Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

Efficient Clustering Network Based on Matrix Factorization

Contrastive learning is a significant research direction in the field of deep learning.However,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods.To address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).Specifically,we design a batched low-rank Singular Value Decomposition(SVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data.Additionally,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters apart.Extensive experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.

Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.

Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis

Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.

Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-View Data Representation

Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency of multi-view data,while neglecting the diversity among different views as well as the high-order relationships of data,resulting in the loss of valuable complementary information.In this paper,we design a hypergraph regularized diverse deep matrix factorization(HDDMF)model for multi-view data representation,to jointly utilize multi-view diversity and a high-order manifold in a multilayer factorization framework.A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data.Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view.An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis.Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms stateof-the-art multi-view learning approaches.

DoS Attack Detection Based on Deep Factorization Machine in SDN

Software-Defined Network(SDN)decouples the control plane of network devices from the data plane.While alleviating the problems presented in traditional network architectures,it also brings potential security risks,particularly network Denial-of-Service(DoS)attacks.While many research efforts have been devoted to identifying new features for DoS attack detection,detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks.To solve this problem,a new method of DoS attack detection based on Deep Factorization Machine(DeepFM)is proposed in SDN.Firstly,we select the Growth Rate of Max Matched Packets(GRMMP)in SDN as detection feature.Then,the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS attacks.After training,the model can be used to infer whether SDN is under DoS attacks,and a DeepFM-based detection method for DoS attacks against client host is implemented.Simulation results show that our method can effectively detect DoS attacks in SDN.Compared with the K-Nearest Neighbor(K-NN),Artificial Neural Network(ANN)models,Support Vector Machine(SVM)and Random Forest models,our proposed method outperforms in accuracy,precision and F1 values.

Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

Data is humongous today because of the extensive use of World WideWeb, Social Media and Intelligent Systems. This data can be very important anduseful if it is harnessed carefully and correctly. Useful information can beextracted from this massive data using the Data Mining process. The informationextracted can be used to make vital decisions in various industries. Clustering is avery popular Data Mining method which divides the data points into differentgroups such that all similar data points form a part of the same group. Clusteringmethods are of various types. Many parameters and indexes exist for the evaluationand comparison of these methods. In this paper, we have compared partitioningbased methods K-Means, Fuzzy C-Means (FCM), Partitioning AroundMedoids (PAM) and Clustering Large Application (CLARA) on secure perturbeddata. Comparison and identification has been done for the method which performsbetter for analyzing the data perturbed using Extended NMF on the basis of thevalues of various indexes like Dunn Index, Silhouette Index, Xie-Beni Indexand Davies-Bouldin Index.

On Factorization of N-Qubit Pure States and Complete Entanglement Analysis of 3-Qubit Pure States Containing Exactly Two Terms and Three Terms

A multi-qubit pure quantum state is called separable when it can be factored as the tensor product of 1-qubit pure quantum states.Factorizing a general multi-qubit pure quantum state into the tensor product of its factors(pure states containing a smaller number of qubits)can be a challenging task,especially for highly entangled states.A new criterion based on the proportionality of the rows of certain associated matrices for the existence of certain factorization and a factorization algorithm that follows from this criterion for systematically extracting all the factors is developed in this paper.3-qubit pure states play a crucial role in quantum computing and quantum information processing.For various applications,the well-known 3-qubit GHZ state which contains two nonzero terms,and the 3-qubit W state which contains three nonzero terms,have been studied extensively.Using the new factorization algorithm developed here we perform a complete analysis vis-à-vis entanglement of 3-qubit states that contain exactly two nonzero terms and exactly three nonzero terms.

