OBJECTIVE To provide reference for medical institutions to establish the record management mode and review rules of off-label use of 5-aminolevulinic acid （ALA） in photodynamic therapy based on the level of evidence. METHODS All ALA-containing outpatient prescriptions in the rational drug use system in our hospital from January 1， 2024 to December 31， 2025 were retrospectively collected. Based on the drug instructions， the current status of off-label use of ALA in photodynamic therapy was identified . The relevant studies in Micromedex， PubMed， CNKI， Wanfang Data and other databases were systematically searched as the relevant evidence-based evidence of ALA off-label use. According to the Off-label Drug Use Filing Standard of the hospital，the evidence-based evaluation method was used to evaluate the evidence-based evidence of ALA off-label use and carry out hierarchical management. RESULTS A total of 1 803 effective prescriptions were included， of which 676 （37.49%） were off-label use， distributed in the dermatology department （564 prescriptions，83.43%） and the plastic surgery department （112 prescriptions，16.57%）. All 676 prescriptions were off-indications medication， involving ten types of skin diseases， primarily including moderate to severe acne （39.94%）， skin warts （25.44%）， Bowen’s disease （11.98%）， and others. According to evidence-based evidence，off-label uses such as moderate to severe acne， actinic keratosis， and Bowen’s disease were managed according to the evidence categoryⅠ orⅡ.The uses of extramammary Paget’s disease and rosacea were managed according to the evidence category Ⅲ.The uses of lichen sclerosus and keloids were managed according to the evidence category Ⅳ.The results of evidence-based evaluation showed that 92.01% of off-label use in our hospital had high-level evidence-based support （ evidence category was gradeⅠ-Ⅱ）. CONCLUSIONS Off-label uses supported by high-level evidence， such as moderate to severe acne， skin warts， and Bowen’s disease， can be managed under filing category Ⅰ or Ⅱ. For the use of lichen sclerosus and keloids， evidence-based evidence is insufficient and should be strictly restricted.The vast majority of ALA off-label use in our hospital has sufficient evidence-based basis.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

ObjectiveTo systematically identify the main chemical components of Linggui Zhugan granules （LGZGG）， and to explore the pharmacodynamic substance basis for the treatment of metabolic dysfunction-associated steatohepatitis （MASH）. MethodsThe chemical components of LGZGG were systematically analyzed by ultra-performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry （UPLC-Q-TOF-MS/MS）. The protein-protein interaction （PPI） network and "compound prescription-disease-component-target" network were constructed by network target analysis to predict the potential pharmacodynamic substances and core targets of LGZGG in the treatment of MASH. The potential pharmacodynamic substances were enriched by macroporous adsorption resin， and the chemical composition of the LGZGG-50% ethanol elution fraction （LGZGG-50） was analyzed by UPLC-Q-TOF-MS/MS. The mouse model of MASH was established by feeding a high-fat， high-cholesterol， and high-fructose diet for 12 weeks. Mice were randomly allocated into normal， model， positive drug （MGL-3196， 1 mg·kg^-1）， LGZGG （crude drug， 34 g·kg^-1·d^-1）， LGZGG-50 （crude drug， 34 g·kg^-1·d^-1） groups. The levels of aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C） in the serum were measured， and the pathological changes in the liver tissue were observed by hematoxylin-eosin （HE） and Sirius red staining. The in vivo efficacy of LGZGG and LGZGG-50 in the treatment of MASH was evaluated on the basis of the findings. The expression levels of adenosine monophosphate-activated protein kinase （AMPK）， phosphorylated AMPK （p-AMPK）， silent information regulator 2-related enzyme 1 （SIRT1）， peroxisome proliferator-activated receptor α （PPARα）， and carnitine palmitoyltransferase 1A （CPT1A） in the liver tissue were determined by Western blot to verify the regulatory effects of core targets. ResultsA total of 83 chemical components were identified from LGZGG， including 20 flavonoids， 13 terpenoids， 17 organic acids， 7 amino acids， 11 glycosides， 3 nucleosides， 3 aromatics， 3 alkaloids， 2 phenylpropanoids， 2 sugars， 1 nucleic acid， and 1 steroid. A total of 134 common targets were obtained by network target analysis. The core targets included SIRT1， PPARα， nuclear factor-kappa B subunit 1 （NF-κB1）， and interleukin-6 （IL-6）. Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis revealed that the targets were mainly enriched in the AMPK， PPAR and other signaling pathways. The topological analysis of the "compound prescription-disease-component-target" network showed that the potential pharmacodynamic substances of LGZGG against MASH were flavonoids and terpenoids. The relative content of flavonoids and terpenes in LGZGG-50 were 74.00% and 62.41% in positive and negative ion modes， respectively， indicating that LGZGG-50 effectively enriched total flavonoids and total terpenoids. The results of in vivo efficacy showed that compared with the model group， LGZGG and LGZGG-50 reducef the body weight， liver weight， serum TG， GLU， and ALT levels of MASH mice （P<0.05，P<0.01）， and LGZGG additionally increased the HDL-C level （P<0.01）. Both groups alleviated the pathological damage in the liver tissue. The results of Western blot showed that compared with the model group， the protein levels of p-AMPK/AMPK， SIRT1， PPARα， and CPT1A were up-regulated in the LGZGG group （P<0.05，P<0.01）， and those of p-AMPK/AMPK and CPT1A were up-regulated in the LGZGG-50 group （P<0.01）. ConclusionLGZGG ameliorates MASH， with the main pharmacodynamic substances being flavonoids and terpenoids. The mechanism may be related to the regulation of AMPK/SIRT1/PPARα signaling pathway， improvement of lipid metabolism， and alleviation of pathological damage in the liver tissue.

