Epma Journal最新文献_第4页

Conceptualised psycho-medical footprint for health status outcomes and the potential impacts for early detection and prevention of chronic diseases in the context of 3P medicine 在3P医学背景下，健康状况结果的概念化心理医学足迹以及对早期发现和预防慢性病的潜在影响

2区医学 Q1 Medicine

Epma Journal

Pub Date : 2023-11-08 DOI: 10.1007/s13167-023-00344-2

Ebenezer Afrifa-Yamoah, Eric Adua, Enoch Odame Anto, Emmanuel Peprah-Yamoah, Victor Opoku-Yamoah, Emmanuel Aboagye, Rashid Hashmi

Abstract Background The Suboptimal Health Status Questionnaire-25 (SHSQ-25) is a distinctive medical psychometric diagnostic tool designed for the early detection of chronic diseases. However, the synaptic connections between the 25 symptomatic items and their relevance in supporting the monitoring of suboptimal health outcomes, which are precursors for chronic diseases, have not been thoroughly evaluated within the framework of predictive, preventive, and personalised medicine (PPPM/3PM). This baseline study explores the internal structure of the SHSQ-25 and demonstrates its discriminatory power to predict optimal and suboptimal health status (SHS) and develop photogenic representations of their distinct relationship patterns. Methods The cross-sectional study involved healthy Ghanaian participants ( n = 217; aged 30–80 years; ~ 61% female), who responded to the SHSQ-25. The median SHS score was used to categorise the population into optimal and SHS. Graphical LASSO model and multi-dimensional scaling configuration methods were employed to describe the network structures for the two populations. Results We observed differences in the structural, node placement and node distance of the synaptic networks for the optimal and suboptimal populations. A statistically significant variance in connectivity levels was noted between the optimal (58 non-zero edges) and suboptimal (43 non-zero edges) networks ( p = 0.024). Fatigue emerged as a prominently central subclinical condition within the suboptimal population, whilst the cardiovascular system domain had the greatest relevance for the optimal population. The contrast in connectivity levels and the divergent prominence of specific subclinical conditions across domain networks shed light on potential health distinctions. Conclusions We have demonstrated the feasibility of creating dynamic visualizers of the evolutionary trends in the relationships between the domains of SHSQ-25 relative to health status outcomes. This will provide in-depth comprehension of the conceptual model to inform personalised strategies to circumvent SHS. Additionally, the findings have implications for both health care and disease prevention because at-risk individuals can be predicted and prioritised for monitoring, and targeted intervention can begin before their symptoms reach an irreversible stage.

背景亚理想健康状况问卷-25 (SHSQ-25)是一种独特的医学心理测量诊断工具，旨在早期发现慢性疾病。然而，在预测、预防和个性化医学(PPPM/3PM)的框架内，尚未对25个症状项目之间的突触连接及其在支持监测亚理想健康结果(慢性疾病的前兆)中的相关性进行全面评估。本基线研究探讨了SHSQ-25的内部结构，并证明了其预测最佳和次最佳健康状态(SHS)的歧视性能力，并开发了它们不同关系模式的上镜表示。方法横断面研究纳入健康的加纳参与者(n = 217;年龄30-80岁;~ 61%为女性)，对SHSQ-25有应答。使用SHS得分中位数将人群分为最优人群和SHS人群。采用图形LASSO模型和多维尺度配置方法描述了两个种群的网络结构。结果观察到最优群体和次优群体突触网络的结构、节点位置和节点距离的差异。在最优(58条非零边)和次优(43条非零边)网络之间的连接水平存在统计学上显著的差异(p = 0.024)。在次优人群中，疲劳是一个突出的中心亚临床状况，而心血管系统领域与最佳人群有最大的相关性。在连接水平的对比和特定亚临床条件的不同突出跨域网络揭示了潜在的健康差异。我们已经证明了创建SHSQ-25结构域与健康状况结果之间关系的动态可视化进化趋势的可行性。这将提供对概念模型的深入理解，从而为规避SHS的个性化策略提供信息。此外，这些发现对卫生保健和疾病预防都有意义，因为可以预测高危人群并优先监测，并且可以在他们的症状达到不可逆转的阶段之前开始有针对性的干预。

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引用次数: 0

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine 预测社区居住老年人队列1年、3年、5年和8年全因死亡率:预测性、预防性和个性化医疗的相关性

2区医学 Q1 Medicine

Epma Journal

Pub Date : 2023-11-03 DOI: 10.1007/s13167-023-00342-4

Yequn Chen, Xiulian Deng, Dong Lin, Peixuan Yang, Shiwan Wu, Xidong Wang, Hui Zhou, Ximin Chen, Xiaochun Wang, Weichai Wu, Kaibing Ke, Wenjia Huang, Xuerui Tan

