Lymphovascular space invasion (LVSI) is a key prognostic factor influencing treatment decisions in endometrial cancer (EC). Here, we evaluate whether diffusion-weighted imaging (DWI)-based habitat imaging can noninvasively identify LVSI in EC. We retrospectively analyzed 101 EC patients who underwent preoperative multi-b-value DWI examination between December 2020 and October 2024. EC lesions were decoded into four habitats determined by unsupervised K-means clustering using true diffusion coefficient (D), perfusion fraction (f), and mean kurtosis (MK) maps, and the volume fractions of each habitat were quantified. LVSI-positive EC ( n = 22 ) exhibited significantly higher volume fractions of Habitat 1 ( H1 ) and lower volume fractions of Habitat 2 ( H2 ) compared to LVSI-negative cases ( p < 0.001 ). Logistic regression identified independent risk factors for LVSI, including H1, FIGO stage, and histologic grade. H1 demonstrated comparable diagnostic performance (AUC, 0.76 ; 95%CI: 0.67, 0.84 ) to pathological indicators, while achieving the highest sensitivity ( 81.82% ). Additionally, H1 correlated positively with tumor volume, while H2 correlated negatively with histologic grade. These findings suggest DWI-based habitat imaging could serve as a valuable preoperative tool for noninvasive LVSI assessment in EC.

Background Precisely estimating risk stratification prior to initial treatment is crucial to guide clinical treatment decision-making in patients with operable cervical cancer. This study aimed to develop and validate deep-learning (DL) models using multi-parameter magnetic resonance imaging (MRI) for automatic segmentation and risk status prediction in operable cervical squamous cell carcinoma (CSCC). Methods Between June 2015 and May 2024, a total of 408 patients were enrolled from four hospitals. The nnUNetV2 architecture was designed for automatic tumor segmentation. For classification task, the 3D DL and 2.5D DL model, clinical model were developed. Two fusion models were developed: the feature-based Combined model (FeatureMerge) and the probability-based Combined model (ProbML). The segmentation performance was assessed using the Dice similarity coefficient (DSC), and the model’s diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC). Results The segmentation model performed well in the internal testing cohort and external testing cohort, with average DSCs of 0.829 and 0.762, respectively. For the prediction of risk stratification, the 2.5D DL model had superior discriminative ability compared to 3D DL model and clinical model. Combined model (ProbML) exhibited improved predictive capacity in the internal testing cohort (AUC, 0.865; 95% CI, 0.773–0.958) and external testing cohort (AUC, 0.805; 95% CI, 0.713–0.897). Conclusion The proposed Combined model (ProbML), which incorporates clinical characteristics, 3D DL and 2.5D DL features, can be used to predict risk stratification in operable CSCC. In addition, the nnUNetV2-based deep-learning model can accurately segment cervical cancer.

To establish an effective radiological risk stratification feature for metachronous metastasis of colon cancer. This retrospective, single-center study enrolled patients with stage II/III colon cancer who underwent curative surgery between December 2016 and September 2020. The ecological spatial characteristics of tumor habitats at the Class-level and Landscape–level were extracted by landscape pattern analysis. Univariate and multivariate COX regression analysis were used to determine the spatial characteristics related to metachronous metastasis and included in the prediction model, and the landscape score value was constructed to stratified the risk of metachronous metastasis. The predictive performance was evaluated using the time-dependent area under the ROC curve (AUC). Metachronous metastases occurred in 31 of 93 patients (median age, 61 years, [IQR, 51–65 years]). GYRATE-MD (HR: 1.67[95

