Preoptimized phage cocktail for use in aerosols against nosocomial transmission of carbapenem-resistant Acinetobacter baumannii: A 3-year prospective intervention study.

Using bacteriophages (phages) as environmental sanitizers has been recognized as a potential alternative method to remove bacterial contamination in vitro; however, very few studies are available on the application of phages for infection control in hospitals. Here, we performed a 3-year prospective intervention study using aerosolized phage cocktails as biocontrol agents against carbapenem-resistant Acinetobacter baumannii (CRAB) infection in the hospital. When a CRAB-infected patient was identified in an intensive care unit (ICU), their surrounding environment was chosen for phage aerosol decontamination. Before decontamination, 501 clinical specimens from the patients were subjected to antibiotic resistance analysis and phage typing. The optimal phage cocktails were a combination of different phage families or were constructed by next-evolutionary phage typing with the highest score for the host lysis zone to prevent the development of environmental CRAB phage resistance. The phage infection percentage of the antibiotic-resistant A. baumannii strains was 97.1%, whereas the infection percentage in the antibiotic-susceptible strains was 79.3%. During the phage decontamination periods from 2017 to 2019, the percentage of carbapenem-resistant A. baumannii in test ICUs decreased significantly from 65.3% to 55%. The rate of new acquisitions of CRAB infection over the three years was 4.4 per 1000 patient-days, which was significantly lower than that in the control wards (8.9 per 1000 patient-days) where phage decontamination had never been performed. In conclusion, our results support the potential of phage cocktails to decrease CRAB infection rates, and the aerosol generation process may make this approach more comprehensive and time-saving.

A Novel 3D Culture Scaffold to Shorten Development Time for Multicellular Tumor Spheroids.

Multicellular tumor spheroids and tumoroids are considered ideal in vitro models that reflect the features of the tumor microenvironment. Biomimetic components resembling the extracellular matrix form scaffolds to provide structure to 3-dimensional (3D) culture systems, supporting the growth of both spheroids and tumoroids. Although Matrigel has long been used to support 3D culture systems, batch variations, component complexity, and the use of components derived from tumors are complicating factors. To address these issues, we developed the ACD 3D culture system to provide better control and consistency. We evaluated spheroid and tumoroid formation using the ACD 3D culture system, including the assessment of cell viability and cancer marker expression. Under ACD 3D culture conditions, spheroids derived from cancer cell lines exhibited cancer stem cell characteristics, including a sphere-forming size and the expression of stem cell marker genes. The ACD 3D culture system was also able to support patient-derived primary cells and organoid cell cultures, displaying adequate cell growth, appropriate morphology, and resistance to oxaliplatin treatment. These spheroids could also be used for drug screening purposes. In conclusion, the ACD 3D culture system represents an efficient tool for basic cancer research and therapeutic development.

[Drug Discrimination Method Based on Near Infrared Reflectance Spectrum and Balance Cascading Sparse Representation Based Classification].

The combination of near infrared spectrum and pattern recognition methods has a wide application prospect in rapid and nondestructive supervision and management of drugs. The traditional identification methods regard the smallest error rate as the goal while the imbalance of classes is ignored. This makes the positive class is overwhelming covered by the negative class and reduces its effect for the classifier, so that the classification results tend to recognize the negative class correctly, which severely affects the identification accuracy. In this paper, we mainly studied the class imbalance problems of true or false drugs via infrared spectral data of its, and then propose a balance cascading and sparse representation based classification method (BC-SRC) by combining the Balance Cascading with SRC. We sampling majority samples from the majority class for several times, which has the same size as minority samples and the majority samples we sampled can contain all the majority class samples entirely (sampling times is ceiling the result of majority samples number divide minority samples number). We can get sets of results, and then obtain the final predict labels form those results. Experiments of three databases achieved on Matlab2012a shows that the method is effective. From the experimental results, it can be seen that the method is superior to the commonly used Partial Least Squares (PLS), Extreme Learning Machine (ELM) and BP. Particularly, for the imbalanced databases, when the imbalance factor is greater than 10, the proposed method has more stable performance with higher classification accuracy than the existing ones mentioned above.

