Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media (preprint)

News media has been utilized as a political tool to stray from facts, presenting biased claims without evidence. Amid the COVID-19 pandemic, politically biased news (PBN) has significantly undermined public trust in vaccines, despite strong medical evidence supporting their efficacy. In this paper, we analyze: (i) how inherent vaccine stances subtly influence individuals' selection of news sources and participation in social media discussions; and (ii) the impact of exposure to PBN on users' attitudes toward vaccines. In doing so, we first curate a comprehensive dataset that connects PBN with related social media discourse. Utilizing advanced deep learning and causal inference techniques, we reveal distinct user behaviors between social media groups with various vaccine stances. Moreover, we observe that individuals with moderate stances, particularly the vaccine-hesitant majority, are more vulnerable to the influence of PBN compared to those with extreme views. Our findings provide critical insights to foster this line of research.

STANCE-C3: Domain-adaptive Cross-target Stance Detection via Contrastive Learning and Counterfactual Generation (preprint)

Stance detection is the process of inferring a person's position or standpoint on a specific issue to deduce prevailing perceptions toward topics of general or controversial interest, such as health policies during the COVID-19 pandemic. Existing models for stance detection are trained to perform well for a single domain (e.g., COVID-19) and a specific target topic (e.g., masking protocols), but are generally ineffectual in other domains or targets due to distributional shifts in the data. However, constructing high-performing, domain-specific stance detection models requires an extensive corpus of labeled data relevant to the targeted domain, yet such datasets are not readily available. This poses a challenge as the process of annotating data is costly and time-consuming. To address these challenges, we introduce a novel stance detection model coined domain-adaptive Cross-target STANCE detection via Contrastive learning and Counterfactual generation (STANCE-C3) that uses counterfactual data augmentation to enhance domain-adaptive training by enriching the target domain dataset during the training process and requiring significantly less information from the new domain. We also propose a modified self-supervised contrastive learning as a component of STANCE-C3 to prevent overfitting for the existing domain and target and enable cross-target stance detection. Through experiments on various datasets, we show that STANCE-C3 shows performance improvement over existing state-of-the-art methods.

Towards Exploring Large Molecular Space: An Efficient Chemical Genetic Algorithm.

Generating molecules with desired properties is an important task in chemistry and pharmacy. An efficient method may have a positive impact on finding drugs to treat diseases like COVID-19. Data mining and artificial intelligence may be good ways to find an efficient method. Recently, both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule's properties. However, existing methods need to be improved in efficiency and performance. To solve these problems, we propose a method named the Chemical Genetic Algorithm for Large Molecular Space (CALM). Specifically, CALM employs a scalable and efficient molecular representation called molecular matrix. Then, we design corresponding crossover, mutation, and mask operators inspired by domain knowledge and previous studies. We apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular optimization. The results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods, where the z tests performed on the results of several experiments show that our method is more than 99% likely to be significant. At the same time, based on the experimental results, we point out the insufficiency in the experimental evaluation standard which affects the fair evaluation of previous work. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-021-0970-3.

Recuperative herbal formula Jing Si maintains vasculature permeability balance, regulates inflammation and assuages concomitants of "Long-Covid".

Coronavirus disease 2019 (COVID-19) is a worldwide health threat that has long-term effects on the patients and there is currently no efficient cure prescribed for the treatment and the prolonging effects. Traditional Chinese medicines (TCMs) have been reported to exert therapeutic effect against COVID-19. In this study, the therapeutic effects of Jing Si herbal tea (JSHT) against COVID-19 infection and associated long-term effects were evaluated in different in vitro and in vivo models. The anti-inflammatory effects of JSHT were studied in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells and in Omicron pseudotyped virus-induced acute lung injury model. The effect of JSHT on cellular stress was determined in HK-2 proximal tubular cells and H9c2 cardiomyoblasts. The therapeutic benefits of JSHT on anhedonia and depression symptoms associated with long COVID were evaluated in mice models for unpredictable chronic mild stress (UCMS). JSHT inhibited the NF-ƙB activities, and significantly reduced LPS-induced expression of TNFα, COX-2, NLRP3 inflammasome, and HMGB1. JSHT was also found to significantly suppress the production of NO by reducing iNOS expression in LPS-stimulated RAW 264.7 cells. Further, the protective effects of JSHT on lung tissue were confirmed based on mitigation of lung injury, repression in TMRRSS2 and HMGB-1 expression and reduction of cytokine storm in the Omicron pseudotyped virus-induced acute lung injury model. JSHT treatment in UCMS models also relieved chronic stress and combated depression symptoms. The results therefore show that JSHT attenuates the cytokine storm by repressing NF-κB cascades and provides the protective functions against symptoms associated with long COVID-19 infection.

