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Abu Zaid, M.

Publications and source records attributed to Abu Zaid, M..

2 recordsLinked to original sources

Isolation of Elementary Nanofibrils of Cellulose from Non-Structural Plant Cells: Hydrothermal Processing as a Generalizable Route

The isolation of cellulose nanofibrils (CNFs), a promising precursor for sustainable and high-performance materials, has relied on chemically intensive, energy-demanding processes. As these processes were originally designed for the isolation of CNFs from wood, we herein show that the intrinsic ultrastructure of non-structural plant cells provides unique opportunities, namely direct access to loosely organized cellulose nanonetworks. We demonstrate that this loose nanofibrillar tissue can be transformed into CNFs with sizes down to elementary nanofibrils ([~]4 nm) at high yields (reaching [~]32%) under exceptionally mild hydrothermal conditions. Three distinct plants were evaluated and the physicochemical properties of the obtained nanonetworks and corresponding CNFs were thoroughly studied, including the hydrodynamics of the resulting gels. Films prepared from the obtained CNFs showed similar performance to those obtained from conventionally isolated wood-based CNFs. Overall, this study demonstrates that CNFs can be obtained through low-intensity, hazard-free, processes from widely available biomass. Thus, this approach offers a unique shift in the range of opportunities to produce CNFs facilitating the integration of their use into the food supply chain, biomedical applications, and other regulatory-constrained applications.

plant biology↗

Diagnostic Evidence GAuge of Single cells (DEGAS): A transfer learning framework to infer impressions of cellular and patient phenotypes between patients and single cells

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single cell and patient bulk tissue transcriptomic datasets from Glioblastoma Multiforme (GBM), Alzheimers Disease (AD), and Multiple Myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on newly generated myeloma single cell transcriptomics led to the identification of PHF19high myeloma cells associated with progression.

bioinformatics↗