Objective assessment of stored blood quality by deep learning.

Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans' assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.

Data-analysis strategies for image-based cell profiling.

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Label-Free Leukemia Monitoring by Computer Vision.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well-recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemotherapy (Campana and Pui: Blood 129 (2017) 1913-1918). Given the potential for machine learning to improve precision medicine, we tested its capacity to monitor disease in children undergoing ALL treatment. Diagnostic and on-treatment bone marrow samples were labeled with an ALL-discriminating antibody combination and analyzed by imaging flow cytometry. Ignoring the fluorescent markers and using only features extracted from bright-field and dark-field cell images, a deep learning model was able to identify ALL cells at an accuracy of >88%. This antibody-free, single cell method is cheap, quick, and could be adapted to a simple, laser-free cytometer to allow automated, point-of-care testing to detect slow early responders. Adaptation to other types of leukemia is feasible, which would revolutionize residual disease monitoring. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Prevalence of antibodies against Hepatitis D virus (HDV) in blood donors in Northern Germany.

OBJECTIVE: To assess the prevalence of HDV infections in German blood donors. METHOD: 167 donors with acute/chronic or resolved HBV infection and detectable antibodies against Hepatitis B core antigen (anti-HBc) were tested for antibodies against HDV (anti-HDV) by competitive ELISA. Samples with detectable anti-HDV or with HBsAg and/or HBV DNA were additionally investigated for HDV RNA. RESULTS: In nine (5.4 %) of the 167 donors, also HBsAg and HBV DNA were detectable. Anti-HDV was detectable in two of the 167 donors (1.2 %), additional four donors (2.4 %) had a borderline result. All of these donors tested negative for HBsAg and HBV DNA. Neither in samples with anti-HDV nor in HBsAg-/HBV DNA-positive samples, HDV RNA was detectable. CONCLUSIONS: At least 1.2 % of anti-HBc-positive blood donors have had an HDV infection. Although there is some evidence for a somewhat higher prevalence of HDV, the overall prevalence of HDV in Northern Germany is low.

Label-Free Identification of White Blood Cells Using Machine Learning.

White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state-of-the-art method for determining WBC differential counts. However, this process requires several sample preparation steps and may adversely disturb the cells. We present a novel label-free approach using an imaging flow cytometer and machine learning algorithms, where live, unstained WBCs were classified. It achieved an average F1-score of 97% and two subtypes of WBCs, B and T lymphocytes, were distinguished from each other with an average F1-score of 78%, a task previously considered impossible for unlabeled samples. We provide an open-source workflow to carry out the procedure. We validated the WBC analysis with unstained samples from 85 donors. The presented method enables robust and highly accurate identification of WBCs, minimizing the disturbance to the cells and leaving marker channels free to answer other biological questions. It also opens the door to employing machine learning for liquid biopsy, here, using the rich information in cell morphology for a wide range of diagnostics of primary blood. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

An open-source solution for advanced imaging flow cytometry data analysis using machine learning.

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using "user-friendly" platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery.

Infectivity of blood products containing cytomegalovirus DNA: results of a lookback study in nonimmunocompromised patients.

BACKGROUND: DNA of human cytomegalovirus (CMV) is frequently detected in plasma of donors with primary CMV infection. It is unknown, however, whether leukoreduced blood products from these donors contain sufficient amounts of infectious virus to cause transfusion-transmitted CMV infections (TT-CMV). STUDY DESIGN AND METHODS: During a 14-year period, CMV DNA-positive donations were identified as part of several previously published studies. Additionally, further donors with seroconversion were tested for CMV DNA. The serostatus of patients who had received a CMV DNA-positive blood product was determined out of pretransfusion samples. Later samples were examined for development of CMV antibodies. Patients with a follow-up of less than 140 days were also tested for CMV DNA. RESULTS: A total of 221 blood products from CMV DNA-positive donations were transfused to 219 recipients. Pretransfusion samples were available for 179 patients, of whom 62 (34.6%) were seronegative. For 39 seronegative recipients of 40 blood products follow-up samples drawn at least 30 days after transfusion were available. The median duration of follow-up was 287 days (range, 38-3784 days). Thirty-six patients were still CMV seronegative in their last sample. Three patients were CMV seropositive due to passive antibody transfer by plasma rich products from seropositive donors, but CMV DNA negative in all tested samples. CONCLUSION: TT-CMV was excluded in all recipients of 40 blood products from CMV DNA-positive donations. This corresponds to a 95% interval of confidence for the risk of TT-CMV of less than 7.4%. Because no patient belonged to a typical at-risk population, the results are only valid for immunocompetent subjects.

