Objective: To compare and analyze the antibacterial effects of platelets against five common clinical pathogenic bacteria including MRSA, SE, SA, E. coli, and CRKP, and to preliminarily explore the role of DCD sensitivity in the observed variations of antibacterial effects. Methods: The same number of platelets were used to establish co-culture systems of platelets and platelet lysates with the five pathogenic bacteria. The antibacterial effects of platelets and platelet lysates on the five pathogenic bacteria were evaluated by observing the turbidity of the bacterial solution, measuring the OD value of the bacterial solution and counting the colonies. The supernatant protein of platelets co-cultured with MRSA was collected for quantitative proteomics analysis to explore the important antibacterial proteins of platelets. The content of DCD in the supernatant after co-culture of platelets and platelet lysates with the five pathogenic bacteria was detected by ELISA to preliminarily analyze the reasons for the different antibacterial effects of platelets on the five pathogenic bacteria. Results: Compared with the control group of MRSA, SA, and SE, the turbidity of the bacterial solution decreased after co-culture of platelets and platelet lysates with MRSA, SA, and SE for 12 h, and the OD value and colony count were significantly reduced (P<0.05). The turbidity of the bacterial solution did not change significantly after co-culture of platelets and platelet lysates with E. coli for 24 h, but the OD value decreased (P<0.05), and the colony count decreased to 10 CFU/mL but the difference was not statistically significant (P>0.05). Compared with the control group of CRKP, the turbidity, OD value, and colony count of the bacterial solution did not change significantly after co-culture of platelets and platelet lysates with CRKP (P>0.05). Proteomics results showed that after co-culture with MRSA, important proteins related to platelet activation, including collagen, fibrinogen, von Willebrand factor, integrin αIIbβ3, platelet glycoprotein V and IV were significantly up-regulated. ELISA results showed that after co-culture with the five pathogenic bacteria, platelets could secrete a large amount of DCD, with the content around 3 μg/mL. Conclusion: The antibacterial effect of platelets on Gram-positive bacteria MRSA, SA, and SE is better than that on Gram-negative bacteria E. coli and CRKP, and platelets have the best antibacterial effect on MRSA. The differences in antibacterial effects of platelets on the five pathogenic bacteria may be related to the sensitivity of DCD antibacterial peptides to the five pathogenic bacteria.

Objective: To compare the changes in the concentration of relevant growth factors released from platelet-rich plasma (PRP) stored at -80℃ by cryopreservation and at 4℃ by refrigerated lyophilization over 2 years, aiming to provide a theoretical basis for prolonging PRP storage duration. Methods: PRP (n=15) was separated using a blood cell separator and stored under -80℃ cryopreservation (F-PRP group) and 4℃ refrigerated freeze-drying conditions (FD-PRP group). The contents of growth factors (PDGF-AA, PDGF-BB, EGF, TGF-β1, and VEGF) in both groups were measured by ELISA at 1, 3, 6, 9, 12 and 24 months. Results: PDGF-AA and VEGF maintained good stability in both groups for up to 24 months. PDGF-BB and TGF-β1 showed high stability in the first 12 months but their stability decreased gradually from 12th to 24th months. EGF demonstrated good stability in the first 6 months, and its stability gradually decreased from the 9th to 24th months. Comparing the F-PRP and FD-PRP groups, the concentrations of the five growth factors in the FD-PRP group were either not statistically different or higher than those in the F-PRP group at all time points. Specifically, the concentrations of EGF were significantly higher in the FD-PRP group at all time points. Conclusion: Both -80℃ freezing and 4℃ freeze-drying enable long-term preservation of PRP. Freeze-drying imposes less stringent storage requirements and facilitates growth factor compared to frozen storage.

