Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning.

Recent advancements in pre-trained language-image models have ushered in a new era of visual comprehension. Leveraging the power of these models, this paper tackles two issues within the realm of visual analytics: (1) the efficient exploration of large-scale image datasets and identification of data biases within them; (2) the evaluation of image captions and steering of their generation process. On the one hand, by visually examining the captions generated from language-image models for an image dataset, we gain deeper insights into the visual contents, unearthing data biases that may be entrenched within the dataset. On the other hand, by depicting the association between visual features and textual captions, we expose the weaknesses of pre-trained language-image models in their captioning capability and propose an interactive interface to steer caption generation. The two parts have been coalesced into a coordinated visual analytics system, fostering the mutual enrichment of visual and textual contents. We validate the effectiveness of the system with domain practitioners through concrete case studies with large-scale image datasets.

Current status and continuing medical education need for general practitioners in Tibet, China: a cross-sectional study.

BACKGROUND: The Tibetan area is one of China's minority regions with a shortage of general practice personnel, which requires further training and staffing. This research helps to understand the current condition and demand for general practitioner (GP) training in Tibetan areas and to provide a reference for promoting GP education and training. METHODS: We conducted a cross-sectional survey using stratified sampling targeting 854 GPs in seven cities within the Tibetan Autonomous Region, utilizing an online questionnaire. Achieving a high response rate of 95.1%, 812 GPs provided invaluable insights. Our meticulously developed self-designed questionnaire, available in both Chinese and Tibetan versions, aimed to capture a wide array of data encompassing basic demographics, clinical skills, and specific training needs of GPs in the Tibetan areas. Prior to deployment, the questionnaire underwent rigorous development and refinement processes, including expert consultation and pilot testing, to ensure its content validity and reliability. In our analysis, we employed descriptive statistics to present the characteristics and current training needs of GPs in the Tibetan areas. Additionally, chi-square tests were utilized to examine discrepancies in training needs across various demographic groups, such as age, job positions, and educational backgrounds of the participating GPs. RESULTS: The study was completed by 812 (812/854, 95.1%) GPs, of whom 62.4% (507/812) were female. The top three training needs were hypertension (81.4%, 661/812), pregnancy management (80.7%, 655/812), and treatment of related patient conditions and events (80.5%, 654/812). Further research shows that the training required by GPs of different ages in "puncturing, catheterization, and indwelling gastric tube use" (64.6% vs. 54.8%, p = 9.5 × 10- 6) varies statistically. GPs in various positions have different training needs in "community-based chronic disease prevention and management" (76.6% vs. 63.9%, p = 0.009). The training needs of GPs with different educational backgrounds in "debridement, suturing, and fracture fixation" (65.6% vs. 73.2%, p = 0.027) were also statistically significant. CONCLUSIONS: This study suggests the need for targeted continuing medical education activities and for updating training topics and content. Course developers must consider the needs of GPs, as well as the age, job positions, and educational backgrounds of GPs practicing in the Tibetan Plateau region. TRIAL REGISTRATION: Not applicable.

A Flexible, Adaptive, and Self-Powered Triboelectric Vibration Sensor with Conductive Sponge-Silicone for Machinery Condition Monitoring.

Vibration sensors for continuous and reliable condition monitoring of mechanical equipment, especially detection points of curved surfaces, remain a great challenge and are highly desired. Herein, a highly flexible and adaptive triboelectric vibration sensor for high-fidelity and continuous monitoring of mechanical vibration conditions is proposed. The sensor is entirely composed of flexible materials. It consists of a conductive sponge-silicone layer and a fluorinated ethylene propylene film. It can detect vibration acceleration of 5 to 50 m s-2 and vibration frequency of 10 to 100 Hz. It has strong robustness and stability, and the output performance barely changes after the durability test of 168 000 working cycles. Additionally, the flexible sensor can work even when the detection point of the mechanical equipment is curved, and the linear fit of the output voltage and acceleration is very close to that when the detection point is flat. Finally, it can be applied to monitoring the working condition of blower and vehicle engine, and can transmit vibration signal to mobile phone application through Wi-Fi module for real-time monitoring. The flexible triboelectric vibration sensor is expected to provide a practical paradigm for smart, green, and sustainable wireless sensor system in the era of Internet of Things.