Abstract Background Population aging is a global public health issue involving increased prevalence of age-related diseases, and concomitant burden on medical resources and the economy. Ninety-two diseases have been identified as age-related, accounting for 51.3% of the global adult disease burden. The economic cost per capita for older people over 60 years is 10 times that of the younger population. From the aspects of predictive, preventive, and personalized medicine (PPPM), developing a risk-prediction model can help identify individuals at high risk for all-cause mortality and provide an opportunity for targeted prevention through personalized intervention at an early stage. However, there is still a lack of predictive models to help community-dwelling older adults do well in healthcare. Objectives This study aims to develop an accurate 1-, 3-, 5-, and 8-year all-cause mortality risk-prediction model by using clinical multidimensional variables, and investigate risk factors for 1-, 3-, 5-, and 8-year all-cause mortality in community-dwelling older adults to guide primary prevention. Methods This is a two-center cohort study. Inclusion criteria: (1) community-dwelling adult, (2) resided in the districts of Chaonan or Haojiang for more than 6 months in the past 12 months, and (3) completed a health examination. Exclusion criteria: (1) age less than 60 years, (2) more than 30 incomplete variables, (3) no signed informed consent. The primary outcome of the study was all-cause mortality obtained from face-to-face interviews, telephone interviews, and the medical death database from 2012 to 2021. Finally, we enrolled 5085 community-dwelling adults, 60 years and older, who underwent routine health screening in the Chaonan and Haojiang districts, southern China, from 2012 to 2021. Of them, 3091 participants from Chaonan were recruited as the primary training and internal validation study cohort, while 1994 participants from Haojiang were recruited as the external validation cohort. A total of 95 clinical multidimensional variables, including demographics, lifestyle behaviors, symptoms, medical history, family history, physical examination, laboratory tests, and electrocardiogram (ECG) data were collected to identify candidate risk factors and characteristics. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) models and multivariable Cox proportional hazards regression analysis. A nomogram predictive model for 1-, 3-, 5- and 8-year all-cause mortality was constructed. The accuracy and calibration of the nomogram prediction model were assessed using the concordance index (C-index), integrated Brier score (IBS), receiver operating characteristic (ROC), and calibration curves. The clinical validity of the model was assessed using decision curve analysis (DCA). Results Nine independent risk factors for 1-, 3-, 5-, and 8-year all-cause mortality were identified, including increased age, male, alcohol status, higher

人口老龄化是一个全球性的公共卫生问题，涉及与年龄相关疾病的患病率增加，以及随之而来的医疗资源和经济负担。已确定有92种疾病与年龄有关，占全球成人疾病负担的51.3%。60岁以上老年人的人均经济成本是年轻人的10倍。从预测、预防和个性化医疗(PPPM)的角度来看，建立风险预测模型可以帮助识别全因死亡率高风险个体，并通过早期个性化干预提供有针对性的预防机会。然而，仍然缺乏预测模型来帮助社区居住的老年人做好医疗保健。目的利用临床多维变量建立准确的1、3、5、8年全因死亡率风险预测模型，探讨影响社区老年人1、3、5、8年全因死亡率的危险因素，指导基层预防。方法采用双中心队列研究。纳入标准:(1)居住在社区的成年人，(2)过去12个月内在潮南区或灏江区居住6个月以上，(3)完成健康检查。排除标准:(1)年龄小于60岁;(2)不完整变量大于30个;(3)未签署知情同意书。该研究的主要结果是通过面对面访谈、电话访谈和2012年至2021年的医疗死亡数据库获得的全因死亡率。最后，我们招募了5085名60岁及以上的社区居住成年人，他们于2012年至2021年在中国南方潮南和濠江区接受了常规健康筛查。其中，从潮南市招募3091名参与者作为主要培训和内部验证研究队列，从浩江市招募1994名参与者作为外部验证队列。共收集95个临床多维变量，包括人口统计学、生活方式行为、症状、病史、家族史、体格检查、实验室检查和心电图(ECG)数据，以确定候选危险因素和特征。使用最小绝对收缩和选择算子(LASSO)模型和多变量Cox比例风险回归分析确定危险因素。构建了1年、3年、5年和8年全因死亡率的nomogram预测模型。采用一致性指数(C-index)、综合Brier评分(IBS)、受试者工作特征(ROC)和校准曲线评估nomogram预测模型的准确性和校准性。采用决策曲线分析(decision curve analysis, DCA)评价模型的临床有效性。结果1年、3年、5年和8年全因死亡率有9个独立危险因素，包括年龄增加、男性、酒精状况、每日饮酒量增加、癌症史、空腹血糖升高、血红蛋白降低、心率加快和发生心脏传导阻滞。危险因素标准的获取成本低、获取方便、便于临床应用，为制定针对高危人群的个性化预防和干预措施提供了新的思路和目标。在训练集、内部验证集和外部验证集，nomogram model的曲线下面积(AUC)分别为0.767、0.776和0.806,c -index分别为0.765、0.775和0.797。IBS小于0.25，表明校准良好。校正曲线和决策曲线显示预测概率与实际概率吻合较好，对PPPM有较好的临床预测价值。结论个性化风险预测模型可以从PPPM的角度识别出全因死亡高危人群，为预防全因死亡提供初级保健服务，并为这些高危人群提供个性化的医疗治疗。严格控制日常饮酒，降低空腹血糖，提高血红蛋白，控制心率，治疗心脏传导阻滞，有利于提高老年人群的生存率。

