A review of the immune activity of chitooligosaccharides

Under the influence of the COVID-19, people's awareness of physical health and immunity has increased significantly. Chitooligosaccharide is an oligomer of β-(1, 4)-linked D-glucosamine, furthermore, is one of the most widely studied immunomodulators. Chitooligosaccharide can be prepared from the chitin or chitosan polymers through enzymatically, chemically or physically processes. Chitooligosaccharide and its derivatives have been proven to have a wide range of biological activities including intestinal flora regulation, immunostimulant, anti-tumor, anti-obesity and anti-oxidation effects. This review summarizes the latest research of the preparation methods, biological activities in immunity and safety profiles of Chitooligosaccharide and its derivatives. We recapped the effect mechanisms of Chitooligosaccharide basing on overall immunity. Comparing the effects of Chitooligosaccharide with different molecular weights and degree of aggregation, a reference range for usage has been provided. This may provide a support for the application of Chitooligosaccharide in immune supplements and food. In addition, future research directions are also discussed. © 2023, Sociedade Brasileira de Ciencia e Tecnologia de Alimentos, SBCTA. All rights reserved.

Evaluating the generalisability of neural rumour verification models

Research on automated social media rumour verification, the task of identifying the veracity of questionable information circulating on social media, has yielded neural models achieving high performance, with accuracy scores that often exceed 90%. However, none of these studies focus on the real-world generalisability of the proposed approaches, that is whether the models perform well on datasets other than those on which they were initially trained and tested. In this work we aim to fill this gap by assessing the generalisability of top performing neural rumour verification models covering a range of different architectures from the perspectives of both topic and temporal robustness. For a more complete evaluation of generalisability, we collect and release COVID-RV, a novel dataset of Twitter conversations revolving around COVID-19 rumours. Unlike other existing COVID-19 datasets, our COVID-RV contains conversations around rumours that follow the format of prominent rumour verification benchmarks, while being different from them in terms of topic and time scale, thus allowing better assessment of the temporal robustness of the models. We evaluate model performance on COVID-RV and three popular rumour verification datasets to understand limitations and advantages of different model architectures, training datasets and evaluation scenarios. We find a dramatic drop in performance when testing models on a different dataset from that used for training. Further, we evaluate the ability of models to generalise in a few-shot learning setup, as well as when word embeddings are updated with the vocabulary of a new, unseen rumour. Drawing upon our experiments we discuss challenges and make recommendations for future research directions in addressing this important problem. © 2022 The Author(s)

S-PDB: Analysis and Classification of SARS-CoV-2 Spike Protein Structures

This paper proposes a novel and efficient method, called S-PDB, for the analysis and classification of Spike (S) protein structures of SARS-CoV-2 and other viruses/organisms in the Protein Data Bank (PDB). The method first finds and identifies protein structures in PDB that are similar to a protein structure of interest (SARS-CoV-2 S) via a protein structure comparison tool. The amino acid (AA) sequences of identified protein structures, downloaded from PDB, and their aligned amino acids (AAA) and secondary structure elements (ASSE), that are stored in three separate datasets, are then used for the reliable detection/classification of SARS-CoV-2 S protein structures. Three classifiers are used and their performance is compared by using six evaluation metrics. Obtained results show that two classifiers for text data (Multinomial Naive Bayes and Stochastic Gradient Descent) performed better and achieved high accuracy on the dataset that contains AAA of protein structures compared to the datasets for AA and ASSE, respectively. © 2022 IEEE.

A New Container Throughput Forecasting Paradigm under COVID-19

COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. This paper proposes a novel decomposition-ensemble forecasting method to forecast container throughput under the impact of major events. Combining this with change-point analysis and empirical mode decomposition (EMD), this paper uses the decomposition-ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, we tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. To validate the proposed method, we apply it to a forecast of the container throughput of Shanghai port. The results show that the proposed forecasting model significantly outperforms its rivals, including EMD-SVR, SVR, and ARIMA.

