呼吸与危重症医学科(pulmonary and critical care medicine,PCCM)专科医师培训是培养高素质、高水平PCCM专科人才的重要途径。PCCM是一门涵盖范围广,疾病分类复杂的实践性学科。典型教学病例库是现代医学教育模式下的一种全新教学方式...呼吸与危重症医学科(pulmonary and critical care medicine,PCCM)专科医师培训是培养高素质、高水平PCCM专科人才的重要途径。PCCM是一门涵盖范围广,疾病分类复杂的实践性学科。典型教学病例库是现代医学教育模式下的一种全新教学方式。该院通过典型案例的教学,能够激发专培医师的主动性和积极性,提高其临床思维、临床实践、临床科研和团队协作能力。展开更多
重症肺炎是一种病情严重、治疗复杂且死亡率相对较高的呼吸系统疾病。近年来,尽管其发病率及死亡率有所下降,但早期诊断,尤其是病原学诊断,仍是提高重症肺炎救治能力的关键环节。随着全球新冠病毒的流行,重症肺炎的病原学谱发生了相应...重症肺炎是一种病情严重、治疗复杂且死亡率相对较高的呼吸系统疾病。近年来,尽管其发病率及死亡率有所下降,但早期诊断,尤其是病原学诊断,仍是提高重症肺炎救治能力的关键环节。随着全球新冠病毒的流行,重症肺炎的病原学谱发生了相应变化。分子诊断技术的使用在过去十年中有所增加,这些技术有助于在一个呼吸道样本中检测多种病毒,使得严重CAP中呼吸道病毒的报告频率越来越高。未来,随着科技的不断进步和医疗技术的不断创新,CAP及SCAP的诊断及治疗将取得更多进展,为患者提供更加精准、有效的治疗服务。本文综述了近年来重症肺炎病原学诊断的最新进展,旨在为提高重症肺炎的救治能力提供参考。Severe community-acquired pneumonia has a complex condition, high treatment costs, and a high mortality rate, posing a great threat to human life and health. In recent years, although its incidence rate and mortality have declined, early diagnosis, especially etiological diagnosis, is still the key link to improve the rescue ability of severe community-acquired pneumonia. With the global prevalence of COVID-19, the pathogenic spectrum of severe pneumonia has changed accordingly. The use of molecular diagnostic techniques has increased in the past decade, which helps detect multiple viruses in a respiratory sample, leading to an increasing frequency of reporting respiratory viruses in severe CAP. In the future, with the continuous advancement of technology and innovation in medical technology, more progress will be made in the diagnosis and treatment of CAP and SCAP, providing patients with more accurate and effective treatment services. This article reviews the latest advances in the etiological diagnosis of severe community-acquired pneumonia in recent years, aiming to provide a reference for improving the treatment capacity of severe community-acquired pneumonia.展开更多
背景:本研究主要探索肥胖对呼吸机相关肺炎(Ventilator-Associated Pneumonia, VAP)患者1年全因死亡率的影响,构建呼吸机相关肺炎的肥胖患者长期死亡风险模型,进一步探究“肥胖悖论”对呼吸机相关肺炎的影响。方法:我们使用大型公共数...背景:本研究主要探索肥胖对呼吸机相关肺炎(Ventilator-Associated Pneumonia, VAP)患者1年全因死亡率的影响,构建呼吸机相关肺炎的肥胖患者长期死亡风险模型,进一步探究“肥胖悖论”对呼吸机相关肺炎的影响。方法:我们使用大型公共数据库提取进入重症监护室(Intensive Care Unit, ICU)内的合适的患者,将患者根据BMI分为肥胖和非肥胖两类。首先,调节协变量后进行倾向性匹配评分(Propensity Score Matching, PSM)减少选择偏差,探究肥胖状态与VAP不良预后的具体关系。其次,为了进一步研究VAP肥胖患者1年全因死亡率的风险因素,我们通过套索回归分析(Least Absolute Shrinkage and Selection Operator, LASSO)联合多因素逻辑回归分析选择特征变量来构建VAP肥胖患者长期死亡率的列线图预测模型。最后,通过绘制校准曲线以评估模型的准确性和可靠性。结果:最终,我们提取到资料齐全的1506名VAP患者,其中肥胖患者有665名。调整一切混杂变量后,进行PSM后发现肥胖状态并不影响VAP患者短期死亡率,无明显统计学差异(P > 0.05);而在1年死亡率中,肥胖患者的死亡风险显著低于非肥胖患者(P Background: This study primarily explores the impact of obesity on the 1-year all-cause mortality rate in patients with Ventilator-Associated Pneumonia (VAP). It aims to construct a long-term mortality risk model for obese patients with VAP and further investigate the effect of “obesity paradox” on VAP. Methods: We extracted data from a large public database for patients admitted to the Intensive Care Unit (ICU) and categorized them into obese and non-obese groups based on BMI. Initially, Propensity Score Matching (PSM) was used after adjusting for covariates to reduce selection bias and explore the relationship between obesity and adverse outcomes in VAP. Subsequently, to further investigate the risk factors for 1-year all-cause mortality in obese VAP patients, we applied Least Absolute Shrinkage and Selection Operator (LASSO) regression