An Overview of the Stability and Delivery Challenges of Commercial Nucleic Acid Therapeutics.

Nucleic acid (NA)-based biopharmaceuticals have emerged as promising therapeutic modalities. NA therapeutics are a diverse class of RNA and DNA and include antisense oligonucleotides, siRNA, miRNA, mRNA, small activating RNA, and gene therapies. Meanwhile, NA therapeutics have posed significant stability and delivery challenges and are expensive. This article discusses the challenges and opportunities for achieving stable formulations of NAs with novel drug delivery systems (DDSs). Here we review the current progress in the stability issues and the significance of novel DDSs associated with NA-based biopharmaceuticals, as well as mRNA vaccines. We also highlight the European Medicines Agency (EMA) and US Food and Drug Administration (FDA)-approved NA-based therapeutics with their formulation profiles. NA therapeutics could impact future markets if the remaining challenges and requirements are addressed. Regardless of the limited information available for NA therapeutics, reviewing and collating the relevant facts and figures generates a precious resource for formulation experts familiar with the NA therapeutics' stability profile, their delivery challenges, and regulatory acceptance.

Examine the relationships between health-related quality of life, achievement motivation and job performance: the case of Taiwan hospitality industry.

BACKGROUND: Employees are considered as one of the most important assets in many organizations, and their health well-being is critical to help achieve a sustainable and motivated workforce that is committed to delivering quality hospitality services through enhanced performance and productivity. Given the extent of the challenges and impact presented by the COVID-19 pandemic to the hospitality industry, it is timely to gain further insights on employees' health well-being. The key purpose of this study is to examine the relationships between health-related quality of life, achievement motivation and job performance in the Taiwan hospitality industry, to acquire a better understanding of their relationships through the job performance pathway models. METHODS: This study has used a purposeful sampling technique to select the 10 highest-earning hospitality companies in Taiwan. A total of 292 questionnaires were collected from the employees of these hospitality companies. Based on the multi-dimensional concept of health-related quality of life (HRQoL), the relationships between the five key dimensions (i.e. psychological health, physical health, social health, achievement motivation, and job performance) were examined. To measure these dimensions, the survey questions were adapted from previous research such as the World Health Organization's WHOQOL-BREF scale, Minnesota Satisfaction Questionnaire. Partial least squares - Structural Equation Modeling method was used to explore these dimensions, and two job performance pathway models (for manager and staff) were subsequently developed. RESULTS AND CONCLUSIONS: Findings showed that psychological health directly affected the manager's job performance and physical health had a similar effect through social health. While psychological health had not affected the staff's job performance, but it could affect achievement motivation through both direct and indirect effects of social health. The pathway models that were developed indicated that the manager's job performance was mainly affected by psychological health and social health, whereas the key dimension that had affected the staff's job performance was achievement motivation.

The initial attempt at home hemodialysis in mainland China.

BACKGROUND: Observational studies have shown home hemodialysis (HHD) to be associated with better survival than facility hemodialysis (HD) and peritoneal dialysis (PD). Patients on HHD have reported higher quality of life and independence. HHD is considered to be an economical way to manage end-stage kidney disease (ESKD). The coronavirus disease 2019 pandemic has had a significant impact on patients with ESKD. Patients on HHD may have an advantage over in-center HD patients because of a lower risk of exposure to infection. PARTICIPANTS AND METHODS: We enrolled HD patients from our dialysis center. We first established the HHD training center. The training center was approved by the Chinese government. Doctors, nurses and engineers train and assess patients separately. There are three forms of patient monitoring: home visits, internet remote monitoring, and outpatient services. Demographic and medical data included age, sex, blood pressure, and dialysis-related data. Laboratory tests were conducted in our central testing laboratory, including hemoglobin (Hgb), serum creatinine (Cr), urea nitrogen (BUN), uric acid (UA), albumin (Alb), calcium (Ca), phosphorus (P), parathyroid hormone (PTH), and brain natriuretic peptide (BNP) levels. RESULTS: Six patients who underwent regular dialysis in the HD center of our hospital were selected for HHD training. We enrolled 6 patients, including 4 males and 2 females. The mean age of the patients was 47.5 (34.7-55.7) years, and the mean dialysis age was 33.5 (11.2-41.5) months. After an average of 16.0 (11.2-25.5) months of training, Alb, P and BNP levels were improved compared with the baseline values. After training, three patients returned home to begin independent HD. During the follow-up, there were no serious adverse events leading to hospitalization or death, but there were several adverse events. They were solved quickly by extra home visits of the technicians or online by remote monitoring. During the follow-up time, the laboratory indicators of all the patients, including Hgb, Alb, Ca, P, PTH, BNP, and ß2-MG levels, remained stable before and after HHD treatment. CONCLUSION: HHD is feasible and safe for ESKD in China, but larger-scale and longer-term studies are needed for further confirmation.

