BACKGROUND: Bone marrow transplantation with minor ABO incompatibility may be followed by moderate delayed hemolysis of the recipient's red cells by donor‐derived ABO antibodies. This reaction may be more severe after transplantation of peripheral blood progenitor cells (PBPCs).
CASE REPORT: A 16‐year‐old boy underwent an allogeneic PBPC transplant from his HLA‐mismatched mother as treatment for acute myeloblastic leukemia that had proved resistant to induction chemotherapy. Transfusion of the unmanipulated PBPCs proceeded without any complication, despite the difference in ABO blood group (donor, O Rh‐positive; recipient, A Rh‐positive). On Day 7, a rapid drop in hemoglobin to 4 g per dL was observed, which was attributed to a massive hemolysis. All the recipient's group A red cells were destroyed within 36 hours. This delayed and rapidly progressive hemolytic anemia was not associated with the transfusion of the donor's plasma. Rather, the anti‐A titer increased in parallel with marrow recovery, which suggested an active synthesis of these antibodies by immunocompetent cells from the donor against the recipient's red cells. The mother's anti‐A titer was retrospectively found to be 2048. Her unusually high titer is probably due to prior sensitization during pregnancies. On Day 12, the patient developed grade IV graft‐versus‐host disease, which proved resistant to all treatments instituted and led to his death on Day 35.
CONCLUSION: PBPC transplantation with minor ABO incompatibility may be associated with significant risk of massive delayed hemolysis.
The technologies used in mineral process engineering are evolving. The digital mineral processing solutions are based on advances in our ability to instrumentally measure phenomena at several stages of the beneficiation circuit, manage the data in real-time, and to analyze these data using machine learning to develop the next generation of process control. The main purpose of this study is to overview various digital solutions for mineral processing plants and characterization laboratories while emphasizing their utilization in the current state of the digitization process of the GTK Mintec. This study highlights the specialized digital technologies that are particularly relevant for mineral processing and beneficiation. The digital solutions studied in this article include digital twin, machine vision, information management system, sensors, smart equipment, machine learning techniques, process control system, robotic cell, and Internet of Things applied across the whole chain of studying materials from the mineralogical examinations through the bench-scale studies to the pilot test trials. The aim is to provide a clear view on the different aspects of digitizing mineral processing plants based upon the lessons learned from the development plans in GTK Mintec.
Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.