Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic.

Clinical evidence suggests that some patients diagnosed with coronavirus disease 2019 (COVID-19) experience a variety of complications associated with significant morbidity, especially in severe cases during the initial spread of the pandemic. To support early interventions, we propose a machine learning system that predicts the risk of developing multiple complications. We processed data collected from 3,352 patient encounters admitted to 18 facilities between April 1 and April 30, 2020, in Abu Dhabi (AD), United Arab Emirates. Using data collected during the first 24 h of admission, we trained machine learning models to predict the risk of developing any of three complications after 24 h of admission. The complications include Secondary Bacterial Infection (SBI), Acute Kidney Injury (AKI), and Acute Respiratory Distress Syndrome (ARDS). The hospitals were grouped based on geographical proximity to assess the proposed system's learning generalizability, AD Middle region and AD Western & Eastern regions, A and B, respectively. The overall system includes a data filtering criterion, hyperparameter tuning, and model selection. In test set A, consisting of 587 patient encounters (mean age: 45.5), the system achieved a good area under the receiver operating curve (AUROC) for the prediction of SBI (0.902 AUROC), AKI (0.906 AUROC), and ARDS (0.854 AUROC). Similarly, in test set B, consisting of 225 patient encounters (mean age: 42.7), the system performed well for the prediction of SBI (0.859 AUROC), AKI (0.891 AUROC), and ARDS (0.827 AUROC). The performance results and feature importance analysis highlight the system's generalizability and interpretability. The findings illustrate how machine learning models can achieve a strong performance even when using a limited set of routine input variables. Since our proposed system is data-driven, we believe it can be easily repurposed for different outcomes considering the changes in COVID-19 variants over time.

Thymoquinone as an anticancer agent: evidence from inhibition of cancer cells viability and invasion in vitro and tumor growth in vivo.

Phytochemical compounds are emerging as a new generation of anticancer agents with limited toxicity in cancer patients. The purpose of this study was to investigate the potential impact of thymoquinone (TQ), the major constituent of black seed, on survival, invasion of cancer cells in vitro, and tumor growth in vivo. Exposure of cells derived from lung (LNM35), liver (HepG2), colon (HT29), melanoma (MDA-MB-435), and breast (MDA-MB-231 and MCF-7) tumors to increasing TQ concentrations resulted in a significant inhibition of viability through the inhibition of Akt phosphorylation leading to DNA damage and activation of the mitochondrial-signaling proapoptotic pathway. We provide evidence that TQ at non-toxic concentrations inhibited the invasive potential of LNM35, MDA-MB-231, and MDA-MB231-1833 cancer cells. Moreover, we demonstrate that TQ synergizes with DNA-damaging agent cisplatin to inhibit cellular viability. The anticancer activity of thymoquinone was also investigated in athymic mice inoculated with the LNM35 lung cells. Administration of TQ (10 mg/kg/i.p.) for 18 days inhibited the LNM35 tumor growth by 39% (P < 0.05). Tumor growth inhibition was associated with significant increase in the activated caspase-3. The in silico target identification suggests several potential targets of TQ mainly HDAC2 proteins and the 15-hydroxyprostaglandin dehydrogenase. In this context, we demonstrated that TQ treatment resulted in a significant inhibition of HDAC2 proteins. In view of the available experimental findings, we contend that thymoquinone and/or its analogues may have clinical potential as an anticancer agent alone or in combination with chemotherapeutic drugs such as cisplatin.