vector cytotoxicity, and the transfection efficiency. Herein, we focus on various headgroup structures (tertiary amines, quaternary ammonium salts, peptides, piperazines, dendrimers, and several others), and we summarize and classify important lipid-based carriers that have been developed in recent years. We also discuss applications of cationic lipids with various headgroups for delivery of nucleic acid drugs, and we analyze how headgroup structure affects transport efficiency and carrier toxicity. Finally, we briefly describe the challenges of developing novel lipid carriers, as well as their prospects.

Removal and assessment of cadmium contamination based on the toxic responds of a soil ciliate Colpoda sp.

Bioremediation of cadmium (Cd) pollution, a recognized low-carbon green environmental protection technology, is significantly enhanced by the discovery of Cd-tolerant microorganisms and their underlying tolerance mechanisms. This study presents Colpoda sp., a soil ciliate with widespread distribution, as a novel bioindicator and bioremediator for Cd contamination. With a 24 h-LC50 of 5.39 mg l-1 and an IC50 of 24.85 µg l-1 in Cd-contaminated water, Colpoda sp. achieves a maximum bioaccumulation factor (BAF) of 3.58 and a Cd removal rate of 32.98 ± 0.74 % within 96 h. The toxic responses of Colpoda sp. to Cd stress were assessed through cytological observation with transmission electron microscopy (TEM), oxidative stress kinase activity, and analysis of Cd-metallothionein (Cd-MTs) and the cd-mt gene via qRT-PCR. The integrated biomarker response index version 2 (IBRv2) and structural equation models (SEM) were utilized to analyze key factors and mechanisms, revealing that the up-regulation of Cd-MTs and cd-mt expression, rather than the oxidative stress system, is the primary determinant of Cd accumulation and tolerance in Colpoda sp. The ciliate's ability to maintain growth under 24.85 µg l-1 Cd stress and its capacity to absorb and accumulate Cd particles from water into cells are pivotal for bioremediation. A new mathematical formula and regression equations based on Colpoda sp.'s response parameters have been established to evaluate environmental Cd removal levels and design remediation schemes for contaminated sites. These findings provide a novel bioremediation and monitoring pathway for Cd remobilization and accumulation in soil and water, potentially revolutionizing the governance of Cd pollution.

Responses of colonization and development of periphytic biofilms to three typical tire wear particles with or without incubation-aging in migrating aqueous phases.

Understanding the behavior of tire wear particles (TWPs) and their impact on aquatic environments after aging is essential. This study explored the characteristics of TWPs generated using different methods (rolling friction, sliding friction, and cryogenic milling) and their transformation after exposure to environmental conditions mimicking runoff and sewage, focusing on their effects on river water and periphytic biofilms. Laboratory experiments indicate that at low exposure levels (0.1 mg/L), TWPs promoted biofilm growth, likely due to zinc release acting as a nutrient and the aggregation of particles serving as biofilm scaffolds. However, at higher concentrations (100 mg/L), TWPs inhibited biofilm development. This inhibition is linked to toxic byproducts like N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone and environmentally persistent free radicals, which reduce biofilm biomass, alter algal diversity, and decrease the production of essential biofilm components such as proteins and polysaccharides, consistent with the inhibitory behavior of TWPs on bis-(3'-5')-cyclic diguanosine monophosphate and quorum sensing signals, including acyl-homoserine lactone and autoinducer-2. Aging processes, particularly after simulated sewage treatment, further affect ecological impacts of TWPs, reducing the benefits observed at low concentrations and intensifying the negative effects at high concentrations. Contribution of here lies in systematically revealing the impact of TWPs on the development of aquatic biofilms, emphasizing the logical relationship between their aging characteristics, environmental behavior, and ecological risks. It assesses not only the release effects of typical additives and conventional size effects but also highlights the emerging photochemical toxicity (persistent free radicals), thus providing valuable insights into the aquatic ecological risk assessment of TWPs.

Metformin-induced changes of the gut microbiota in patients with type 2 diabetes mellitus: results from a prospective cohort study.

