Introduction:In immunohematology case studies the knowledge about variants of the blood group gene of interest can facilitate antibody diagnosis. Known gene variants can be rapidly genotyped by specific methods, but the identification of unknown variants requires sequencing of the gene. High throughput next-generation sequencing (NGS) technologies represent important tools for DNA sequencing of many targets in larger numbers of samples but are less suitable for the analysis of one or few genes in single samples. Nanopore sequencing (Oxford Nanopore Technologies, ONT) is a fast sequencing technology that could fulfill the requirements for targeted gene sequencing in case studies. Here, we describe an optimized protocol for long-read nanopore sequencing of blood group genes that enables analysis of whole genes within less than 7 h from DNA extraction to genotype determination. Methods:Primers for long-range PCR (LR-PCR) were designed for the blood group genes ACKR1, CD151, BCAM, KEL, SLC14A1, GYPA, GYPB, GYPE, RHD, and RHCE with amplicon sizes in the range of 2.4-15.8 kilo base pairs (kbp). For evaluation of the sequencing data, 22 samples with 25 known gene variants were selected. The optimized sequencing workflow included DNA extraction from EDTA blood, LR-PCR amplification, library preparation, nanopore sequencing on the MinION Mk1D sequencing device with FLO-MIN114 (MinION) flow cells and data analysis including variant detection and genotyping. In addition, the workflow was tested on the MinION sequencing device with FLO-FLG114 (Flongle) flow cells and the PromethION 2 Solo sequencing device with FLO-PRO114 (PromethION) flow cells. Results:Using the outlined long-read nanopore sequencing protocol, sequencing data for reliable variant calling were obtained. All alleles were identified and the zygosity could be determined based on the read counts, except for GYPB in one sample. Besides sequencing on the MinION Mk1D sequencing device MinION flow cells, successful application of the sequencing protocol to the Flongle flow cells and the PromethION sequencing device using PromethION flow cells was demonstrated. As expected, mean coverage and mean Q scores varied between the flow cells and devices. Conclusion:The optimized nanopore sequencing protocol enabled the generation of long-read sequence data and identification of blood group gene variants within a working day. This approach is suitable for molecular analyses of different blood group genes in immunohematology case studies under the same LR-PCR and sequencing conditions.

BACKGROUND:In immune thrombocytopenia (ITP), platelet-intrinsic alterations and their potential contribution to disease pathogenesis remain incompletely understood. Since platelets, although anucleate, retain a functional transcriptome, transcriptomic profiling may provide insight into disease-associated changes. OBJECTIVES:To investigate platelet-intrinsic transcriptomic changes in ITP and to determine whether these alterations are disease-specific compared with non-ITP. METHODS:Total RNA sequencing was performed on purified platelets from patients with active ITP (n = 6), chemotherapy-induced thrombocytopenia as a non-ITP control (n = 6), ITP in treatment-free remission (n = 6), and healthy controls (n = 8). RESULTS:Platelets from patients with active ITP exhibited higher total RNA content, consistent with enrichment of young, recently released platelets. Transcriptomic analysis identified a profile dominated by platelet activation pathways, including integrin- and calcium-dependent signaling, cytoskeletal remodeling, and vesicle trafficking. In contrast, genes involved in mitochondrial biogenesis and oxidative phosphorylation were downregulated, accompanied by a reduced proportion of mitochondrial-derived RNA. Although both active ITP and chemotherapy-induced thrombocytopenia showed elevated RNA content relative to healthy controls, reflecting enhanced platelet turnover, the coordinated upregulation of activation-associated transcripts was unique to active ITP. Platelets from ITP patients in treatment-free remission showed partial normalization of the transcriptional profile, with loss of the activation signature and intermediate expression patterns between active ITP and healthy controls. CONCLUSION:RNA sequencing data show distinct platelet transcriptomic changes in ITP, marked by coordinated upregulation of platelet activation pathways and reduced mitochondrial gene expression. This profile is consistent with an activation-primed, metabolically altered platelet state and may reflect disease-related changes in platelet biology in ITP.

