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.

ObjectiveBased on traditional quality evaluation of Gardeniae Fructus（GF） recorded in historical materia medica， this study systematically compared the quality differences between wild and cultivated GF from morphological characteristics， microscopic features， and contents of primary and secondary metabolites. MethodsVernier calipers and analytical balances were used to measure the length， diameter and individual fruit weight of wild and cultivated GF， and the aspect ratio was calculated. A colorimeter was used to determine the chromaticity value of wild and cultivated GF， and the paraffin sections of them were prepared by safranin-fast green staining and examined under an optical microscope to observe their microstructure. Subsequently， the contents of water-soluble and alcohol-soluble extracts of wild and cultivated GF were detected by hot immersion method under the general rule 2201 in volume Ⅳ of the 2020 edition of the Pharmacopoeia of the People's Republic of China， the starch content was measured by anthrone colorimetric method， the content of total polysaccharides was determined by phenol-sulfuric acid colorimetric method， the sucrose content was determined by high performance liquid chromatography coupled with evaporative light scattering detection（HPLC-ELSD）， and the contents of representative components in them were measured by ultra-performance liquid chromatography（UPLC）. Finally， correlation analysis was conducted between quality traits and phenotypic traits， combined with multivariate statistical analysis methods such as principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， key differential components between wild and cultivated GF were screened. ResultsIn terms of traits， the wild GF fruits were smaller， exhibiting reddish yellow or brownish red hues with significant variation between batches. While the cultivated GF fruits are larger， displaying deeper orange-red or brownish red. The diameter and individual fruit weight of cultivated GF were significantly greater than those of wild GF， while the blue-yellow value（b^*） of wild GF was significantly higher than that of cultivated GF. In the microstructure， the mesocarp of wild GF contained numerous scattered calcium oxalate cluster crystals， while the endocarp contained stone cell class round， polygonal or tangential prolongation， undeveloped seeds were visible within the fruit. In contrast， the mesocarp of cultivated GF contained few calcium oxalate cluster crystals， or some batches exhibited extremely numerous cluster crystals. The stone cells in the endocarp were predominantly round-like， with the innermost layer arranged in a grid pattern. Seeds were basically mature， and only a few immature seeds existed in some batches. Regarding primary metabolite content， wild GF exhibited significantly higher total polysaccharide level than cultivated GF（P<0.01）. In category-specific component content， wild GF exhibited significantly higher levels of total flavonoids and total polyphenols compared to cultivated GF（P<0.01）. Analysis of 12 secondary metabolites revealed that wild GF exhibited significantly higher levels of Shanzhiside， deacetyl asperulosidic acid methyl ester， gardenoside and chlorogenic acid compared to cultivated GF（P<0.01）. Conversely， the contents of genipin 1-gentiobioside， geniposide and genipin were significantly lower in wild GF（P<0.01）. ConclusionThere are significant differences between wild and cultivated GF in terms of traits， microstructure， and contents of primary and secondary metabolites. At present， the quality evaluation system of cultivated GF remains incomplete， and this study provides a reference for guiding the production of high-quality GF medicinal materials.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

Cholangiocarcinoma (CCA) is a malignancy characterized by tumor cells originating in the liver or bile ducts, exhibiting features of cholangiocyte differentiation. It poses a significant clinical challenge due to the limited diagnostic and therapeutic options available. Robust animal models are essential for advancing our understanding of CCA pathogenesis and developing effective treatments. This review provides a comprehensive overview of CCA mouse models, highlighting various approaches, including chemical induction, genetically engineered models, and tumor xenografts. Each model is discussed in terms of its establishment techniques, pathological characteristics, and research significance, with a focus on intrahepatic CCA. Chemical induction models, such as diethylnitrosamine- and azoxymethane-induced models, offer insights into tumorigenesis processes, whereas genetically modified models involving alterations in key genes such as Kirsten rat sarcoma viral oncogene homolog, tumor protein 53, and isocitrate dehydrogenase serve as important tools for studying the molecular mechanisms underlying CCA. Xenograft models, including patient-derived xenografts, bridge the gap between experimental research and clinical applications, allowing for precise therapeutic evaluations. By comparing these models, this review underscores their respective advantages and limitations, paving the way for future studies aiming to optimize and innovate CCA modeling strategies.

