ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

Nuclear factor E2-related factor 2 (Nrf2) is a major regulator of redox homeostasis in cells, and Nrf2 signaling pathway has anti-inflammatory, anti-oxidative stress, and anti-fibrosis effects while plays an important role in wound healing and pathological scar formation and progression. This article reviewed the related research regarding the effect of oxidative stress and Nrf2 signaling pathway on pathological scars, furthermore, it investigated the relationship between Nrf2 signaling pathway, oxidative stress and pathological scars, providing a new perspective for the study of mechanism and clinical prevention and treatments of pathological scars.

Nuclear factor E2-related factor 2 (Nrf2) is a major regulator of redox homeostasis in cells, and Nrf2 signaling pathway has anti-inflammatory, anti-oxidative stress, and anti-fibrosis effects while plays an important role in wound healing and pathological scar formation and progression. This article reviewed the related research regarding the effect of oxidative stress and Nrf2 signaling pathway on pathological scars, furthermore, it investigated the relationship between Nrf2 signaling pathway, oxidative stress and pathological scars, providing a new perspective for the study of mechanism and clinical prevention and treatments of pathological scars.

This article reviews relevant literature on the prevention and treatment of cancer with hesperidin published in the past 10 years by searching electronic databases such as China National Knowledge Infrastructure（CNKI）， Wanfang， and PubMed， and summarizes the research progress on the anticancer mechanism of hesperidin. Hesperidin has a wide range of pharmacological effects， including anti-inflammatory， antioxidant， antibacterial， antiviral， anticancer， immune-regulatory， anti-radiation， neuroprotective and cardiovascular protective properties and so on. Its anticancer mechanisms mainly include inhibiting cancer cell proliferation， promoting apoptosis， reducing angiogenesis， inhibiting invasion and migration of cancer cells， regulating immunity and autophagy， and exerting antioxidant and anti-inflammatory effects. As a broad-spectrum anticancer drug， hesperidin manifests chemo-preventive and therapeutic effects across various cancers， contingent upon its multifaceted anticancer mechanisms. Furthermore， this article summarizes the synergistic effects of hesperidin in combination with cisplatin， doxorubicin， cyclophosphamide and paclitaxel. It elucidates that hesperidin can enhance the cytotoxicity of these anticancer drugs against cancer cells while mitigating drug resistance and adverse side effects. Nonetheless， the clinical use is somewhat constrained due to its poor water solubility and limited bioavailability. Therefore， this article also outlines the current strategies for enhancing hesperidin's bioavailability， including structural modification， combination with other chemical substances， and utilization of nano drug carriers.The discovery of derivatives of hesperidin not only preserves the anticancer efficacy of hesperidin, but also effectively overcomes the shortcomings of poor water solubility and low bioavailability of hesperidin, effectively predicting the good application prospects of hesperidin and its derivatives.

The sympathetic nervous system(SNS)plays a pivotal role in maintaining organ homeosta-sis and the pathogenesis of various ailments.Studies have unveiled a profound interconnection between sympathet-ic nerves and the development of liver cirrhosis,cirrhotic cardiomyopathy,and hepatorenal syndrome.Therefore,researchers have proposed SNS as a candidate therapeutic target for liver-related disorders.This article reviewed the research progress of sympathetic nerves in liver cirrhosis,cirrhotic cardiomyopathy,and hepatorenal syn-drome,aiming to enrich the knowledge about the roles of sympathetic nerves in cirrhosis and its complications and provide new ideas for the treatment of liver cirrhosis and its complications.

Objective:To evaluate the efficacy and safety of gold micrhenedle radiofrequency and other photoelectric methods in the treatment of facial acne depression scar by using a meta-analysis.Methods:From January 2015 to August 2022, gold microneedles and radio frequence for treatment of facial acne depression scar of randomized controlled trial were retrieved from CNKI, Wanfang Database, VIP, China Biomedical Literature Service System, PubMed database, Cochrane Library and Embase database, including 12 papers. There were 6 Chinese and 6 English literatures, with a sample size of 612 cases.Results:Gold microneedling radio-frequency showed better efficacy in the treatment of facial acne depression scar ( P<0.05). After subgroup analysis, the effective rate in the observation group was higher than that in the control group after 4 treatments, and the difference was statistically significant ( P<0.05). Clinical acne scarring assessment scale, pain score and recovery time had statistically significant difference ( P<0.05). Conclusions:Gold microneedling radiofrequency alone or in collaboration with other photoelectricity in the treatment of acne depression scar has short rest period, slight pain, and obvious improvement of scar effect. However, the improvement effect on icicle depression scar is limited.

Objective:To understand the iron content and transferrin receptor 1 (TfR1) expression levels in keloid and normal skin tissues, an in vitro model of keloid fibroblasts (KFB) ferroptosis induced by Erastin was constructed, and the effects of Erastin and Ferrostatin-1(Fer-1) on cell viability, ferrous ion (Fe 2+) content and lipid peroxidation, ferroptosis and fibrosis-related regulatory factors were examined. Methods:Six keloid tissues and six normal skin tissues were collected from the First Affiliated Hospital of Xinjiang Medical University from March to June 2022, and the tissue iron content kit was used to determine the iron content in the dermis of the two tissues, and TfR1 protein expression was detected in the two tissues by Western blot. Primary KFB and normal skin fibroblasts (NFB) were obtained by tissue block culture method, and the effect of different concentrations of Erastin and Fer-1 on cell activity was detected by using Erastin-induced KFB ferroptosis model and CCK-8 method to screen the appropriate drug concentration. The follow-up experiments were divided into five groups: NFB group, control group, Erastin (0.6 μmol/L) group, Fer-1 (1 μmol/L) group, and Erastin (0.6 μmol/L) +Fer-1 (1 μmol/L) group, while KFB was used as the experimental object in the last 4 groups. Cell migration ability was detected by scratch assay, and malondialdehyde (MDA), reactive oxygen species (ROS) and Fe 2+ content in each group of cells were detected by fluorescent probe method and kits; the protein expression of TfR1, glutathione peroxidase 4 (GPx4), solute carrier family 7 member 11 (SLC7A11), α-smooth muscle actin (α-SMA) and type I collagen (COL-1) in each group of cells were detected by Western blot, and the protein expression and localization of TfR1, Gpx4 in KFB were detected by immunofluorescence. GraphPad Prism 9.0 statistical software was used, and the measurement data were expressed as Mean±SD. Independent sample t-test was used for comparison between 2 groups, and one-way ANOVA was used for comparison between multiple groups, then LSD-t test was used for pairwise comparison between groups. P< 0.05 indicated that the differences were statistically significant. Results:The iron content and TfR1 protein expression were significantly higher in keloid compared with normal skin tissue ( P<0.01). The proliferation rate of KFB with different Erastin concentrations decreased gradually, the IC 50 was 0.61 μmol/L, and Fer-1 had no obvious toxicity to KFB in the range of 0.1~20 μmol/L. Scratch test showed that the migration rate of control group was significantly higher than that of NFB group ( P< 0.01) ; compared with the control group, KFB migration rate decreased significantly after Erastin intervention ( P<0.01) ; compared with the Erastin group, KFB migration was significantly accelerated in the Erastin+Fer-1 group ( P<0.01). Compared with the NFB group, ROS, MDA levels were significantly increased in the control group ( P< 0.01) ; compared with the control group, ROS, MDA levels and Fe 2+ content were significantly higher in the Erastin group ( P<0.01), while ROS, MDA levels and Fe 2+ content were significantly lower in the Fer-1 group ( P<0.01) ; compared with the Erastin group, MDA, ROS levels and Fe 2+content in the Erastin+ Fer-1 group were significantly decreased ( P<0.01). Western Blot showed that, compared with the NFB group, iron death indexes of SLC7A11 and GPx4 protein expression were significantly reduced ( P<0.01 or P<0.05), TfR1 protein expression was increased ( P<0.01), and fibrosis indexes of α-SMA and COL-1 protein expression were significantly increased ( P<0.01) in the control group; compared with the control group, SLC7A11 expression was reduced ( P<0.01) and TfR1, COL-1 expression increased ( P<0.01), while SLC7A11, GPx4 expression increased ( P< 0.01) and TfR1, α-SMA, COL-1 expression significantly decreased ( P<0.01) in the Fer-1 group; compared with the Erastin group, the GPx4, SLC7A11 expression was increased ( P< 0.01) and TfR1, α-SMA, COL-1 expression was significantly decreased ( P< 0.01) in the Erastin+Fer-1 group, suggesting that Fer-1 was able to reverse Erastin-induced ferroptosis and pro-fibrotic effects in KFB. Immunofluorescence showed that GPx4 was expressed in both nucleus and cytoplasm. Compared with control group, Fer-1 increased the fluorescence intensity of GPx4 in KFB ( P<0.01). Compared with Erastin group, the fluorescence intensity of GPx4 in Erastin+ Fer-1 group was significantly increased ( P<0.01). TfR1 was mainly expressed in cytoplasm. Compared with control group, Erastin increased the fluorescence intensity of TfR1 in KFB ( P< 0.05), while Fer-1 group significantly decreased it ( P < 0.01). Compared with Erastin group, the fluorescence intensity of TfR1 in Erastin+Fer-1 group was significantly reduced ( P<0.01). Conclusions:Iron overload and free iron increase in keloids. Erastin induces ferroptosis in KFB and aggravates keloid fibrosis. Fer-1 reverses the oxidative damage and iron accumulation induced by Erastin and effectively inhibits ferroptosis and keloid fibrosis in KFB.