Background: Sepsis-associated delirium (SAD) is a common acute brain dysfunction in elderly patients in the intensive care unit (ICU), which significantly increases the length of hospital stay, medical costs, and the risk of death. Despite the availability of multiple delirium prediction tools, there are few models specific to the elderly septic population, and most lack external validation. Methods: This retrospective cohort study enrolled 5034 elderly ICU patients with sepsis. A prediction model was constructed based on the MIMIC-IV database and externally validated using 281 patients admitted to the First Affiliated Hospital of Jilin University between January 2019 and November 2024. A workflow was developed using R software. Candidate predictors were first identified using the LASSO regression method, then incorporated into a multivariate logistic regression model and visualized as a nomogram. Patients were randomly divided into a training set and an internal validation set in a 6:4 ratio. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow test, calibration plots, Brier scores, and decision curve analysis (DCA). Results: The overall incidence of delirium was 46.44%. Seven variables were ultimately included in the final model: body temperature, SOFA score, hemoglobin level, serum sodium concentration, history of neurological disease, mechanical ventilation, and midazolam use. The model demonstrated good discriminatory performance, with area under the receiver operating characteristic curve (AUC) values of 0.794 (95% CI: 0.777–0.810) in the training set, 0.784 (95% CI: 0.763–0.804) in the internal validation set, and 0.815 (95% CI: 0.765–0.864) in the external validation set. The Hosmer–Lemeshow goodness-of-fit test showed no significant deviation between predicted and observed outcomes (P > 0.05), indicating good calibration. The Brier scores were 0.185, 0.188, and 0.176 for the training, internal validation, and external validation sets, respectively. Decision curve analysis (DCA) further confirmed the model’s potential clinical utility. Conclusion: The SAD risk prediction model developed in this study features a simple structure and relies on readily available clinical variables. It demonstrated favorable discrimination and calibration in the external validation cohort, suggesting its potential utility as a practical tool for early detection and targeted intervention of delirium in elderly ICU patients with sepsis.

User-generated content (UGC), which generates vast amounts of content in real-time through social networks, offers a significant opportunity for mining new knowledge. The survival of information technology products such as mobile APPs (mAPPs) depends on continuance. The exploration of the impact mechanism underlying continuance intention is crucial given the low continued usage of many mAPPs. Mining real-world UGC provides an efficient approach for user experience and product evaluation compared to traditional interviews or surveys. This study proposes a novel UGC-driven, Kano model focused, pipelined framework to automatically identify impact factors and determine the impact mechanism underlying continuance intention. The above method framework involves unsupervised clustering text analysis to identify user needs and product functions from UGC, followed by the construction of a statistical model to explore the relationship between these factors and user satisfaction and dissatisfaction (Kano model). Additionally, a model to distinguish the attributes of factors that significantly affect satisfaction or dissatisfaction is proposed. Finally, user satisfaction and dissatisfaction are used as mediators to build models of continuance and discontinuance intention. Empirical validation of the proposed method is conducted with a case study of mobile health apps, involving the mining of 86,423 user reviews and structural equation modeling based on 1,025 user responses. The results indicate that the UGC-driven method effectively explores the impact mechanism of continuance and discontinuance intention.

Objective: Little has been known on the effect of chronic glyphosate exposure on osteoarthritis (OA). The aim of this study was to investigate the association between glyphosate exposure and OA and to further investigate the different moderating effects of leisure time physical activity (LTPA) and body mass index (BMI) types on the association between glyphosate exposure and OA. Methods: Cross-sectional data from 2540 participants in the 2015 -2018 National Health and Nutrition Examination Survey (NHANES) were used to explore the association between glyphosate exposure and OA. Multivariate logistic regression models and restricted cubic spline models were used to investigate the association between glyphosate exposure and OA, and further analyses were conducted to determine the association between glyphosate exposure and OA under different LTPA and BMI types. Results: Of the 2540 participants, 346 had OA. Participants with the highest glyphosate concentration (Q4) had a higher incidence of OA compared to participants with the lowest glyphosate concentration (Q1) (OR, 1.88; 95 % confidence interval [CI]: 1.13, 3.13), there was no nonlinear association between glyphosate and OA (non -linear P = 0.343). In the no LTPA group, glyphosate concentration in the Q4 group was correlated with OA (OR, 2.65; 95%CI: 1.27, 5.51). In the obese group, glyphosate concentration in the Q4 group was correlated with OA (OR, 2.74; 95 % CI: 1.48, 5.07). Among people with high BMI and inactive in LTPA, glyphosate concentrations in Q4 were associated with OA (OR, 2.19; 95 % CI: 1.07, 4.48). Conclusions: Glyphosate is associated with OA odd, and physical activity and moderate weight loss can mitigate this association to some degree. This study provides a scientific basis for rational prevention of OA by regulation of LTPA and BMI under glyphosate exposure.

