ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveTo investigate the current professional competency among full-time and part-time personnel of the nosocomial infection control administration in Shanghai, so as to provide a scientific basis for future training programmes. MethodsIn December 2024, a questionnaire survey was conducted by the Shanghai Nosocomial Infection Quality Control Center among full-time and part-time personnel of the nosocomial infection control administration across medical institutions at various levels and types in Shanghai using convenience sampling method. The questionnaire consisted of two parts: demographic information and professional competency assessment. The professional competency scale comprised four dimensions: fundamental cognition, basic skills, professional expertise, and personal qualities, totaling 35 items. ResultsA total of 1 179 questionnaires were distributed, with 1 144 valid responses collected, yielding an effective response rate of 97.03%. Statistically significant differences were observed among full-time and part-time personnel of the nosocomial infection control administration in terms of age (t=5.32, P=0.021), professional background (χ²=9.90, P=0.019), educational qualifications (χ²=19.10, P<0.001), professional titles (χ²=12.60, P=0.002), and the levels of medical institutions (χ²=111.08, P<0.001). The scores of full-time personnel of the nosocomial infection control administration in fundamental cognition [92 (82, 99) points] and basic skills [88 (78, 96) points] were significantly higher than those of part-time personnel(Z=-2.21, P=0.027;Z=-2.74, P=0.006). Statistically significant differences were found in fundamental cognition scores between full-time and part-time personnel of the nosocomial infection control administration regarding occupational safety protection, definition of healthcare-associated infection outbreaks, types of drug-resistant bacteria and their prevention and control strategies, and transmission routes of different infectious diseases (all P<0.05). Statistically significant differences were also observed in basic skills scores including proficient use of monitoring platforms, formulation and revision of standard operating procedures (SOPs), independent completion of targeted surveillance, guidance on basic infection control skills, guidance for key departments, and follow-up of personnel with occupational exposure (all P<0.05). However, no statistically significant differences were found in scores of professional knowledge and personal qualities (P>0.05). ConclusionThere are certain differences in professional competency between full-time and part-time personnel of the nosocomial infection control administration in Shanghai in terms of fundamental cognition and basic skills. Part-time personnel can effectively improve their professional competency through systematic training on basic infection control knowledge and practical skills, thereby comprehensively enhancing the overall quality of the nosocomial infection administration team.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Lung cancer is the leading cause of cancer-related deaths worldwide， and tumor metastasis is a key factor contributing to the mortality of most lung cancer patients. Aberrant activation of epithelial-mesenchymal transition （EMT） is a major driver of lung cancer progression and metastasis. EMT is characterized by the loss of apical-basal polarity and intercellular adhesion in highly differentiated， polarized， and organized epithelial cells， which acquire motility， migratory potential， and invasive properties. During this process， cells undergo cytoskeletal remodeling and transform into a mesenchymal phenotype， accompanied by associated changes in cellular markers. The EMT process is highly complex and is tightly regulated by intricate networks involving multiple transcription factors， post-translational controls， epigenetic modifications， and non-coding RNAs. Therefore， therapies targeting the mechanisms of malignant transformation and their associated pathways in lung cancer are of significant clinical importance. In recent years， EMT has attracted increasing attention as a potential target for cancer therapy. Chinese medicine， with its characteristics of multi-target action， low side effects， and good therapeutic efficacy， has demonstrated an important role in anticancer treatment. A series of studies have investigated the role of Chinese medicine in inhibiting EMT in lung cancer. Active ingredients of Chinese medicine， including flavonoids， glycosides， phenols， terpenoids， saccharides， and alkaloids， as well as Chinese medicine compound formulas， have shown significant regulatory effects on EMT. Their mechanisms mainly involve multiple pathways， targets， and links， including signaling pathways， exosomes， microRNAs （miRNAs）， and the tumor-associated immune microenvironment. This article summarizes the mechanisms by which EMT promotes malignant tumor progression and reviews the current research on how Chinese medicine active ingredients， monomers， and compound formulas inhibit EMT and suppress lung cancer cell migration and invasion. This study is expected to provide comprehensive theoretical information for basic and translational research on lung cancer.

