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。     

精神科护士夜班经历创伤事件后成长体验的质性研究

目的 探讨精神科护士夜班经历创伤事件后的成长体验,以期为护士不同阶段心理情况进行针对性干预和指导,并为护士职业生涯早期心理建设提供方向。方法 采用扎根理论的研究方法,2020年3月—7月对广东省某三级甲等医院12名夜班经历创伤事件的精神科护士进行深度访谈,采用Strauss和Corbin扎根理论分析资料,分为开放式登录、轴心式登录和选择式登录3个阶段,持续比较分析资料。结果 发现精神科护士夜班经历创伤事件后经历了应激期、调适期、成长期3个不同阶段。结论 精神科护士夜班经历创伤事件后的成长为动态过程,管理者或干预者可根据不同阶段进行针对性干预和指导,并在护士早期培训中促使其提前做好相关知识、技能的储备和必备心理素质的塑造。     

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.     

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.     

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.     

脐带间充质干细胞分泌的外泌体对骨性关节炎模型大鼠的镇痛作用

目的观察脐带间充质干细胞分泌的外泌体对骨性关节炎模型大鼠疼痛行为、软骨修复及背根神经节(DRG)中转录激活因子3(ATF-3)及生长相关蛋白43(GAP-43)表达的影响, 并探讨外泌体治疗关节炎疼痛的可能机制。方法采用随机数字表法将54只雄性SD大鼠分为假手术组、模型组和外泌体组, 每组18只, 除假手术组外, 其余2组均于左后肢膝关节腔注射4 mg/50 μl单碘乙酸钠(MIA)建立疼痛模型, 假手术组大鼠关节腔注射50 μl生理盐水作为对照。造模14天后, 假手术组和模型组大鼠左后肢膝关节腔注射50 μl生理盐水, 外泌体组大鼠注射50 μl外泌体。于造模前1天、造模后第7、14天及给药后第7、14、28天对各组大鼠机械痛阈和热痛阈进行测定;于测试后取出相应时间点DRG, 采用免疫蛋白印迹法检测ATF-3及GAP-43表达情况;并取出相应时间点各组大鼠膝关节, 采用苏木素伊红(HE)染色检测软骨修复情况。结果与模型组比较, 外泌体组在给药后第7天时, 机械痛阈值及热痛阈值均明显增加, 直到给药后第28天时差异均具有统计学意义(P<0.05);DRG水平ATF-3蛋白表达显著...     

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.     

Automated assessment of the substantia nigra on susceptibility map-weighted imaging using deep convolutional neural networks for diagnosis of Idiopathic Parkinson's disease

ObjectivesDespite its use in determining nigrostriatal degeneration, the lack of a consistent interpretation of nigrosome 1 susceptibility map-weighted imaging (SMwI) limits its generalized applicability. To implement and evaluate a diagnostic algorithm based on convolutional neural networks for interpreting nigrosome 1 SMwI for determining nigrostriatal degeneration in idiopathic Parkinson's disease (IPD).MethodsIn this retrospective study, we enrolled 267 IPD patients and 160 control subjects (125 patients with drug-induced parkinsonism and 35 healthy subjects) at our institute, and 24 IPD patients and 27 control subjects at three other institutes on approval of the local institutional review boards. Dopamine transporter imaging served as the reference standard for the presence or absence of abnormalities of nigrosome 1 on SMwI. Diagnostic performance was compared between visual assessment by an experienced neuroradiologist and the developed deep learning-based diagnostic algorithm in both internal and external datasets using a bootstrapping method with 10000 re-samples by the “pROC” package of R (version 1.16.2).ResultsThe area under the receiver operating characteristics curve (AUC) (95% confidence interval [CI]) per participant by the bootstrap method was not significantly different between visual assessment and the deep learning-based algorithm (internal validation, .9622 [0.8912–1.0000] versus 0.9534 [0.8779-0.9956], P = .1511; external validation, 0.9367 [0.8843-0.9802] versus 0.9208 [0.8634-0.9693], P = .6267), indicative of a comparable performance to visual assessment.ConclusionsOur deep learning-based algorithm for assessing abnormalities of nigrosome 1 on SMwI was found to have a comparable performance to that of an experienced neuroradiologist.