ObjectiveTo evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

Objective To explore the adverse event signals of children using macrolide drugs （azithromycin, clarithromycin, and erythromycin）, and provide reference for rational medicine use in clinical practice. Methods Data from children under 12 years old were extracted from the US FAERS database spanning from the first quarter of 2004 to the second quarter of 2023. The adverse drug reaction （ADR） signal mining for three macrolide antibiotics was conducted using the Reporting Odds Ratio （ROR） and Bayesian Confidence Propagation Neural Network （BCPNN） methods. Special emphasis was placed on analyzing and contrasting the differences in adverse events among the three drugs. Results A total of 1 615 reports for children under 12 years old were retrieved from the FAERS database, including 1 024 reports of azithromycin, 460 reports of clarithromycin, and 131 reports of erythromycin. Among azithromycin and erythromycin, there were more reports from boys than girls, while for clarithromycin, there were more reports from girls than boys. Oral administration was the most common route of administration for all three drugs. Regarding the outcome of adverse events reported, azithromycin and clarithromycin were primarily associated with other serious adverse events, whereas erythromycin was mainly associated with hospitalization and other serious adverse events. The number of adverse events reported decreased with increasing age, with a higher number of reports in the 0-3 age group. Using the ROR and BCPNN methods for signal detection, 86 signals were identified for azithromycin, 91 for clarithromycin, and 34 for erythromycin. These signals involved 22 System Organ Classes （SOCs）, with azithromycin mainly concentrated in skin and subcutaneous tissue disorders （n=21）, clarithromycin in gastrointestinal disorders （n=15）, and erythromycin in gastrointestinal disorders （n=8）. Twenty-four signals of moderate to high risk were detected, with 13 for azithromycin, 9 for clarithromycin, and 2 for erythromycin. Conclusion The adverse events induced by the three drugs with different risks in different systems. When clinically treating Mycoplasma pneumoniae pneumonia in children, the risk profiles of drugs in different systems should be considered, and personalized dosing should be implemented.

Background Lead is a typical persistent environmental pollutant that can accumulate in bones for decades. During pregnancy, alterations in calcium metabolism promote the mobilization of bone lead, resulting in secondary exposure; however, the mechanisms by which pregnancy-associated bone lead mobilization affects maternal renal function remain unclear. Objective To investigate the role of mitochondrial dysfunction in pregnancy-related bone lead mobilization-induced renal injury. Methods Newly weaned female Wistar rats were randomly assigned to a control or a lead-exposed group administered either 0.05% sodium acetate or 0.05% lead acetate in drinking water. Following a 4-week lead exposure and a 4-week washout period, the females were co-housed with healthy age-matched males for mating. Rats were sacrificed at early (gestational day 3) and late (gestational day 17) pregnancystages, respectively. Renal histopathology was assessed using hematoxylin and eosin staining staining. Mitochondria-related indicators, including oxidative stress, inflammatory responses, and energy metabolism, were measured. Differential metabolites were identified using serum metabolomics. Results Renal injury in the lead-exposed pregnant rats progressed in a time-dependent manner, characterized by degeneration of proximal tubular epithelial cells, glomerular hyaline changes, and interstitial inflammatory cell infiltration. Repeated measures ANOVA indicated a significant interaction between the treatment factor (lead exposure) and the temporal factor (gestational stage) on renal injury (P<0.001). Further analysis of mitochondrial function-related indicators in late-pregnancy renal tissue revealed that the lead exposure group exhibited significantly increased levels of malondialdehyde (MDA) and reactive oxygen species (ROS) (P<0.05), accompanied by a reduction in superoxide dismutase (SOD) and reduced glutathione (GSH) activities (P<0.05); regarding inflammatory markers, levels of interleukin-18 (IL-18) and interleukin-1β (IL-1β) were elevated (P<0.01), whereas interleukin-33 (IL-33) was decreased in the lead-exposed group (P<0.05); energy metabolism-related indicators, including adenosine triphosphate (ATP) level, Na⁺-K⁺-ATPase and Ca²⁺-Mg²⁺-ATPase activities, and mitochondrial respiratory chain complexes I, III, and V activities, were significantly reduced (P<0.05) in the lead-exposed gorup. The typical differential metabolite N-methylisoleucine, identified through serum metabolomics analysis, was negatively correlated with blood lead levels, kidney injury scores, and IL-1β, while positively correlated with catalase (CAT) activity and Ca²⁺-Mg²⁺-ATPase. Conclusions Mitochondrial dysfunction may play a critical role in renal injury induced by bone lead mobilization during late gestation.

