Abstract The para-Bombay phenotype is a rare H-deficient blood group variant characterized by markedly reduced or absent expression of H on red blood cells (RBCs) because of mutations in FUT1, with variable secretor status influenced by FUT2. The Bombay phenotype is relatively more common in the Indian population, but the para-Bombay phenotype remains exceedingly rare and is frequently underrecognized because of diagnostic challenges during routine serologic testing. We report molecularly confirmed para-Bombay siblings from India in whom rouleaux-induced pseudoagglutination mimicked anti-H reactiv ity. The index case demonstrated absent reactivity with anti-H lectin and weak reactivity with pooled group O RBCs during reverse grouping and thermal amplitude testing, initially raising suspicion of clinically significant anti-H. However, detailed serologic evaluation, including microscopic examination, saline replacement, adsor ption–elution studies, and saliva inhibition testing, favored rouleaux-induced pseudoagglutination over true anti-H–mediated agglutination. Molecular analysis identified homozygous FUT1 c.328G>A and FUT2 c.357C>T mutations in both affected siblings, consistent with para-Bombay secretor phenotype and similar to previously reported variants in the Telangana population. This case highlights rouleaux-induced pseudoagglutination as an impor tant diagnostic pitfall in para-Bombay phenotypes. Integration of conventional serologic methods with molecular characterization was essential for accurate diagnosis, appropriate transfusion planning, and avoidance of unnecessary restrictions on compatible blood components.
Abstract The D–– phenotype is an exceptionally rare Rh blood group phenotype characterized by expression of D in the complete absence of C, c, E, and e. Individuals with this phenotype are at risk of developing anti-Rh17 (anti-Hr 0 ), a clinically significant alloantibody that can severely complicate transfusion support and pregnancy management because compatible blood is exceptionally difficult to obtain. This review critically synthesizes the available published evidence regarding the D–– phenotype and anti-Rh17 alloimmunization, integrating data from case reports, case series, family investigations, and molecular studies from different geographic regions. Current evidence regarding molecular mechanisms, immunohematologic character istics, serologic identification, transf usion management, pregnancy outcomes, and rare donor strategies is summarized, w ith emphasis on practical considerations relevant to transfusion medicine. Although the D–– phenotype is exceptionally rare, early recognition, comprehensive immunohematologic evaluation, and proactive transfusion planning, including collaboration with rare donor programs, are essential for optimizing patient outcomes.
Abstract G is present on virtually all red blood cells expressing either D or C; thus, identification of anti-G, anti-D, and anti-C in a patient’s plasma can be challenging. While identification of anti-G is usually of little clinical significance, it becomes increasingly important in the context of prenatal care. Proper antibody differentiation is necessary to determine candidacy for Rh immune globulin administration to prevent anti-D immunization during pregnancy. We conducted an analytical experimental study of 18 plasma samples from donors and patients with apparent anti-D and anti-C upon initial antibody identification. Samples were tested in duplicate using two established anti-G differentiation methods. Method A consisted of sequential adsorption and elution techniques, and Method B consisted of double adsorption and elution techniques. Of the 18 samples obtained, 14 fit the criteria for inclusion in this study. Both methods successfully identified anti-G and anti-D when present. Method A failed to identify anti-C in one sample. Our results generated a percent positive agreement of 92.9 percent with a 95 percent confidence interval of 68.5–98.7. Our findings demonstrated that Method B detected more antibodies than Method A and allowed for a reduction in testing time, making it the preferred method in this study.
Abstract Artificial intelligence (AI) and machine learning (ML) are transforming immunohematology, offering significant enhancements in precision, accuracy, and personalized transfusion care. This expert overview summarizes current applications of AI/ML in phenotypic and genotypic red blood cell (RBC) antigen determination, antibody detection, crossmatching, and quality control. Recent studies demonstrate the efficacy of image-based and deep learning methods, with accuracies reaching 99 percent for automated phenotyping and genotyping. AI-assisted models for antibody detection and compatibility assessment have also emerged, providing rapid, standardized decision-making that reduces human error and improves transf usion outcomes. Notably, AI-driven platforms like the Haem-Match Consortium’s bloodMatcher potentially reduce alloimmunization risks by as much as 80 percent, highlighting the technology’s clinical relevance. AI also bolsters hemovigilance and quality control by analyzing vast clinical data for early detection of adverse events. Challenges remain, however, including algorithmic bias, data privacy, and the need for extensive real-world validation to ensure safe and ethical adoption. Integration with existing hospital information systems and development of robust regulatory frameworks are crucial to fully harness these tools. Future research should prioritize validation in diverse populations and transparent AI development practices. Overall, AI and ML hold promise to revolutionize immunohematology by enhancing accuracy, minimizing adverse transfusion reactions, and supporting patient-centric transfusion care.
Abstract Antibodies to Class I human leukocyte antigens (HLAs) are a major immunologic cause of platelet transfusion refractoriness (PTR). HLA inheritance is highly related to ethnicity. Because most platelet donors are White, Hispanic patients may be at an increased risk of developing HLA antibodies due to mismatch between donor and recipient. This retrospective study analyzed 16 Hispanic patients with PTR to evaluate HLA antibodies associated with PTR. Baseline mean fluorescent intensity (MFI) was used to measure HLA antibody detection. Additive MFI values and clinical cut points were evaluated for Class I HLAs. Additionally, eplet exposition scores for antibody-verified eplets were totaled and compared with corrected count increment (CCI). Significant negative correlations were found between CCI with total additive MFI ( r = –0.656) and allele-specific MFI for HLA A*03:01 ( r = –0.728), C*14:02 ( r = –0.795), and C*01:02 ( r = –0.727). Analysis of cut points yielded significance between negative and strong MFI for HLA B*07:02. Data reveal that antibodies against common HLA alleles, such as A*03:01, correlate with PTR, highlighting the potential impact on alloimmunization and PTR risks in Hispanic patients.