Congenital platelet disorders are rare and targeted treatment is usually not possible. Inherited platelet function disorders (iPFDs) can affect surface receptors and multiple platelet responses such as defects of platelet granules, signal transduction, and procoagulant activity. If iPFDs are also associated with a reduced platelet count (thrombocytopenia), it is not uncommon to be misdiagnosed as immune thrombocytopenia. Because the bleeding tendency of the different platelet disorders is variable, a correct diagnosis of the platelet defect based on phenotyping, function analysis, and genotyping is essential, especially in the perioperative setting. In the case of a platelet receptor deficiency, such as Bernard-Soulier syndrome or Glanzmann thrombasthenia, not only the bleeding tendency but also the risk of isoimmunization after platelet transfusions or pregnancy has to be considered. Platelet granule disorders are commonly associated with either intrinsically quantitative or qualitative granule defects due to impaired granulopoiesis, or granule release defects, which can also affect additional signaling pathways. Functional platelet defects require expertise in the clinical bleeding tendency in terms of the disorder when using antiplatelet agents or other medications that affect platelet function. Platelet defects associated with hematological-oncological diseases require comprehensive information about the patient including the clinical implication of the genetic testing. This review focuses on genetics, clinical presentation, and laboratory platelet function analysis of iPFDs with or without reduced platelet number. As platelet defects affecting the cytoskeleton usually show thrombocytopenia, but less impaired or normal platelet functional responses, they are not specifically addressed.

In this article, our goal is to offer an introduction and overview of the diagnostic approach to inherited platelet function defects (iPFDs) for clinicians and laboratory personnel who are beginning to engage in the field. We describe the most commonly used laboratory methods and propose a diagnostic four-step approach, wherein each stage requires a higher level of expertise and more specialized methods. It should be noted that our proposed approach differs from the ISTH Guidance on this topic in some points. The first step in the diagnostic approach of iPFD should be a thorough medical history and clinical examination. We strongly advocate for the use of a validated bleeding score like the ISTH-BAT (International Society on Thrombosis and Haemostasis Bleeding Assessment Tool). External factors like diet and medication have to be considered. The second step should rule out plasmatic bleeding disorders and von Willebrand disease. Once this has been accomplished, the third step consists of a thorough platelet investigation of platelet phenotype and function. Established methods consist of blood smear analysis by light microscopy, light transmission aggregometry, and flow cytometry. Additional techniques such as lumiaggregometry, immune fluorescence microscopy, and platelet-dependent thrombin generation help confirm and specify the diagnosis of iPFD. In the fourth and last step, genetic testing can confirm a diagnosis, reveal novel mutations, and allow to compare unclear genetics with lab results. If diagnosis cannot be established through this process, experimental methods such as electron microscopy can give insight into the underlying disease.

Thrombozyten, auch Blutplattchen genannt, sind zellulare Blutbestandteile, die fur ihre wichtige Rolle bei der Blutstillung bekannt sind. In der primaren Hamostase sorgen die Thrombozyten fur den Verschluss verletzter Gefa ss e durch Bildung des sogenannten wei ss en Thrombus. An diesem Prozess sind verschiedene Glykoproteine beteiligt und sorgen sowohl fur die Anheftung von Thrombozyten an subendotheliale Matrixproteine als auch fur deren Aktivierung und Quervernetzung. Aktivierte Thrombozyten setzen zudem eine Vielzahl unterschiedlicher Mediatoren, Cytokine und Chemokine frei. Dies lasst vermuten, dass Thrombozyten uber die Hamostase hinaus auch an anderen physiologischen und pathophysiologischen Prozessen beteiligt sind. Mit der zunehmenden Erkenntnis uber die molekularen und zellularen Mechanismen wurden diese Prozesse als Thrombo- Inflammation oder Immunothrombosis bezeichnet.

