Platelet components associated with adverse reactions: predictive value of mitochondrial DNA relative to biological response modifiers.

BACKGROUND: Biological response modifiers (BRMs), secreted by platelets (PLTs) during storage, play a role in adverse events (AEs) associated with transfusion. Moreover, mitochondrial DNA (mtDNA) levels in PLT components (PCs) are associated with AEs. In this study we explore whether there is a correlation between pathogenic BRMs and mtDNA levels and whether these markers can be considered predictors of transfusion pathology. STUDY DESIGN AND METHODS: We investigated a series of reported AEs after PC transfusion, combining clinical observations and mathematical modeling systems. RESULTS: mtDNA was consistently released during the first days of PC storage; however, mtDNA release was earlier in "pathogenic" than in nonpathogenic PCs. PC supernatants with high levels of mtDNA along with soluble CD40 ligand (sCD40L) were significantly associated with occurrences of AEs. The fact that mtDNA did not associate with the 14 BRMs tested suggests the role of mtDNA in PC transfusion-linked inflammation is independent of that of BRMs, known to be associated with AEs. We present evidence that PLTs generate distinct pathogenic secretion profiles of BRMs and mtDNA. The calculated area under the curve for mtDNA was significantly associated with AEs, although less stringently predictive than those of sCD40L or interleukin-13, standard predictors of AE. The established model predicts that distinct subtypes of AEs can be distinguished, dependent on mtDNA levels and PC storage length. CONCLUSIONS: Further work should be considered to test the propensity of mtDNA in PLT concentrates to generate inflammation and cause an AE.

On the benefits of self-taught learning for brain decoding.

CONTEXT: We study the benefits of using a large public neuroimaging database composed of functional magnetic resonance imaging (fMRI) statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to reconstruct these maps. Then, we use this trained encoder to initialize a supervised convolutional neural network to classify tasks or cognitive processes of unseen statistic maps from large collections of the NeuroVault database. RESULTS: We show that such a self-taught learning process always improves the performance of the classifiers, but the magnitude of the benefits strongly depends on the number of samples available both for pretraining and fine-tuning the models and on the complexity of the targeted downstream task. CONCLUSION: The pretrained model improves the classification performance and displays more generalizable features, less sensitive to individual differences.

A computerized prediction model of hazardous inflammatory platelet transfusion outcomes.

BACKGROUND: Platelet component (PC) transfusion leads occasionally to inflammatory hazards. Certain BRMs that are secreted by the platelets themselves during storage may have some responsibility. METHODOLOGY/PRINCIPAL FINDINGS: First, we identified non-stochastic arrangements of platelet-secreted BRMs in platelet components that led to acute transfusion reactions (ATRs). These data provide formal clinical evidence that platelets generate secretion profiles under both sterile activation and pathological conditions. We next aimed to predict the risk of hazardous outcomes by establishing statistical models based on the associations of BRMs within the incriminated platelet components and using decision trees. We investigated a large (n = 65) series of ATRs after platelet component transfusions reported through a very homogenous system at one university hospital. Herein, we used a combination of clinical observations, ex vivo and in vitro investigations, and mathematical modeling systems. We calculated the statistical association of a large variety (n = 17) of cytokines, chemokines, and physiologically likely factors with acute inflammatory potential in patients presenting with severe hazards. We then generated an accident prediction model that proved to be dependent on the level (amount) of a given cytokine-like platelet product within the indicated component, e.g., soluble CD40-ligand (>289.5 pg/109 platelets), or the presence of another secreted factor (IL-13, >0). We further modeled the risk of the patient presenting either a febrile non-hemolytic transfusion reaction or an atypical allergic transfusion reaction, depending on the amount of the chemokine MIP-1α (<20.4 or >20.4 pg/109 platelets, respectively). CONCLUSIONS/SIGNIFICANCE: This allows the modeling of a policy of risk prevention for severe inflammatory outcomes in PC transfusion.