ObjectiveThis study aimed to investigate the action mechanism by which Banxia Xiexintang （BXT） inhibits epithelial-mesenchymal transition （EMT） in chronic atrophic gastritis （CAG） rats by regulating the interleukin-17（IL-17）/extracellular regulated protein kinases（ERK）/CCAAT enhancer binding protein β（C/EBPβ）signaling pathway， thereby providing new theoretical evidence for the treatment of CAG with classic traditional Chinese medicine formulas. MethodsA CAG rat model was established by using the combined factor method. After successful modeling， the rats were randomly divided into the model group， low-， medium-， and high-dose groups （0.549， 1.098， 2.196 g·kg^-1， respectively） of BXT， and the positive drug group （vitacoenzyme， 0.3 g·kg^-1）. A normal control group was also set up. After 8 weeks of intervention， the pathological changes of gastric tissue were evaluated. The enzyme-linked immunosorbent assay （ELISA） was used to detect the contents of IL-17， tumor necrosis factor-α （TNF-α）， cyclooxygenase-2 （COX-2）， and C/EBPβ in serum， as well as the contents of EMT markers in gastric mucosal tissue including E-cadherin， N-cadherin， and vimentin. The immunohistochemistry method was employed to determine the localization and protein expression levels of IL-17， p-ERK， and C/EBPβ in gastric mucosal tissue. Western blot was used to detect the protein expressions of C/EBPβ， ERK， and its phosphorylated form （p）-ERK in gastric mucosa. Real-time polymerase chain reaction （Real-time PCR） was applied to measure the mRNA expression levels of ERK， COX-2， and C/EBPβ in gastric mucosa. ResultsCompared with those in the normal control group， the rats in the model group showed gastric mucosal glandular atrophy and inflammatory cell infiltration. The protein and their related mRNA expressions of C/EBPβ， ERK， and p-ERK in gastric mucosa were significantly increased （P<0.05，P<0.01）. The levels of IL-17， TNF-α， COX-2， and C/EBPβ in serum were significantly increased （P<0.01）. The contents of N-cadherin and vimentin in gastric mucosal tissue were significantly increased， while the content of E-cadherin was significantly decreased （P<0.01）. Compared with the model group， after intervention with different doses of BXT， the pathological damage of the gastric mucosa was improved to varying degrees. The protein and mRNA expressions of C/EBPβ， ERK， and p-ERK in gastric mucosa were significantly reduced （P<0.05，P<0.01）. The levels of IL-17， TNF-α， COX-2， and C/EBP β in serum were significantly decreased （P<0.01）. The contents of N-cadherin and vimentin in gastric mucosa tissue were decreased， while the content of E-cadherin was increased （P<0.05，P<0.01）. ConclusionBXT can effectively improve the pathological damage of gastric mucosal tissue in CAG rats. Its action mechanism may be related to reducing the levels of IL-17 and TNF-α in serum， regulating the IL-17/ERK/C/EBPβ signaling pathway and inhibiting the EMT process.

To introduce the general thinking, guidelines, work objectives and elaboration process of the general technical requirements adopted by volume Ⅳ of the Chinese Pharmacopoeia 2025 Edition, and to summarize and figure out the main characteristics on dosage forms, physico-chemical testing, microbial and biological testing, reference standards and guidelines The newly revised general chapters of pharmacopoeia give full play to the normative and guiding role of the Chinese Pharmacopoeia standard, track the frontier dynamics of international drug regulatory science and the elaboration of monographs, expand the application of state-of-the-art technologies, and steadily promote the harmonization and unification with the ICH guidelines； further enhance the overall capacity of TCM quality control, actively implement the 3 R principles on animal experiments, and practice the concept of environmental-friendly； replace and/or reduce the use of toxic and hazardous reagents, strengthen the requirements of drug safety control This paper aims to provide a full-view perspective for the comprehensive, correct understanding and accurate implementation of general technical requirements included in the Chinese Pharmacopoeia 2025 Edition.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

