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.

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...

Coronavirus disease 2019 (COVID-19) patients were classified into four clinical stages (uncomplicated illness, mild, severe and critical pneumonia) depending on disease severity. We aim to investigate the corresponding clinical, radiological and laboratory characteristics between different clinical stages. A retrospective, single-centre study of 101 confirmed patients with COVID-19 at Renmin Hospital of Wuhan University from 2 January to 28 January 2020 was enrolled; follow-up endpoint was on 8 February 2020. Clinical data were collected and compared during the course of illness. The median age of the 101 patients was 51.0 years and 33.6% were medical staff. Fever (68%), cough (50%) and fatigue (23%) are the most common symptoms. About 26% patients underwent the mechanical ventilation and 98% patients were treated with antibiotics. Thirty-seven per cent patients were cured and 11 died. On admission, the number of patients with uncomplicated illness, mild, severe and critical pneumonia were 2 [2%], 86 [85%], 11 [11%] and 2 [2%]. Forty-four of the 86 mild pneumonia progressed to severe illness within 4 days, with nine patients worsened due to critical pneumonia within 4 days. Two of the 11 severe patients improved to mild condition while three others deteriorated. Significant differences were observed among groups of different clinical stages in numbers of influenced pulmonary segments (6 vs. 12 vs. 17, P < 0.001). A significantly upward trend was witnessed in ground-glass opacities overlapped with striped shadows (33% vs. 42% vs. 55% vs. 80%, P < 0.001), while pure ground-glass opacities gradually decreased as disease progressed (45% vs. 35% vs. 24% vs. 13%, P < 0.001) within 12 days. Lymphocytes, prealbumin and albumin showed a downtrend as disease progressed from mild to severe or critical condition, an uptrend was found in white blood cells, C-reactive protein, neutrophils and lactate dehydrogenase. The proportions of serum amyloid A > 300 mg/l in mild, severe and critical conditions were 18%, 46% and 71%, respectively.

No abstract

Early classification of time series aims to predict the class value of a sequence accurately as early as possible, not wait for the full-length data, which is significant in many time-sensitive applications and has attracted great interest in recent years. For instance, early diagnosis can help patients get early treatment and even save their lives. The problem of early classification is how to determine whether the collected data are sufficient to output the class value. Moreover, in practical applications, users also need to know the confidence (reliability) of the prediction results for more appropriate processing. For example, giving a healthy patient the possibility of suffering from some disease can assist physicians in an optimal therapy. However, existing work has not provided an effective measure to indicate how accurate the classification is. Therefore, in this paper, we propose an effective confidence-based early classification of time series. Firstly, based on a set of base time series classifiers trained at different timestamps, we propose a dynamic decision fusion method to measure the confidence of a predicted result by fusing the results of multiple base classifiers. Secondly, by analyzing the distribution of confidence values, we develop an adaptive learning method for the confidence threshold to simultaneously optimize the two conflicting objectives: accuracy and earliness. Finally, the experimental results conducted on 45 equal-length datasets and 8 variable-length datasets clearly show that our proposed approach can achieve the superior in early classification compared to state-of-the-art approaches.INDEX TERMS Time series classification, early classification, confidence measure, decision fusion. I. INTRODUCTION

The objective of this study was to examine the chemical composition and oxidative stability of microalgae oil, also to explore in vitro bioaccessibility and antioxidant activity of microalgae oil after simulated gastrointestinal digestion. In total, more than 50 fatty acids were identified by GC-MS analysis, with both palmitic acid (38.3%) and DHA (34.5%) being identified as major fatty acids. The contents of total phenolics and flavonoids in the various solvent extracts were measured spectrometrically, and their amounts were 39.33 AE 0.34 mg gallic acid/g and 16.08 AE 4.3 mg rutin/g, respectively. HPLC analysis showed that the contents of b-carotene, a-tocopherols, band g-tocopherols (not separated) and d-tocopherols were 136 mg/100 g, 164.4 mg/g, 317.3 mg/g, and 43.2 mg/g, respectively. Concerning sterols, cholesterol was the principal sterol at 4210.5 mg/kg and the other six main sterols were campesterol (121.4 mg/kg), 24-methylene cholesterol (192.8 mg/kg), 24-methyl-colest-7-en-3b-ol (144.6 mg/kg), ergosterol (144.8 mg/kg), stigmasterol (260.1 mg/kg) and D7,24-stigmastadienol (150.5 mg/kg), respectively. The overall chemical properties of the tested oils indicated that microalgae oil had a great oil quality. A Schaal oven test was used to evaluate the oxidative stability of microalgae oil. Furthermore, in vitro simulated gastrointestinal digestion was performed, and the antioxidant ability of digestion oil was determined by using a 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging assay, a 3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical cation decolourisation activity assay, a reducing power assay, a b-carotene bleaching assay and an oxygen radical absorbance capacity (ORAC) antioxidant assay. The results showed that following simulated gastrointestinal digestion, microalgae oil displayed a good in vitro bioaccessibility and moderate antioxidant capacity. Thus, the antioxidant activity of the microalgae oil was mainly contributed by its abundant antioxidant constituents.Practical applications: Schizochytrium aggregatum oil is a good source of DHA and of effective, bioaccessible antioxidants.

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.