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引用次数: 0

Emerging frontiers of artificial intelligence and machine learning in ischemic stroke: a comprehensive investigation of state-of-the-art methodologies, clinical applications, and unraveling challenges 人工智能和机器学习在缺血性中风中的新兴前沿:对最先进的方法、临床应用和解开挑战的全面调查

2区医学 Q1 Medicine

Epma Journal

Pub Date : 2023-11-02 DOI: 10.1007/s13167-023-00343-3

Yishu Fan, Zhenshan Song, Mengqi Zhang

引用次数: 0

Causal relationship between gut Prevotellaceae and risk of sepsis: a two-sample Mendelian randomization and clinical retrospective study in the framework of predictive, preventive, and personalized medicine 肠道Prevotellaceae与脓毒症风险之间的因果关系:一项预测性、预防性和个性化医学框架下的双样本孟德尔随机化和临床回顾性研究

2区医学 Q1 Medicine

Epma Journal

Pub Date : 2023-10-12 DOI: 10.1007/s13167-023-00340-6

Yinghao Luo, Yang Zhou, Pengfei Huang, Qianqian Zhang, Feiyu Luan, Yahui Peng, Jieling Wei, Nana Li, Chunying Wang, Xibo Wang, Jiannan Zhang, Kaijiang Yu, Mingyan Zhao, Changsong Wang

引用次数: 0

Development and validation of a short-form suboptimal health status questionnaire 一个简短的亚理想健康状况问卷的开发和验证

2区医学 Q1 Medicine

Epma Journal

Pub Date : 2023-09-12 DOI: 10.1007/s13167-023-00339-z

Shuyu Sun, Hongzhi Liu, Zheng Guo, Qihua Guan, Yinghao Wang, Jie Wang, Yan Qi, Yuxiang Yan, Youxin Wang, Jun Wen, Haifeng Hou

引用次数: 1

Machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy. 机器学习提出了针对易患青光眼性视神经病变的原发性前房角闭合患者的个性化特征的治疗算法。

IF 6.5 2区医学 Q1 Medicine

Epma Journal

Pub Date : 2023-08-17 eCollection Date: 2023-09-01 DOI: 10.1007/s13167-023-00337-1

Natalia I Kurysheva, Oxana Y Rodionova, Alexey L Pomerantsev, Galina A Sharova, Olga Golubnitschaja

Background: Primary angle closure glaucoma (PACG) is still one of the leading causes of irreversible blindness, with a trend towards an increase in the number of patients to 32.04 million by 2040, an increase of 58.4% compared with 2013. Health risk assessment based on multi-level diagnostics and machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure are considered essential tools to reverse the trend and protect vulnerable subpopulations against health-to-disease progression.

Aim: To develop a methodology for personalized choice of an effective method of primary angle closure (PAC) treatment based on comparing the prognosis of intraocular pressure (IOP) changes due to laser peripheral iridotomy (LPI) or lens extraction (LE).

Methods: The multi-parametric data analysis was used to develop models predicting individual outcomes of the primary angle closure (PAC) treatment with LPI and LE. For doing this, we suggested a positive dynamics in the intraocular pressure (IOP) after treatment, as the objective measure of a successful treatment. Thirty-seven anatomical parameters have been considered by applying artificial intelligence to the prospective study on 30 (LE) + 30 (LPI) patients with PAC.