The Complement Receptor 1 (CR1) carries the Knops blood group antigens (KN; ISBT 022).1, 2 CR1 consists of 30 complement control protein (CCP) domains grouped into four long homologous repeats (LHR-A to -D). The antigens KN1 to KN10 are located in LHR-D while the antithetical antigens KN11 and KN12 are located in LHR-C.3 Antibodies to Knops antigens are not clinically significant but they are often found in patients causing problems in ruling out additional relevant antibodies. In a female patient (M.B.; index case) of Ethiopian origin with transfusion history and a severe COVID-19 pneumonia, we found an antibody to a high prevalence antigen. After ruling out antibodies to a number of high prevalence antigens, an antibody to a Knops antigen was suspected, because it could be inhibited by the Knops/DACY recombinant protein. Molecular analysis of CR1 was conducted for M.B. and a nonrelated African patient (Y.U.) who was nonreactive with M.B.'s plasma. Antibody identification was performed by the gel technique in the indirect antiglobulin test using different commercial panels (BioRad, Switzerland) with untreated and papain treated red cells negative for high prevalence antigens. Recombinant proteins Chido, Rodgers, JMH, Kn(a), and DACY (imunsyn GmbH, Hannover, Germany) were used for inhibition assays of M.B.'s plasma. Furthermore, recombinant CR1 proteins for LHR-C, LHR-C_1097Pro, LHR-C_1100Gly, and LHR-C_1097Pro-1100Gly were produced according to previously described procedures and used for inhibition assays.4 Molecular analysis of CR1 was included in targeted next generation sequencing of all exons of the blood group genes encoding the systems ISBT 001 to 043. Genomic DNA from M.B. and Y.U. was sequenced according to standard protocols for amplicon-based library generation with the iSeq 100 system (illumina Inc., Berlin, Germany). For data analysis, the Variant Interpreter (illumina Inc.) and the Integrative Genomics Viewer (IGV) tools were used.5 Genotyping of CR1 c.3290T>C (rs200111726) was performed according to standard PCR-SSP protocol with forward primers for the wild type allele (5′-GTGACCTACCGCTGCAATCT-3′) and the variant allele (5′-GTGACCTACCGCTGCAATCC-3′), a reverse primer (5′-TGGAGGCGTGCATTTGTTAGG-3′), and primers for an internal control amplified from the HBB gene.6 The antibody of M.B. was reactive with all test cells of the antibody identification panels and 29 test red cells negative for different high prevalence antigens. It was nonreactive with Y.U.'s red cells only and with all papain-treated red cells. An antibody to known KN antigens was ruled out. Recombinant Kn(a), Chido, Rodgers and JMH did not inhibit the antibody, whereas, it was inhibited by the DACY recombinant protein, indicating that the corresponding antigen is probably located in LHR-C. Molecular analysis revealed 5 homozygous missense variants in CR1 of both patients: 3 known CR1 variants (c.3623A>G, c.4801A>G, c.4843A>G) and 2 variants in exon 21 c.3290T>C (p.Leu1097Pro; rs200111726) and c.3298A>G (p.Arg1100Gly; rs202070239). PCR-SSP for c.3290T>C confirmed the sequencing results. Inhibition assays using the different recombinant CR1 proteins showed inhibition of the patient's antibody with LHR-C and LHR-C_1100Gly but no inhibition with LHR-C_1097Pro and LHR-C_1097Pro-1100Gly (Figure 1). The inhibition with LHR-C and LHR-C_1100Gly proved that the antibody is directed against p.1097Leu. Using an antibody to a high prevalence antigen found in a previously transfused Ethiopian patient we identified a new Knops blood group antigen located in LHR-C region of the Knops protein. In commemoration of the index patient who died from severe COVID-19 the provisional antigen name (KNMB) was derived from the initials. The antigen number KN13 was assigned by the ISBT working party on Red Cell Immunogenetics and Blood Group Terminology at the ISBT meeting in Gothenburg, Sweden, in June 2023. KNMB is defined by p.1097Leu and homozygosity for p.1097Pro in both patients caused the KNMB-negative phenotype. The underlying CR1 variant rs200111726T>C is rare (0.02%) in the European population, but more frequent (3.9%) in the African population.7 Accordingly, only 1 of 25,000,000 Europeans (0.000004%) but 1 of 625 Africans (0.16%) are expected to be KNMB negative. The authors have disclosed no conflicts of interest. Open Access funding enabled and organized by Projekt DEAL.

