As the core vehicle for preserving and transmitting traditional Chinese medicine（TCM） academic thought and clinical experience， the establishment of a robust quality evaluation system for TCM clinical case reports is a crucial component in the current standardization and modernization of TCM. Based on the practical experience of constructing the China Clinical Cases Library of Traditional Chinese Medicine by the China Association of Chinese Medicine， this study conducted a comprehensive analysis of critical challenges， including insufficient authenticity and unfocused evaluation criteria. It proposed a three-dimensional evaluation framework grounded in the structure-process-outcome logic， encompassing three dimensions of authenticity and standardization， characteristics and advantages， application and translational impact. This framework integrated 12 key evaluation indicators in a systematic manner. The model preserved the academic characteristics of TCM syndrome differentiation and treatment， while aligning with modern scientific research standards， achieving a balance between individualized TCM experience and standardized evaluation. Concurrently， this study provided theoretical foundations and methodological guidance for evaluating the quality of TCM clinical cases， contributing significantly to the inheritance of TCM knowledge， evidence-based practice， and the reform of talent evaluation mechanisms.

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.

ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

ObjectiveTo investigate the liver-protecting and anti-liver fibrosis effects of astragaloside Ⅳ （AS-Ⅳ） in vitro and in vivo， as well as its mechanism of action in intervention against liver fibrosis. MethodsIn the animal experiment， C57BL/6J mice were divided into control group， model group， low-dose AS-Ⅳ （20 mg/kg） group， and high-dose AS-Ⅳ （80 mg/kg） group. The mice were given intraperitoneal injection of carbon tetrachloride for 6 weeks to induce liver fibrosis， and since week 3 of injection， the mice in the low-dose AS-Ⅳ group and the high-dose AS-Ⅳ group were given AS-Ⅳ by gavage at a dose of 20 mg/kg and 80 mg/kg， respectively. The serum levels of alanine aminotransferase （ALT） and aspartate aminotransferase （AST） were measured after 4 weeks of administration， as well as the serum levels of hyaluronic acid （HA）， laminin （LN）， procollagen Ⅲ N-terminal peptide （PⅢNP）， and collagen type Ⅳ （Col-Ⅳ）. HE staining， picrosirius red staining， and Masson staining were used to observe liver histopathology and collagen deposition； RT-qPCR was used to measure the mRNA expression levels of Acta2， Col1a1， and Col3a1 in liver tissue， and Western blot was used to measure the protein expression levels of α-smooth muscle actin （α-SMA）， collagen type Ⅲ （Col-Ⅲ）， phosphatidylinositol 3-kinase （PI3K）， phosphorylated PI3K （pPI3K）， protein kinase B （Akt）， and phosphorylated AKT （p-Akt） in liver tissue； transcriptome sequencing was performed for liver tissue to identify differentially expressed genes and perform a bioinformatics analysis. In the cell experiment， transforming growth factor-β （TGF-β） was used to induce the activation of LX-2 cells， and the PI3K inhibitor LY294002 and the PI3K activator 740 Y-P were used for intervention. The cells were divided into control group， model group， AS-Ⅳ group， LY294002 group， and AS-Ⅳ+740 Y-P group， and the cells were harvested after 36 hours of intervention. Changes in the protein expression levels of α-SMA， Col-Ⅲ， pPI3K/PI3K， and pAkt/Akt in LX-2 cells were measured， as well as changes in the relative mRNA expression levels of Acta2， Col1a1， and Col3a1. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsIn the animal experiment， compared with the model group， the AS-Ⅳ treatment group had significant reductions in the serum levels of ALT， AST， HA， LN， PⅢNP， and Col-Ⅳ （all P<0.01）， the mRNA expression levels of Acta2， Col1a1， and Col3a1 in liver tissue （all P<0.05）， and the protein expression levels of α-SMA， Col-Ⅲ， pPI3K， and pAkt （Ser473） in liver tissue （all P<0.05）. In the cell experiment， compared with the control group， the model group had significant increases in the protein expression levels of α-SMA， Col-Ⅲ， pPI3K， and pAkt （Ser473） after TGF-β induction （all P<0.05）； compared with the model group， the AS-Ⅳ group had significant reductions in the protein expression levels of α-SMA， Col-Ⅲ， pPI3K， and pAkt （Ser473） （all P<0.05）， and both the AS-Ⅳ group and the LY294002 group had significant reductions in the protein expression level of pPI3K and the relative mRNA expression levels of Acta2， Col1a1， and Col3a1 （all P<0.05）. Compared with the AS-Ⅳ group， there were significant increases in the protein expression level of pPI3K and the relative mRNA expression levels of Acta2， col1a1， and Col3a1 after 740 Y-P intervention （all P<0.05）. ConclusionAS-Ⅳ can inhibit hepatic stellate cell activation and improve liver fibrosis， possibly by inhibiting the PI3K/Akt signaling pathway.