combined with multivariate logistic regression analysis to select feature variables and constructed a nomogram prediction model for predicting long-term mortality in obese patients with VAP. Finally, we assessed the accuracy and reliability of the model through calibration curves. Results: A total of 1506 VAP patients with complete data were included, of whom 665 were obese. After adjusting for all confounding variables and performing PSM, it was found that obesity status did not affect the short-term mortality of VAP patients, with no statistically significant difference (P > 0.05). However, in terms of 1-year mortality, obese patients exhibited a significantly lower risk of death compared to non-obese patients (P < 0.05). Thus, after selecting characteristic variables by LASSO regression combined with multivariate logistic regression, we constructed a nomogram prediction model to predict the 1-year all-cause mortality in obese VAP patients, and found that the most important factors influencing mortality were age, Charlson Comorbidity Index (CCI), whether or not tracheal intubation, and renal replacement therapy (RRT). Conclusion: Our study found that the obesity paradox still exists in VAP. Older age, higher CCI, and the need for RRT during hospitalization may be associated with a higher long-term mortality risk in these patients.展开更多
慕课(massive open online courses,MOOCs)作为信息教育时代一种新型的教育模式,改变了传统授课的模式,具有开放性、共享性、满足个性化需求的优势,实现了与传统教学模式的融合与互补,有助于发挥医学生的学习主观能动性,培养临床思维能...慕课(massive open online courses,MOOCs)作为信息教育时代一种新型的教育模式,改变了传统授课的模式,具有开放性、共享性、满足个性化需求的优势,实现了与传统教学模式的融合与互补,有助于发挥医学生的学习主观能动性,培养临床思维能力,提高教学质量。在呼吸系统整合课程中引入慕课,可利用国内外高校的精品课程,较好地整合教学资源,弥补实际教学中的不足,缩小教育资源和教学水平的地方差异。学生在自我评价方面,在自我管理、病案分析、团队协作等能力在慕课教学中均得到提升,对慕课的总体学习体验感到满意。同时,慕课教学也对教师的教学能力、医学院校的软硬件投入、学生的自学能力提出了更高的要求。文章结合慕课,探讨在呼吸系统整合课程教学中应用的可行性,从慕课在呼吸系统整合课程中的优势、慕课的指标评价体系、慕课所面临的机遇和挑战等方面,分析对教学模式的影响,发挥优势,避免劣势,为整合系统课程的教学改进给予建议,在教学实践中提供新思路。展开更多
肺癌是目前发病率和死亡率居首位的恶性肿瘤,对肺癌进行早期诊断是降低死亡率的关键。早期肺癌主要表现为肺结节,目前肺结节的检出率越来越高,而其中实性肺结节是检出最多的结节类型,对实性肺结节进行良恶性鉴别是重点。本文综述了实性...肺癌是目前发病率和死亡率居首位的恶性肿瘤,对肺癌进行早期诊断是降低死亡率的关键。早期肺癌主要表现为肺结节,目前肺结节的检出率越来越高,而其中实性肺结节是检出最多的结节类型,对实性肺结节进行良恶性鉴别是重点。本文综述了实性肺结节的影像学及临床危险因素。影像学表现如结节大小、形态、边缘特征及生长速度,结合患者的年龄、吸烟史、家族史等临床信息,有助于评估实性肺结节的良恶性。Lung cancer is the malignant tumor with the highest morbidity and mortality rate, and early diagnosis of lung cancer is the key to reducing the mortality rate. Early lung cancer is mainly manifested as lung nodules, and the detection rate of lung nodules is getting higher and higher, among which solid lung nodules are the most frequently detected nodule type, and it is important to differentiate the benign and malignant nature of solid lung nodules. This article reviews the imaging and clinical risk factors of solid lung nodules. Imaging manifestations such as nodule size, morphology, margin characteristics, and growth rate, combined with clinical information such as the patient’s age, smoking history, and family history, are helpful in assessing the benign or malignant nature of solid lung nodules.展开更多
