Development of specific nanobodies (VHH) for CD19 immunotargeting of human B-Lymphocytes

Objective(s): CD19 is a transmembrane glycoprotein of immunoglobulin superfamily. In order to treat lymphoma, monoclonal antibodies (mAb) can target different antigens, including CD19, CD20 and CD22 on the surface of B-cells. Along with biotechnology progress, a new generation of antibodies is introduced, with the purpose of eliminating the defects of the previous generation. Among the most developed one are nanobodies (Nb). Nbs are a unique kind of camelid single domain antibody fragments with a broad range of medical applications. Unique physicochemical properties of Nbs have made them ideal candidates for therapeutic and diagnostic applications. Materials and Methods: An immune gene library was created, and several CD19 specific Nbs were selected through antigen panning process, and their molecular properties as well as specificity, sensitivity, affinity and immunoreactivity against CD19 positive and negative cells were evaluated. Results: The Nb library was prepared with 7.2 �107 members. We managed to isolate a panel of CD19- specific Nbs after the last round of selection with the affinity of isolated Nbs being estimated at the standard range of 15-35 nM. Sequence analysis of positive clones was indicative of the fact that 12 variable sequences were confirmed. Of all these 12 clones, 2 clones with the greatest level signal in ELISA underwent subsequent analysis. Our sequencing results indicated high sequence homology (approximately 90) between the Nb and Homa variable immunoglobulin domains. Conclusion: Specific Nbs possess the potential to be used as novel therapeutic approaches in order to treat autoimmune diseases and B-cell lymphoma. © 2018, Mashhad University of Medical Sciences. All rights reserved

Development of specific nanobodies (VHH) for CD19 immunotargeting of human B-Lymphocytes

Objective(s): CD19 is a transmembrane glycoprotein of immunoglobulin superfamily. In order to treat lymphoma, monoclonal antibodies (mAb) can target different antigens, including CD19, CD20 and CD22 on the surface of B-cells. Along with biotechnology progress, a new generation of antibodies is introduced, with the purpose of eliminating the defects of the previous generation. Among the most developed one are nanobodies (Nb). Nbs are a unique kind of camelid single domain antibody fragments with a broad range of medical applications. Unique physicochemical properties of Nbs have made them ideal candidates for therapeutic and diagnostic applications. Materials and Methods: An immune gene library was created, and several CD19 specific Nbs were selected through antigen panning process, and their molecular properties as well as specificity, sensitivity, affinity and immunoreactivity against CD19 positive and negative cells were evaluated. Results: The Nb library was prepared with 7.2 �107 members. We managed to isolate a panel of CD19- specific Nbs after the last round of selection with the affinity of isolated Nbs being estimated at the standard range of 15-35 nM. Sequence analysis of positive clones was indicative of the fact that 12 variable sequences were confirmed. Of all these 12 clones, 2 clones with the greatest level signal in ELISA underwent subsequent analysis. Our sequencing results indicated high sequence homology (approximately 90) between the Nb and Homa variable immunoglobulin domains. Conclusion: Specific Nbs possess the potential to be used as novel therapeutic approaches in order to treat autoimmune diseases and B-cell lymphoma. © 2018, Mashhad University of Medical Sciences. All rights reserved

The effect of superparamagnetic iron oxide nanoparticles surface engineering on relaxivity of magnetoliposome