智能时代人因科学研究的新范式取向及重点

本文首先提出了“人因科学”这一创新的学科群概念来表征工程心理学、人因工程、工效学、人机交互等相近领域。尽管这些领域的研究角度不一样,但是它们分享共同的研究理念、对象以及目的。我们近期的研究表明,人工智能(AI)新技术带来了一系列新的人因问题,而作为人因科学研究对象的人机关系呈现出从“人机交互”向“人智组队式合作”的跨时代演进。这些变化对人因科学研究提出了新问题和新挑战,需要我们重新审视基于非智能技术的人因科学研究范式和重点。在此背景下,本文梳理人因科学研究范式取向跨时代的演进,总结我们近5年所提出的一系列用于丰富人因科学研究范式的新概念模型和框架,其中包括人智协同认知系统、人智协同认知生态系统以及智能社会技术系统的模型和框架。本文进一步从人因科学研究范式取向的角度进一步提升这些概念模型和框架,提出智能时代人因科学研究的三种新范式取向,分析相应的应用意义,并展望今后的研究方向。同时,针对智能时代人因科学研究重点的跨时代转移新特征,本文从“人智交互”“智能人机界面”“人智组队合作”三个方面展望了今后人因科学的研究重点,揭示出人因科学新研究范式取向对未来研究重点的作用。我们认为,人因科学的研究范式取向和研究重点互为影响,互为促进,智能时代的人因科学研究需要多样化、创新的研究范式取向,从而进一步推动人因科学的发展。

混合式教学模式下影响学生学科成绩的因素分析——以医学免疫学课程为例

随着信息技术快速发展,混合式教学模式逐渐成为多数高校的主流教学模式。如何评价学生的学习成效,分析影响学生成绩的因素,是该教学模式的重点研究方向。本文以新乡医学院医学免疫学课程混合式教学为例,介绍混合式教学的实施过程和评价体系,分析影响学生成绩和学习成效的主要因素,着重探讨形成性评价和“翻转课堂”对学生学科成绩的影响。

数据要素化背景下的档案数字转型行动空间展望

数据要素化已成为我国引领全球数字转型的关键路径。档案领域作为数据的关键参与者和贡献者,为了及时跟进档案数字转型进程,必须充分协调与数据要素化的衔接。文章从数据要素化背景出发,解析数据作为生产要素的基本认知、流通交易、安全治理以及试点先行的基本内容,进一步探讨档案数字转型同其关联要素,主要展现为档案作为数据要素的内涵扩充、多维参与数据流通交易、档案工作融入数据治理体系、实践推进导向下的试点示范场景构建,并基于此对档案数字转型可展开的行动空间提出展望。

单增李斯特菌毒力因子及调控机制研究进展

单增李斯特菌(Listeria monocytogenes)是一种革兰氏阳性食源性致病菌,能在低温、高温、高渗透压等多种不利条件下生长存活。在宿主体内,单增李斯特菌首先寄生于胃肠道,随后穿过肠道屏障,再通过血液传播到靶器官引发系列疾病,整个过程与单增李斯特菌独特的毒力因子及调控机制密切相关。该文首先回顾整理了单增李斯特菌在感染期间关键毒力因子的主要作用及目前新进展,介绍了毒力调控因子(Prf A)和转录调控因子(σ~B),着重讨论了调控因子在宿主外环境和内环境的相互作用,最后对目前新的毒力调控因子进行归纳总结,为全面理解单增李斯特菌的感染机制和进一步开展精准防控提供一定参考。

