[摘 要] 目的：通过多组学整合分析，解析肠道菌群在胆管癌（CCA）发生发展中的潜在作用机制并识别相关关键基因。方法：基于SRA数据库的16S rRNA测序数据，比较CCA患者与健康对照者的肠道菌群组成；采用孟德尔随机化（MR）分析评估菌群与CCA风险的遗传关联。通过gutMGene和GeneCards数据库获取相关代谢物与基因，进行代谢和功能富集分析。整合GEO单细胞转录组数据（GSE213452），解析肿瘤微环境的细胞组成，重点关注T细胞亚群及其功能状态，并结合TCGA-CHOL数据集验证关键候选基因的表达差异。结果：与健康对照组相比，CCA患者肠道菌群组成发生显著改变，变形菌门下菌群异常富集（LDA > 4）。MR分析进一步证实，肠杆菌目与肠杆菌科的遗传易感性均与CCA风险呈正向关联。代谢通路富集分析提示，菌群相关代谢物主要参与嘌呤代谢及糖酵解/糖异生等通路；功能富集分析显示，相关基因显著富集于NOD样受体、IL-17、Toll样受体及NF-κB信号通路等炎症免疫通路。单细胞转录组分析结果显示，CCA组织中肿瘤细胞比例显著升高（P < 0.05），T细胞比例由20.7%增至39.2%；拟时序分析结果表明，MKI67⁺ T细胞处于分化末期并呈高增殖特征，其差异基因与菌群相关基因存在交集，其中SERPINA1和IFNG表达在肿瘤免疫微环境中显著变化（P < 0.001），可能发挥核心调控作用。TCGA-CHOL数据集验证显示，SERPINA1在CCA肿瘤组织中显著下调（P < 0.001），而IFNG在肿瘤与正常组织间无显著差异（P > 0.05）。结论：肠道菌群失衡（尤其是肠杆菌科异常增殖）可能通过代谢-免疫调控网络促进CCA进展，T细胞功能变化与关键基因（SERPINA1和IFNG）在MKI67+ T细胞中的差异化表达模式密切相关。

Objective To explore the relationship between intestinal flora imbalance and pulmonary function in patients with chronic obstructive pulmonary disease (COPD), and to analyze the related influencing factors. Methods A total of 310 patients with COPD who were admitted to Air Force Military Medical University Tangdu Hospital from June 2022 to December 2024 were retrospectively analyzed. Based on intestinal flora status, the enrolled patients were classified into imbalance group (n=83) and non-imbalance group (n=227). Logistic regression analysis was conducted to analyze the independent related factors of intestinal flora imbalance in COPD patients. Based on the above factors, a prediction model was constructed, and ROC curve analysis model was applied to analyze the predictive value of the model on intestinal flora imbalance. Results Logistic regression analysis revealed that age, IL-6, albumin, pulmonary function, long-term bed rest and long-term use of antibiotics were related to intestinal flora imbalance in COPD patients (all P<0.05). ROC results of the Logistic prediction model showed that the area under the curve, sensitivity, specificity and 95%CI were 0.961, 0.880, 0.996 and 0.932-0.989 respectively. Conclusion The intestinal flora imbalance in patients with COPD is closely related to lung function, age, inflammatory status, nutritional indicators, activity ability and antibiotic use.

[摘 要] 目的：探讨环指蛋白8（RNF8）对子宫内膜癌（EC）细胞顺铂（CDDP）敏感性的影响及可能机制。方法：基于在线数据库分析RNF8在EC组织中的表达及拷贝数变异；采用慢病毒介导shRNA稳定敲低Ishikawa细胞中RNF8表达并进行CDDP处理，按sh-NC+溶剂对照、sh-RNF8+溶剂对照、sh-NC + CDDP、sh-RNF8 + CDDP分组处理，并设置sh-RNF8 + CDDP + KU60019抑制实验。采用CCK-8、克隆形成、Annexin V-FITC/PI流式细胞术、Hoechst 33258/PI染色、WB及γ-H2AX免疫荧光检测细胞活力、克隆形成、凋亡、DNA损伤累积及ATM/CHK2通路相关蛋白表达。结果：RNF8在EC组织和细胞中表达升高。RNF8敲低降低细胞存活率和IC50，抑制克隆形成，并进一步增强CDDP诱导的凋亡；同时BAX、Cleaved-Caspase-3和γ-H2AX表达升高，BCL-2、p-ATM、p-CHK2和RAD21表达降低。KU60019处理进一步促进DNA损伤累积和细胞凋亡，并降低细胞活力。结论：RNF8可能通过ATM/CHK2轴促进DNA损伤应答并降低EC细胞对CDDP的敏感性；敲低RNF8可增加DNA损伤累积并增强CDDP敏感性。

