ObjectiveThis paper aims to investigate and evaluate the staged efficacy and safety of the representative empirical prescription of the “Zhu Ke Jiao” theory， Qijia Rougan prescription， combined with entecavir in the treatment of hepatic fibrosis in chronic hepatitis B. MethodsA multicenter randomized controlled clinical study was conducted， and 101 patients diagnosed with chronic hepatitis B-related hepatic fibrosis （CHB-HF） who met the diagnosis and inclusion criteria were randomly assigned to an observation group （Qijia Rougan prescription + entecavir） and a control group （entecavir）. The treatment duration was 24 weeks. Liver stiffness measurement （LSM）， fibrosis-4 index （FIB-4）， portal vein diameter， hepatitis B serology， biochemical indicators， hepatic fibrosis markers in serum ［hyaluronic acid （HA）， laminin （LN）， procollagen Ⅲ peptide （PⅢP）， and type Ⅳ collagen （Ⅳ-C）］， and traditional Chinese medicine syndrome scores were used as efficacy evaluation indicators. Efficacy assessments and explorations of different staged subgroups of Qijia Rougan prescription were conducted according to LSM values based on the Metavir pathological staging standard. ResultsA total of 98 cases were included for statistical analysis， with 49 cases in the observation group and 49 in the control group. The general data of the patients in both groups were comparable. Compared with the same group before treatment， the observation group showed a significant reduction in LSM and FIB-4 （P<0.01）， as well as notable improvements in LN， Ⅳ-C， and various TCM syndrome scores （P<0.05， P<0.01）. When compared to the control group after treatment， the observation group demonstrated significant improvements in LSM， FIB-4， and various TCM syndrome score indicators （P<0.05， P<0.01）， indicating that the observation group performed better than the control group. Subgroup analysis of the regression of hepatic fibrosis stages showed that compared to the same group before treatment， the observation group had better improvement in regression of stages F2 and F3 （P<0.05）. When compared to the control group after treatment， the observation group exhibited superior improvement in regression of stage F3 （P<0.05）. No adverse events occurred in either group during the treatment period. ConclusionCompared with entecavir alone， the combination of Qijia Rougan prescription and entecavir significantly improves the degree of hepatic fibrosis and clinical TCM symptoms in patients. The optimal intervention period is primarily during stage F3， which is a potential “interception” point of the “Zhu Ke Jiao” theory.

At present， the systematic excavation of the clinical experience and academic thought of the Sichuan school of Chinese medicine vis-à-vis its dominant disease entities remains fragmentary， and replicable paradigms are scarce. Confronted with empirical fragmentation， data heterogeneity and decision-making subjectivity， the standardised distillation， inheritance and clinical translation of these distinctive experiences has become a critical bottleneck constraining the development of the Sichuan school. The integration of artificial-intelligence technologies in data processing， pattern recognition and intelligent decision-making has rendered deep mining of traditional Chinese medicine（TCM） clinical knowledge and patterns imperative. Constructing an intelligent modern TCM diagnostic-therapeutic-evaluative system is now the obligatory route for inheritance and innovation in Chinese medicine， and simultaneously provides a technological breakthrough for intelligent decision paradigms in the dominant diseases of the Sichuan school. Accordingly， this study adopts the regional academic school as its point of entry， focuses on the dominant diseases of the Sichuan school， and proposes an innovative pathway of "four-dimensional data-multi-modal fusion-multi-agent decision-making". Specifically， four data dimensions are defined and instantiated： （Ⅰ） knowledge from classical medical literature and historical case records. （Ⅱ） objective four-diagnosis phenotypic data. （Ⅲ） master physicians' prescribing regularities. （Ⅳ） characteristic mechanisms of renowned formulae. Leveraging multi-modal data fusion and generative artificial intelligence， the entire causal chain of Famous Physicians and Renowned Formulas is explicated to reconstruct the diagnostic-therapeutic cognitive logic of the regional school. Finally， a multi-agent collective-decision model is established and refined for the dominant diseases of the Sichuan school， capable of generating precise， individualised treatment regimens and thereby advancing an intelligent diagnostic-therapeutic paradigm that delivers more efficient and accurate clinical decision support.