Unpacking the effects of personality traits on algorithmic awareness: The mediating role of previous knowledge and moderating role of internet use

The COVID-19 pandemic has accelerated the integration of algorithms in online platforms to facilitate people’s work and life. Algorithms are increasingly being utilized to tailor the selection and presentation of online content. Users’ awareness of algorithmic curation influences their ability to properly calibrate their reception of online content and interact with it accordingly. However, there has been a lack of research exploring the factors that contribute to users’ algorithmic awareness, especially in the roles of personality traits. In this study, we explore the influence of Big Five personality traits on internet users’ algorithmic awareness of online content and examine the mediating effect of previous knowledge and moderating effect of breadth of internet use in in China during the pandemic era. We adapted the 13-item Algorithmic Media Content Awareness Scale (AMCA-scale) to survey users’ algorithmic awareness of online content in four dimensions. Our data were collected using a survey of a random sample of internet users in China (n = 885). The results of this study supported the moderated mediation model of open-mindedness, previous knowledge, breadth of internet use, and algorithmic awareness. The breadth of internet use was found to be a negative moderator between previous knowledge and algorithmic awareness.

Machine Learning Prediction of COVID-19 Severity Levels From Salivaomics Data (preprint)

The clinical spectrum of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the strain of coronavirus that caused the COVID-19 pandemic, is broad, extending from asymptomatic infection to severe immunopulmonary reactions that, if not categorized properly, may be life-threatening. Researchers rate COVID-19 patients on a scale from 1 to 8 according to the severity level of COVID-19, 1 being healthy and 8 being extremely sick, based on a multitude of factors including number of clinic visits, days since the first sign of symptoms, and more. However, there are two issues with the current state of severity level designation. Firstly, there exists variation among researchers in determining these patient scores, which may lead to improper treatment. Secondly, researchers use a variety of metrics to determine patient severity level, including metrics involving plasma collection that require invasive procedures. This project aims to remedy both issues by introducing a machine learning framework that unifies severity level designations based on noninvasive saliva biomarkers. Our results show that we can successfully use machine learning on salivaomics data to predict the severity level of COVID-19 patients, indicating the presence of viral load using saliva biomarkers.

Evaporation of liquid nanofilms: A minireview Note: This paper is part of the special topic, Flow and the Virus

Evaporation of virus-loaded droplets and liquid nanofilms plays a significant role in the pandemic of COVID-19. The evaporation mechanism of liquid nanofilms has attracted much attention in recent decades. In this minireview, we first introduce the relationship between the evaporation process of liquid nanofilms and the pandemic of COVID-19. Then, we briefly provide the frontiers of liquid droplet/nanofilm evaporation on solid surfaces. In addition, we discuss the potential application of machine learning in liquid nanofilm evaporation studies, which is expected to be helpful to build up a more accurate molecular model and to investigate the evaporation mechanism of liquid nanofilms on solid surfaces.

RT-LAMP assay for rapid detection of the R203M mutation in SARS-CoV-2 Delta variant.

