大美育观下广西民族医药“医+X”美育课程思政育人模式新探——以广西卫生职业技术学院为例

大美育观为广西民族医药“医+X”美育课程思政育人模式的构建提供了经验启迪和思想理论指导。广西卫生职业技术学院通过广西民族医药文化资源开发与活化重构“医+X”美育模式,融合“三香一美”、“和合校园”文化、健康科普“四进”工作等开展美育资源开发、科普队伍建设和科普路径拓展,丰富“医+X”美育活动体系,深化广西民族医药“医+X”美育进社团、进课堂、进岗位的实践改革,完善多维评价机制,以期全方位提升美育课程思政育人成效,提升医学生的综合职业素养。

“医教协同”视域下基础医学课程思政建设情况调查

文章以江苏省25所职业院校中教授基础医学课程的教师及学习该课程的学生为研究对象,采用线上线下问卷调查相结合及访谈的方式,对“医教协同”视域下基础医学课程思政建设情况进行调查,总结了“医教协同”视域下基础医学课程思政建设基本情况、“医教协同”视域下基础医学课程思政建设存在的问题,以及“医教协同”视域下基础医学课程思政建设存在问题的成因,进而提出了相应建议。

城市社区老年人“医体结合”健康促进服务需求的调查研究

伴随着人口老龄化的持续推进,我国在养老方面的形势日益严峻,该文具体结合了医体结合的理念背景,分析城市社区老年人医体结合健康促进服务的需求现状。采用问卷调查法对城市社区老年人的体育健康需求、医疗健康需求进行调查分析。结果发现,目前,在城市社区内部,医疗和体育两大模块的配合度不足,同时老年人对其的需求却比较高。基于此,该文进一步就老年人医体结合的健康促进服务,提出相应的建议,希望进一步丰富相关研究,为城市社区老年人健康服务新模式的提出提供一定的参考。

老龄化视域下“医养护体”融合养老模式研究

随着全球人口结构的变化,老龄化已成为一个不可忽视的社会问题,特别是在发展中国家,这一问题尤为突出,因为这些国家常常缺乏应对老龄化挑战的充分准备。在这样的背景下,探讨和实施有效的养老服务模式变得尤为重要。“医养护体”融合养老模式作为一种创新的服务模式,以其整合医疗、养老和护理服务的特点应运而生,旨在更好地满足老年人的综合需求。

“医美热”何以持续?——医美实践中的逻辑依赖与社会学反思

既有研究关注“医美热”背后的结构性因素,但难以回答“医美热”何以持续的理论问题。为此,文章采用希林“肉身实在论”的理论框架,关注身体生成性特征与社会约束性特征之间的联系,并基于某中高档医疗美容机构的田野调查对这一问题进行探究。文章认为,借由身体实现的医美与社会结构的互构使得“医美热”不断持续。在这一过程中,散见于不同生活领域中同美貌相关的歧视性社会规则,如亲密关系领域中“郎才女貌”的婚姻标准,劳动力市场领域里的“美容溢价”,以及日常生活领域中外貌同个人发展机会的关联,使得美貌不断累积优势,并抬高个体寻求医美的意愿。而新自由主义文化下的医美实践,则借“赋权”与“自由”之名,以个体主体性的身体改造为代价,更以其他未经身体修饰的大多数人的身体贬值为代价,利用、加深并巩固了前述隐蔽的社会歧视性规则,以最大化身体修饰的效益。这或许是理解当前“医美热”争议的核心。

天津市重点人群家庭医生服务利用度与满意度现况调查分析

目的调查分析天津市重点人群家庭医生服务利用度与满意度情况,从而为提升天津市家庭医生团队服务能力,促进家庭医生签约服务的开展提供参考。方法采用随机抽样方法,选取天津市16个行政区的社区居民作为研究对象,从机构的家庭医生团队签约底册中随机抽取被试样本,采用电话收集数据的方式完成对签约居民的问卷调查,根据问卷调查结果对居民服务利用情况、家庭医生服务效果等进行分析。结果居民对家庭医生签约服务的利用度、依从性和配合度较高,对家庭医生服务较认可。影响居民家庭医生服务利用度的主要因素为年龄、性别、文化程度、区域、供需不匹配以及签约服务内容等方面,签约居民对目前家庭医生签约服务总体满意度较高。结论需进一步提高家庭医生服务能力、水平及服务质量,细分居民群体,以更好地满足居民个性化需求,增加居民对签约的知晓度,优化基层医疗资源配置,提升居民满意度。

