目的   通过机器学习方法构建脓毒症谵妄患者30 d死亡的预测模型，并识别关键预测因子。方法   采用基于医疗信息集成重症监护数据库（Medical Information Mart for Intensive Care IV）的回顾性队列研究方法，boruta筛选重要特征，并通过决策树，K近邻，LightGBM，随机森林，支持向量机，XGBoost构建模型进行分析，通过ROC曲线下面积进行评估，利用F1分数、召回率、精确率、特异度、灵敏度和阳性预测值比较模型表现。结果  XGBoost模型在训练集和验证集中的ROC曲线下面积分别为0.906和0.762，表明该模型具有良好的预测能力，入院年龄、红细胞分布宽度和白细胞计数是最重要的预测因子。结论   基于机器学习的脓毒症谵妄患者预后预测模型展现出良好的预测效能，为临床早期干预提供了重要参考依据。

       Objective  To construct a  30-day mortality  prediction model for  patients with  sepsis-associated  delirium using machine learning methods and identify key predictive factors．Methods  A  retrospective cohort study was conducted based on the Medical Information Mart for Intensive Care IV database．Important features were selected using the Boruta algorithm，and models including Decision Tree，K-Nearest Neighbors，LightGBM，Random Forest，Support Vector Machine，and XGBoost were constructed and analyzed．Model performance was evaluated using the area under the reciver operater characteristic（ROC）curve（AUC），along with F1 score，recall，precision，specificity，sensitivity，and positive predictive value．Results  The XGBoost model demonstrated strong predictive performance，with AUC values of 0.906 in the training set and 0.762 in the test set．Key predictors identified included admission age，red blood cell distribution width，and white blood cell count．Conclusions  The machine learning-based prediction model for sepsis-associated delirium prognosis exhibits robust predictive efficacy，providing a valuable tool for early clinical intervention．

目的 分析产后出血预测评分与产妇凝血指标的相关性,以及出血预测评分对阴道分娩产后出血的预测效能。方法 采用回顾性研究,纳入2021年1月—2022年12月河南科技大学第二附属医院收治的136例阴道分娩产妇,根据产后出血情况,将合并产后出血的36例患者列为病例组,其余100例列为对照组,比较两组患者的产后出血预测评分及凝血指标,经Spearman相关性系数验证产后出血预测评分结果与凝血指标的相关性,依据实际出血情况,验证产后出血预测评分、各凝血指标对产后出血的预测效能。结果 病例组患者的产后出血预测评分为（7.33±2.46）分,D-二聚体（D-D）为（2.62±0.41）mg/L,均高于对照组[（6.14±2.06）分、（2.17±0.45）mg/L],纤维蛋白原（FIB）为（4.42±1.25）g/L,低于对照组（5.23±1.16）g/L;活化部分凝血活酶时间（APTT）为（37.44±10.25）s,凝血酶原时间（PT）为（15.45±4.12）s,凝血酶时间（TT）为（16.77±4.25）s,均高于对照组[（30.11±10.12）s、（12.49±4.11）s、（13.34±4.18）s],差异具有统计学意义（P<0.05）。经Spearman相关性系数分析,产后出血预测评分与经阴道分娩产妇的D-D、APTT、PT、TT呈正相关,与FIB呈负相关。通过绘制受试者工作特征曲线（ROC）后得知,产后出血预测评分及凝血指标对产后出血均有一定预测价值,但产后出血预测评分的AUC值大于各凝血指标。结论 产后出血预测评分与产妇凝血功能指标呈正相关,将产后出血预测评分与凝血指标检测相结合能实现对产后出血的早期识别及诊断。