Cytomegalovirus Serostatus as Predictor for Adverse Events After Cardiac Surgery: A Prospective Observational Study.

OBJECTIVE: To clarify whether reactivated cytomegalovirus (CMV) infections in critically ill patients lead to worse outcome or just identify more severely ill patients. If CMV has a pathogenic role, latently infected (CMV-seropositive) patients should have worse outcome than seronegative patients because only seropositive patients can experience a CMV reactivation. DESIGN: Post-hoc analysis of a prospective observational study. SETTING: Single university hospital. PARTICIPANTS: The study comprised 983 consecutive patients scheduled for on-pump surgery. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: CMV antibodies were analyzed in preoperative plasma samples. Postoperative adverse events (reintubation, low cardiac output or reinfarction, dialysis, stroke) and 30-day and 1-year mortality were evaluated prospectively. The plasma of reintubated patients and matched control patients was tested for CMV deoxyribonucleic acid, and 618 patients were found to be seropositive for CMV (63%). Among these, the risk for reintubation was increased (10% v 4%, p = 0.001). This increase remained significant after correction for confounding factors (odds ratio 2.70, p = 0.003) and was detectable from the third postoperative day throughout the whole postoperative period. Other outcome parameters were not different. Reintubated seropositive patients were more frequently CMV deoxyribonucleic acid-positive than were matched control patients (40% v 8%, p<0.001). CONCLUSIONS: CMV-seropositive patients had an increased risk of reintubation after cardiac surgery, which was associated with reactivations of their CMV infections. Additional studies should determine whether this complication may be prevented by monitoring of latently infected patients and administering antiviral treatment for reactivated CMV infections.

Synchronization in human musical rhythms and mutually interacting complex systems.

Though the music produced by an ensemble is influenced by multiple factors, including musical genre, musician skill, and individual interpretation, rhythmic synchronization is at the foundation of musical interaction. Here, we study the statistical nature of the mutual interaction between two humans synchronizing rhythms. We find that the interbeat intervals of both laypeople and professional musicians exhibit scale-free (power law) cross-correlations. Surprisingly, the next beat to be played by one person is dependent on the entire history of the other person's interbeat intervals on timescales up to several minutes. To understand this finding, we propose a general stochastic model for mutually interacting complex systems, which suggests a physiologically motivated explanation for the occurrence of scale-free cross-correlations. We show that the observed long-term memory phenomenon in rhythmic synchronization can be imitated by fractal coupling of separately recorded or synthesized audio tracks and thus applied in electronic music. Though this study provides an understanding of fundamental characteristics of timing and synchronization at the interbrain level, the mutually interacting complex systems model may also be applied to study the dynamics of other complex systems where scale-free cross-correlations have been observed, including econophysics, physiological time series, and collective behavior of animal flocks.

Comparison of Two Test Strategies for Clarification of Reactive Results for Anti-HBc in Blood Donors.

OBJECTIVE: Testing for antibodies against hepatitis B core antigen (anti-HBc) was introduced to detect blood donors suffering from occult hepatitis B infection. Confirmation of specification of reactive results in the anti-HBc screening assay is still a challenge for blood donation services. METHODS: Two different test strategies for confirmation of specification of reactive anti-HBc tests, one performed in our institute and one suggested by the German authority (Paul-Ehrlich-Institut (PEI)), were compared. The first strategy is based on one supplemental anti-HBc test, the other requires two supplemental anti-HBc tests. RESULTS: 389 samples from 242 donors were considered. Both test strategies yielded concordant results in 117 reactive samples termed 'true-positive' or 'specificity confirmed', in 156 reactive samples termed 'false-positive' or 'specificity not confirmed', and in 99 negative samples. In 17 samples obtained from 11 donors, both test strategies gave discrepant results ('false-positive' but 'specificity confirmed'). In 10 of 11 donors, a real HBV infection was very unlikely, one remained unclear. 30 donors considered 'false-positive' became negative in all anti-HBc tests after follow-up testing and thus eligible for donor re-entry. CONCLUSIONS: The test strategy suggested by the PEI yielded no additional information but induced an overestimation of HBV infections and unnecessary look-back procedures. Many anti-HBc-reactive donors can be regained after follow-up testing.