儿童重症肺炎支原体肺炎危险因素的Meta分析

背景近年来,儿童肺炎支原体肺炎的发病率持续上升,重症肺炎支原体肺炎的发病人数也相应升高,引起了临床医师的广泛关注。了解与重症肺炎支原体肺炎相关的危险因素,以判断患儿病情的严重程度、预防重症发生和减少后遗症,一直是研究的热点。虽然已经有许多关于重症肺炎支原体肺炎危险因素的研究,但这些研究在时间和地理区域上存在差异,因此需要进行系统综述及分析以对其进行全面了解。目的系统评价重症肺炎支原体肺炎的危险因素。方法计算机检索中国知网(CNKI)、万方数据知识服务平台(Wanfang Data)、维普网(VIP)、中国生物医学文献数据库(CBM)、独秀学术搜索数据库(Duxiu)、中华医学期刊全文数据库(Yiigle)、Cochrane Library、PubMed、Embase、Web of Science、Science Direct和BioMed Central,检索涉及儿童重症肺炎支原体肺炎危险因素的相关研究,检索时限均从建库至2023年8月。由2位研究者独立筛选文献、提取资料并评价纳入研究的偏倚风险后,采用Stata 14.0和RevMan 5.4软件进行Meta分析。结果共纳入22个研究,均为回顾性病例对照研究,包括4531例患儿。Meta分析结果显示,C反应蛋白(CRP)(OR=1.92,95%CI=1.72~2.15,P<0.00001)、红细胞沉降率(ESR)(OR=2.61,95%CI=2.12~3.22,P<0.00001)、降钙素原(PCT)(OR=2.60,95%CI=1.43~4.75,P=0.002)、D-二聚体(OR=4.36,95%CI=2.93~6.50,P<0.00001)、白细胞计数(WBC)(OR=1.98,95%CI=1.66~2.36,P<0.00001)、肺下叶病变(OR=5.70,95%CI=3.48~9.35,P<0.00001)、肺大片状病变(OR=6.37,95%CI=4.09~9.92,P<0.00001)、高肺炎支原体抗体滴度(OR=2.83,95%CI=1.78~4.49,P<0.0001)、乳酸脱氢酶(LDH)(OR=1.03,95%CI=1.00~1.05,P=0.05)、发热时间(OR=8.33,95%CI=3.38~20.56,P<0.00001)是儿童重症肺炎支原体肺炎的影响因素。结论炎性标志物(CRP、ESR、PCT、LDH、WBC)的升高、出现影像学特征性改变(大片状实变、下叶病变)、高肺炎支原体抗体滴度、D-二聚体升高以及发热时间延长可能为儿童重症肺炎支原体肺炎的危险因素。未来需要更高质量的研究来进一步探讨其他临床、影像学和实验室结果与儿童重症肺炎支原体肺炎之间的关系,并基于发现的危险因素建立预后模型。

基于蒙特卡罗模拟的透射式XRD背景的定量分析

XRD背景主要来源于空气、装置和样品中光子的散射,XRD分析需准确扣除背景,常用的扣背景方法具有一定的局限。蒙特卡罗模拟可实现光子的甄别与统计,因此可将其用于XRD背景来源的定量分析。相干散射的模拟中需考虑物质的Form Factor,这可由Baró等的拟合式、独立原子模型或德拜公式给出;在20 keV能量及透射几何模式下,通过对骨、尼龙、水等物质的XRD模拟,对探测器中光子来源进行了区分和统计。结果表明,德拜公式计算的空气背景大于独立原子模型;空气散射在小角度时背景占比大,随散射角的增加从100%减小至40%以下,而样品中康普顿散射的计数占比则从0增大至60%以上。该方法可对不同样品组成以及复杂装置结构的XRD背景进行定量分析。

自身免疫疾病应用肿瘤坏死因子-α抑制剂后并发结核病33例临床特征分析

目的探讨自身免疫疾病患者应用肿瘤坏死因子-α抑制剂后并发结核病的临床特点、治疗及预后。方法收集2019年1月至2023年3月期间广州市胸科医院收治的33例应用生物制剂(肿瘤坏死因子-α抑制剂)后并发结核病患者的临床资料,其中男性25例,女性8例,中位年龄32岁,回顾性分析其临床症状、实验室检查结果、影像学及气管镜特征、病理特点、治疗及转归进行综合分析。结果常见临床表现为咳嗽(26/33)、咳痰(23/33)、发热(17/33)。发病多见于肺结核(32/33)、支气管结核(15/33)、纵隔及肺门淋巴结结核(11/33)。结核菌双侧全肺播散(21/33),结核菌肺内组织(支气管、纵隔肺门淋巴结、胸膜)播散(19/33),肺外结核(18/33),肺结核伴肺内组织或肺外结核(26/33)。血CD4+T淋巴细胞检测正常(23/33),血IGRA检测阳性(27/33)。肺部影像学粟粒性结节(8/33)。淋巴结组织病理为不典型肉芽肿结节。抗结核治疗疗程8~32个月。死亡1例。结论自身免疫疾病应用肿瘤坏死因子-α抑制剂后并发结核病患者以双肺病灶多见,易播散到肺内组织及全身多个器官,免疫功能下降,多需延长治疗疗程,经综合治疗多预后良好。

土壤微塑料影响植物生长的因素与机制研究进展

土壤中的微塑料可通过多种方式影响植物生长,并且其在植物体内积累会最终通过食物链进入人体,厘清微塑料对植物生长的影响及机制,有助于系统掌握其在土壤-植物体系中的环境行为。微塑料的赋存状态和理化特征均可影响其对植物的作用效果,本文从粒径、形状、浓度、种类、塑料添加剂和老化程度等方面,梳理了土壤微塑料影响植物生长的主要因素及作用机制,并对未来研究的重点内容提出展望,以期为进一步明晰微塑料对土壤生态系统的影响提供参考。