高校后勤承担着学校教学、科研和日常生活保障任务,是高校的重要组成部分.人力资源管理作为高校后勤管理的核心工作,对推动高校后勤长足发展及稳定运营起着至关重要的作用. 随着高校社会化改革的逐年深入,高校后勤建立了相应的人事管理部门,人力资源管理模式也越来越清晰,人力资源管理机制建设也越来越完善,但是仍然存在人员结构不平衡、组织机构建设不细化、薪酬管理不标准、绩效考核及激励考核作用不充分、业务审批流程不顺畅等诸多问题.

China is the largest global consumer of antimicrobials and improving surveillance methods could help to reduce antimicrobial resistance (AMR) spread. Here we report the surveillance of ten large-scale chicken farms and four connected abattoirs in three Chinese provinces over 2.5 years. Using a data mining approach based on machine learning, we analysed 461 microbiomes from birds, carcasses and environments, identifying 145 potentially mobile antibiotic resistance genes (ARGs) shared between chickens and environments across all farms. A core set of 233 ARGs and 186 microbial species extracted from the chicken gut microbiome correlated with the AMR profiles of Escherichia coli colonizing the same gut, including Arcobacter, Acinetobacter and Sphingobacterium , clinically relevant for humans, and 38 clinically relevant ARGs. Temperature and humidity in the barns were also correlated with ARG presence. We reveal an intricate network of correlations between environments, microbial communities and AMR, suggesting multiple routes to improving AMR surveillance in livestock production.

Diabetes is a chronic disease characterized by hyperglycemia. According to the statistics of estimated the number of people with diabetes reached 537 million in 2021. The rapid increase in the number of diabetics makes diabetes become a global health disease threatening mankind. Diabetes is usually accompanied by a long period of diagnosis. Before diabetes is discovered, the body is in a state of high blood sugar for a long time, which will cause serious complications for patients, early detection and treatment of diabetes can greatly alleviate the harm caused by the disease. In this context, this paper proposes a Diabetes prediction algorithm model based on PIMA Indians Diabetes Dataset(PID) published by the University of California at Irvine. In terms of model architecture, the prediction algorithm model is stored in the cloud server center to give full play to the efficient computing power of the cloud server, and it is more convenient to iteratively update the model algorithm. The algorithm model is divided into two parts. The first part uses the neural network to raise the dimension of the data. Inspired by the Cover theorem, the nonlinear dimension of the data is easier to distinguish the data. In the process of raising dimension, the loss function is used to make the data separability after raising dimension stronger. The second part uses the raised dimension data to train the base classification algorithm and integrate the trained algorithms with stacking methods to obtain the final model. Finally, the prediction accuracy of the algorithm model is 89.72% on the test set, which is significantly improved compared with other algorithms proposed on the PID data set.

以现有专利预警相关文献为研究对象,通过系统回顾与深度分析,对多元主体需求下的专利预警研究现状进行总结与梳理,分析其主要特征,并提出促进专利预警研究创新发展的改进思路与方向.研究发现:目前针对专利预警的概念内涵主要围绕专利威胁态势、专利竞争风险和专利侵权纠纷等视角展开探讨,关于专利预警机制建立的必要性、基本原则、关键作用、核心内容、主要措施等方面的研究构成了专利预警机制研究的理论体系,主要从预警主体、预警平台和预警指标等方面对专利预警体系的关键构成要素展开探索,专利预警的核心方法包括自然语言处理、机器学习、专利组合分析和专利地图分析,采集的信息资源包含专利信息及市场信息;然而在多源数据资源利用、预警模型构建及实时动态监测方面尚存在一定的不足.鉴于此,对后续研究提出两点展望:一是要进一步探索基于多源数据融合的专利预警实现机制;二是要深入探究基于扫描统计量的实时动态监测及预警方法,进而提高专利预警的有效性.