The immunomodulatory， repair， and regeneration-promoting functions of mesenchymal stem cells make them one of the potential treatment methods for liver diseases. At present， viral and non-viral delivery methods have been developed to genetically modify mesenchymal stem cells， and gene modification can promote the survival， homing， and cytokine secretion of mesenchymal stem cells， thereby enhancing the ability of mesenchymal stem cells to treat liver diseases. This article mainly summarizes the research advances in gene-modified mesenchymal stem cells in the treatment of liver diseases， in order to provide new insights and strategies for the clinical treatment of liver diseases.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

The mechanism of action of traditional Chinese medicine (TCM) remains unclear. Historically, research on TCM has mainly focused on exploring the mechanisms of active components acting on single targets. However, it is insufficient to explain the complex mechanisms by which these active components in TCM treat diseases. In recent years, the emergence of molecular glues (MGs) theory has provided new strategies to address this issue. MGs are small molecules that can promote interactions between proteins at their interface. The characteristic of MGs is to establish connections between diverse protein structures, thereby enabling a chemically-mediated proximity effect that triggers a wide spectrum of biological functions. Natural products are the result of billions of years of evolutionary processes in the natural environment. Thus, the extensive structural diversity of natural products renders them a rich source of MGs, including polyketides, terpenoids, steroids, lignans, organic acids, alkaloids and other classes. Currently, several well-known natural MGs, including the immunosuppressants cyclosporin A (CsA) and tacrolimus (FK506), as well as the anticancer agent taxol, have been incorporated into clinical practice. Meanwhile, the advancement of new technologies is propelling the discovery of novel MGs from natural products. Thus, we primarily summarize a growing variety of MGs from natural origins reported in recent years and categorize them based on the chemical structural types. Moreover, the main sources of TCM are natural products. The discovery of natural MGs promises to provide a new perspective for the elucidation of the molecular mechanism behind the efficiency of TCM. In summary, this review aims to provide insights from the perspective of natural products that could potentially influence TCM and modern drug development.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

During orthodontic treatment, clinical monitoring of patients is a crucial factor in determining treatment success. It aids in timely problem detection and resolution, ensuring adherence to the intended treatment plan. In recent years, digital technology has increasingly permeated orthodontic clinical diagnosis and treatment, facilitating clinical decision-making, treatment planning, and follow-up monitoring. This review summarizes recent advancements in digital technology for monitoring orthodontic tooth movement, related complications, and appliance-wearing compliance. It aims to provide insights for researchers and clinicians to enhance the application of digital technology in orthodontics, improve treatment outcomes, and optimize patient experience. The digitization of diagnostic data and the visualization of dental models make chair-side follow-up monitoring more convenient, accurate, and efficient. At the same time, the emergence of remote monitoring technology allows orthodontists to promptly identify oral health issues in patients and take corresponding measures. Furthermore, the multimodal data fusion method offers valuable insights into the monitoring of the root-alveolar relationship. Artificial intelligence technology has made initial strides in automating the identification of orthodontic tooth movement, associated complications, and patient compliance evaluation. Sensors are effective tools for monitoring patient adherence and providing data-driven support for clinical decision-making. The application of digital technology in orthodontic monitoring holds great promise. However, challenges like technical bottlenecks, ethical considerations, and patient acceptance remain.

By analyzing the current application of multi-modal data in the diagnosis of gastric "inflammation-cancer" transformation， this study explored the feasibility and strategies for constructing a disease-syndrome integrated diagnosis and treatment system. Based on traditional Chinese medicine （TCM） phenomics， we proposed utilizing multi-modal data from literature research， cross-sectional studies， and cohort follow-ups， combined with artificial intelligence technology， to establish a multi-dimensional diagnostic and treatment index system. This approach aims to uncover the complex pathogenesis and transformation patterns of gastric "inflammation-cancer" progression. Additionally， by dynamically collecting TCM four-diagnostic information and modern medical diagnostic information through a long-term follow-up system， we developed three major modules including information extraction， multi-modal phenotypic modeling， and information output， to make it enable real-world clinical data-driven long-term follow-up and treatment of chronic atrophic gastritis. This system can provide technical support for clinical diagnosis， treatment evaluation， and research， while also offering insights and methods for intelligent TCM diagnosis.