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Abstract Periodontitis is a critical risk factor for the occurrence and development of diabetes. Porphyromonas gingivalis may participate in insulin resistance (IR) caused by periodontal inflammation, but the functional role and specific mechanisms of P. gingivalis in IR remain unclear. In the present study, clinical samples were analysed to determine the statistical correlation between P. gingivalis and IR occurrence. Through culturing of hepatocytes, myocytes, and adipocytes, and feeding mice P. gingivalis orally, the functional correlation between P. gingivalis and IR occurrence was further studied both in vitro and in vivo. Clinical data suggested that the amount of P. gingivalis isolated was correlated with the Homeostatic Model Assessment for IR score. In vitro studies suggested that coculture with P. gingivalis decreased glucose uptake and insulin receptor (INSR) protein expression in hepatocytes, myocytes, and adipocytes. Mice fed P. gingivalis tended to undergo IR. P. gingivalis was detectable in the liver, skeletal muscle, and adipose tissue of experimental mice. The distribution sites of gingipain coincided with the downregulation of INSR. Gingipain proteolysed the functional insulin-binding region of INSR. Coculture with P. gingivalis significantly decreased the INSR–insulin binding ability. Knocking out gingipain from P. gingivalis alleviated the negative effects of P. gingivalis on IR in vivo. Taken together, these findings indicate that distantly migrated P. gingivalis may directly proteolytically degrade INSR through gingipain, thereby leading to IR. The results provide a new strategy for preventing diabetes by targeting periodontal pathogens and provide new ideas for exploring novel mechanisms by which periodontal inflammation affects the systemic metabolic state.

Abstract Background Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that has been found to be associated with dysregulation of gastrointestinal functions and gut microbial homeostasis (the so-called “gut-brain axis”). ASD is often accompanied by poor performances in social interaction and repetitive behaviors. Studies on the gut-brain axis provide novel insights and candidate targets for ASD therapeutics and diagnosis. Based on the ASD mice model, this work aims to reveal the mechanisms behind the interaction of intestinal barrier function and probiotics in ASD mouse models. Results We found an altered intestinal barrier in both BTBR T+ Itpr3tf/J (BTBR) and valproic acid (VPA) mice, including increased intestinal permeability, decreased expression of intestinal tight junction proteins (claudin1, claudin3, and occludin), and increased levels of IL-6, TNF-α, and IFN-γ. Based on intestinal microbial alternation, C. butyricum can drive reduced expression of histone deacetylases 1 (HDAC1) and enhanced intestinal barrier function, significantly promoting behavioral abnormalities of ASD in BTBR mice. In parallel, we confirmed that C. butyricum was involved in the regulation of intestinal function by the Trek1 channel, indicating that it is a target of C. butyricum/butyric acid to improve intestinal barrier function in ASD mice. Conclusions Our finding provides solid evidence for the gut microbiota involved in ASD through the brain-gut axis. In addition, the probiotics C. butyricum hold promise to improve gut health and ameliorate behavioral abnormalities associated with ASD.

Abstract The increased penetration of renewable energy sources and the intensification of peak‐valley differences present challenges to peak regulation in the power system. Fulfilling the peak regulation needs of the power system solely through generation‐side resources proves to be challenging. Large‐scale fixed frequency air‐conditioning (FFAC) and inverter air‐conditioning (IAC) are high‐quality flexible load resources. This paper proposes a hierarchical coordinated control strategy of air‐conditioning (AC) loads for peak regulation service. In the first layer of the control strategy, the load aggregator collaborates with multiple AC groups to ensure the completion of peak regulation tasks with specific power‐constrained requirements. In the second layer of the control strategy, the control centre of each group optimizes the temperature adjustment values for each AC load, aiming to reduce incentive costs. Finally, the proposed method is validated through simulations, demonstrating its capability to achieve coordinated control of FFAC and IAC loads under various power‐constrained requirements. Furthermore, the simulation demonstrates the effectiveness of the control strategy in reducing user discomfort and the AC's incentive costs.

