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 construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Occupational chronic n-hexane poisoning incidents have been effectively curtailed in traditional printing and footwear industries, but its hazards are emerging in new industries. In recent years, two cluster incidents involving eight patients with occupational chronic n-hexane poisoning had occurred in Longgang District, Shenzhen City. Unlike the cleaning processes of electronic components in the electronics industry, these two incidents occurred during cleaning operations of non-electronic products. The rapid on-site detection tubes indicated the presence of n-hexane in the organic solvents used at the work site, and subsequent analysis of volatile components of the organic solvents further confirmed the involvement of n-hexane. Although the n-hexane exposure concentration of short term in the workplace air samples were below its occupational exposure limit, all eight cases were diagnosed as occupational chronic n-hexane poisoning, based on occupational exposure history, clinical manifestations, field investigations, and laboratory test results. These two poisoning incidents highlight that in air-conditioned or enclosed workshops with substandard occupational disease prevention facilities, the use of n-hexane containing organic solvents may result in occupational chronic n-hexane poisoning, even when the air monitoring results do not exceed the occupational exposure limits.

BACKGROUND: Molecular subtyping is an essential complementarity after pathological analyses for targeted therapy. This study aimed to investigate the consistency of next-generation sequencing (NGS) results between circulating tumor DNA (ctDNA)-based and tissue-based in non-small cell lung cancer (NSCLC) and identify the patient characteristics that favor ctDNA testing. METHODS: Patients who diagnosed with NSCLC and received both ctDNA- and cancer tissue-based NGS before surgery or systemic treatment in Lung Cancer Center, Sichuan University West China Hospital between December 2017 and August 2022 were enrolled. A 425-cancer panel with a HiSeq 4000 NGS platform was used for NGS. The unweighted Cohen's kappa coefficient was employed to discriminate the high-concordance group from the low-concordance group with a cutoff value of 0.6. Six machine learning models were used to identify patient characteristics that relate to high concordance between ctDNA-based and tissue-based NGS. RESULTS: A total of 85 patients were enrolled, of which 22.4% (19/85) had stage III disease and 56.5% (48/85) had stage IV disease. Forty-four patients (51.8%) showed consistent gene mutation types between ctDNA-based and tissue-based NGS, while one patient (1.2%) tested negative in both approaches. Patients with advanced diseases and metastases to other organs would be suitable for the ctDNA-based NGS, and the generalized linear model showed that T stage, M stage, and tumor mutation burden were the critical discriminators to predict the consistency of results between ctDNA-based and tissue-based NGS. CONCLUSION ctDNA-based NGS showed comparable detection performance in the targeted gene mutations compared with tissue-based NGS, and it could be considered in advanced or metastatic NSCLC.
Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Circulating Tumor DNA/blood* ; High-Throughput Nucleotide Sequencing/methods* ; Female ; Male ; Lung Neoplasms/pathology* ; Middle Aged ; Mutation/genetics* ; Aged ; Adult ; Aged, 80 and over

Emerging evidences have indicated the role of ferroptosis in the progression of metabolic-associated fatty liver disease (MAFLD); thus, inhibiting ferroptosis is a promising strategy for the development of MAFLD therapeutics. Recent studies have demonstrated the antioxidative effect of the gut commensal bacterium Akkermansia muciniphila (A. muc); however, whether it can alleviate ferroptosis remains unclear. The current study indicates A. muc intervention efficiently reversed high-fat high-fructose diet (HFHFD)-induced lipid peroxidation and ferroptosis in the liver. These beneficial effects were mediated by activation of the hepatic AMPK/SIRT1/PGC-1α axis, as evidenced by the finding that AMPK deficiency abrogated the amelioration of lipid peroxidation in vitro and in vivo. Furthermore, the short-chain fatty acids (SCFAs) were enriched upon A. muc treatment, and acetate was identified as a key activator of hepatic AMPK signalling. Mechanistically, microbiota-derived acetate was transported to the liver and metabolized to adenosine monophosphate (AMP), which triggered AMPK activation. Furthermore, a colonization assay in germ-free mice confirmed that A. muc mediated antiferroptotic effects in the absence of other microbes. These data indicated that A. muc exerts antiferroptotic effects against MAFLD, at least partially by producing acetate, which activates the hepatic AMPK/SIRT1/PGC-1α axis to alleviate ferroptosis via the inhibition of polyunsaturated fatty acid (PUFA) synthesis.

Ferroptosis of chondrocytes is a significant contributor to osteoarthritis (OA), for which there is still a lack of safe and effective therapeutic drugs targeting ferroptosis. Here, we screen for anti-ferroptotic drugs in Food and Drug Administration (FDA)-approved drug library via a high-throughput manner in chondrocytes. We identified a group of FDA-approved anti-ferroptotic drugs, among which vitamin K showed the most powerful protective effect. Further study demonstrated that vitamin K effectively inhibited ferroptosis and alleviated the extracellular matrix (ECM) degradation in chondrocytes. Intra-articular injection of vitamin K inhibited ferroptosis and alleviated OA phenotype in destabilization of the medial meniscus (DMM) mouse model. Mechanistically, transcriptome sequencing and knockdown experiments revealed that the anti-ferroptotic effects of vitamin K depended on growth arrest-specific 6 (Gas6). Furthermore, exogenous expression of Gas6 was found to inhibit ferroptosis through the AXL receptor tyrosine kinase (AXL)/phosphatidylinositol 3-kinase (PI3K)/AKT serine/threonine kinase (AKT) axis. Together, we demonstrate that vitamin K inhibits ferroptosis and alleviates OA progression via enhancing Gas6 expression and its downstream pathway of AXL/PI3K/AKT axis, indicating vitamin K as well as Gas6 to serve as a potential therapeutic target for OA and other ferroptosis-related diseases.

