Hasil untuk "Therapeutics. Pharmacology"

Faruk Alam, Surabhi Mandal, Bhupendra Shrestha et al.

Background: Liposomes are widely used as drug delivery systems because of their reduced systemic toxicity. Over the past few decades, numerous drug-loaded liposomes have been approved for clinical use in the treatment of cancer, viral, and fungal infections. Various liposomal formulations have progressed to later phases of clinical trials. Liposomes are spherical vesicles composed of a single or multiple phospholipid bilayers surrounding an aqueous core. Drug-loaded liposomes can exhibit controlled or targeted drug delivery, low immunogenicity, high biocompatibility, biodegradability, prolonged drug half-life, increased efficiency, reduced systemic toxicity, and enhanced pharmacokinetic properties. Methodology: This review article addresses the characteristics and types of liposomes; novel methods for their preparation, such as the Supercritical Anti-solvent Method and the Dual Asymmetric Centrifugation Method; lipid preferences; future directions for liposomes; marketed liposomal formulations; and associated patents. Results and Discussion: It has the potential to protect the drug against degradation. The aforementioned drug delivery system increases in vivo drug distribution toward target sites. PEGylated liposomes can prolong circulation time. It requires expertise in techniques, such as thin-film hydration and reverse-phase evaporation, for preparation. It has been utilized in nanomedicine. This particular delivery system requires characterizations like size, drug loading, drug release, etc. Conclusion: Liposome-embedded delivery systems advance nanotechnology and biopharmaceutics. The role of modern medicine has continued to expand, particularly in the management of chronic diseases.

Youjin Choi, Jaeyoung Moon, Jinyoung Yoo et al.

Songwriting has long served as a powerful medium for expressing unconscious emotions and fostering self-awareness in psychotherapy. Due to the auditory-centric nature of traditional approaches, Deaf and Hard-of-Hearing (DHH) individuals have often been excluded from music's therapeutic benefits. In response, this study presents a music psychotherapy tool co-designed with therapists, integrating conversational agents (CAs) and music generative AI as symbolic and therapeutic media. Through a usage study with 23 DHH individuals, we found that collaborative song writing with the CA enabled them to experience emotional release, reinterpretation, and deeper self-understanding. In particular, the CA's strategies -- supportive empathy, example response options, and visual-based metaphors -- were found to facilitate musical dialogue effectively for DHH individuals. These findings contribute to inclusive AI design by showing the potential of human-AI collaboration to bridge therapeutic artistic practices.

Jiying Zhang, Shuhao Zhang, Pierre Vandergheynst et al.

G-protein-coupled receptors (GPCRs), primary targets for over one-third of approved therapeutics, rely on intricate conformational transitions to transduce signals. While Molecular Dynamics (MD) is essential for elucidating this transduction process, particularly within ligand-bound complexes, conventional all-atom MD simulation is computationally prohibitive. In this paper, we introduce GPCRLMD, a deep generative framework for efficient all-atom GPCR-ligand simulation.GPCRLMD employs a Harmonic-Prior Variational Autoencoder (HP-VAE) to first map the complex into a regularized isometric latent space, preserving geometric topology via physics-informed constraints. Within this latent space, a Residual Latent Flow samples evolution trajectories, which are subsequently decoded back to atomic coordinates. By capturing temporal dynamics via relative displacements anchored to the initial structure, this residual mechanism effectively decouples static topology from dynamic fluctuations. Experimental results demonstrate that GPCRLMD achieves state-of-the-art performance in GPCR-ligand dynamics simulation, faithfully reproducing thermodynamic observables and critical ligand-receptor interactions.

Tianmeng Hu, Yongzheng Cui, Biao Luo et al.

The inverse design of RNA three-dimensional (3D) structures is crucial for engineering functional RNAs in synthetic biology and therapeutics. While recent deep learning approaches have advanced this field, they are typically optimized and evaluated using native sequence recovery, which is a limited surrogate for structural fidelity, since different sequences can fold into similar 3D structures and high recovery does not necessarily indicate correct folding. To address this limitation, we propose RIDER, an RNA Inverse DEsign framework with Reinforcement learning that directly optimizes for 3D structural similarity. First, we develop and pre-train a GNN-based generative diffusion model conditioned on the target 3D structure, achieving a 9% improvement in native sequence recovery over state-of-the-art methods. Then, we fine-tune the model with an improved policy gradient algorithm using four task-specific reward functions based on 3D self-consistency metrics. Experimental results show that RIDER improves structural similarity by over 100% across all metrics and discovers designs that are distinct from native sequences.