获得性异位骨化的分子生物学机制

背景:异位骨化是一个动态发展的过程,临床中不同亚型的异位骨化具有不同病因或诱导因素,但在异位骨化中后期进展阶段又表现出相似的病理过程,其中由于创伤等因素引起的获得性异位骨化具有较高的发病率。目的:对近年获得性异位骨化发生发展相关分子生物学机制研究进行综述。方法:以“分子生物学,异位骨化,机制”为检索词在中国知网、万方医学数据库进行检索,以“molecular biological,heterotopic ossification,mechanisms”为检索词分别在PubMed、Embase、Web of Science及Google Scholar数据库进行检索,检索时限为2016年1月至2022年8月,并根据获得文献进行补充检索,对所得文献进行筛选,最终纳入131篇文献进行综述分析。结果与结论:①获得性异位骨化的发生和发展是一个动态的过程,具有一定的隐匿性,因此该疾病的诊疗有一定的困难。②文章通过综述国内外相关文献,发现获得性异位骨化涉及骨形态发生蛋白、转化生长因子β、Hedgehog、Wnt及mTOR共5条主要信号通路,同时在局部微环境中涉及到Runx2、血管内皮生长因子、缺氧诱导因子、成纤维细胞生长因子及Sox9共5项核心枢纽因子,核心机制可能是不同信号通路之间的相互作用,影响机体成骨细胞前体细胞、成骨细胞微环境以及与此相关的细胞因子,进而影响机体骨代谢并导致获得性异位骨化的发生。③未来可以以异位骨化相关单细胞的成骨内稳态为研究方向,以成骨细胞前体细胞-成骨微环境-信号通路及细胞因子作为研究要素,探索各部分要素在不同时空条件下的特征,比较不同种类、不同个体细胞成骨内稳态异同,从整体视角观察异位骨化分子信号网络调控机制,有利于未来临床防治异位骨化新方法的探究。④以中医药及靶向治疗为代表的治疗方法是近年的研究热点,如何将中医药与体内成骨稳态联系,并与靶向治疗相结合,也是未来研究方向之一。⑤目前获得性异位骨化的研究仍多限于基础实验研究,临床防治方法仍存在疗效不确切及不良反应明显等缺陷,相关靶向防治药物临床应用安全性及有效性仍有待验证,如何以基础实验研究为基础,结合发生发展机制,为临床防治提供新方向将是未来研究的重点。

不同激活剂对富血小板血浆生长因子的影响

背景:生长因子是富血小板血浆在临床治疗中发挥作用的关键效应分子,不同激活剂激活富血小板血浆后生长因子浓度存在差异,是影响临床疗效的重要因素。目的:分析不同激活剂对富血小板血浆中生长因子质量浓度的影响。方法:招募12名健康志愿者,采集EDTA-K2抗凝静脉血,应用二次离心法制备富血小板血浆。比较静脉血与富血小板血浆中生长因子的质量浓度差异。将富血小板血浆分别与4种激活剂(生理盐水、凝血酶、葡萄糖酸钙、葡萄糖酸钙+凝血酶)按照体积比10∶1混匀,置于37℃恒温水浴箱孵育30 min,离心后提取上清液,检测生长因子质量浓度;利用血琼脂平板检测上清液中的细菌生长情况;采用Pearson相关分析不同激活剂与富血小板血浆中生长因子质量浓度的相关性,血小板计数值与富血小板血浆中生长因子质量浓度的相关性。结果与结论:(1)富血小板血浆中血小板衍生生长因子BB、血小板衍生生长因子AB、血管内皮生长因子、表皮生长因子的质量浓度分别是静脉血中对应生长因子质量浓度的8.7,22.2,2.3,2.8倍(P <0.05);(2)与生理盐水组比较,凝血酶组、葡萄糖酸钙组、葡萄糖酸钙+凝血酶组中血小板衍生生长因子BB、血小板衍生生长因子AB、血管内皮生长因子、表皮生长因子质量浓度升高(P <0.05);凝血酶组、葡萄糖酸钙组血小板衍生生长因子BB质量浓度高于葡萄糖酸钙+凝血酶组(P <0.05),凝血酶组血小板衍生生长因子AB质量浓度高于葡萄糖酸钙组、葡萄糖酸钙+凝血酶组(P <0.05),凝血酶组表皮生长因子质量浓度低于葡萄糖酸钙组、葡萄糖酸钙+凝血酶组(P <0.05);(3)血琼脂平板实验结果显示4组上清液中均无细菌生长;(4)Pearson相关分析显示,富血小板血浆中血小板衍生生长因子BB质量浓度与凝血酶存在正向强相关性(r=0.683,P <0.05),血管内皮生长因子质量浓度与凝血酶、葡萄糖酸钙、葡萄糖酸钙+凝血酶激混合剂存在正向强相关性(r=0.730,0.789,0.686,P <0.05);富血小板中血小板计数值与4种生长因子质量浓度均无相关性(P> 0.05);(5)结果提示,不同激活剂对富血小板血浆中生长因子浓度产生不同影响,建议临床根据不同生长因子质量浓度和治疗需求选择不同激活剂,以提高临床疗效。