Results and data interpretation in the framework of 3p medicine: Based on the anatomical and topographic features of the patients with PAC, mathematical models have been developed that provide a personalized choice of LE or LPI in the treatment. Multi-level diagnostics is the key tool in the overall advanced approach. To this end, for the future application of AI in the area, it is strongly recommended to consider the following:Clinically relevant phenotyping applicable to advanced population screeningSystemic effects causing suboptimal health conditions considered in order to cost-effectively protect affected individuals against health-to-disease transitionClinically relevant health risk assessment utilizing health/disease-specific molecular patterns detectable in body fluids with high predictive power such as a comprehensive tear fluid analysis.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-023-00337-1.

背景：原发性闭角型青光眼（PACG）仍然是不可逆失明的主要原因之一，到2040年，患者人数有增加的趋势，达到3204万，与2013年相比增加了58.4%。基于多层次诊断和机器学习的健康风险评估，针对原发性前房角闭合患者的个性化特征量身定制的治疗算法被认为是扭转这一趋势并保护弱势亚群免受健康向疾病发展影响的重要工具。目的：在比较激光周边虹膜切开术（LPI）或晶状体摘除术（LE）引起的眼压（IOP）变化的预后的基础上，开发一种个性化选择有效原发性闭角（PAC）治疗方法的方法。方法：采用多参数数据分析建立模型，预测LPI和LE一期闭角（PAC）治疗的个体疗效。为此，我们提出了治疗后眼压（IOP）的正动力学，作为成功治疗的客观衡量标准。通过将人工智能应用于30（LE）的前瞻性研究，已经考虑了37个解剖参数 + 30例（LPI）PAC患者。3p医学框架下的结果和数据解释：基于PAC患者的解剖和地形特征，已经开发了数学模型，为治疗中的LE或LPI提供了个性化选择。多级诊断是整体高级方法中的关键工具。为此，对于人工智能在该领域的未来应用，强烈建议考虑以下因素：适用于高级人群筛查的临床相关表型考虑导致次优健康状况的系统影响，以经济有效地保护受影响的个体免受健康到疾病的转变利用可检测的健康/疾病特异性分子模式进行临床相关健康风险评估具有高预测能力的体液，例如全面的泪液分析。补充信息：在线版本包含补充材料，可访问10.1007/s13167-023-00337-1。

{"title":"Machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy.","authors":"Natalia I Kurysheva, Oxana Y Rodionova, Alexey L Pomerantsev, Galina A Sharova, Olga Golubnitschaja","doi":"10.1007/s13167-023-00337-1","DOIUrl":"10.1007/s13167-023-00337-1","url":null,"abstract":"Background: Primary angle closure glaucoma (PACG) is still one of the leading causes of irreversible blindness, with a trend towards an increase in the number of patients to 32.04 million by 2040, an increase of 58.4% compared with 2013. Health risk assessment based on multi-level diagnostics and machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure are considered essential tools to reverse the trend and protect vulnerable subpopulations against health-to-disease progression.Aim: To develop a methodology for personalized choice of an effective method of primary angle closure (PAC) treatment based on comparing the prognosis of intraocular pressure (IOP) changes due to laser peripheral iridotomy (LPI) or lens extraction (LE).Methods: The multi-parametric data analysis was used to develop models predicting individual outcomes of the primary angle closure (PAC) treatment with LPI and LE. For doing this, we suggested a positive dynamics in the intraocular pressure (IOP) after treatment, as the objective measure of a successful treatment. Thirty-seven anatomical parameters have been considered by applying artificial intelligence to the prospective study on 30 (LE) + 30 (LPI) patients with PAC.Results and data interpretation in the framework of 3p medicine: Based on the anatomical and topographic features of the patients with PAC, mathematical models have been developed that provide a personalized choice of LE or LPI in the treatment. Multi-level diagnostics is the key tool in the overall advanced approach. To this end, for the future application of AI in the area, it is strongly recommended to consider the following:Clinically relevant phenotyping applicable to advanced population screeningSystemic effects causing suboptimal health conditions considered in order to cost-effectively protect affected individuals against health-to-disease transitionClinically relevant health risk assessment utilizing health/disease-specific molecular patterns detectable in body fluids with high predictive power such as a comprehensive tear fluid analysis.Supplementary information: The online version contains supplementary material available at 10.1007/s13167-023-00337-1.","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":"14 3","pages":"527-538"},"PeriodicalIF":6.5,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10425844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The correlation between primary open-angle glaucoma (POAG) and gut microbiota: a pilot study towards predictive, preventive, and personalized medicine. 原发性开角型青光眼（POAG）与肠道微生物群之间的相关性：一项针对预测性、预防性和个性化药物的初步研究。

IF 6.5 2区医学 Q1 Medicine

Epma Journal

Pub Date : 2023-08-11 eCollection Date: 2023-09-01 DOI: 10.1007/s13167-023-00336-2

Si Chen, Nan Wang, Siqi Xiong, Xiaobo Xia

Background: Glaucoma is the leading cause of irreversible blindness worldwide. Emerged evidence has shown that glaucoma is considered an immune system related disorder. The gut is the largest immune organ in the human body and the gut microbiota (GM) plays an irreversible role in maintaining immune homeostasis. But, how the GM influences glaucoma remains unrevealed. This study aimed at investigating the key molecules/pathways mediating the GM and the glaucoma to provide new biomarkers for future predictive, preventive, and personalized medicine.