唇腭裂患儿母亲产后抑郁情况及影响因素分析

目的:调查唇腭裂患儿母亲产后抑郁发生情况并分析其影响因素。方法:选取2022年4月—2023年4月于广州市妇女儿童医疗中心携唇腭裂患儿就诊的母亲204名作为研究对象。收集唇腭裂患儿母亲一般资料,根据爱丁堡产后抑郁量表结果分为抑郁组与非抑郁组,采用Logistic回归分析唇腭裂患儿母亲产后抑郁的影响因素。结果:204名母亲中,产后抑郁64名(占比31.4%),产后未抑郁140名(占比68.6%)。抑郁组农村居住占比、有不良孕产史占比高于非抑郁组,受教育程度初中及以下、睡眠质量很差占比高于非抑郁组,领悟社会支持量表评分低于非抑郁组,差异有统计学意义(P<0.05)。无不良孕产史、睡眠质量好、领悟社会支持量表评分高是唇腭裂患儿母亲产后抑郁的独立保护因素(P<0.05)。结论:影响唇腭裂患儿母亲产后抑郁的因素较多,医护人员需要重点关注有不良孕产史、睡眠质量差、社会支持度低的患儿母亲产后的身心状态。

视觉显示终端使用影响多因素logistic回归分析及“医教协同”模式干预效果研究

目的:探讨视觉显示终端(VDT)使用影响多因素logistic回归分析及“医教协同”模式干预效果。方法:2021年9月—2022年8月在静安区42所小学中抽取4所小学为调查学校,从调查学校中抽取小学一、二年级共1 060名学生为对象,所有学生均完成视力筛查,根据筛查结果分为视力不良组(n=462)和非视力不良组(n=598)。查阅两组资料,对学生视觉显示终端使用对视力的影响可能因素进行单因素及多因素logistic回归分析;视力不良组采用随机数字表法分为两组,对照组采用常规干预,观察组联合“医教协同”模式干预,比较两组视力屈光和健康使用VDT知识、态度、行为。结果:1 060名学生经视力筛查462名近视,确诊率为43.58%。单因素及多因素结果表明:VDT使用时长、使用强度、使用时眼睛对屏幕的距离、屏幕分辨率和亮度均为学生近视发生的独立危险因素(P<0.05);两组干预12个月后VDT健康使用情况得到改善(P<0.05),视力屈光下降不明显(P>0.05);观察组使用VDT知识、态度、行为评分高于对照组(P<0.05)。结论:视觉显示终端使用会对学生视力产生影响,对视力不良患者给予“医教协同”模式干预,能延缓视力不良加重,提升健康使用VDT知识、态度、行为水平,值得推广应用。

农村失能老人长期照护问题研究--以河南省N县为例

在我国老龄化程度日益严重及失能老人数量日益增多的情况下,失能老人照护服务已成为一种刚性需求。尤其是在经济发展状况较差、基础设施落后的农村,失能老人的数量要远远大于城市,因此其照护问题亟待关注。同时,考虑到居家养老、机构养老和社区养老等养老模式均面临诸多挑战,再加上随着社会经济的发展,老龄化、少子化、空巢化趋势日益加剧,失能老人的规模和比例仍在不断扩大。因此,解决农村地区失能老人的长期照护问题尤为迫切和重要。

“医联体”模式下县级医院人才建设路径研究

随着社会经济持续发展,我国卫生健康事业也取得了良好发展,医疗服务规模与质量成效显著。然而,从医疗资源的总体分布上来看,仍呈现出城乡发展不协调的情况,结构化矛盾相对明显。近几年,“医联体”模式建设以福建省三明市作为试点经验,指导不同省市在“医联体”方面的建设,分级诊疗、资源下沉、基层医疗机构人才培养等要求也被相继提出。文章结合三明市“医联体”建设试点经验,从人才下沉需求、基层医疗卫生服务发展需求与薪酬制度改革需求三个方面着手,综合探讨当前县级医院人才建设路径,旨在提供相应的理论指导。

Holistic Integrative Medicine Declaration-Secondary publication

Holistic integrative medicine,abbreviated as HIM,has been officially proposed since 2012.Its theoretical system has been continuously improved,and its practical methods have become increasingly diverse,becoming an inevitable choice and path for the medical development in the new era.This article demonstrates ten major propositions for HIM,elaborating on the connotation and extension of HIM fromthe perspectives of epistemology and methodology,in order to achieve the transformation and adaptive evolution of modern medicine.

Correlation Between Granulomatous Mastitis and Autoimmune Function and the Immuneregulating Role of Traditional Chinese Medicine

Granulomatous mastitis(GM)is a benign granulomatous condition,and its pathogenesis may be related to autoimmune disorders.Cellular immunity,humoral immunity,immunoglobulins,and complement could all play a role in the disease process,showing certain clinical patterns.Corticosteroids can quickly control disease progression,and immunosuppressants can be used for complex and refractory GM cases.In traditional Chinese medicine(TCM),“healthy qi”is similar to immune system function.For GM with deficient healthy qi,TCM treatments such as internal and external herbal applications can help regulate immune function and shorten disease duration by staged and TCM treatment,regulating viscera,reinforcing healthy qi,and eliminating pathogenic factors.

