Purpose: This study assessed the effect of oxygen affinity and molecular weight (MW) of o-raffinose cross-linked hemoglobin based oxygen carriers (HBOCs) on cerebral oxygen delivery and mean arterial blood pressure (MAP) following hemorrhage and resuscitation in rats.
Methods: Isoflurane anesthetized rats (n = 6-7 per group) underwent 30% hemorrhage and resuscitation with an equivalent volume of one of three different HBOCs: 1) High P50 Poly o-raffinose hemoglobin (Poly OR-Hb, P50 = 70 mmHg); 2) High P50 > 128 Poly OR-Hb (MW > 128 kDa, P50 = 70 mmHg) and 3) Low P50 > 128 Poly OR-Hb (MW >128 kDa, P50 = 11 mmHg). Hippocampal cerebral tissue oxygen tension, regional cerebral blood flow (rCBF), MAP, total hemoglobin concentration and arterial blood gases were measured. Data analysis by two-way ANOVA and post hoc Tukey tests determined significance (P < 0.05, mean +/- SD).
Results: Hippocampal tissue oxygen tension increased in all HBOC groups following resuscitation. The rCBF remained unchanged after HBOC resuscitation in all groups. Following resuscitation, the peak MAP was higher in the High P50 Poly OR-Hb group (152 +/- 13 mmHg) when compared to either the Low or High P50 large MW, (> 128 kDa) HBOC group (119 +/- 15 mmHg or 127 +/- 18 respectively, P < 0.05 for both).
Conclusions: O-raffinose polymerized HBOC, with or without lower MW components, maintained cerebral tissue oxygen delivery following hemorrhage and resuscitation in rats. The higher MW HBOCs showed a decrease in peak MAP, which did not alter oxygen delivery. No significant effect of oxygen affinity on cerebral tissue oxygen tension or blood flow was observed.
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http://dx.doi.org/10.1007/BF03022533 | DOI Listing |
J Thorac Dis
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Department of Surgery, Yale University School of Medicine, Yale University, New Haven, CT, USA.
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January 2025
Department of Plant Biology, Faculty of AgriSciences, Mendel University in Brno, Zemědělská, 1665/1, 61300, Brno, Czech Republic.
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Indiana Alzheimer Disease Research Center and Center for Neuroimaging, Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis Indiana USA.
Introduction: The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.
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for the CKiD Study Investigators and the NIDDK CKD Biomarkers Consortium, 3500 Civic Center Boulevard, Philadelphia, PA, 19041, USA.
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