The highly infectious Delta variant strain of SARS-CoV-2 remains globally dominant and undermines COVID-19 vaccines. Rapid detection of the Delta variant is crucial for the identification and quarantine of infected individuals. In this study, our aim was to design and validate a genotyping RT-LAMP method to detect Delta variants specifically. R203M in the N gene of SARS-CoV-2 was chosen as the Delta variant-specific mutation for genotyping. To target the R203M-harboring region and the conserved sequence of the N gene, two sets of primers were designed, and a Cq (quantification cycle) ratio-based RT-LAMP for SARS-CoV-2 and R203M detection was developed by analyzing the significant discrepancy in amplification efficiency of the two sets of primers. We validated the RT-LAMP method on 498 clinical specimens in parallel with RT-qPCR, and 84 Delta variants from 198 positive samples were determined by sequencing. Compared with traditional RT-qPCR analyses, RT-LAMP appears to be 100% accurate in detecting SARS-CoV-2 clinical samples. RT-LAMP has a good ability to distinguish between Delta and non-Delta variants under a Cq ratio threshold of 1.80. Furthermore, the AUC (area under the curve) of this method was 1.00; the sensitivity, specificity and accuracy were all 100%. In summary, we have proposed a rapid, accurate and cost-effective RT-LAMP method to detect SARS-CoV-2 and Delta variants, which may facilitate the surveillance of COVID-19.

Dynamic common correlated effects of pandemic uncertainty on environmental quality: fresh insights from East-Asia and Pacific countries

It is well known that pandemic-related uncertainty affects various macroeconomic indicators, including environmental quality. Due to pandemic outbreaks, the reduction in economic activities affects the environmental quality in many economies. The study explores the impact of pandemic uncertainty on environmental quality in East-Asia and Pacific countries. Most past research use only CO2 emissions, which is an inappropriate measurement of environmental quality. Besides CO2 emissions, we have utilized other pollutants like N2O and CH4 emissions along with ecological footprint. The traditional econometric approaches ignore cross-sectional dependence and heterogeneity and give biased outcomes. Hence, we have employed a new method, “Dynamic Common Correlated Effects (DCCE),” which can excellently deal with the problems mentioned above. The short-run and long-run DCCE estimations show a negative and significant influence of pandemic uncertainty on ecological footprint, CO2 and CH4 emissions in whole and lower-income group of East-Asia and Pacific region. Moreover, pandemic uncertainty has a negative relationship with all indicators of environmental quality in higher-income economies. The study provides a unique opportunity to examine how pandemic uncertainty through anthropogenic activities affects environmental quality and serves as a significant resource for policymakers in planning and estimating the effectiveness of environmental quality measures. It is necessary to carry out sustainable environmental policies in East-Asia and Pacific region according to the vulnerabilities and resilience to global pandemic uncertainty.

Evaporation of liquid nanofilms: A minireview.

Evaporation of virus-loaded droplets and liquid nanofilms plays a significant role in the pandemic of COVID-19. The evaporation mechanism of liquid nanofilms has attracted much attention in recent decades. In this minireview, we first introduce the relationship between the evaporation process of liquid nanofilms and the pandemic of COVID-19. Then, we briefly provide the frontiers of liquid droplet/nanofilm evaporation on solid surfaces. In addition, we discuss the potential application of machine learning in liquid nanofilm evaporation studies, which is expected to be helpful to build up a more accurate molecular model and to investigate the evaporation mechanism of liquid nanofilms on solid surfaces.

A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media.

BACKGROUND: Rumor detection is a popular research topic in natural language processing and data mining. Since the outbreak of COVID-19, related rumors have been widely posted and spread on online social media, which have seriously affected people's daily lives, national economy, social stability, etc. It is both theoretically and practically essential to detect and refute COVID-19 rumors fast and effectively. As COVID-19 was an emergent event that was outbreaking drastically, the related rumor instances were very scarce and distinct at its early stage. This makes the detection task a typical few-shot learning problem. However, traditional rumor detection techniques focused on detecting existed events with enough training instances, so that they fail to detect emergent events such as COVID-19. Therefore, developing a new few-shot rumor detection framework has become critical and emergent to prevent outbreaking rumors at early stages. METHODS: This article focuses on few-shot rumor detection, especially for detecting COVID-19 rumors from Sina Weibo with only a minimal number of labeled instances. We contribute a Sina Weibo COVID-19 rumor dataset for few-shot rumor detection and propose a few-shot learning-based multi-modality fusion model for few-shot rumor detection. A full microblog consists of the source post and corresponding comments, which are considered as two modalities and fused with the meta-learning methods. RESULTS: Experiments of few-shot rumor detection on the collected Weibo dataset and the PHEME public dataset have shown significant improvement and generality of the proposed model.