A novel analytical method was developed in this study by combining online solid phase extraction with ultra performance liquid chromatography-tandem mass spectrometry(Online SPE-UPLC-MS/MS)for simultaneous determination of 50 kinds of steroid hormones in surface water.Specifically,after high-speed centrifugation of 4 mL water samples,the supernatant was directly injected into an Oasis HLB online SPE column for enrichment and purification.Subsequently,the target compounds were transferred to the analytical column via valve switching for separation and analysis.The chromatographic separation was performed on a Thermo Acclaim RSLC C18 column(100 mm×2.1 mm,2.2 μm),using a mobile phase composed of 5 mmol/L ammonium fluoride aqueous solution and acetonitrile.Mass spectrometric detection was conducted in positive ion mode,utilizing multiple reaction monitoring(MRM)with quantification achieved by the internal standard method.The method validation demonstrated that the limits of detection(LOD)for the 50 kinds of steroid hormones ranged from 0.02 to 0.50 ng/L,while the limits of quantification(LOQ)were between 0.08 and 1.67 ng/L.The average recoveries in surface water samples at spiked concentrations of 5,20 and 200 ng/L were between 74.1％and 119％,with relative standard deviations(RSDs)of 0.2％to 9.9％.This method was applied to analyze 11 surface water samples collected from sites surrounding a pharmaceutical and chemical industrial park.A total of 44 kinds of steroid hormones were detected,with concentrations ranging from 0.11 to 88.6 ng/L,revealing the presence of hormone contamination in the environmental waters surrounding industrial areas.Compared with the traditional offline SPE methods,the proposed online SPE technique significantly reduced sample volume requirements and pretreatment time,while minimizing the loss of target compounds during the pretreatment process.Moreover,compared to reported online SPE techniques,this method achieved high-throughput analysis of multiple classes of steroid hormones,with lower detection limits and higher recoveries.Overall,this method provided rapid sample preparation,high sensitivity,and excellent stability,making it suitable for the direct analysis of trace steroid hormones in surface water.

Objective To establish an efficient and stable one-step real-time quantitative PCR(qRT-PCR)method for detecting mi-croRNA-like small RNAs(milRNAs)encoded by novel Bunyavirus and evaluate its clinical application value.Methods Three kinds of milRNAs encoded by novel Bunyavirus,including sRNA S-1480,sRNA M-692,and sRNA L-4706,were selected based on prelimi-nary screening.The specific qRT-PCR primers and reaction systems for them were designed and optimized.The sensitivity,specificity,and clinical applicability of the method were further evaluated by standard curve drawing,primer concentration/sequence optimization,fluorescent dye optimization,product sequencing verification,and serum specimen detection of 20 patients infected with novel Bunya-virus and 20 healthy controls.Results A one-step qRT-PCR detection system was successfully established.The standard curve con-structed with standard substance showed a good linear relationship in the range of 10 fmol/L to 100 nmol/L(R2>0.98).The detection limits for three kinds of milRNAs were 10 fmol/L.The analysis results of the receiver operating characteristics(ROC)curve showed that sRNA S-1480(AUCROC=0.972 5),sRNA M-692(AUCROC=0.757 5),sRNA L-4706(AUCROC=0.957 5),and their combina-tion(AUCROC=0.995 0)could effectively distinguish the patients infected with novel Bunyavirus from healthy controls.Conclusion The established one-step qRT-PCR detection system exhibits high sensitivity and provides an efficient and reliable alternative for the clinical diagnosis of novel Bunyavirus infection.

Objective:To investigate the epidemiological dynamics and spatiotemporal diffusion trend of brucellosis in China from 2010 to 2024.Methods:Data on reported human brucellosis cases in mainland China from January 1, 2010, to December 31, 2024, were collected via the"China Information System for Disease Control and Prevention", including detailed information on the date of onset, gender, age, occupation, and residential address of the cases. The Joinpoint regression and spatial interpolation techniques were used to investigate the spatiotemporal dynamics and population distribution characteristics of human brucellosis in pastoral/semi-pastoral areas and other regions, as well as urban and rural areas, and explore the epidemic trends of the disease.Results:From 2010 to 2024, pastoral/semi-pastoral regions reported 252 094 brucellosis cases, with a mean annual incidence rate of 36.57±7.28 per 100 000. In contrast, other regions cumulatively recorded 519 748 cases during the same period, demonstrating a significantly lower mean annual incidence rate of 2.54±0.74 per 100 000. The incidence rate of human brucellosis in pastoral/semi-pastoral regions exhibited a declining-rebounding-declining trend. Specifically, the incidence rate decreased significantly from 2010 to 2017 (APC=-7.20; P<0.001) and increased notably from 2017 to 2021 (APC=18.00; P=0.015) with a decline again from 2021 to 2024 (APC=-7.53; P=0.027). In other regions, the incidence rate showed a fluctuating upward trend. Specifically, the incidence rate increased significantly from 2010 to 2015 (APC=20.37; P<0.001) and decreased notably from 2015 to 2018 (APC=-21.78; P<0.001), followed by an increase again from 2018 to 2024, a significant upward trend in incidence rate from 2018 to 2021 (APC=26.73; P<0.001) and a non-significant decline from 2021 to 2024 (APC=-0.99; P=0.735), resulting in the maintenance of a relatively high incidence level. Rural areas demonstrated significantly higher brucellosis incidence rates than urban settings (all P<0.001). Brucellosis exhibited a diffusion trend from the northern epidemic areas of China to neighboring regions, along with sporadic diffusion in southern regions between 2010 and 2024. The age structure of patients in pastoral/semi-pastoral areas differed significantly from that in other regions. Specifically, in pastoral/semi-pastoral areas, the incidence rate was higher among the 35-49 age groups, while in other regions, the incidence rate was higher among those aged 55-64. Conclusion:There are notable disparities in the incidence of human brucellosis between pastoral/semi-pastoral areas and other regions in China. Human brucellosis exhibits a diffusion trend from the northern epidemic areas of China to neighboring regions, along with sporadic diffusion in southern regions.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