Objective To analyze the correlation between postpartum bleeding prediction score and maternal blood coagulation index and the prediction efficiency of postpartum bleeding in vaginal delivery．Methods This is a retrospective study．The cases were included from January 2021 to December 2022．The subjects of the study were 136 vaginal delivery mothers． According to the delivery situation,36 patients with postpartum bleeding were included in the case group,and the rest 100 patients were included in the control group．The postpartum bleeding prediction score and coagulation indicators of the two groups were compared．The correlation between postpartum bleeding prediction score and coagulation indicators was verified by Spearman correlation coefficient．According to the actual bleeding situation,verify the predictive score for postpartum bleeding and the diagnostic efficacy of various coagulation indicators on postpartum bleeding．Results According to the test,the predictive score for postpartum bleeding in the case group was（7.33±2.46）,D-dimer（D-D）was（2.62±0.41）mg/L,which were higher than those in the control group [（6.14±2.06）,（2.17±0.45）mg/L]．Fibrinogen（FIB）was（4.42±1.25）g/L,lower than the control group（5.23±1.16）g/L,activated partial thromboplastin time（APTT）was（37.44±10.25）s,prothrombin time（PT）was（15.45±4.12）s,and thrombin time（TT）was（16.77±4.25）s．Compared with the control group [（30.11±10.12）s,（12.49±4.11）s,and（13.34±4.18）s）],the above indicators were all higher（P<0.05）．Through Spearman correlation coefficient analysis,the predictive score of postpartum bleeding was positively correlated with the D-D,APTT,PT,TT,negatively correlated with the FIB of the parturient who delivered through vagina．After drawing the ROC curve,it was found that both the postpartum hemorrhage prediction score and coagulation indicators had certain predictive value for postpartum hemorrhage,but the AUC value of the postpartum hemorrhage prediction score was greater than each coagulation indicator．Conclusions The prediction score of postpartum bleeding is positively correlated with the coagulation function indicators of the parturient,combining the score and indicators can achieve early identification and diagnosis of postpartum bleeding．

胶质瘤是颅内最常见的原发性恶性肿瘤,其分级对患者治疗方式的选择和预后至关重要。尽管目前组织病理学仍是其最为可靠的分级手段,但需通过有创性手术以获取组织样本,存在一定的风险。相较之下,磁共振成像（MRI）作为一种非侵入性影像诊断工具,在胶质瘤分级中发挥着不可或缺的作用。然而,传统MRI评估受限于医师个体主观性强和可重复性差的问题,一定程度上影响了准确的分级结果。近年来,影像组学技术的崭露头角为解决上述难题开辟了新视角,通过高通量提取影像数据特征捕捉并量化肿瘤的影像学表现,避免因主观因素而导致的不确定性,协助医师更准确地评估肿瘤的恶性程度。本文对近五年来MRI影像组学在胶质瘤术前分级预测方面的相关研究进行了简要综述,旨在为相关领域研究者提供有益的参考和借鉴,以推动MRI影像组学在临床实践中的应用。

Glioma is the most common primary malignant brain tumor,and its grading is crucial for treatment decisions and prognosis．Currently,histopathology remains the gold standard for grading,but it requires invasive procedures and carries inherent risks．In contrast,magnetic resonance imaging（MRI）,a non-invasive diagnostic tool,plays an indispensable role in glioma grading．However,traditional MRI assessment is hampered by interobserver subjectivity and limited repeatability,which compromise grading accuracy．In recent years,radiomics,a burgeoning field,has offered a promising solution to address these challenges．By extracting high-dimensional imaging data features,radiomics enables the quantification of tumor radiological characteristics and elimination of subjectivity-related discrepancies．This technology assists clinicians in more precisely assessing the malignancy of gliomas．This article summarizes relevant studies in the past five years on the application of MRI radiomics in preoperative glioma grading,aiming to provide valuable insights and guidance to researchers in the field and promote the clinician implementation of MRI radiomics．

目的 构建并验证机械通气患儿肠内营养支持发生误吸的风险预测模型。方法 回顾性分析中山市博爱医院2021年3月—2023年3月儿童重症监护病房330例行机械通气并进行肠内营养的患儿临床资料,通过二元Logistic回归,获取机械通气患儿肠内营养支持发生误吸的预测因素,绘制列线图模型,并进行模型评价及验证。结果 330例机械通气患儿中,104例患儿发生误吸、226例未发生误吸。两组患儿在意识状态、机械通气方式、管饲量、胃残留量、胃管置入深度、促胃动力药、镇静剂等方面对比差异具有统计学意义（P<0.05）。二元Logistic结果显示,胃残留量、机械通气方式、管饲量、意识状态、胃管置入深度、促胃动力药、镇静剂是机械通气患儿肠内营养支持发生误吸的影响因素（P<0.05）。建模组AUC为0.810（95%CI：0.760~0.860）,Hosmer-Lemesh结果显示,χ²=3.245,P=0.846;外部验证组AUC为0.873（95%CI：0.831~0.914）,Hosmer-Lemesh结果显示,χ²=3.567,P=0.875。建模组和训练组DCA曲线大部分落于Y=0上方。建模组与外部验证组校准曲线均与参考曲线高度贴合,预测概率与实际概率接近,校准度良好。结论 基于胃残留量、机械通气方式、管饲量、意识状态、胃管置入深度、促胃动力药、镇静剂等7项指标构建的风险预测模型具有一定的临床价值,可作为医护人员识别肠内营养机械通气误吸高危患儿的工具。