Objective: To evaluate the feasibility of exempting Asian-type DEL pregnant women from anti-D monitoring and RhD immunoglobulin prophylaxis injections by comparing and analyzing the clinical incidence of anti-D alloimmunization between Asian-type DEL pregnant women and true RhD-negative pregnant women. Methods: A total of 165 pregnant women who were initially screened as RhD negative by the saline method and received medical treatment in our hospital from December 2022 to August 2024 were collected as the research subjects. Absorption and elution tests, DEL genotyping, and gene sequencing were used to divide the pregnant women into the Asian-type DEL group and the true negative group. After obtaining informed consent, the following clinical management plan was implemented for pregnant women with Asian-type DEL: exemption from routine anti-D antibody detection, exemption from RhD immunoglobulin prophylaxis, and transfusion of RhD-positive red blood cells. Blood samples of newborns were sent for examination of hemolytic disease of the fetus and newborn (HDFN). The routine management plan was implemented for true negative pregnant women. The incidence of alloimmunization and HDFN was comparatively analyzed between the two groups. Results: Among 165 initially screened RhD negative pregnant women, serological testing and genotyping confirmed 42 as Asian-type DEL, 9 as D variant, and 114 as true negative. Among 42 pregnant women with Asian-type DEL, 3 cases tested positive for HDFN due to receiving RhD immunoglobulin prophylaxis injection. The remaining 39 cases were exempted from anti-D testing after being fully informed of the risk, and did not receive RhD immunoglobulin prophylaxis. The HDFN tests were all negative. In the true negative group, anti-D antibodies were detected in 20 cases, of which 6 cases tested positive for HDFN. A pregnant woman with Asian -type DEL did not show RhD homologous immune response after receiving 2 units of RhD positive red blood cells. Statistical analysis revealed a significantly lower risk of anti-D alloimmunization in Asian-type DEL carriers compared to true D-negative pregnant women (P<0.05). Conclusion: Pregnant women with Asian-type DEL can be exempted from routine anti-D antibody testing and do not require routine RhD immunoglobulin prophylaxis injections.

Objective: To explore the risk factors of allogeneic blood transfusion during lung transplant surgery and prognostic effects of transfusion by analyzing the basic data, surgical details, laboratory tests results, and intraoperative blood transfusion details during the perioperative period of lung transplant, so as to guide clinical blood use. Methods: A retrospective analysis was conducted on the data of 319 patients who underwent lung transplantation surgery in our hospital from January 2022 to December 2023. The patients were divided into a non-transfusion group (n=70) and a transfusion group (n=249) based on their intraoperative blood transfusion status. The clinical data, surgical details, perioperative laboratory results and other relevant preoperative and postoperative parameters were compared between the two groups, and the postoperative prognosis (improvement, non-recovery, and death) was analyzed. Results: After comparison between the two groups of patients, it was found that the non-transfusion group had higher levels of preoperative Hb (g/L)(144.41±17.66 vs 129.78±20.44), preoperative Hct [43.25(40.23, 47.5) vs 40.7(37, 43.55)], preoperative TBIL (μmol/L)[11.45(9.15, 15.3)vs 9.9(6.88, 13.33)], and postoperative PLT (×10 /L)(167.74±64.43 vs 132.37±54.84) than the transfusion group (all P<0.05). The non-transfusion group had lower levels of preoperative pCO (mmHg)[41.4(37.4, 45.8)vs 45.3(40, 52.48)], postoperative TBIL (μmol/L)[25.45(17.68, 33.95)vs 30.8(21.55, 43.05)], postoperative pH (7.41±0.09 vs 7.45±0.10), bilateral lung transplantation [27(38.6%) vs 157(63.1%)], surgical duration (h) [5(4, 7)vs 6.5(5, 8)], use of ECMO [52(74.3%) vs 232(93.2%)], and intraoperative blood loss (mL)[600(500, 800)vs 1 000(800, 1 500)] compared to the transfusion group (all P<0.05). The items with P<0.1 in the compared indicators were included in the binary logistic regression analysis, and the results showed that bilateral lung transplantation, intraoperative blood loss, preoperative TBIL, postoperative PLT, postoperative TBIL, preoperative pCO2, and postoperative pH were significantly correlated with whether blood transfusion was performed (P<0.05). The P values of the Chi-square test for postoperative improvement and mortality in the non transfusion group and transfusion group were both greater than 0.05, indicating no statistically significant difference in the prognosis rate between the two groups of patients. Conclusion: Bilateral lung transplantation, intraoperative blood loss, preoperative TBIL, and preoperative pCO are risk factors for blood transfusion during lung transplantation. Intraoperative blood transfusion has a significant impact on postoperative PLT, postoperative TBIL, and postoperative pH indicators, but has no significant effect on prognosis. A comprehensive evaluation of laboratory indicators and surgical details can help developing blood transfusion strategies more effectively.