BACKGROUND: The influence of the microbiota on hypoglycemic agents is becoming more apparent. The effects of metformin, a primary anti-diabetes drug, on gut microbiota are still not fully understood. RESEARCH DESIGN AND METHODS: This prospective cohort study aims to investigate the longitudinal effects of metformin on the gut microbiota of 25 treatment-naïve diabetes patients, each receiving a daily dose of 1500 mg. Microbiota compositions were analyzed at baseline, and at 1, 3, and 6 months of medication using 16S rRNA gene sequencing. RESULTS: Prior to the 3-month period of metformin treatment, significant improvements were noted in body mass index (BMI) and glycemic-related parameters, such as fasting blood glucose (FPG) and hemoglobin A1c (HbA1c), alongside homeostasis model assessment indices of insulin resistance (HOMA-IR). At the 3-month mark of medication, a significant reduction in the α-diversity of the gut microbiota was noted, while ß-diversity exhibited no marked variances throughout the treatment duration. The Firmicutes to Bacteroidetes ratio. markedly decreased. Metformin treatment consistently increased Escherichia-Shigella and decreased Romboutsia, while Pseudomonas decreased at 3 months. Fuzzy c-means clustering identified three longitudinal trajectory clusters for microbial fluctuations: (i) genera temporarily changing, (ii) genera continuing to decrease (Bacteroides), and (iii) genera continuing to increase(Lachnospiraceae ND3007 group, [Eubacterium] xylanophilum group, Romboutsia, Faecalibacterium and Ruminococcaceae UCG-014). The correlation matrix revealed associations between specific fecal taxa and metformin-related clinical parameters HbA1c, FPG, Uric Acid (UA), high-density lipoproteincholesterol (HDL-C), alanine aminotransferase (ALT), hypersensitive C-reactive protein (hs-CRP), triglyceride (TG) (P < 0.05). Metacyc database showed that metformin significantly altered 17 functional pathways. Amino acid metabolism pathways such as isoleucine biosynthesis predominated in the post-treatment group. CONCLUSIONS: Metformin's role in glucose metabolism regulation may primarily involve specific alterations in certain gut microbial species rather than an overall increase in microbial species diversity. This may suggest gut microbiota targets in future studies on metabolic abnormalities caused by metformin.

Morphological variation in Schizothorax oconnori, Schizothorax waltoni (Teleostei: Cyprinidae: Schizothoracinae), and their natural hybrids from the middle Yarlung Zangbo River, Tibet.

The morphological variation in Schizothorax oconnori, Schizothorax waltoni, and their natural hybrids was examined using conventional and image-based analysis approaches. In total, 38 specimens of S. oconnori, 35 of S. waltoni, and 37 natural hybrids were collected from the Shigatse to the Lhasa section of the Yarlung Zangbo River during June and July 2021. A total of 21 morphometric, 4 meristic, and 27 truss variables were employed for the classification of S. oconnori, S. waltoni, and natural hybrids. Principal component analysis (PCA) and factor analysis (FA), as well as discriminant function analysis (DFA) and cluster analysis (CA), were conducted to identify differences based on traditional and truss measurements. Four principal components explained 75.92% of the variation among the morphometric characters, while five principal components accounted for 79.69% of the variation among the truss distances. FA results showed that factor 1 was associated with head shape, and factor 2 was associated with fins based on morphometric characters. Among the truss characters, factor 1 was related to head shape, and factor 2 was related to chest shape. In DFA, morphometric measurements achieved higher accuracy (100%) compared to truss distances (94.55%). The head morphology of hybrids exhibited intermediate traits between S. oconnori and S. waltoni. Both morphometry-based and truss-based clustering indicated that the morphology of natural hybrids leaned toward S. oconnori. In conclusion, the combination of morphometric and truss analysis is beneficial for classifying S. oconnori, S. waltoni, and their natural hybrids. The presence of natural hybrids could be considered an evolutionary response to the differentiation of nutritional and spatial niches in the middle Yarlung Zangbo River.