Seroprevalence and incidence of hepatitis E virus infection in German blood donors.

BACKGROUND: Hepatitis E virus (HEV) is transmissible by transfusion. More data are needed about seroprevalence, incidence, and viremia in blood donors for the assessment of risk of transfusion-transmitted (TT)-HEV infections. STUDY DESIGN AND METHODS: Samples from 1019 whole blood donors were tested for anti-HEV immunoglobulin (Ig)G by enzyme-linked immunosorbent assay and Western blot. The incidence of HEV and presence of HEV RNA in donors who seroconverted were determined by testing archive samples and recipients of viremic donations were traced. Anti-HEV IgM and alanine transaminase (ALT) testing were also performed to assess the value of such measures in the prevention of TT-HEV infections. RESULTS: A total of 69 of 1019 donors tested positive for anti-HEV IgG (6.8% seroprevalence), and seroconversion for anti-HEV IgG occurred in seven of 69 donors within 2 years (incidence, 0.35%/year). Three of seven (42.8%) seroconverting donors provided an archive sample in which HEV RNA was detectable. One recipient of these donations was traceable; anti-HEV IgG, IgM, and HEV RNA testing were negative 41 days after transfusion. Neither ALT levels nor anti-HEV IgM detection correlated with the presence of HEV RNA. CONCLUSIONS: The seroprevalence of HEV was 6.8%, and the annual incidence 0.35%. HEV RNA was detectable in several seroconverting donors, without evidence for HEV transmission in the only traceable recipient. Since neither ALT nor anti-HEV IgM testing correlate with the presence of HEV RNA, HEV nucleic acid testing currently provides the only method for the prevention of TT-HEV infection. However, before implementation, more data about clinical relevance of TT-HEV infections and infectious dose of HEV are required.

Prevention of Transfusion-Transmitted Cytomegalovirus Infections: Which is the Optimal Strategy?

Traditionally, leukoreduction and selection of blood products from seronegative donors have been used as alternative strategies to reduce the risk of transfusion-transmitted cytomegalovirus infections (TT-CMV) in atrisk patients. After the introduction of universal leukoreduction for red blood cell and platelet concentrates in Germany, a controversy evolved as to whether the additional selection of blood products from seronegative donors would reduce or even increase the risk of TT-CMV. This review summarizes the current knowledge about CMV infections in blood donors and the implications of this information on the effect of potential transfusion strategies. Even though there are conflicting data about the incidence of TT-CMV remaining after the introduction of leukodepletion, it has been clearly shown that both prevalence and concentration of CMV DNA in peripheral blood are highest in newly seropositive donors. Therefore, avoidance of blood products from these donors is the most important goal of any transfusion strategy. This goal can be reached by: i) selection of blood products from seronegative donors, ii) provision of CMV DNA-negative blood products, or iii) provision of blood from long-term seropositive donors. In cases of suspected TT-CMV, all implicated donors should be investigated carefully to gather further knowledge on which donors confer the lowest risk for TT-CMV.

Parvovirus b19 infections and blood counts in blood donors.

OBJECTIVES: Parvovirus B19 (B19V) is a transfusion-transmissible virus. To obtain data about the prevalence, incidence, the course of B19V infection in blood donors and whether B19V might impair their blood counts, samples from blood donors with B19V infection were investigated. METHODS: Blood donations were screened for B19V DNA using the Cobas TaqScreen DPX Test® in mini-pools. B19V DNA concentration, anti-B19V IgG antibody titer and blood counts were determined in positive donors. RESULTS: 157/23,889 (0.66%) donors provided 347 B19V DNA-positive samples. Prevalence of B19V infection was 0.45%, incidence 0.20%. B19V DNA concentrations were predominantly low; only in 8 samples were viral loads of ≥10(5) IU B19V DNA/ml plasma detectable. Besides a slight decrease in hemoglobin, hematocrit, mean corpuscular volume, mean cellular hemoglobin and mean hemoglobin concentration, no major differences in blood counts occurred in B19V DNA-positive samples. In samples with a low B19V DNA concentration, anti-B19V IgG titers were rather high. 98 donors provided at least 1 B19V DNA-positive follow-up sample, indicating a prolonged viremia. CONCLUSIONS: B19V infection induced no major impairment in the blood counts. In donors with low-level viremia, infectivity through their donations is probably reduced by high antibody titers. Low-level viremia is prolonged, probably exceeding 1 year in many cases.