Methods: Datasets from the primary open-angle glaucoma (POAG) patients (GSE138125) and datasets for target genes of GM/GM metabolites were downloaded from a public database. For GSE138125, the differentially expressed genes (DEGs) between healthy and POAG samples were identified. And the online Venn diagram tool was used to obtain the DEGs from POAG related to GM. After which GM-related DEGs were analyzed by correlation analysis, pathway enrichment analysis, and protein-protein interaction (PPI) network analysis. Human trabecular meshwork cells were used for validation, and the mRNA level of hub genes was verified by quantitative real-time polymerase chain reaction (RT-qPCR) in the in vitro glaucoma model.

Results: A total of 16 GM-related DEGs in POAG were identified from the above 2 datasets (9 upregulated genes and 7 downregulated genes). Pathway enrichment analysis indicated that these genes are mostly enriched in immune regulation especially macrophages-related pathways. Then 6 hub genes were identified by PPI network analysis and construction of key modules. Finally, RT-qPCR confirmed that the expression of the hub genes in the in vitro glaucoma model was consistent with the results of bioinformatics analysis of the mRNA chip.

Conclusion: This bioinformatic study elucidates NFKB1, IL18, KITLG, TLR9, FKBP2, and HDAC4 as hub genes for POAG and GM regulation. Immune response modulated by macrophages plays an important role in POAG and may be potential targets for future predictive, preventive, and personalized diagnosis and treatment.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-023-00336-2.

背景：青光眼是世界范围内不可逆失明的主要原因。新出现的证据表明青光眼被认为是一种与免疫系统相关的疾病。肠道是人体最大的免疫器官，肠道微生物群在维持免疫稳态方面发挥着不可逆的作用。但是，转基因是如何影响青光眼的还没有被揭示。本研究旨在研究介导GM和青光眼的关键分子/途径，为未来的预测、预防和个性化医学提供新的生物标志物。方法：从公共数据库下载原发性开角型青光眼（POAG）患者的数据集（GSE138125）和GM/GM代谢产物靶基因的数据集。对于GSE138125，鉴定了健康和POAG样品之间的差异表达基因（DEG）。使用在线Venn图工具从POAG中获得与GM相关的DEG。然后通过相关性分析、通路富集分析和蛋白质-蛋白质相互作用（PPI）网络分析对GM相关的DeG进行分析。使用人小梁网细胞进行验证，并通过定量实时聚合酶链反应（RT-qPCR）在体外青光眼模型中验证hub基因的mRNA水平。结果：从上述2个数据集中共鉴定出16个POAG中的GM相关DEG（9个上调基因和7个下调基因）。通路富集分析表明，这些基因主要富集于免疫调节，尤其是巨噬细胞相关通路。然后通过PPI网络分析和关键模块的构建，鉴定出6个枢纽基因。最后，RT-qPCR证实hub基因在体外青光眼模型中的表达与mRNA芯片的生物信息学分析结果一致。结论：本生物信息学研究阐明NFKB1、IL18、KITLG、TLR9、FKBP2和HDAC4是POAG和GM调节的枢纽基因。巨噬细胞调节的免疫反应在POAG中发挥着重要作用，可能成为未来预测、预防和个性化诊断和治疗的潜在靶点。补充信息：在线版本包含补充材料，可访问10.1007/s13167-023-00336-2。

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引用次数: 1

Quantitative phosphoproteomics reveals molecular pathway network alterations in human early-stage primary hepatic carcinomas: potential for 3P medical approach. 定量磷酸蛋白质组学揭示人类早期原发性肝癌的分子通路网络改变：3P医学方法的潜力。

IF 6.5 2区医学 Q1 Medicine

Epma Journal

Pub Date : 2023-08-10 eCollection Date: 2023-09-01 DOI: 10.1007/s13167-023-00335-3

Yuping Zhang, Na Li, Lamei Yang, Wenshuang Jia, Zhijun Li, Qianwen Shao, Xianquan Zhan