ObjectiveTo identify the primary hemostatic active components in Moutan Cortex Carbonisata（MCC） based on the spectrum-effect relationship between the fingerprint and hemostatic efficacy， thereby providing a basis for characterizing its active constituents. MethodsUltra-performance liquid chromatography-quadrupole-electrostatic field orbitrap high-resolution mass spectrometry（UPLC-Q-Orbitrap MS/MS） was employed to establish the fingerprint profiles of 16 batches of MCC aqueous extracts and identify the common peaks. Activated partial thromboplastin time（APTT）， an in vitro coagulation activity indicator， was measured for the 16 batches of samples using a semi-automated coagulometer. Grey relational analysis（GRA）， Pearson correlation analysis， and partial least squares regression（PLSR） were comprehensively applied to screen potential hemostatic active components. For the identified active components， multi-dimensional pharmacological validation was conducted through in vitro coagulation assays measuring APTT， prothrombin time（PT）， and thrombin time（TT）， evaluation of hemostasis rate using a zebrafish cerebral hemorrhage model， and real-time quantitative polymerase chain reaction（Real-time PCR） detection of coagulation factor X（FⅩ） mRNA expression level. ResultsThe UPLC fingerprint of the aqueous extract of MCC was successfully established， identifying 12 common peaks. Among these， 9 chemical components were subsequently characterized using UPLC-Q-Orbitrap MS. Comprehensive application of GRA， Pearson correlation analysis， and PLSR analysis identified 5-hydroxymethylfurfural（5-HMF）， gallic acid， 1-O-galloylglucose， and p-hydroxybenzoic acid as key hemostatic active constituents in MCC. In vitro coagulation assays confirmed that all four active components significantly shortened APTT and PT（P<0.05， P<0.01）. The zebrafish cerebral hemorrhage model further validated their in vivo hemostatic efficacy， with each component significantly reducing hemorrhage area（P<0.05， P<0.01）， yielding hemostasis rates of 31.20% for 5-HMF， 68.85% for gallic acid， 45.45% for 1-O-galloylglucose， and 45.60% for p-hydroxybenzoic acid， and demonstrating overall concentration-dependent effects. Real-time PCR analysis demonstrated that all active components significantly upregulated FⅩ mRNA expression（P<0.05， P<0.01）， synergistically enhancing hemostasis. ConclusionBy integrating spectrum-effect relationship analysis and multi-dimensional efficacy validation， this study identified four hemostatic constituents from MCC， providing a scientific basis for elucidating its hemostatic material basis.

ObjectiveTo investigate differential metabolites associated with browning in the post-harvest processing of Pinelliae Rhizoma， providing data support for elucidating the key metabolites and metabolic pathways involved in browning， and developing safe and efficient sulfur-free processing techniques. MethodsUltra-performance liquid chromatography-triple quadrupole/linear ion trap mass spectrometry（UPLC-QTRAP-MS/MS） was used to detect the metabolites of Pinelliae Rhizoma at different browning stages（0， 8， 16 h） for widely targeted metabolomics. Subsequently， Multivariate statistical analysis of metabolites was conducted using principal component analysis（PCA）， hierarchical cluster analysis（HCA）， orthogonal partial least squares-discriminant analysis（OPLS-DA）， and K-means cluster analysis. Differential metabolites at different browning stages were screened based on variable importance in the projection（VIP） value>1 and |log₂fold change（FC）|≥1， and metabolic pathway enrichment analysis was performed using Kyoto Encyclopedia of Genes and Genomes（KEGG）. ResultsA total of 1 416 metabolites were identified across the three browning stages of Pinelliae Rhizoma， predominantly comprising amino acids and their derivatives（239）， lipids（219）， alkaloids（156）， phenolic acids（121）， terpenoids（113）， and flavonoids（111）. A two-by-two comparison of the three browning phases， yielded 622 differential metabolites that were significantly enriched in the phenylpropanoid biosynthesis， flavone and flavonol biosynthesis， and purine metabolic pathway. Further analysis revealed that carbohydrates such as D-mannose and turanose， phenolic acids such as 1-O-caffeoyl-6-O-glucosyl-β-D-glucose， dicaffeoylshikimic acid， and flavonoids such as epigallocatechin gallate， vitexin-7-O-rutinoside， luteolin-7-O-（6″-malonyl）glucoside-5-O-arabinoside， catechin gallate， epicatechin gallate， isovitexin-7-O-glucoside-2″-O-rhamnoside， apigenin-7-O-rutinoside-4ʹ-O-sophoroside， 3，5，3ʹ，4ʹ，5ʹ-penta-hydroxyflavan-7-gallate may act as browning substrates and play important roles in the browning process. ConclusionCarbohydrates， phenolic acids， and flavonoids may serve as key substrates in the browning process of Pinelliae Rhizoma， involving pathways such as phenylpropanoid biosynthesis， flavone and flavonol biosynthesis， and purine metabolism， which can provide a theoretical basis for further exploration of the browning mechanism.