The impact of donor cytomegalovirus DNA on transfusion strategies for at-risk patients.

BACKGROUND: Cytomegalovirus (CMV) DNA is frequently detected in plasma of newly seropositive donors. Selection of leukoreduced blood products from donors with remote CMV infection could avoid transfusion-transmitted CMV infections (TT-CMV) due to primarily infected donors. However, there are no data about the prevalence of reactivations in long-term seropositive donors compared to the incidence of window period donations in seronegative donors. Therefore, the optimal transfusion strategy for at-risk patients is unclear. STUDY DESIGN AND METHODS: Whole blood samples from 22,904 donations were tested for CMV DNA, and CMV DNA-positive donations were categorized as donations from 1) seronegative donors, 2) newly seropositive donors, and 3) long-term seropositive donors. RESULTS: Twenty-one donors were reproducibly CMV DNA-positive (0.09%). Frequency of detection and concentration of CMV DNA in whole blood were comparable for seronegative and long-term seropositive donors. Nonreproducibly positive results for CMV DNA in whole blood were more frequent in long-term seropositive donors (0.16% vs. 0.01%, p<0.01). Only low concentrations of CMV DNA in plasma were detectable in two seronegative donors and one long-term seropositive donor. Highest concentrations of CMV DNA in both whole blood and plasma, however, were found in newly seropositive donors. CONCLUSION: Prevalences of window period donations among seronegative donors and reactivations among long-term seropositive donors, as well as the CMV DNA concentration in whole blood and plasma samples from these donors, are comparable. Therefore, blood products from both groups could be used for patients at risk for TT-CMV, while those of newly seropositive donors seem to bear an increased risk.

Window period donations during primary cytomegalovirus infection and risk of transfusion-transmitted infections.

BACKGROUND: Donors with short interdonation intervals (e.g., apheresis donors) have an increased risk of window period donations. The frequency of cytomegalovirus (CMV) window period donations is important information to decide whether selection of seronegative donors might be advantageous for patients at risk for transfusion-transmitted CMV infections (TT-CMV). STUDY DESIGN AND METHODS: CMV seroconversion in 93 donors with positive results in routine CMV antibody testing within at most 35 days after the last seronegative sample was evaluated by Western blot and/or a second antibody test. In donors with unconfirmed seroconversion, an additional later sample was tested. Concentration of CMV DNA was determined in pre- and postseroconversion samples. RESULTS: CMV seroconversion was confirmed in 12 donors (13%). Among these, the last seronegative sample was CMV DNA positive in three donors (25%, below 30 IU/mL). The first seropositive sample was CMV DNA positive in 10 donors (83%, maximum 1600 IU/mL). Both prevalence and median concentration of CMV DNA were higher in the first seropositive sample (p = 0.004 and p = 0.02), with maximum concentrations being reached about 2 weeks after seroconversion. No CMV DNA was detected in samples from donors with unconfirmed seroconversion. CONCLUSION: At least in donors with short interdonation intervals, most suspected CMV seroconversions are due to false-positive results of the screening test. As window period donations are rare and contain less CMV DNA than the first seropositive donation, avoidance of blood products from primarily seropositive donors is especially helpful to avoid TT-CMV if donors with short interdonation intervals are concerned.

Evaluation of algorithms for the diagnostic assessment and the reentry of blood donors who tested reactive for antibodies against hepatitis B core antigen.