Abstract Background Gastric intestinal metaplasia (GIM) is an essential precancerous lesion. Although the reversal of GIM is challenging, it potentially brings a state-to-art strategy for gastric cancer therapeutics (GC). The lack of the appropriate in vitro model limits studies of GIM pathogenesis, which is the issue this work aims to address for further studies. Method The air-liquid interface (ALI) model was adopted for the long-term culture of GIM cells in the present work. This study conducted Immunofluorescence (IF), quantitative real-time polymerase chain reaction (qRT-PCR), transcriptomic sequencing, and mucoproteomic sequencing (MS) techniques to identify the pathways for differential expressed genes (DEGs) enrichment among different groups, furthermore, to verify novel biomarkers of GIM cells. Result Our study suggests that GIM-ALI model is analog to the innate GIM cells, which thus can be used for mucus collection and drug screening. We found genes MUC17, CDA, TRIM15, TBX3, FLVCR2, ONECUT2, ACY3, NMUR2, and MAL2 were highly expressed in GIM cells, while GLDN, SLC5A5, MAL, and MALAT1 showed down-regulated, which can be used as potential biomarkers for GIM cells. In parallel, these genes that highly expressed in GIM samples were mainly involved in cancer-related pathways, such as the MAPK signal pathway and oxidative phosphorylation signal pathway. Conclusion The ALI model is validated for the first time for the in vitro study of GIM. GIM-ALI model is a novel in vitro model that can mimic the tissue micro-environment in GIM patients and further provide an avenue for studying the characteristics of GIM mucus. Our study identified new markers of GIM as well as pathways associated with GIM, which provides outstanding insight for exploring GIM pathogenesis and potentially other related conditions.

Abstract Background Nonalcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases globally. Hepatic stellate cells (HSCs) are the major effector cells of liver fibrosis. HSCs contain abundant lipid droplets (LDs) in their cytoplasm during quiescence. Perilipin 5 (PLIN 5) is a LD surface‐associated protein that plays a crucial role in lipid homeostasis. However, little is known about the role of PLIN 5 in HSC activation. Methods PLIN 5 was overexpressed in HSCs of Sprague–Dawley rats by lentivirus transfection. At the same time, PLIN 5 gene knockout mice were constructed and fed with a high‐fat diet (HFD) for 20 weeks to study the role of PLIN 5 in NAFLD. The corresponding reagent kits were used to measure TG, GSH, Caspase 3 activity, ATP level, and mitochondrial DNA copy number. Metabolomic analysis of mice liver tissue metabolism was performed based on UPLC‐MS/MS. AMPK, mitochondrial function, cell proliferation, and apoptosis‐related genes and proteins were detected by western blotting and qPCR. Results Overexpression of PLIN 5 in activated HSCs led to a decrease in ATP levels in mitochondria, inhibition of cell proliferation, and a significant increase in cell apoptosis through AMPK activation. In addition, compared with the HFD‐fed C57BL/6J mice, PLIN 5 knockout mice fed with HFD showed reduced liver fat deposition, decreased LD abundance and size, and reduced liver fibrosis. Conclusion These findings highlight the unique regulatory role of PLIN 5 in HSCs and the role of PLIN 5 in the fibrosis process of NAFLD.

Abstract Background Ureteropelvic junction obstruction (UPJO) is a common obstructive disease of the urinary tract. UPJO patients commonly exhibit coexistent renal calculi. The main aim of therapy is to relieve the obstruction and remove the stones at the same time. Methods This retrospective study included 110 patients diagnosed with UPJO coexisting with multiple renal calculi at Shanxi Bethune Hospital and the First Hospital of Shanxi Medical University between March 2016 and January 2022. Patients were divided according to the methods used for dealing with UPJO and renal calculi. In Group A, patients underwent traditional open pyeloplasty and pyelolithotomy. In Group B, patients underwent percutaneous nephrolithotomy first and then laparoscopic pyeloplasty. In Group C, patients underwent flexible cystoscopy to remove stones and then laparoscopic pyeloplasty. In Group D, patients underwent flexible vacuum-assisted ureteral access sheath (FV-UAS)assisted flexible ureteroscopy (f-URS) and underwent laparoscopic pyeloplasty. The stones were broken up using a holmium laser. The pyeloplasty success rate, stone clearance rate, operation time, bleeding amount, complication occurrence rate, postsurgical pain, length of stay, and hospitalization cost were compared between the groups. The follow-up period was at least 2 years. Results The use of f-URS and the FV-UAS, significantly increased the renal stone clearance rate and significantly reduced the complication incidence and operation time in UPJO patients with multiple coexisting renal calculi. Conclusions Laparoscopic pyeloplasty combined with f-URS and FV-UAS is safe and effective for treating UPJO in patients complicated by renal caliceal stones. Trial registration Retrospectively registered.