ObjectiveTo compare the performance of different automatic evaluation methods for detecting pattern hallucinations generated by large language models （LLMs） in traditional Chinese medicine （TCM） syndrome differentiation and to identify a strategy with higher overall discriminative performance against expert manual judgment. MethodsA standardized TCM syndrome-differentiation dataset containing 598 cases was constructed from case reports published in Chinese core journals indexed by Peking University Core Journals or Chinese Science Citation Database. Each of the 598 cases was submitted to five Chinese LLMs， with each model generating one syndrome-pattern output per case， yielding 2990 outputs in total. The 598 outputs generated by Qwen3-Next-80B-A3B-Thinking were independently annotated for syndrome-pattern hallucinations by two licensed TCM physicians with intermediate or higher professional titles. Disagreements were adjudicated by a third licensed TCM physician with a senior associate professional title and more than 10 years of clinical experience. The resulting consensus annotations served as the reference labels for training and validating the automated evaluation method. Based on these reference labels， three categories of automated evaluation methods were developed and compared following a progressive strategy from text-level semantic similarity， to syndrome-element structural consistency， and finally multi-feature fusion. Method A employed sentence-embedding models based on semantic similarity between pattern texts. Method B adopted a rule-based threshold method using weighted Jaccard coefficients of disease-location and disease-nature syndrome elements. Method C utilized a machine-learning classifier integrating syndrome-element matching scores， missing and redundant syndrome-element counts， and multiple semantic-similarity features. Five-fold cross-validation was used to evaluate the discriminative performance of each method against manual judgment. The optimal method was then applied to uniformly assess the pattern hallucination rates of five LLMs. ResultsIn method A， text2vec-large-chinese showed best overall performance， with an area under the receiver operating characteristic curve （AUROC）， area under the precision-recall curve （PR-AUC）， F1-score， sensitivity， and specificity of 0.849， 0.884， 0.794， 0.801， and 0.691， respectively. In method B， the corresponding metrics of the rule-based syndrome-element Jaccard threshold method were 0.848， 0.851， 0.817， 0.845， and 0.684， respectively； those of the LLM-based syndrome-element Jaccard method were 0.872， 0.906， 0.795， 0.764， and 0.780， respectively. In method C， the multilayer perceptron （MLP） classifier showed the best overall performance， with AUROC， PR-AUC， F1-score， sensitivity， and specificity of 0.933， 0.951， 0.871， 0.857， and 0.846， respectively. Under the unified evaluation framework combining DeepSeek-R1 syndrome-element extraction and MLP classifier， the estimated pattern hallucination rates of the five LLMs ranged from 56.0% to 77.4%. ConclusionAmong the three automatic evaluation methods， Method C integrating syndrome-element discrepancy features and text semantic features using an MLP classifier showed the highest overall discriminative performance against manual judgment， and is more suitable for evaluating syndrome hallucinations in LLM-based TCM syndrome differentiation tasks.