[目的/意义]构建企业专利威胁预警的概念模型,完善专利威胁预警理论,为专利威胁预警方法研究提供理论依据.[方法/过程]以竞争战略和竞争优势理论框架为基础,从产业环境角度辨识出能够影响企业专利威胁形成的关键因素,构建企业专利威胁预警的概念模型,分析其预警机理,并进行实证研究.[结果/结论]研究结果表明,构建的企业专利威胁预警的"E-L"模型可以从产业环境角度对企业所面临的专利威胁进行评估,能够更加有效地对专利威胁进行识别和预警.

[目的/意义]构建企业专利威胁预警的概念模型,完善专利威胁预警理论,为企业参与专利威胁预警工作提供理论支撑.[目的/意义]从影响企业专利威胁形成的技术研发人员因素出发,确定评估维度,构建企业专利威胁预警的概念模型,分析其预警机理,并进行实证研究.[结果/结论]研究结果表明,利用构建的企业专利威胁预警的"卡带"模型可以指导企业从技术研发人员角度对企业在各项技术领域中所面临的专利威胁进行评估,有助于实现对企业专利威胁进行更为有效的识别与预警.[创新/局限]本文的创新主要体现在以HCM理论框架为基础,从创新型人力资本存量、人力资本质量和人力资本维持时间3个维度对企业专利威胁进行评估,进而构建企业专利威胁预警的概念模型.研究局限主要表现在对于相对技术研发能力维度的测度主要采用的是技术研发人员的专利申请数量,尚未从专利申请质量角度对该评估维度进行测度.

从基础竞争态势、高被引专利特性、专利原创性与普遍性及综合竞争角度建立专利竞争态势分析模型,从多个维度综合分析东北亚主要竞争体在心血管系统疾病药物领域的优势与劣势,发现中国专利数量较多,是技术活跃者,但专利质量与日韩差距较大;日本、韩国是潜在技术竞争者,专利质量较高;俄罗斯在该领域技术相对落后;A61P-009/08(血管舒张药)、A61P-009/12(抗高血压药)是各国重点布局的技术领域.根据当前问题提出了重视提升专利质量、加强与技术强国的联系、找到技术创新的突破口等建议.

目的:构建技术竞争对手多维动态识别模型,为企业技术竞争对手的识别提供参考.方法:在动态竞争理论的基础上探讨技术竞争对手的特征,构建技术竞争对手动态识别模型.结果:该模型可以从多个维度动态识别目标企业的技术竞争对手和潜在的核心技术竞争对手.结论:技术竞争对手多维动态识别模型,可以为企业技术竞争对手的识别提供参考.

针对情报学方法在新冠肺炎疫情期间的实际应用,从3个方面探讨了重大疫情防控中的情报学方法问题,即重大疫情防控中情报学理论问题、情报学方法在危机情境下的适用性和新冠疫情防控中的情报支撑,提出了情报界在做好常规的信息管理工作和信息系统建设的基础上,应聚焦"情报",主动介入重大疫情防控主战场,运用大数据和人工智能等技术服务于重大疫情防控,为应急管理决策提供的支撑等建议.

Background: Medical informatics (MI) is a multidisciplinary field in which researchers pursue scientific exploration, problem-solving, and decision-making to facilitate the effective use of biomedical data, information and knowledge for the improvement of human health. The purpose of this study is to identify research fronts in the field of MI and ultimately elucidate research activities and trends in this field. Methods: This study used topic model to identify research topics in the field of MI based on the latent Dirichlet allocation method (LDA). And the topic cloud is utilized to visualize the research topics. For identifying the research front topics, we proposed the indicators of identifying research front topics. In addition, we investigated how front topics change over time, and divided them into five categories based on the life cycle theory. Results: The data were collected from 35981 published journal abstracts between 2007 and 2016. In the topic distribution of MI, we found that the scope of MI related research has become increasingly interdisciplinary, particular for medical data analysis. Also, in the analysis of research fronts of MI, we found that the use of natural language processing and medical text knowledge extraction play an essential role for systematic analysis and indexing of the underlying semantic contents. Conclusions: By categorizing the research fronts, the results shows that there are twelve growing, five stable and two declining research fronts. We hope that this work will facilitate greater exploration of the method of identifying the research fronts. Moreover, the findings of this study provide an insight on the research fronts and trends in MI.