A risk-aware scheduling method of multienergy virtual power plant (MEVPP) is proposed to measure the uncertainty in MEVPP. First, a novel day-ahead uncertainty scenario generation method based on denoising diffusion probabilistic model is proposed, and historical data are employed to learn the error relationship between real power curves and predict power curves. The probability distribution of the prediction error which describes the day-ahead output power curve of renewable energy source is learned by parameter training. Subsequently, the effect of risk aversion on decision-making is investigated by implementing conditional value-at-risk in the optimization model, MEVPP operation mode under the carbon trading and green certificate trading mechanism is analyzed. Finally, the proposed scheme is implemented on a test MEVPP with carbon trading, and green certificate trading is addressed in detail through a numerical study. Moreover, the effects of the operator’s risk-averse behavior on the MEVPP are investigated.

ObjectiveGastric intestinal metaplasia (IM) is a precancerous stage associated with gastric cancer. Despite the observed beneficial effects of metformin on IM, its molecular mechanism remains not fully elucidated. This study aims to reveal the effects and potential mechanisms of metformin in treating IM based on both bioinformatics and in vivo investigations.MethodsThe seven public databases (GeneCards, DisGeNET, OMIM, SuperPred, Pharm Mapper, Swiss Target Prediction, TargetNet) were used in this work to identify targeted genes related to intestinal metaplasia (IM) and metformin. The shared targeted genes between metformin and IM were further analyzed by network pharmacology, while the interactions in-between were investigated by molecular docking. In parallel, the therapeutic effect of metformin was evaluated in IM mice model, while the core targets and pathways effected by metformin were verified in vivo.ResultsWe screened out 1,751 IM-related genes and 318 metformin-targeted genes, 99 common genes identified in between were visualized by constructing the protein-protein interaction (PPI) network. The top ten core targeted genes were EGFR, MMP9, HIF1A, HSP90AA1, SIRT1, IL2, MAPK8, STAT1, PIK3CA, and ICAM1. The functional enrichment analysis confirmed that carcinogenesis and HIF-1 signaling pathways were primarily involved in the metformin treatment of IM. Based on molecular docking and dynamics, we found metformin affected the function of its targets by inhibiting receptor binding. Furthermore, metformin administration reduced the progression of IM lesions in Atp4a−/− mice model significantly. Notably, metformin enhanced the expression level of MUC5AC, while inhibited the expression level of CDX2. Our results also showed that metformin modulated the expression of core targets in vivo by reducing the activity of NF-κB and the PI3K/AKT/mTOR/HIF-1α signaling pathway.ConclusionThis study confirms that metformin improves the efficacy of IM treatment by regulating a complex molecular network. Metformin plays a functional role in inhibiting inflammation/apoptosis-related pathways of further IM progression. Our work provides a molecular foundation for understanding metformin and other guanidine medicines in IM treatment.

The randomness and fluctuation of large-scale distributed photovoltaic (PV) power will affect the stable operation of the distribution network. The energy storage system (ESS) can effectively suppress the power output fluctuation of the PV system and reduce the PV curtailment rate through charging/discharging states. In order to improve the operation capability of the distribution network and PV consumption rate, an optimal multi-objective strategy is proposed based on PV power prediction. First, the back propagation (BP) neural network with an improved genetic algorithm (GA) is used to predict PV power output. Furthermore, an adaptive variability function is added to the GA to improve the prediction accuracy. Then, the distribution network model containing distributed PV and the ESS is constructed. The optimal object contains network power loss, voltage deviation, and PV consumption. The model is solved based on the improved multi-objective particle swarm optimization (MOPSO) algorithm of Pareto optimality. The probabilistic amplitude is adopted to encode the particles for avoiding local optimal. Finally, the proposed optimal strategy is verified by the IEEE 33-bus distribution network. The results show that the proposed strategy has an obvious effect on reducing the network power loss and voltage deviation, as well as improving the PV consumption rate.