Stellate ganglion block is a treatment method commonly used in clinical practice. In recent years， more and more studies have shown that stellate ganglion block can effectively alleviate central post-stroke pain， central pain after spinal cord injury， and central pain in Parkinson disease， which has a broad application prospect in the treatment of central pain. This article reviews the studies on stellate ganglion block for the treatment of central pain in order to explore feasible therapeutic methods for the treatment of central pain and provide a reference for its clinical application.
Stroke ; Parkinson Disease

Objective: To construct a hypertension risk prediction model for residents aged 18-79 years, so as to provide an assessment tool for early screening and prevention of hypertension in high-risk groups. Methods: The permanent residents aged 18-79 years from 6 townships (streets) in Fangshan District of Beijing Municipality were selected as the study subjects using a multi-stage stratified random sampling method from March to June 2023. Demographic information, lifestyle, body mass index (BMI), blood pressure, fasting blood glucose, and blood lipid were collected through questionnaire survey, physical examination, and laboratory tests. Subjects were randomly divided into training and validation sets at a 7∶3 ratio. The logistic regression model was used to screen the risk factors of hypertension, and a hypertension risk prediction nomogram was constructed. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis were used to verify the discrimination, fit, and clinical application value of the model. Results: A total of 4 438 subjects were included, including 2 365 males (53.29%) and 2 073 females (46.71%), with a mean age of (44.99±14.90) years. The prevalence of hypertension was 35.29% (1 566 cases), and the standardized prevalence was 24.74%. The logistic regression model screened out 9 influencing factors of hypertension. The nomogram was established as ln[p/ (1-p)]= -2.873 + 0.935×40-<50 years + 1.463×50-<60 years + 1.908×60-<70 years + 2.346×70-79 years + 0.298×male-0.675×college degree or above + 0.384×smoking + 0.227×drinking + 0.572×overweight + 1.449×obesity + 0.557×heart rate ≥80 beats/min + 0.428×diabetes + 0.484×dyslipidemia. The area under the ROC curve of the validation set was 0.821 (95%CI: 0.798-0.843), and the calibration curve results showed that the calibration curve fitted the actual curve well. Decision curve analysis showed that the threshold probability was in the range of 0.10 to 0.70, and the model had good predictive value and clinical application value. Conclusion The nomogram based on age, gender, educational level, smoking, drinking, body mass index, heart rate, diabetes, and dyslipidemia can be used to predict the risk of hypertension among residents aged 18-79 years.

Objective To develop and validate risk prediction models utilizing five machine learning algorithms for assessing postoperative pulmonary infection(PPI)risk in lung cancer patients undergoing grade Ⅳ thoracoscopic surgery.Methods A retrospective cohort study included 2,380 lung cancer patients who underwent grade Ⅳ thoracoscopic surgery at a tertiary hospital in Shanghai(January 2022 to June 2024).Patients were stratified into training(n=1,665)and validation(n=715)cohorts.Five machine learning algorithms—Logistic regression(LR),artificial neural network(ANN),support vector machine(S VM),random forest(RF),and extreme gradient boosting(XGB)—were employed to construct predictive models.A nomogram was developed for clinical utility.Results Among 2,380 patients,226(9.5％)developed PPI.The Least Absolute Shrinkage and Se-lection Operator(LASSO)regression identified eight predictive variables:daily cigarette consumption,diabetes history,preoperative diffusing capacity,maximal tumor diameter,24-hour postoperative chest drainage volume,perioperative oral nutritional supplementation(ONS),postoperative urinary cathe-terization,and intraoperative pleural adhesion severity.All models demonstrated robust discrimina-tion,with area under the curve(AUC)values ranging from 0.862 to 0.947.The XGB model a-chieved superior performance(AUC=0.947,95％CI,0.937 to 0.962),followed closely by the LR model(AUC=0.926,95％CI,0.918 to 0.933).Conclusion Machine learning-based algo-rithms models effectively stratify PPI risk in lung cancer patients following grade Ⅳ thoracoscopic surgery.The derived nomogram provides a practical tool for perioperative risk management by healthcare providers.