目的:探究麻醉恢复室全麻成年患者苏醒期躁动的危险因素.方法:2018年6月-2019年6月期间成年全麻手术患者400例,全程监测并进行镇静躁动评分,根据是否发生苏醒期躁动分为观察组(发生躁动)与对照组(无躁动),Logistic回归分析筛选躁动的危险因素.结果:性别、年龄、术前白细胞计数都是躁动危险因素.结论:成年患者全麻苏醒期躁动的危险因素包括性别、年龄以及术前白细胞计数等,对包含此类危险因素的患者需要充分重视,采取预防措施,从而预防坠床、拔管等事件.

及时准确地把握研究前沿有助于为科技政策的制定和科研部署提供更加全面的决策依据和参考.大数据环境下,有效协同利用多源数据识别研究前沿成为当下情报学领域的研究重点.文章通过文献调研和内容分析法对研究前沿的识别方法和多源数据融合方法进行深入分析.依据研究对象不同,本文将研究前沿识别方法划分为基于引文的、基于词汇的、基于主题的和基于融合的方法,并对比阐述了基于融合方法的必要性.在多源数据融合方法方面,依据融合深度不同,本文从载体融合和关系融合两个方面梳理现有方法的特点和不足.为实现基于多源数据和深入语义层面的研究前沿识别,本文构建了面向多源科技文本融合的载体-特征-关系融合模型,并以研究前沿的核心特征为切入点,提出关注度、新颖度和中心度3个识别指标,丰富了基于多源数据融合的研究前沿识别方法.

Purposes: This study aims to identify the comorbidity patterns of older men with lung cancer in China. Methods: We analyzed the electronic medical records (EMRs) of lung cancer patients over age 65 in the Jilin Province of China. The data studied were obtained from 20 hospitals of Jilin Province in 2018. In total, 1510 patients were identified. We conducted a rank–frequency analysis and social network analysis to identify the predominant comorbidities and comorbidity networks. We applied the association rules to mine the comorbidity combination with the values of confidence and lift. A heatmap was utilized to visualize the rules. Results: Our analyses discovered that (1) there were 31 additional medical conditions in older patients with lung cancer. The most frequent comorbidities were pneumonia, cerebral infarction, and hypertension. (2) The network-based analysis revealed seven subnetworks. (3) The association rules analysis provided 41 interesting rules. The results revealed that hypertension, ischemic cardiomyopathy, and pneumonia are the most frequent comorbid combinations. Heart failure may not have a strong implicating role in these comorbidity patterns. Cerebral infarction was rarely combined with other diseases. In addition, glycoprotein metabolism disorder comorbid with hyponatremia or hypokalemia increased the risk of anemia by more than eight times in older lung cancer patients. Conclusions: This study provides evidence on the comorbidity patterns of older men with lung cancer in China. Understanding the comorbidity patterns of older patients with lung cancer can assist clinicians in their diagnoses and contribute to developing healthcare policies, as well as allocating resources.

Hepatocellular carcinoma (HCC) is a common and fatal cancer. People with HCC report higher odds of comorbidity compared with people without HCC. To explore the association between HCC and medical comorbidity, we used routinely collected clinical data and applied a network perspective. In the network perspective, we used correlation analysis and community detection tests that described direct relationships among comorbidities. We collected 14,891 patients with HCC living in Jilin Province, China, between 2016 and 2018. Cirrhosis was the most common comorbidity of HCC. Hypertension and renal cysts were more common in male patients, while chronic viral hepatitis C, hypersplenism, hypoproteinemia, anemia and coronary heart disease were more common in female patients. The proportion of chronic diseases in comorbidities increased with age. The main comorbidity patterns of HCC were: HCC, cirrhosis, chronic viral hepatitis B, portal hypertension, ascites and other common complications of cirrhosis; HCC, hypertension, diabetes mellitus, coronary heart disease and cerebral infarction; and HCC, hypoproteinemia, electrolyte disorders, gastrointestinal hemorrhage and hemorrhagic anemia. Our findings provide comprehensive information on comorbidity patterns of HCC, which may be used for the prevention and management of liver cancer.