ObjectiveTo establish a regional risk assessment system for hospital-acquired infections in neurosurgery department of general hospital, and to evaluate its prevention and control effectiveness. MethodsFailure mode and effect analysis (FMEA) was used to identify the core risk factors for infections in neurosurgery department. The risk priority number (RPN) of each risk factor was calculated to determine the priority intervention targets. Targeted interventions were developed and continuously refined through the plan-do-check-act (PDCA) cycles. Data from January to June 2023 (control group) and July to December 2023 (intervention group) were collected to compare the differences in environmental hygiene monitoring qualification rate, incidence rate of hospital-acquired infections among inpatients, and detection rate of bacterial antimicrobial resistance. ResultsHigh-risk factors for hospital-acquired infections in neurosurgery department included patient-related risk factors, inadequate implementation of isolation measures for special infections, and poor compliance with surgical site infection (SSI) prevention protocols. After intervention, the environmental hygiene qualification rate significantly increased from 81.55% to 100.00% (χ²=120.49, P<0.001). The overall hospital-acquired infection rate among inpatients decreased from 2.62% to 2.45%, the infection rate of per case declined from 3.12% to 2.84%, and the detection rate of multidrug-resistant organism infections reduced from 43.72% to 36.79%. Additionally, antimicrobial utilization rate decreased from 48.75% to 42.53% (χ²=34.09, P<0.001). ConclusionThe FMEA-based risk assessment system can effectively identify critical infection risks in neurosurgery department, and targeted interventions can significantly improve infection prevention and control performance.

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 recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.

Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.

Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.

Objective To construct and validate a prediction model for urosepsis in patients after upper urinary tract stone surgery using various machine learning algorithms.Methods A total of 7 464 upper urinary tract stone patients who underwent surgery at the Sixth Affiliated Hospital of Guangxi Medical University from Jun.2018 to Jun.2023 were enrolled and randomly assigned to training(5 224 cases)or validation sets(2 240 cases)at a ratio of 7∶3.Among them,622(8.33%)cases developed urosepsis postoperatively.Six machine learning algorithms,including extreme gradient boosting(XGBoost),logistic regression,light gradient boosting machine(LightGBM),random forest(RF),adaptive boosting(AdaBoost),and gradient boosting decision tree(GBDT),were used to construct prediction models for postoperative urosepsis.The model's predictive ability and clinical benefits were evaluated using receiver operating characteristic(ROC)curves,Shapley additive explanation(SHAP)analysis,calibration curves,and decision curve analysis(DCA).Results The clinical features included body mass index(BMI),number of surgeries,heart rate,Barthel index,venous thrombo embolism(VTE)risk assessment,gender,American Society of Anesthesiologists(ASA)grade,urinary nitrite,and urinary leukocyte in the models.In the training set,the XGBoost,LightGBM,and RF models performed excellently,with area under curve(AUC)values of ROC curves reaching 1.00.In the validation set,the logistic regression model performed the best,with an AUC value of ROC curve of 0.76,showing good predictive stability and calibration.The AdaBoost and GBDT models followed with AUC values of 0.74 and 0.75,respectively,while the AUC values of the LightGBM,XGBoost,and RF models were 0.71,0.70,and 0.68.In terms of model interpretability,SHAP analysis showed the contribution of variables in a descending order as:heart rate,urinary leukocytes,gender,BMI,Barthel index,VTE risk assessment,urinary nitrite,number of surgeries,and ASA grade.Conclusion A logistic regression model for early risk prediction of postoperative urosepsis in upper urinary tract stone patients has been successfully constructed.This model has good predictive performance and calibration,and can effectively assist clinical diagnosis.

