Vox Sanguinis最新文献

Background and objectives: Numerous A and B subgroups can cause discrepancies between forward and reverse typing, weak reactivity or unexpected antibody patterns. This study aimed to identify the most prevalent ABO subgroups in a population of Southeast Brazil, characterize their serological profiles and evaluate their impact on ABO typing.

Study design and methods: Donor samples collected in São Paulo State, Brazil, were included in the study and classified into two groups according to predefined selection criteria. Group 1 comprised 27 samples with reduced or negative reactivity with anti-A1 lectin, selected from 225 donors with routine Group A typing. Group 2 comprised 33 samples selected over a 2-year period due to atypical ABO typing and subsequently subjected to complementary serological testing. All samples were molecularly characterized using polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) and Sanger sequencing.

Results: In Group 1, the predominant allele was ABO*A2.01, representing 10.2% of all type A donors. In Group 2, the most frequent variant allele was ABO*AW.09 (33%), followed by ABO*AW.31 (11%) and ABO*AEL.01 (11%). All these three subgroups were implicated in cases of ABO mistyping. B subgroups were identified in two samples and were associated with molecular changes in intron 1 (+5.8-kb site).

Conclusion: Although ABO subgroups are uncommon, they occur in our population and directly affect blood typing. The results demonstrate the presence of clinically significant variants and highlight the need for vigilance in donor screening, including careful interpretation of typing results, the use of complementary serological testing and molecular analysis when necessary.

Background and objectives: Pathogen reduction (PR) using amotosalen-UVA was implemented for 100% of platelet concentrates (PCs) in France in November 2017. No bacterial testing was in place earlier. The impact of PR on the risk of transfusion-transmitted infections (TTIs) from November 2017 to December 2022 (vs. January 2013 to October 2017) has been published previously. To further assess the impact of PR implementation, the evaluation period was expanded to December 2024.

Materials and methods: PC-associated TTIs occurring in 2023 and 2024 were analysed and included in the assessment.

Results: Two cases of PC-associated transfusion-transmitted bacterial infections (TTBIs) were reported. The first involved Bacillus mobilis in a split pooled whole blood-derived PC. This Grade 2 TTBI was diagnosed following identification of the same pathogen in the second PC unit, quarantined for failed swirling test. The second involved Staphylococcus ureilyticus in an apheresis PC transfused to a patient who subsequently died. TTBI frequency decreased from 1/97,098 PC (n = 15 over 5 years, 2 deaths) to 1/787,662 PC (n = 3, over 7 years, 1 death) after PR implementation (p < 0.001). No human immunodeficiency virus (HIV), hepatitis B virus (HBV), hepatitis C virus (HCV), human T-cell lymphotropic virus (HTLV) or arboviruses TTI were reported from 2013 to 2024. No further cases of PC-associated hepatitis E virus (HEV) TTI were reported, concomitantly to HEV-nucleic acid testing implementation. A PC-associated parvovirus B19 TTI occurred in 2024. The use of PR PCs facilitated PC supply during arboviral outbreaks.

Conclusion: The reduction in PC-related TTBIs following PR implementation is confirmed. However, the recent occurrence of two severe TTBI cases highlights a persistent risk. Prevention of PR-PC-associated TTIs by non-enveloped viruses requires specific screening.

Background and objectives: Accurate detection of transfusion-transmissible infections, such as human immunodeficiency virus (HIV) and hepatitis C and B viruses (HCV/HBV), is critical to ensure blood safety, and screening assays must demonstrate high specificity and sensitivity. In this study, we compared the performances of Elecsys® HIV Duo, HCV Duo, Anti-HCV II and HBsAg II immunoassays with those of comparator assays HIV Ag/Ab Combo, Anti-HCV II and HBsAg on the Alinity® s platform and HIV Ag/Ab Combo, Anti-HCV and HBsAg Qualitative II on the Alinity i platform.

Materials and methods: Approximately 2050 plasma samples from first-time blood donors and commercial seroconversion panels were used. Specificity was assessed as the proportion of true non-reactive samples identified by each assay. Seroconversion sensitivity was evaluated based on the detection time and average interval from nucleic acid testing (NAT)-positivity to assay reactivity.

Results: Overall, all Elecsys assays evaluated had similar specificities as the corresponding Alinity s/i assays, although the absolute difference in specificity between Alinity i Anti-HCV assay and the other HCV assays was statistically significant. The seroconversion sensitivities of Elecsys HIV Duo and HBsAg II assays were similar to the corresponding Alinity s/i assays. For HCV, Elecsys HCV Duo assay detected infection earlier than Alinity s/i assays for most panels (87.0% and 91.8%, respectively), with an average detection time from NAT positivity for HCV RNA of 1.5 days versus 21.4 and 23.8 days, respectively.

Conclusion: The robust specificities and early detection capabilities of the evaluated Elecsys assays support their routine use in blood donor screening and diagnosis.