Objective: To investigate the effects of blood component transfusion on the prognosis of patients with varying severity of traumatic brain injury (TBI). Methods: A retrospective analysis was conducted on clinical data from 621 TBI patients admitted between January 2012 and December 2022. The patients in the blood transfusion group were categorized into three groups based on Glasgow Coma Scale (GCS) scores: severe impairment (GCS 3-8, n=302), moderate impairment (GCS 9-12, n=186), and mild impairment (GCS 13-14, n=133). General clinical data and laboratory test indexes were analyzed. Patients were further divided into two subgroups based on in-hospital mortality: death group (n=72) vs survival group (n=549). Univariate and multivariate logistic regression analysis was used to analyze the effects of different blood component transfusion volumes on the prognosis of TBI patients. ROC curve was used to evaluate the prognostic value of red blood cell transfusion volume. Results: Patients with GCS scores 3-8 had significantly longer hospital stays (21.73±15.89 vs 20.83±11.54 vs 15.5±7.76) and higher RBC transfusion volumes (6.16±6.79 vs 4.67±2.81 vs 3.67±3.20) than the other two groups (P<0.05). NLR, PCT, CRP, PT, Fib, FDP and DDI after the last transfusion showed significant differences from pre-transfusion values (P<0.05). The death group exhibited higher transfusion volumes of RBCs, plasma, platelets, and cryoprecipitate compared with the survival group (P<0.05). Univariate (OR: 1.541, 95%CI: 1.412-1.682) and multivariate (OR: 1.522, 95%CI: 1.362-1.700) logistic regression analyses showed that the RBC transfusion volume was a risk factor affecting the prognostic factors of TBI patients after infusion of blood components. ROC curve analysis showed that RBC transfusion volume could serve as a prognostic marker (sensitivity: 0.708, specificity: 0.812). Conclusion: Blood component transfusion alters inflammatory and coagulation markers in patients with different degrees of TBI, and RBC transfusion volume is a viable prognostic indicator for TBI outcomes.

Objective: To evaluate the clinical effect of blood transfusion treatment in elderly critically ill patients under different blood transfusion initiation thresholds. Methods: A total of 144 elderly critically ill patients aged >70 years who underwent red blood cell transfusion in the elderly intensive care unit (ICU) of our hospital from January 2021 to January 2023 were included. According to different blood transfusion initiation thresholds, the patients were divided into restrictive blood transfusion group (n=77, Hb<70 g/L before blood transfusion) and liberal blood transfusion group (n=67, Hb 70-100 g/L before blood transfusion). Acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score, estimated mortality and general data collection were performed when the two groups of patients entered the ICU. Blood transfusion details of these patients in the ICU were collected and documented, including pre-transfusion Hb levels, volume and number of red blood cell transfusion, and post- transfusion Hb levels. Propensity score matching (PSM) was used to match the baseline data of the two groups of patients, and the clinical outcomes were compared and analyzed after matching. Results: After PSM matching, 52 pairs of patients were successfully matched. The matched restrictive and liberal transfusion groups showed comparable characterists, including age, APACHE Ⅱ score, the number of cases with APACHE Ⅱ score >20, estimated mortality, incidence of comorbidities and primary diseases (P>0.05). The number of red blood cell transfusions and transfusion volume (U) in the ICU of the two groups were 7.77±4.73 vs 12.19±10.41, 11.64±7.65 vs 19.14±16.14 (all P<0.05), and the Hb levels (g/L) before and after red blood cell transfusion in the ICU was 59.92±5.98 vs 77.44±8.60,77.88±17.21 vs 87.56±15.23 (all P<0.05). In terms of clinical outcomes, there was no significant difference between the two groups (all P>0.05): ICU length of stay (d) 39.56±36.80 vs 40.10±49.29, three-week mortality rate (%) 21.2 vs 21.2, in-hospital mortality rate (%) 46.2 vs 53.9, mortality rate in subgroup with APACHE Ⅱ score ≤ 20 (%) 11.5 vs 1.9, the incidence of severe infection (%) 78.8 vs 73.1, the incidence of heart failure (%) 57.7 vs 44.2, and the incidence of pulmonary edema (%) 26.9 vs 19.2. Conclusion: Elderly ICU patients can tolerate lower blood transfusion thresholds. Therefore, the restrictive transfusion strategy can reduce the total amount of blood transfusion, save valuable blood resources, and achieve the same blood transfusion effect as the liberal transfusion strategy.