[Sparse Denoising Autoencoder Application in Identification of Counterfeit Pharmaceutical].

Near-infrared(NIR)As a fast and non-destructive testing technology, spectroscopy techniques is very suitable for pharmaceutical discrimination. Autoencoder network, as a hot research topic, has drawn widespread attention in machine learning research in recent years. Compared with traditional surface learning algorithm models, Autoencoder network has more powerful modeling capability as a typical deep networks model. Based on the unsupervised greedy layer-wise pre-training, autoencoder trains the network layer by layer while minimizing the error in reconstructing. Each layer is pre-trained with an unsupervised learning algorithm, learning a nonlinear transformation of the input of each layer which is the output of the previous layer. Pre-whitening process could get the inner structural features of the data more effectively. The supervised fine-tuning is followed with the unsupervised pre-training which sets the stage for a final training phase. The deep architecture is fine-tuned with respect to a supervised training criterion with gradient-based optimization. In this paper, firstly, the preprocessing step and pre-whitening transformation were used to treat near-infrared spectroscopy data of erythromycin ethylsuccinate, The pre-whitening transformation would reduce the correlation of the features, which gave each feature the same variance. Experimental results showed that the pre-whitening process had improved the classification accuracy of Sparse Denoising Autoencoder (SDAE) effectively. The SDAE with two hidden layers combined with pre-whitening was used to build the classification model for the identification of counterfeit pharmaceutical. The BP neural networks was compared with SVM algorithm for the classification accuracy and mean absolute difference (MAD). SDAE algorithm had higher classification accuracy than BP neural networks which had the same network structure with the SDAE networks, and SDAE algorithm also performed better than the SVM algorithm when the train datasets achieved a certain amount. As to the generalization performances, SDAE algorithm had less mean absolute difference of classification accuracy than SVM and BP Neural Networks. This result showed that SDAE algorithm could be effectively used to discriminate the counterfeit pharmaceutical.

[Drug discrimination by near infrared spectroscopy based on summation wavelet extreme learning machine].

As an effective technique to identify counterfeit drugs, Near Infrared Spectroscopy has been successfully used in the drug management of grass-roots units, with classifier modeling of Pattern Recognition. Due to a major disadvantage of the characteristic overlap and complexity, the wide bandwidth and the weak absorption of the Spectroscopy signals, it seems difficult to give a satisfactory solutions for the modeling problem. To address those problems, in the present paper, a summation wavelet extreme learning machine algorithm (SWELM(CS)) combined with Cuckoo research was adopted for drug discrimination by NIRS. Specifically, Extreme Learning Machine (ELM) was selected as the classifier model because of its properties of fast learning and insensitivity, to improve the accuracy and generalization performances of the classifier model; An inverse hyperbolic sine and a Morlet-wavelet are used as dual activation functions to improve convergence speed, and a combination of activation functions makes the network more adequate to deal with dynamic systems; Due to ELM' s weights and hidden layer threshold generated randomly, it leads to network instability, so Cuckoo Search was adapted to optimize model parameters; SWELM(CS) improves stability of the classifier model. Besides, SWELM(CS) is based on the ELM algorithm for fast learning and insensitivity; the dual activation functions and proper choice of activation functions enhances the capability of the network to face low and high frequency signals simultaneously; it has high stability of classification by Cuckoo Research. This compact structure of the dual activation functions constitutes a kernel framework by extracting signal features and signal simultaneously, which can be generalized to other machine learning fields to obtain a good accuracy and generalization performances. Drug samples of near in- frared spectroscopy produced by Xian-Janssen Pharmaceutical Ltd were adopted as the main objects in this paper. Experiments for binary classification and multi-label classification were conducted, and the conclusion proved that the proposed method has more stable performance, higher classification accuracy and lower sensitivity to training samples than the existing ones, such as the BP neural network, ELM and-ELM by particle swarm optimization.

A highly efficient and accurate method of detecting and subtyping Influenza A pdm H1N1 and H3N2 viruses with newly emerging mutations in the matrix gene in Eastern Taiwan.

