Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets.

Erythrocyte concentrates recovered from under-collected whole blood: experimental and clinical results.

Background: Although periodic blood shortages are widespread in major Chinese cities, approximately 1 x 10(5) U of whole blood are discarded yearly because of under-collection. To reduce the wastage of acid citrate dextrose solution B (ACD-B) anticoagulated under-collected whole blood (UC-WB), this study was performed to elucidate the effect of extracellular pH and holding time on erythrocyte quality. Mannitol-adenine-phosphate (MAP) erythrocyte concentrates (UC-RBCs) were prepared with UC-WB to assess the safety and efficacy of this component.

[Long-term clinical observation of an accommodative 1CU after implantation].

Objective: To evaluate the clinical effectiveness and accommodative range after implanting the 1CU.

Methods: It was a prospective case series study. From March in 2004 to December in 2007, 23 cases (28 eyes) had phacoemulsification and implantation of 1CU (HumanOptics).