ObjectiveThis paper aims to conduct a secondary analysis of a randomized controlled trial on the treatment of myelodysplastic syndrome （MDS） with deficiency of both the spleen and kidney and blockage of toxin and blood stasis with an arsenic-containing traditional Chinese medicine compound， by applying the mixed model for repeated measure （MMRM） and the method of stratified composite outcome with win ratio. The analysis includes the assessment of hematological efficacy and the composite outcome evaluation of adverse reactions， so as to more comprehensively assess the therapy of this regimen. MethodsThe MMRM and win ratio methods were used to evaluate the efficacy of a prospective，multi-center，double-blind，randomized controlled study. The blood routine （hemoglobin concentration，neutrophil count， and platelet count） and biochemical indexes （aspartate aminotransferase，alanine aminotransferase，serum creatinine，and serum ferritin） of the patients were detected at the time of enrollment and at the end of each course of treatment in the laboratory department of Xiyuan Hospital. The patients' syndromes at the time of enrollment and after treatment were recorded and scored according to the therapy standard of traditional Chinese medicine for diseases and syndromes. MMRM was used to analyze the blood routine indexes of the experimental group and the control group. This method has the advantages of high data reliability and dynamic efficacy under intervention and time. The win ratio method was used to evaluate the composite outcome of traditional Chinese medicine syndrome scores and biochemical indexes according to the priority and to verify the clinical safety of arsenic-containing traditional Chinese medicine compound. ResultsThe results of MMRM analysis showed that the hemoglobin concentration of patients in the group with arsenic-containing traditional Chinese medicine compound increased significantly compared with that before treatment in the group，while that in the placebo group decreased significantly （P<0.01）. When compared with that after treatment in the placebo group，the hemoglobin concentration of patients in the group with arsenic-containing traditional Chinese medicine compound increased significantly，and the mean difference of least squares （LS） was statistically significant （P<0.01）. When compared with those before treatment in the group，there were no statistically significant differences in the neutrophil count and platelet count in both groups. After treatment，there were no statistically significant differences in the neutrophil count， platelet count， and the mean difference of LS between the two groups. The analysis results of win ratio showed that the group with arsenic-containing traditional Chinese medicine compound had a significant advantage in the comparison of composite outcomes，with a win ratio （95% CI） of 2.01 （1.24-3.27） （P<0.01），and that the possibility of "winning" in terms of safety was 2.01 times that of the placebo group. The safety advantage of the group with arsenic-containing traditional Chinese medicine compound mainly came from the traditional Chinese medicine syndrome scores，renal function indexes， and iron reserve capacity indexes，and the number of winning times was less than that of losing times in the comparison of liver function outcomes. ConclusionThe MMRM analysis proves that the arsenic-containing traditional Chinese medicine compound can significantly improve the hemoglobin concentration of patients with myelodysplastic syndrome with refractory cytopenia and multilineage dysplasia （MDS-RCMD） of the type of deficiency of both the spleen and kidney and blockage of toxin and blood stasis. This conclusion is not interfered with by time trends and individual relationships and methodologically improves the credibility of the therapy of the arsenic-containing traditional Chinese medicine compound in treating MDS. Four outcomes are evaluated by the win ratio method，namely traditional Chinese medicine syndromes，liver function，renal function， and iron reserve capacity，proving that the arsenic-containing traditional Chinese medicine compound has the comprehensive advantages of improving the survival quality of the patients and reducing adverse reactions. The win ratio outcome provides clear comparative indexes for the evaluation of adverse reactions，making it easier for regulatory authorities，medical staff， and patients to understand the safety of the arsenic-containing traditional Chinese medicine compound in clinical application.

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.