Ganglioside-Enriched Phospholipid Vesicles Induce Cooperative Aß Oligomerization and Membrane Disruption.

A major hallmark of Alzheimer's disease (AD) is the accumulation of extracellular aggregates of amyloid-ß (Aß). Structural polymorphism observed among Aß fibrils in AD brains seem to correlate with the clinical subtypes suggesting a link between fibril polymorphism and pathology. Since fibrils emerge from a templated growth of low-molecular-weight oligomers, understanding the factors affecting oligomer generation is important. Membrane lipids are key factors to influence early stages of Aß aggregation and oligomer generation, which cause membrane disruption. We have previously demonstrated that conformationally discrete Aß oligomers can be generated by modulating the charge, composition, and chain length of lipids and surfactants. Here, we extend our studies into liposomal models by investigating Aß oligomerization on large unilamellar vesicles (LUVs) of total brain extracts (TBE), reconstituted lipid rafts (LRs), or 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC). Varying the vesicle composition by specifically increasing the amount of GM1 gangliosides as a constituent, we found that only GM1-enriched liposomes induce the formation of toxic, low-molecular-weight oligomers. Furthermore, we found that the aggregation on liposome surface and membrane disruption are highly cooperative and sensitive to membrane surface characteristics. Numerical simulations confirm such a cooperativity and reveal that GM1-enriched liposomes form twice as many pores as those formed in the absence GM1. Overall, this study uncovers mechanisms of cooperativity between oligomerization and membrane disruption under controlled lipid compositional bias, and refocuses the significance of the early stages of Aß aggregation in polymorphism, propagation, and toxicity in AD.

A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research.

COVID-19 is a global health crisis that has altered human life and still promises to create ripples of death and destruction in its wake. The sea of scientific literature published over a short time-span to understand and mitigate this global phenomenon necessitates concerted efforts to organize our findings and focus on the unexplored facets of the disease. In this work, we applied natural language processing (NLP) based approaches on scientific literature published on COVID-19 to infer significant keywords that have contributed to our social, economic, demographic, psychological, epidemiological, clinical, and medical understanding of this pandemic. We identify key terms appearing in COVID literature that vary in representation when compared to other virus-borne diseases such as MERS, Ebola, and Influenza. We also identify countries, topics, and research articles that demonstrate that the scientific community is still reacting to the short-term threats such as transmissibility, health risks, treatment plans, and public policies, underpinning the need for collective international efforts towards long-term immunization and drug-related challenges. Furthermore, our study highlights several long-term research directions that are urgently needed for COVID-19 such as: global collaboration to create international open-access data repositories, policymaking to curb future outbreaks, psychological repercussions of COVID-19, vaccine development for SARS-CoV-2 variants and their long-term efficacy studies, and mental health issues in both children and elderly.

Automated data abstraction for quality surveillance and outcome assessment in radiation oncology.

Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic abstraction of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data abstraction, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software abstracts structured DICOM-RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site-specific "Smart" templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data abstracted from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well-defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider.