Silver nanoparticles reduce the apoptosis induced by tumor necrosis factor-α

Silver nanoparticles (AgNPs) are widely known to have anti-inflammatory properties, but the exact mechanism underlying this anti-inflammatory effect is not clearly understood. Tumor necrosis factor-α (TNFα) is a major pro-inflammatory cytokine that is expressed in the early stage of cell inflammation and induces apoptosis by several known pathways. Our study aimed to investigate the effect of AgNPs on the response of lung epithelial cells to TNFα and the molecular mechanism of this response. Lung epithelial cell line NCI-H292 cells were exposed to AgNPs (5 µg/mL) and/or TNFα (20 ng/mL) for 24 h, then cellular uptake was analyzed using flow cytometry. Our results showed that AgNPs were taken up by cells in a dose-dependent manner and that the cellular uptake ratio of AgNPs was significantly increased in the presence of TNFα. Apoptosis assays indicated that exposure to AgNPs significantly decreased the apoptotic effect of TNFα. Confocal microscopy was used to localize the tumor necrosis factor receptor 1 (TNFR1) and revealed that TNFR1 localized on the surface of cells exposed to TNFα. In contrast, TNFR1 localized inside cells exposed to both AgNPs and TNFα, with very few receptors scattered on the cell membrane. The results indicated that AgNPs reduced the cell surface TNFR1 expression level. The results suggested that the reduction of surface TNFR1 reduced cellular response to TNFα, resulting in an anti-apoptotic effect.     

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.     

一种新型模拟肺模型的实验研究

建立了一种新型的可用于呼吸机性能测试的玻璃瓶模拟肺模型,通过物理推导得到模拟肺的顺应性与玻璃瓶的大小及其内部温度有关,并用相关的实验证明了模型的可行性.研究表明:在恒温条件下其顺应性与玻璃瓶的大小成比例关系;在玻璃瓶内放入一定量的铜网可以使其在一定的频率下保持恒温状态,这种模拟肺可用于精确测试呼吸机特性.     

A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features

In this paper, a novel computer-based approach is proposed for malignancy risk assessment of thyroid nodules in ultrasound images. The proposed approach is based on boundary features and is motivated by the correlation which has been addressed in medical literature between nodule boundary irregularity and malignancy risk. In addition, local echogenicity variance is utilized so as to incorporate information associated with local echogenicity distribution within nodule boundary neighborhood. Such information is valuable for the discrimination of high-risk nodules with blurred boundaries from medium-risk nodules with regular boundaries. Analysis of variance is performed, indicating that each boundary feature under study provides statistically significant information for the discrimination of thyroid nodules in ultrasound images, in terms of malignancy risk. k-nearest neighbor and support vector machine classifiers are employed for the classification tasks, utilizing feature vectors derived from all combinations of features under study. The classification results are evaluated with the use of the receiver operating characteristic. It is derived that the proposed approach is capable of discriminating between medium-risk and high-risk nodules, obtaining an area under curve, which reaches 0.95.     

Diagnosis of lung nodule using Moran’s index and Geary’s coefficient in computerized tomography images

This paper analyzes the application of Moran’s index and Geary’s coefficient to the characterization of lung nodules as malignant or benign in computerized tomography images. The characterization method is based on a process that verifies which combination of measures, from the proposed measures, has been best able to discriminate between the benign and malignant nodules using stepwise discriminant analysis. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these in predicting the classification for each nodule. In order to verify this application we also describe tests that were carried out using a sample of 36 nodules: 29 benign and 7 malignant. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. The two analyzed functions and its combinations have provided above 90% of accuracy and a value area under receiver operation characteristic (ROC) curve above 0.85, that indicates a promising potential to be used as nodules signature measures. The preliminary results of this approach are very encouraging in characterizing nodules using the two functions presented.

Scanning electron microscopic observations on micro-organisms in the root nodules of Tribulus terrestris L. (Zygophyllaceae)

Scanning electron microscopic observations were made on the micro-organisms of root nodules of Tribulus terrestris L. The results showed that nodules of T. terrestris contained dual infection consisting of Rhizobium sp. and Newmania karachiensis. Based on these observations, T. terrestris should be grouped with nonlegume Parasponia-type bacterial nodules.     

蛙皮素受体亚型-3在肺疾病中的研究进展

蛙皮素受体亚型-3（BRS-3）属G蛋白偶联受体,至今未发现天然配体。BRS-3具有广泛的生物学作用,可调控神经内分泌功能、能量代谢及细胞生长增殖。在啮齿类动物及哺乳动物体内都有分布,大多数组织表达水平很低（包括正常肺组织）,但在某些肺部疾病及胎肺中表达增加。就BRS-3受体特点、激活后信号转导机制及其与肺肿瘤、肺发育、慢性炎症性肺病的联系作一综述。     

大气混合污染物对大鼠肺脏中血红素氧合酶1表达的影响

通过观察大气污染物引起大鼠肺损伤的病理组织学变化,分析肺组织内血红素氧合酶1（HO-1）的表达量的变化,为大气污染所致肺损伤的生物学标记物的研究提供科学依据。实验方法如下：将30只Wistar大鼠,随机分为3个实验组（3d、7d、30d组）和3个对照组（3d、7d、30d组）,对实验组大鼠染尘染毒后提取所有大鼠肺组织,观察其病理组织学变化;RT-PCR技术检测各组肺组织中HO-1的mRNA表达;Western-blot技术检测各组肺组织中HO-1蛋白的表达。结果显示：实验组（7d及30d）的肺组织病理形态学评分显著高于对照组（7d及30d）,实验组（7d及30d）大鼠肺组织HO-1mRNA表达与对照组相比,具有显著性差异（P均〈0.05）。随着吸入大气混合污染物的时间延长,肺组织内的HO-1蛋白表达水平逐渐增高,实验组（7d及30d）与对照组（7d及30d）相比,差异有显著性意义（P〈0.05）。在吸入大气混合污染物后早期（7d）,HO-1转录水平升高。持续吸入混合大气污染物（30d）,大鼠肺组织出现明显的病理组织学变化,HO-1转录水平、蛋白表达水平均比对照组明显升高。得出结论：可将HO-1作为大气混合污染物所致的肺损伤的早期生物学标记物;采取某些手段增加肺组织HO-1的表达,可能会减少混合大气污染物对肺组织造成的损伤。     

基于局部闭值和聚类中心迭代的肺结节检测算法

肺部疾病通常以肺结节的形式表现出来。为了对肺部疾病进行诊断治疗,需要对肺结节进行准确的检测。提出了基于局部阈值和聚类中心迭代的肺结节检测算法。首先,对肺实质图像采用局部阈值算法,提取感兴趣区域(ROIs),并且计算ROIs的形态特征、灰度特征和纹理特征;其次,结合规则、聚类中心迭代和欧式距离,对ROIs进行分类。实验结果表明,所提算法能够较好地检测出孤立性结节、低对比度结节和粘连肺壁结节。     

大洋多金属结核的非冶金加工应用

基于大洋多金属结核具有的特殊物理化学性质,本文重点介绍国内外开展的大洋多金属结核非冶金加工利用的研究情况。