The purpose of this paper is to explore the decoction and dosing methods of rhubarb root and rhizome in classical prescriptions and to provide a reference basis for the clinical use of rhubarb root and rhizome. By collating the relevant classical prescriptions of rhubarb root and rhizome in Shanghan Lun and Jingui Yaolüe, the relationship between its decoction and dosing methods and the syndrome was analyzed. The decoction of rhubarb root and rhizome in classical prescriptions can be divided into three categories: simultaneous decoction, decoction later, and other methods (impregnation in Mafei decoction, decoction with water from the well spring first taken in the morning, and pills). If it enters the blood level or wants to slow down, rhubarb root and rhizome should be decocted at the same time with other drugs. If it enters the qi level and wants to speed up, rhubarb root and rhizome should be decocted later. If it wants to upwardly move, rhubarb root and rhizome should be immersed in Mafei decoction. If it wants to suppress liver yang, rhubarb root and rhizome should be decocted with water from the well spring first taken in the morning. If the disease is prolonged, rhubarb root and rhizome should be taken in pill form. The dosing methods of rhubarb root and rhizome can be divided into five categories: draught, twice, three times, before meals, and unspecified. For acute and serious illnesses with excess of pathogenic qi and adequate vital qi, we choose draught. For gastrointestinal diseases, we choose to take the medicine twice. For achieving a moderate and long-lasting effect, we choose to take the medicine three times. If the disease is located in the lower part of the heart and abdomen, we choose to take it before meals. The use of rhubarb root and rhizome in clinical practice requires the selection of the appropriate decoction and dosing methods according to the location of the disease, the severity of the disease, the patient′s constitution, and the condition after taking the medicine.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

ObjectiveFor prepubertal and urgently treated malignant tumor patients, ovarian tissue cryopreservation and transplantation represent more appropriate fertility preservation methods. Current clinical practices often involve freezing ovarian tissue with high concentrations of cryoprotectants (CPAs) and thawing with water baths. These processes lead to varying degrees of toxicity and devitrification damage to ovarian tissue. Therefore, this paper proposes optimized methods for vitrification of ovarian tissues based on sodium alginate hydrogel encapsulation and magnetic induction nanowarming technology. MethodsFirstly, the study investigated the effects of sodium alginate concentration, the sequence of hydrogel encapsulation and CPAs loading on vitrification efficiency of encapsulated ovarian tissue. Additionally, the capability of sodium alginate hydrogel encapsulation to reduce the required concentration of CPAs was validated. Secondly, a platform combining water bath and magnetic induction nanowarming was established to rewarm ovarian tissue under various concentrations of magnetic nanoparticles and magnetic field strengths. The post-warming follicle survival rate, antioxidant capacity, and ovarian tissue integrity were evaluated to assess the efficacy of the method. ResultsThe study found that ovarian tissue encapsulated with 2% sodium alginate hydrogel exhibited the highest follicle survival rate after vitrification. The method of loading CPAs prior to encapsulation proved more suitable for ovarian tissue cryopreservation, effectively reducing the required concentration of CPAs by 50%. A combination of 8 g/L Fe₃O₄ nanoparticles and an alternating magnetic field of 300 Gs showed optimal warming effectiveness for ovarian tissue. Combining water bath rewarming with magnetic induction nanowarming yielded the highest follicle survival rate, enhanced antioxidant capacity, and preserved tissue morphology. ConclusionSodium alginate hydrogel encapsulation of ovarian tissue reduces the concentration of CPAs required during the freezing process. The combination of magnetic induction nanowarming with water bath provides an efficient method ovarian tissue rewarming. This study offers novel approaches to optimize ovarian tissues vitrification.

[Objective] To analyze the loss rate of platelet donors and evaluate the strategies for establishing a platelet donor bank. [Methods] A total of 1 443 donors who joined the HLA and HPA gene donor bank for platelets in Henan Province from 2018 to 2020 were included in this study. Data on the total number of apheresis platelet donations, annual donation frequency, age at enrollment, donation habits (including the number of platelets donated per session and whether they had previously donated whole blood), and enrollment location were collected from the platelet donor information management system. Donor loss was determined based on the date of their last donation. The loss rates of different groups under various conditions were compared to assess the enrollment strategies. [Results] By the time the platelet bank was officially operational in 2022, 421 donors had been lost, resulting in an loss rate of 29% (421/1 443). By the end of 2023, the overall cumulative loss rate reached 52% (746/1 443). The loss rate was lower than the overall level in groups meeting any of the following conditions: total apheresis platelet donations exceeding 50, annual donation frequency of 10 or more, age at enrollment of 40 years or older, donation of more than a single therapeutic dose per session, or a history of whole blood donation two or more times. Additionally, loss rates varied across different enrollment locations, with higher enrollment numbers generally associated with higher loss rates. [Conclusion] Through a comprehensive analysis of donor loss, our center has adjusted its strategies for establishing the donor pool. These findings also provide valuable insights for other blood collection and supply institutions in building platelet donor banks.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objectives:To investigate the epidemiological characteristics and ethnic differences of cardiovascular-kidney-metabolic syndrome(CKM)among the Hani,Dai,Bai,and Lisu populations in Yunnan Province,and to provide evidence for developing effective prevention and control strategies for CKM.Methods:A cross-sectional survey was conducted among four ethnic minority groups.A total of 3 906 permanent residents aged 18 years and older were enrolled using a multistage cluster random sampling method.CKM stages(0-4)were defined based on the 2023 American Heart Association criteria,stages 3-4 were classified as advanced CKM.Descriptive statistics and chi-square tests were used to compare the prevalence of CKM stages across ethnic groups.Modified Poisson regression was applied to estimate relative risk(RR)and 95%confidence intervals(CI)for factors associated with advanced CKM.Results:The prevalence rates of CKM stage 1 and above among the Hani,Dai,Bai and Lisu ethnic groups were 80.1%,87.3%,84.8%and 67.8%,respectively.The prevalence of CKM was generally higher in males than in females,and the prevalence of CKM increased significantly with age.The Dai ethnic group had the highest prevalence of advanced CKM(24.7%,95%CI:22.1%-27.4%),while the Lisu ethnic group had the lowest prevalence of advanced CKM(13.7%,95%CI:11.5%-15.9%).Modified Poisson regression analysis showed that older age and higher body mass index were common risk factors for advanced CKM across all four ethnic groups.Additionally,except for the Lisu ethnic group,the other three ethnic groups had specific individual risk factors:among the Hani ethnic group,low educational attainment(RR=2.18,95%CI:1.12-4.25)and low income(RR=1.47,95%CI:1.00-2.18)were the primary risk factors of CKM.Among the Dai ethnic group,smoking(RR=1.60,95%CI:1.07-2.37)and a family history of cardiovascular disease(RR=1.61,95%CI:1.14-2.27)are the primary risk factors of CKM.Among the Bai ethnic group,male gender(RR=0.48,95%CI:0.29-0.79)was the primary risk factor of CKM.Conclusions:The prevalence of CKM stage 1 or higher is relatively high among the four minority ethnic groups in Yunnan province.There are significant differences in staging characteristics and primary risk factors across ethnic groups,necessitating the development of stratified,differentiated intervention strategies to achieve precise prevention and control and ethnic health equity in terms of CKM.