Potential targets and traditional Chinese medicine treatment strategy of cough after SARS-CoV-2 infection.

The severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) Omicron variants BA.5.2 and BF.7 have become the main epidemic strains in China since the quarantine policy was lifted in 7th December 2022. Cough is one of the main symptoms induced by SARS-CoV-2 infection. SARS-CoV-2 infection-associated cough injuries the lung and upper respiratory tract, while the infected people cough out virus and liquid which forms virus-containing aerosols, a medium for quickly spreading. Furthermore, cough is one of primary sequelae of discharged patients in corona virus disease 2019 (COVID-19). By now, there are no efficacious drugs for treatment of upper respiratory tract infection associated cough induced by omicron. Traditional Chinese medicine (TCM) has a long history on treating cough. By reviewing the mechanisms of the occurrence of cough after SARS-CoV-2 infection, potential therapeutic targets and cough suppressant herbs with significant efficacy in clinical and basic research, we provide a reference for the treatment of cough after SARS-Cov-2 infection and a basis for the majority of infected patients to select appropriate herbs for cough relief under guidance of physicians.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

An ultrasensitive electrochemiluminescence immunosensor for SARS-CoV-2 nucleocapsid protein detection based on signal amplification strategy of DMSN@QDs

Based on signal amplification strategy of dendritic mesoporous silica nanospheres loaded with CdSe/ZnS quantum dots (DMSN@QDs), an ultrasensitive electrochemiluminescence (ECL) immunosensor with magnetic separation was constructed for the detection of SARS-CoV-2 nucleocapsid protein (NP). DMSN, a mesoporous material with abundant radial pores, large specific surface area and high porosity, can increase the loading capacity of QDs and hinder their aggregation as the nanocarrier. DMSN@QDs with good ECL efficiency were used as signal labels to construct a sandwich immunosensor. The designed ECL immunosensor displayed a good linear relationship for NP concentrations ranging from 0.005 ng mL(-1) to 50 ng mL(-1), with a limit of detection of 3.33 pg mL(-1). The ECL immunosensor was successfully applied to detect NP in human serum samples with satisfactory recovery. This strategy provided a new method for detecting NP and expanded the application field of DMSN.

Effects of regulatory focus on online learning engagement of high school students: The mediating role of self-efficacy and academic emotions

Background Since the outbreak of COVID-19, online courses have been extensively used from K-12 to higher education. Online learning engagement, an important factor in online learning success, is currently at a low level in high school. Meanwhile, the research on the factors that influence high school students' online learning engagement is still limited. Objectives Based on the theories of regulatory focus and value control, this study developed a multi-mediation model to investigate whether self-efficacy and academic emotions can mediate the relationship between regulatory focus and online learning engagement. Methods A total of 926 high school student (52.16% female, mean age = 16.47 years) were recruited to participate in this study and completed self-report measures of regulatory focus, online learning engagement, online learning self-efficacy and academic emotions. And we used SPSS macro PROCESS developed by Hayes to examine the mediating role of online learning self-efficacy and academic emotions. Results and Conclusions The results indicated that promotion focus had a stable positive effect on online learning engagement of high school students, whereas prevention focus had a significant negative effect on the same. Self-efficacy and positive emotions had a significant positive mediating effect between promotion focus and online learning engagement. Moreover, positive emotions had a significant positive mediating effect between the prevention focus and online learning engagement, while negative emotions had a significant negative mediating effect between them.

Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification

Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic. However, due to complex feature behaviors and expensive annotations of US images, it is difficult to apply Artificial Intelligence (AI) assisting approaches for the lung's multi-symptom (multi-label) classification. To overcome these difficulties, we propose a novel semi-supervised Two-Stream Active Learning (TSAL) method to model complicated features and reduce labeling costs in an iterative manner. The core component of TSAL is the multi-label learning mechanism, in which label correlation information is used to design a multi-label margin (MLM) strategy and a confidence validation for automatically selecting informative samples and confident labels. In this framework, a multi-symptom multi-label (MSML) classification network is proposed to learn discriminative features of lung symptoms, and a human-machine interaction (HMI) is exploited to confirm the final annotations that are used to fine-Tune MSML. Moreover, a novel lung US dataset named COVID19-LUSMS is built, currently containing 71 clinical patients with 6, 836 images sampled from 678 videos. Experimental evaluations show that TSAL can achieve superior performance to the baseline and the state-of-The-Art using only 20% data. Qualitatively, visualization of the attention map confirms a good consistency between the model prediction and the clinical knowledge. © 2020 IEEE.

Correlation of the renal function changes to prognosis in critical patients with COVID-19

Objective: To get the message of kidney injury and its causes in patients with COVID-19, and analyze the correlation of kidney injury to COVID-19 typing and prognosis, so provide a reference for the treatment and prognosis evaluation of COVID-19. Methods: According to the retrospective cohort study protocol, the clinical data and prognosis of 319 confirmed patients with COVID-19, admitted in the General Hospital of Central Theater Command (Wuhan) from Jan. 1st to Mar. 14th, 2020, were collected. The correlation of COVID-19 patients' renal function changes to the classification and prognosis of diseases were analyzed using univariate and multivariate logistic regression analysis. Results: The mean age of the 319 confirmed patients with COVID-19 was (55.2±17.0) years. The proportion of non-critical group (mild+moderate type) and critical group (severe+critical type) were 62.1% (198/319) and 37.9% (121/319), respectively. The fatality rate of present study cohort was 5.6% (18/319). About 3.8% cases (12/319) were with elevated blood urea nitrogen (BUN) and serum creatinine (SCr) at admission, and about 5.6% cases (18/319) were with elevated BUN only at admission. Univariate logistic regression analysis revealed that the age, the levels of SCr and BUN at admission and one week after admission, the combination of diabetes mellitus, and chronic kidney disease were the risk factors associated with the death in critical group patients (P<0.05). Multivariate logistic regression analysis revealed that the elevated levels of BUN at admission and one week after admission were the independent risk factors of death in the critical group patients. Conclusions: The elevated levels of BUN at admission and one week after admission were the important clinical features and independent risk factors associated with the death of critical COVID-19 patients. More attention should be paid to all kinds of clinical factors that may lead to increase the level of BUN.