Objective: To retrospectively analyze the early monitoring indicators before and after admission, the use of coagulation/anticoagulant medications, and the red blood cell transfusion within 24 hours in emergency trauma patients, and to identify the indicators related to the volume of red blood cells transfused during the first 24 hours of hopitalization, thereby assisting clinical judgment of the probability and required quantity of red blood cell transfusion. Methods: Data of 117 emergency trauma patients admitted to intensive care unit (ICU) from January 2022 to March 2024 were retrospectively analyzed. Patients were categorized according to whether the volume of red blood cells transfused within 24 hours exceeded specific quartile thresholds (Q1, Q2, Q3). Non-parametric tests were used for numerical variables and Chi-square tests were used for categorical variables to identify statistically significant single-factor indicators, which were subsequently incoporated into a binary logistic regression model to obtain a combined predictive probability. ROC curve analysis was performed on the multi-factor indicators and their combined predictive probability derived from the binary logistic regression model. Results: 1) The initial hemoglobin (Hb) and hematocrit (Hct) levle were independent influencing factors in the group with red blood cell transfusion volume exceeded Q1 (P<0.05), and the combined predictive probability demostrated by ROC curve analysis was AUC=0.858 (P<0.05). 2) In the group of red blood cell transfusion volume exceeding Q2, the initial Fib, transhulitic acid, human prothrombin complex, trauma category and primary trauma site were independent influencing factors (P<0.05), and the combined predictive probability of ROC curve analysis was AUC=0.966 (P<0.05). 3) Pulse pressure and trauma category were independent influencing factors in the group with red blood cell transfusion volume exceeding Q3 (P<0.05), and ROC curve analysis revealed that combined prediction probability was AUC=0.944 (P<0.05). Conclusion: Early monitoring indicators and the use of coagulation medications, before and after admission in emergency trauma patients show diagnostic value in predicting the amount of red blood cells transfused on the first day of admission. Early warning alerts established through patient monitoring indicators can reduce incidents of untimely blood supply from the blood transfusion department (blood bank) for emergency trauma patients with massive hemorrhage, especially for patients with rare blood types or during blood supply shortage.

Objective: To evaluate the performance of platelet efficacy score (PEscore) in predicting platelet transfusion efficacy in hematological patients. Methods: A total of 485 patients with hematological diseases, including 298 males (62.09±15.45 years) and 187 females (59.17±16.52 years) who received platelet transfusion from January 1, 2021 to December 31, 2024 were enrolled in this study. Clinical data of the patients such as diagnosis, gender, age, number of platelet transfusion, and platelet antibody data were analyzed to investigate the incidence and influencing factors of platelet transfusion refractoriness in hematological patients at our hospital. ROC curve was used to evaluate the performance of PEscore model in predicting platelet transfusion efficacy. The predictive performance of PEscore model was validated by calculating its sensitivity, specificity, and accuracy in 115 clinical cases. Results: The incidence of platelet transfusion refractoriness in 485 cases was 29.90% (145/485). Significant differences (P<0.05) were observed between the effective and ineffective platelet transfusion groups regarding the following factors: diagnosis: lymphoma [55.32% (26/47) vs 44.68% (21/47)], the number of previous platelet transfusions [≥25: 60.78% (31/51) vs 39.22% (20/51)], platelet antibody screening result [positive: 33.76% (53/157) vs 66.24% (104/157)], and platelet transfusion volume (×10 /L) [>6: 62.71% (74/118) vs 37.29% (44/118)]. The area under the ROC curve of PEscore was 0.876. The cut-off points and corresponding sensitivity and specificity were 19.90.59% and 69.44%, respectively. The results of clinical application showed that the sensitivity, specificity and accuracy of the PEscore model for predicting platelet transfusion were 87.50%, 93.41% and 92.17%, respectively. Conclusion: The incidence of platelet transfusion refractoriness in hematological patients is relatively high. PEscore prediction model has a good performance in predicting the effect of platelet transfusion, which can provide a reliable basis for predicting the effect of platelet transfusion in hematological patients before blood transfusion.

