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"The above summary does not do justice to Martyanov's truly seminal book. I can only say that I consider this book as an absolutely indispensable "must read" for every person in the USA who loves his/her country and for every person who believes that wars, especially nuclear ones, must be avoided at all costs. Just like many others (I think of Paul Craig Roberts), Martyanov is warning us that "the day of reckoning is upon us" and that the risks of war are very real, even if for most of us such an event is also unthinkable. Those in the USA who consider themselves patriots should read this book with special attention, not only because it correctly identifies the main threat to the USA, but also because it explains in detail what circumstances have resulted in the current crisis. Waving (mostly Chinese made) US flags is simply not an option anymore, neither is looking away and pretending that none of this is real. Martynov's book will also be especially interesting to those in the US armed forces who are observing the tremendous decline of US military power from inside. Who better than a former Soviet officer could not only explain, but also understand the mechanisms which have made such a decline possible?"

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.