Background: Revealing the role of anoikis resistance plays in CRC is significant for CRC diagnosis and treatment. This study integrated the CRC anoikis-related key genes (CRC-AKGs) and established a novel model for improving the efficiency and accuracy of the prognostic evaluation of CRC. Methods: CRC-ARGs were screened out by performing differential expression and univariate Cox analysis. CRC-AKGs were obtained through the LASSO machine learning algorithm and the LASSO Risk-Score was constructed to build a nomogram clinical prediction model combined with the clinical predictors. In parallel, this work developed a web-based dynamic nomogram to facilitate the generalization and practical application of our model. Results: We identified 10 CRC-AKGs and a risk-related prognostic Risk-Score was calculated. Multivariate COX regression analysis indicated that the Risk-Score, TNM stage, and age were independent risk factors that significantly associated with the CRC prognosis(p

BackgroundBilateral ductus arteriosus (BDA) is a relatively rare vascular malformation. According to the double arch theory, BDA is formed when the distal ends of the sixth pairs of primitive arches on the left and right sides have not regressed. We describe a fetus with prenatal echocardiographic findings of BDA and right aortic arch mirror-image branching (RAA-MIB) combined with congenital heart disease. Furthermore, to gain a deeper understanding of the embryological mechanism of BDA, we review the literature on all combinations of BDA present in 40 fetuses/infants.Case summaryA 22-year-old female patient underwent fetal echocardiography at 23 weeks of gestation. Both the two-dimensional (2D) grayscale image and color Doppler flow imaging (CDFI) revealed dextro-transposition of the great arteries combined with a ventricular septal defect and RAA-MIB. The following scan revealed a rare vascular ring, which was identified as BDA extending from the confluent of the left pulmonary artery and right pulmonary artery, completely encircling the trachea to form an “O”-shaped vascular ring before finally converging into the descending aorta. A persistent left superior vena cava was also observed. We subsequently used four-dimensional (4D) color Doppler imaging with the spatiotemporal image correlation (STIC) HD live flow and STIC HD live flow silhouette mode to clearly display ventricular arterial connectivity and the direction of vessel travel. Adjusting the image quality and display angle is very important when applying STIC. The 4D images confirmed our diagnosis. After multidisciplinary counseling and discussion with her family, this female patient decided to terminate the pregnancy.ConclusionOur review of the literature summarized nine combinations classified into three types of BDA and aortic arch pathology. However, our case differs because it is a novel combination of intracardiac structural abnormalities and vascular rings in a fetus. Prenatal ultrasound diagnosis of BDA is important and requires a combination of 2D grayscale, CDFI, and STIC images to assist in scanning.

BackgroundThere is a lack of research evidence on the association between sugar-sweetened beverage (SSB) consumption and gestational diabetes mellitus (GDM) in China. ObjectiveTo explore the association between frequency of SSB consumption before pregnancy and risk of GDM in pregnant women in Shaanxi Province, and to provide a scientific basis for targeted interventions to control maternal blood glucose. MethodsThe recruitment to the China Birth Cohort study started in October 2020. Pregnant women at 6-16 weeks who had their first prenatal examination at five hospitals in Shaanxi Province were recruited. A maternal health questionnaire was used to collect basic information about pregnant women. A semi-quantitative food frequency questionnaire was used to collect the consumption of carbonated beverages, fruit and vegetable juice beverages, coffee beverages, and milk tea beverages in one year before pregnancy, which were summed to obtain the SSB consumption. Pregnant women were divided into three groups according to SSB consumption, namely 0.05) between SSB consumption and maternal age, pre-pregnancy BMI, or first labor or not. For each additional SSB consumption per day, the risk of GDM increased by 94% (OR=1.94, 95%CI: 1.37, 2.75); and the maternal OGTT 1-hour glucose and 2-hour glucose increased by 0.33 mmol·L−1 and 0.18 mmol·L−1, respectively (P0.05). ConclusionHigher SSB consumption before pregnancy increases the risk of GDM in pregnant women.

Abstract A method for identifying vulnerable nodes in distribution networks is proposed, which is based on complex networks and optimized TOPSIS. This method aims to address the issues of one‐sided evaluation indicators and inaccurate indicator weights that are present in existing methods for identifying vulnerable nodes in distribution networks. Based on the theory of complex networks, a comprehensive set of vulnerability indicators for distribution network nodes is constructed by considering both the topology structure and system operation status of the distribution network. The TOPSIS comprehensive evaluation model for optimization is proposed to enhance the selection process of optimal and worst indicator values. The advantages and disadvantages of each indicator are characterized using Mahalanobis distance. The calculation of proximity is optimized by establishing a virtual negative ideal solution, which makes the identification of vulnerable nodes more reasonable. The simulation results demonstrate that this method is more effective in identifying vulnerable nodes in the power grid compared to traditional methods, and has significant practical applications.

