OBJECTIVE To establish a method for simultaneously determining three anti-tuberculosis drugs in the plasma of patients with spinal tuberculosis and apply it in clinical practice. METHODS LC-MS/MS method was established for the quantitative determination of the concentrations of isoniazid， rifampicin and pyrazinamide in the plasma of patients with spinal tuberculosis， using diphenhydramine as the internal standard. The determination was carried out using Chemalink CM-C18T column， with mobile phase consisting of 0.1% formic acid-methanol solution （gradient elution）， at the flow rate of 0.4 mL/min and an injection volume of 2 μL. Multiple reaction monitoring was conducted using an electrospray ionization source in positive ion mode. The ion pairs used for quantitative analysis were m/z 138.0→121.0（ for isoniazid）， m/z 823.3→791.3（ for rifampicin）， m/z 124.1→ 79.0 （for pyrazinamide）， and m/z 256.0→167.0 （for diphenhydramine）. Fifty-three patients diagnosed with spinal tuberculosis in Qinghai Provincial People’s Hospital from January 2023 to June 2025 were selected， and the plasma concentrations of isoniazid， rifampicin and pyrazinamide in these patients were measured using the above method. RESULTS The linear ranges for isoniazid， rifampicin and pyrazinamide were 0.5-16， 2-64， and 2.5-80 μg/mL， respectively （r≥0.998 7）. The accuracy ranged from 90.20% to 108.64% （n＝5）. RSDs for intra-day precision were all less than 6.63% （n＝5）， while those for inter-day precision were all less than 8.42% （n＝3）. The matrix effects ranged from 88.60% to 115.41% （n＝5）. The relative deviations in the stability tests were all within the ±15% range， and the carry-over effect did not interfere with the determination. The results of clinical application showed that the mean plasma drug concentrations of isoniazid， rifampicin and pyrazinamide in patients with spinal tuberculosis were （3.62±2.80）， （8.55±4.57）， and （20.12±6.56） μg/mL， respectively. The incidences of plasma drug concentrations falling below the effective peak concentrations were 49.06%， 58.49% and 60.38%， respectively. CONCLUSIONS The method established in this study is rapid， accurate， and demonstrates good stability， making it suitable for clinical monitoring of the plasma concentrations of isoniazid， rifampicin and pyrazinamide in patients with spinal tuberculosis.

OBJECTIVE To investigate the neuroprotective effect and mechanism of eleutheroside B （ELB） on Parkinson’s disease （PD） model mice by regulating the IκB kinase β （IKKβ）/nuclear factor-κB （NF-κB） signaling pathway. METHODS Fifty mice were randomly divided into normal control group， model group， positive control group （selegiline hydrochloride， 10 mg/kg）， and ELB low-dose and high-dose groups （80， 160 mg/kg）， with 10 mice in each group. Each group was given relevant medicine or normal saline intragastrically for 14 consecutive days. Starting from the 10th day of administration， the model group and all administration groups were intraperitoneally injected with 1-methyl-4-phenyl-1，2，3，6-tetrahydropyridine （MPTP） 30 mg/kg， for five consecutive days to establish the chronic PD model. After the last administration for 24 h， six mice were randomly selected from each group to test their behavioral abilities； detect the levels of interleukin-1β （IL-1β）， IL-10， tumor necrosis factor-α （TNF-α） in brain tissue and their mRNA expressions were measured， and positive expression of tyrosine hydroxylase （TH）， protein expressions of TH， α -synuclein （ α -syn）， ionized calcium-binding adaptor molecule 1 （Iba-1）， as well as phosphorylation levels of IKKβ and NF-κB p65 proteins in the brain tissue were detected. The ultrastructure of neurons in substantia nigra was observed. RESULTS Compared with the model group， rotarod endurance time and climbing score of each administration group （except for the ELB low-dose group） were increased significantly （ P ＜0.05）， while the levels and mRNA expressions of IL-1β， TNF-α， α -syn， and Iba-1， as well as phosphorylation levels of IKKβ and NF-κB p65 proteins in brain tissue were decreased significantly （except for TNF-α in the ELB low-dose group）. Conversely， the level and mRNA expression of IL-10 （except for the ELB low-dose group）， TH positive expression and protein expressions were significantly increased （ P ＜0.05）. Typical neurodegenerative pathological changes， such as neuronal karyopyknosis， mitochondrial swelling and vacuolization， and endoplasmic reticulum dilation， all showed varying degrees of improvement. CONCLUSIONS ELB may exert neuroprotective effects by inhibiting the activation of the IKKβ/NF-κB signaling pathway， alleviating inflammatory responses， reducing abnormal α -syn aggregation and neuronal loss， and further improving motor dysfunction in PD mice.