在精准医疗时代,肺癌患者的治疗方案制定高度依赖于精确的病理诊断和预后评估。近年来,全切片成像技术与人工智能的迅速发展极大地推动了病理组学技术的进步。病理组学在肺癌病理图像分析中,尤其是在肿瘤区域识别、预后预测、肿瘤微环...在精准医疗时代,肺癌患者的治疗方案制定高度依赖于精确的病理诊断和预后评估。近年来,全切片成像技术与人工智能的迅速发展极大地推动了病理组学技术的进步。病理组学在肺癌病理图像分析中,尤其是在肿瘤区域识别、预后预测、肿瘤微环境表征等方面展现出巨大的潜力。本文回顾了病理组学在肺癌诊疗及预后评估领域的最新研究进展,分析了其在当前应用中的局限,并对病理组学的未来发展方向进行了展望。In the era of precision medicine, the treatment plans for lung cancer patients heavily rely on accurate pathological diagnosis and prognostic evaluation. In recent years, the rapid development of whole-slide imaging technology and artificial intelligence has significantly advanced the progress of pathological histology technology. Pathomics has shown great potential in the analysis of lung cancer pathological images, especially in tumor region identification, prognostic prediction, and tumor microenvironment characterization. This article reviews the latest research developments in the field of pathomics for lung cancer diagnosis, treatment, and prognostic evaluation, analyzes its limitations in current applications, and provides an outlook on the future direction of pathomics.展开更多
肺腺癌(LUAD)是非小细胞肺癌(NSCLC)的主要类型,其病理分级对于评估肿瘤侵袭性和预后至关重要。不同病理级别的LUAD在基因表达、TP53基因突变、转录因子FOSB、循环肿瘤细胞(CTC)以及外周血炎症因子等生物标志物表达上存在显著差异,可预...肺腺癌(LUAD)是非小细胞肺癌(NSCLC)的主要类型,其病理分级对于评估肿瘤侵袭性和预后至关重要。不同病理级别的LUAD在基因表达、TP53基因突变、转录因子FOSB、循环肿瘤细胞(CTC)以及外周血炎症因子等生物标志物表达上存在显著差异,可预测病理分级。因此,确定可靠的病理分级预测的生物标志物对治疗方案的选择至关重要。本文围绕LUAD病理分级预测相关的生物标志物进行综述,以期制定个体化治疗方案、改善预后。Lung adenocarcinoma (LUAD) is the predominant type of non-small cell lung cancer (NSCLC), and its pathological grading is crucial for assessing tumor aggressiveness and prognosis. LUADs of different pathological grades exhibit significant differences in the expression of biomarkers such as gene expression, TP53 gene mutations, transcription factor FOSB, circulating tumor cells (CTC), and peripheral blood inflammatory factors, which can predict pathological grading. Therefore, identifying reliable biomarkers for predicting pathological grading is essential for selecting appropriate treatment regimens. This review focuses on biomarkers related to LUAD pathological grading prediction, aiming to facilitate the development of individualized treatment plans and improve prognosis.展开更多
文摘呼吸与危重症医学科(pulmonary and critical care medicine,PCCM)专科医师培训是培养高素质、高水平PCCM专科人才的重要途径。PCCM是一门涵盖范围广,疾病分类复杂的实践性学科。典型教学病例库是现代医学教育模式下的一种全新教学方式。该院通过典型案例的教学,能够激发专培医师的主动性和积极性,提高其临床思维、临床实践、临床科研和团队协作能力。
文摘重症肺炎是一种病情严重、治疗复杂且死亡率相对较高的呼吸系统疾病。近年来,尽管其发病率及死亡率有所下降,但早期诊断,尤其是病原学诊断,仍是提高重症肺炎救治能力的关键环节。随着全球新冠病毒的流行,重症肺炎的病原学谱发生了相应变化。分子诊断技术的使用在过去十年中有所增加,这些技术有助于在一个呼吸道样本中检测多种病毒,使得严重CAP中呼吸道病毒的报告频率越来越高。未来,随着科技的不断进步和医疗技术的不断创新,CAP及SCAP的诊断及治疗将取得更多进展,为患者提供更加精准、有效的治疗服务。本文综述了近年来重症肺炎病原学诊断的最新进展,旨在为提高重症肺炎的救治能力提供参考。Severe community-acquired pneumonia has a complex condition, high treatment costs, and a high mortality rate, posing a great threat to human life and health. In recent years, although its incidence rate and mortality have declined, early diagnosis, especially etiological diagnosis, is still the key link to improve the rescue ability of severe community-acquired pneumonia. With the global prevalence of COVID-19, the pathogenic spectrum of severe pneumonia has changed accordingly. The use of molecular diagnostic techniques has increased in the past decade, which helps detect multiple viruses in a respiratory sample, leading to an increasing frequency of reporting respiratory viruses in severe CAP. In the future, with the continuous advancement of technology and innovation in medical technology, more progress will be made in the diagnosis and treatment of CAP and SCAP, providing patients with more accurate and effective treatment services. This article reviews the latest advances in the etiological diagnosis of severe community-acquired pneumonia in recent years, aiming to provide a reference for improving the treatment capacity of severe community-acquired pneumonia.