Objective: Hepatic carcinoma is one of the most common types of malignant tumors in the digestive system, and its biological characteristics determine its high rate of metastasis and recurrence after radical resection, leading to a poor prognosis for patients. Increasing evidence demonstrates that phosphoproteins and phosphorylation-mediated molecular pathways influence the occurrence and development of hepatic carcinoma. It is urgent need to develop early-stage biomarkers for improving diagnosis, therapy, medical service, and prognostic assessment. We hypothesize that phosphoproteome and phosphorylation-mediated signaling pathway networks significantly differ in human early-stage primary hepatic carcinomas relative to control liver tissues, which will identify the key differentially phosphorylated proteins and phosphorylation-mediated signaling pathway network alterations in human early-stage primary hepatic carcinoma to innovate predictive diagnosis, prognostic assessment, and personalized medical services and progress beyond the state of the art in the framework of predictive, preventive, and personalized medicine (PPPM).Methods: Tandem mass tag (TMT)-based quantitative proteomics coupled with TiO2 enrichment of phosphopeptides was used to identify phosphorylation profiling, and bioinformatics was used to analyze the pathways and biological functions of phosphorylation profiling between early-stage hepatic carcinoma tissues and tumor-adjacent normal control tissues. Furthermore, the integrative analysis with transcriptomic data from TCGA database obtained differently expressed genes (DEGs) corresponding to differentially phosphorylated proteins (DPPs) and overall survival (OS)-related DPPs.Results: A total of 1326 phosphopeptides derived from 858 DPPs in human early-stage primary hepatic carcinoma were identified. KEGG pathway network analysis of 858 DPPs revealed 33 statistically significant signaling pathways, including spliceosome, glycolysis/gluconeogenesis, B-cell receptor signaling pathway, HIF-1 signaling pathway, and fatty acid degradation. Gene Ontology (GO) analysis of 858 DPPs revealed that protein phosphorylation was involved in 57 biological processes, 40 cellular components, and 37 molecular functions. Protein-protein interaction (PPI) network constructed multiple high-combined scores and co-expressed DPPs. Integrative analysis of transcriptomic data and DPP data identified 105 overlapped molecules (DPPs; DEGs) between hepatic carcinoma tissues and control tissues and 125 OS-related DPPs. Overlapping Venn plots showed 14 common molecules among datasets of DPPs, DEGs, and OS-related DDPs, including FTCD, NDRG2, CCT2, PECR, SLC23A2, PNPLA7, ANLN, HNRNPM, HJURP, MCM2, STMN1, TCOF1, TOP2A, and SSRP1. The drug sensitivities of OS-related DPPs were identified, including LMOD1, CAV2, UBE2E2, RAPH1, ANXA5, HDLBP, CUEDC1, APBB1IP, VCL, SRSF10, SLC23A2, EPB41L2,

目的：肝癌是消化系统最常见的恶性肿瘤之一，其生物学特性决定了其根治性切除后的高转移率和复发率，导致患者预后不佳。越来越多的证据表明，磷蛋白和磷酸化介导的分子途径影响肝癌的发生和发展。迫切需要开发早期生物标志物来改善诊断、治疗、医疗服务和预后评估。我们假设磷酸化蛋白质组和磷酸化介导的信号通路网络在人类早期原发性肝癌中相对于对照肝组织显著不同，其将鉴定人类早期原发性肝癌中关键的差异磷酸化蛋白和磷酸化介导的信号通路网络改变，以创新预测诊断、预后评估和个性化医疗服务，并在预测、预防、，方法：采用基于串联质谱标签（TMT）的定量蛋白质组学结合磷酸肽的TiO2富集来鉴定磷酸化谱，并利用生物信息学分析早期肝癌组织和肿瘤邻近正常对照组织之间磷酸化谱的途径和生物学功能。此外，通过与TCGA数据库转录组学数据的整合分析，获得了与差异磷酸化蛋白（DPPs）和总生存期（OS）相关的DPPs相对应的差异表达基因（DEGs）。对858个DPP的KEGG通路网络分析揭示了33个具有统计学意义的信号通路，包括剪接体、糖酵解/糖异生、B细胞受体信号通路、HIF-1信号通路和脂肪酸降解。对858个DPP的基因本体论（GO）分析显示，蛋白质磷酸化参与了57个生物过程、40个细胞成分和37个分子功能。蛋白质-蛋白质相互作用（PPI）网络构建了多个高综合评分和共表达DPPs。转录组数据和DPP数据的综合分析确定了肝癌组织和对照组织之间的105个重叠分子（DPPs；DEG）和125个OS相关的DPPs。重叠Venn图显示了DPP、DEG和OS相关DDP数据集中的14个常见分子，包括FTCD、NDRG2、CCT2、PECR、SLC23A2、PNPLA7、ANLN、HNRNPM、HJURP、MCM2、STMN1、TCOF1、TOP2A和SSRP1。鉴定了OS相关DPP的药物敏感性，包括LMOD1、CAV2、UBE2E2、RAPH1、ANXA5、HDLBP、CUEDC1、APBB1IP、VCL、SRSF10、SLC23A2、EPB41L2、ESR1、PLEKHA4、SAFB2、SMARCAD1、VCAN、PSD4、RDH16、NOP56、MEF2C、BAIAP2L2、NAGS、SRSF2、FHOD3，结论：人类早期原发性肝癌组织中磷酸化蛋白质组和磷酸化介导的信号通路的鉴定和注释为肿瘤的预防和治疗提供了新的方向，有助于丰富磷酸化功能研究和开发新的生物标志物；（ii）丰富磷酸化介导的信号通路，以更深入地了解早期原发性肝癌的潜在机制；以及（iii）开发促进靶向磷酸化位点的抗肿瘤药物。我们推荐在早期原发性肝癌中进行定量磷酸化蛋白质组学，这为深入了解早期原发癌的分子机制、发现有效的治疗靶点/药物以及构建可靠的磷酸化相关生物标志物以用于患者分层、预测诊断、预后评估提供了巨大的前景，以及PPPM框架内的个性化医疗服务。补充信息：在线版本包含补充材料，可访问10.1007/s13167-023-00335-3。