Objective: To investigate the factors influencing intraoperative blood transfusion in patients with hip fractures and to develop a machine learning (ML) model for predicting this risk. Methods: A total of 424 patients with hip fractures who underwent surgical treatment between November 2022 and March 2025 in our hospital were selected. Key feature variables of intraoperative blood transfusion risk were identified using the Boruta algorithm. Four different ML algorithms—support vector machine (SVM), linear discriminant analysis (LDA), mixed discriminant analysis (MDA), and extreme gradient boosting (XGBoost)—were used to develop predictive models for intraoperative blood transfusion risk. The predictive performance of the four ML models were evaluated using accuracy, precision, receiver operating characteristic (ROC) curves, precision-recall curves (PRC), precision-recall gain curves (PRGC), and F1 scores. Shapley additive interpretation (SHAP) was used to interpret the final model. Results: Among the 424 patients, 77(18.2%) received intraoperative blood transfusion. The Boruta algorithm identified albumin (ALB), activated partial thromboplastin time (APTT), types of anesthesia, types of fracture, and hemoglobin (Hb) as key feature variables for predicting intraoperative blood transfusion risk. In model evaluation, the SVM model outperforms the other three models across multiple metrics, including the area under the receiver operating characteristic curve (AUC), recall, recall gain, accuracy, precision, F1 score, and the area under the precision-recall curve (PRC-AUC). The SVM model, interpreted and visualized based on SHAP values, effectively predicted intraoperative blood transfusion risk in patients with hip fracture. A visual online application was developed based on the SVM model (https://pbo-nomogram.shinyapps.io/blood/). Conclusion: Preoperative low ALB and Hb levels, prolonged APTT, general anesthesia, and intertrochanteric fractures are risk factors for intraoperative blood transfusion in hip fracture patients. The risk prediction model for intraoperative blood transfusion constructed based on the SVM algorithm has optimal performance, which provides new ideas and methods for the clinical early identification of hip fracture patients with high transfusion risk and the implementation of targeted interventions.

AIM:To clarify the expression characteristics and regulatory role of the long non-coding RNA(lncRNA)metastasis-associated lung adenocarcinoma transcript 1(MALAT1)in oxidative stress and apoptosis of H2O2-induced human lens epithelial cell line HLE-B3.METHODS: HLE-B3 cells were divided into Control group and H2O2 group(cultured with 200 μmol/L H2O2 for 24 h). Levels of reactive oxygen species(ROS)and cell apoptosis rates were detected by flow cytometry. Activities of superoxide dismutase(SOD)and catalase(CAT)were measured using kits, MALAT1 expression was quantified by real-time quantitative PCR, and its cellular localization was determined by fluorescence in situ hybridization. After selecting the Si-MALAT1 sequence with the highest silencing efficiency, the groups were established as Control, H2O2, H2O2+Si-NC, and H2O2+Si-MALAT1. Immunofluorescence staining was employed to detect the apoptosis marker TUNEL and Nrf2 nuclear translocation. RESULTS:Compared with the Control group, the H2O2group exhibited significantly elevated ROS levels, markedly decreased SOD and CAT activities(both P<0.001), and a significantly increased apoptosis rate(P<0.01). MALAT1 expression was upregulated(P<0.01)and localized in the nucleus. After silencing MALAT1, H2O2-induced cell apoptosis was significantly inhibited(P<0.01).CONCLUSION: H2O2-induced oxidative stress can increase the expression of MALAT1, and silencing MALAT1 reduces oxidative stress damage and apoptosis, providing a potential target for preventing and treating cataracts.

This article discusses ethical issues related to corneal donation and transplantation, including the boundaries of living donor donation, the fairness of allocation, the limits of incentives and the risks of xenotransplantation. Based on ethical principles, a dialectical analysis is conducted in conjunction with laws, regulations and clinical practice. Living corneal donation should distinguish between therapeutic eye removal (acceptable) and corneal normal but retinal and optic nerve blindness donation (strictly limited); the current decentralized distribution system has regional unfairness and a unified national mechanism needs to be established; in the incentive policies, spiritual incentives should be advocated, economic incentives should adhere to the bottom line of no compensation but can be supplemented with humanitarian assistance, and rights incentives require national regulations; xenotransplantation faces animal ethics, religious taboos and public health risks, and a national regulatory framework for xenotransplantation needs to be established. On the basis of adhering to ethical principles, a special situation living donor review mechanism, a unified national distribution system, incentive policy implementation details and a national regulatory framework for xenotransplantation should be constructed to promote the sustainable development of corneal donation.