BACKGROUND: Screening of blood donations for antibodies against hepatitis B core antigen (anti-HBc) is an accepted method to prevent some transfusion-transmitted hepatitis B virus (HBV) infections. However, anti-HBc testing may result in donor loss due to unspecific results in the currently available anti-HBc tests. Algorithms to distinguish true-positive from false-positive results and for reentry of those donors who tested false anti-HBc positive were evaluated retrospectively. STUDY DESIGN AND METHODS: Samples that tested reactive for anti-HBc by chemiluminescent microparticle immunoassay (CMIA) were investigated for anti-HBc by microparticle immunoassay, for anti-HBs and hepatitis B surface antigen (HBsAg) by CMIA, and for HBV DNA by individual-donor nucleic acid testing. Results were classified true positive, indeterminate, and false positive for anti-HBc. Donors who tested indeterminate and false positive were admitted for reentry if follow-up testing for anti-HBc became negative and no further evidence for an HBV infection was apparent. RESULTS: A total of 554 of 148,000 samples, taken from 30,000 individuals within 3 years tested reactive for anti-HBc by CMIA. Of those, 553 could be further classified: 142 (26%) true positive, 76 (14%) indeterminate, and 335 (60%) false positive. A total of 214 of 411 (52%) samples termed indeterminate or false positive were admitted for reentry and able to provide further donations. In one donor, anti-HBc-positive/HBsAg- and HBV DNA-negative HBV DNA was detectable during follow-up. CONCLUSION: According to our proposed algorithm, 26% of anti-HBc-reactive results tested by CMIA were true positive. Many donors tested indeterminate or false positive can provide future donations if our proposed algorithm for reentry is applied. One donor at risk for transmitting HBV was identified solely by anti-HBc testing.

Hematopoietic stem-cell senescence and myocardial repair - Coronary artery disease genotype/phenotype analysis of post-MI myocardial regeneration response induced by CABG/CD133+ bone marrow hematopoietic stem cell treatment in RCT PERFECT Phase 3.

BACKGROUND: Bone marrow stem cell clonal dysfunction by somatic mutation is suspected to affect post-infarction myocardial regeneration after coronary bypass surgery (CABG). METHODS: Transcriptome and variant expression analysis was studied in the phase 3 PERFECT trial post myocardial infarction CABG and CD133+ bone marrow derived hematopoetic stem cells showing difference in left ventricular ejection fraction (∆LVEF) myocardial regeneration Responders (n=14; ∆LVEF +16% day 180/0) and Non-responders (n=9; ∆LVEF -1.1% day 180/0). Subsequently, the findings have been validated in an independent patient cohort (n=14) as well as in two preclinical mouse models investigating SH2B3/LNK antisense or knockout deficient conditions. FINDINGS: 1. Clinical: R differed from NR in a total of 161 genes in differential expression (n=23, q<0•05) and 872 genes in coexpression analysis (n=23, q<0•05). Machine Learning clustering analysis revealed distinct RvsNR preoperative gene-expression signatures in peripheral blood acorrelated to SH2B3 (p<0.05). Mutation analysis revealed increased specific variants in RvsNR. (R: 48 genes; NR: 224 genes). 2. Preclinical:SH2B3/LNK-silenced hematopoietic stem cell (HSC) clones displayed significant overgrowth of myeloid and immune cells in bone marrow, peripheral blood, and tissue at day 160 after competitive bone-marrow transplantation into mice. SH2B3/LNK-/- mice demonstrated enhanced cardiac repair through augmenting the kinetics of bone marrow-derived endothelial progenitor cells, increased capillary density in ischemic myocardium, and reduced left ventricular fibrosis with preserved cardiac function. 3. VALIDATION: Evaluation analysis in 14 additional patients revealed 85% RvsNR (12/14 patients) prediction accuracy for the identified biomarker signature. INTERPRETATION: Myocardial repair is affected by HSC gene response and somatic mutation. Machine Learning can be utilized to identify and predict pathological HSC response. FUNDING: German Ministry of Research and Education (BMBF): Reference and Translation Center for Cardiac Stem Cell Therapy - FKZ0312138A and FKZ031L0106C, German Ministry of Research and Education (BMBF): Collaborative research center - DFG:SFB738 and Center of Excellence - DFG:EC-REBIRTH), European Social Fonds: ESF/IV-WM-B34-0011/08, ESF/IV-WM-B34-0030/10, and Miltenyi Biotec GmbH, Bergisch-Gladbach, Germany. Japanese Ministry of Health : Health and Labour Sciences Research Grant (H14-trans-001, H17-trans-002) TRIAL REGISTRATION: ClinicalTrials.gov NCT00950274.