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引用次数: 0

Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies. 使用综合组学方法和机器学习策略，在预测、预防和个性化医学的背景下，识别特发性肺纤维化中的氧化应激相关生物标志物。

IF 6.5 2区医学 Q1 Medicine

Epma Journal

Pub Date : 2023-07-31 eCollection Date: 2023-09-01 DOI: 10.1007/s13167-023-00334-4

Fan Yang, Wendusubilige, Jingwei Kong, Yuhan Zong, Manting Wang, Chuanqing Jing, Zhaotian Ma, Wanyang Li, Renshuang Cao, Shuwen Jing, Jie Gao, Wenxin Li, Ji Wang

Background: Idiopathic pulmonary fibrosis (IPF) is a rare interstitial lung disease with a poor prognosis that currently lacks effective treatment methods. Preventing the acute exacerbation of IPF, identifying the molecular subtypes of patients, providing personalized treatment, and developing individualized drugs are guidelines for predictive, preventive, and personalized medicine (PPPM / 3PM) to promote the development of IPF. Oxidative stress (OS) is an important pathological process of IPF. However, the relationship between the expression levels of oxidative stress-related genes (OSRGs) and clinical indices in patients with IPF is unclear; therefore, it is still a challenge to identify potential beneficiaries of antioxidant therapy. Because PPPM aims to recognize and manage diseases by integrating multiple methods, patient stratification and analysis based on OSRGs and identifying biomarkers can help achieve the above goals.Methods: Transcriptome data from 250 IPF patients were divided into training and validation sets. Core OSRGs were identified in the training set and subsequently clustered to identify oxidative stress-related subtypes. The oxidative stress scores, clinical characteristics, and expression levels of senescence-associated secretory phenotypes (SASPs) of different subtypes were compared to identify patients who were sensitive to antioxidant therapy to conduct differential gene functional enrichment analysis and predict potential therapeutic drugs. Diagnostic markers between subtypes were obtained by integrating multiple machine learning methods, their expression levels were tested in rat models with different degrees of pulmonary fibrosis and validation sets, and nomogram models were constructed. CIBERSORT, single-cell RNA sequencing, and immunofluorescence staining were used to explore the effects of OSRGs on the immune microenvironment.Results: Core OSRGs classified IPF into two subtypes. Patients classified into subtypes with low oxidative stress levels had better clinical scores, less severe fibrosis, and lower expression of SASP-related molecules. A reliable nomogram model based on five diagnostic markers was constructed, and these markers' expression stability was verified in animal experiments. The number of neutrophils in the immune microenvironment was significantly different between the two subtypes and was closely related to the degree of fibrosis.Conclusion: Within the framework of PPPM, this work comprehensively explored the role of OSRGs and their mediated cellular senescence and immune processes in the progress of IPF and assessed their capabilities aspredictors of high oxidative stress and disease progression,targets of the vicious loop between regulated pulmonary fibrosis and OS for targeted secondary and tertiary prevention, andreferences for personalized antioxidant and antifibrotic therapies.Supplement