The rapid identification of Influenza A virus and its variants, which cause severe respiratory diseases, is imperative to providing timely treatment and improving patient outcomes. Conventionally, two separate assays (total test duration of up to 6 h) are required to initially differentiate Influenza A and B viruses and subsequently distinguish the pdm H1N1 and H3N2 serotypes of Influenza A virus. In this study, we developed a multiplex real-time RT-PCR method for simultaneously detecting Influenza A and B viruses and subtyping Influenza A virus, with a substantially reduced test duration. Clinical specimens from hospitalized patients and outpatients with influenza-like symptoms in Eastern Taiwan were collected between 2011 and 2015, transported to Hualien Tzu Chi Hospital, and analyzed. Conventional RT-PCR was used to subtype the isolated Influenza A viruses. Thereafter, for rapid identification, the multiplex real-time RT-PCR method was developed and applied to identify the conserved regions that aligned with the available primers and probes. Accordingly, a multiplex RT-PCR assay with three groups of primers and probes (MAF and MAR primers and MA probe; InfAF and InfAR primers and InfA probe; and MBF and MBR primers and MB probe) was established to distinguish these viruses in the same reaction. Thus, with this multiplex RT-PCR assay, Influenza B, Influenza A pdm H1N1, and Influenza A H3N2 viruses were accurately detected and differentiated within only 2.5 h. This multiplex RT-PCR assay showed similar analytical sensitivity to the conventional singleplex assay. Further, the phylogenetic analyses of our samples revealed that the characteristics of these viruses were different from those reported previously using samples collected during 2012-2013. In conclusion, we developed a multiplex real-time RT-PCR method for highly efficient and accurate detection and differentiation of Influenza A and B viruses and subtyping Influenza A virus with a substantially reduced test duration for diagnosis.

Sufficient virus-neutralizing antibody in the central nerve system improves the survival of rabid rats.

BACKGROUND: Rabies is known to be lethal in human. Treatment with passive immunity for the rabies is effective only when the patients have not shown the central nerve system (CNS) signs. The blood-brain barrier (BBB) is a complex functional barrier that may compromise the therapeutic development in neurological diseases. The goal of this study is to determine the change of BBB integrity and to assess the therapeutic possibility of enhancing BBB permeability combined with passive immunity in the late stage of rabies virus infection. METHODS: The integrity of BBB permeability in rats was measured by quantitative ELISA for total IgG and albumin levels in the cerebrospinal fluid (CSF) and by exogenously applying Evans blue as a tracer. Western blotting of occludin and ZO-1, two tight junction proteins, was used to assess the molecular change of BBB structure.The breakdown of BBB with hypertonic arabinose, recombinant tumor necrosis factor-alpha (rTNF-γ), and focused ultrasound (FUS) were used to compare the extent of BBB disruption with rabies virus infection. Specific humoral immunity was analyzed by immunofluorescent assay and rapid fluorescent focus inhibition test. Virus-neutralizing monoclonal antibody (mAb) 8-10E was administered to rats with hypertonic breakdown of BBB as a passive immunotherapy to prevent the death from rabies. RESULTS: The BBB permeability was altered on day 7 post-infection. Increased BBB permeability induced by rabies virus infection was observed primarily in the cerebellum and spinal cord. Occludin was significantly decreased in both the cerebral cortex and cerebellum. The rabies virus-specific antibody was not strongly elicited even in the presence of clinical signs. Disruption of BBB had no direct association with the lethal outcome of rabies. Passive immunotherapy with virus-neutralizing mAb 8-10E with the hypertonic breakdown of BBB prolonged the survival of rabies virus-infected rats. CONCLUSIONS: We demonstrated that the BBB permeability was altered in a rat model with rabies virus inoculation. Delivery of neutralizing mAb to the infected site in brain combined with effective breakdown of BBB could be an aggressive but feasible therapeutic mode in rabies when the CNS infection has been established.