Deficiency of purinergic P2X4 receptor alleviates experimental autoimmune hepatitis in mice.

Purinergic P2X4 receptor (P2X4R) has been shown to have immunomodulatory properties in infection, inflammation, and organ damage including liver regeneration and fibrosis. However, the mechanisms and pathophysiology associated with P2X4R during acute liver injury remain unknown. We used P2X4R-/- mice to explore the role of P2X4R in three different models of acute liver injury caused by concanavalin A (ConA), carbon tetrachloride, and acetaminophen. ConA treatment results in an increased expression of P2X4R in the liver of mice, which was positively correlated with higher levels of aspartate aminotransferase and alanine aminotransferase in the serum. However, P2X4R gene ablation significantly reduced the severity of acute hepatitis in mice caused by ConA, but not by carbon tetrachloride or acetaminophen. The protective benefits against immune-mediated acute hepatitis were achieved via modulating inflammation (Interleukin (IL)-1ß, IL-6, IL-17A, interferon-γ, tumor necrosis factor-α), oxidative stress (malondialdehyde, superoxide dismutase, glutathione peroxidase, and catalase), apoptosis markers (Bax, Bcl-2, and Caspase-3), autophagy biomarkers (LC3, Beclin-1, and p62), and nucleotide oligomerization domain-likereceptorprotein 3(NLRP3) inflammasome-activated pyroptosis markers (NLRP3, Gasdermin D, Caspase-1, ASC, IL-1ß). Additionally, administration of P2X4R antagonist (5-BDBD) or agonist (cytidine 5'-triphosphate) either improved or worsened ConA-induced autoimmune hepatitis, respectively. This study is the first to reveal that the absence of the P2X4 receptor may mitigate immune-mediated liver damage, potentially by restraining inflammation, oxidation, and programmed cell death mechanisms. And highlight P2X4 receptor is essential for ConA-induced acute hepatitis.

Visual Analytics for Machine Learning: A Data Perspective Survey.

The past decade has witnessed a plethora of works that leverage the power of visualization (VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML, keeps growing at a fast pace. To better organize the enormous works and shed light on the developing trend of VIS4ML, we provide a systematic review of these works through this survey. Since data quality greatly impacts the performance of ML models, our survey focuses specifically on summarizing VIS4ML works from the data perspective. First, we categorize the common data handled by ML models into five types, explain the unique features of each type, and highlight the corresponding ML models that are good at learning from them. Second, from the large number of VIS4ML works, we tease out six tasks that operate on these types of data (i.e., data-centric tasks) at different stages of the ML pipeline to understand, diagnose, and refine ML models. Lastly, by studying the distribution of 143 surveyed papers across the five data types, six data-centric tasks, and their intersections, we analyze the prospective research directions and envision future research trends.

NRF1 promotes primordial germ cell development, proliferation and survival.

Primordial germ cells (PGCs) are the germline precursors that give rise to oocytes and sperm, ensuring the continuation of life. While the PGC specification is extensively studied, it remains elusive how the PGC population is sustained and expanded after they migrate to embryonic gonads before birth. This study demonstrates that NRF1, a known regulator for mitochondrial metabolism, plays critical roles in post-migrating PGC development. We show that NRF1 protein level gradually increases in post-migrating PGCs during embryonic development. Conditional Nrf1 knockout from embryonic germ cells leads to impaired PGC proliferation and survival. In addition, NRF1 may also actively drive PGC derivation from pluripotent stem cells. Using whole genome transcriptome profiling and ChIP-seq analyses, we further reveal that NRF1 directly regulates key signalling molecules in PGC formation, transcription factors in proliferation and cell cycle and enzymes in mitochondrial metabolism. Overall, our findings highlight an essential requirement of NRF1 in regulating a broad transcriptional network to support post-migrating PGC development both in vitro and in vivo.

