A review of the health effects and exposure-responsible relationship of diesel particulate matter for underground mines

The increasing use of diesel-powered equipment in confined spaces(underground mines) has the potential to over expose underground miners under the threat of diesel particulate matter(DPM). Miners in underground mines can be exposed to DPM concentrations far more than works in other industries. A great number of animal and epidemiological studies have shown that both short-term and long-term DPM exposure have adverse health effect. Based on reviews of related studies, especially some recent evidence, this paper investigated the long and short-term health effects based on animal studies and epidemiological studies. The exposure-response relationship studies were also explored and compared to the current DPM regulation or standards in some countries. This paper found that the DPM health effect studies specifically for miners are not sufficient to draw solid conclusions, and a recommendation limit of DPM concentration can be put in place for better protection of miners from DPM health risk. Current animal studies lack the use of species that have similar lung functions as human for understanding the cancer mode of action in human. And finally, the DPM health hazard will continue to be a challenging topic before the mode of action and reliable exposure-response relationship are established.     

提高腺病毒对肿瘤细胞转染效率的药物效果评价

为了解决在肿瘤基因治疗中腺病毒载体介导的转染细胞效率低的问题,高效、低毒的转染试剂为其提供了一条新途径.采用正十二烷基-β-D-麦芽糖苷(n-Dodecyl-β-D-Maltoside,DDM)、DC-Chol、SDS联合腺病毒e GFP-Ad转染肝癌细胞Hep G2、食管癌细胞EC109及人正常肝细胞L-02与食管正常细胞Het-1A,48 h后观察绿色荧光蛋白表达情况并计阳性细胞率,评价联合转染试剂处理提高e GFP-Ad转染肿瘤细胞的效率.实验结果表明,DDM(3μg/m L)、DC-Chol(1 000μg/m L)、SDS(0.5μg/m L)联合e GFP-Ad转染肿瘤细胞Hep G2和EC109的效率分别可达85%和78%、80%和76%、45%和41%,与单独e GFP-Ad相比,转染效率分别提高16倍和14.6倍、15倍和14.2倍、8倍和7.2倍;且与正常细胞相比,DDM能显著提高对肿瘤细胞的转染效率,二者差异具有高度统计学意义.作为非脂质体类试剂,DDM能显著提高腺病毒对肿瘤细胞的转染效率,为进一步提高腺病毒介导的肿瘤基因治疗提供有效方法与技术参考.     

神经生长因子及其前体蛋白在晚期胰腺癌组织中的表达研究

采用2011年1月-2014年6月在云南省第一人民医院确诊并行手术治疗的58例胰腺癌患者,切除部分肿瘤组织作为实验标本,癌旁组织作为对照,利用免疫组化及Western blot检测晚期胰腺癌组织内神经生长因子(nerve growth factor,NGF)及神经生长因子前体(precursor of nerve growth factor,pro NGF)的表达,Gel-Pro analyzer 4图像分析系统分析Western条带密度.试验重复3次,取3次IOD值行统计学分析.免疫组化及Western blot结果显示肿瘤组织NGF的表达较癌旁组织明显增高,而pro NGF的表达较癌旁组织明显降低.半定量Western blot显示癌旁组织和肿瘤组织NGF表达的IOD值分别为113.33±8.54和285.00±15.13,pro NGF表达的IOD值分别为245.00±7.55和86.33±7.37,差异有统计学意义(P0.05).因此,晚期胰腺癌患者肿瘤组织内NGF/pro NGF的比例发生改变,组织内NGF蓄积,而pro NGF减少,这可能是导致胰腺癌肿瘤细胞凋亡减少,生长、浸润和迁徙功能增强的重要机制之一.     

Network views for personalized medicine

Clinical Proteomics has traveled a long way pinpointing potential biomarkers for a variety of diseases. However, the absence of clinical implementation of proteomics findings has led to a frank evaluation and reconsideration of applied practices in biomarker discovery, recruitment of technological tools for biomarker verification and generation of new guidelines for data reporting. Nevertheless, considering the need for vast clinical resources for biomarker validation, the frequent lack of clear definitions of contexts of use, in combination to the biomarker “high offer,” progress toward biomarker implementation will even more require the adoption of an extensive open-minded approach: disease-focused networks are needed to ensure rapid exchange of information, initiation of appropriate studies, parallel validation of multiple biomarkers and sharing of valuable clinical resources. This viewpoint article targets to reflect on these issues and advocates the added value of multidisciplinary networks in biomarker development using bladder cancer as a paradigm.     

