积极降压对脑小血管病防治策略的影响

2017年,美国心脏病学会和美国心脏协会提出将高血压诊断标准降为130/80 mm Hg (1 mm Hg=0. 133 kPa),这与其他指南有所不同。降压治疗的核心目标在于靶器官的保护,近年来降压治疗与脑小血管病预防的相关问题日益受到临床重视。目前研究结果表明,降压治疗对腔隙性卒中患者的二级预防以及白质病变进展的预防可能有一定积极意义,但确切的降压目标值并未确定。血压与临床结局可能呈现J型关系,血压过低或过高可能均有害,而取得最大获益的降压目标有待进一步探索。     

α_1-抗胰蛋白酶的制备及其防治急性肺损伤的疗效

目的　以FIV 1为原料 ,制备较高纯度α1 AT制剂 ,用该制剂干预急性肺损伤动物模型作疗效考核。方法　FIV 1抽提液经PEG沉淀 ,离子交换 ,病毒灭活、超滤、除菌、分装 ,冻干制备α1 AT制剂。用急性肺损伤动物模型 ,比较静脉注射与雾化吸入α1 AT的治疗效果。结果　 3批制剂纯度 >70 % ,无菌、热原、安全试验均符合生物制品规程要求。静脉注射或雾化吸入 ,可降低由内毒素诱发急性肺损伤程度。结论　α1 AT制备工艺适合大规模生产。在防治急性肺损伤时有一定效果     

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.     

Evaluation of cartilage repair tissue after biomaterial implantation in rat patella by using T2 mapping

To evaluate the ability of MR T2 mapping (8.5 T) to characterize ex vivo longitudinally, morphologically and quantitatively, alginate-based tissue engineering in a rat model of patellar cartilage chondral focal defect. Calibrated rat patellar cartilage defects (1.3 mm) were created at day 0 (D0) and alginate sponge with (Sp/C+) or without (Sp/C–) autologous chondrocytes were implanted. Animals were sacrificed sequentially at D20, D40 and D60 after surgery and dissected patellae underwent MRI exploration (8.5 T). T2 values were calculated from eight SE images by using nonlinear least-squares curve fitting on a pixel-by-pixel basis (constant repetition time of 1.5 s, eight different echo times: 5.5, 7.5, 10.5, 12.5, 15.0, 20.0, 25.0 and 30.0 ms). On the T2 map, acquired in a transversal plane through the repair zone, global T2 values and zonal variation of T2 values of repair tissue were evaluated versus control group and compared with macroscopic score and histological studies (toluidine blue, sirius red and hematoxylin-eosin). Partial, total and hypertrophic repair patterns were identified. At D40 and D60, Sp/C+ group was characterized by a higher proportion of total repair in comparison to Sp/C– group. At D60, the proportion of hypertrophic repair was two fold in Sp/C– group versus Sp/C+ group. As confirmed morphologically and histologically, the T2 map also permitted the distinction of three types of repair tissue: total, partial and hypertrophic. Total repair tissue was characterized by high T2 values versus normal cartilage (p<0.05). Zonal variation, reflecting the collagen network organization, appeared only at D60 for Sp/C+ group (p<0.05). Hypertrophic tissue, mainly observed at D60, presented high T2 global values without zonal variation with cartilage depth. These results confirm the potency of the MR T2 map (8.5 T) to characterize macroscopically and microscopically the patterns of the scaffold guided-tissue repair of a focal chondral lesion in the rat patella (total, partial and hypertrophic). On T2 map, three parameters (i.e. MRI macroscopic pattern, T2 global values and zonal variation of T2 values) permit to characterize chondral repair tissue, as a virtual biopsy.     