An In-Situ Visual Analytics Framework for Deep Neural Networks.

The past decade has witnessed the superior power of deep neural networks (DNNs) in applications across various domains. However, training a high-quality DNN remains a non-trivial task due to its massive number of parameters. Visualization has shown great potential in addressing this situation, as evidenced by numerous recent visualization works that aid in DNN training and interpretation. These works commonly employ a strategy of logging training-related data and conducting post-hoc analysis. Based on the results of offline analysis, the model can be further trained or fine-tuned. This strategy, however, does not cope with the increasing complexity of DNNs, because (1) the time-series data collected over the training are usually too large to be stored entirely; (2) the huge I/O overhead significantly impacts the training efficiency; (3) post-hoc analysis does not allow rapid human-interventions (e.g., stop training with improper hyper-parameter settings to save computational resources). To address these challenges, we propose an in-situ visualization and analysis framework for the training of DNNs. Specifically, we employ feature extraction algorithms to reduce the size of training-related data in-situ and use the reduced data for real-time visual analytics. The states of model training are disclosed to model designers in real-time, enabling human interventions on demand to steer the training. Through concrete case studies, we demonstrate how our in-situ framework helps deep learning experts optimize DNNs and improve their analysis efficiency.

Elamipretide(SS-31) Attenuates Idiopathic Pulmonary Fibrosis by Inhibiting the Nrf2-Dependent NLRP3 Inflammasome in Macrophages.

Idiopathic pulmonary fibrosis (IPF) is a progressive fatal lung disease with a limited therapeutic strategy. Mitochondrial oxidative stress in macrophages is directly linked to IPF. Elamipretide(SS-31) is a mitochondrion-targeted peptide that has been shown to be safe and beneficial for multiple diseases. However, whether SS-31 alleviates IPF is unclear. In the present study, we used a bleomycin (BLM)-induced mouse model followed by SS-31 injection every other day to investigate its role in IPF and explore the possible mechanism. Our results showed that SS-31 treatment significantly suppressed BLM-induced pulmonary fibrosis and inflammation, with improved histological change, and decreased extracellular matrix deposition and inflammatory cytokines release. Impressively, the expression percentage of IL-1ß and IL-18 was downregulated to lower than half with SS-31 treatment. Mechanistically, SS-31 inhibited IL-33- or lipopolysaccharide(LPS)/IL-4-induced production of IL-1ß and IL-18 in macrophages by suppressing NOD-like receptor thermal protein domain associated protein 3(NLRP3) inflammasome activation. Nuclear factor erythroid 2-related factor 2(Nrf2) was dramatically upregulated along with improved mitochondrial function after SS-31 treatment in activated macrophages and BLM-induced mice. Conversely, there was no significant change after SS-31 treatment in Nrf2-/- mice and macrophages. These findings indicated that SS-31 protected against pulmonary fibrosis and inflammation by inhibiting the Nrf2-mediated NLRP3 inflammasome in macrophages. Our data provide initial evidence for the therapeutic efficacy of SS-31 in IPF.

Coaxial Flexible Fiber-Shaped Triboelectric Nanogenerator Assisted by Deep Learning for Self-Powered Vibration Monitoring.

Self-powered vibration sensor is highly desired for distributed and continuous monitoring requirements of Industry 4.0. Herein, a flexible fiber-shaped triboelectric nanogenerator (F-TENG) with a coaxial core-shell structure is proposed for the vibration monitoring. The F-TENG exhibits higher adaptability to the complex surfaces, which has an outstanding application prospect due to vital compensation for the existing rigid sensors. Initially, the contact characteristics between the dielectric layers, that related to the perceiving performance of the TENG, are theoretically analyzed. Such a TENG with 1D structure endows high sensitivity, allowing for accurately responding to a wide range of vibration frequencies (0.1 to 100 Hz). Even applying to the real diesel engine, the error in detecting the vibration frequencies is only 0.32% compared with the commercial vibration sensor, highlighting its potential in practical application. Further, assisted by deep learning, the recognition accuracy in monitoring nine operating conditions of the system achieves 97.87%. Overall, the newly designed F-TENG with the merits of high-adaptability, cost-efficiency, and self-powered, has offered a promising solution to fulfill an extensive range of vibration sensing applications in the future.

