Effect of Co-culture of mesenchymal stem cell and glomerulus endothelial cell to promote endothelialization under optimized perfusion flow rate in whole renal ECM scaffold.

In recent era, many researches on implantable bio-artificial organs has been increased owing to large gap between donors and receivers. Comprehensive organ based researches on perfusion culture for cell injury using different flow rate have not been conducted at the cellular level. The present study investigated the co-culture of rat glomerulus endothelial cell (rGEC) and rat bone marrow mesenchymal stem cells (rBMSC) to develop micro vascularization in the kidney scaffolds culturing by bioreactor system. To obtain kidney scaffold, extracted rat kidneys were decellularized by 1% sodium dodecyl sulfate (SDS), 1% triton X-100, and distilled water. Expanded rGECs were injected through decellularized kidney scaffold artery and cultured using bioreactor system. Vascular endothelial cells adhered and proliferated on the renal ECM scaffold in the bioreactor system for 3, 7 and 14 days. Static, 1 â€‹ml/min and 2 â€‹ml/min flow rates (FR) were tested and among them, 1 â€‹ml/min flow rate was selected based on cell viability, glomerulus character, inflammation/endothelialization proteins expression level. However, the flow injury was still existed on primary cell cultured at vessel in kidney scaffold. Therefore, co-culture of rGEC â€‹+ â€‹rBMSC found suitable to possibly solve this problem and resulted increased cell proliferation and micro-vascularization in the glomerulus, reducing inflammation and cell death which induced by flow injury. The optimized perfusion rate under rGEC â€‹+ â€‹rBMSC co-culture conditions resulted in enhanced endocellularization to make ECM derived implantable renal scaffold and might be useful as a way of treatment of the acute renal failure.

Caloric restriction ameliorates acrolein-induced neurotoxicity in rats.

OBJECTIVE: Acrolein, a highly reactive unsaturated aldehyde, is a ubiquitous environmental pollutant and oxidative damage induced by acrolein is hypothesized to involve in the etiology of Alzheimer's disease (AD). Calorie restriction (CR) is the only non-genetic intervention that has consistently been verified to retard aging by ameliorating oxidative stress. Therefore, we investigated the effects of CR on acrolein-induced neurotoxicity in Sprague-Dawley (SD) rats. METHODS: A total of 45 weaned and specific-pathogen-free SD rats (male, weighing 180-220 g) were gavage-fed with acrolein (2.5 mg/kg/day) and fed ab libitum of 10 g/day or 7 g/day (representing 30% CR regimen), or gavage-fed with same volume of tap water and fed al libitum as vehicle control for 12 weeks. After behavioral test conducted by Morris Water Maze, SD rats were sacrificed and brain tissues were prepared for histochemical evaluation and Western blotting to detect alterations in oxidative stress, BDNF/TrkB pathway and key enzymes involved in amyloid precursor protein (APP) metabolism. RESULTS: Treatment with 30% CR in SD rats significantly attenuated acrolein-induced cognitive impairment. Oxidative damage including deletion of glutathione and superoxide dismutase and sharp rise in malondialdehyde were notably improved by 30% CR. Further study suggested that 30% CR showed protective effects against acrolein by modulating BDNF/TrkB signaling pathways. Moreover, 30% CR restored acrolein-induced changes of APP, ß-secretase, α-secretase and receptor for advanced glycation end products. CONCLUSION: These findings suggest that CR may provide a promising approach for the treatment of AD, targeting acrolein.

Structurally enhanced incremental neural learning for image classification with subgraph extraction.

In this paper, a structurally enhanced incremental neural learning technique is proposed to learn a discriminative codebook representation of images for effective image classification applications. In order to accommodate the relationships such as structures and distributions among visual words into the codebook learning process, we develop an online codebook graph learning method based on a novel structurally enhanced incremental learning technique, called as "visualization-induced self-organized incremental neural network (ViSOINN)". The hidden structural information in the images is embedded into the graph representation evolving dynamically with the adaptive and competitive learning mechanism. Afterwards, image features can be coded using a sub-graph extraction process based on the learned codebook graph, and a classifier is subsequently used to complete the image classification task. Compared with other codebook learning algorithms originated from the classical Bag-of-Features (BoF) model, ViSOINN holds the following advantages: (1) it learns codebook efficiently and effectively from a small training set; (2) it models the relationships among visual words in metric scaling fashion, so preserving high discriminative power; (3) it automatically learns the codebook without a fixed pre-defined size; and (4) it enhances and preserves better the structure of the data. These characteristics help to improve image classification performance and make it more suitable for handling large-scale image classification tasks. Experimental results on the widely used Caltech-101 and Caltech-256 benchmark datasets demonstrate that ViSOINN achieves markedly improved performance and reduces the computational cost considerably.

XCSc: a novel approach to clustering with extended classifier system.

In this paper, we propose a novel approach to clustering noisy and complex data sets based on the eXtend Classifier Systems (XCS). The proposed approach, termed XCSc, has three main processes: (a) a learning process to evolve the rule population, (b) a rule compacting process to remove redundant rules after the learning process, and (c) a rule merging process to deal with the overlapping rules that commonly occur between the clusters. In the first process, we have modified the clustering mechanisms of the current available XCS and developed a new accelerate learning method to improve the quality of the evolved rule population. In the second process, an effective rule compacting algorithm is utilized. The rule merging process is based on our newly proposed agglomerative hierarchical rule merging algorithm, which comprises the following steps: (i) all the generated rules are modeled by a graph, with each rule representing a node; (ii) the vertices in the graph are merged to form a number of sub-graphs (i.e. rule clusters) under some pre-defined criteria, which generates the final rule set to represent the clusters; (iii) each data is re-checked and assigned to a cluster that it belongs to, guided by the final rule set. In our experiments, we compared the proposed XCSc with CHAMELEON, a benchmark algorithm well known for its excellent performance, on a number of challenging data sets. The results show that the proposed approach outperforms CHAMELEON in the successful rate, and also demonstrates good stability.

[Implementation and application of a digital cytopathological lung cancer diagnosing system].

OBJECTIVE: To evaluate the efficacy of the digital cytopathological lung cancer diagnosing system (DCLCDS) utilizing the latest computer technologies (including reinforcement learning, image segmentation and classifier) and the cytopathological knowledge on lung cancer cells. METHODS: Separate the overlapped lung cancer cells in a slice image applying the improved deBoor-Cox B-Spline algorithm; Segment cell regions in a slice image using an image segmentation algorithm based on reinforcement learning; Ensemble different classifiers, including Decision Tree classifier, Support Vector Machine (SVM) classifier and Bayesian classifier, to achieve an accurate result of cytopathological lung cancer diagnosis. RESULTS: The accurate diagnosis rate for lung cancer identification of 224 images of small lung lesions aspiration biopsy from 120 cases randomly selected was 92.3%. The accurate diagnosis rate for type classification of lung cancer was 82.5%. The identification rate for abnormal nuclear cells was 71.6%. CONCLUSIONS: The DCLCDS achieves a high accuracy on cytopathological lung cancer diagnosis by solving some major problems on the cytology smears, including cell overlapping, uneven coloration and impurity. It provides a relatively objective, standard tool on cytopathological lung cancer diagnosis. It has good efficacy on early diagnosis of lung cancer.

Lung cancer cell identification based on artificial neural network ensembles.

An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.