Objective To establish and verify the risk prediction model of enteral nutritional aspiration in children with mechanical ventilation．Methods The clinical data of 330 children who underwent mechanical ventilation and enteral nutrition in the PICU of Zhongshan Boai Hospital from March 2021 to March 2023 were retrospectively analyzed．The independent predictive factors of enteral nutrition support aspiration in children with mechanical ventilation were obtained by binary Logistic regression,and the nomographic model was drawn,and the model was evaluated and verified. Results Among 330 children with mechanical ventilation,104 had aspiration and 226 did not．There were statistically significant differences between the two groups in consciousness state,mechanical ventilation mode,tube feeding amount,gastric residual amount,gastric tube insertion depth,gastric motivity drugs,sedatives,etc.（P<0.05）．Binary Logistic results showed that gastric residual amount,mechanical ventilation mode,tube feeding amount,state of consciousness,depth of gastric tube insertion,gastric motonics and sedatives were the influential factors of enteral nutritional aspiration in children with mechanical ventilation（P<0.05）．The AUC of the modeling group was 0.810（95%CI：0.760-0.860）,and the Hosmer-Lemesh result showed that χ²=3.245,P=0.846．The AUC of the external verification group was 0.873（95%CI：0.831-0.914）,and the Hosmer-Lemesh result showed that χ²=3.567,P=0.875．The DCA curves of modeling group and training group mostly were above Y=0．The calibration curves of the modeling group and the external verification group are highly fit to the reference curves,and the prediction probability was close to the actual probability,and the calibration degree was good．Conclusion sThe risk prediction model based on 7 indexes,including stomach residual amount,mechanical ventilation mode,tube feeding amount,state of consciousness,depth of gastric tube insertion,gastric motivity drug and sedative,with certain clinical value,and can be used as a tool for medical staff to identify children at high risk of enteral nutritional mechanical aspiration．

目的 探讨动态对比增强磁共振成像（DCE-MRI）多参数定量特征对乳腺癌腋窝淋巴结转移（ALNM）风险的预测价值。方法 回顾性收集2020年3月—2022年11月在佛山市高明区人民医院经手术病理确诊的155例乳腺癌患者临床资料,根据患者是否发生ALNM分为ALNM 组（n=39）和无ALNM 组（n=116）。采用单因素分析和多因素Logistic回归分析乳腺癌发生ALNM的影响因素。结果 ALNM组和无ALNM 组患者的肿块质地、肿块直径、肿块部位、肿块形状、肿块内部强化特征等指标比较差异无统计学意义（t/χ²=2.249、0.977、1.369、0.524、2.158,P>0.05）。两组患者肿块表观扩散系数（ADC）值、腋窝淋巴结（ALN）短径、肿块边缘、动态增强时间-信号强度曲线（TIC）曲线等指标比较,差异有统计学意义（t/χ²=6.573、9.873、29.441、2.031,P<0.05）。二元Logistic回归模型结果显示,肿块ADC值、ALN 短径（≥5 mm）、TIC曲线（流出型）为乳腺癌ALNM发生的危险因素（OR=0.251、0.106、0.002,P<0.05）。结论 DCE-MRI多参数定量特征中,乳腺癌患者的肿块ADC值低、ALN 短径（≥5 mm）、TIC曲线（流出型）为乳腺癌ALNM发生的危险因素。

Objective To investigate the predictive value of multi-parameter quantitative features of dynamic contrast-enhanced magnetic resonance imaging（DCE-MRI）in the risk of axillary lymph node metastasis（ALNM）in breast cancer．Methods Clinical data of 155 patients with breast cancer diagnosed by surgery and pathology in Foshan Gaoming District People's hospital from March 2020 to November 2022 were retrospectively collected．According to whether the patients had ALNM,they were divided into ALNM group（n=39）and non-ALNM group（n=116）．Univariate analysis and multiple Logistic regression models were used to explore the influencing factors of ALNM in breast cancer．Results There was no significant difference in mass texture,mass diameter,mass location,mass shape and internal enhancement between the ALNM group and the non-ALNM group（t/χ²=2.249,0.977,1.369,0.524,2.158,P>0.05）．There were significant differences in ADC value,ALN short diameter,tumor margin and TIC curve between the two groups（t/χ²=6.573,9.873,29.441,2.031,P<0.05）．Binary Logistic regression model showed that ADC value,ALN short diameter（≥5 mm）and tumor margin（blur）were risk factors for the occurrence of breast cancer ALNM（OR=0.251,0.106,0.002,P<0.05）．Conclusions Among the multi-parameter quantitative features of DCE-MRI,the ADC value of breast cancer,the short diameter of ALN（≥5 mm）,and the edge of the tumor（blur）are the risk factors for the occurrence of ALNM in breast cancer．