Glutamate-pantothenate pathway promotes antibiotic resistance of Edwardsiella tarda.

Although cellular metabolic states have been shown to modulate bacterial susceptibility to antibiotics, the interaction between glutamate (Glu) and chloramphenicol (CAP) resistance remains unclear because of the specificity of antibiotics and bacteria. We found that the level of Glu was upregulated in the CAP-resistant strain of Edwardsiella tarda according to a comparative metabolomics approach based on LC-MS/MS. Furthermore, we verified that exogenous metabolites related to Glu, the tricarboxylic acid (TCA) cycle, and glutathione (GSH) metabolism could promote CAP resistance in survival assays. If GSH metabolism or the TCA cycle is inhibited by L-buthionine sulfoximine or propanedioic acid, the promotion of CAP resistance by Glu in the corresponding pathway disappears. According to metabolomic analysis, exogenous Glu could change pantothenate metabolism, affecting GSH biosynthesis and the TCA cycle. These results showed that the glutamate-pantothenate pathway could promote CAP resistance by being involved in the synthesis of GSH, entering the TCA cycle by direct deamination, or indirectly affecting the metabolism of the two pathways by pantothenate. These results extend our knowledge of the effect of Glu on antibiotic resistance and suggest that the potential effect, which may aggravate antibiotic resistance, should be considered before Glu and GSH administration in the clinic.

Naringenin confers protection against experimental autoimmune encephalomyelitis through modulating the gut-brain axis: A multiomics analysis.

Multiple sclerosis (MS) is a disease of the central nervous system that involves the immune system attacking the protective covering of nerve fibers. This disease can be influenced by both environmental and genetic factors. Evidence has highlighted the critical role of the intestinal microbiota in MS and its animal model, experimental autoimmune encephalomyelitis (EAE). The composition of gut microflora is mainly determined by dietary components, which, in turn, modulate host homeostasis. A diet rich in naringenin at 0.5% can effectively mitigate the severity of EAE in mice. However, there is little direct data on the impact of naringenin at optimal doses on EAE development, as well as its intestinal microbiota and metabolites. Our study revealed that 2.0% naringenin resulted in the lowest clinical score and pathological changes in EAE mice, and altered the gene expression profiles associated with inflammation and immunity in spinal cord tissue. We then used untargeted metabolomics and 16S rRNA gene sequences to identify metabolites and intestinal microbiota, respectively. Naringenin supplementation enriched gut microbiota in EAE mice, including increasing the abundance of Paraprevotellaceae and Comamonadaceae, while decreasing the abundance of Deltaproteobacteria, RF39, and Desulfovibrionaceae. Furthermore, the changes in gut microbiota affected the production of metabolites in the feces and brain, suggesting a role in regulating the gut-brain axis. Finally, we conducted a fecal transplantation experiment to validate that gut microbiota partly mediates the effect of naringenin on EAE alleviation. In conclusion, naringenin has potential immunomodulatory effects that are influenced to some extent by the gut microbiome.

Role of Tim-3 in COVID-19: a potential biomarker and therapeutic target.

T cell immunoglobulin and mucin domain containing protein 3 (Tim-3), an immune checkpoint, is important for maintaining immune tolerance. There is increasing evidence that Tim-3 is aberrantly expressed in patients with COVID-19, indicating that it may play an important role in COVID-19. In this review, we discuss the altered expression and potential role of Tim-3 in COVID-19. The expression of Tim-3 and its soluble form (sTim-3) has been found to be upregulated in COVID-19 patients. The levels of Tim-3 on T cells and circulating sTim-3 have been shown to be associated with the severity of COVID-19, suggesting that this protein could be a potential biomarker of COVID-19. Moreover, this review also highlights the potential of Tim-3 as a therapeutic target of COVID-19.