In the realm of human motion recognition systems, the augmentation of 3D human pose data plays a pivotal role in enriching and enhancing the quality of original datasets through the generation of synthetic data. This augmentation is vital for addressing the current research gaps in diversity and complexity, particularly when dealing with rare or complex human movements. Our study introduces a groundbreaking approach employing Generative Adversarial Networks (GANs), coupled with Support Vector Machine (SVM) and DenseNet, further enhanced by robot-assisted technology to improve the precision and efficiency of data collection. The GANs in our model are responsible for generating highly realistic and diverse 3D human motion data, while SVM aids in the effective classification of this data. DenseNet is utilized for the extraction of key features, facilitating a comprehensive and integrated approach that significantly elevates both the data augmentation process and the model's ability to process and analyze complex human movements. The experimental outcomes underscore our model's exceptional performance in motion quality assessment, showcasing a substantial improvement over traditional methods in terms of classification accuracy and data processing efficiency. These results validate the effectiveness of our integrated network model, setting a solid foundation for future advancements in the field. Our research not only introduces innovative methodologies for 3D human pose data enhancement but also provides substantial technical support for practical applications across various domains, including sports science, rehabilitation medicine, and virtual reality. By combining advanced algorithmic strategies with robotic technologies, our work addresses key challenges in data augmentation and motion quality assessment, paving the way for new research and development opportunities in these critical areas.

A two-stage robust planning method for energy storage in distribution networks based on load prediction is proposed to address the uncertainty of active load in energy storage planning. First, considering the uncertainty of active load, a short-term load forecasting model combining the mutual information method and BiLSTM is established based on k-means++ clustering. Second, based on the results of load forecasting, a comprehensive norm-constrained uncertainty set is constructed, and a two-stage robust model for distribution network energy storage planning is established. The first stage aims to minimize the annual investment cost of the energy storage system, while the second stage aims to minimize the daily operating cost of the distribution network. At the same time, a second-order cone relaxation transformation model with non-convex constraints is introduced to ultimately achieve the optimal economy of the distribution network in energy storage planning. Finally, the effectiveness of the proposed method and model is validated on the IEEE 33-node distribution network model using the MATLAB platform.

Abstract Background Women with gestational diabetes mellitus (GDM) are at greater risk of abnormal birth weight. Since the level of biochemical indicators could often affect the intrauterine growth and development of the fetus, it is of great practical significance to understand the changes of biochemical levels across pregnancy in women with GDM and to find out the indicators that play an important role in predicting birth weight. Methods The data source of this study was from the Xi'an Longitudinal Mother–Child Cohort study (XAMC), in which women with GDM with normal and high pre-pregnancy body mass index (BMI) and their newborns between January 1st and March 31st in 2018 were included. The data of ferritin, serum lipid profile and fasting plasma glucose (FPG) of mothers in the three trimesters of pregnancy, as well as birth weight of newborns were all collected from medical records. Multiple linear regression and multivariate logistic regression analyses were used to explore the association of the biochemical indexes and birth weight. A P value

Abstract Background Caesarean-section (C-section) may influence children’s long-term health by affecting bacterial colonization. However, few studies have focused on the association between C-section delivery (CSD) and dental caries, and previous conclusions have been conflicting. This study aimed to explore whether CSD would increase the risk of early childhood caries (ECC) in preschool children in China. Methods This study was a retrospective cohort study. Three-year-old children with full primary dentition were included through the medical records system. Children in the nonexposure group were vaginally delivered (VD), while children in the exposure group were delivered through C-section. The outcome was the occurrence of ECC. After agreeing to participate in this study, guardians of included children completed a structured questionnaire on maternal sociodemographic factors, children’s oral hygiene and feeding habits. The chi-square test was used to determine differences in the prevalence and severity of ECC between the CSD and VD groups and to analyse the prevalence of ECC according to sample characteristics. Subsequently, potential risk factors for ECC were preliminarily identified through univariate analysis, and the adjusted odds ratios (ORs) were further calculated through multiple logistic regression analysis after controlling for confounding factors. Results The VD group included 2115 participants while CSD group included 2996 participants. The prevalence of ECC was higher in CSD children than in VD children (27.6% vs. 20.9%, P