Involvement of extraneural tissues and upregulation of inducible nitric oxide synthase after experimental infection with rabies virus in BALB/c mice and LEW/SsN rats.

Rabies virus can cause fatal encephalomyelitis, but the involvement of extraneural organs has not been well characterized. In this study, we investigated the histopathological changes and the distribution of viral antigens in extraneural organs after pathogenic rabies virus infection in mouse and rat models. In histopathological examination, classical viral encephalitis and rabies-specific Negri body were observed in the brain. In addition to the central nervous system (CNS), inflammatory responses were found in other organs, such as the heart, kidney, liver, and lung. Similarly, immunohistochemical staining and reverse transcription-polymerase chain reaction revealed the presence of rabies virus in the CNS and extraneural tissues. Moreover, macrophages, especially in the lung and heart, were involved in the infection. Transcriptional analyses of the expression of inducible nitric oxide synthase (iNOS) demonstrated that rabies virus potentiated the gene expression of iNOS in the brain, lung, and heart. The immunoreactive iNOS-positive macrophages were detected adjacent to the infection. These results suggest that macrophages are involved in the extraneural infection and the expression of iNOS in macrophages contributes to the formation of tissue inflammation. Our study indicates the involvement of extraneural organs following rabies virus infection, which may aggravate the progression of this deadly disease.

[Parallel PLS algorithm using MapReduce and its aplication in spectral modeling].

Partial least squares (PLS) has been widely used in spectral analysis and modeling, and it is computation-intensive and time-demanding when dealing with massive data To solve this problem effectively, a novel parallel PLS using MapReduce is proposed, which consists of two procedures, the parallelization of data standardizing and the parallelization of principal component computing. Using NIR spectral modeling as an example, experiments were conducted on a Hadoop cluster, which is a collection of ordinary computers. The experimental results demonstrate that the parallel PLS algorithm proposed can handle massive spectra, can significantly cut down the modeling time, and gains a basically linear speedup, and can be easily scaled up.

[A novel online process monitoring method based on near infrared spectroscopy and its application to the column chromatographic separation for traditional Chinese medicine].

Near infrared spectroscopy (NIRS) is a process analysis and monitoring tool with many advantages, while it needs to set up quantitative or discriminative calibration models in advance, and needs to adjust these models when the process conditions are varied, which makes it difficult for ordinary user to take its full advantage of it. To tackle this problem, this paper presented a novel, simple and model-free methodology for online process monitoring based on two reciprocal viewpoints of measuring the variability of spectroscopy-both the similarity and dissimilarity of process spectrum, i.e., the adaptive moving window standard deviation function(AMWSW) and similarity function(S). The methodology was validated by a column chromatography process of traditional Chinese medicine using near infrared spectroscopy. The online trend curves of AMWSW and S obtained by proposed method were validated by a comparison with the content variation curves of multiple indicative components analyzed by high performance liquid chromatography (HPLC), and these trend curves demonstrated their potential for real-time process status monitoring, accurately determining the beginning point, the peak point, the end point of the elution, and the phase change from water solution to ethanol solution. The proposed methodology can also be used to other process analysis techniques, such as ultraviolet/visible, infrared, Raman, fluorescence, chromatograph and mass spectrum.

[Real-time monitoring of industrial acetic acid production process using a new type of online NIR analysis system].

The safe and real-time monitoring of the production process of acetic acid is always a key technical problem. The conventional online chromatographic analysis can't satisfy the requirements of real-time analysis for its inherent disadvantages. A new type of on-line near-infrared analysis system has been developed for real-time analysis of the concentration of each component in acetic acid reaction kettles instantly. Its features and configuration were described in detail. Both the laboratory modeling and field application results have confirmed that this system is of high stability and accuracy. The proposed system can effectively solve the key technical problems in the manufacture and ensure the safety and stability of production process of acetic acid.

Mesenchymal stem cell-derived exosomes improve motor function and attenuate neuropathology in a mouse model of Machado-Joseph disease.