目的 基于影像组学方法,探讨多层螺旋CT（MSCT）四期增强扫描单一/不同期相及不同容积感兴趣区（VOI）的选择,在术前预测原发性肝细胞癌（HCC）微血管侵犯（MVI）中的价值。方法 回顾性收集88例经手术病理证实为HCC并行术前MSCT四期增强扫描的患者,其中包括47例MVI阳性患者和41例MVI阴性患者。在MSCT增强扫描的动脉早期、动脉晚期、门静脉期及延迟期图像中手动逐层勾画肿瘤ROI,获得瘤体容积感兴趣区VOI（Vt）,然后基于计算机自动膨胀算法将Vt外扩10 mm获得瘤体及瘤周VOI（Vt+Vp）。使用Pyradiomics软件分别从Vt和Vt+Vp中提取影像组学特征,随后采用15种特征选择方法和10种分类器构建150个预测模型,并通过十折交叉检验以验证模型的效能。使用准确度、敏感度、特异度、受试者工作特性曲线下面积（AUC）评估模型的效能,并比较性能最优的前三个预测模型。结果 MSCT四期增强扫描图像中预测HCC MVI状态的影像组学模型在门静脉期的表现优于其它期相及各期相的不同组合,其中最大的AUC值在Vt和Vt+Vp两种ROI中分别为0.768和0.782。此外,基于Vt+Vp的影像组学模型对MVI的预测效能优于基于Vt的影像组学模型,基于Vt+Vp性能最优的预测模型的AUC值、准确度、敏感度和特异度分别0.782、0.728、0.745和0.705。结论 采用影像组学方法术前无创性预测HCC MVI状态首选增强扫描的门静脉期,ROI首选瘤体联合瘤周10 mm区域。

Objective To investigate the value of single or different phases of contrast-enhanced multi-slice spiral CT（MSCT）in different volumetric regions of interest（ROI）to preoperatively predict the state of microvascular invasion in primary hepatocellular carcinoma（HCC）based on radiomics methods．Methods A total of 88 patients with HCC confirmed by surgical pathology who underwent preoperative MSCT quadruple-enhanced scan were retrospectively recruited,including 47 MVI-positive patients and 41 MVI-negative patients．The ROI was manually delineated slice-by-slice in the early arterial phase,late arterial phase,portal venous phase,and equilibrium phase of enhanced MSCT images to obtain the volume of tumor VOI（Vt）,and then Vt was expanded by 10 mm through the computer expansion algorithm automatically to obtain the volume of tumor and peritumor（Vt+Vp）．Pyradiomics software was used to extract radiomic features from Vt and Vt+Vp,followed by 150 discriminant models constructed with 150 feature selection methods and 10 classifiers,and then 10-fold cross-validation was used to evaluate the performance of these models．Using accuracy,sensitivity,specificity,area under the receiver operating characteristic curve（AUC）to assess model performance．The top three predictive models with the best performance were also compared．Results The radiomics model for predicting HCC MVI status in portal venous phase among quadruple-enhanced MSCT images outperformed other phases and different combinations of phases,achieving the highest AUC values of 0.768 and 0.782 in Vt and Vt+Vp respectively．In addition,the prediction performance of the radiomics model based on Vt+Vp was superior to models based on Vt．AUC value,accuracy,sensitivity,and specificity of the model with the best performance based on Vt+Vp were 0.782,0.728,0.745 and 0.705 respectively．Conclusions Radiomics models based on the portal venous phase of contrast-enhanced MSCT and tumor combined with the 10mm peritumoral area were more recommended to be employed to preoperative non-invasively predict the state of HCC MVI．