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMRI-based MDD models suffer from limitations, including neglecting dynamic network traits, lacking interpretability, and struggling with small datasets. We present DSFGNN, a novel graph neural network framework addressing these issues for improved MDD diagnosis. DSFGNN employs a graph isomorphism encoder to model static and dynamic brain networks, achieving effective fusion of temporal and spatial information through a spatiotemporal attention mechanism, thereby enhancing interpretability. Furthermore, we incorporate a causal disentangling module and orthogonal regularization module to augment the model's expressiveness. We evaluate DSFGNN on the Rest-meta-MDD dataset, yielding superior results compared to the best baseline. Besides, extensive ablation studies and interpretability analysis confirm DSFGNN's effectiveness and potential for biomarker discovery.

PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning.

Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA-disease interactions. Although there have been computational methods proposed for the detection of piRNA-disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA-disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA-disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA-disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN.

Open access EEG dataset of repeated measurements from a single subject for microstate analysis.

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.

Long-read sequencing unveils novel somatic variants and methylation patterns in the genetic information system of early lung cancer.

Lung cancer poses a global health challenge, necessitating advanced diagnostics for improved outcomes. Intensive efforts are ongoing to pinpoint early detection biomarkers, such as genomic variations and DNA methylation, to elevate diagnostic precision. We conducted long-read sequencing on cancerous and adjacent non-cancerous tissues from a patient with lung adenocarcinoma. We identified somatic structural variations (SVs) specific to lung cancer by integrating data from various SV calling methods and differentially methylated regions (DMRs) that were distinct between these two tissue samples, revealing a unique methylation pattern associated with lung cancer. This study discovered over 40,000 somatic SVs and over 180,000 DMRs linked to lung cancer. We identified approximately 700 genes of significant relevance through comprehensive analysis, including genes intricately associated with many lung cancers, such as NOTCH1, SMOC2, CSMD2, and others. Furthermore, we observed that somatic SVs and DMRs were substantially enriched in several pathways, such as axon guidance signaling pathways, which suggests a comprehensive multi-omics impact on lung cancer progression across various biological investigation levels. These datasets can potentially serve as biomarkers for early lung cancer detection and may hold significant value in clinical diagnosis and treatment applications.

Evaluation and analysis of surgical treatment for single-level or multi-level lumbar degenerative disease based on radiography.

Background: Radiography has a low level of radiation exposure while providing valuable information. Due to its cost effectiveness and widespread availability, the preoperative radiographic imaging examination is a valuable approach for assessing patients with spinal disease. This study aimed to examine the influence of preoperative X-ray evaluation on the surgical treatment of patients with single- or multi-level lumbar degenerative disease (LDD). Methods: A retrospective cohort analysis was conducted of 172 patients diagnosed with LDD who underwent transforaminal lumbar interbody fusion (TLIF) or posterior lumbar interbody fusion (PLIF) surgery between December 2021 and February 2023 at the Shanghai Changzheng Hospital. Various parameters were measured on preoperative radiographs, including the iliac crest height, median iliac angle (MIA), lumbar lordosis (LL), intervertebral facet joint degeneration, lumbosacral angle (LSA), intervertebral foramen height (IFH), and surgical segment. The surgical treatment was evaluated based on the operative time, intraoperative blood loss, and postoperative complications. A correlation analysis and independent sample t-tests were used to assess the relationship between preoperative radiographic variables and surgical treatments. Further, a multivariate linear regression analysis was employed to identify the risk factors affecting the clinical outcomes. Results: The correlation analysis and t-test results showed that the MIA, height of the iliac crest, intervertebral facet joint degeneration, and surgical segment were significantly correlated with the surgical treatments (P<0.05). Specifically, the height of the iliac crest, intervertebral facet joint degeneration, and surgical segment were positively correlated with the surgical treatments. Conversely, the MIA was negatively correlated with the surgical treatments. However, no significant differences were observed between the IFH, LSA, and LL in relation to posterior lumbar surgery (P>0.05). The multiple linear regression analysis showed that the height of the iliac crest, MIA, intervertebral facet joint degeneration, and surgical segment were independent factors affecting the surgical treatments of patients with single- or multi-level LDD. These findings highlight the importance of considering these factors when planning and performing lumbar surgery. Conclusions: The measurements taken from radiographs, including the height of the iliac crest, MIA, intervertebral facet joint degeneration, and surgical segment, demonstrate potential influences on the treatment of single- and multi-level lumbar spine surgery. These variables can be captured in plain film imaging and can provide valuable insights into the surgical procedure and offer guidance for the operation. By analyzing these radiographic measurements, surgeons can gain a better understanding of a patient's condition and tailor the surgical approach accordingly, thus optimizing the outcomes of the surgery.

KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery.

The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug-target interactions (DTIs) and drug-drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32$\%$ in AUROC and 3.56$\%$ in AUPR for DTIs and 6.56$\%$ in AUROC and 8.17$\%$ in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein-protein interactions prediction, ablation studies and case studies further validate its effectiveness.

Implementation of Underwater Electric Field Communication Based on Direct Sequence Spread Spectrum (DSSS) and Binary Phase Shift Keying (BPSK) Modulation.

Stable communication technologies in complex waters are a prerequisite for underwater operations. Underwater acoustic communication is susceptible to multipath interference, while underwater optical communication is susceptible to environmental impact. The underwater electric field communication established based on the weak electric fish perception mechanism is not susceptible to environmental interference, and the communication is stable. It is a new type of underwater communication technology. To address issues like short communication distances and high bit error rates in existing underwater electric field communication systems, this study focuses on underwater electric field communication systems based on direct sequence spread spectrum (DSSS) and binary phase shift keying (BPSK) modulation techniques. To verify the feasibility of the established spread spectrum electric field communication system, static communication experiments were carried out in a swimming pool using the DSSS-based system. The experimental results show that in fresh water with a conductivity of 739 µS/cm, the system can achieve underwater current electric field communication within a 11.2 m range with 10-6 bit errors. This paper validates the feasibility of DSSS BPSK in short-range underwater communication, and compact communication devices are expected to be deployed on underwater robots for underwater operations.

Acupressure for depression: A systematic review and meta-analysis.

Depression is a common mental disorder caused by the interaction of social, psychological, and biological factors. Treatments include psychotherapy, pharmacotherapy, and other therapies, but they have limitations. Particularly, the COVID-19 pandemic may have a negative impact on depressed people. Thus, developing more potential treatments for depression has currently been an urgent challenge. A growing number of studies have found that acupressure is effective in relieving the symptoms of depression. Thus, this study aimed to evaluate the efficacy and safety of acupressure in people with depression. English (PubMed, CENTRAL, EMBASE, APA PsycINFO, and CINAHL) and Chinese databases (CBM, CNKI, Wanfang, and VIP), ClinicalTrials.gov and Chinese Clinical Trial Registry were searched for randomized controlled trials (RCTs) on patients diagnosed with depression from study inception until March 31, 2023. Studies that compared acupressure with sham acupressure, conventional treatments (i.e., medication, usual care, etc.), and acupressure as an adjunct to conventional treatment for depression were included. The primary outcome was depression level measured using the Hamilton Depression Scale, Self-Rating Depression Scale, or Geriatric Depression Scale. A total of 19 RCTs involving 1686 participants were included. The pooled results showed that acupressure exhibited a significant beneficial effect on reducing the severity of depression compared with sham acupressure and served as an adjunct to conventional treatment, although the evidence level was moderate. Thus, acupressure may be a potential treatment for depression.

Enhancing the Conductivity of PEDOT:PSS Films by the Confinement of Ice Crystals.