Objective To analyze the nonlinear relationship between hypothermic machine perfusion (HMP) parameters and delayed graft function (DGF) and optimize the construction of a predictive model for DGF. Methods The data of 923 recipients who underwent kidney transplantation from deceased donors were retrospectively analyzed. According to the occurrence of DGF, the recipients were divided into DGF group (n=823) and non-DGF group (n=100). Donor data, HMP parameters and recipient data were analyzed for both groups. The nonlinear relationship between HMP parameters and the occurrence of DGF was explored based on restricted cubic splines (RCS). Over-sampling, under-sampling and balanced sampling were used to address the imbalance in the proportion of DGF to construct logistic regression predictive models. The area under the curve (AUC) of each model was compared in the validation set, and a nomogram model was constructed. Results Donor BMI, cold ischemia time of the donor kidney, and HMP parameters (initial and final pressures, resistance, and perfusion time) were significantly different between the DGF and non-DGF groups (all P<0.05). The RCS analysis revealed a threshold-like nonlinear relationship between HMP parameters and the risk of DGF. Among the models constructed using different sampling methods, the balanced sampling model had the highest AUC. Using this model, a nomogram was constructed to stratify recipients based on risk scores. Recipients in the high-risk group had higher serum creatinine levels at 1, 6, and 12 months after kidney transplantation compared to those in the low-risk group (all P<0.05). Conclusions There is a nonlinear relationship between HMP parameters and the risk of DGF, and the threshold is helpful for organ quality assessment and monitoring of graft function after transplantation. The predictive model for DGF constructed on the base of balanced sampling algorithms helps perioperative decision-making and postoperative graft function monitoring of kidney transplantation.

The Fragment of Dunhuang Ancient Tibetan Moxibustion Therapy contains rich content on fire moxibustion therapy of Tubo-period Tibetan medicine, characterized by distinctive clinical features of Tibetan acupuncture and strong regional attributes. This paper systematically reviews the relevant materials on moxibustion in the Fragment and summarizes the findings as follows: Tibetan fire moxibustion mainly uses mugwort as the material, and terms like "fine mugwort", "broad bean" and "sheep dung pellet" refer to the size of the moxa cone. The number of moxa cones used is predominantly odd numbers, usually ranging from 5 to 21. The main indications for fire moxibustion cover internal medicine, external medicine, gynecology, pediatrics, and various pain syndromes. The therapy advocates for treating acute conditions and heat syndromes with moxibustion. The manuscript also records detailed contraindications, including time-based and seasonal taboos. Moxibustion is applied to both local and distal acupoints, reflecting the therapeutic concept of treating both proximal and distal regions. Furthermore, it documents simple and practical acupoint localization methods such as surface anatomical markers, proportional bone measurement, finger measurement, and hand-span measurement. Compared with contemporaneous Chinese medical moxibustion techniques, the moxibustion methods recorded in this Fragment are rich in content and present unique Tibetan theoretical characteristics. It provides valuable data and evidence for the excavation, application, and further research of Tibetan acupuncture and moxibustion.
Moxibustion/instrumentation* ; Humans ; History, Ancient ; Medicine, Tibetan Traditional/history* ; Tibet ; Acupuncture Points

Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.

Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.

The Mofeng Ointment Formulation （MOF， 摩风膏方） is recorded in the Dunhuang manuscript Unnamed Treatise on the Pulse （《亡名氏脉经》） and stands as a representative ancient external therapeutic formula in traditional Chinese medicine （TCM）. Known for dispelling wind， activating blood circulation， warming the meridians， and relieving pain， it is particularly effective in treating rheumatic arthralgia. Through literature research， this paper systematically reviews the composition， preparation techniques， and clinical application characteristics of MOF. In recent years， advancements in modern pharmaceutical technology have led to the development of various innovative ointment-based formulations derived from the traditional recipe， such as Dunhuang Xiaoding Ointment （敦煌消定膏）， Dunhuang Xiaozhong and Zhentong Ointment （Patch） （敦煌消肿镇痛膏/贴）， Xiaoding Ointment （消定膏） gel patch， and Xiaoding Ointment （消定膏） cataplasm. These innovations explore pathways for transforming classical prescriptions into modern applications， providing a theoretical basis for the external TCM treatment of pain-related and orthopedic conditions.

