Design, construction and in vivo functional assessment of a hinge truncated sFLT01.

Gene therapy for the treatment of ocular neovascularization has reached clinical trial phases. The AAV2-sFLT01 construct was already evaluated in a phase 1 open-label trial administered intravitreally to patients with advanced neovascular age-related macular degeneration. SFLT01 protein functions by binding to VEGF and PlGF molecules and inhibiting their activities simultaneously. It consists of human VEGFR1/Flt-1 (hVEGFR1), a polyglycine linker, and the Fc region of human IgG1. The IgG1 upper hinge region of the sFLT01 molecule makes it vulnerable to radical attacks and prone to causing immune reactions. This study pursued two goals: (i) minimizing the immunogenicity and vulnerability of the molecule by designing a truncated molecule called htsFLT01 (hinge truncated sFLT01) that lacked the IgG1 upper hinge and lacked 2 amino acids from the core hinge region; and (ii) investigating the structural and functional properties of the aforesaid chimeric molecule at different levels (in silico, in vitro, and in vivo). Molecular dynamics simulations and molecular mechanics energies combined with Poisson-Boltzmann and surface area continuum solvation calculations revealed comparable free energy of binding and binding affinity for sFLT01 and htsFLT01 to their cognate ligands. Conditioned media from human retinal pigment epithelial (hRPE) cells that expressed htsFLT01 significantly reduced tube formation in HUVECs. The AAV2-htsFLT01 virus suppressed vascular development in the eyes of newborn mice. The htsFLT01 gene construct is a novel anti-angiogenic tool with promising improvements compared to existing treatments.

sFLT01 modulates invasion and metastasis in prostate cancer DU145 cells by inhibition of VEGF/GRP78/MMP2&9 axis.

BACKGROUND: About 90% of cancer-related deaths are due to metastasis of cancer cells, and angiogenesis is a critical step in this process. sFLT01 is a novel fusion protein and a dual-targeting agent that neutralizes both VEGF and PlGF proangiogenic activities. GRP78 dual effect in tumor growth and angiogenesis could be activated under VEGF stimulation. The current study was designed to investigate the inhibitory impact of sFLT01 protein on VEGF/GRP78 axis. To this point, sFLT01 construct was synthesized, recombinant plasmid was expressed in eukaryotic host cells, sFLT01-HisTag protein was extracted and analyzed. The functional activity of sFLT01 on VEGF-enhanced tube formation and angiogenesis of HUVEC cells were examined. Eventually, the inhibitory impact of sFLT01 on growth, invasiveness, and migration of human prostate cancer cell line, DU145, was assessed. Real-time PCR evaluated the level of GRP78 and its effect on the downstream factors; matrix metallopeptidase proteins 2&9 (MMP2&9) along with tissue inhibitor of metalloproteinase proteins1&2 (TIMP1&2) under sFLT01 stimulation. RESULTS: According to the data, sFLT01 protein showed modulatory impact on proliferation, invasion, and migration of DU145 cells along with the potential of HUVECs angiogenesis. Real-Time PCR analysis depicted a significant downregulation in GRP78, MMP2 and MMP9 transcripts' levels, and a subsequent elevation of TIMP1 and TIMP2 expression under sFLT01 stimulation was detected. CONCLUSION: Overall, these data indicated that the inhibitory impact of sFLT01 on cancer cells growth and invasiveness could be mediated through the modulation of VEGF/GRP78/MMP2&9 axis and activation of TIMPs.

Breast mass classification on sonographic images on the basis of shape analysis.

PURPOSE: To evaluate the efficiency of novel shape features for classification of benign and malignant sonographic breast masses. METHODS: Mass regions were extracted from the region of interest (ROI) sub-image by applying a segmentation algorithm based on the level set method. Six features (difference area with five features of mass pixel number viewed at different angles) were then extracted for further classification. A multilayered perceptron neural network (MLP) classifier was used to classify breast mass. The leave-one-case-out procedure was used on a database of 81 pathologically proved breast sonographic images of patients (47 benign cases and 34 malignant cases) to evaluate our method. RESULTS: The classification results showed overall accuracy was 93.83%, sensitivity 91.18%, specificity 95.74%, positive predictive value 93.94%, and negative predictive value 93.75%. CONCLUSION: The experimental results showed that this diagnostic system with the features proposed can improve the positive rate of biopsies, provide a second opinion for physicians, and be used as a useful tool for mass classification.

Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model.

Segmentation of the arterial wall boundaries from intravascular ultrasound images is an important image processing task in order to quantify arterial wall characteristics such as shape, area, thickness and eccentricity. Since manual segmentation of these boundaries is a laborious and time consuming procedure, many researchers attempted to develop (semi-) automatic segmentation techniques as a powerful tool for educational and clinical purposes in the past but as yet there is no any clinically approved method in the market. This paper presents a deterministic-statistical strategy for automatic media-adventitia border detection by a fourfold algorithm. First, a smoothed initial contour is extracted based on the classification in the sparse representation framework which is combined with the dynamic directional convolution vector field. Next, an active contour model is utilized for the propagation of the initial contour toward the interested borders. Finally, the extracted contour is refined in the leakage, side branch openings and calcification regions based on the image texture patterns. The performance of the proposed algorithm is evaluated by comparing the results to those manually traced borders by an expert on 312 different IVUS images obtained from four different patients. The statistical analysis of the results demonstrates the efficiency of the proposed method in the media-adventitia border detection with enough consistency in the leakage and calcification regions.

Mobile Cardiac Health-care Monitoring and Notification with Real Time Tachycardia and Bradycardia Arrhythmia Detection.

BACKGROUND: The increasing trend of heart disease has turned the attention of researchers toward the use of portable connected technologies. The necessity of continuous special care for cardiovascular patients is an inevitable fact. METHODS: In this research, a new wireless electrocardiographic (ECG) signal-monitoring system based on smartphone is presented. This system has two main sections. The first section consists of a sensor which receives ECG signals via an amplifier, then filters and digitizes the signal, and prepares it to be transmitted. The signals are stored, processed, and then displayed in a mobile application. The application alarms in dangerous situations and sends the location of the cardiac patient to family or health-care staff. RESULTS: The results obtained from the analysis of the electrocardiogram signals on 20 different people have been compared with the traditional ECG in hospital by a cardiologist. The signal is instantly transmitted by 200 sample per second to mobile phone. The raw data are processed, the anomaly is detected, and the signal is drawn on the interface in about 70 s. Therefore, the delay is not noticeable by the patient. With respect to rate of data transmission to hospital, different internet connections such as 2G, 3G, 4G, WiFi, WiMax, or Long-Term Evolution (LTE) could be used. Data transmission ranges from 9.6 kbps to 20 Mbps. Therefore, the physician could receive data with no delay. CONCLUSIONS: A performance accuracy of 91.62% is obtained from the wireless ECG system. It conforms to the hospital's diagnostic standard system while providing a portable monitoring anywhere at anytime.

Classification of benign and malignant breast masses based on shape and texture features in sonography images.

The purpose of this research was evaluating novel shape and texture feature' efficiency in classification of benign and malignant breast masses in sonography images. First, mass regions were extracted from the region of interest (ROI) sub-image by implementing a new hybrid segmentation approach based on level set algorithms. Then two left and right side areas of the masses are elicited. After that, six features (Eccentricity_feature, Solidity_feature, DeferenceArea_Hull_Rectangular, DeferenceArea_Mass_Rectangular, Cross-correlation-left and Cross-correlation-right) based on shape, texture and region characteristics of the masses were extracted for further classification. Finally a support vector machine (SVM) classifier was utilized to classify breast masses. The leave-one-case-out protocol was utilized on a database of eighty pathologically-proven breast sonographic images of patients (forty-seven benign cases and thirty-three malignant cases) to evaluate our method. The classification results showed an overall accuracy of 95.00%, sensitivity of 90.91%, specificity of 97.87%, positive predictive value of 96.77%, negative predictive value of 93.88%, and Matthew's correlation coefficient of 89.71%. The experimental results declare that our proposed method is actually a beneficial tool for the diagnosis of the breast cancer and can provide a second opinion for a physician's decision or can be used for the medicine training especially when coupled with other modalities.