Metastasis is the leading cause of death from cutaneous melanoma. Identifying metastasis-related targets and developing corresponding therapeutic strategies are major areas of focus. While functional genomics strategies provide powerful tools for target discovery, investigations at the protein level can directly decode the bioactive epitopes on functional proteins. Aptamers present a promising avenue as they can explore membrane proteomes and have the potential to interfere with cell function. Herein, we developed a target and epitope discovery platform, termed functional aptamer evolution-enabled target identification (FAETI), by integrating affinity aptamer acquisition with phenotype screening and target protein identification. Utilizing the aptamer XH3C, which was screened for its migration-inhibitory function, we identified the Chondroitin Sulfate Proteoglycan 4 (CSPG4), as a potential target involved in melanoma migration. Further evidence demonstrated that XH3C induces cytoskeletal rearrangement by blocking the interaction between the bioactive epitope of CSPG4 and integrin α4. Taken together, our study demonstrates the robustness of aptamer-based molecular tools for target and epitope discovery. Additionally, XH3C is an affinity and functional molecule that selectively binds to a unique epitope on CSPG4, enabling the development of innovative therapeutic strategies.

Acute kidney injury (AKI) has high morbidity and mortality, but effective clinical drugs and management are lacking. Previous studies have suggested that macrophages play a crucial role in the inflammatory response to AKI and may serve as potential therapeutic targets. Emerging evidence has highlighted the importance of forkhead box protein O1 (FoxO1) in mediating macrophage activation and polarization in various diseases, but the specific mechanisms by which FoxO1 regulates macrophages during AKI remain unclear. The present study aimed to investigate the role of FoxO1 in macrophages in the pathogenesis of AKI. We observed a significant upregulation of FoxO1 in kidney macrophages following ischemia-reperfusion (I/R) injury. Additionally, our findings demonstrated that the administration of FoxO1 inhibitor AS1842856-encapsulated liposome (AS-Lipo), mainly acting on macrophages, effectively mitigated renal injury induced by I/R injury in mice. By generating myeloid-specific FoxO1-knockout mice, we further observed that the deficiency of FoxO1 in myeloid cells protected against I/R injury-induced AKI. Furthermore, our study provided evidence of FoxO1's pivotal role in macrophage chemotaxis, inflammation, and migration. Moreover, the impact of FoxO1 on the regulation of macrophage migration was mediated through RhoA guanine nucleotide exchange factor 1 (ARHGEF1), indicating that ARHGEF1 may serve as a potential intermediary between FoxO1 and the activity of the RhoA pathway. Consequently, our findings propose that FoxO1 plays a crucial role as a mediator and biomarker in the context of AKI. Targeting macrophage FoxO1 pharmacologically could potentially offer a promising therapeutic approach for AKI.

Genetic transformation is a fundamental tool in molecular biology research of medicinal plants. Tailoring transgenic technologies to each distinct medicinal plant would necessitate a substantial investment of time and effort. Here, we present a simple hairy root transformation method that does not require sterile conditions, utilizing Agrobacterium rhizogenes strain K599 and the visible RUBY reporter system. Transgenic hairy roots were obtained for six tested medicinal plant species, roots or rhizomes of which have recognized medicinal value, spanning four botanical families and six genera (Platycodon grandiflorus, Atractylodes macrocephala, Scutellaria baicalensis, Codonopsis pilosula, Astragalus membranaceus, and Glycyrrhiza uralensis). Furthermore, two previously identified Glycyrrhiza uralensis UGTs that convert liquiritigenin into liquiritin in heterologous systems were studied in planta using the method. Our results indicate that overexpression of GuUGT1 but not GuUGT10 and Cas9-mediated knockout of GuUGT1 profoundly influenced the accumulation of liquiritin and isoliquiritin in licorice roots. Therefore, the method described here represents a simple, rapid and widely applicable hairy root transformation method that enables fast gene functional study in medicinal plants.