Background

Hemoglobin S percentages are used in the management of patients who have sickle cell disease. However, hemoglobin S measurements often are not routinely or rapidly performed. Rapid and accurate methods to estimate hemoglobin S levels after simple transfusion may improve the care of patients with sickle cell disease.

Study design and methods

A comprehensive review of the electronic medical record identified 24 stable patients with sickle cell disease who received simple red blood cell transfusions and had hemoglobin S measurements before and after the transfusion that were less than 72 hours apart. Examination of these patients identified 62 separate transfusions that met our criteria. Three simple equations that utilized complete blood count values and readily available information from the medical record were used to predict the post-transfusion hemoglobin S level after transfusion (Equation 1: predicted post-transfusion hemoglobin = pre-transfusion hemoglobin S × [pre-transfusion hemoglobin/post-transfusion hemoglobin]; Equation 2: predicted post-transfusion hemoglobin S = pre-transfusion hemoglobin S × [pre-transfusion hematocrit/post-transfusion hematocrit]; and Equation 3: predicted post-transfusion hemoglobin S = pre-transfusion hemoglobin S × total pre-transfusion hemoglobin/[total pre-transfusion hemoglobin + (red blood cell volume × 20)]).

Results

The predicted hemoglobin S values for all three equations showed a highly significant correlation with the measured post-hemoglobin S value. The coefficient of determination (R² ) for Equations 1, 2, and 3 was 0.95, 0.92, and 0.97, respectively. Predicting the post-transfusion hemoglobin S value using estimates of the patient's total hemoglobin and the transfused hemoglobin (Equation 3) was the most precise.

Conclusion

Reductions in hemoglobin S values in patients with sickle cell disease who receive simple red blood cell transfusions can be reliably predicted using complete blood cell measurements and simple arithmetic equations.

Background

Platelet clumping is a common occurrence during peripheral blood hematopoietic stem cell (HSC) collection using the Spectra Optia mononuclear cell (MNC) protocol. If clumping persists, it may prevent continuation of the collection and interfere with proper MNC separation. This study is the first to report the incidence of clumping, identify precollection factors associated with platelet clumping, and describe the degree to which platelet clumping interferes with HSC product yield.

Study design and methods

In total, 258 HSC collections performed on 116 patients using the Optia MNC protocol were reviewed. Collections utilized heparin in anticoagulant citrate dextrose to facilitate large-volume leukapheresis. Linear and logistic regression models were utilized to determine which precollection factors were predictive of platelet clumping and whether clumping was associated with product yield or collection efficiency.

Results

Platelet clumping was observed in 63% of collections. Multivariable analysis revealed that a lower white blood cell count was an independent predictor of clumping occurrence. Chemotherapy mobilization and a lower peripheral blood CD34+ cell count were predictors of the degree of clumping. Procedures with clumping had higher collection efficiency but lower blood volume processed on average, resulting in no difference in collection yields. Citrate toxicity did not correlate with clumping.

Conclusion

Although platelet clumping is a common technical problem seen during HSC collection, the total CD34+ cell-collection yields were not affected by clumping. WBC count, mobilization approach, and peripheral blood CD34+ cell count can help predict clumping and potentially drive interventions to proactively manage clumping.

Obesity is associated with an increased risk of developing breast cancer and is also associated with worse clinical prognosis. The mechanistic link between obesity and breast cancer progression remains unclear, and there has been no development of specific treatments to improve the outcome of obese cancer patients. Here we show that obesity-associated NLRC4 inflammasome activation/ interleukin (IL)-1 signalling promotes breast cancer progression. The tumour microenvironment in the context of obesity induces an increase in tumour-infiltrating myeloid cells with an activated NLRC4 inflammasome that in turn activates IL-1β, which drives disease progression through adipocyte-mediated vascular endothelial growth factor A (VEGFA) expression and angiogenesis. Further studies show that treatment of mice with metformin inhibits obesity-associated tumour progression associated with a marked decrease in angiogenesis. This report provides a causal mechanism by which obesity promotes breast cancer progression and lays out a foundation to block NLRC4 inflammasome activation or IL-1β signalling transduction that may be useful for the treatment of obese cancer patients.