Artificial intelligence (AI) and machine learning (ML) are beginning to enhance key workflows across blood banking (BB) and transfusion medicine (TM), including donor engagement, inventory forecasting, immunohaematology interpretation, haemovigilance and clinical decision support. These tools can extract signals from large, diverse datasets to improve efficiency, consistency and timeliness of care while enabling more proactive patient blood management. At the same time, TM is uniquely sensitive to safety and trust, so the field must pair innovation with disciplined validation and governance. This review surveys emerging applications and distils a balanced ethics and oversight framework tailored to TM and BB practice. Core elements include outcome-focused validation and continuous monitoring; targeted fairness testing where biological or scientific plausibility suggests subgroup differences; layered transparency that distinguishes transparency, interpretability and explainability; and privacy by design with proportionate consent. We emphasize human oversight at every step: clinicians and laboratorians evaluate, monitor and troubleshoot models, perform tasks AI cannot, integrate multiple inputs to make decisions and interact with AI through guardrails such as intended-use statements, confidence thresholds and clear override pathways. Accountability is shared across users, service directors, institutions and regulators. When implemented with these safeguards, AI can reduce preventable errors, improve resource allocation and support more equitable access to safe transfusion. The goal is principled pragmatism that protects patients and donors while enabling timely adoption of well-validated tools.

Background and objectives: Platelet antibody screening and identification are crucial and challenging. MacKay Memorial Hospital has been dedicated to this for nearly four decades. We reviewed our experience with platelet antibody detection methods and prevalence, and presented a possible case of post-transfusion purpura caused by anti-CD36.

Materials and methods: We analyzed platelet antibody screening results from 1988 to 2024. We used solid phase red cell adherence test (SPRCA) from 1988 to 2018 (9268 samples), and parallel SPRCA/enzyme-linked immunosorbent assay (ELISA) from 2019 to 2024 (2037 samples). Positive results are further identified through flow cytometry, monoclonal antibody immobilization of platelet antigens (MAIPA), or in-house platelet antigen panels. We also studied a possible case of post-transfusion purpura (PTP) caused by anti-CD36. A 79-year-old woman was treated with numerous platelet transfusions that were ineffective, with generalized purpura appearing a few days later following massive transfusion.

Results: From 1988-2018, 2800 of 9268 samples (30.2%) were positive. Six cases of neonatal alloimmune thrombocytopenia (NAITP) were identified (two anti-HPA-3a, two anti-HPA-5a, and two anti-CD36). From 2019-2024, 778 of 2037 samples (38.19%) were positive. Specific antibodies were identified in eight cases, and six of them were anti-CD36. Introduction of ELISA resulted in a 15-fold detection rate of anti-CD36 (0.02% to 0.29%), suggesting previous underestimation. In the possible PTP case, massive transfusion was started on Day 8 of hospitalization, purpura appeared on Day 15, anti-CD36 was identified on Day 21, and CD36-negative platelet transfusion was initiated on the following day. However, the patient eventually died of multiorgan failure on Day 29.

Conclusion: The prevalence of anti-CD36 antibody in the Taiwanese population was previously underestimated. Clinicians should be aware that not only human platelet antigen (HPA) but CD36 should also be considered when platelet refractoriness persists despite HLA-matched transfusion, as anti-CD36 may cause serious complications.

Background and objectives: Cytomegalovirus (CMV) causes harm in at-risk populations. Selection of seronegative donors has been used to prevent transmission. Leucodepletion reduces the potential for CMV transmission; however, the residual risk is uncertain, leading to variability in practice. This study systematically reviews the risk of CMV transmission in leucodepleted blood products compared to seronegative blood products.

Materials and methods: A systematic review identified comparative studies of CMV infection rates following transfusion of leucodepleted blood from CMV-negative or unselected donors. Preclinical studies on blood product CMV transmission, reported cases and studies that informed population risk were also reviewed. Meta-analysis was performed on comparative studies.

Results: There was no difference in the rate of infection following transfusion of leucodepleted cellular products with or without donor CMV seronegativity selection, with a relative risk of 1.21 (95% confidence interval [CI]: 0.42-3.49). No confirmed cases of CMV transmission were found. Preclinical studies showed a significant reduction in transmissible virus with leucodepletion, although no threshold could be defined. Cell-free CMV is not removed by filtration, and although it may remain a potential source of infection, there was no evidence of transmission through plasma, possibly due to detectable virus not reflecting intact transmissible virus.

Conclusion: Selecting CMV-seronegative donors did not reduce the risk of transmission when transfusing leucodepleted blood products because of the high efficiency of filters in removing transmissible cellular virus. This finding suggests CMV donor negative selection does not substantially contribute to donor safety.

Background and objectives: China faces a tight balance between clinical blood supply and demand. Vasovagal reactions (VVRs) hinder donor recruitment and retention. This study aimed to develop and validate a predictive model for the early identification of high-risk donors to guide targeted interventions.

Materials and methods: This unmatched case-control study enrolled whole-blood donors from a blood station in Xinjiang, China. VVR was defined according to the International Society of Blood Transfusion (ISBT) criteria for donation-related complications. Significant predictors identified by multivariable logistic regression were used to develop a prediction model. Model performance and calibration were evaluated using receiver operating characteristic (ROC) analysis and the Hosmer-Lemeshow (H-L) test.

Results: A total of 319 donors experienced VVRs, and 1276 served as controls. The final prediction model incorporated nine significant predictors, categorized as follows-demographic factors: age, pulse, systolic blood pressure (SBP) and body mass index (BMI); psychological factors: perceived stress level; and behavioural factors: number of donations, regular exercise, postprandial interval (≥4 h) and average sleep duration. The model demonstrated good discrimination, with areas under the ROC curve of 0.830 and 0.827 in the modelling and validation sets, respectively. Good calibration was indicated by H-L test results (modelling set: χ² = 13.817, p = 0.129; validation set: χ² = 8.698, p = 0.466).

Conclusion: The constructed prediction model is effective, providing a reference for blood station staff to assess the risk of VVRs in whole-blood donors and implement early interventions.