Roles of p53-Mediated Host-Virus Interaction in Coronavirus Infection.

The emergence of the SARS-CoV-2 coronavirus has garnered global attention due to its highly pathogenic nature and the resulting health crisis and economic burden. Although drugs such as Remdesivir have been considered a potential cure by targeting the virus on its RNA polymerase, the high mutation rate and unique 3' to 5' exonuclease with proofreading function make it challenging to develop effective anti-coronavirus drugs. As a result, there is an increasing focus on host-virus interactions because coronaviruses trigger stress responses, cell cycle changes, apoptosis, autophagy, and the dysregulation of immune function and inflammation in host cells. The p53 tumor suppressor molecule is a critical regulator of cell signaling pathways, cellular stress responses, DNA repair, and apoptosis. However, viruses can activate or inhibit p53 during viral infections to enhance viral replication and spread. Given its pivotal role in cell physiology, p53 represents a potential target for anti-coronavirus drugs. This review aims to summarize the relationship between p53 and coronaviruses from various perspectives, to shed light on potential targets for antiviral drug development and vaccine design.

Triacetyl Resveratrol Inhibits PEDV by Inducing the Early Apoptosis In Vitro.

PEDV represents an ancient Coronavirus still causing huge economic losses to the porcine breeding industry. Resveratrol has excellent antiviral effects. Triacetyl resveratrol (TCRV), a novel natural derivative of resveratrol, has been recently discovered, and its pharmacological effects need to be explored further. This paper aims to explore the relationship between PEDV and TCRV, which offers a novel strategy in the research of antivirals. In our study, Vero cells and IPEC-J2 cells were used as an in vitro model. First, we proved that TCRV had an obvious anti-PEDV effect and a strong inhibitory effect at different time points. Then, we explored the mechanism of inhibition of PEDV infection by TCRV. Our results showed that TCRV could induce the early apoptosis of PEDV-infected cells, in contrast to PEDV-induced apoptosis. Moreover, we observed that TCRV could promote the expression and activation of apoptosis-related proteins and release mitochondrial cytochrome C into cytoplasm. Based on these results, we hypothesized that TCRV induced the early apoptosis of PEDV-infected cells and inhibited PEDV infection by activating the mitochondria-related caspase pathway. Furthermore, we used the inhibitors Z-DEVD-FMK and Pifithrin-α (PFT-α) to support our hypothesis. In conclusion, the TCRV-activated caspase pathway triggered early apoptosis of PEDV-infected cells, thereby inhibiting PEDV infections.

Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study.

BACKGROUND: Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology. METHODS: A retrospective study of patients' data was conducted using the Emergency Rescue Database of Chinese People's Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity and the areas under receiver operator characteristic curves (ROC-AUC). RESULTS: The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the Logistic regression were cardiac troponin T (CTnT) (coefficient=0.346, OR = 1.413), temperature (T) (coefficient=0.235, OR = 1.265), respiratory rate (RR) (coefficient= -0.206,OR = 0.814), serum kalium (K) (coefficient=0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient= -0.101, OR = 0.904), and albumin (ALB) (coefficient= -0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heartrate, and systolic blood pressure. CONCLUSIONS: The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.