BACKGROUND: Machado-Joseph disease is the most common autosomal dominant hereditary ataxia worldwide without effective treatment. Mesenchymal stem cells (MSCs) could slow the disease progression, but side effects limited their clinical application. Besides, MSC-derived exosomes exerted similar efficacy and have many advantages over MSCs. The aim of this study was to examine the efficacy of MSC-derived exosomes in YACMJD84.2 mice. METHODS: Rotarod performance was evaluated every 2 weeks after a presymptomatic administration of intravenous MSC-derived exosomes twice in YACMJD84.2 mice. Loss of Purkinje cells, relative expression level of Bcl-2/Bax, cerebellar myelin loss, and neuroinflammation were assessed 8 weeks following treatment. RESULTS: MSC-derived exosomes were isolated and purified through anion exchange chromatography. Better coordination in rotarod performance was maintained for 6 weeks in YACMJD84.2 mice with exosomal treatment, compared with those without exosomal treatment. Neuropathological changes including loss of Purkinje cells, cerebellar myelin loss, and neuroinflammation were also attenuated 8 weeks after exosomal treatment. The higher relative ratio of Bcl-2/Bax was consistent with the attenuation of loss of Purkinje cells. CONCLUSIONS: MSC-derived exosomes could promote rotarod performance and attenuate neuropathology, including loss of Purkinje cells, cerebellar myelin loss, and neuroinflammation. Therefore, MSC-derived exosomes have a great potential in the treatment of Machado-Joseph disease.

[Isomap-PLS nonlinear modeling method for near infrared spectroscopy].

For modeling the nonlinear relationship existing between samples' near infrared (NIR) spectra and their chemical or physical properties, a novel modeling method was put forward in the present paper, which builds model by combining Isomap and partial least squares (PLS). Isomap is a newly proposed nonlinear dimension reduction algorithm, and belongs to the algorithm family of manifold learning, which is a new branch of machine learning. Isomap is based on multidimensional scaling (MDS) algorithm; however, it replaces the Euclidean distance in MDS with an approximated geodesic distance, so it can effectively find out the intrinsic low dimensional structure from high dimensional data. By combining Isomap and PLS, refered to as Isomap-PLS, a novel nonlinear modeling method for NIR spectra analysis was proposed. In this method, Isomap was used to extract nonlinear information from high dimensional NIR spectra while keeping the invariance of geometric property, and then PLS was adopted to remove linear information redundancy and build a calibration model. The parameters of the Isomap, i.e. the number of the nearest neighbor k and output dimension d, can affect the performance of the method. In this paper, a grid search approach was used for parameter optimization. The Isomap-PLS modeling method was applied to two public benchmark NIR datasets, and the modeling results were compared with that of PLS. The results demonstrated that in both datasets, each model built with Isomap-PLS had a smaller rooted mean square error of cross-validation (RMSECV) than the corresponding model built with PLS. Moreover, for some properties, the RMSECV of Isomap-PLS was significantly reduced by a factor of 2-5 compared with that of PLS. It can be concluded that by taking the virtue that Isomap can reflect the intrinsic nonlinear structure of NIR spectra, Isomap-PLS can effectively model the nonlinear correlations between spectra and physicochemical properties of the samples, and so it gains more power in calibration and prediction than PLS.

Simultaneous quantification of 11 pivotal metabolites in neural tube defects by HPLC-electrospray tandem mass spectrometry.

One-carbon metabolism that involves folate metabolism and homocysteine metabolism plays a powerful role in embryonic development. Any impairment to this metabolism during the neurulation process would trigger the occurrence of neural tube defects (NTDs). The great importance of one-carbon metabolism necessitates the establishment of methodology to determine the relative compounds involved in the metabolic cycles. We have developed a sensitive method for measurement of 11 pivotal compounds by using high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS/MS) in sera of pregnant women. Use of an aqueous chromatography column increased retention time and separation of the polar compounds in the system, resulting in fewer co-elution and interference from the other compounds that can lead to ion suppression. Calibration curves suitable for the analysis of maternal serum were linear (r(2)>0.997) with limits of detection from 0.05 to 1ng/mL. Intra-day coefficients of variation (CVs) and inter-day CVs were both lower than 11%. With the developed method, 96 serum samples including 46 cases and 50 controls were analyzed. The established method provided a reliable method for quantifying most of the compounds involved in the one-carbon metabolism simultaneously, thus made it possible to elucidate NTDs with multiple factors instead of one single and provided a solid foundation for the diagnosis and prevention of NTDs as well as some other one-carbon metabolism related diseases.