Deep learning-based recognition of stained tongue coating images

Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.

稳心颗粒联合胺碘酮治疗心力衰竭并发室性心律失常有效性及安全性的Meta分析

目的:系统评价稳心颗粒结合胺碘酮治疗心力衰竭并发室性心律失常的疗效与安全性。方法:计算机检索中国知网(CNKI)、维普网(VIP)、万方、中国生物医学文献数据库(CBM)、PubMed、Web of Science和the Cochrane Library等数据库,检索有关稳心颗粒联合胺碘酮治疗心力衰竭并发室性心律失常的随机对照试验(RCT)。由2名研究者分别对文献进行筛选、提取数据和评价纳入研究的偏倚风险后,采取RevMan 5.3及R语言统计软件进行Meta分析。结果:共纳入18项研究,涉及1 527例病人。Meta分析结果显示,稳心颗粒联合胺碘酮治疗心力衰竭并发室性心律失常,在提高临床总有效率[RR=1.19,95%CI(1.14,1.25),P<0.000 1]、减少室性期前收缩次数[SMD=-2.42,95%CI(-4.66,-0.18),P=0.03]、稳定心率[SMD=-1.66,95%CI(-1.94,-1.37),P<0.000 1]、增加左室射血分数[SMD=1.57,95%CI(0.54,2.61),P=0.003]均优于单独使用胺碘酮,且不良反应发生率低于单独使用胺碘酮[RR=0.59,95%CI(0.43,0.80),P=0.000 8]。结论:现有证据表明,稳心颗粒联合胺碘酮在治疗心力衰竭并发室性心律失常,能够明显提高临床疗效、LVEF,且不良反应更少,安全性较好。

Research on the LA-UMamba Model for Asymmetric Modules with Added Auxiliary Information

Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling capability of the model through the self-attention mechanism,better segmentation performance can be achieve.Moreover,the high computational cost of Transformer has motivated researchers to explore more efficient models,such as the Mamba model based on state-space modeling(SSM),and for the field of medical segmentation,reducing the number of model parameters is also necessary.In this study,a novel asymmetric model called LA-UMamba was proposed,which integrates visual Mamba module to efficiently capture complex visual features and remote dependencies.The classical design of U-Net was adopted in the upsampling phase to help reduce the number of references and recover more details.To mitigate the information loss problem,an auxiliary U-Net downsampling layer was designed to focus on sizing without extracting features,thus enhancing the protection of input information while maintaining the efficiency of the model.The experiments were conducted on the ACDC MRI cardiac segmentation dataset,and the results showed that the proposed LA-UMamba achieves proved performance compared to the baseline model in several evaluation metrics,such as IoU,Accuracy,Precision,HD and ASD,which improved that the model is successful in optimizing the detail processing and reducing the complexity of the model,providing a new perspective for further optimization of medical image segmentation techniques.

Constitution identification model in traditional Chinese medicine based on multiple features

Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

An interpretability model for syndrome differentiation of HBV-ACLF in traditional Chinese medicine using small-sample imbalanced data

Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clinically interpretable and highly accurate.Methods We collected medical records from 261 patients diagnosed with HBV-ACLF,including three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaundice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly analyzed.Furthermore,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublingual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accuracy on clinical data characterized by small sample sizes and a class imbalance.

银发经济背景下“医养游”融合的参与意愿及影响因素——以十堰市为例

目的:为迎接蓬勃发展的银发经济,围绕“一主四优多支撑”康养产业,通过调查老年群体参与“医养游”的意愿及影响因素。方法:引用现有成熟量表对十堰市中心城区内的323名老年人进行问卷调查,对调研数据进行描述性统计和线性回归分析。结果:大部分老年人对参与“医养游”有较高兴趣,愿意增加开支和参与频次。目的地的人文景观和生态环境的丰富性是老年人选择的主要因素。他们更倾向于与家人一同参与,主要出于对体验文化和生态旅居的考虑。结论:总体而言,老年人对“医养游”融合持高度认可。十堰市应充分整合医疗、文旅、康养等资源优势,加大对老年人的宣传力度,为我国“医–养–游”融合新型养老模式的发展提供路径参考。

“医养结合”模式下养老建筑设计策略分析

“医养结合”模式的提出,为养老机构的设计理念和实践带来了深远的影响。传统的养老设施设计主要考量的是如何提供基本的生活支持,而医养结合模式要求养老机构的建筑设计需要更加人性化、功能化,以及具备更高的适应性和灵活性。这意味着,在建筑布局上,不仅要考虑到医疗区域的合理设置,保证医疗服务的便捷性和有效性,同时也要为老年人创造一个舒适、安心的生活环境,让他们能够享受到家的温暖和社会的关怀。本文将针对“医养结合”模式下养老建筑设计途径展开详细分析,以供参考。