Hanchi Zhang, Xinrui Qi, Lin Yang et al.

Chronic inflammation, a major global health burden, is a significant contributor to various diseases, including liver and kidney failure, inflammatory bowel disease, myocardial dysfunction, rheumatoid arthritis, diabetes, sepsis, and even cancer. Anthocyanins (ACNs), natural pigments widely distributed in diverse angiosperms, are renowned for their biological properties, such as anti-inflammatory, antioxidant, anti-tumor, and antibacterial effects. They have been demonstrated to play a pivotal role in modulating inflammation and treating a broad spectrum of inflammatory disorders. However, low bioavailability, instability, and uncontrollable distribution and excretion of ACNs have significantly restricted their applications. Encapsulation of ACNs into nanomaterials has therefore emerged as a viable strategy to overcome these limitations. Despite growing interest in this field, no comprehensive reviews has yet systematically examined the anti-inflammatory activity of ACN-loaded nanoparticles (NPs). In this review, we provide an in-depth summary of the preparation techniques, physicochemical properties, and functional characteristics of ACNs-loaded NPs based on polysaccharides, proteins, lipids, and metal NPs, and critically evaluate their direct and indirect anti-inflammatory effects. Furthermore, we discuss the therapeutic potential and underlying regulatory mechanisms of ACNs-loaded NPs in inflammatory diseases, including colitis, neuritis, and wound inflammation. This review aims to offer a comprehensive reference for the development of function-enhanced novel ACNs-loaded NPs and paves the way for their future clinical application.

Haoning Guo, Liling Tang, Miaoquan He et al.

BackgroundKetamine and esketamine have demonstrated rapid, short-term antidepressant effects in major depressive disorder (MDD), but their relative safety remains unclear. This review aims to update the evidence on the safety of two agents for MDD and indirectly compare their safety and tolerability.MethodWe systematically searched PubMed, PsycINFO, Embase, and Cochrane databases up to 1 May 2025. Eligible studies compared ketamine or esketamine with placebo, active psychotropic agents, or electroconvulsive therapy in adults with MDD.ResultsWe retrieved 5,473 articles, 47 of which met the inclusion criteria. For ketamine versus placebo, both dropout and incidence rates of adverse events (AEs) were statistically significant, with number needed to harm (NNH) values of 12 and 2, respectively. A similar pattern of effect sizes was found for esketamine, but with higher corresponding NNH values. Conversely, neither the meta-analysis nor NNH analyses of the incidence of serious AEs for ketamine and esketamine were statistically significant. A series of AEs like dizziness, dissociation, nausea, vertigo, and vision blurred, with relatively low NNH values, would be more likely to occur in clinical practice and exhibit dose-dependent effects. Moreover, ketamine or esketamine was associated with transient and significant psychiatric side-effects, blood pressure increases, and sedation post-dose. No significant abnormalities were observed in cognitive impairments, laboratory results, bladder symptoms, nasal examination, or addiction-related evaluations for either drug.ConclusionAlthough further promising evidence supports the safety of ketamine and esketamine for MDD, the findings of this study highlight a potential tolerability advantage with esketamine over ketamine for short-term use for MDD. These findings require further validation through direct head-to-head clinical trials comparing these two drugs.Systematic Review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42023389486.

JunJie Wee, Jian Jiang

Topological Data Analysis (TDA) has emerged as a powerful framework for extracting robust, multiscale, and interpretable features from complex molecular data for artificial intelligence (AI) modeling and topological deep learning (TDL). This review provides a comprehensive overview of the development, methodologies, and applications of TDA in molecular sciences. We trace the evolution of TDA from early qualitative tools to advanced quantitative and predictive models, highlighting innovations such as persistent homology, persistent Laplacians, and topological machine learning. The paper explores TDA's transformative impact across diverse domains, including biomolecular stability, protein-ligand interactions, drug discovery, materials science, and viral evolution. Special attention is given to recent advances in integrating TDA with machine learning and AI, enabling breakthroughs in protein engineering, solubility and toxicity prediction, and the discovery of novel materials and therapeutics. We also discuss the limitations of current TDA approaches and outline future directions, including the integration of TDA with advanced AI models and the development of new topological invariants. This review aims to serve as a foundational reference for researchers seeking to harness the power of topology in molecular science.