Rapid developments in organic electronics demand highly conductive and freestanding (PEDOT:PSS) films. However, the synthesis of highly conductive PEDOT:PSS films requires toxic reagents, such as high-concentration acids and bases. Herein, an eco-friendly and cost-effective strategy is reported for improving the conductivity of PEDOT:PSS films through the confinement of ice crystals. The crystallization of water swelled by the film facilitated the phase separation of PEDOT and PSS, and the excess PSS in the skin layer is effectively removed. Moreover, under the confinement effect, the carrier mobility of the film is enhanced through the formation of a well-crystallized PEDOT molecular morphology. A detailed elucidation of aggregate structure evolution in PEDOT:PSS films during annealing, solvent post-treatment, and subsequent confined crystallization is presented herein. After multiple water crystallization cycles, the conductivity of the PEDOT:PSS film increased by over 85%, achieving a maximum of 2564 ± 142 S cm-1. Finally, compared to post-treatment with dimethyl sulfoxide (DMSO), the current strategy can improve the Seebeck coefficient by 5.6% and the power factor by 139%.

6,7-Dimethoxycoumarin, Gardenoside and Rhein combination improves non-alcoholic fatty liver disease in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: This study explores the potential therapeutic benefits of using a three-component DGR (composed of specific compounds) to target the NLRP3 inflammasome in the context of non-alcoholic fatty liver disease (NAFLD). AIM OF THE STUDY: To assess the impact of a three-component DGR on NAFLD, specifically examining its effects on liver lipid accumulation, inflammation, and the diversity of intestinal microbial communities. METHODS: NAFLD was induced in 8-week-old Sprague Dawley rats by feeding them a high-fat emulsion diet every morning for 8 consecutive weeks. Oral administration of DGR or its constituent equivalents in the afternoon. The pharmacological effects of DGR were evaluated using H&E, ORO and ELISA methods to determine the changes in serum and liver tissue indexes of rat-models. Immunohistochemical staining and Western blot were used to assess the interaction between DGR, NLRP3 and IL-1ß. RESULTS: The induction of NAFLD resulted in elevated hepatic triglycerides (TG), total cholesterol (TC), and free fatty acids (FFA). However, these alterations were ameliorated upon administration of DGR. It is noteworthy that DGR exhibited superior efficacy in comparison to its constituent compounds, manifesting augmented antioxidant activity, diminished hepatic damage, and the attenuation of pro-inflammatory factors. Both DGR and its individual monomeric constituents exhibited the capacity to attenuate the activation of the NLRP3 inflammasome in the liver, leading to an amelioration of the pathological characteristics associated with NAFLD. An analysis of the intestinal flora unveiled an elevated abundance of p_Firmicutes (1.1-fold), p_Cyanobacteria (5.76-fold), and p_Verrucomicrobia (5.2-fold), accompanied by a heightened p_Firmicutes to p_Bacteroidetes ratio (5.49-fold). CONCLUSIONS: In the non-alcoholic fatty liver disease (NAFLD) rat model, the concurrent administration of three-component DGR effectively regulated lipid deposition, suppressed liver inflammation, and restored balance in the intestinal flora, thereby improving NAFLD pathology. These findings propose a promising therapeutic strategy for NAFLD, centered on inhibiting the NLRP3 inflammasome through the use of the three-component DGR.

Ionic Liquids: An Ideal Solvent for Tuning the UCST Phase Behavior of Polymer Gels.

Liquid-liquid phase separation (LLPS) is a common stimulus-responsive phenomenon widely studied and applied in constructing intelligent systems such as microfluidic valves, smart windows, and biosensors. However, LLPS in an aqueous solution has limited applicability confined to a narrow temperature range within 0-100 °C. In addition, for easy exploitation of thermoresponsive behavior, phase separation must be stable and accurately predictable under varying conditions. This study proposes a gel system exhibiting UCST phase behavior using ionic liquids (ILs) and hydrophilic polymers, whose phase transition temperature can be linearly tuned within a wide range (from subzero to over 100 °C) by varying the mixing ratio of ILs in their blends. Similar to the mixing of ILs with structurally similar cations, mixing ILs containing different anions proved to be an effective ideal random mixing method based on experimental results and molecular dynamics simulations. This mixing mechanism of ILs accounts for the linear regulation of the UCST of the ionogels when the mixing ratio of ILs in their blends varies. Moreover, the unique feature of ILs was further demonstrated using other hydrophilic polymer networks and multiple combinations of ILs, suggesting the generality of this strategy for UCST regulation in the ionogels.