Objective: To investigate the distribution characteristics of Kidd blood group antigens, phenotypes and genotypes in Xinjiang and their influence on the risk of coronary heart disease. Methods: Samples from 7 981 patients treated at People's Hospital of Xinjiang Uygur Autonomous Region from August 1, 2023 to May 31, 2024 were collected for Jk(a-b-) phenotype screening via urea hemolysis test, followed by the third-generation sequencing (TGS). Kidd blood group Jk and Jk antigens in 1 081 patients with coronary heart disease and 1 021 healthy people were detected, and their phenotype frequency distribution was analyzed and corresponding gene frequencies were calculated. Correlation analysis and logistic regression were used to evaluate the influence of Kidd blood group antigen expression on coronary heart disease risk. Results: Two Jk(a-b-) phenotype samples were detected, both resulting from novel gene mutation combinations. Comparative analysis of two groups revealed a higher proportion of the Jk(a-b+) phenotype in the case group (22.5%, 243/1 081) than in the control group (18.5%, 189/1 021). Moreover, Kidd blood group phenotype distribution varied significantly across all ethnic groups in the case group (P<0.05). In the control group, the Hui ethnic group exhibited the highest JK JK genotype frequency 64.15% (34/53). In the case group, the highest JK allele frequency was observed in Mongol ethnic group 56.31% (125/222), and the lowest in Han patients 45.71% (341/746). The expression of Jk antigen was negatively correlated with coronary heart disease (P<0.05). Conclusion: The distribution of Kidd blood group system varied across ethnic groups in Xinjiang. The expression of Jk antigen may have protective effect on coronary heart disease, which provides a basis for future clinical blood transfusion treatment and the mechanism study of the correlation between Kidd blood group and coronary heart disease.

Objective: To analyze the distribution characteristics of immunoglobulin A (IgA) concentration in Nanning Zhuang blood donors by measuring the concentration of plasma IgA. Methods: Enzyme-linked immunosorbent assay (ELISA) was performed to measure the absorbance of 2 000 plasma samples from Zhuang blood donors. The IgA concentration in samples was calculated using the ELISA Calc regression/fitting technology program. Results: The standard curve demonstrated that ELISA detection of plasma IgA concentration exhibited good precision. The frequency of IgA deficiency was 0/2 000. No statistically significant difference in the distribution of IgA concentration was observed between males and females (P>0.05). The distribution of IgA concentration varied significantly across age groups: younger individuals (18-39 years old) had lower plasma IgA levels (mg/dL) compared to older individuals (40-56 years old): 5-89.99 mg/dL group, 8.80% (176/2 000) vs 17.20% (344/2 000); 90-450 mg/dL group,20.65% (413/2 000) vs 51.20% (1 024/2 000); >450 mg/dL group, 0.45%, (9/2 000) vs 1.70% (34/2 000), P<0.05. No significant difference in IgA concentration was found among different ABO blood types in Zhuang blood donors (P>0.05). Spearman correlation analysis revealed a positive correlation between age and IgA concentration (R =0.114, P<0.05). Conclusion: No individuals with IgA deficiency were screened out among the Zhuang blood donors in Nanning area, and plasma IgA levels progressively increase with age.