The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests. We developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergencyroom with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response. We have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases. This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation (This tool is available at https://covid19-blood-ml.herokuapp.com/).

AbstractObjectivesThe outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to date, the epidemic has gradually spread to 209 countries worldwide with more than 1.5 million infected people and 100,000 deaths. Amplification of viral RNA by rRT-PCR serves as the gold standard for confirmation of infection, yet it needs a long turnaround time (3–4 h to generate results) and shows false-negative rates as large as 15%–20%. In addition, the need of certified laboratories, expensive equipment and trained personnel led many countries to limit the rRT-PCR tests only to individuals with pronounced respiratory syndrome symptoms. Thus, there is a need for alternative, less expensive and more accessible tests.MethodsWe analyzed the plasma levels of white blood cells (WBCs), platelets, C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyl transpeptidase (GGT), alkaline phosphatase and lactate dehydrogenase (LDH) of 207 patients who, after being admitted to the emergency room of the San Raffaele Hospital (Milan, Italy) with COVID-19 symptoms, were rRT-PCR tested. Of them, 105 tested positive, whereas 102 tested negative.ResultsStatistically significant differences were observed for WBC, CRP, AST, ALT and LDH. Empirical thresholds for AST and LDH allowed the identification of 70% of either COVID-19-positive or -negative patients on the basis of routine blood test results.ConclusionsCombining appropriate cutoffs for certain hematological parameters could help in identifying false-positive/negative rRT-PCR tests. Blood test analysis might be used as an alternative to rRT-PCR for identifying COVID-19-positive patients in those countries which suffer from a large shortage of rRT-PCR reagents and/or specialized laboratory.

The supply system model incorporates a combination of values and relationships obtained from other national laboratories, publications, consultation with academia and staff from the U.S. Department of Agriculture and the U.S. Forest Service, and published and unpublished INL data. Further details on the model are provided in Appendix F. Equipment lists for the feed handling and drying area, as well as other pertinent information, are provided in Appendix A and Appendix B. All purchased and installed equipment costs for this area are shown as zero for the plant economics because all of these capital costs are included in the delivered feedstock cost. 3.2 Area 200: Gasification The following section presents an overview, basis for design, and cost estimates for construction of the gasification facilities.

Escherichia coli Dps belongs to a family of bacterial stress-induced proteins to protect DNA from oxidative damage. It shares with Listeria innocua ferritin several structural features, such as the quaternary assemblage and the presence of an unusual ferroxidase center. Indeed, it was recently recognized to be able to oxidize and incorporate iron. Since ferritins are endowed with the unique capacity to direct iron deposition toward formation of a microcrystalline core, the structure of iron deposited in the E. coli Dps cavity was studied. Polarized single crystal absorption microspectrophotometry of iron-loaded Dps shows that iron ions are oriented. The spectral properties in the high spin 3d 5 configuration point to a crystal form with tetrahedral symmetry where the tetrahedron center is occupied by iron ions and the vertices by oxygen. Crystals of iron-loaded Dps also show that, as in mammalian ferritins, iron does not remain bound to the site after oxidation has taken place. The kinetics of the iron reduction/release process induced by dithionite were measured in the crystal and in solution. The reaction appears to have two phases, with t1 ⁄2 of a few seconds and several minutes at neutral pH values, as in canonical ferritins. This behavior is attributed to a similar composition of the iron core.