Lipid nanoparticle library towards development next generation genomic medicines

Ionizable amino lipids are a major constituent of the lipid nanoparticles for delivering nucleic acid therapeutics (e.g., DLin-MC3-DMA in ONPATTRO , ALC-0315 in Comirnaty , SM-102 in Spikevax ). Scarcity of lipids that are suitable for cell therapy, vaccination, and gene therapies continue to be a problem in advancing many potential diagnostic/therapeutic/vaccine candidates to the clinic. Herein, we describe the development of novel ionizable lipids to be used as functional excipients for designing vehicles for nucleic acid therapeutics/vaccines in vivo or ex vivo use in cell therapy applications. We first studied the transfection efficiency (TE) of LNP-based mRNA formulations of these ionizable lipid candidates in primary human T cells and established a workflow for engineering of primary immune T cells. We then adapted this workflow towards bioengineering of CAR constructs to T cells towards non-viral CAR T therapy. Lipids were also tested in rodents for vaccine applications using self-amplifying RNA (saRNA) encoding various antigens. We have then evaluated various ionizable lipid candidates and their biodistribution along with the mRNA/DNA translation exploration using various LNP compositions. Further, using ionizable lipids from the library, we have shown gene editing of various targets in rodents. We believe that these studies will pave the path to the advancement in nucleic acid based therapeutics and vaccines, or cell gene therapy agents for early diagnosis and detection of cancer, and for targeted genomic medicines towards cancer treatment and diagnosis.

ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals.

The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA.

Explainable Deep Neural Models for COVID-19 Prediction from Chest X-Rays with Region of Interest Visualization

COVID-19 has been designated as a once-in-a-century pandemic, and its impact is still being felt severely in many countries, due to the extensive human and green casualties. While several vaccines are under various stage of development, effective screening procedures that help detect the disease at early stages in a non-invasive and resource-optimized manner are the need of the hour. X-ray imaging is fairly accessible in most healthcare institutions and can prove useful in diagnosing this respiratory disease. Although a chest X-ray scan is a viable method to detect the presence of this disease, the scans must be analyzed by trained experts accurately and quickly if large numbers of tests are to be processed. In this paper, a benchmarking study of different preprocessing techniques and state-of-the-art deep learning models is presented to provide comprehensive insights into both the objective and subjective evaluation of their performance. To analyze and prevent possible sources of bias, we preprocessed the dataset in two ways-first, we segmented the lungs alone, and secondly, we formed a bounding box around the lung and used only this area to train. Among the models chosen to benchmark, which were DenseNet201, EfficientNetB7, and VGG-16, DenseNet201 performed better for all three datasets. © 2021 IEEE.

COVIDDX: AI-based Clinical Decision Support System for Learning COVID-19 Disease Representations from Multimodal Patient Data

The COVID-19 pandemic has affected the world on a global scale, infecting nearly 68 million people across the world, with over 1.5 million fatalities as of December 2020. A cost-effective early-screening strategy is crucial to prevent new outbreaks and to curtail the rapid spread. Chest X-ray images have been widely used to diagnose various lung conditions such as pneumonia, emphysema, broken ribs and cancer. In this work, we explore the utility of chest X-ray images and available expert-written diagnosis reports, for training neural network models to learn disease representations for diagnosis of COVID-19. A manually curated dataset consisting of 450 chest X-rays of COVID-19 patients and 2,000 non-COVID cases, along with their diagnosis reports were collected from reputed online sources. Convolutional neural network models were trained on this multimodal dataset, for prediction of COVID-19 induced pneumonia. A comprehensive clinical decision support system powered by ensemble deep learning models (CADNN) is designed and deployed on the web*. The system also provides a relevance feedback mechanism through which it learns multimodal COVID-19 representations for supporting clinical decisions.

COVID-19 pandemic in East Africa: analisys and prospects

The article examines the impact of the COVID-19 pandemic on the economic and political situation in East Africa, drawing from reports by leading international organizations in the field issued from March to June 2020. According to these findings, it is tourism, an important pillar of East African economies, and the nascent aviation industries that are going to take the biggest hit, leading to an inevitable spike in external borrowing. The slowly growing interregional trade is unlikely to compensate for these losses. Besides, the pervasive refugee and IDPs problem, as well as widespread HIV are further exacerbating the epidemiological situation. The article also outlines a number of opportunities to manage the unfolding crisis, which include, inter alia, the existing regional integration tools and instruments, in particular, the ones offered by the East African Community. In addition to the national strategies employed by the countries individually, the EAC could assist in pooling foreign aid to combat COVID-19, which to date remains very modest. In these circumstances, writing off debt is one crucial thing the international community could do to alleviate the burden of the pandemic in East Africa. Another factor that could possibly be playing into the hands of the countries in question is their successful experience in fighting other lethal diseases, such as Ebola fever. Politically, the way East African governments handle the pandemic might well become a litmus test showing how much confidence the populations have in their leaders, either reinforcing or undermining their positions. If unsuccessful, anti-COVID-19 policies could fuel popular discontent;the opposite scenario implies a stronger vertical of power and enhanced personal authority of the leaders in charge. The authors believe that despite its obviously pernicious influence, this pandemic may well become an incentive encouraging some long-awaited change in the region. В статье представлен анализ воздействия пандемии COVID-19 на экономическую и политическую ситуацию в Восточной Африке с опорой на доклады крупнейших международных организаций, вышедшие в марте-июне 2020 г., а также намечены возможные пути выхода из кризиса, в том числе через задействование существующих региональных механизмов. Авторы предполагают, что при всех её безусловно губительных последствиях, в частности, социально-экономического характера, нынешняя пандемия может стать для региона своеобразным импульсом, который подтолкнёт его к давно назревшим переменам.