The purpose of this work is evaluating the effect of ultra small superparamagnetic iron oxide nanoparticles (USPIONs) coatings on encapsulation efficiency in liposomes and cellular cytotoxicity assay. Moreover, we assessed the effects of surface engineering on the relaxivity of magnetoliposome nanoparticles in order to create a targeted reagent for the intelligent diagnosis of cancers by MRI. For estimating the effect of nanoparticle coatings on encapsulation, several kinds of USPIONs coated by dextran, PEG5000 and citrate were used. All kinds of samples are monodispersed and below 100 ± 10 nm and the coatings of USPIONs have no significant effect on magnetoliposome diameter. The coating of USPIONs could have effect on percentage of encapsulation. The dextran coated USPIONs have more stability and quality accordingly the encapsulation increased up to 92, then the magnetoliposome nano particles have been targeted by Herceptin and anti-HER2 VHH, separately. Over storage period of four weeks the resulting particles were stable and physico-chemical properties such as size and zetapotential did not show any significant changes. The relaxivity of contrast agents was measured using a 1.5 T MRI. The r2/r1 ratio was more than two for all samples which demonstrate the negative contrast enhancing of all SPION embedded specimens. The high ratio of r2/r1 as well as high r2 is the best combination of a negative contrast agent as it is obtained for pure magnetite. The value of r2/r1 for all other samples including Herceptin targeted magnetoliposome, anti-HER2 VHH targeted magnetoliposome and non-targeted magnetoliposome were between ~21 to ~28, which show the magnetite embedded samples have enough negative contrast to be detectable by MRI. Therefore the HER2 targeted magnetoliposomes are a good and stable candidate as contrast agents in clinical radiology and biomedical research with minimal cytotoxicity and biocompatibility effects. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd

Quantifying the Effects of Prosody Modulation on User Engagement and Satisfaction in Conversational Systems

As voice-based assistants such as Alexa, Siri, and Google Assistant become ubiquitous, users increasingly expect to maintain natural and informative conversations with such systems. However, for an open-domain conversational system to be coherent and engaging, it must be able to maintain the user's interest for extended periods, without sounding boring or annoying. In this paper, we investigate one natural approach to this problem, of modulating response prosody, i.e., changing the pitch and cadence of the response to indicate delight, sadness or other common emotions, as well as using pre-recorded interjections. Intuitively, this approach should improve the naturalness of the conversation, but attempts to quantify the effects of prosodic modulation on user satisfaction and engagement remain challenging. To accomplish this, we report results obtained from a large-scale empirical study that measures the effects of prosodic modulation on user behavior and engagement across multiple conversation domains, both immediately after each turn, and at the overall conversation level. Our results indicate that the prosody modulation significantly increases both immediate and overall user satisfaction. However, since the effects vary across different domains, we verify that prosody modulations do not substitute for coherent, informative content of the responses. Together, our results provide useful tools and insights for improving the naturalness of responses in conversational systems.Comment: Published in CHIIR 2020, 4 page

Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems

Predicting user satisfaction in conversational systems has become critical, as spoken conversational assistants operate in increasingly complex domains. Online satisfaction prediction (i.e., predicting satisfaction of the user with the system after each turn) could be used as a new proxy for implicit user feedback, and offers promising opportunities to create more responsive and effective conversational agents, which adapt to the user's engagement with the agent. To accomplish this goal, we propose a conversational satisfaction prediction model specifically designed for open-domain spoken conversational agents, called ConvSAT. To operate robustly across domains, ConvSAT aggregates multiple representations of the conversation, namely the conversation history, utterance and response content, and system- and user-oriented behavioral signals. We first calibrate ConvSAT performance against state of the art methods on a standard dataset (Dialogue Breakdown Detection Challenge) in an online regime, and then evaluate ConvSAT on a large dataset of conversations with real users, collected as part of the Alexa Prize competition. Our experimental results show that ConvSAT significantly improves satisfaction prediction for both offline and online setting on both datasets, compared to the previously reported state-of-the-art approaches. The insights from our study can enable more intelligent conversational systems, which could adapt in real-time to the inferred user satisfaction and engagement.Comment: Published in CIKM '19, 10 page