Safety and efficacy of oral lopinavir/ritonavir in pediatric patients with coronavirus disease: a nationwide comparative analysis.

OBJECTIVE: Because of the limited treatment options available, oral lopinavir/ritonavir (LPR) was used for treating coronavirus disease (COVID-19) in pediatric patients. This study aimed to assess the efficacy and safety of LPR in COVID-19 pediatric patients with mild symptoms. PATIENTS AND METHODS: This retrospective multicenter analysis included hospitalized children with mild COVID-19 who received LPR at one of 13 hospitals in China from January 1, 2020, to June 1, 2020. Patients treated with LPR were matched with patients not treated with LPR (1:4) according to age, sex, and length of symptom onset and hospitalization. Descriptive statistics and non-parametric tests were applied to compare differences between groups. Kaplan-Meier probability curves and Cox regression models were used to analyze nasal swab turning negative time (recovery time) and hospital discharge days. RESULTS: In total, 23 patients treated with LPR were matched with 92 untreated controls. The median age of patients was 6 years, and 56.52% of them were male. All patients were discharged from the hospital after being cured. The treatment group had a longer nasal swab turning negative time (hazard ratio [HR] 5.33; 95% CI: 1.94-14.67; p = 0.001) than the control group. LPR treatment was also associated with a longer hospitalization time (HR 2.01; 95% CI: 1.24-3.29; p = 0.005). After adjusting for the influence of LPR treatment, adverse drug reaction events were associated with a longer nasopharyngeal swab negative time (HR 4.67; 95% CI 1.35-16.11; p = 0.015). CONCLUSIONS: For children with mild COVID-19, LPR is inferior to conventional treatment in reducing virus shedding time and hospitalization duration and is associated with increased adverse reactions.

A wireless sensor technology' application in clinical nursing of hospital internet of things

Background : With the continuous development of sensing technology, people begin to use all kinds of sensors to monitor the human condition, such as early rising ECG instrument. Although this kind of equipment has the characteristics of high precision and high sensitivity, it has the defects of large volume, high power consumption, harsh use conditions and high cost. With the continuous development of wireless sensor network technology and integration technology, it is more and more possible to design a device with small size, high mobility and greatly reduced cost on the premise of maintaining the advantages of dual height. The purpose of this study is to explore how to apply wireless sensor network technology to clinical field, and to study the exact benefits of wireless sensor network technology for clinical neighborhood. Methods : Body temperature is an important monitoring index of whether there is abnormality in human body. Taking this as a breakthrough, we designed a real- time temperature acquisition device that can be worn on the arm of patients for a long time by using wireless sensor network technology and high- sensitivity sensor (sensitivity < 0.1°C). In addition, in order to complete the normal transmission of body temperature data, we also designed data aggregation node, gateway node and data processing platform. Through the system, the patient's temperature data can be monitored by the medical staff in real time. Results : Compared with the traditional methods of body temperature measurement (mercury thermometer, infrared thermometer), With the same accuracy, it can record the patient's temperature change in more detail (usually the temperature measurement in the hospital is tested every 6 hours, while the system can be tested every 30 minutes, 1 hour and 2 hours, and the test interval only needs to be completed through the system setting). The use of the system can greatly reduce the workload of medical staff (from the perspective of temperature measurement alone, it subverts the traditional way of temperature measurement, from a way of medical staff to a way of patient to a way of system automatically recording the patient's temperature data, which can be viewed by medical staff when they need to), and it can realize the non- contact temperature monitoring between doctors and patients, which can control infectious diseases such as SARS, novel coronavirus is of great significance Conclusions : The combination of sensor technology, low power consumption technology, integration technology and wireless sensor network makes it possible to realize medical grade physical examination equipment with high precision, long time and easy to carry. This kind of equipment can greatly reduce the nursing workload of medical staff for patients. Reliable and accurate data also provides powerful data support for the treatment of patients later.