The chronicity of infectious diseases is an important field in the collaborative research of traditional Chinese and Western medicine. The warm disease theory of pathogen invading nutrient and blood aspects in traditional Chinese medicine （TCM） takes the struggle between healthy Qi and pathogenic Qi and cementation of Yin as the core pathogenesis， providing a unique theoretical framework for explaining the common pathology of infectious chronic diseases. This theory originated from Yin-Yang interaction in the Internal Classic and was enriched with WU Youke's theory of intruding pathogen interacting and lingering in blood vessels and YE Tianshi's theory of long-term illness entering collaterals. Combining the theory with modern medical knowledge， our team has condensed the dynamic pathogenesis model of deficiency （nutrient and blood aspects） and excess （pathogen） interacting in the blood collaterals of Yin aspect， the core feature of which is the four-dimensional interactions of cause （pathogen characteristics）， location （three Yin locations of diseases）， nature （deficiency and excess）， and potential （transmission trend）. The common pathology of infectious chronic diseases is reflected in interactions. That is， the interactions between nutrient and blood deficiency （immune exhaustion and metabolic disorder） and pathogen excess （pathogen persistence and fibrous hyperplasia） in the liver collaterals （Jueyin）， kidney collaterals （Shaoyin）， lung collaterals （Taiyin） and other blood collaterals of Yin aspect form the pathological damage characterized by immune inflammatory response-continuous tissue damage with excessive repair. Taking the inheritance and innovative development of classics as the main line， this paper systematically discusses the scientific connotation of the theory of pathogen invading nutrient and blood aspects and the paths of inheritance and innovation and clarifies the original significance of this theory in the chronic development of infectious diseases. Furthermore， taking clinical diseases as an example， this paper reflects the guiding value of this classical theory in the modern diagnosis and treatment of infectious diseases with integrated traditional Chinese and Western medicine and the application potential of this theory in solving complex medical problems through the construction of the innovative paradigm of precise diagnosis and treatment with integrated traditional Chinese and Western medicine.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

Recent studies have indicated that the expression of ubiquitin-specific protease 51 (USP51), a novel deubiquitinating enzyme (DUB) that mediates protein degradation as part of the ubiquitin‒proteasome system (UPS), is associated with tumor progression and therapeutic resistance in multiple malignancies. However, the underlying mechanisms and signaling networks involved in USP51-mediated regulation of malignant phenotypes remain largely unknown. The present study provides evidence of USP51's functions as the prominent DUB in chemoresistant triple-negative breast cancer (TNBC) cells. At the molecular level, ectopic expression of USP51 stabilized the 78 kDa Glucose-Regulated Protein (GRP78) protein through deubiquitination, thereby increasing its expression and localization on the cell surface. Furthermore, the upregulation of cell surface GRP78 increased the activity of ATP binding cassette subfamily B member 1 (ABCB1), the main efflux pump of doxorubicin (DOX), ultimately decreasing its accumulation in TNBC cells and promoting the development of drug resistance both in vitro and in vivo. Clinically, we found significant correlations among USP51, GRP78, and ABCB1 expression in TNBC patients with chemoresistance. Elevated USP51, GRP78, and ABCB1 levels were also strongly associated with a poor patient prognosis. Importantly, we revealed an alternative intervention for specific pharmacological targeting of USP51 for TNBC cell chemosensitization. In conclusion, these findings collectively indicate that the USP51/GRP78/ABCB1 network is a key contributor to the malignant progression and chemotherapeutic resistance of TNBC cells, underscoring the pivotal role of USP51 as a novel therapeutic target for cancer management.

Clinical management of atopic dermatitis (AD) is challenged by its susceptibility to recurrence, side effects, and high costs. We found that Portulaca oleracea L.-derived nanovesicles (PDNV) exert anti-inflammatory effects by modulating macrophage M1/M2 polarization. These effects were achieved through pathways including inhibition of nuclear factor-κB (NF-κB) and stimulator of interferon genes (STING) protein expression in diseased tissues, demonstrating their potential to ameliorate AD symptoms. To increase the transdermal permeation of PDNV, dissolvable microneedles composed primarily of hyaluronic acid (HA) were developed as an adjunctive means of delivery. Meanwhile, polysaccharides of Portulaca oleracea L., which were synergistic with PDNV, were used as microneedle constituent materials to enhance the mechanical properties and physical stability of HA. This new means of delivery significantly improves the treatment of AD and also provides new options for the efficient utilization of plant extracellular vesicles and the treatment of AD. In addition, transcriptomic analysis of PDNV showed that the mRNAs of Portulaca oleracea L. are closest to those of ferns, which may shed light on related evolutionary and plant species identification studies.

