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McGlothlin, M.

Publications and source records attributed to McGlothlin, M..

2 recordsLinked to original sources

Differential regulation of myeloid Scavenger Receptor A (CD204) expression in humans versus mice in response to sepsis

Sepsis is the number one cause of mortality in the critically ill patient. The underlying pathophysiology of sepsis is not fully understood. Scavenger Receptor A (SR-A) is a promiscuous receptor of myeloid cells that binds multiple pathogen associated molecular patterns (PAMPs). We have demonstrated that SR-A promotes mortality in murine sepsis. The present study sought to examine the effect of sepsis on myeloid SR-A expression in septic mice and compare it to sepsis patients. Leukocytes were isolated from mice after cecal ligation and puncture. Blood leukocytes were isolated from sepsis patients. Myeloid cells were isolated from healthy mice or human subjects and were treated with PAMPs. Leukocyte phenotype and SR-A expression were determined by flow cytometry. CLP increased SR-A expression when compared to sham operated animals on both neutrophils and monocytes/macrophages. In contrast, SR-A expression was not changed on either neutrophils or monocytes from sepsis patients when compared to healthy control subjects. Treatment of murine neutrophils and macrophages with Pam3CSK4 and treatment of macrophages with LPS increased SR-A expression. Human neutrophil SR-A expression was not altered by any PAMPs, and only treatment with C. albicans glucan increased SR-A expression on human monocytes. Myeloid SR-A expression is increased in septic mice, and this increase plays a detrimental role in the response to sepsis, while SR-A expression is not changed in leukocytes from septic patients and does not correlate with SOFA II scores. Thus, myeloid SR-A expression does not appear to play significant a role in the pathophysiology of human sepsis.

immunology↗

A Comparison of Lossless Compression Methods in Microscopy Data Storage Applications

Modern high-throughput microscopy methods such as light-sheet imaging and electron microscopy are capable of producing petabytes of data inside of a single experiment. Storage of these large images, however, is challenging because of the difficulty of moving, storing, and analyzing such vast amounts of data, which is often collected at very high data rates (>1GBps). In this report, we provide a comparison of the performance of several compression algorithms using a collection of published and unpublished datasets including confocal, fMOST, and pathology images. We also use simulated data to demonstrate the efficiency of each algorithm as image content or entropy increases. As a result of this work, we recommend the use of the BLOSC algorithm combined with ZSTD for various microscopy applications, as it produces the best compression ratio over a collection of conditions. CCS CONCEPTS* Applied computing [->] Bioinformatics; Imaging.

bioinformatics↗