Objective:To establish the reference interval of 12 types of cytokines(IL-1β,IL-2,IL-4,IL-5,IL-6,IL-8,IL-10,IL-12p70,IL-17,IFN-γ,IFN-α,TNF-α)in adult plasma based on multiple microsphere flow immunofluorescence(MBFFI).Methods:A total of 140 healthy adult patients who were examined at Xuzhou Central Hospital between January 2022 and December 2023 were included in the study.Plasma cytokine levels were detected and reference intervals were established by the flow cytometer and the assay kits produced by Qingdao Raisecare Biotechnology Co.,Ltd and Jiangsu BioPredia Biotechnology Co.,Ltd.Results:All of the cytokines exhibited a non-normal distribution,and there was a discrepancy in the 95％reference interval between the two re-agents.The reference intervals for the 12 cytokine kits produced by Qingdao Raisecare Biotechnology Co.,Ltd.were as follows:IFN-α:<4.91 pg/ml,IL-12 p70:<1.95 pg/ml,IL-5:<12.72 pg/ml,IL-8:<60.68 pg/ml,IL-1β:<27.67 pg/ml,IL-2:<5.01 pg/ml,IL-4:<1.22 pg/ml,IL-6:<6.11 pg/ml,TNF-α:<2.92 pg/ml,IL-17:<10.27 pg/ml,IL-10:<6.88 pg/ml,IFN-γ:<17.68 pg/ml.The reference intervals of the 12 cytokines produced by Jiangsu BioPredia Biotechnology Co.,Ltd.were as follows:IFN-α:<4.05 pg/ml,IL-12 p70:<7.33 pg/ml,IL-5:<7.80 pg/ml,IL-8:<13.24 pg/ml,IL-1β:<19.24 pg/ml,IL-2:<2.42 pg/ml,IL-4:<0.99 pg/ml,IL-6:<2.10 pg/ml,TNF-α:<0.87 pg/ml,IL-17:<1.42 pg/ml,IL-10:<1.10 pg/ml,IFN-γ:<1.34 pg/ml.Conclusion:In this study,the ref-erence range of two reagents for the detection of 12 kinds of cytokines in plasma of healthy adults is established by MBFFI,which pro-vides a valuable reference for the diagnosis and treatment of clinical-related diseases.

Objective To investigate the influencing factors for frailty in elderly patients with mild ischemic stroke(MIS),construct a risk prediction model based on random forest algorithm and evaluate its application performance.Methods A total of 322 elderly MIS patients subjected by cluster sampling in the Neurodiagnosis and Treatment Center of our hospital during January and June 2022 were enrolled and divided into non-frailty group(n=261)and frailty group(n=61)according to the results of frailty screening scale.The general clinical data,and the scores of Daily Living Ability Scale,Simplified Geriatric Depression Scale,Social Support Rating Scale,36-Item Concise Health Questionnaire,and Simplified Intelligent State Examination Scale were compared between two groups.Random forest algorithm was used to construct a risk prediction model for frailty in the elderly MIS patients,and ROC curve was plotted to analyze the predictive value of the model.Results The incidence of frailty in elderly MIS patients was 18.94％.There were sta-tistical differences in age,marital status,monthly income,medical expense payment,sleep condi-tion and comorbidities in the frail group(P＜0.01).The frail group had significantly lower scores of support utilization,concise health status,physiological function,body pain,general health,social function,and emotional function,and higher scores of Activities of Daily Living Scale and the Simplified Geriatric Depression Scale than the non-frail group(P＜0.05).The random forest model obtained an accuracy of 88.28％,a sensitivity of 88.24％,a specificity of 88.89％,an F1 score of 0.933,and an AUC value of 0.816.Random forest algorithm ranked the important varia-bles influencing frailty in elderly MIS patients through variable importance scores,and the top five predictors were types of basic diseases(≥2 types),sleep condition,quality of life score,self-care ability score,and personal monthly income(＜2000 Yuan).Conclusion A risk prediction model for frailty in elderly MIS patients is constructed by using random forest algorithm.The model is helpful to identify key factors of frailty as early as possible,provide empirical evidence for clinical medical staff to implement early intervention,improve the health and quality of elderly MIS patients,and prevent or delay the occurrence of frailty.