Clinical applications of capillary electrophoresis coupled to mass spectrometry in biomarker discovery: Focus on bladder cancer

A major requirement in the application of proteins as clinical biomarkers is that they provide a highly sensitive and specific result in disease assessment. Since single biomarkers are generally of limited accuracy, a group or panel of well-characterized biomarkers appears appropriate, providing a more robust and sensitive MS-based analytical platform. CE coupled to MS has been successfully used in biomarker discovery and application, as it enables the selective detection of peptides and small proteins, combining the high separation capacity of CE with the advanced sensitivity of MS. CE-MS allows the characterization of highly complex samples (such as urine, plasma, and other biofluids) in a consistent and reproducible way. It has a range of applications, many focusing especially in studies on urinary peptide biomarkers in kidney and cardiovascular diseases. Another major field of interest has been malignancy of the genitourinary system. In the first part of this review, we cover technical aspects and performance characteristics of CE-MS, with special focus on the requirements for biomarker discovery and clinical application. In the second part, we review the potential and development of CE-MS in the management of genitourinary cancers, especially bladder cancer. CE-MS has been employed in several studies aimed at discovering biomarkers for bladder cancer that may be useful in diagnosis, monitoring for recurrence, and prediction of the risk for the muscle-invasive stage. In the last part of the review, we discuss current challenges and provide an outlook for ongoing and possible future developments.     

Construction of human monoclonal single-chain Fv antibodies against small-cell lung cancer by phage display libraries derived from cell-immunized SCID mice engrafted with human peripheral blood lymphocytes

In this study, we describe a phage display strategy to obtain human monoclonal single-chain Fv (scFv) antibodies binding target cancer cell surface proteins. By developing a cancer cell immunization protocol for SCID mice engrafted with human peripheral blood lymphocytes in combination with an antibody phage display method, we have isolated phage antibodies binding small-cell lung cancer cell line H889 by subtractive selection. One of the isolated scFv antibodies, 12EAb, recognized the E2 component of pyruvate dehydrogenase complex (PDC-E2) by immunoprecipitation according to MALDI-TOF MS analysis. Furthermore, we have confirmed the plasma membrane localization of PDC-E2 in small-cell lung cancer cells by immunocytochemistry and cell surface protein biotinylation, although PDC-E2 is usually located in the mitochondrial matrix. These results, including unique localization of identified antigens, were obtained by proteomic approaches. The present methods can be applied to generate human monoclonal scFv antibodies against tumor cells and to identify new molecular targets for immunotherapy and markers for diagnosis.     

The Golden Touch by Light: A Finely Engineered Luminogen Empowering High Photoactivatable and Photodynamic Efficiency for Cancer Phototheranostics

Photoactivatable agent is a powerful tool in biomedicine studies due to high-precision spatiotemporal control of light. However, those previously reported agents generally suffer from short wavelength, fluorescence self-quenching effect, and the lack of photosensitizing property, which severely restrict their practical applications. To address these issues, molecular engineering of 1,4-dihydropyridine derivatives is conducted to obtain an optimized agent, namely TPA-DHPy-Py, which exhibits low oxidation potential, high photoactivation efficiency, and excellent type I/II combined photodynamic activity. Concurrently, its photoactivated counterpart is featured by aggregation-induced near-infrared emission and remarkable reactive oxygen species (ROS) production efficiency. Upon photoactivation, TPA-DHPy-Py is capable of precisely identifying cancer cells from co-culturing cancer cells and normal cells without the assistance of any extra targeting units, and in situ monitoring lipid droplets and endoplasmic reticulum alteration under ROS stress, as well as achieving fluorescent visualization of tumor in vivo with supremely high imaging contrast. Furthermore, the unprecedented performance on photodynamic cancer therapy is demonstrated by the significant inhibition of tumor growth. Therefore, the photoactivatable TPA-DHPy-Py with dual-organelle-targeted and excellent photodynamic activity associated with self-monitoring ability is highly promising for cancer theranostics in clinical trials.     

Horizontal Voting Ensemble Based Predictive Modeling System for ColonCancer

Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture. Ten thousand (10000) microscopic images are taken to test the classification performance of the proposed PMS with the HVE method. The microscopic images obtained from the colon tissues are classified into ACA or benign by the proposed PMS. Results prove that the proposed PMS has ~8% performance improvement over the architecture without using the HVE method. The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2% of sensitivity and 99.4% of specificity. The overall accuracy of the proposed PMS is 99.3%, and without using the HVE method, it is only 91.3%.     

Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

Breast cancer is one of the deadly diseases prevailing in women. Earlier detection and diagnosis might prevent the death rate. Effective diagnosis of breast cancer remains a significant challenge, and early diagnosis is essential to avoid the most severe manifestations of the disease. The existing systems have computational complexity and classification accuracy problems over various breast cancer databases. In order to overcome the above-mentioned issues, this work introduces an efficient classification and segmentation process. Hence, there is a requirement for developing a fully automatic methodology for screening the cancer regions. This paper develops a fully automated method for breast cancer detection and segmentation utilizing Adaptive Neuro Fuzzy Inference System (ANFIS) classification technique. This proposed technique comprises preprocessing, feature extraction, classifications, and segmentation stages. Here, the wavelet-based enhancement method has been employed as the preprocessing method. The texture and statistical features have been extracted from the enhanced image. Then, the ANFIS classification algorithm is used to classify the mammogram image into normal, benign, and malignant cases. Then, morphological processing is performed on malignant mammogram images to segment cancer regions. Performance analysis and comparisons are made with conventional methods. The experimental result proves that the proposed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.