Azam Bayat, Aref Khalkhali, Ali Reza Mahjoub

Summary Osteoporosis is a skeletal disorder, characterized by a decrease in bone strength and puts the individual at risk for fracture. On the other hand, rheumatoid arthritis is a systemic disease of unknown etiology that causes inflammation of the joints of the organs. Purpose Due to the destructive effects of these diseases and its increasing prevalence and lack of appropriate medication for treatment, the present study aimed to evaluate the therapeutic effect of a new type of healthy and live food supplement on rheumatoid arthritis and induced osteoporosis in rats. Methods In this research, healthy and live food powder were synthesized by a new and green route. This organic biomaterial was named NBS. The NBS food supplement had various vitamins, macro and micro molecules, and ingredients. The new healthy and nutritious diet showed that the use of this supplement led to the return of the parameters to normal levels. Results The concentration of 12.5 mg/ kg showed the least therapeutic effect and 50 mg/ kg had the highest therapeutic effect for osteoporosis. The results of blood parameters involved in inflammation in both healthy and patient groups showed that the use of complete adjuvant induction causes joint inflammation. In the study of the interaction of the concentrations, it was observed that the concentration of 50 mg/ kg had the highest therapeutic effect against the disease in the studied mice. Conclusion The results showed that the new healthy and viable supplement restores the blood osteoporotic and rheumatoid factors of the mice to normal.

Salini Mohanty, Jui-Hua Tsai, Ning Ning et al.

As higher-valent pneumococcal conjugate vaccines (PCVs) become available for pediatric populations in the US, it is important to understand healthcare provider (HCP) preferences for and acceptability of PCVs. US HCPs (pediatricians, family medicine physicians and advanced practitioners) completed an online, cross-sectional survey between March and April 2023. HCPs were eligible if they recommended or prescribed vaccines to children age <24 months, spent ≥25% of their time in direct patient care, and had ≥2 y of experience in their profession. The survey included a discrete choice experiment (DCE) in which HCPs selected preferred options from different hypothetical vaccine profiles with systematic variation in the levels of five attributes. Relative attribute importance was quantified. Among 548 HCP respondents, the median age was 43.2 y, and the majority were male (57.9%) and practiced in urban areas (69.7%). DCE results showed that attributes with the greatest impact on HCP decision-making were 1) immune response for the shared serotypes covered by PCV13 (31.4%), 2) percent of invasive pneumococcal disease (IPD) covered by vaccine serotypes (21.3%), 3) acute otitis media (AOM) label indication (20.3%), 4) effectiveness against serotype 3 (17.6%), and 5) number of serotypes in the vaccine (9.5%). Among US HCPs, the most important attribute of PCVs was comparability of immune response for PCV13 shared serotypes, while the number of serotypes was least important. Findings suggest new PCVs eliciting high immune responses for serotypes that contribute substantially to IPD burden and maintaining immunogenicity against serotypes in existing PCVs are preferred by HCPs.

Mohammad Ghazi Vakili, Christoph Gorgulla, AkshatKumar Nigam et al.

The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology. Researchers have increasingly leveraged novel computational techniques to streamline the drug development process to increase hit rates and reduce the costs associated with bringing a drug to market. To this end, we introduce a quantum-classical generative model that seamlessly integrates the computational power of quantum algorithms trained on a 16-qubit IBM quantum computer with the established reliability of classical methods for designing small molecules. Our hybrid generative model was applied to designing new KRAS inhibitors, a crucial target in cancer therapy. We synthesized 15 promising molecules during our investigation and subjected them to experimental testing to assess their ability to engage with the target. Notably, among these candidates, two molecules, ISM061-018-2 and ISM061-22, each featuring unique scaffolds, stood out by demonstrating effective engagement with KRAS. ISM061-018-2 was identified as a broad-spectrum KRAS inhibitor, exhibiting a binding affinity to KRAS-G12D at $1.4 μM$. Concurrently, ISM061-22 exhibited specific mutant selectivity, displaying heightened activity against KRAS G12R and Q61H mutants. To our knowledge, this work shows for the first time the use of a quantum-generative model to yield experimentally confirmed biological hits, showcasing the practical potential of quantum-assisted drug discovery to produce viable therapeutics. Moreover, our findings reveal that the efficacy of distribution learning correlates with the number of qubits utilized, underlining the scalability potential of quantum computing resources. Overall, we anticipate our results to be a stepping stone towards developing more advanced quantum generative models in drug discovery.