OBJECTIVE: Ulcerative colitis is closely associated with intestinal stem cell (ISC) loss and impaired intestinal mucus barrier. Sinisan (SNS), a compound Chinese herbal medicine, has a long history in the treatment of intestinal dysfunction, yet whether SNS can relieve acute experimental colitis by modulating ISC proliferation and secretory cell differentiation has not been studied. Our study tested the effect of SNS against acute colitis and focused on the mechanisms involving intestinal barrier recovery. METHODS: Network pharmacology analysis and blood entry component analysis of SNS were used to explore the underlying mechanism by which SNS affects the acute dextran sulfate sodium (DSS)-induced murine colitis model. RNA-sequencing was used to demonstrate the mechanism. Further, reverse transcription-quantitative polymerase chain reaction, immunofluorescence staining, and alcian blue and periodic acid-Schiff staining were performed in vivo and in the colonic organoids to investigate the cell lineage differentiation-related mechanism of SNS. Furthermore, potential active ingredients from SNS were predicted by network pharmacology analysis. RESULTS: SNS dramatically suppressed DSS-induced acute colonic inflammation in mice. RNA-sequencing analysis revealed downregulation of inflammation and apoptosis-related genes, and upregulation of lipid metabolism and proliferation-related genes, such as Irf7, Pparα, Clspn and Hspa5. Additionally, ISC renewal and intestinal secretory cell lineage commitment were significantly promoted by SNS both in vivo and in vitro in colonic organoids, leading to enhanced mucin expression. Furthermore, potential active ingredients from SNS that mediated inflammation, lipid metabolism, proliferation, apoptosis, stem cells and secretory cells were predicted using a network pharmacology approach. CONCLUSION Our study shed light on the underlying mechanism of SNS in attenuating acute colitis from the perspective of ISC renewal and secretory lineage cell differentiation, suggesting a of novel therapeutic strategy against colitis. Please cite this article as: Cai YJ, Lan JH, Li S, Feng YN, Li FH, Guo MY, et al. Sinisan, a compound Chinese herbal medicine, alleviates acute colitis by facilitating colonic secretory cell lineage commitment and mucin production. J Integr Med. 2025; 23(4): 429-444.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Mice ; Colon/pathology* ; Mucins/metabolism* ; Mice, Inbred C57BL ; Cell Differentiation/drug effects* ; Male ; Colitis/metabolism* ; Cell Lineage/drug effects* ; Dextran Sulfate ; Stem Cells/drug effects* ; Disease Models, Animal

Objective To develop and validate a fast Monte Carlo(MC)-based patient-specific quality assurance(PSQA)tool using the treatment log files that is suitable to be used in the online adaptive radiotherapy for pencil beam scanning proton therapy(PBSPT-ART).Methods The proposed tool first used the delivery log file of a PBSPT plan to reversely reconstruct the PBSPT(rPBSPT)plan,and then used an in-house developed graphic processing unit(GPU)-accelerated virtual particle MC(VPMC)dose engine to calculate the dose distribution of the rPBSPT plan.The rPBSPT dose calculated by VPMC was then compared to the rPBSPT dose calculated by another independent MC dose engine(MCsquare),using 3D gamma analysis to verify the accuracy of VPMC calculation.As a demonstration of the feasibility of developed log file-based PSQA,the VPMC calculated dose of the rPBSPT plan was compared to the pre-delivery second check dose of the corresponding PBSPT plan calculated by MCsquare,using 3D gamma analysis.3D gamma analysis employes a criterion of 2 mm/2%/10%.Twenty patients with different disease sites were representatively selected to validate the efficiency and accuracy of the tool.Results The average calculation time of a rPBSPT plan by VPMC was(5.88±4.00)s in the accuracy verification.Compared to MCsquare,the passing rate of the 3D gamma analysis was 99.47%±0.72%.In the proposed PSQA tool demonstration,the passing rate of comparing the VPMC calculated rPBSPT dose to MCsquare calculated second check dose of the corresponding PBSPT plan was 98.91%±0.92%.Conclusion The accuracy and efficiency of the tool can meet the requirements of PSQA in the online PBSPT-ART workflow.

Objective:To explore the safety and efficacy of intrasaccular flow disruptor in intracranial aneurysms, including wide-necked aneurysms.Methods:A retrospective analysis was performed; 102 patients with intracranial aneurysms treated with intrasaccular flow disruptor at Department of Neurointervention, First Affiliated Hospital of Zhengzhou University from August 2022 to November 2024 were enrolled; their clinical and imaging data were collected. The aneurysm characteristics and perioperative complications were summarized; aneurysm occlusion was evaluated by Woven EndoBridge occlusion scale (WOS) immediately after surgery and during the follow-up period.Results:Among the 102 aneurysms, 33 were anterior cerebral artery aneurysms, 40 were middle cerebral artery aneurysms, 17 were internal carotid artery aneurysms and 12 were basilar artery aneurysms; 73 aneurysms were regular intracranial saccular aneurysms and 29 were irregular saccular ones (17 of them with daughter sacs); 12 aneurysms were ruptured at acute phase. The surgical success rate was 100%: single intrasaccular flow disruptor was implanted into 95 patients, and stent-assisted or salvage treatments were given to 7 patients). Immediately after surgery, WOS grading A was noted in 5 patients, grading B in 7, grading C in 31, and grading D in 59. Two patients suffered severe perioperative complications and passed away: one patient died for ischemic complications that ensued after stent salvage treatment (parent artery being compressed by intrasaccular flow disruptor); the other patient died for hemorrhagic complications triggered by rupture of an initially unruptured aneurysm following intrasaccular flow disruptor implantation. Among them, 52 patients completed postoperative DSA follow-up, with a median follow-up of 205 (168, 292) days; WOS grading A was noted in 31 patients, grading B in 9, grading C in 8, and grading D in 4 at the last follow-up; the full occlusion rate was 92.3%, and no ischemic or hemorrhagic adverse events occurred.Conclusion:Intrasaccular flow disruptor shows high short-term safety and good efficacy in the treatment of intracranial aneurysms.