基于小波系数Hu矩的生物组织损伤监测方法

提出了一种基于B超图像的小波系数Hu矩特征值并结合支持向量机监测生物组织损伤的方法。利用高强度聚焦超声(HIFU)对新鲜离体猪肉组织进行辐照,实时获取辐照前后的B超图像,并对其进行预处理获取减影图像。提取减影图像的Hu矩、小波系数均值和基于小波系数的Hu矩3个特征值,分别利用支持向量机对生物组织样本进行学习、分类处理。结果表明:小波系数Hu矩特征值比Hu矩和小波系数均值的总辨识率分别高出2.70%和2.05%,从而可以更有效地监测HIFU治疗中生物组织损伤情况。该方法可以帮助临床医生客观地监控HIFU治疗过程,对提高HIFU疗效有实际意义。     

深度学习多部位病灶检测与分割

目的 多部位病灶具有大小各异和类型多样的特点,对其准确检测和分割具有一定的难度。为此,本文设计了一种2.5D深度卷积神经网络模型,实现对多种病灶类型的计算机断层扫描（computed tomography,CT）图像的病灶检测与分割。方法 利用密集卷积网络和双向特征金字塔网络组成的骨干网络提取图像中的多尺度和多维度信息,输入为带有标注的中央切片和提供空间信息的相邻切片共同组合而成的CT切片组。将融合空间信息的特征图送入区域建议网络并生成候选区域样本,再由多阈值级联网络组成的Cascade R-CNN（region convolutional neural networks）筛选高质量样本送入检测与分割分支进行训练。结果 本文模型在DeepLesion数据集上进行验证。结果表明,在测试集上的平均检测精度为83.15%,分割预测结果与真实标签的端点平均距离误差为1.27 mm,直径平均误差为1.69 mm,分割性能优于MULAN（multitask universal lesion analysis network for joint lesion detection,tagging and segmentation）和Auto RECIST（response evaluation criteria in solid tumors）,且推断每幅图像平均时间花费仅91.7 ms。结论 对于多种部位的CT图像,本文模型取得良好的检测与分割性能,并且预测时间花费较少,适用病变类别与DeepLesion数据集类似的CT图像实现病灶检测与分割。本文模型在一定程度上能满足医疗人员利用计算机分析多部位CT图像的需求。     

Short communication: Genetic characterization of digital cushion thickness

Dairy cow lameness is a serious animal welfare issue. It is also a significant cause of economic losses, reducing reproductive efficiency and milk production and increasing culling rates. The digital cushion is a complex structure composed mostly of adipose tissue located underneath the distal phalanx and has recently been phenotypically associated with incidence of claw horn disruption lesions (CHDL); namely, sole ulcers and white line disease. The objective of this study was to characterize digital cushion thickness genetically and to investigate its association with body condition score (BCS), locomotion score (LOCO), CHDL, and milk production. Data were collected from 1 large closely monitored commercial dairy farm located in upstate New York; 923 dairy cows were used. Before trimming, the following data were collected by a member of the research team: BCS, cow height measurement, and LOCO. Presence or not of CHDL (sole ulcer or white line disease, or both) was recorded at trimming. Immediately after the cows were hoof trimmed, they underwent digital sonographic B-mode examination for the measurement of digital cushion thickness. Factors such as parity number, stage of lactation, calving date, mature-equivalent 305-d milk yield (ME305MY), and pedigree information were obtained from the farm’s dairy management software (DairyCOMP 305; Valley Agricultural Software, Tulare, CA). Univariate animal models were used to obtain variance component estimations for each studied trait (CHDL, BCS, digital cushion thickness average, LOCO, height, and ME305MY) and a 6-variate analysis was conducted to estimate the genetic, residual, and phenotypic correlations between the studied traits. The heritability estimate of DCTA was 0.33 ± 0.09, whereas a statistically significant genetic correlation was estimated between DCTA and CHDL (−0.60 ± 0.29). Of the other genetic correlations, significant estimates were derived for BCS with LOCO (−0.49 ± 0.19) and ME305MY (−0.48 ± 0.20). Digital cushion thickness is moderately heritable and genetically strongly correlated with CHDL.     

