我国老年人认知水平变化轨迹及其分化

目的 探究影响我国老年人认知水平的变化趋势，分离出年龄、队列效应。方法 基于CLHLS（2002—2018）多重队列追踪数据，以Stata16.0软件为工具，运用分层生长曲线模型进行统计分析。结果 本研究发现，个体行为生活方式、社会经济地位、性别、慢性病数量对认知水平均具有统计学意义；年龄、队列对认知水平的变化具有独立效应；随着年龄的增长，我国老年人认知水平下降，认知水平的城乡、性别差异明显；较年轻出生队列的老年人认知水平较好，认知水平的城乡差异随着队列的年轻化而变大，性别差异在较年轻队列有略微缩小的趋势。结论 影响认知水平因素复杂，认知障碍会增加医疗成本及照护负担，因此需准确把握老年认知水平的变化规律与作用路径，从而为卫生服务、养老保障、长期医疗照护的资源配置提供科学依据。     

Pharmacokinetics and Early Tumor Response to Conventional Transarterial Chemoembolization with Sorafenib and Doxorubicin in a VX2 Rabbit Tumor Model

PurposeTo investigate the pharmacokinetics (PK) and early effects of conventional transarterial chemoembolization (TACE) using sorafenib and doxorubicin on tumor necrosis, hypoxia markers, and angiogenesis in a rabbit VX2 liver tumor model.Materials and MethodsVX2 tumor-laden New Zealand White rabbits (N = 16) were divided into 2 groups: 1 group was treated with hepatic arterial administration of ethiodized oil and doxorubicin emulsion (DOX-TACE), and the other group was treated with ethiodized oil, sorafenib, and doxorubicin emulsion (SORA-DOX-TACE). Animals were killed within 3 days of the procedure. Levels of sorafenib and doxorubicin were measured in blood, tumor, and adjacent liver using mass spectrometry. Tumor necrosis was determined by histopathological examination. Intratumoral hypoxia-inducible factor (HIF) 1α, vascular endothelial growth factor (VEGF), and microvessel density (MVD) were determined by immunohistochemistry.ResultsThe median intratumoral concentration of sorafenib in the SORA-DOX-TACE group was 17.7 μg/mL (interquartile range [IQR], 7.42–33.5 μg/mL), and its maximal plasma concentration (C_max) was 0.164 μg/mL (IQR, 0.0798–0.528 μg/mL). The intratumoral concentration and C_max of doxorubicin were similar between the groups: 4.08 μg/mL (IQR, 3.18–4.79 μg/mL) and 0.677 μg/mL (IQR, 0.315–1.23 μg/mL), respectively, in the DOX-TACE group and 1.68 μg/mL (IQR, 0.795–4.08 μg/mL) and 0.298 μg/mL (IQR, 0.241–0.64 μg/mL), respectively, in the SORA-DOX-TACE group. HIF-1α expression was increased in the SORA-DOX-TACE group than in the DOX-TACE group. Tumor volume, tumor necrosis, VEGF expression, and MVD were similar between the 2 groups.ConclusionsThe addition of sorafenib to DOX-TACE delivered to VX2 liver tumors resulted in high intratumoral and low systemic concentrations of sorafenib without altering the PK of doxorubicin.     

主动脉出口血压对左心室血流特性的影响

采用Mimics对心脏进行三维重构，用3-matic进行模型优化以及模型误差分析，对左心室室壁运动做速度分布假设，基于UDF宏文件对左心室室壁运动编写程序，将血液视为非牛顿流体，采用动网格技术研究不同血压对左心室血液流动的影响。模拟发现当左心室收缩时，压力梯度明显，内部压力减小。当左心室舒张时，内部压力逐渐增高。二尖瓣口处的速率先增大后减小。血压升高，左心室内剪切应力持续增大，极易破坏红细胞结构，产生溶血现象，导致心脏功能紊乱。     

Feasibility of a method for low contrast CT image quality assessment using difference detail curves for abdominal scans

This work describes a measurement method for assessing dose-related image-quality of CT scans based on the difference detail curve (DDC) method, and showcases its use in a low contrast setting. The method is based on a phantom consisting of elliptical slices of different sizes into which contrast object modules can be inserted. These modules contain contrast objects based on (synthetic) resin mixtures with sucrose (native) or sodium iodine (contrast medium). Mixing ratios are provided to achieve a range of clinically relevant CT-numbers with these materials. The phantom is characterized in terms of contrast accuracy, energy dependency and long-term drift with satisfying results. Contrast accuracy and energy dependency are similar to that of water or soft tissue. Image quality of 655 scans of the phantom acquired at 30 different clinical institutions and with 16 different CT scanner models from 4 manufacturers was assessed by calculating a difference detail curve (DDC) from evaluation of up to 5 human observers using a custom-made software (RadiVates) described in this work. Based on these measurements, inter-observer variability was quantified using a bootstrap method and was shown to be a large contributor to the overall variability. This work demonstrates that assessment of CT image quality is feasible with the aforementioned phantom and DDC method.     

Preoperative comprehensive malignancy risk estimation for thyroid nodules: Development and verification of a network-based prediction model

BackgroundIn order to avoid excessive treatment of thyroid nodules in the clinic, it is necessary to find a simple and practical analysis method to comprehensively and accurately reflect benign or malignant thyroid nodules. This study aimed to construct and validate a comprehensive and reliable network-based predictive model using a variety of imaging and laboratory criteria for thyroid nodules to stratify the risk of malignancy prior to surgery.MethodsWe retrospectively analyzed data from patients who underwent surgical treatment for thyroid nodules at the Thyroid and Breast Diagnosis and Treatment Center of Weifang Hospital of Traditional Chinese Medicine between January 2018 and December 2020. Binary logical regression analysis was performed to predict whether nodules were malignant or benign. The developmental dataset included 457 patients (January 2018–December 2020). The validation set included separate data points (n = 225, January 2018–December 2020).ResultsIn this study, criteria that showed significant predictive value for malignant nodules included TI-RADS: 4b (p = 0.065); Bethesda IV, Bethesda V, Bethesda VI (P < 0.0001); BRAF^V600E mutation (P < 0.0001); Calcitonin>5 pg/ml (p = 0.0037); and FNA-Tg>30 ng/ml (p = 0.0003). A 10-grade risk scoring system was developed. The risk of malignancy risk ranged from 2.06% to 100% and was positively associated with increasing risk grade. The areas under the receiver-operating characteristic curve of the development and validation sets were 0.972 and 0.946, respectively.ConclusionA simple, comprehensive and reliable web-based predictive model was designed using a variety of imaging and laboratory criteria to stratify thyroid nodules by probability of malignancy.     

Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.     

老年下肢骨折患者行手术室人性化护理的效果

目的评价手术室人性化护理干预对老年下肢骨折患者手术以及术后恢复情况的影响,以加速老年骨折患者康复。方法选择我院近年来收治手术治疗的老年下肢骨折患者,总计100例。结合手术室护理方法进行对照组和研究组随机分组,对照组老年下肢骨折患者采取常规护理,研究组老年下肢骨折患者采取手术室人性化护理干预。比较两组老年下肢骨折患者的手术情况,包括情绪评分、生活质量评分、术后并发症以及护理满意度。结果研究组老年下肢骨折患者SAS、SDS、SF-36评分均明显优于对照组,且安全性和护理满意度均明显高于对照组,观察指标数据差异有统计学意义,P<0.05。结论予以老年下肢骨折患者手术室人性化护理干预满足了患者的护理需求,进一步稳定了患者的情绪、身体,从而提高了患者手术安全性。