目的 基于随机森林方法构建甲状腺功能减退（简称甲减）患病风险预测模型。方法 从MIMIC-IV数据库纳入5 735名甲减患者为病例组,4 803名非甲减患者为对照组,基于随机森林模型进行建模。同时利用逻辑回归、贝叶斯正则化神经网络、XGBoost作为比较模型。最后用准确率、F1分数、精确率、召回率、特异性以及AUC值评价四个机器学习模型性能。结果 随机森林模型准确率为0.85,F1分数为0.84,精确率为0.84,召回率为0.84,特异性为0.86,AUC值为0.91。在该模型中,促甲状腺激素、年龄、绝对淋巴细胞计数、血液中红细胞数、中性白细胞、性别、碱性磷酸酶、丙氨酸氨基转移酶、嗜酸性粒细胞绝对计数、尿素氮为甲减患者诊断重要性排前10的指标。结论 采用随机森林方法构建的甲减患病预测模型为甲减的早期诊断有潜在应用价值。

Objective To construct a risk prediction model for hypothyroidism based on the random forest model．Methods A total of 5 735 hypothyroidism patients were included from the MIMIC-IV database as the case group, and 4 803 non-hypothyroidism patients were included as the control group．Random forest models were constructed for both groups, and logistic regression, Bayesian regularized neural network, and XGBoost were used as comparative models．The performance of the four machine learning models was evaluated using accuracy, F1 score, precision, recall, specificity, and AUC value．Results The random forest model had an accuracy of 0.85, an F1 score of 0.84, a precision of 0.84, a recall of 0.84, a specificity of 0.86, and an AUC value of 0.91．In this model, thyroid-stimulating hormone, age, absolute lymphocyte count, red blood cell count in blood, neutrophil, gender, alkaline phosphatase, aspartate aminotransferase, absolute eosinophil count, and blood urea nitrogen were the top 10 indicators for diagnosing hypothyroidism patients．Conclusions The hypothyroidism disease prediction model constructed using the random forest method has potential application value for the early diagnosis of hypothyroidism．

目的 探讨子宫瘢痕的超声弹性成像结合厚度分析对剖宫产后再妊娠产妇子宫破裂的预测应用。方法 选择2020年1月—2021年12月在中山市中医院分娩的剖宫产术后再次妊娠经阴道分娩（VBAC）产妇作为研究对象。根据纳入和排除标准,共纳入子宫破裂的VBAC产妇32例、非子宫破裂的VBAC产妇90例。通过住院病历信息系统查询研究对象的基本信息及其在妊娠晚期（≥37周）用B超对研究对象行子宫瘢痕厚度和弹性的测量结果,采用受试者工作特征曲线（ROC）曲线分析子宫瘢痕厚度和弹性SI值对子宫破裂的预测作用。结果 子宫破裂组中年龄>35岁、妊娠>2次、与上次剖宫产间隔<2年、新生儿体质量≥3 kg、单层缝合者的比例高于非子宫破裂组（P<0.05）。122例产妇子宫瘢痕厚度的均值为（3.42±0.49）mm,SI的均值为（2.57±0.45）。ROC曲线分析结果显示：子宫瘢痕厚度单独预测子宫破裂的曲线下面积（AUC）为0.805（95%CI：0.730~0.880,P<0.05）,cut off值为3.05 mm,灵敏度为0.726,特异度为0.910,约登指数为0.636;子宫瘢痕SI单独预测子宫破裂的AUC为0.730（95%CI：0.635~0.824,P<0.05）,cut off值为2.11,灵敏度为0.767,特异度为0.781,约登指数为0.548;子宫瘢痕厚度联合预测子宫破裂的AUC为0.874（95%CI：0.812~0.937,P<0.01）,灵敏度为0.875,特异度为0.811,约登指数为0.686。子宫瘢痕厚度结合子宫瘢痕SI值预测子宫破裂的AUC高于单独使用子宫瘢痕厚度（Z=7.611,P=0.041）和子宫瘢痕SI值（Z=25.864,P=0.025）。结论 子宫瘢痕的超声弹性成像SI值联合子宫厚度可有效提高超声对于VBAC产妇子宫破裂的预测效能,具有一定的应用意义。