BACKGROUND:Previous studies by our research group discovered that Jiawei Xiaoyao San has a significant anti-liver cancer effect,but the specific mechanism of action was unclear. OBJECTIVE:To investigate the regulatory effects of the traditional Chinese medicine formula Jiawei Xiaoyao San on the levels of miRNAs in plasma exosomes of rats with diethylnitrosamine chronically induced primary liver cancer,based on high-throughput sequencing combined with bioinformatics. METHODS:SD rats were randomly divided into a blank control group,a liver cancer model group,and a Jiawei Xiaoyao San treatment group.Liver cancer models were induced by continuous administration of diethylnitrosamine for 12 weeks.Starting from the 17th week,rats in the Jiawei Xiaoyao San treatment group were administered Jiawei Xiaoyao San once daily until the end of the 20th week,while rats in the blank control and liver cancer model groups were given an equivalent volume of saline.Anti-hepatocellular carcinoma effects were validated by assessing the morphological structure of rat liver tissues,along with the expression of the hepatocellular carcinoma markers,Glypican-3 protein and serum alpha-fetoprotein.Plasma exosomes from each group of rats were isolated using ultracentrifugation.High-throughput sequencing technology was used to screen for differentially expressed miRNAs in rat plasma exosomes.Bioinformatics was used to predict the potential biomarkers through which Jiawei Xiaoyao San exerts its anti-liver cancer effects via liver cancer-derived exosomal miRNAs,followed by functional analysis. RESULTS AND CONCLUSION:(1)Jiawei Xiaoyao San significantly improved the morphological structure of liver tissues in a rat model of liver cancer.Compared with the liver cancer model group,the expression of liver cancer markers Glypican-3 protein and serum alpha-fetoprotein was significantly reduced in the Jiawei Xiaoyao San treatment group.(2)Bioinformatics analysis showed that in the Jiawei Xiaoyao San group,upregulated miR-223-3p in the liver cancer model group had target binding sites with genes E2F1 and NCOA1,which were closely related to liver cancer survival and prognosis.Therefore,Jiawei Xiaoyao San has a therapeutic effect on liver cancer,possibly by targeting negative regulation of NCOA1/E2F1 through liver cancer plasma-derived exosomal miR-223-3p,thereby playing anti-liver cancer effect.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective To investigate the value of enhanced MRI image features combined with apparent diffusion coefficient(ADC)in the differential diagnosis of primary hepatic mucosa-associated lymphoid tissue(MALT)lymphoma and hepatocellular carcinoma(HCC).Methods The clinical data of 10 patients with pathology-confirmed primary MALT lymphoma admitted in our hospital from January 1997 to May 2025 were retrospectively collected.Their enhanced MRI images were analyzed,mainly including the number of lesions,morphology,signal characteristics,enhancement methods,and presence or absence of vessel floating signs,and the ADC value was calculated in order to screen statistically significant variables.Firth regression analysis was used to screen independent predictors of primary MALT lymphoma in the liver.Receiver operating characteristic(ROC)curve was plotted,and the area under the curve(AUC)was calculated to evaluate the diagnostic efficacy.Results Compared with the HCC group,the primary MALT lymphoma group had significantly higher incidence in the females(P=0.026),alpha-fetoprotein(AFP)<8 ng/mL(P<0.001),lower T2WI signal(P=0.001),lower ADC value(P<0.001),and mostly continuously enhanced lesions(P<0.001),and more vessel floating signs(P=0.008).Univariate Firth regression analysis showed that sex(95%CI:1.36～52.10,P=0.020),T2 value(95%CI:3.89～5 121.00,P<0.001),AFP(95%CI:0～0.21,P<0.001),ADC value(95%CI:0～0.21,P<0.001),and dynamic reinforcement(95%CI:0～0.23,P<0.001),and vessel floating sign(95%CI:0～0.40,P=0.004)were diagnostic predictors for primary MALT lymphoma of the liver.Multivariate Firth regression analysis indicated that ADC value(95%CI:0～0.62,P=0.006),AFP(95%CI:0～0.047,P=0.01),and dynamic reinforcement(95%CI:0～0.39,P=0.005)were independent predictors for primary MALT in the liver.ROC curve analysis indicated that the AUC value of ADC value in diagnosing primary MALT lymphoma was 0.96,with a sensitivity of 0.8 and a specificity of 1.0,and the AUC value of AFP level in diagnosing primary MALT lymphoma was 0.85,with a sensitivity of 1.0 and a specificity of 0.7.The AUC value of dynamic reinforcement in diagnosing primary MALT lymphoma was 0.85,with a sensitivity of 0.8,and a specificity of 0.9.Conclusion Dynamic reinforcement and ADC value can be used as differential markers for primary MALT lymphoma and HCC,providing reliable reference for clinical preoperative evaluation.