Introduction: The molecular diagnosis of the A(1) blood group is based on the exclusion of ABO gene variants causing blood groups A(2), B, or O. A specific genetic marker for the A(1) blood group is still missing. Recently, long-read ABO sequencing revealed four sequence variations in intron 1 as promising markers for the ABO*A1 allele. Here, we evaluated the diagnostic values of the 4 variants in blood donors with regular and weak A phenotypes and genotypes. Methods: ABO phenotype data (A, B, AB, or O) were taken from the blood donor files. The ABO genotypes (low resolution) were known from a previous study and included the variants c.261delG, c.802G>A, c.803G>C, and c.1061delC. ABO variant alleles (ABO*AW.06,*AW.08,*AW.09,*AW.13, *AW.30, and *A3.02) were identified in weak A donors by sequencing the ABO exons before. For genotyping of the ABO intron 1 variants rs532436, rs1554760445, rs507666, and rs2519093, we applied TaqMan assays with endpoint fluorescence detection according to a standard protocol. Genotypes of the variants were compared with the ABO phenotype and genotype. Evaluation of diagnostic performance included sensitivity, specificity, positive (PPV), and negative predictive value (NPV). Results: In 1,330 blood donors with regular ABO phenotypes and genotypes, the intron 1 variants were significantly associated with the proposed A(1) blood group. In 15 donors, we found discrepancies to the genotype of at least one of the 4 variants. For the diagnosis of the ABO*A1 allele, the variants showed 98.79-99.48% sensitivity, 99.66-99.81% specificity, 98.80-99.31% PPV, and 99.66-99.86% NPV. Regarding the A phenotype, the diagnostic values were 99.02-99.41% sensitivity, 99.63-99.76% specificity, 99.41-99.61% PPV, and 99.39-99.63% NPV. The *A1 marker allele of all intron 1 variants was also associated with the *AW.06, *AW.13, and *AW.30 variants. Samples with *AW.08, *AW.09, and *A3.02 variants lacked this association. Conclusion: The ABO intron 1 variants revealed significant association with the ABO*A1 allele and the A phenotype. However, the intron 1 genotype does not exclude variant alleles causing weak A phenotypes. With the introduction of reliable tag, single nucleotide variants for the A(1), A(2), B, and O blood groups and the genotyping instead of phenotyping of the ABO blood group are getting more feasible on a routine basis.

Artificial intelligence (AI) has rapidly evolved over the past few decades and has now become an integral part of our daily lives. From virtual assistants like Siri and Alexa to personalized shopping recommendations, AI technology has made our lives easier, more convenient, and efficient. AI has also found applications in fields such as healthcare, finance, transportation, and education, where it is being used to solve complex problems and enhance decisionmaking processes. With the advent of smart homes, selfdriving cars, and intelligent personal assistants, AI is poised to transform the way we live, work, and interact with the world around us. As AI technology continues to advance, it is likely that we will see even more widespread adoption of AI applications in our daily lives. AI and machine learning have shown potential in various medical fields, including transfusion medicine. AI can be applied to transfusion medicine to improve the safety and efficacy of blood transfusions. For example, machine learning algorithms can be used to identify potential adverse reactions to blood transfusions and predict the need for transfusions in critically ill patients. AI can also assist in the matching of blood types, ensuring that patients receive the correct blood transfusion and minimizing the risk of complications. Additionally, AI can be used to monitor and track blood inventory levels, reducing wastage and ensuring that an adequate supply of blood products is always available. As the technology continues to advance, AI has the potential to transform transfusion medicine, improving patient outcomes and enhancing the safety and efficiency of blood transfusions. In the first paper in this special issue of Transfusion Medicine and Hemotherapy, D’Alessandro [1] and Lopes et al. [2] summarize key advances in the field of omics, big data, and AI in transfusion medicine. Omics and big data, coupled with machine learning algorithms, are revolutionizing transfusion medicine by enabling the analysis of large, complex datasets and the identification of new biomarkers for diagnosis and treatment. Omics refers to the study of various biological molecules, including genomics (study of DNA polymorphisms in donors and recipients), transcriptomics (study of RNAs and miRNAs in stored blood units), proteomics (study of proteins and post-translational modifications, like oxidation, phosphorylation, and methylation), and metabolomics (study of metabolites). These omics data can be analyzed with machine learning algorithms to identify patterns and relationships between different biological molecules, which can be used to develop new biomarkers of storage quality and predictors of transfusion efficacy. For example, machine learning algorithms can be used to analyze genomics data to predict patient blood group phenotypes – as summarized by Thun et al. [3] in this issue, which can be useful in blood transfusion matching – especially for rare blood groups. Similarly, proteomics data can be analyzed to identify potential biomarkers for transfusion reactions, and metabolomics data can be used to monitor the development of the storage lesion and metabolic responses to blood transfusions. As the volume and complexity of omics data continue to grow, the use of big data and machine learning will become increasingly important in transfusion medicine, leading to improved patient outcomes and personalized treatments.