Objective To study the application of ultrasound elasticity imaging combined thickness analysis of uterine scar in predicting uterine rupture in women pregnant after cesarean section．Methods Pregnant women with vaginal birth after cesarean（VBAC）from January 2020 to December 2021 in Zhongshan Hospital of Traditional Chinese Medicine were selected as the research subjects．A total of 32 VBAC parturients with uterine rupture and 90 VBAC parturients without uterine rupture were included according to the inclusion and exclusion criteria．The basic information of the subjects was queried through the medical record information system of the hospital．In the third trimester（≥37 weeks）,the thickness and elasticity of uterine scar were measured by ultrasound,and the predictive effect of uterine scar thickness and elastic SI value on uterine rupture was analyzed by ROC curve．Results Chi-square test showed that the incidence of uterine rupture was higher in patients with age>35 years,pregnancy>2 times,interval from last cesarean section<2 years,newborn weight≥3kg,and the proportion of uterine rupture in single suture was higher than that in double suture（P<0.05）．The mean uterine scar thickness of 122 subjects was（3.42±0.49）mm,and the mean SI was（2.57±0.45）．The area under curve（AUC）of uterine scar thickness alone for predicting uterine rupture was 0.805（95%CI：0.730-0.880,P<0.05）,the cut off value was 3.05 mm,the sensitivity was 0.726,the specificity was 0.910,and the Youden coefficient was 0.636 by ROC curve analysis．The AUC of uterine scar SI alone for predicting uterine rupture was 0.730（95%CI：0.635-0.824,P<0.05）,the cut off value was 2.11,the sensitivity was 0.767,the specificity was 0.781,and the Youden coefficient was 0.548 by ROC curve analysis．The AUC of uterine scar thickness combination for predicting uterine rupture was 0.874（95%CI：0.812-0.937,P<0.01）,the sensitivity was 0.875,the specificity was 0.811,and the Youden coefficient was 0.686 by ROC curve analysis．The AUC predicted by uterine scar thickness combined with uterine scar SI value was higher than that predicted by uterine scar thickness alone（Z=7.611,P=0.041）and uterine scar SI value（Z=25.864,P=0.025）．Conclusions Elastic SI value of ultrasound imaging of uterine scar combined with uterine thickness can effectively improve the prediction efficiency of ultrasound for VBAC maternal uterine rupture,which has certain application significance,but further demonstration is still needed．

目的 探讨老年吸入性肺炎的危险因素,建立风险预测模型,以期降低老年吸入性肺炎的发病率。方法 选取2017年8月28日—2020年 10月30日广州市第一人民医院老年病科住院治疗的老年肺炎患者205例,按照是否发生吸入性肺炎分为吸入性肺炎组和非吸入性肺炎组,对比2组患者的各项指标,分析老年吸入性肺炎的危险因素,建立风险预测模型,采用ROC曲线对模型进行预测效果检验。结果 多因素Logistic回归分析结果显示,脑梗塞、帕金森、留置胃管、长期卧床为老年吸入性肺炎的危险因素(P<0.05)。模型公式为Logit(P)=-2.952+1.221X₂+2.417X₃+2.388X₈+1.683X₁₀。该模型ROC曲线下面积为0.894。结论 本研究中的模型预测效果良好,可为医护人员预测老年患者发生吸入性肺炎的概率,及时采取相应的预见性护理及干预性治疗。

Objective To explore the risk factors of aspiration pneumonia in the elderly and establish the risk prediction model, in order to reduce the incidence of aspiration pneumonia in the elderly. Methods A total of 205 elderly patients with pneumonia who were hospitalized in the department of geriatrics, Guangzhou First People's Hospital from August 28, 2017 to October 30, 2020, were divided into aspiration pneumonia group and non-aspiration pneumonia group according to whether aspiration pneumonia occurred. The indicators of the two groups of patients were compared, the risk factors of aspiration pneumonia in the elderly were analyzed, the risk prediction model was established, and the prediction effect of the model was tested by receiver operating characteristic curve. Results Multivariate Logistic regression analysis showed that cerebral infarction, Parkinson's disease, indwelling nasogastric tube, and being bedridden were risk factors for aspiration pneumonia in elderly patients (P<0.05). The model formula was Logit (P)=-2.952+1.221X₂+2.417X₃+2.388X₈+1.683X₁₀. The area under receiver operating characteristic curve of this model was 0.894. Conclusion The prediction effect of the model in this study was good, which could predict the probability of aspiration pneumonia in elderly patients for medical staff, and to timely take the corresponding predictive care and interventional treatment.

