Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.

Background:The importance of telomerase in multiple myeloma (MM) is well established; however, its response to bortezomib has not been addressed.Methods:The effect of bortezomib on telomerase activity and cell proliferation was evaluated in four MM cell lines and in myeloma cells obtained from eight patients. The mechanism of telomerase regulation on epigenetic, transcriptional, and post-translational levels was further assessed in two selected cell lines: ARP-1 and CAG. Clinical data were correlated with the laboratory findings.Results:Bortezomib downregulated telomerase activity and decreased proliferation in all cell lines and cells obtained from patients, albeit in two different patterns of kinetics. ARP-1 cells demonstrated higher and earlier sensitivity than CAG cells due to differential phosphorylation of hTERT by PKCα. Methylation of hTERT promoter was not affected. Transcription of hTERT was similarly inhibited in both lines by decreased binding of SP-1 and not of C-Myc and NFκB. The ex vivo results confirmed the in vitro findings and suggested existence of clinical relevance.Conclusion:Bortezomib downregulates telomerase activity in MM cells both transcriptionally and post-translationally. MM cells, both in vitro and in patients, exhibit different sensitivity to the drug due to different post-translational response. The effect of bortezomib on telomerase activity may correlate with resistance to bortezomib in patients, suggesting its potential utility as a pre-treatment assessment.

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