Seungheun Baek, Soyon Park, Yan Ting Chok et al.

Predicting cellular responses to various perturbations is a critical focus in drug discovery and personalized therapeutics, with deep learning models playing a significant role in this endeavor. Single-cell datasets contain technical artifacts that may hinder the predictability of such models, which poses quality control issues highly regarded in this area. To address this, we propose CRADLE-VAE, a causal generative framework tailored for single-cell gene perturbation modeling, enhanced with counterfactual reasoning-based artifact disentanglement. Throughout training, CRADLE-VAE models the underlying latent distribution of technical artifacts and perturbation effects present in single-cell datasets. It employs counterfactual reasoning to effectively disentangle such artifacts by modulating the latent basal spaces and learns robust features for generating cellular response data with improved quality. Experimental results demonstrate that this approach improves not only treatment effect estimation performance but also generative quality as well. The CRADLE-VAE codebase is publicly available at https://github.com/dmis-lab/CRADLE-VAE.

Bohao Xu, Yingzhou Lu, Yoshitaka Inoue et al.

Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics. Traditional machine learning models often struggle with the complexity and variability inherent in predicting protein functions, necessitating more sophisticated approaches. In this work, we introduce Protein-Mamba, a novel two-stage model that leverages both self-supervised learning and fine-tuning to improve protein function prediction. The pre-training stage allows the model to capture general chemical structures and relationships from large, unlabeled datasets, while the fine-tuning stage refines these insights using specific labeled datasets, resulting in superior prediction performance. Our extensive experiments demonstrate that Protein-Mamba achieves competitive performance, compared with a couple of state-of-the-art methods across a range of protein function datasets. This model's ability to effectively utilize both unlabeled and labeled data highlights the potential of self-supervised learning in advancing protein function prediction and offers a promising direction for future research in drug discovery.

Julieana Moon, Naimul Khan

Within the evolving field of digital intervention, serious games emerge as promising tools for evidence-based interventions. Research indicates that gamified therapy, whether employed independently or in conjunction with online psychoeducation or traditional programs, proves more efficacious in delivering care to patients. As we navigate the intricate realm of serious game design, bridging the gap between therapeutic approaches and creative design proves complex. Professionals in clinical and research roles demonstrate innovative thinking yet face challenges in executing engaging therapeutic serious games due to the lack of specialized design skills and knowledge. Thus, a larger question remains: How might we aid and educate professionals in clinical and research roles the importance of game design to support their innovative therapeutic approaches? This study examines potential solutions aimed at facilitating the integration of gamification design principles into clinical study protocols, a pivotal aspect for aligning therapeutic practices with captivating narratives in the pursuit of innovative interventions. We propose two solutions, a flow chart framework for serious games or a comprehensive reference document encompassing gamification design principles and guidelines for best design practices. Through an examination of literature reviews, it was observed that selected design decisions varied across studies. Thus, we propose that the second solution, a comprehensive reference design guide, is more versatile and adaptable.

E. M. Arroyo-Urea, María Muñoz-Hernando, Marta Leo-Barriga et al.

AbstractNanoemulsions (NE) are lipid nanocarriers that can efficiently load hydrophobic active compounds, like palmitoyl-L-carnitine (pC), used here as model molecule. The use of design of experiments (DoE) approach is a useful tool to develop NEs with optimized properties, requiring less experiments compared to trial-and-error approach. In this work, NE were prepared by the solvent injection technique and DoE using a two-level fractional factorial design (FFD) as model was implemented for designing pC-loaded NE. NEs were fully characterized by a combination of techniques, studying its stability, scalability, pC entrapment and loading capacity and biodistribution, which was studied ex-vivo after injection of fluorescent NEs in mice. We selected the optimal composition for NE, named pC-NEU, after analysis of four variables using DoE. pC-NEU incorporated pC in a very efficient manner, with high entrapment efficiency (EE) and loading capacity. pC-NEU did not change its initial colloidal properties stored at 4 °C in water during 120 days, nor in buffers with different pH values (5.3 and 7.4) during 30 days. Moreover, the scalability process did not affect NE properties and stability profile. Finally, biodistribution study showed that pC-NEU formulation was predominantly concentrated in the liver, with minimal accumulation in spleen, stomach, and kidneys.