文摘背景:本研究主要探索肥胖对呼吸机相关肺炎(Ventilator-Associated Pneumonia, VAP)患者1年全因死亡率的影响,构建呼吸机相关肺炎的肥胖患者长期死亡风险模型,进一步探究“肥胖悖论”对呼吸机相关肺炎的影响。方法:我们使用大型公共数据库提取进入重症监护室(Intensive Care Unit, ICU)内的合适的患者,将患者根据BMI分为肥胖和非肥胖两类。首先,调节协变量后进行倾向性匹配评分(Propensity Score Matching, PSM)减少选择偏差,探究肥胖状态与VAP不良预后的具体关系。其次,为了进一步研究VAP肥胖患者1年全因死亡率的风险因素,我们通过套索回归分析(Least Absolute Shrinkage and Selection Operator, LASSO)联合多因素逻辑回归分析选择特征变量来构建VAP肥胖患者长期死亡率的列线图预测模型。最后,通过绘制校准曲线以评估模型的准确性和可靠性。结果:最终,我们提取到资料齐全的1506名VAP患者,其中肥胖患者有665名。调整一切混杂变量后,进行PSM后发现肥胖状态并不影响VAP患者短期死亡率,无明显统计学差异(P > 0.05);而在1年死亡率中,肥胖患者的死亡风险显著低于非肥胖患者(P Background: This study primarily explores the impact of obesity on the 1-year all-cause mortality rate in patients with Ventilator-Associated Pneumonia (VAP). It aims to construct a long-term mortality risk model for obese patients with VAP and further investigate the effect of “obesity paradox” on VAP. Methods: We extracted data from a large public database for patients admitted to the Intensive Care Unit (ICU) and categorized them into obese and non-obese groups based on BMI. Initially, Propensity Score Matching (PSM) was used after adjusting for covariates to reduce selection bias and explore the relationship between obesity and adverse outcomes in VAP. Subsequently, to further investigate the risk factors for 1-year all-cause mortality in obese VAP patients, we applied Least Absolute Shrinkage and Selection Operator (LASSO) regression combined with multivariate logistic regression analysis to select feature variables and constructed a nomogram prediction model for predicting long-term mortality in obese patients with VAP. Finally, we assessed the accuracy and reliability of the model through calibration curves. Results: A total of 1506 VAP patients with complete data were included, of whom 665 were obese. After adjusting for all confounding variables and performing PSM, it was found that obesity status did not affect the short-term mortality of VAP patients, with no statistically significant difference (P > 0.05). However, in terms of 1-year mortality, obese patients exhibited a significantly lower risk of death compared to non-obese patients (P < 0.05). Thus, after selecting characteristic variables by LASSO regression combined with multivariate logistic regression, we constructed a nomogram prediction model to predict the 1-year all-cause mortality in obese VAP patients, and found that the most important factors influencing mortality were age, Charlson Comorbidity Index (CCI), whether or not tracheal intubation, and renal replacement therapy (RRT). Conclusion: Our study found that the obesity paradox still exists in VAP. Older age, higher CCI, and the need for RRT during hospitalization may be associated with a higher long-term mortality risk in these patients.
