Effect of cold agglutinins on red blood cell parameters in a trauma patient: a case report

The presence of cold agglutinins (CAs) in samples intended for complete blood count (CBC) using automated haematology analysers might cause serious preanalytical errors. In this report we describe the case of a 90-year old female patient admitted to the Emergency department following trauma injuries. A blood testing on admission revealed surprisingly low red blood cell count (0.99 x 1012/L), low haematocrit (0.102 L/L) which did not correlate with haemoglobin concentration (100 g/L), and high erythrocytes indices (mean corpuscular haemoglobin, 101 pg; mean corpuscular haemoglobin concentration, 980 g/L). In the second sample, after repeated collection, almost equal results were observed. Blood smear examination under the microscope revealed clusters of erythrocytes. Cold agglutinins presence was suspected and, in order to get valid results, sample was warmed to 37 °C. Correction of CBC was observed. Furthermore, we performed some additional analysis to confirm the presence of CAs in this patient. The aim of this report was to present the laboratory findings in a case of CAs and propose a laboratory procedure for whole blood samples with suspected CAs

Proteome sequence features carry signatures of the environmental niche of prokaryotes

<p>Abstract</p> <p>Background</p> <p>Prokaryotic environmental adaptations occur at different levels within cells to ensure the preservation of genome integrity, proper protein folding and function as well as membrane fluidity. Although specific composition and structure of cellular components suitable for the variety of extreme conditions has already been postulated, a systematic study describing such adaptations has not yet been performed. We therefore explored whether the environmental niche of a prokaryote could be deduced from the sequence of its proteome. Finally, we aimed at finding the precise differences between proteome sequences of prokaryotes from different environments.</p> <p>Results</p> <p>We analyzed the proteomes of 192 prokaryotes from different habitats. We collected detailed information about the optimal growth conditions of each microorganism. Furthermore, we selected 42 physico-chemical properties of amino acids and computed their values for each proteome. Further, on the same set of features we applied two fundamentally different machine learning methods, Support Vector Machines and Random Forests, to successfully classify between bacteria and archaea, halophiles and non-halophiles, as well as mesophiles, thermophiles and mesothermophiles. Finally, we performed feature selection by using Random Forests.</p> <p>Conclusions</p> <p>To our knowledge, this is the first time that three different classification cases (domain of life, halophilicity and thermophilicity) of proteome adaptation are successfully performed with the same set of 42 features. The characteristic features of a specific adaptation constitute a signature that may help understanding the mechanisms of adaptation to extreme environments.</p