The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

The radiolysis of the isomers L-, D-and DL-of the aspartic acid, in solid polycrystalline state, was investigated at room temperature. The analysis of their ESR spectra indicated the formation of at least two radicalic entities. The radical, identified as R 3 , resulting from the deamination of the acid, exhibits the highest concentration and thermal resistance. Possible mechanisms of formation of three radical species are suggested, based also on literature data. The kinetics of the disappearance of radical R 3 indicated a complex mechanism. Three possible variants were suggested for this mechanism.

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The SARS-COV-2 pandemic has put pressure on Intensive Care Units, and made the identification of early predictors of disease severity a priority. We collected clinical, biological, chest CT scan data, and radiology reports from 1,003 coronavirus-infected patients from two French hospitals. Among 58 variables measured at admission, 11 clinical and 3 radiological variables were associated with severity. Next, using 506,341 chest CT images, we trained and evaluated deep learning models to segment the scans and reproduce radiologists' annotations. We also built CT image-based deep learning models that predicted severity better than models based on the radiologists' reports. Finally, we showed that adding CT scan information-either through radiologist lesion quantification or through deep learning-to clinical and biological data, improves prediction of severity. These findings show that CT scans contain novel and unique prognostic information, which we included in a 6-variable ScanCov severity score.

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The energies of combustion and fusion of 5-cyano-5H-dibenzo [a,d]cycloheptene (1) and (5E,11E)-dibenzo[a,e]cyclooctene-5,11-dicarbonitrile (2) were measured by means of microbomb calorimetry and DSC, respectively. The derived enthalpies of formation in solid state are 320 ± 18 for nitrile 1 and 470 ± 31 kJ mol -1 for nitrile 2, respectively. The experimental enthalpies of formation are discussed in relationship with values calculated at the G3(MP2)//B3LYP level of quantum chemical theory, by means of group additivity and isodesmic reactions. The two nitriles are not stabilized by dibenzoannelation.Keywords Dibenzocycloalkane nitrile Á Enthalpies of combustion and of formation Á Enthalpies of fusion, vaporization and sublimation Á G3(MP2)//B3LYP quantum chemical calculations Á Group additivity Á Isodesmic reactions