Figure 4.
Greater accumulated bending energy is associated with higher acid SMase activity and increased mature erythrocyte adhesion. (A) Accumulated bending energy distributions of mature erythrocytes under hypoxia vary widely between patients. Three example distributions are shown, with varying distributions for the demonstration. Distributions are represented by 2 parameters for statistical analysis: mean accumulated bending energy and percent cells above 12.5 nJ cutoff. (B) Accumulated bending energy distribution parameters associated with serum acid SMase activity of patients with SCD that are not recently been transfused (P = .022, for mean accumulated bending energy, and P = .030 for percent cells above 12.5 nJ cutoff, N = 5). (C) An increase in mature erythrocyte adhesion with hypoxia is associated with mean accumulated bending energy (P = .002, N = 16) and the percentage of adhered mature erythrocytes with relatively higher accumulated bending energy (P < .001, N = 16). P values are calculated using linear regression.

Greater accumulated bending energy is associated with higher acid SMase activity and increased mature erythrocyte adhesion. (A) Accumulated bending energy distributions of mature erythrocytes under hypoxia vary widely between patients. Three example distributions are shown, with varying distributions for the demonstration. Distributions are represented by 2 parameters for statistical analysis: mean accumulated bending energy and percent cells above 12.5 nJ cutoff. (B) Accumulated bending energy distribution parameters associated with serum acid SMase activity of patients with SCD that are not recently been transfused (P = .022, for mean accumulated bending energy, and P = .030 for percent cells above 12.5 nJ cutoff, N = 5). (C) An increase in mature erythrocyte adhesion with hypoxia is associated with mean accumulated bending energy (P = .002, N = 16) and the percentage of adhered mature erythrocytes with relatively higher accumulated bending energy (P < .001, N = 16). P values are calculated using linear regression.

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