DNA methylation based age prediction is a new method in the toolbox of forensic genetics. Typically, the method is applied in the course of police investigation e.g. to predict the age of an unknown person that has left a biological trace at a crime scene. The method can also be used to answer other forensic questions, for example to estimate the age of unknown human bodies in the course of the identification process. In the present study, we tested for a potential impact of biogeographic ancestry (BGA) on age predictions using five age dependent methylated CpG sites within the genetic regions of ELOVL2, MIR29B2CHG, FHL2, KLF14 and TRIM59. We collected 102 blood samples each from donors living in Iraq, Middle East (ME) and Germany, Central Europe (EU). Both sample sets were matched in sex and age ranging from 18 to 68 years with exactly one male and female sample per year of age. All samples were analyzed by bisulfite pyrosequencing applying a multiplex pre-amplification strategy based on a single input of 35 ng converted DNA in the PCR. For the CpGs in MIR29B2CHG, FHL2 and KLF14, we observed significantly different methylation levels between the two populations. While we were able to train two highly accurate prediction models for the respective population with mean absolute deviations between predicted and actual ages (MAD) of 3.34 years for the ME model, and 2.72 years for the EU model, we found an absolute prediction difference between the two population specific models of more than 4 years. A combined model for both populations compensated the methylation difference between the two populations, providing MADs of prediction of only 3.81 years for ME and 3.31 years for EU samples. In total, the results of the present study strongly support the benefit of BGA information for more reliable methylation based age predictions.

Background: The duration of anti-SARS-CoV-2-antibody detectability up to 12 months was examined in individuals after either single convalescence or convalescence and vaccination. Moreover, variables that might influence an anti-RBD/S1 antibody decline and the existence of a post-COVID-syndrome (PCS) were addressed. Methods: Forty-nine SARS-CoV-2-qRT-PCR-confirmed participants completed a 12-month examination of anti-SARS-CoV-2-antibody levels and PCS-associated long-term sequelae. Overall, 324 samples were collected. Cell-free DNA (cfDNA) was isolated and quantified from EDTA-plasma. As cfDNA is released into the bloodstream from dying cells, it might provide information on organ damage in the late recovery of COIVD-19. Therefore, we evaluated cfDNA concentrations as a biomarker for a PCS. In the context of antibody dynamics, a random forest-based logistic regression with antibody decline as the target was performed and internally validated. Results: The mean percentage dynamic related to the maximum measured value was 96 (±38)% for anti-RBD/S1 antibodies and 30 (±26)% for anti-N antibodies. Anti-RBD/S1 antibodies decreased in 37%, whereas anti-SARS-CoV-2-anti-N antibodies decreased in 86% of the subjects. Clinical anti-RBD/S1 antibody decline prediction models, including vascular and other diseases, were cross-validated (highest AUC 0.74). Long-term follow-up revealed no significant reduction in PCS prevalence but an increase in cognitive impairment, with no indication for cfDNA as a marker for a PCS. Conclusion: Long-term anti-RBD/S1-antibody positivity was confirmed, and clinical parameters associated with declining titers were presented. A fulminant decrease in anti-SARS-CoV-2-anti-N antibodies was observed (mean change to maximum value 30 (±26)%). Anti-RBD/S1 antibody titers of SARS-CoV-2 recovered subjects boosted with a vaccine exceeded the maximum values measured after single infection by 235 ± 382-fold, with no influence on preexisting PCS. PCS long-term prevalence was 38.6%, with an increase in cognitive impairment compromising the quality of life. Quantified cfDNA measured in the early post-COVID-19 phase might not be an effective marker for PCS identification.