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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 a Harvard cancer index-based risk predictive model for perioperative venous thromboembolism (VTE) in elderly patients with femoral neck fracture and assess its predictive effectiveness.Methods:A retrospective cohort study was conducted to analyze the clinical data of 610 elderly patients with femoral neck fracture admitted to Peking Union Medical College Hospital between January 2013 and December 2022, including 193 males and 417 females, aged 60-99 years [(77.3±9.0)years]. The patients were divided into VTE group ( n=125) and non-VTE group ( n=485) according to occurrence of VTE during the perioperative period. The two groups were compared in terms of gender, age, body mass index, smoking status, alcohol consumption, time from fracture to admission, surgical waiting time, comorbidities, perioperative electrolyte disorders, past or present history of malignancy, past history of deep vein thrombosis (DVT) or pulmonary embolism (PE), and preoperative use of oral anticoagulants. Univariate analysis and multivariable stepwise Logistic regression analysis were conducted to evaluate and identify independent risk factors for perioperative VTE in elderly patients with femoral neck fracture. A perioperative VTE risk predictive model for elderly patients with femoral neck fracture was constructed using the Harvard cancer index: (1) assigning a risk score to each variable according to the corresponding conversion criteria of the Harvard cancer index and risk score, based on the magnitude of their ORs; (2) determining the exposure rate of each risk factor based on the population distribution observed in this study; (3) calculating the average population risk score; (4) computing the individual VTE risk score; (5) deriving the ratio (X) of each individual ′s VTE risk score to the population average. Based on the Harvard cancer index classification criteria for disease risk levels, individual VTE risk categories were determined. The predictive performance of the risk stratification was evaluated by comparing the incidence of VTE across different risk levels. The predictive performance of the model was evaluated based on sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) test and internal validation was performed using the bootstrap resampling method with 1000 iterations. Results:Univariate analysis showed that gender, age, time from fracture to admission, surgical waiting time, previous cerebral infarction, stroke within the past month, Alzheimer′s disease, primary Parkinson′s syndrome, hysterectomy with bilateral adnexectomy, perioperative electrolyte disorders, history of DVT or PE, and preoperative use of oral anticoagulant drug were moderately associated with the occurrence of VTE in elderly patients with femoral neck fracture ( P<0.10). Multivariable stepwise logistic regression analysis demonstrated that female gender ( OR=2.26, 95% CI 1.34, 3.80, P<0.01), time from fracture to admission>1 day ( OR=3.70, 95% CI 2.24, 6.12, P<0.01), surgical waiting time>70 hours ( OR=2.06, 95% CI 1.29, 3.30, P<0.01), previous cerebral infarction ( OR=3.78, 95% CI 1.04, 13.76, P<0.05), stroke within the past month ( OR=11.57, 95% CI 1.21, 110.44, P<0.05), Alzheimer′s disease ( OR=3.26, 95% CI 1.12, 9.49, P<0.05), primary Parkinson ′s syndrome ( OR=3.47, 95% CI 1.22, 9.85, P<0.05), previous hysterectomy with bilateral adnexectomy ( OR=4.75, 95% CI 2.09, 10.80, P<0.01), perioperative electrolyte disorders ( OR=2.73, 95% CI 1.39, 5.35, P<0.01), and preoperative oral anticoagulant use ( OR=3.86, 95% CI 1.18, 12.67, P<0.05) were significantly associated with the occurrence of perioperative VTE in elderly patients with femoral neck fracture. Based on the above 10 risk factors, a perioperative VTE risk predictive model for elderly patients with femoral neck fracture was constructed with the Harvard cancer index. The formula was as follows: X=[10×(female gender)+25×(time from fracture to admission>1 day)+10×(surgical waiting time>70 hours)+25×(previous cerebral infarction)+50×(stroke within the past month)+25×(Alzheimer′s disease)+25×(primary Parkinson′s disease)+25×(previous hysterectomy with bilateral adnexectomy)+10×(perioperative electrolyte disorders)+25×(preoperative use of oral anticoagulant drug)]/33. Individualized VTE risk was classified into five levels: very low, low, moderate, high, and very high, with corresponding VTE rates of 4.8%, 11.8%, 14.9%, 32.3%, and 73.5%, respectively ( χ2=87.71, P<0.01). The VTE risk predictive model demonstrated an AUC of 0.74 (95% CI 0.69, 0.79, P<0.01), with a sensitivity of 63.2% and specificity of 74.8%. The H-L goodness-of-fit test indicated satisfactory model calibration ( P>0.05). The internal validation with the bootstrap method confirmed that the AUC remained 0.74. Conclusions:Female gender, time from fracture to admission>1 day, surgical waiting time>70 hours, previous cerebral infarction, stroke within the past month, Alzheimer′s disease, primary Parkinson′s syndrome, hysterectomy with bilateral adnexectomy, perioperative electrolyte disorders, and preoperative use of oral anticoagulant drug are independent risk factors for perioperative VTE in elderly patients with femoral neck fracture. Based on these factors, the perioperative VTE risk predictive model constructed using the Harvard cancer index demonstrates good clinical predictive value. Individualized VTE risk stratification can effectively identify high-, intermediate-, and low-risk populations, providing a valuable reference for tailoring anticoagulant prophylaxis strategies and enhancing postoperative surveillance.