Charles Brobbey, Shasha Yin, Liu Liu et al.

Abstract Protein arginine methyltransferase 5 (PRMT5) catalyzes mono-methylation and symmetric di-methylation on arginine residues and has emerged as a potential antitumor target with inhibitors being tested in clinical trials. However, it remains unknown how the efficacy of PRMT5 inhibitors is regulated. Here we report that autophagy blockage enhances cellular sensitivity to PRMT5 inhibitor in triple negative breast cancer cells. Genetic ablation or pharmacological inhibition of PRMT5 triggers cytoprotective autophagy. Mechanistically, PRMT5 catalyzes monomethylation of ULK1 at R532 to suppress ULK1 activation, leading to attenuation of autophagy. As a result, ULK1 inhibition blocks PRMT5 deficiency-induced autophagy and sensitizes cells to PRMT5 inhibitor. Our study not only identifies autophagy as an inducible factor that dictates cellular sensitivity to PRMT5 inhibitor, but also unearths a critical molecular mechanism by which PRMT5 regulates autophagy through methylating ULK1, providing a rationale for the combination of PRMT5 and autophagy inhibitors in cancer therapy.

Valeria Castelli, Enric Sastre-Escolà, Pedro Puerta-Alcalde et al.

Objectives: to assess the current epidemiology, antibiotic therapy and outcomes of onco- hematological patients with bacteremic skin and soft-tissue infections (SSTIs), and to identify the risk factors for Gram-negative bacilli (GNB) infection and for early and overall mortality. Methods: episodes of bacteremic SSTIs occurring in cancer patients at two hospitals were prospectively recorded and retrospectively analyzed. Results: Of 164 episodes of bacteremic SSTIs, 53% occurred in patients with solid tumors and 47% with hematological malignancies. GNB represented 45.5% of all episodes, led by <i>Pseudomonas aeruginosa</i> (37.8%). Multidrug resistance rate was 16%. Inadequate empirical antibiotic therapy (IEAT) occurred in 17.7% of episodes, rising to 34.6% in those due to resistant bacteria. Independent risk factors for GNB infection were corticosteroid therapy and skin necrosis. Early and overall case-fatality rates were 12% and 21%, respectively. Risk factors for early mortality were older age, septic shock, and IEAT, and for overall mortality were older age, septic shock and resistant bacteria. Conclusions: GNB bacteremic SSTI was common, particularly if corticosteroid therapy or skin necrosis. IEAT was frequent in resistant bacteria infections. Mortality occurred mainly in older patients with septic shock, resistant bacteria and IEAT. These results might guide empirical antibiotic therapy in this high-risk population.

Ning Dai, Wei Yu Tang, Tianshuo Zhou et al.

The tasks of designing RNAs are discrete optimization problems, and several versions of these problems are NP-hard. As an alternative to commonly used local search methods, we formulate these problems as continuous optimization and develop a general framework for this optimization based on a generalization of classical partition function which we call "expected partition function". The basic idea is to start with a distribution over all possible candidate sequences, and extend the objective function from a sequence to a distribution. We then use gradient descent-based optimization methods to improve the extended objective function, and the distribution will gradually shrink towards a one-hot sequence (i.e., a single sequence). As a case study, we consider the important problem of mRNA design with wide applications in vaccines and therapeutics. While the recent work of LinearDesign can efficiently optimize mRNAs for minimum free energy (MFE), optimizing for ensemble free energy is much harder and likely intractable. Our approach can consistently improve over the LinearDesign solution in terms of ensemble free energy, with bigger improvements on longer sequences.