Cerebral Hyperperfusion after Revascularization Inhibits Development of Cerebral Ischemic Lesions Due to Artery-to-Artery Emboli during Carotid Exposure in Endarterectomy for Patients with Preoperative Cerebral Hemodynamic Insufficiency: Revisiting the “Impaired Clearance of Emboli” Concept

The purpose of the present study was to determine whether cerebral hyperperfusion after revascularization inhibits development of cerebral ischemic lesions due to artery-to-artery emboli during exposure of the carotid arteries in carotid endarterectomy (CEA). In patients undergoing CEA for internal carotid artery stenosis (≥70%), cerebral blood flow (CBF) was measured using single-photon emission computed tomography (SPECT) before and immediately after CEA. Microembolic signals (MES) were identified using transcranial Doppler during carotid exposure. Diffusion-weighted magnetic resonance imaging (DWI) was performed within 24 h after surgery. Of 32 patients with a combination of reduced cerebrovascular reactivity to acetazolamide on preoperative brain perfusion SPECT and MES during carotid exposure, 14 (44%) showed cerebral hyperperfusion (defined as postoperative CBF increase ≥100% compared with preoperative values), and 16 (50%) developed DWI-characterized postoperative cerebral ischemic lesions. Postoperative cerebral hyperperfusion was significantly associated with the absence of DWI-characterized postoperative cerebral ischemic lesions (95% confidence interval, 0.001–0.179; p = 0.0009). These data suggest that cerebral hyperperfusion after revascularization inhibits development of cerebral ischemic lesions due to artery-to-artery emboli during carotid exposure in CEA, supporting the “impaired clearance of emboli” concept. Blood pressure elevation following carotid declamping would be effective when embolism not accompanied by cerebral hyperperfusion occurs during CEA.     

筛选诱导烟草对烟草花叶病毒产生系统抗性的真菌

真菌产生的代谢产物可激活烟草体内抗病防御相兲酶的表达,诱导烟草产生系统抗性,增强烟草对病毒的抗性。本研究对38种植物病原真菌及45种分离自（恩施、襄樊）土壤、烟叶中的真菌进行了过敏性反应实验和系统抗性实验。结果表明,筛选出了对烟草花叶病毒抗性较强的真菌。在烟草上产生过敏反应的菌株有32个;系统抗性实验中对烟草花叶病毒抑制率较高的菌株有16个,其中,油茶炭疽菌、棉花黄萎病菌、esf-13、esf-3、小麦赤霉、立枯丝核菌、E1、esf-6、xfpf-6等菌株的抗性较高,枯斑抑制率均大于70%,最高可达96.44%。抗性较高的真菌可开发为烟草病毒病诱抗剂。     

A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network

The principle restorative step in the treatment of ischemic stroke depends on how fast the lesion is delineated from the Magnetic Resonance Imaging (MRI) images. This will serve as a vital aid to estimate the extent of damage caused to the brain cells. However, manual delineation of the lesion is time-consuming and it is subjected to intra-observer and inter-observer variability. Most of the existing methods for ischemic lesion segmentation rely on extracting handcrafted features followed by application of a machine learning algorithm. Identifying such features demand multi-domain expertise in Neuro-radiology as well as Image processing. This can be accomplished by learning the features automatically using Convolutional Neural Network (CNN). To perform segmentation, the spatial arrangement of pixel needs to be preserved in addition to learning local features of an image. Hence, a deep supervised Fully Convolutional Network (FCN) is presented in this work to segment the ischemic lesion. The highlight of this research is the application of Leaky Rectified Linear Unit activation in the last two layers of the network for a precise reconstruction of the ischemic lesion. By doing so, the network was able to learn additional features which are not considered in the existing U-Net architecture. Also, an extensive analysis was conducted in this research to select optimal hyper-parameters for training the FCN. A mean segmentation accuracy of 0.70 has been achieved from the experiments conducted on ISLES 2015 dataset. Experimental observations show that our proposed FCN method is 10% better than the existing works in terms of Dice Coefficient.