Membrane treatment of liquid wastes from radiological decontamination operations

The paper focuses on the evaluation of membrane filtration for the treatment of liquid radioactive streams generated in area decontamination operations. In this work, semi-permeable membranes were demonstrated to be effective reducing the volume of wastewater containing cesium and cobalt by two orders of a magnitude. The efficiency of membrane separation was enhanced by employing additives that enlarged the size of target radionuclide species and improved their rejection by the membranes. This was achieved by chelation with synthetic water-soluble polymers and by adsorption on micro particles of adsorbent coupled with micelle formation. The effect of wastewater composition and that of the radionuclide-binding additives on the volume reduction was investigated. Membrane treatment is expected to help simplify further processing and decrease disposal costs.     

Welfare of calves - 2. Increase in vertebral artery blood flow following exsanguination by neck sticking and evaluation of chest sticking as an alternative slaughter method

The role of the vertebral arteries in delaying loss of sensibility following neck sticking in slaughter calves was investigated. Vertebral artery blood flow was measured using probes before, during and after electrical stunning and slaughter. Systemic blood pressure, electrocorticogram, visually evoked responses and the occurrence of carotid occlusions were also recorded. When carotid occlusion occurred, the time to onset of brain failure was delayed based on the development of an isoelectric state. In addition, when carotid occlusion occurred the mean arterial blood pressure was sustained for longer following slaughter, and concurrently vertebral artery blood flow could be maintained at about 30% of its initial level for up to 3 min. In some animals vertebral artery flow increased substantially following sticking. When chest sticking was used no occlusion of vessels occurred, mean arterial blood pressure fell promptly (within 8 s) and the onset of an isoelectric state did not extend beyond one minute. In addition, visually evoked responses were not present after 5 s following chest sticking.     

Artificial convolution neural network techniques and applications for lung nodule detection

We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.     

Reduction of false positives in lung nodule detection using a two-level neural classification

The authors have developed a neural-digital computer-aided diagnosis system, based on a parameterized two-level convolution neural network (CNN) architecture and on a special multilabel output encoding procedure. The developed architecture was trained, tested, and evaluated specifically on the problem of diagnosis of lung cancer nodules found on digitized chest radiographs. The system performs automatic "suspect" localization, feature extraction, and diagnosis of a particular pattern-class aimed at a high degree of "true-positive fraction" detection and low "false-positive fraction" detection. In this paper, the authors aim at the presentation of the two-level neural classification method in reducing false-positives in their system. They employed receiver operating characteristics (ROC) method with the area under the ROC curve (A(z)) as the performance index to evaluate all the simulation results. The two-level CNN showed superior performance (A(z)=0.93) to the single-level CNN (A(z)=0.85). The proposed two-level CNN architecture is proven to be promising and to be extensible, problem-independent, and therefore, applicable to other medical or difficult diagnostic tasks in two-dimensional (2-D) image environments.     

Differentially expressed proteins of MCF-7 human breast cancer cells affected by Zilongjin, a complementary Chinese herbal medicine

Purpose : Zilongjin, a complementary Chinese herbal medicine, has been used to alleviate the adverse effects of chemotherapeutic drugs in cancer therapy. However, the mechanisms of anti‐cancer activity of Zilongjin are still largely unkonwn. Experimental design : First, the proteomic approach of combined 2‐DE and ESI‐MS/MS was used to investigate the effect of Zilongjin on the protein expression in MCF‐7 cells. Then, the differential expression of some proteins was confirmed by Western blot, cytoimmunofluoresecnce, and quantitative real‐time RT‐PCR analysis. Results : The identified proteins with differential expression, involved in such events as protein translation, cellular signal transduction, cytoskeleton formation and transportation, include seven downregulating proteins, such as Eukaryotic translation initiation factor 3 subunit I, Eukaryotic translation initiation factor 1A Y‐chromosomal, Ran‐specific GTPase‐activating protein, Ubiquitin‐conjugating enzyme E2 N, Tropomodulin‐3, Macrophage‐capping protein, and Tumor protein D52, as well as two upregulating proteins, HSP β‐1 and keratin18. Moreover, the differential expression of three proteins was confirmed. Conclusions and clinical relevance : (i) These results provide a new insight into the molecular mechanisms of Zilongjin on therapy for breast cancer. (ii) The application of the proteomic approaches will result in the more extended appreciation of Chinese medicine than those known at present.     

Top-down proteomic analysis by MALDI-TOF profiling: Concentration-independent biomarkers

Although numerous protein biomarkers have been correlated with advanced disease states, no new clinical assays have been developed. Goals often anticipate disease-specific protein changes that exceed values among healthy individuals, a property common to acute phase reactants. This review considers somewhat different approaches. It focuses on intact protein isoform ratios that present a biomarker without change in the total concentration of the protein. These will seldom be detected by peptide level analysis or by most antibody-based assays. For example, application of an inexpensive method to large sample groups resulted in observation of several polymorphisms, including the first structural polymorphism of apolipoprotein C1. Isoform distribution of this protein was altered and was eventually linked to increased obesity. Numerous other protein isoforms included C- and N-terminal proteolysis, changes of glycoisoform ratios and certain types of sulfhydryl oxidation. While many of these gave excellent statistical correlation with advanced disease, clinical utility was not apparent. More important may be that protein isoform ratios were very stable in each individual. Diagnosis by longitudinal analysis of the same individual might increase sensitivity of protein biomarkers by 20-fold or more. Protein changes that exceed the range of values found among healthy individuals may be uncommon.     

Proteomics integrated with Escherichia coli vector-based vaccines and antigen microarrays reveals the immunogenicity of a surface sialidase-like protein of Propionibacterium acnes

Proteomics is a powerful tool for the identification of proteins, which provides a basis for rational vaccine design. However, it is still a highly technical and time‐consuming task to examine a protein's immunogenicity utilizing traditional approaches. Here, we present a platform for effectively evaluating protein immunogenicity and antibody detection. A tetanus toxin C fragment (Tet‐c) was used as a representative antigen to establish this platform. A cell wall‐anchoring sialidase‐like protein (SLP) of Propionibacterium acnes was utilized to assess the efficacy of this platform. We constructed an Escherichia coli vector‐based vaccine by overexpressing Tet‐c or SLP in E. coli and utilized an intact particle of E. coli itself as a vaccine (E. coli Tet‐c or SLP vector). After ultraviolet (UV) irradiation, the E. coli vector‐based vaccines were administered intranasally into imprinting control region mice without adding exogenous adjuvants. For antibody detection, we fabricated antigen microarrays by printing with purified recombinant proteins including Tet‐c and SLP. Our results demonstrated that detectable antibodies were elicited in mice 6 weeks after intranasal administration of UV‐irradiated E. coli vector‐based vaccines. The antibody production of Tet‐c and SLP was significantly elevated after boosting. Notably, the platform with main benefits of using E. coli itself as a vaccine carrier provides a critical template for applied proteomics aimed at screening novel vaccine targets. In addition, the novel immunogenic SLP potentially serves as an antigen candidate for the development of vaccines targeting P. acnes‐associated diseases.