Xiao Yuan

The process of identifying and characterizing B-cell epitopes, which are the portions of antigens recognized by antibodies, is important for our understanding of the immune system, and for many applications including vaccine development, therapeutics, and diagnostics. Computational epitope prediction is challenging yet rewarding as it significantly reduces the time and cost of laboratory work. Most of the existing tools do not have satisfactory performance and only discriminate epitopes from non-epitopes. This paper presents a new deep learning-based multi-task framework for linear B-cell epitope prediction as well as antibody type-specific epitope classification. Specifically, a sequenced-based neural network model using recurrent layers and Transformer blocks is developed. We propose an amino acid encoding method based on eigen decomposition to help the model learn the representations of epitopes. We introduce modifications to standard cross-entropy loss functions by extending a logit adjustment technique to cope with the class imbalance. Experimental results on data curated from the largest public epitope database demonstrate the validity of the proposed methods and the superior performance compared to competing ones.

Yujin Cho, Mingeon Kim, Seojin Kim et al.

This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents. With the rapid advancement of artificial intelligence, particularly in natural language processing, LLMs present a novel opportunity to augment traditional psychological counseling methods. This research primarily focuses on evaluating the LLM's ability to engage in empathetic, adaptable, and contextually appropriate interactions within a therapeutic setting. A comprehensive evaluation was conducted by a panel of clinical psychologists and psychiatrists using a specially developed scorecard. The assessment covered various aspects of the LLM's performance, including empathy, communication skills, adaptability, engagement, and the ability to establish a therapeutic alliance. The study avoided direct testing with patients, prioritizing privacy and ethical considerations, and instead relied on simulated scenarios to gauge the LLM's effectiveness. The results indicate that LLMs hold significant promise as supportive tools in therapy, demonstrating strengths in empathetic engagement and adaptability in conversation. However, challenges in achieving the depth of personalization and emotional understanding characteristic of human therapists were noted. The study also highlights the importance of ethical considerations in the application of AI in therapeutic contexts. This research provides valuable insights into the potential and limitations of using LLMs in psychological counseling for autistic adolescents. It lays the groundwork for future explorations into AI's role in mental health care, emphasizing the need for ongoing development to enhance the capabilities of these models in therapeutic settings.

Li L, Du Y, Wang Y et al.

Ling Li,1,2,&ast; Yaoyao Du,1,&ast; Yang Wang,3 Ning He,2 Bing Wang,1,4 Tong Zhang1 1School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 2School of Pharmacy, Anhui University of Chinese Medicine, Hefei, People’s Republic of China; 3Metabo-Profile Biotechnology (Shanghai) Co. Ltd, Shanghai, People’s Republic of China; 4Chinese Academy of Sciences, Shanghai Institute of Materia Medica, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bing Wang; Tong Zhang, Email bwang@simm.ac.cn; zhangtdmj@hotmail.comBackground: Gastric ulcer (GU) is the most common multifactor gastrointestinal disorder affecting millions of people worldwide. There is evidence that gut microbiota is closely related to the development of GU. Atractylone (ATR) has been reported to possess potential biological activities, but research on ATR alleviating GU injury is unprecedented.Methods: Helicobacter pylori (H. pylori)-induced GU model in zebrafish and ethanol-induced acute GU model in rat were established to evaluate the anti-inflammatory and ulcer inhibitory effects of ATR. Then, 16S rRNA sequencing and metabolomics analysis were performed to investigate the effect of ATR on the microbiota and metabolites in rat feces and their correlation.Results: Therapeutically, ATR inhibited H. pylori-induced gastric mucosal injury in zebrafish. In the ulceration model of rat, ATR mitigated the gastric lesions damage caused by ethanol, decreased the ulcer area, and reduced the production of inflammatory factors. Additionally, ATR alleviated the gastric oxidative stress injury by increasing the activity of superoxide dismutase (SOD) and decreasing the level of malondialdehyde (MDA). Furthermore, ATR played a positive role in relieving ulcer through reshaping gut microbiota composition including Parabacteroides and Bacteroides and regulating the levels of metabolites including amino acids, short-chain fatty acids (SCFAs), and bile acids.Conclusion: Our work sheded light on the mechanism of ATR treating GU from the perspective of the gut microbiota and explored the correlation between gut microbiota, metabolites, and host phenotype.Keywords: atractylone, gastric ulcer, inflammation, oxidative stress, gut microbiota, metabolomics