Objective: We investigated long-term effects of intracoronary CD34+ cell transplantation in dilated cardiomyopathy and the relationship between intramyocardial cell homing and clinical response. Methods and Results:
A fter an acute myocardial injury, recruitment of stem cells may significantly influence the repair process. Studies have shown that serum stromal-derived factor-1 (SDF-1) levels rise significantly after an acute myocardial infarction, which increases homing of stem cells to the damaged tissue. 1 In contrast, in patients with chronic heart failure, local homing signals may be less intense. This difference is especially pronounced in patients with nonischemic dilated cardiomyopathy (DCM), which involves significant downregulation of several homing factors, including SDF-1 2 . The importance of homing is especially prominent when considering stem cell therapy in patients with heart failure.3 In a recent study of patients with nonischemic DCM, we demonstrated that the response to intracoronary CD34 + cell therapy is dependent on the degree of myocardial cell retention. 4 These findings suggest that the efficacy of intracoronary cell therapy may be limited by the number of cells retained in the myocardium.Compared with intracoronary delivery, intramyocardial (IM) cell delivery is consistently associated with higher myocardial cell retention rates in both early and late phases after acute myocardial infarction. 5,6 In preclinical models of ischemic heart failure, IM injection of higher doses of bone marrow mononuclear cells was associated with incremental benefit, 7 and late cardiac functional recovery was more prominent in Background-In an open-label blinded study, we compared intracoronary and transendocardial CD34 + cell transplantation in patients with nonischemic dilated cardiomyopathy. Methods and Results-Of the 40 patients with dilated cardiomyopathy, 20 were randomized to receive intracoronary injection and 20 received transendocardial CD34 + cell delivery. In both groups, CD34 + cells were mobilized by filgrastim, collected via apheresis, and labeled with technetium-99m radioisotope for single-photon emission computed tomographic imaging. In the intracoronary group, cells were injected intracoronarily in the artery supplying segments of greater perfusion defect on myocardial perfusion scintigraphy. In the transendocardial group, electroanatomic mapping was used to identify viable but dysfunctional myocardium, and transendocardial cell injections were performed. Nuclear single-photon emission computed tomographic imaging for quantification of myocardial retention was performed 18 hours thereafter. At baseline, groups did not differ in age, sex, left ventricular ejection fraction, or N-terminal pro-brain natriuretic peptide levels. The number of CD34 + cells was also comparable (105±31×10 6 in the transendocardial group versus 103±27×10 6 in the intracoronary group, P=0.62). At 18 hours after procedure, myocardial retention was higher in the transendocardial group (19.2±4.8%) than in the intracoronary group (4.4±1.2%, P<0.01). At 6 months, left ventricular ejection fraction improved more in the transendocardial group (+8.1±4.3%) than in the intracoronary group (+4.2±2.3%, P=0.03). The same pattern was observed...
Quick and accurate medical diagnoses are crucial for the successful treatment of diseases. Using machine learning algorithms and based on laboratory blood test results, we have built two models to predict a haematologic disease. One predictive model used all the available blood test parameters and the other used only a reduced set that is usually measured upon patient admittance. Both models produced good results, obtaining prediction accuracies of 0.88 and 0.86 when considering the list of five most likely diseases and 0.59 and 0.57 when considering only the most likely disease. The models did not differ significantly, which indicates that a reduced set of parameters can represent a relevant “fingerprint” of a disease. This knowledge expands the model’s utility for use by general practitioners and indicates that blood test results contain more information than physicians generally recognize. A clinical test showed that the accuracy of our predictive models was on par with that of haematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone can be successfully applied to predict haematologic diseases. This result and could open up unprecedented possibilities for medical diagnosis.
Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected the operational ROC point at a sensitivity of 81.9% and a specificity of 97.9%. The cross-validated AUC was 0.97. The five most useful routine blood parameters for COVID-19 diagnosis according to the feature importance scoring of the XGBoost algorithm were: MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. t-SNE visualization showed that the blood parameters of the patients with a severe COVID-19 course are more like the parameters of a bacterial than a viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results represent a significant contribution to improvements in COVID-19 diagnosis.
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