目的 利用网络药理学技术,分析黄甲软肝颗粒治疗肝纤维化的作用网络,以及黄甲软肝颗粒治疗肝纤维化的潜在作用机制,并在体内动物实验进行初步验证。方法 采用中药系统药理学分析平台中寻找黄甲软肝颗粒中10味中药相关的化学成分和作用靶点,通过GeneCards等数据库筛选肝纤维化疾病相关的靶标;对药物与疾病靶点相映射得到黄甲软肝颗粒治疗肝纤维化的作用靶点,运用cytoscape将疾病靶点与复方活性成分靶点的交集-交集部分对应的活性成分”构建“C(成分)-T(靶点)”作用网络。将交集靶点利用 DAVID数据库进行GO富集分析和KEGG富集分析,以获得其潜在作用机制。最后,通过黄甲软肝颗粒防治CCl₄导致SD大鼠肝纤维化的体内实验进行初步验证,考察末次给药后大鼠体质量和肝脏指数,采用微板法检测SD大鼠血清中天冬氨酸氨基转移酶(AST)、丙氨酸氨基转移酶(ALT)水平,苏木精-伊红染色观察肝脏病理学变化。结果 预测筛选得到黄甲软肝颗粒共有117个潜在活性成分,266个活性成分对应靶点,161个交集靶点,关键成分有槲皮素、山奈酚、丹参酮IIA、芒柄花黄素等,关键靶点有PTGS2、PTGS1、NCOA1、ACHE、HTR、RXRA、ADRB2、IL1B等。GO 分析共包含 960条富集结果,其中生物过程845 条,分子功能 63条,细胞组成 52 条;KEGG 分析共得出68条通路,与本次研究较相关的通路主要包括TNF信号通路、Toll样受体信号通路、Rap1信号通路、胞质DNA传感途径、ErbB信号通路、VEGF信号通路等。体内动物实验研究表明,黄甲软肝颗粒能显著降低大鼠的肝脏指数和血清ALT、AST,改善肝组织病理学指标。结论 黄甲软肝颗粒可通过多成分、多途径、多靶点协同发挥治疗肝纤维化的作用,本研究为黄甲软肝颗粒治疗肝纤维化疾病的物质基础、作用机制及临床应用的进一步研究奠定基础。

Objective To analyze the effective network of Huangjia Ruangan Granules in treating liver fibrosis and its potential mechanism by using network pharmacology, and preliminary verify by animal in vivo experiments. Methods From the Chinese Medicine System Pharmacology Analysis Platform, we searched for the chemical constituents and targets of 10 Chinese herbs in Huangjia Ruangan Granules, and screened the targets related to liver fibrosis diseases through GeneCards and other databases. The drug and disease target were mapped to the target of Huangjia Ruangan Granules for the treatment of liver fibrosis, and the active component corresponding to the intersection of the disease target and the compound active component target was constructed using cytoscape “C (component)-T (target)” action network. The intersection target was used for GO enrichment analysis and KEGG enrichment analysis with DAVID database to obtain its potential mechanism of action. Finally, through the in vivo experiment of using Huangjia Ruangan Granules to prevent and treat CCl₄ leaded liver fibrosis in SD rats, the rats' body weight and liver index after the last dose were recorded, and the levels of aminotransferase (AST) and alanine aminotransferase (ALT) in the serum of SD rats were detected by the microplate method, hematoxylin-eosin staining were used to observe liver pathological changes. Results Predictive screening showed that Huangjia Ruangan Granules had 117 potential active ingredients, 266 active ingredients corresponded to targets, and 161 intersection targets. The key ingredients was quercetin, kaempferol, tanshinone IIA, formononetin, etc. The key targets were PTGS2, PTGS1 NCOA1, ACHE, HTR, RXRA, ADRB2, IL1B, etc. GO analysis showed a total of 960 enrichment results, including 845 biological processes, 63 molecular functions, and 52 cell compositions; KEGG analysis revealed a total of 68 pathways, the related pathways included TNF signaling pathway, Toll-like receptor signaling pathway, Rap1 signaling pathway, cytoplasmic DNA sensing pathway, ErbB signaling pathway and VEGF signaling pathway, etc. In vivo animal experiments had shown that Huangjia Ruangan Granules could significantly reduce the liver index and serum ALT and AST levels of rats, and improve liver histopathological indicators. Conclusions Huangjia Ruangan Granules treated liver fibrosis through multi-component, multi-pathway and multi-target synergy. This research laid the groundwork for the material basis, mechanism and clinical application of Huangjia Ruangan Granules in treating liver fibrosis diseases.