The C-type lectin-like receptor 2 (CLEC-2) is expressed on platelets and mediates binding to podoplanin (PDPN) on various cell types. The binding to circulating tumor cells (CTCs) leads to platelet activation and promotes metastatic spread. An increased level of soluble CLEC-2 (sCLEC-2), presumably released from activated platelets, was shown in patients with thromboinflammatory and malignant disease. However, the functional role of sCLEC-2 and the mechanism of sCLEC-2 release are not known. In this study, we focused on the effect of platelet activation on CLEC-2 expression and the sCLEC-2 plasma level in patients with cancer. First, citrated blood from healthy volunteer donors (n = 20) was used to measure the effect of platelet stimulation by classical agonists and PDPN on aggregation, CLEC-2 expression on platelets with flow cytometry, sCLEC-2 release to the plasma with ELISA and total CLEC-2 expression with Western blot analysis. Second, sCLEC-2 was determined in plasma samples from healthy donors (285) and patients with colorectal carcinoma (CRC; 194), melanoma (160), breast cancer (BC; 99) or glioblastoma (49). PDPN caused a significant increase in the aggregation response induced by classical agonists. ADP or PDPN stimulation of platelets caused a significant decrease in CLEC-2 on platelets and sCLEC-2 in the plasma, whereas total CLEC-2 in platelet lysates remained the same. Thus, the increased plasma level of sCLEC-2 is not a suitable biomarker of platelet activation. In patients with CRC (median 0.9 ng/mL), melanoma (0.9 ng/mL) or BC (0.7 ng/mL), we found significantly lower sCLEC-2 levels (p < 0.0001), whereas patients with glioblastoma displayed higher levels (2.6 ng/mL; p = 0.0233) compared to healthy controls (2.1 ng/mL). The low sCLEC-2 plasma level observed in most of the tumor entities of our study presumably results from the internalization of sCLEC-2 by activated platelets or binding of sCLEC-2 to CTCs.

COVID-19 convalescent plasma (CCP) with high neutralizing antibodies has been suggested in preventing disease progression in COVID-19. In this study, we investigated the relationship between clinical donor characteristics and neutralizing anti-SARS-CoV-2 antibodies in CCP donors. COVID-19 convalescent plasma donors were included into the study. Clinical parameters were recorded and anti-SARS-CoV-2 antibody levels (Spike Trimer, Receptor Binding Domain (RBD), S1, S2 and nucleocapsid protein) as well as ACE2 binding inhibition were measured. An ACE2 binding inhibition < 20% was defined as an inadequate neutralization capacity. Univariate and multivariable logistic regression analysis was used to detect the predictors of inadequate neutralization capacity. Ninety-one CCP donors (56 female; 61%) were analyzed. A robust correlation between all SARS-CoV-2 IgG antibodies and ACE2 binding inhibition, as well as a positive correlation between donor age, body mass index, and a negative correlation between time since symptom onset and antibody levels were found. We identified time since symptom onset, normal body mass index (BMI), and the absence of high fever as independent predictors of inadequate neutralization capacity. Gender, duration of symptoms, and number of symptoms were not associated with SARS-CoV-2 IgG antibody levels or neutralization. Neutralizing capacity was correlated with SARS-CoV-2 IgG antibodies and associated with time since symptom onset, BMI, and fever. These clinical parameters can be easily incorporated into the preselection of CCP donors.

Background and Objectives Non-invasive assays for predicting foetal blood group status in pregnancy serve as valuable clinical tools in the management of pregnancies at risk of detrimental consequences due to blood group antigen incompatibility. To secure clinical applicability, assays for non-invasive prenatal testing of foetal blood groups need to follow strict rules for validation and quality assurance. Here, we present a multi-national position paper with specific recommendations for validation and quality assurance for such assays and discuss their risk classification according to EU regulations. Materials and Methods We reviewed the literature covering validation for in-vitro diagnostic (IVD) assays in general and for non-invasive foetal RHD genotyping in particular. Recommendations were based on the result of discussions between co-authors. Results In relation to Annex VIII of the In-Vitro-Diagnostic Medical Device Regulation 2017/746 of the European Parliament and the Council, assays for non-invasive prenatal testing of foetal blood groups are risk class D devices. In our opinion, screening for targeted anti-D prophylaxis for non-immunized RhD negative women should be placed under risk class C. To ensure high quality of non-invasive foetal blood group assays within and beyond the European Union, we present specific recommendations for validation and quality assurance in terms of analytical detection limit, range and linearity, precision, robustness, pre-analytics and use of controls in routine testing. With respect to immunized women, different requirements for validation and IVD risk classification are discussed. Conclusion These recommendations should be followed to ensure appropriate assay performance and applicability for clinical use of both commercial and in-house assays.