Objective To explore the expression profile of adenosine A1 receptor in the paraventricular thalamic nucleus(PVT)in wakefulness/sleep state and its role in regulating sleep.Methods Male C57BL/6J mice(6～12 weeks old,weighing 22～28 g)were randomly divided into ZT0,ZT6,ZT10,ZT12,and sleep deprivation and recover0y sleep groups(n=16 or n=4).RT-qPCR and fluorescence in situ hybridization were used to observe the changes of adenosine A1 receptor in PVT.Whole-cell patch-clamp recording was conducted to determine the effects of adenosine on the membrane potential and discharge frequency of PVT neurons.The experimental animals were divided into adenosine A1 receptor interference group(n=8)and interference control group(n=7).After RNA interference,electroencephalography(EEG)and electromyography(EMG)were applied simultaneously to observe the changes in wake and sleep time between the 2 groups during sleep deprivation for 6 h and sleep recovery for 4 h.Results RT-qPCR and fluorescence in situ hybridization confirmed that the expression of adenosine A1 receptor in PVT was affected by wakefulness/sleep state,with the level in the ZT0 group(active stage)significantly higher than that in the ZT12 group(non-active stage,P<0.05).Whole-cell patch clamp recording indicated that adenosine exerted inhibitory effects on PVT neurons through 2 distinct response types(P<0.05),and the inhibition was in a diminishing trend with a decrease in the expression level of adenosine A1 receptor.After sleep deprivation,the expression level of adenosine A1 receptor showed significant intergroup differences:the level was significantly higher in the sleep deprivation group than the recovery sleep group(P<0.05),while that of the recovery sleep group was higher than that of the ZT6 group and that of the ZT10 group(P<0.05).After knocking down adenosine A1 receptor(shRNA-A1R)in the PVT,the wakefulness timing of the shRNA-A1R group was significantly increased,while the sleep timing was significantly decreased within 1 h after sleep recovery when compared with the interference control group(P<0.05).Conclusion The expression of adenosine A1 receptor in the PVT is dynamically regulated by sleep pressure,which was increasing as sleep pressure rises.

The shortage of donor organs is the primary factor limiting the availability of liver transplantation (LT) and is a leading cause of death among patients on the waiting list. The reuse of liver allografts, while rare, represents a significant and unconventional donor resource, offering a promising strategy to expand the donor pool. This approach has been documented in international literature, demonstrating favorable surgical outcomes and long-term follow-up results. Here, we report the first case of liver allograft reuse in the Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University. In this case, the first recipient underwent orthotopic LT for acute liver failure and hepatic encephalopathy. However, their condition deteriorated on the seventh postoperative day, culminating in brain death. Following evaluation and maintenance, the liver allograft was successfully re-transplanted into a second recipient, who had undergone LT six days earlier but experienced acute hepatic artery embolism leading to rapid liver function deterioration. The second recipient's liver function recovered smoothly after surgery, and they were discharged on the 28th postoperative day. This case highlights the significant value of liver allograft reuse in expanding the donor pool and providing life-saving options for critically ill patients requiring urgent LT.

Objective:To explore the relationship between general demographic characteristics,inflammatory indicators,nutritional indicators,pathological data and lymph node metastasis in endometrial cancer(EC)pa-tients,and to construct and validate a model for preoperative prediction of lymph node status in endometrial canc-er patients.Methods:The preoperative clinical data of 473 patients with EC who underwent surgical treatment in the Zhu Jiang Hospital of Southern Medical University from January 2010 to April 2024 were retrospectively ana-lyzed.The independent risk factors of lymph node metastasis of endometrial cancer were screened by univariate and multivariate Logistic regression analyses,and the nomogram prediction model was constructed by R soft-ware.The performance of the model was evaluated by the receiver operating characteristic(ROC)curve,calibra-tion curve and clinical decision curve.Results:Menopausal status,high grade biopsy pathology,CA125 ≥24.47U/ml,systemic immune inflammatory index(SII)≥710.91,and prognostic nutritional index(PNI)＜52.90 were in-dependent risk factors for lymph node metastasis in endometrial cancer(OR＞1,P＜0.05).The nomogram model constructed based on these five factors had an AUC of 0.853 in the training set and 0.871 in the test set.The cali-bration curve fitted well,and the clinical decision curve shows a positive benefit.Conclusions:The endometrial cancer lymph node metastasis prediction model constructed based on menopausal status,biopsy pathology,CA125,SII,and PNI has good accuracy and fit,with certain clinical application value.