No abstract

Natural triterpenes represent a group of pharmacologically active and structurally diverse organic compounds. The focus on these phytochemicals has been enormous in the past few years, worldwide. Asiatic acid (AA), a naturally occurring pentacyclic triterpenoid, is found mainly in the traditional medicinal herb Centella asiatica. Triterpenoid saponins, which are the primary constituents of C. asiatica, are commonly believed to be responsible for their extensive therapeutic actions. Published research work has described the molecular mechanisms underlying the various biological activities of AA and its derivatives, which vary for each chronic disease. However, a compilation of the various pharmacological properties of AA has not yet been done. Herein, we describe in detail the pharmacological properties of AA and its derivatives that inhibit multiple pathways of intracellular signaling molecules and transcription factors that are involved in the various stages of chronic diseases. Furthermore, the pharmacological activities of AA were compared with two natural compounds: curcumin and resveratrol. This review summarizes the research on AA and its derivatives and helps to provide future directions in the area of drug development.

Liver transplantation patients are at increased risk for methicillin-resistant Staphylococcus aureus (MRSA) infection, but the molecular mechanism remains unclear. We found that genetic predisposition to low pannexin 1 (PANX1) expression in donor livers was associated with MRSA infection in human liver transplantation recipients. Using Panx1 and Il-33-knockout mice for liver transplantation models with MRSA tail vein injection, we demonstrated that Panx1 deficiency increased MRSA-induced liver injury and animal death. We found that decreased PANX1 expression in the liver led to reduced release of adenosine triphosphate (ATP) from hepatocytes, which further reduced the activation of P2X2, an ATP-activating P2X receptor. Reduced P2X2 function further decreased the NLRP3-mediated release of interleukin-33 (IL-33), reducing hepatic recruitment of macrophages and neutrophils. Administration of mouse IL-33 to Panx1−/− mice significantly (P = 0.011) ameliorated MRSA infection and animal death. Reduced human hepatic IL-33 protein abundance also associated with increased predisposition to MRSA infection. Our findings reveal that genetic predisposition to reduced PANX1 function increases risk for MRSA infection after liver transplantation by decreasing hepatic host innate immune defense, which can be attenuated by IL-33 treatment.

Over the past 3 months, coronavirus disease 2019 (COVID-19) has emerged across China and developed into a worldwide outbreak [1]. The disease has caused varying degrees of illness. The proportion of patients with COVID-19 with non-severe illness was 84.3% on admission, and severe cases accounted for 15.7% [2]. Most of the non-severe pneumonia patients would gradually alleviate and be cured with treatment, while others would rapidly progress to severe illness, which has a poor prognosis [3, 4]. As recently reported, the cumulative risk of the composite end-point was 3.6% in all COVID-19 patients, and the cumulative risk was 20.6% for severe illness [2]. However, it is still unknown whether early identification and intervention for non-severe patients with COVID-19 could prevent progression into severe disease. According to the experience of treating other diseases, there might be a large promoting effect of treatment. In this paper, we aim to build a predictive model for identifying high-risk non-severe pneumonia patients at an early stage. 86 patients with COVID-19 and non-severe pneumonia on admission were recruited as the training cohort at Renmin Hospital of Wuhan University from 2 to 20 January, 2020, and another 62 patients were prospectively enrolled as the validation cohort from 28 January to 9 February, 2020. COVID-19 was confirmed by real-time PCR. Disease severities of COVID-19 were defined as severe and non-severe pneumonia based on the criteria of American Thoracic Society guidelines for community-acquired pneumonia [2, 5]. The exclusion criteria included: 1) degrees of severity were not available on admission or during follow-up; 2) diagnosed with severe illness at the time of admission; 3) confirmed with COVID-19 and treated at other hospitals; 4) medication was administered within 15 days before admission; 5) received oxygen support during follow-up. Patients were divided into "progressed" or "non-progressed" groups, based on whether they progressed to severe illness or not during the 14-day follow-up period. Comorbidity included diabetes, hypertension, cardiovascular and cerebrovascular diseases, COPD, malignant tumour, chronic liver disease, chronic kidney disease, tuberculosis and immunodeficiency diseases, etc. Clinical characteristics and laboratory findings were extracted from electronic medical records. Radiological features were extracted from chest computed tomography (CT) imaging using a double-blind method [6]. To evaluate the lesion size accurately, a diagnosis system for COVID-19 based on artificial intelligence (AI) was employed to measure volume ratio of pneumonia automatically by analysing CT values [7, 8]. Logistic regression was used as the classifier to build the predictive model. The discriminative performance of the predictive model was quantified by the value of the area under the receiver operating characteristic curve (AUC) in the cross-validation of the training and validation datasets. Risk index calculated with the weight of each variable in the model was used to identify...