Background: The coronavirus disease-2019 (COVID-19) is a systemic disease with severe implications on the vascular and coagulation system. A procoagulant platelet phenotype has been reported at least in the acute disease phase. Soluble P-selectin (sP-sel) in the plasma is a surrogate biomarker of platelet activation. Increased plasma levels of sP-sel have been reported in hospitalized COVID-19 patients associated with disease severity. Here, we evaluated in a longitudinal study the sP-sel plasma concentration in blood donors who previously suffered from moderate COVID-19. Methods: 154 COVID-19 convalescent and 111 non-infected control donors were recruited for plasma donation and for participation in the CORE research trial. First donation (T1) was performed 43-378 days after COVID-19 diagnosis. From most of the donors the second (T2) plasma donation including blood sampling was obtained after a time period of 21-74 days and the third (T3) donation after additional 22-78 days. Baseline characteristics including COVID-19 symptoms of the donors were recorded based on a questionnaire. Platelet function was measured at T1 by flow cytometry and light transmission aggregometry in a representative subgroup of 25 COVID-19 convalescent and 28 control donors. The sP-sel plasma concentration was determined in a total of 704 samples by using a commercial ELISA. Results: In vitro platelet function was comparable in COVID-19 convalescent and control donors at T1. Plasma samples from COVID-19 convalescent donors revealed a significantly higher sP-sel level compared to controls at T1 (1.05 +/- 0.42 ng/mL vs. 0.81 +/- 0.30 ng/mL; p < 0.0001) and T2 (0.96 +/- 0.39 ng/mL vs. 0.83 +/- 0.38 ng/mL; p = 0.0098). At T3 the sP-sel plasma level was comparable in both study groups. Most of the COVID-19 convalescent donors showed a continuous decrease of sP-sel from T1 to T3. Conclusion: Increased sP-sel plasma concentration as a marker for platelet or endothelial activation could be demonstrated even weeks after moderate COVID-19, whereas, in vitro platelet function was comparable with non-infected controls. We conclude that COVID-19 and additional individual factors could lead to an increase of the sP-sel plasma level.

Background: Genetic mutations in various pancreatic enzymes or their counteracting proteins have been linked to chronic pancreatitis. In particular, variants in the genes encoding pancreatic lipase (PNLIP) and carboxyl ester lipase (CEL) have been associated with pancreatitis. Therefore, we investigated pancreatic phospholipase A2 (PLA2G1B) as a promising candidate gene in patients with chronic pancreatitis. Methods: We analyzed all coding exons and adjacent intronic regions of PLA2G1B in 416 German patients with non-alcoholic chronic pancreatitis (NACP) and 186 control subjects by direct DNA sequencing. Results: We detected 2 frequent synonymous variants in exon 3: c.222T>C (p.Y74 = ) and c.294G>A (p.S98 = ). The genotype and allele frequencies of these variants were similar between patients and controls (c.222 TC: 9.6% in NACP vs. 9.7% in controls; c.222CC: 0.2% in NACP vs. 0% in controls; c.294 GA: 31.3% in NACP vs. 28.0% in controls; c.294AA: 2.4% in NACP vs. 1.1% in controls). All p-values were nonsignificant. In addition, we found one synonymous variant, c.138C>T (p.N46 = ) and one non synonymous variant, c.244A>G (p.S82G), in a single case each. Conclusions: Our results suggest that genetic alterations in PLA2G1B do not predispose to the development of non-alcoholic chronic pancreatitis. (c) 2022 IAP and EPC. Published by Elsevier B.V. All rights reserved.

A gradual decay in humoral and cellular immune responses over time upon SAR1S-CoV-2 vaccination may cause a lack of protective immunity. We conducted a longitudinal analysis of antibodies, T cells, and monocytes in 25 participants vaccinated with mRNA or ChAdOx1-S up to 12 weeks after the 3rd (booster) dose with mRNA vaccine. We observed a substantial increase in antibodies and CD8 T cells specific for the spike protein of SARS-CoV-2 after vaccination. Moreover, vaccination induced activated T cells expressing CD69, CD137 and producing IFN-γ and TNF-α. Virus-specific CD8 T cells showed predominantly memory phenotype. Although the level of antibodies and frequency of virus-specific T cells reduced 4-6 months after the 2nd dose, they were augmented after the 3rd dose followed by a decrease later. Importantly, T cells generated after the 3rd vaccination were also reactive against Omicron variant, indicated by a similar level of IFN-γ production after stimulation with Omicron peptides. Breakthrough infection in participants vaccinated with two doses induced more SARS-CoV-2-specific T cells than the booster vaccination. We found an upregulation of PD-L1 expression on monocytes but no accumulation of myeloid cells with MDSC-like immunosuppressive phenotype after the vaccination. Our results indicate that the 3rd vaccination fosters antibody and T cell immune response independently from vaccine type used for the first two injections. However, such immune response is attenuated over time, suggesting thereby the need for further vaccinations.