Green tea EGCG inhibits naïve CD4+ T cell division and progression in mice: An integration of network pharmacology, molecular docking and experimental validation.

Dietary green tea epigallocatechin-3-gallate (EGCG) could attenuate experimental autoimmune encephalomyelitis via the modification of the balance of CD4+ T helper (Th) cells. Moreover, EGCG administration in vitro has a direct impact on the regulatory cytokines and differentiation of CD4+ T cells. Here, we aim to determine whether EGCG directly affects the cell division and progression in naive CD4+ T cells. We first investigate the effect of EGCG on naïve CD4+ T cell division and progression in vitro. An integrated analysis of network pharmacology and molecular docking was utilized to further identify the targets of EGCG for T cell-mediated autoimmune diseases and multiple sclerosis (MS). EGCG treatment prevented naïve CD4+ T cells from progressing through the cell cycle when stimulated with anti-CD3/CD28 antibodies. This was achieved by increasing the proportion of cells arrested in the G0/G1 phase by 8.6% and reducing DNA synthesis activity by 51% in the S phase. Furthermore, EGCG treatment inhibited the expression of cyclins (cyclin D1, cyclin D3, cyclin A, and cyclin B1) and CDKs (CDK2 and CDK6) during naïve CD4+ T cell activation in response to anti-CD3/CD28 stimulation. However, EGCG inhibited the decrease of P27Kip1 (CDKN1B) during naïve CD4+ T cell activation, whereas it inhibited the increase of P21Cip1 (CDKN1A) expression 48 h after mitogenic stimulation. The molecular docking analysis confirmed that these proteins (CD4, CCND1, and CDKN1A) are the primary targets for EGCG, T cell-mediated autoimmune diseases, and MS. Finally, target enrichment analysis indicated that EGCG may affect the cell cycle, T cell receptor signaling pathway, Th cell differentiation, and NF-κB signaling pathway. These findings reveal a crucial role of EGCG in the division and progression of CD4+ T cells, and underscore other potential targets of EGCG in T cell-mediated autoimmune diseases such as MS.

Structural transformations in single-crystalline AgPd nanoalloys from multiscale deep potential molecular dynamics.

AgPd nanoalloys often undergo structural evolution during catalytic reactions; the mechanism underlying such restructuring remains largely unknown due to the use of oversimplified interatomic potentials in simulations. Herein, a deep-learning potential is developed for AgPd nanoalloys based on a multiscale dataset spanning from nanoclusters to bulk configurations, exhibits precise predictions of mechanical properties and formation energies with near-density functional theory accuracy, calculates the surface energies closer to experimental values compared to those obtained by Gupta potentials, and is applied to investigate the shape reconstruction of single-crystalline AgPd nanoalloys from cuboctahedron (Oh) to icosahedron (Ih) geometries. The Oh to Ih shape restructuring is thermodynamically favorable and occurs at 11 and 92 ps for Pd55@Ag254 and Ag147@Pd162 nanoalloys, respectively. During the shape reconstruction of Pd@Ag nanoalloys, concurrent surface restructuring of the (100) facet and internal multi-twinned phase change are observed with collaborative displacive characters. The presence of vacancies can influence the final product and reconstructing rate of Pd@Ag core-shell nanoalloys. The Ag outward diffusion on Ag@Pd nanoalloys is more pronounced in Ih geometry compared to Oh geometry and can be further accelerated by the Oh to Ih deformation. The deformation of single-crystalline Pd@Ag nanoalloys is characterized by a displacive transformation involving the collaborative displacement of a large number of atoms, distinguishing it from the diffusion-coupled transformation of Ag@Pd nanoalloys.

Atomic strain and catalytic properties of formate oxidation and dehydrogenation in AgPd nanoalloys.

