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Role of Krüppel-Like Factors in Endothelial Cell Function and Shear Stress–Mediated Vasoprotection
Published in Juhyun Lee, Sharon Gerecht, Hanjoong Jo, Tzung Hsiai, Modern Mechanobiology, 2021
Antiphospholipid syndrome is an autoimmune disease characterized by recurrent arterial and venous thrombosis in the presence of antiphospholipid antibodies (APLA). It is most frequently seen in systemic lupus erythematosus. The majority of APLA antagonize phospholipid-binding proteins, particularly against β2-glycoprotein I (β2GPI). Anti-β2GPI antibodies activate endothelial cells to induce inflammation through the downregulation of KLF2/ KLF4. This in turn leads to upregulation of NF-xB and the expression of proinflammatory molecules. As described earlier, KLFs sequester the cotranscriptional activator CBP/p300, making it unavailable for NF-xB. Competition between KLF and NF-xB for CBP/p300 is likely the mechanism of this phenotype as restoration of KLF2 or KLF4 expression inhibits NF-xB transactivation and blocks APLA/anti-β2GPI-mediated endothelial activation [94].
Intravenous Immunoglobulin at the Borderline of Nanomedicines and Biologicals: Antithrombogenic Effect via Complement Attenuation
Published in Raj Bawa, János Szebeni, Thomas J. Webster, Gerald F. Audette, Immune Aspects of Biopharmaceuticals and Nanomedicines, 2019
Antiphospholipid syndrome (APS) is an autoimmune, hypercoagulable state caused by antibodies to negatively charged phospholipids (aPL). APS is characterized by thrombosis in both arteries (myocardial infarction, stroke) and veins (deep vein thrombosis, pulmonary embolism) as well as pregnancy-related complications such as miscarriage, stillbirth, preterm delivery, or severe preeclampsia. In rare cases, APS leads to rapid organ failure due to generalized thrombosis; this is termed “catastrophic antiphospholipid syndrome” (CAPS) and is associated with a high mortality rate [15].
Gender-specific associations between polymorphisms in the Toll-like receptor (TLR) genes and lung function among workers in swine operations
Published in Journal of Toxicology and Environmental Health, Part A, 2018
Zhiwei Gao, James A. Dosman, Donna C. Rennie, David A. Schwartz, Ivana V. Yang, Jeremy Beach, Ambikaipakan Senthilselvan
Decision tree analysis has gained attention among computational, biomedical, and medical researchers. The advantages and disadvantages of this method were comprehensively reviewed by Song and Zhang (2014) and Song and Lu (2015). Recently, due to rapid advances in microarray technology, investigators are facing the challenge of how to effectively analyze large numbers of genetic markers from limited numbers of samples in microarray data. Machine learning has been widely used for microarray analysis to identify genetic markers to improve diagnosis and prediction of prognosis or responses in patients receiving a particular treatment of many diseases such as systemic lupus erythematosus, primary antiphospholipid syndrome (Armananzas et al. 2009) and cancers (Deist et al. 2018; Wang 2014). Simplicity and easy interpretation makes this method popular. However, this method has the disadvantage of being unstable, i.e. the optimal decision tree based upon a small dataset is generally unstable, and suffers from overfitting, which limits its generalizability and robustness.