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys

Background: Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and without laboratory-based measurements, and the corresponding risk charts for 182 countries to predict 10-year risk of fatal and non-fatal CVD in adults aged 40–74 years. Methods: Based on our previous laboratory-based prediction model (Globorisk), we used data from eight prospective studies to estimate coefficients of the risk equations using proportional hazard regressions. The laboratory-based risk score included age, sex, smoking, blood pressure, diabetes, and total cholesterol; in the non-laboratory (office-based) risk score, we replaced diabetes and total cholesterol with BMI. We recalibrated risk scores for each sex and age group in each country using country-specific mean risk factor levels and CVD rates. We used recalibrated risk scores and data from national surveys (using data from adults aged 40–64 years) to estimate the proportion of the population at different levels of CVD risk for ten countries from different world regions as examples of the information the risk scores provide; we applied a risk threshold for high risk of at least 10% for high-income countries (HICs) and at least 20% for low-income and middle-income countries (LMICs) on the basis of national and international guidelines for CVD prevention. We estimated the proportion of men and women who were similarly categorised as high risk or low risk by the two risk scores. Findings: Predicted risks for the same risk factor profile were generally lower in HICs than in LMICs, with the highest risks in countries in central and southeast Asia and eastern Europe, including China and Russia. In HICs, the proportion of people aged 40–64 years at high risk of CVD ranged from 1% for South Korean women to 42% for Czech men (using a ≥10% risk threshold), and in low-income countries ranged from 2% in Uganda (men and women) to 13% in Iranian men (using a ≥20% risk threshold). More than 80% of adults were similarly classified as low or high risk by the laboratory-based and office-based risk scores. However, the office-based model substantially underestimated the risk among patients with diabetes. Interpretation: Our risk charts provide risk assessment tools that are recalibrated for each country and make the estimation of CVD risk possible without using laboratory-based measurements

Cosmological phase transitions in warped space: gravitational waves and collider signatures

We study the electroweak phase transition within a 5D warped model including a scalar potential with an exponential behavior, and strong back-reaction over the metric, in the infrared. By means of a novel treatment of the superpotential formalism, we explore parameter regions that were previously inaccessible. We nd that for large enough values of the t'Hooft parameter (e.g. N = 25) the holographic phase transition occurs, and it can force the Higgs to undergo a rst order electroweak phase transition, suitable for electroweak baryogenesis. The model exhibits gravitational waves and colliders signatures. It typically predicts a stochastic gravitational wave background observable both at the Laser Interferometer Space Antenna and at the Einstein Telescope. Moreover the radion tends to be heavy enough such that it evades current constraints, but may show up in future LHC runs.The work of EM is supported by the Spanish MINEICO under Grant FPA2015-64041-C2-1-P and FIS2017-85053-C2-1-P, by the Junta de Andaluc a under Grant FQM-225, by the Basque Government under Grant IT979-16, and by the Spanish Consolider Ingenio 2010 Programme CPAN (CSD2007-00042). The research of EM is also supported by the Ram on y Cajal Program of the Spanish MINEICO, and by the Universidad del Pa s Vasco UPV/EHU, Bilbao, Spain, as a Visiting Professor. GN is supported by the Swiss National Science Foundation (SNF) under grant 200020-168988. The work of MQ is partly supported by Spanish MINEICO under Grant CICYT-FEDER-FPA2014- 55613-P and FPA2017-88915-P, by the Severo Ochoa Excellence Program of MINEICO under Grant SEV-2016-0588, and by CNPq PVE fellowship project 405559/2013-5

Image Segmentation based on Multi-region Multi-scale Local Binar Fitting and Kullback-Leibler Divergence

The inhomogeneity of intensity and the noise of image are the two major obstacles to accurate image segmentation by region-based level set models. To provide a more general solution to these challenges and address the difficulty of image segmentation methods to handle an arbitrary number of regions, we propose a region-based multi-phase level set method, which is based on the multi-scale local binary fitting (MLBF) and the Kullback–Leibler (KL) divergence, called KL–MMLBF. We first apply the multi-scale theory and multi-phase level set framework to the local binary fitting model to build the multi-region multi-scale local binary fitting (MMLBF). Then the energy term measured by KL divergence between regions to be segmented is incorporated into the energy function of MMLBF. KL–MMLBF utilizes the between-cluster distance and the adaptive kernel function selection strategy to formulate the energy function. Being more robust to the initial location of the contour than the classical segmentation models, KL–MMLBF can deal with blurry boundaries and noise problems. The results of experiments on synthetic and medical images have shown that KL–MMLBF can improve the effectiveness of segmentation while ensuring the accuracy by accelerating this minimization of this energy function and the model has achieved better segmentation results in terms of both accuracy and efficiency to analyze the multi-region image