Formate is a promising hydrogen carrier for safe storage and transport and a fuel for direct formate fuel cells. However, the sluggish kinetics of catalysts for formate dehydrogenation (FDH) and oxidation reactions (FORs) significantly limit the potential applications of formate. Strain effects can effectively modulate catalytic properties by altering the electronic structure. Nevertheless, the lack of theoretical principles to quantify atomic strain and its effects on FDH and FOR catalytic activity has made experimental efforts laborious. In this work, we establish a database of atomic strain distributions for AgPd nanoalloys, reveals that the presence of compressive strain at the edges and corners and compressive strain exerted on the surface of Ag@Pd nanoalloys, particularly the one with an icosahedral shape, boost the FDH and FOR catalytic activity by shifting down the d-band center, thus weakening the adsorption of key intermediate Had. This study provides a theoretical perspective on the development and use of formate as a hydrogen carrier and fuel.

Ivermectin Protects Against Experimental Autoimmune Encephalomyelitis in Mice by Modulating the Th17/Treg Balance Involved in the IL-2/STAT5 Pathway.

Multiple sclerosis (MS), a T-cell-mediated autoimmune disease that affects the central nervous system (CNS), is characterized by white matter demyelination, axon destruction, and oligodendrocyte degeneration. Ivermectin, an anti-parasitic drug, has anti-inflammatory, anti-tumor, and antiviral properties. However, to date, there are no in-depth studies on the effect of ivermectin on the function effector of T cells in murine experimental autoimmune encephalomyelitis (EAE), an animal model of MS. Here, we conducted in vitro experiments and found that ivermectin inhibited the proliferation of total T cells (CD3+) and their subsets (CD4+ and CD8+ T cells) as well as T cells secreting the pro-inflammatory cytokines IFN-γ and IL-17A; ivermectin also increased IL-2 production and IL-2Rα (CD25) expression, which was accompanied by an increase in the frequency of CD4+CD25+Foxp3+ regulatory T cells (Treg). Importantly, ivermectin administration reduced the clinical symptoms of EAE mice by preventing the infiltration of inflammatory cells into the CNS. Additional mechanisms showed that ivermectin promoted Treg cells while inhibiting pro-inflammatory Th1 and Th17 cells and their IFN-γ and IL-17 secretion; ivermectin also upregulated IL-2 production from MOG35-55-stimulated peripheral lymphocytes. Finally, ivermectin decreased IFN-γ and IL-17A production and increased IL-2 level, CD25 expression, and STAT5 phosphorylation in the CNS. These results reveal a previously unknown etiopathophysiological mechanism by which ivermectin attenuates the pathogenesis of EAE, indicating that it may be a promising option for T-cell-mediated autoimmune diseases such as MS.

How Does Attention Work in Vision Transformers? A Visual Analytics Attempt.

Vision transformer (ViT) expands the success of transformer models from sequential data to images. The model decomposes an image into many smaller patches and arranges them into a sequence. Multi-head self-attentions are then applied to the sequence to learn the attention between patches. Despite many successful interpretations of transformers on sequential data, little effort has been devoted to the interpretation of ViTs, and many questions remain unanswered. For example, among the numerous attention heads, which one is more important? How strong are individual patches attending to their spatial neighbors in different heads? What attention patterns have individual heads learned? In this work, we answer these questions through a visual analytics approach. Specifically, we first identify what heads are more important in ViTs by introducing multiple pruning-based metrics. Then, we profile the spatial distribution of attention strengths between patches inside individual heads, as well as the trend of attention strengths across attention layers. Third, using an autoencoder-based learning solution, we summarize all possible attention patterns that individual heads could learn. Examining the attention strengths and patterns of the important heads, we answer why they are important. Through concrete case studies with experienced deep learning experts on multiple ViTs, we validate the effectiveness of our solution that deepens the understanding of ViTs from head importance, head attention strength, and head attention pattern.

SDRQuerier: A Visual Querying Framework for Cross-National Survey Data Recycling.