BACKGROUND:Knee osteoarthritis is a common degenerative disease that significantly impacts patients'quality of life and increases the societal healthcare burden.Early and accurate diagnosis of knee osteoarthritis is crucial for the treatment and prognosis of patients.Traditional diagnostic methods are not only subjective and time-consuming but also do not guarantee consistently high accuracy.OBJECTIVE:To develop an automatic diagnostic method for knee osteoarthritis based on deep learning,utilizing deep learning networks to improve diagnostic accuracy and efficiency.METHODS:A new network model,YOLOV8-ViT,was proposed by replacing the backbone network of YOLOv8n with the Efficient-ViT network and incorporating attention mechanisms for the automatic identification and classification of X-ray images of knee osteoarthritis.The experimental dataset included 5 078 X-ray images of patients with knee osteoarthritis obtained from the Third Affiliated Hospital of Guangzhou University of Chinese Medicine.Three imaging physicians annotated the sites of knee osteoarthritis and classified them according to the Kellgren-Lawrence grading standard using Labelme software,and the results were combined.The evaluation indicators used in this study included Precision,F1 score,mean average precision(mAP),Recall,val/box_loss,val/cls_loss,and val/dfl_loss.RESULTS AND CONCLUSION:The experimental results showed that the YOLOV8-ViT model outperformed the YOLOv5n,YOLOv8n,and YOLOv9n models in terms of precision,mAP50,mAP50-95,F1 score,and Recall,while lowering val/box_loss,val/cls_loss,and val/dfl_loss by 0.496,0.45,and 0.523;1.037,0.305,and 0.728;and 0.267,0.654,and 0.854,respectively.These experimental data validate that this model has high detection accuracy.

Objective To explore the role of miR-204-3p in silicosis and to elucidate the mechanism by which it affects silicosis fibers by regulating silica dust-induced alveolar epithelial-mesenchymal transition(EMT)in rats.Methods Forty SD rats were divided randomly into 4 groups:Control,Silicosis,AAV-Control,and AAV-miR-204-3p groups.The pathology of lung tissue damage was detected by hematoxylin and eosin(HE)and Masson staining.Relative expression levels of miR-204-3p and EMT marker genes in lung tissues from rats in each group were analyzed by real-time fluorescence reverse transcription quantitative PCR(RT-qPCR),and protein expression levels of EMT-related markers in lung tissues were detected by Western blot.Results The alveolar structure was damaged,the lung septa showed interstitial fibrosis,and expression levels of mesenchymal markers were elevated in the Silicosis group compared with the Control group(P＜0.05,P＜0.01,P＜0.001).The alveolar structure was more complete,the EMT process was alleviated,fibrosis was improved,and mesenchymal marker expression was reduced in the AAV-miR-204-3p group compared with the AAV-Control group(P＜0.05,P＜0.01,P＜0.001).Conclusions Free silica dust induces EMT in rat lung tissue.Overexpression of miR-204-3p can attenuate the EMT process induced by free silica dust in rats,and may thus affect silicosis fibrosis.

Ovarian tumor (OT) is the most lethal form of gynecologic malignancy, with minimal improvements in patient outcomes over the past several decades. Metastasis is the leading cause of ovarian cancer-related deaths, yet the underlying mechanisms remain poorly understood. Psychological stress is known to activate the glucocorticoid receptor (NR3C1), a factor associated with poor prognosis in OT patients. However, the precise mechanisms linking NR3C1 signaling and metastasis have yet to be fully elucidated. In this study, we demonstrate that chronic restraint stress accelerates epithelial-mesenchymal transition (EMT) and metastasis in OT through an NR3C1-dependent mechanism involving nuclear protein 1 (NUPR1). Mechanistically, NR3C1 directly regulates the transcription of NUPR1, which in turn increases the expression of snail family transcriptional repressor 2 (SNAI2), a key driver of EMT. Clinically, elevated NR3C1 positively correlates with NUPR1 expression in OT patients, and both are positively associated with poorer prognosis. Overall, our study identified the NR3C1/NUPR1 axis as a critical regulatory pathway in psychological stress-induced OT metastasis, suggesting a potential therapeutic target for intervention in OT metastasis.