文摘慕课(massive open online courses,MOOCs)作为信息教育时代一种新型的教育模式,改变了传统授课的模式,具有开放性、共享性、满足个性化需求的优势,实现了与传统教学模式的融合与互补,有助于发挥医学生的学习主观能动性,培养临床思维能力,提高教学质量。在呼吸系统整合课程中引入慕课,可利用国内外高校的精品课程,较好地整合教学资源,弥补实际教学中的不足,缩小教育资源和教学水平的地方差异。学生在自我评价方面,在自我管理、病案分析、团队协作等能力在慕课教学中均得到提升,对慕课的总体学习体验感到满意。同时,慕课教学也对教师的教学能力、医学院校的软硬件投入、学生的自学能力提出了更高的要求。文章结合慕课,探讨在呼吸系统整合课程教学中应用的可行性,从慕课在呼吸系统整合课程中的优势、慕课的指标评价体系、慕课所面临的机遇和挑战等方面,分析对教学模式的影响,发挥优势,避免劣势,为整合系统课程的教学改进给予建议,在教学实践中提供新思路。
文摘肺癌是目前发病率和死亡率居首位的恶性肿瘤,对肺癌进行早期诊断是降低死亡率的关键。早期肺癌主要表现为肺结节,目前肺结节的检出率越来越高,而其中实性肺结节是检出最多的结节类型,对实性肺结节进行良恶性鉴别是重点。本文综述了实性肺结节的影像学及临床危险因素。影像学表现如结节大小、形态、边缘特征及生长速度,结合患者的年龄、吸烟史、家族史等临床信息,有助于评估实性肺结节的良恶性。Lung cancer is the malignant tumor with the highest morbidity and mortality rate, and early diagnosis of lung cancer is the key to reducing the mortality rate. Early lung cancer is mainly manifested as lung nodules, and the detection rate of lung nodules is getting higher and higher, among which solid lung nodules are the most frequently detected nodule type, and it is important to differentiate the benign and malignant nature of solid lung nodules. This article reviews the imaging and clinical risk factors of solid lung nodules. Imaging manifestations such as nodule size, morphology, margin characteristics, and growth rate, combined with clinical information such as the patient’s age, smoking history, and family history, are helpful in assessing the benign or malignant nature of solid lung nodules.
文摘在精准医疗时代,肺癌患者的治疗方案制定高度依赖于精确的病理诊断和预后评估。近年来,全切片成像技术与人工智能的迅速发展极大地推动了病理组学技术的进步。病理组学在肺癌病理图像分析中,尤其是在肿瘤区域识别、预后预测、肿瘤微环境表征等方面展现出巨大的潜力。本文回顾了病理组学在肺癌诊疗及预后评估领域的最新研究进展,分析了其在当前应用中的局限,并对病理组学的未来发展方向进行了展望。In the era of precision medicine, the treatment plans for lung cancer patients heavily rely on accurate pathological diagnosis and prognostic evaluation. In recent years, the rapid development of whole-slide imaging technology and artificial intelligence has significantly advanced the progress of pathological histology technology. Pathomics has shown great potential in the analysis of lung cancer pathological images, especially in tumor region identification, prognostic prediction, and tumor microenvironment characterization. This article reviews the latest research developments in the field of pathomics for lung cancer diagnosis, treatment, and prognostic evaluation, analyzes its limitations in current applications, and provides an outlook on the future direction of pathomics.
文摘肺腺癌(LUAD)是非小细胞肺癌(NSCLC)的主要类型,其病理分级对于评估肿瘤侵袭性和预后至关重要。不同病理级别的LUAD在基因表达、TP53基因突变、转录因子FOSB、循环肿瘤细胞(CTC)以及外周血炎症因子等生物标志物表达上存在显著差异,可预测病理分级。因此,确定可靠的病理分级预测的生物标志物对治疗方案的选择至关重要。本文围绕LUAD病理分级预测相关的生物标志物进行综述,以期制定个体化治疗方案、改善预后。Lung adenocarcinoma (LUAD) is the predominant type of non-small cell lung cancer (NSCLC), and its pathological grading is crucial for assessing tumor aggressiveness and prognosis. LUADs of different pathological grades exhibit significant differences in the expression of biomarkers such as gene expression, TP53 gene mutations, transcription factor FOSB, circulating tumor cells (CTC), and peripheral blood inflammatory factors, which can predict pathological grading. Therefore, identifying reliable biomarkers for predicting pathological grading is essential for selecting appropriate treatment regimens. This review focuses on biomarkers related to LUAD pathological grading prediction, aiming to facilitate the development of individualized treatment plans and improve prognosis.