Public opinion surveys constitute a widespread, powerful tool to study peoples' attitudes and behaviors from comparative perspectives. However, even global surveys can have limited geographic and temporal coverage, which can hinder the production of comprehensive knowledge. To expand the scope of comparison, social scientists turn to ex-post harmonization of variables from datasets that cover similar topics but in different populations and/or at different times. These harmonized datasets can be analyzed as a single source and accessed through various data portals. However, the Survey Data Recycling (SDR) research project has identified three challenges faced by social scientists when using data portals: the lack of capability to explore data in-depth or query data based on customized needs, the difficulty in efficiently identifying related data for studies, and the incapability to evaluate theoretical models using sliced data. To address these issues, the SDR research project has developed the SDRQuerier, which is applied to the harmonized SDR database. The SDRQuerier includes a BERT-based model that allows for customized data queries through research questions or keywords (Query-by-Question), a visual design that helps users determine the availability of harmonized data for a given research question (Query-by-Condition), and the ability to reveal the underlying relational patterns among substantive and methodological variables in the database (Query-by-Relation), aiding in the rigorous evaluation or improvement of regression models. Case studies with multiple social scientists have demonstrated the usefulness and effectiveness of the SDRQuerier in addressing daily challenges.

Mechanochemical Degradation and Recycling of Synthetic Polymers.

The accumulation of plastic waste, due to lack of recycling, has led to serious environmental pollution. Although mechanical recycling can alleviate this issue, it inevitably reduces the molecular weight and weakens the mechanical properties of materials and is not suitable for mixed materials. Chemical recycling, on the other hand, breaks the polymer into monomers or small-molecule constituents, allowing for the preparation of materials of quality comparable to that of the virgin polymers and can be applied to mixed materials. Mechanochemical degradation and recycling leverages the advantages of mechanical techniques, such as scalability and efficient energy use, to achieve chemical recycling. We summarize recent progress in mechanochemical degradation and recycling of synthetic polymers, including both commercial polymers and those designed for more efficient mechanochemical degradation. We also point out the limitations of mechanochemical degradation and present our perspectives on how the challenges can be mitigated for a circular polymer economy.

P2X4 receptor modulates gut inflammation and favours microbial homeostasis in colitis.

BACKGROUND: Inflammatory bowel disease (IBD) is a non-specific chronic inflammatory disease of the intestine. In addition to genetic susceptibility, environmental factors and dysregulated host immunity, the gut microbiota is implicated in the pathogenesis of Crohn's disease (CD) or ulcerative colitis (UC), the two primary types of IBD. The P2X4 receptor has been demonstrated to have a crucial role in preventing infection, inflammation, and organ damage. However, it remains unclear whether the P2X4 receptor affects IBD and the underlying mechanisms. METHODS: Colitis was induced in mice administrated with dextran sodium sulphate (DSS). 16S rDNA sequencing was used to analyze the gut microbiota in knockout and wild-type mice. Clinical and histopathological parameters were monitored throughout the disease progression. RESULTS: Gene Expression Omnibus analysis showed the downregulation of P2RX4 (P2rx4) expression in colonic tissues from patients or mice with IBD. However, its expression at the protein levels was upregulated on day 4 or 6 and then downregulated on day 7 in C57BL/6 mice treated with DSS. Gene ablation of P2rx4 aggravated DSS-induced colitis accompanying gut microbiota dysbiosis in mice. Moreover, P2X4 receptor-positive modulator ivermectin alleviated colitis and corrected dysregulated microbiota in wild-type C57BL/6 mice. Further antibiotic-treated gut microbiota depletion, cohousing experiment, and fecal microbiota transplantation proved that gut microbiota dysbiosis was associated with the aggravation of colitis in the mouse model initiated by P2rx4. CONCLUSIONS: Our findings elaborate on an unrevealed etiopathophysiological mechanism by which microbiota dysbiosis induced by the P2X4 receptor influences the development of colitis, indicating that the P2X4 receptor represents a promising target for treating patients with CD and UC.