Gendered Factors Associated with Preventive Behaviors and Mental Health among Chinese Adults during the COVID-19 Pandemic Home Quarantine

The outbreak of the coronavirus disease in 2019 (COVID-19) has greatly impacted the life and mental health of many people globally. The objective of this study was to investigate the factors associated with preventive behaviors and mental health among Chinese adults during their home quarantine in the COVID-19 period. An online questionnaire survey was administered in March 2020. The study participants were adults aged between 18 and 70 years old from 31 provinces in China. Of the 3878 participants, 1314 reported moderate levels of anxiety, and the remaining participants reported moderate to severe levels of anxiety. Findings revealed that females aged between 18 and 30 years old who had higher educational qualifications, greater levels of preventive knowledge, trust in the government, and resided in urban and medium-risk areas (R2 = 0.100, F = 27.97, p <0.001) were more likely to exhibit preventive behaviors. In contrast, a higher negative emotional response was generally seen in males who had low levels of preventive knowledge and behaviors, higher risk perception of infection, lower trust in the government, and unhealthy lifestyles (R2 = 0.127, F = 32.33, p <0.001). In addition, the high-risk perception of infection was positively associated with high odds of anxiety (AOR = 1.17, 95% CI: 1.10–1.24), whereas a greater level of preventive knowledge (AOR = 0.36, 95% CI: 0.19–0.70) and behaviors (AOR = 0.69, 95% CI: 0.57–0.84), higher trust in the government’s COVID-19 pandemic mitigation measures (AOR = 0.77, 95% CI: 0.71–0.83), and a healthier lifestyle (AOR = 0.89, 95% CI: 0.79–0.99) were negatively associated with high odds of anxiety. Results showed that a lower level of anxiety and negative emotional response were associated with better preventive behaviors against COVID-19, which were influenced by preventive knowledge, risk perception, trust in the government’s COVID-19 pandemic mitigation measures, and healthy lifestyle. Findings in this study could help formulate health interventions for vulnerable groups related to gendered vulnerabilities in the COVID-19 environment to improve their mental health and preventive behaviors, especially during the period of a pandemic.

A preliminary study on the anxiety and depression situation and psychological intervention of the first-line medical staff in our hospital during the COVID-19 epidemic.

To assess the anxiety and depression situation and psychological intervention effect of the first-line medical staff in our hospital during the COVID-19 epidemic. A total of 384 front-line medical staff in our hospital from January 25 to March 8, 2020 were selected as subjects, which were divided into group A and group B respectively. PHQ-9 depression scale and GAD-7 self-rating anxiety scale questionnaire were used to investigate their anxiety and depression. After 1 month, all subjects were re-self-assessed for anxiety and depression, which were named as A1 and B1 group respectively. The GAD-7 anxiety scale had mild, moderate, and severe anxiety scores before group A, which were significantly higher than those in group B (P < 0.05); after psychological intervention, group A1 had significantly reduced anxiety scores (P < 0.05). And there were no markedly difference of PHQ-9 scale scores before and after psychological intervention between groups A and B, A and A1, and B and B1 (P > 0.05). The first-line medical staff in our hospital have different degree of anxiety and depression during COVID-19. Early positive psychological intervention has an effect on ameliorating the anxiety.

Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels.

Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.