背景：特发性肺纤维化（IPF）是一种罕见的间质性肺病，预后不良，目前缺乏有效的治疗方法。预防IPF急性加重，识别患者的分子亚型，提供个性化治疗，开发个性化药物是预测性、预防性和个性化药物（PPPM/3PM）促进IPF发展的指南。氧化应激（OS）是IPF的一个重要病理过程。然而，IPF患者氧化应激相关基因（OSRGs）的表达水平与临床指标之间的关系尚不清楚；因此，确定抗氧化疗法的潜在受益者仍然是一个挑战。由于PPPM旨在通过整合多种方法来识别和管理疾病，因此基于OSRGs的患者分层和分析以及识别生物标志物可以帮助实现上述目标。方法：将250例IPF患者的转录组数据分为训练集和验证集。核心OSRG在训练集中被鉴定，随后被聚类以鉴定氧化应激相关的亚型。比较不同亚型的氧化应激评分、临床特征和衰老相关分泌表型（SASP）的表达水平，以确定对抗氧化治疗敏感的患者，从而进行差异基因功能富集分析并预测潜在的治疗药物。通过整合多种机器学习方法获得亚型之间的诊断标志物，在不同程度肺纤维化的大鼠模型和验证集中测试其表达水平，并构建列线图模型。CIBERSORT、单细胞RNA测序和免疫荧光染色用于探索OSRGs对免疫微环境的影响。结果：核心OSRGs将IPF分为两个亚型。被分为低氧化应激水平亚型的患者具有更好的临床评分、较轻的纤维化和较低的SASP相关分子表达。基于五种诊断标记构建了一个可靠的列线图模型，并在动物实验中验证了这些标记的表达稳定性。免疫微环境中中性粒细胞的数量在两种亚型之间有显著差异，并且与纤维化程度密切相关。结论：在PPPM的框架内，本工作全面探讨了OSRGs及其介导的细胞衰老和免疫过程在IPF进展中的作用，并评估了它们作为高氧化应激和疾病进展的预测因子、调节性肺纤维化和OS之间的恶性循环靶点的能力，以进行有针对性的二级和三级预防，以及个性化抗氧化剂和抗纤维化疗法的参考文献。补充信息：在线版本包含补充材料，可访问10.1007/s13167-023-00334-4。

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引用次数: 0

Royal jelly: a predictive, preventive and personalised strategy for novel treatment options in non-communicable diseases. 蜂王浆：一种预测性、预防性和个性化的非传染性疾病治疗策略。

IF 6.5 2区医学 Q1 Medicine

Epma Journal

Pub Date : 2023-07-18 eCollection Date: 2023-09-01 DOI: 10.1007/s13167-023-00330-8

Beatriz G Baptista, Ligia S Lima, Marcia Ribeiro, Isadora K Britto, Livia Alvarenga, Julie A Kemp, Ludmila Fmf Cardozo, Andresa A Berretta, Denise Mafra

Royal jelly (RJ) is a bee product produced by young adult worker bees, composed of water, proteins, carbohydrates and lipids, rich in bioactive components with therapeutic properties, such as free fatty acids, mainly 10-hydroxy-trans-2-decenoic acid (10-H2DA) and 10-hydroxydecanoic acid (10-HDA), and major royal jelly proteins (MRJPs), as well as flavonoids, most flavones and flavonols, hormones, vitamins and minerals. In vitro, non-clinical and clinical studies have confirmed its vital role as an antioxidant and anti-inflammatory. This narrative review discusses the possible effects of royal jelly on preventing common complications of non-communicable diseases (NCDs), such as inflammation, oxidative stress and intestinal dysbiosis, from the viewpoint of predictive, preventive and personalised medicine (PPPM/3PM). It is concluded that RJ, predictively, can be used as a non-pharmacological therapy to prevent and mitigate complications related to NCDs, and the treatment must be personalised.

蜂王浆（RJ）是由年轻成年工蜂生产的蜂产品，由水、蛋白质、碳水化合物和脂质组成，富含具有治疗作用的生物活性成分，如游离脂肪酸，主要是10-羟基反式-2-癸烯酸（10-H2DA）和10-羟基癸酸（10-HDA），以及主要的蜂王浆蛋白（MRJPs），维生素和矿物质。在体外，非临床和临床研究已经证实了它作为抗氧化剂和抗炎药的重要作用。这篇叙述性综述从预测性、预防性和个性化医学（PPPM/3PM）的角度讨论了蜂王浆在预防非传染性疾病常见并发症（如炎症、氧化应激和肠道微生态失调）方面的可能作用。结论是，可以预见，RJ可以作为一种非药物治疗来预防和减轻与非传染性疾病相关的并发症，并且治疗必须个性化。

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引用次数: 0