Affinity-Switchable Interaction of Biotin and Streptavidin for the Signal-ON Detection of Small Molecules.

Lateral flow assay (LFA) based on gold nanoparticles (AuNPs) is a widely used analytical device for the rapid analysis of environmental hazards and biomarkers. Typically, a sandwich-type format is used for macromolecule detection, in which the appearance of a red test line indicates a positive result (Signal-ON). In contrast, small molecule detection usually relies on a competitive assay, where the absence of a test line indicates positive testing (Signal-OFF). However, such a "Signal-OFF" reading is usually detected within a narrower dynamic range and tends to generate false-negative signals at a low concentration. Moreover, inconsistent readings between macromolecule and small molecule testing might lead to misinterpretation when used by nonskilled individuals. Herein, we report a "Signal-ON" small molecule competitive assay based on the sterically modulated affinity-switchable interaction of biotin and streptavidin. In the absence of a small molecule target, a large steric hindrance can be imposed on the biotin to prevent interaction with streptavidin. However, in the presence of the small molecule target, this steric effect is removed, allowing the biotin to bind to streptavidin and generate the desired test line. In this article, we demonstrate the selective detection of two small molecule drugs, sulfonamides and trimethoprim, using this simple and modular affinity-switchable lateral flow assay (ASLFA). We believe that this affinity-switchable approach can also be adapted in drug discovery and clinical diagnosis, where the competitive assay format is always used for the rapid analysis of small molecules.

Brain Structural Magnetic Resonance Imaging for Joint Independent Component Analysis in Schizophrenic Patients.

BACKGROUND: In past magnetic resonance imaging studies, normal participants and schizophrenia patients have usually been compared using imaging processing modes with only one parameter. A more extensive evaluation of significant differences between gray and white matter in Schizophrenic patents was necessary. METHODS: Voxel based morphometry was used to separate brain images into gray matter and white matter. Then, the images were mapped to Montreal Neurological Institute space, and DARTEL analytic template was applied for image calibration with statistical parametric mapping. Finally, joint independent component analysis was employed to analyze the gray and white matter of brain images from Schizophrenic patients and normal controls. In this study, joint independent component analysis was used to discriminate clinical differences in magnetic resonance imaging signals between Schizophrenic patients and normal controls. RESULTS: Region of interest analyses has repeatedly shown gray matter reduction in the superior temporal gyrus of Schizophrenic patients. CONCLUSION: These results strongly support previous studies regarding brain volume in schizophrenic patients. The connection networks in frontal and temporal lobes evidently did not differ between normal participants and schizophrenia patients.

Analysis of EEG entropy during visual evocation of emotion in schizophrenia.

BACKGROUND: In this study, the international affective picture system was used to evoke emotion, and then the corresponding signals were collected. The features from different points of brainwaves, frequency, and entropy were used to identify normal, moderately, and markedly ill schizophrenic patients. METHODS: The signals were collected and preprocessed. Then, the signals were separated according to three types of emotions and five frequency bands. Finally, the features were calculated using three different methods of entropy. For classification, the features were divided into different sections and classification using support vector machine (principal components analysis on 95%). Finally, simple regression and correlation analysis between the total scores of positive and negative syndrome scale and features were used. RESULTS: At first, we observed that to classify normal and markedly ill schizophrenic patients, the identification result was as high as 81.5%, and therefore, we further explored moderately and markedly ill schizophrenic patients. Second, the identification rate in both moderately and markedly ill schizophrenic patient was as high as 79.5%, which at the Fz point signal in high valence low arousal fragments was calculated using the ApEn methods. Finally, the total scores of positive and negative syndrome scale were used to analyze the correlation with the features that were the five frequency bands at the Fz point signal. The results show that the p value was less than .001 at the beta wave in the 15-18 Hz frequency range.

Development of an Automatic Testing Platform for Aviator's Night Vision Goggle Honeycomb Defect Inspection.

Due to the direct influence of night vision equipment availability on the safety of night-time aerial reconnaissance, maintenance needs to be carried out regularly. Unfortunately, some defects are not easy to observe or are not even detectable by human eyes. As a consequence, this study proposed a novel automatic defect detection system for aviator's night vision imaging systems AN/AVS-6(V)1 and AN/AVS-6(V)2. An auto-focusing process consisting of a sharpness calculation and a gradient-based variable step search method is applied to achieve an automatic detection system for honeycomb defects. This work also developed a test platform for sharpness measurement. It demonstrates that the honeycomb defects can be precisely recognized and the number of the defects can also be determined automatically during the inspection. Most importantly, the proposed approach significantly reduces the time consumption, as well as human assessment error during the night vision goggle inspection procedures.