Development and validation of a highly sensitive and specific electrochemical assay to quantify anti-SARS-CoV-2 IgG antibodies to facilitate pandemic surveillance and monitoring of vaccine response (preprint)

Amperial is a novel assay platform that uses immobilized antigen in a conductive polymer gel followed by an electrochemical detection. A highly specific and sensitive assay was developed to quantify levels of IgG antibodies to SARS-CoV-2 in saliva. After establishing linearity and limit of detection we established a reference range of 5 standard deviations above the mean. There were no false positives in 667 consecutive saliva samples obtained prior to 2019. Saliva was obtained from 34 patients who had recovered from documented COVID-19 or had documented positive serologies. All of the patients with symptoms severe enough to seek medical attention had positive antibody tests and 88% overall had positive results. We obtained blinded paired saliva and plasma samples from 14 individuals. The plasma was analyzed using an EUA-FDA cleared ELISA kit and the saliva was analyzed by our Amperial assay. All 5 samples with negative plasma titers were negative in saliva testing. Eight of the 9 positive plasma samples were positive in saliva and 1 had borderline results. A CLIA validation was performed as a laboratory developed test in a high complexity laboratory. A quantitative non-invasive saliva based SARSCoV-2 antibody test was developed and validated with sufficient specificity to be useful for population-based monitoring and monitoring of individuals following vaccination.

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Xuebijing injection in the treatment of COVID-19: a retrospective case-control study.

BACKGROUND: Neither a vaccine nor specific therapeutic drugs against 2019 novel coronavirus have been developed. Some studies have shown that Xuebijing injection (XBJ) can exert an anti-inflammatory effect by inhibiting the production of interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and other cytokines. This study aimed to investigate the effect of XBJ on coronavirus disease 2019 (COVID-19) and its effects on IL-6 and tumor necrosis alpha TNF-α. METHODS: A total of 42 patients, who were diagnosed with COVID-19 and treated with XBJ combined with routine treatment at Chongqing University Three Gorges Hospital between January 20, 2020, and March 11, 2020, were selected as the observation group. A control group comprising 16 patients who received routine treatment was also established, and cases were matched from the observation group on a 1:1 basis according to age, comorbidities, and mild and severe disease. The clinical symptoms, laboratory test indexes, and changes in computed tomography (CT) scans of patients in the two groups were observed at the time of admission and 7 days after treatment, and the time taken for the patients to produce a negative nucleic acid test was also recorded. RESULTS: There were no significant differences in baseline data between the two groups. After treatment, there were significant improvements in IL-6 levels and body temperature in the observation group as compared with the control group. Particularly in severe patients, the reduction in body temperature in the observation group was greater than that in the control group (P<0.05). A higher number of patients in the observation group showed improved CT imaging results compared with the control group, and the time taken to produce a negative nucleic acid test was shorter in the observation group than in the control group; however, the differences were not statistically significant (P>0.05). Furthermore, there were no significant differences in TNF-α and IL-10 between the two groups. CONCLUSIONS: The results of this study suggest that routine treatment combined with XBJ can better improve the clinical outcomes of COVID-19 patients.

How to Screen Suitable Service Improve Community Health Care Services by University Students in Taiwan.

Engaging in social contributions to enhance social participation and attending community experiential service learning or internship courses have become an essential learning experience for university students. On the basis of postmodern education theories, this study adopted images and oral accounts involving personal experiences to construct a postmodern education research scheme by using the method of collaborative ethnography. This study selected and performed the following services: filming a community documentary, administering community health dance classes, and archiving community cultural artifacts in databases. Interviews were also administered to facilitate implementation of the actual services. Community health services commonly seen in Taiwan and abroad were compiled, and the resources required for each service were examined. Subsequently, factor analysis was performed to explore the characteristic of these services in order to recommend feasible services for university students to undertake. The results indicated that the eight resources required for the 59 common community health services were (1) a designated space or venue, (2) materials, (3) monetary resources, (4) human resources, (5) expertise, (6) professional equipment, (7) patience, and (8) empathy. The results revealed three principal components, namely labor services, high-resource services, and professional services, for a total explanatory power of 67.99%; the individual explanatory power of these components accounted for 25.04%, 21.81%, and 21.15%, respectively. Next, community health care services suitable for university students to perform were selected and implemented, and these services were well received. The study results indicated that community and environmental justice can be realized by identifying with the value of community health services and promoting postmodern education theories and social norms. The research results are suitable for implementation after the COVID-19 pandemic.