Objective: Vitamin D3 has the general properties of a lipid-soluble vitamin, but is also an active steroid hormone that can regulate the proliferation, apoptosis and differentiation of many tumor cells, and exerts anticancer activity against numerous malignancies. However, the mechanism underlying the effects of vitamin D3 on tumors is not fully understood. Here, we used network pharmacology and in vitro experimental approaches to explore the mechanism of vitamin D3 activity in the context of gastric cancer.Methods: The Targetnet, SuperPred, SwissTargetPrediction, and PharmMapper databases were screened for potential drug-related targets, while we used data from the PharmGKB, Drugbank, OMIM, DisGeNET, CTD, and GeneCards databases to identify potential targets associated with gastric cancer. Disease-drug crossover genes were obtained by constructing Venn diagrams. Gene ontology and Kyoto Encyclopedia of Genomes (KEGG) enrichment analyses of crossover genes were conducted and STRING was used to generate protein interaction networks and identify core targets. CCK-8 experiments were performed and apoptosis detected to assess the effect of vitamin D3 on gastric cancer cells. Western blotting was applied to detect p53/AMPK/mTOR signaling, as well as autophagy-, cell cycle-, and apoptosis-related proteins.Results: A total of 485 targets of vitamin D3 activity were obtained and 1200 gastric cancer disease-related targets discovered. Further, 60 potential targets for vitamin D3 in gastric cancer treatment were identified. KEGG analysis indicated that potential targets were mainly involved in the cell cycle, HIF-1 signaling, and the AMPK pathway, among other pathways. These findings were validated using cellular experiments, which demonstrated that the viability of AGS and SGC-7901 cells was impeded by vitamin D3. Further, vitamin D3 promoted apoptosis and inhibited the cell cycle in those cell lines, as well as activating the p53/AMPK/mTOR pathway, which promotes autophagy and inhibits tumor development.Conclusion: Our network pharmacological analyses provide preliminarily data supporting a role for vitamin D3 in promoting autophagy and apoptosis in gastric cancer cells, and in activating the p53/AMPK/mTOR pathway, which inhibits gastric cancer cell proliferation. Our findings demonstrate the molecular mechanism underlying the effect of vitamin D3 in cure of gastric cancer.

The development of cancer is not just the growth and proliferation of a single transformed cell, but its tumor microenvironment (TME) also coevolves with it, which is primarily involved in tumor initiation, development, metastasis, and therapeutic responses. Recent years, TME has been emerged as a potential target for cancer diagnosis and treatment. However, the clinical efficacy of treatments targeting the TME, especially its specific components, remains insufficient. In parallel, the gut microbiome is an essential TME component that is crucial in cancer immunotherapy. Thus, assessing and constructing frameworks between the gut microbiota and the TME can significantly enhance the exploration of effective treatment strategies for various tumors. In this review the role of the gut microbiota in human cancers, including its function and relationship with various tumors was summarized. In addition, the interaction between the gut microbiota and the TME as well as its potential applications in cancer therapeutics was described. Furthermore, it was summarized that fecal microbiota transplantation, dietary adjustments, and synthetic biology to introduce gut microbiota-based medical technologies for cancer treatment. This review provides a comprehensive summary for uncovering the mechanism underlying the effects of the gut microbiota on the TME and lays a foundation for the development of personalized medicine in further studies.

Dioctyl phthalate, commonly known as bis(2-ethylhexyl) phthalate (DEHP), is a widely used plasticizer in various industries and has been shown to directly or indirectly impact human health. However, there is a lack of comprehensive studies evaluating the potential health risks associated with DEHP accumulation in different organs across various age groups. This study aimed to assess the effects of low (50 mg/kg·bw) and high (500 mg/kg·bw) doses of DEHP on five different organs in mice at young (4-week-old) and aged (76-week-old) life stages. Our findings revealed that both low and high doses of DEHP exposure led to significant dose-dependent inflammation in the liver, spleen, and kidney. Furthermore, regardless of age, DEHP exposure resulted in elevated activity of alanine aminotransferase (ALT) and alkaline phosphatase (ALP) in the liver, as well as increased levels of creatinine (Cr) and urea in the kidney. Moreover, analysis of the fecal microbiota using 16S rRNA sequencing demonstrated that DEHP exposure disrupted the homeostasis of the gut microbiota, characterized by an increased abundance of pathogenic bacteria such as Desulfovibrio and Muribaculum, and a decreased abundance of beneficial bacteria like Lactobacillus. This study provides compelling evidence that DEHP at different concentrations can induce damage to multiple organs and disrupt gut microbiota composition. These findings lay the groundwork for further investigations into DEHP toxicity in various human organs, contributing to a better understanding of the potential health risks associated with DEHP exposure.