Immunohistochemical study of scrub typhus: a report of two cases.

Scrub typhus is a zoonotic disease caused by Orientia tsutsugamushi, which is transmitted by chiggers. The target cells of this rickettsia are poorly defined in humans. Immunohistochemical staining of tissue sections of patients with scrub typhus is helpful in investigating the target cells of this rickettsia in different organs. We studied two autopsy specimens by immunohistochemical staining using a specific antibody against O. tsutsugamushi. Rickettsiae were located in endothelial cells in all of the organs evaluated, namely heart, lung, brain, kidney, appendix and skin, within cardiac muscle cells and renal tubular epithelial cells, and in macrophages located in the lymph node, liver and spleen. In conclusion, O. tsutsugamushi may disseminate into multiple organs through endothelial cells and macrophages, resulting in the development of fatal complications.

[LLE-PLS nonlinear modeling method for near infrared spectroscopy and its application].

The traditional near infrared (NIR) spectra modeling algorithm-partial least squares (PLS) can't effectively reflect the nonlinear correlations existing between the near infrared spectra and the chemical or physical properties of samples. Locally linear embedding (LLE) is a newly proposed nonlinear dimension reduction algorithm, which is a kind of manifold learning algorithm. It can find out the intrinsic dimension from high dimensional data effectively, and map the high dimensional input data points to a global low dimensional coordinates while keeping the spatial relations of the adjacent points, i. e. the geometry structure of the high dimensional space. No application of LLE in the information processing of NIR spectra has been reported. By combining LLE and PLS, a novel nonlinear modeling method LLE-PLS for NIR spectra was proposed. In the proposed method, LLE and PLS were adopted to deduct the dimensions of NIR spectra and build regressor, respectively. The LLE-PLS method was applied to correlate the NIR spectra with the concentrations of salvia acid B in the elution of column chromatography of Salvianolate. The results showed that LLE-PLS outperformed other preprocessing methods such as multiplicative scattering correction, the 1st derivative, vector normalization, minimum-maximum normalization, detrend, debias, and the 2nd derivative. After parameter optimization, LLE-PLS can accurately predict the concentration of salvia acid B, with a minimum RMSECV of 0.128 mg x mL(-1) and r2 of 0.9988, suggesting that LLE-PLS is better than PLS in modeling and prediction. The parameter of the number of nearest neighbor k of LLE-PLS and output dimension d can affect the performance of the method. The research showed that k is robust to RMSECV, and an excessively low or high output dimension d will result in a greater error because of insufficient or excessive information extraction. It can be concluded that LLE-PLS can effectively model the nonlinear correlations between spectra and physicochemical properties of the samples. And it is feasible to actualize online monitoring of the process of column chromatography of Salvianolate by coupling NIR spectra with LLE-PLS modeling method.

Acute respiratory distress syndrome associated with rabies: a case report.

Acute respiratory distress syndrome (ARDS) is the first potentially lethal complication in rabies virus infection, although its occurrence is rare. We report on a fatal case of rabies virus infection in a 45-year-old woman from Hu-Nan Province, China. The neurologic signs of limb numbness and water phobia occurred from 61 days after the dog bite; the clinical course was progressive, with the most severe clinical manifestations being fever, encephalitis, and ARDS. The woman expired 12 days after admission to the hospital. An autopsy proved rabies encephalitis, mainly involving the medulla oblongata, the thalamus, part of the pons, the cerebellum, and the hippocampus. The lung pathologic examination revealed the organizing phase of ARDS with diffuse alveolar damage, hyaline membrane formation, type II alveolar cell hyperplasia accompanied by proliferation of fibroblasts and infiltration of mononuclear cells into the interstitial space. Immunohistochemistry stain and reverse transcription-polymerase chain reaction for rabies virus failed to demonstrate the organism in the lung tissue. Strong expression of inducible nitric oxide synthase (iNOS) was detected in the alveolar macrophages. An immunologic mechanism with iNOS expression in the absence of direct invasion of the organism may participate in the pathogenesis of ARDS associated with rabies.