1990—2019年中国结直肠癌疾病负担变化趋势分析

目的 分析1990-2019年中国结直肠癌疾病负担变化趋势及定量估计影响因素。方法 利用2019年全球疾病负担研究结果,分析1990-2019年我国结直肠癌疾病负担以及危险因素归因疾病负担的变化情况,并采用Gupta分解法定量估算人口增长、老龄化、年龄别患病率和疾病严重程度的改变对疾病负担变化的贡献。结果 1990-2019年我国结直肠癌标化伤残调整寿命年率(DALY rates)呈上升趋势,2019年DALY值较1990年增长了191.12%,其中34.54%可归因于人口增长、111.36%可归因于人口老龄化、77.56%可归因于年龄别患病率的增加、-32.34%归因于疾病严重程度。牛奶摄入不足位列2019年结直肠癌的首要危险因素,其次是谷物摄入不足和钙摄入不足;11种可改变的危险因素中,高BMI所致标化DALY率较1990年增幅最大(225.15%),年均增长4.14%,纤维素摄入不足所致标化DALY率较1990年降幅最大(-44.23%),年均减少2.00%。结论 1990-2019年,我国结直肠癌疾病负担呈上升趋势,人口老龄化是导致结直肠癌疾病负担增长最主要的原因,卫生行政部门应采取相应措施应对老龄化带来的不利影响。

公立三甲医院医师继续医学教育现状及存在问题与对策

目的了解公立三甲医院医师继续医学教育的现状,探讨存在的问题以及对策,为后续制定针对性的继续教育项目,提升继续教育质量提供科学依据。方法选取百色市右江区三所公立三甲医院的220名医师作为研究对象,对其进行问卷调查,采集一般人口学资料、对开展继续教育的内容和形式需求,并分析影响参加继续教育的相关因素。此外,选取15人进行半结构式访谈,了解目前公立三甲医院医师继续医学教育存在的主要问题。结果共发出问卷220份,有效问卷208份,有效回收率94.5%。单因素分析结果显示,年龄、性别、职称和学历与公立三甲医院医师对继续教育必要性认知有关(P<0.05)。多因素logistic回归分析结果显示,年龄30~<40岁(β=1.704,P=0.033,OR=2.927,95%CI=1.092~7.845)、中级职称(β=1.211,P=0.029,OR=3.357,95%CI=1.133~9.943)以及单位认为继续教育必要的态度(β=1.369,P<0.001,OR=3.931,95%CI=2.390~6.468)是影响三甲医院医师参加继续教育的独立影响因素。公立三甲医院医师在继续教育的内容、形式、时长和费用分担上有一定的倾向。既往参加过继续教育的116名研究对象满意度仅为61.2%。研究对象认为目前继续教育存在的主要问题集中在科室支持力度不够、继续教育的内容和形式个性化不足、工作太忙没有时间、精力参与继续教育。结论公立三甲医院医师继续教育必要性的认知还有加强,医院以及科室也要积极主动地为其提供继续教育的条件和机会。此外,继续教育的管理部门应根据参与继续教育的学员特点,提供个性化、满足其需求的培训方案,优化全区的继续教育体系,更好地促进公立三甲医院医师继续教育的质量提升。