The facial expression of schizophrenic patients applied with infrared thermal facial image sequence.

BACKGROUND: Schizophrenia is a neurological disease characterized by alterations to patients' cognitive functions and emotional expressions. Relevant studies often use magnetic resonance imaging (MRI) of the brain to explore structural differences and responsiveness within brain regions. However, as this technique is expensive and commonly induces claustrophobia, it is frequently refused by patients. Thus, this study used non-contact infrared thermal facial images (ITFIs) to analyze facial temperature changes evoked by different emotions in moderately and markedly ill schizophrenia patients. METHODS: Schizophrenia is an emotion-related disorder, and images eliciting different types of emotions were selected from the international affective picture system (IAPS) and presented to subjects during ITFI collection. ITFIs were aligned using affine registration, and the changes induced by small irregular head movements were corrected. The average temperatures from the forehead, nose, mouth, left cheek, and right cheek were calculated, and continuous temperature changes were used as features. After performing dimensionality reduction and noise removal using the component analysis method, multivariate analysis of variance and the Support Vector Machine (SVM) classification algorithm were used to identify moderately and markedly ill schizophrenia patients. RESULTS: Analysis of five facial areas indicated significant temperature changes in the forehead and nose upon exposure to various emotional stimuli and in the right cheek upon evocation of high valence low arousal (HVLA) stimuli. The most significant P-value (lower than 0.001) was obtained in the forehead area upon evocation of disgust. Finally, when the features of forehead temperature changes in response to low valence high arousal (LVHA) were reduced to 9 using dimensionality reduction and noise removal, the identification rate was as high as 94.3%. CONCLUSIONS: Our results show that features obtained in the forehead, nose, and right cheek significantly differed between moderately and markedly ill schizophrenia patients. We then chose the features that most effectively distinguish between moderately and markedly ill schizophrenia patients using the SVM. These results demonstrate that the ITFI analysis protocol proposed in this study can effectively provide reference information regarding the phase of the disease in patients with schizophrenia.

A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images.

Patients with schizophrenia suffer from symptoms such as hallucination and delusion. There are currently a number of publications that discuss the treatment, diagnosis, prognosis, and damage in schizophrenia. This study utilized joint independent component analysis to process the images of GMV and WMV and incorporated the Wisconsin card sorting test (WCST) and the positive and negative syndrome scale (PANSS) to examine the correlation of obtained brain characteristics. We also used PANSS score to classify schizophrenic patients into acute and subacute cases, to analyze the brain structure differences. Finally, we used brain structure images and the error rate of the WCST as eigenvalues in support vector machine learning and classification. The results of this study showed that the frontal and temporal lobes of a normal brain are more apparent than those of a schizophrenia brain. The highest level of classification recognition reached 91.575%, indicating that the WCST error rate and characteristic changes in brain structure volume can be used to effectively distinguish schizophrenia and normal brains. Similarly, this result confirmed that the WCST and brain structure volume are correlated with the differences between schizophrenia and normal participants.

Herpesvirus entry mediator: A switch in immune response and its role in anti-tumor immunity / 第二军医大学学报

The interaction between co-stimulatory ligands and their receptors of T cells and antigen presenting cells is crucial for the activation or resting of the immune cells. The tumor necrosis factor receptor superfamily and CD28-B7 family contain many such regulatory molecules. Herpesvirus entry mediator (HVEM), also named as tumor necrosis factor superfamily member 14, is noted for its pivotal role in regulating immune responses through binding LIGHT as a ligand to develop T-cell immunity, and in interacting with B and T lymphocyte attenuator (BTLA) as a receptor to negatively regulate T-cell responses. In antitumor immunity, increasing amount of evidence demonstrates that HVEM is an indispensable receptor on lymphocyte for LIGHT to co-stimulate tumor antigen-specific CTL. This article discusses the role of HVEM in regulating immune response and antitumor-immunity.

The functional expression of humanized ScFv-urokinase fusion protein in Escherichia coli / 生物工程学报

The fusion protein of Humanized mouse anti-human fibrin ScFv and the low molecular weight urokinase (IIn-UK) contained seven disulfide bonds and formed inclusion body while expressing in normal E. coli strain. By coexpressing DsbC and using the special E. coli strain Origami(DE3) which was trxB/gor double mutant, the fusion protein IIn-UK was functionally expressed in the cytoplasm of E. coli. The expressed fusion protein in the soluble fraction was purified by using affinity chromatography specific against urokinase. The purified fusion protein could combine the thrombus in vitro, and the specific activity of urokinase reached 80,000 IU/mg fusion protein. The result showed that the fusion protein retained the activity of two moieties, and this study laid a foundation for further research of targeting thrombolytic agent.