Human risk factors associated with pilots in runway excursions.

A breakdown analysis of civil aviation accidents worldwide indicates that the occurrence of runway excursions represents the largest portion among all aviation occurrence categories. This study examines the human risk factors associated with pilots in runway excursions, by applying a SHELLO model to categorize the human risk factors and to evaluate the importance based on the opinions of 145 airline pilots. This study integrates aviation management level expert opinions on relative weighting and improvement-achievability in order to develop four kinds of priority risk management strategies for airline pilots to reduce runway excursions. The empirical study based on experts' evaluation suggests that the most important dimension is the liveware/pilot's core ability. From the perspective of front-line pilots, the most important risk factors are the environment, wet/containment runways, and weather issues like rain/thunderstorms. Finally, this study develops practical strategies for helping management authorities to improve major operational and managerial weaknesses so as to reduce the human risks related to runway excursions.

Piezoelectric immunochip for the detection of dengue fever in viremia phase.

The global prevalence of dengue fever has grown so dramatically in recent years that it is endemic in more than 100 countries and has become a major international public health concern. Moreover, since the flu-like symptoms that accompany dengue fever are atypical and varied, the detection procedures currently used to identify it are cumbersome and time-consuming, making early stage epidemiological control and effective medical treatment of this epidemic almost impossible. In this study, a QCM-based detection system was developed in which two monoclonal antibodies against dengue E and NS-1 protein, respectively, were control orientated immobilized on QCM via protein A to produce an immunochip. Various sample pretreatment procedures were evaluated to ascertain the most suitable combination, and both the simulating samples and the clinical specimen were examined by the immunochip. The results revealed that the cibacron blue 3GA gel-heat denature (CB-HD) method was the most effective sample pretreatment technique. Due to the complex composition of the serum, the immunochip could only effectively quantify dengue viral antigens in a 1/1000 untreated simulated sample. With the help of the CB-HD method, the dilution folds were found to capable of being reduced from 1000 to 100, and the detection limit lowered to 1.727 microg/ml (E protein) and 0.740 microg/ml (NS-1 protein) in the original sample. While the cocktail immunochip could not quantify both antigens separately, the higher signal level rendered it a more effective qualification tool for suspect screening. Moreover, the results of the analysis of clinical specimens also proved the ability and future potential of cocktail immunochip in discriminating dengue-positive cases from negative serum specimens in the viremia phase.

Thermal aggregation of SARS-CoV membrane protein.

SARS-CoV membrane protein could be detected easily using Western blotting in non-denaturing condition but not regular denaturing treatment. Boiling treatment, causing the aggregation of SARS-CoV membrane protein in the stacking gels, results in the failure to detect the membrane protein in the separating gels. Aggregated membrane proteins could not be dissociated by 1% Triton-X 100, 6M urea, or 2% SDS. The region with amino acid residues from 51 to 170 is responsible for thermal aggregation of SARS-CoV membrane protein. Hydrophobic regions with amino acid residues from 61 to 90, from 91 to 100, from 136 to 170, are essential for this protein aggregation. Thermal aggregation of SARS-CoV membrane protein is not unique among structural proteins of coronaviruses. However, SARS-CoV membrane protein seems to be more sensitive to heat treatment, since the membrane protein of MHV-JHM, another member of the Coronaviridae, would not aggregate after the same treatment. Therefore, if SARS-CoV membrane protein needs to be analyzed using SDS-PAGE, boiling should be avoided. Thermal aggregation of SARS-CoV membrane protein may be one of the reasons for the inactivation of this virus by heat. The unusual property of SARS-CoV membrane protein aggregation induced by heat also provides a model for the study of protein aggregation.