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.

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.

The disease-syndrome combination model of spleen deficiency with dampness pattern in ulcerative colitis（SDDP-UC） is an important experimental carrier for traditional Chinese medicine （TCM） research on the prevention and treatment of ulcerative colitis （UC）， and the quality of model construction and evaluation directly influences the scientific rigor and translational value of related research conclusions. However， this field still lacks methodological synthesis and a standardized consensus. Based on a comprehensive review of existing literature， this paper summarized isomorphic cues between the spleen deficiency with dampness pattern and UC across four dimensions， including energy metabolism， immune homeostasis， mucosal barrier， and intestinal microecology. The cues were mainly involved in impaired mitochondrial energy supply and glucose metabolic reprogramming， a lowered pro-inflammatory threshold of innate immunity with insufficient adaptive immune regulation， disruption of epithelial barrier gating accompanied by compromised repair capacity， and attenuation of the luminal hypoxia barrier with accumulation of toxic metabolites. A mutually reinforcing process between local "form damage" and systemic "Qi depletion" was further interpreted from a holistic perspective. Regarding modeling strategies， existing studies predominantly use rats as the carrier， apply combined interventions such as improper diet， external damp exposure， and fatigue-related dysregulation to establish the spleen deficiency with dampness pattern background， and subsequently superimpose chemical stimulation to induce UC-like colonic damage， with a total modeling period generally spanning three to four weeks. In terms of the evaluation system， a multidimensional framework integrating syndrome assessment， histopathology， mechanistic indices， and pharmacodynamic counter-verification was outlined. On this basis， current methodological bottlenecks of models were systematically identified， including syndrome drift risk and compounded stress dilemma in temporal sequencing， syndrome confounding from etiological simulation， cross-sectional evaluation bias related to modeling duration， inadequate disease-syndrome linkage and control design within the evaluation system， and limited controls with overly single-track decision logic in formula-based syndrome verification. To address the above issues， a construction and evaluation strategy emphasizing streamlining of core etiological factors， multi-node dynamic monitoring， integration of core disease-syndrome indicator clusters， and establishment of a formula-based syndrome verification system was proposed， providing a reference for the standardized construction and scientific evaluation of the SDDP-UC model.

Rabbit syndrome (RS), characterized by fine, rapid, rhythmic movements along the mouth’s vertical axis, is typically linked to prolonged antipsychotic medication use. Emerging evidence suggests newer antipsychotics’ involvement in RS, prompting investigation into its association with long-acting injectable antipsychotics (LAIs) for schizophrenia or bipolar disorder. We report a case of RS observed in a patient diagnosed with bipolar I disorder and treated with Abilify Maintena, highlighting the importance of vigilance in monitoring adverse effects. The patient, a 53-year-old male, experienced persistent manic episodes despite prior treatments. Upon initiation of Abilify Maintena 400 mg, RS symptoms manifested seven months later, remaining resistant to medication adjustments. This case emphasizes the significance of RS in LAIs-treated patients and emphasizes the need for further research into its mechanisms and optimal management strategies. Additionally, an updated review of RS associated with newer generation antipsychotics is provided to enhance understanding and clinical management.

Breast milk is the optimal natural food for newborns. However， some newborns cannot receive maternal breast milk due to reasons such as mother-infant separation or insufficient lactation. The establishment of human milk banks （HMB） can effectively address these issues， thereby increasing the breastfeeding rate among hospitalized newborns and improving their quality of survival. However， HMB in China is still in the development and improvement stage. Its implementation involves a series of ethical issues， such as informed consent， privacy protection， economic incentives， quality and safety， and fair resource distribution， which hinder HMB’s widespread promotion. Therefore， discussing the ethical dilemmas faced by the widespread establishment of HMB in China’s hospitals and analyzing coping strategies are crucial for improving the breastfeeding rate of newborns. This paper deeply analyzed and sorted out the ethical issues and challenges currently faced by HMB in China， and proposed corresponding strategies， including “ensuring informed consent and voluntary participation of both donors and recipients，” “protecting the privacy of donors and recipients，” “establishing an ethics-based moral incentive and social support system，” “strictly controlling quality and safety issues”， and “developing fair and rational policies，” aiming to provide a reference solution for addressing ethical concerns in the establishment and operation of HMB.