背景前期研究发现,青春期与睡眠的生理变化有关,且不同地区的青少年睡眠时间存在明显差异,中国青少年睡眠时间少、学习压力大,容易遭受失眠的困扰,进而影响身心健康发展。目的系统梳理中国青少年失眠的评估工具和影响因素,为该领域未来的研究提供方向。方法本研究于2022年10月开展,系统检索Web of Science Core Collection、PubMed、中国知网、万方数据知识服务平台、维普网5个数据库,检索时间为建库至2023年3月1日。由两名研究者独立筛选12~18岁中国青少年失眠的有关文章,并提取文章中的作者、发表年份、地区、研究类型、样本量、评估工具、患病率和影响因素信息。结果通过检索数据库获得文献1440篇,最终纳入符合要求的文献39篇,包括英文文献34篇、中文文献5篇,共涉及23项研究,样本量为62~33692,研究类型以横截面研究为主(15项)。23项研究共使用8种失眠评估工具;1项研究提供了失眠的客观测量数据;3项研究涉及量表本土化评估或开发;共检验了5种自评工具并提供了信效度数据,其内部一致性信度为0.50~0.83,2周后重测信度为0.40~0.82,受试者工作特征曲线下面积为0.79~0.85。中国青少年失眠的影响因素复杂而多样,包括人口学因素(年龄、性别等)、生理因素(遗传、身体健康状况等)、心理因素(焦虑、抑郁等)及行为因素(运动、吸烟等)。结论中国青少年失眠评估工具种类多样且影响因素复杂,目前对评估工具的信效度检验不足,失眠的客观测量数据匮乏,失眠与影响因素的因果关系尚未明确,未来仍需进一步研究。
急性缺血性脑卒中(AIS)是一种严重影响人类健康相关的疾病,其拥有高发病率和高死亡率,并与吸烟、高脂饮食等不良生活习惯相关,人工智能(AI)比如机器学习(ML)和深度学习(DL),可以实现从临床及辅助检查尤其是成像学检查中提取特征数据,经过算法处理,得出可信结果。近几年AI更多地应用于医院系统的工作中,并成为临床工作及科研项目有力的帮手。本文全面综述了AI预测急性缺血性脑卒中(AIS)患者在经过血管内治疗,尤其是经过血栓切除术治疗后的预后情况,从而实现精准有效的临床管理和护理决策。此外,本文还批判性地评估了现有研究的局限性,并且指出了新的研究方向,最终目标是提高AIS患者的生存率。Acute ischemic stroke (AIS) is a serious human health-related disease that is characterized by elevated morbidity and mortality rates. It is often linked to detrimental lifestyle behaviors, including smoking and high-fat dietary intake. The advent of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) methodologies, facilitates the extraction and analysis of feature data derived from clinical and ancillary assessments, particularly imaging studies. These data are processed through sophisticated algorithms to yield reliable outcomes. In recent years, AI has been increasingly integrated into hospital systems, emerging as a formidable tool in both clinical practice and research initiatives. This paper presents a comprehensive analysis of AI applications in predicting the prognosis of acute ischemic stroke (AIS) patients following endovascular interventions, with a particular focus on thrombectomy procedures. The objective is to enhance the accuracy and efficacy of clinical management and care decision-making processes. Furthermore, the study critically examines the limitations inherent in current research and identifies prospective avenues for future investigation, ultimately aiming to improve the survival outcomes of AIS patients.
本文报道一例孕中期先兆流产伴多重耐药菌感染患者,通过有效抗感染及保胎治疗,为有生机儿争取到了宝贵的促胎肺成熟时间、更大的出生体重以及更高的存活几率。妊娠期生殖道感染可造成不良妊娠结局的发生,个体化的治疗方案,尤其是药物种类、剂量和治疗周期的选择,对改善妊娠结局十分重要。This paper reports a case of a patient with threatened abortion with a multi-drug-resistant bacteria infection in the second trimester. Through effective anti-infection and fetal protection treatment, it gained valuable lung maturity time, greater birth weight and a higher survival rate for vigorous children. Reproductive tract infection during pregnancy can cause adverse pregnancy outcomes. A personalized treatment plan, especially the choice of drug type, dose and treatment cycle, is very important to improve pregnancy outcomes.
目的:探讨医生助理协助的健康管理模式对孕产妇保健的效果。方法:选取2022年1月至2023年12月在暨南大学附属第一医院门诊产检自愿选择医生助理协助通过线上线下健康管理指导至产后42天的全流程服务的健康管理组孕妇1020人,随机配对选取同期规律产检提供常规保健服务的1020名孕妇作为对照组,比较两组孕妇对孕期知识掌握度及就医满意度,对两组孕妇心境情绪进行评估,对妊娠常见合并症和并发症及分娩结局进行比较。结果:通过加强孕期保健管理后,健康管理组的孕产妇孕期知识掌握度及就医满意度明显高于对照组,健康管理组的孕妇心理疾病及妊娠期并发症发病率低,其中妊娠期糖尿病孕妇的孕期血糖控制良好,且巨大儿、新生儿窒息率低,围产期并发症总发生率低,差异有统计学意义(P Objective: To explore the effectiveness of a health management model assisted by physician assistants for maternal and child health care. Methods: A total of 1020 pregnant women who voluntarily chose to receive a full-process health management service (including both online and offline health guidance) from January 2022 to December 2023 at the outpatient obstetrics clinic of the First Affiliated Hospital of Jinan University were selected as the health management group. Another 1020 pregnant women who received routine prenatal care were randomly selected as the control group. The two groups were compared in terms of knowledge acquisition about pregnancy, satisfaction with healthcare services, mood assessments, common pregnancy-related complications, and delivery outcomes. Results: Enhanced antenatal care management significantly improved pregnancy-related knowledge and satisfaction with medical services among the health management group compared to the control group. The incidence of mental health issues and pregnancy complications was lower in the health management group. Specifically, pregnant women with gestational diabetes in the health management group had better blood glucose control, and the rates of macrosomia and neonatal asphyxia were lower. The overall incidence of perinatal complications was also lower, with statistically significant differences (P < 0.05). Conclusion: The health management model for pregnant women assisted by physician assistants, combining both offline and online services, significantly improved maternal and child health care outcomes and better ensured the safety of mothers and infants. This model is worthy of cli
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