Towards Exploring Large Molecular Space: An Efficient Chemical Genetic Algorithm

Generating molecules with desired properties is an important task in chemistry and pharmacy. An efficient method may have a positive impact on finding drugs to treat diseases like COVID-19. Data mining and artificial intelligence may be good ways to find an efficient method. Recently, both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule's properties. However, existing methods need to be improved in efficiency and performance. To solve these problems, we propose a method named the Chemical Genetic Algorithm for Large Molecular Space (CALM). Specifically, CALM employs a scalable and efficient molecular representation called molecular matrix. Then, we design corresponding crossover, mutation, and mask operators inspired by domain knowledge and previous studies. We apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular optimization. The results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods, where the z tests performed on the results of several experiments show that our method is more than 99% likely to be significant. At the same time, based on the experimental results, we point out the insufficiency in the experimental evaluation standard which affects the fair evaluation of previous work. Supplementary Information The online version contains supplementary material available at 10.1007/s11390-021-0970-3.

Ultrasensitive Strain Sensor Utilizing a AgF-AgNW Hybrid Nanocomposite for Breath Monitoring and Pulmonary Function Analysis.

Breath monitoring and pulmonary function analysis have been the prime focus of wearable smart sensors owing to the COVID-19 outbreak. Currently used lung function meters in hospitals are prone to spread the virus and can result in the transmission of the disease. Herein, we have reported the first-ever wearable patch-type strain sensor for enabling real-time lung function measurements (such as forced volume capacity (FVC) and forced expiratory volume (FEV) along with breath monitoring), which can avoid the spread of the virus. The noninvasive and highly sensitive strain sensor utilizes the synergistic effect of two-dimensional (2D) silver flakes (AgFs) and one-dimensional (1D) silver nanowires (AgNWs), where AgFs create multiple electron transmission paths and AgNWs generate percolation networks in the nanocomposite. The nanocomposite-based strain sensor possesses a high optimized conductivity of 7721 Sm-1 (and a maximum conductivity of 83,836 Sm-1), excellent stretchability (>1000%), and ultrasensitivity (GFs of 35 and 87 when stretched 0-20 and 20-50%, respectively), thus enabling reliable detection of small strains produced by the body during breathing and other motions. The sensor patching site was optimized to accurately discriminate between normal breathing, quick breathing, and deep breathing and analyze numerous pulmonary functions, including the respiratory rate, peak flow, FVC, and FEV. Finally, the observed measurements for different pulmonary functions were compared with a commercial peak flow meter and a spirometer, and a high correlation was observed, which highlights the practical feasibility of continuous respiratory monitoring and pulmonary function analysis.

An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study

Objective: The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models—radiomics (MRM), clinical (MCM), and combined clinical–radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans. Methods: We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, Results: The three out of the top five features identified using Conclusion: The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

BACKGROUND: Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death. METHODS: DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N.ß=.ß80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N.ß=.ß805; D2, N.ß=.ß1917; D3, N.ß=.ß169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity. FINDINGS: The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93...0.96] on the independent validation cohort (N.ß=.ß49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR.ß=.ß1.50, 95% CI [1.20...1.88], P.ß<.ß.001). INTERPRETATION: The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N.ß=.ß2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment. FUNDING: For a full list of funding bodies, please see the Acknowledgements.

Traditional Chinese Medicine in Treating Children With Coronavirus Disease 2019: A Scoping Review.

Coronavirus disease 2019 (COVID-19) is currently widely spread across the world. Traditional Chinese Medicine (TCM) plays an important role in the overall treatment process. As a special group of population, the treatment outcome of children with COVID-19 has attracted much attention. Our study summarizes the current situation of TCM treatment of children with COVID-19. The results showed that TCM displayed a positive role in the treatment process, and that no significant adverse reactions were found. Our findings provide analytical evidence for the efficacy and safety of TCM participation in the treatment of COVID-19 in children.

Rejection of Acellular Porcine Corneal Stroma Transplantation During Coronavirus Disease 2019 Pandemic.

ABSTRACT: To report 2 successfully managed cases of graft rejection with acellular porcine corneal stroma (APCS) transplantation in patients with fungal corneal ulcer. Two patients were diagnosed with fungal corneal ulcer and received APCS transplantation. Graft rejection developed due to the lost follow-up during the period of coronavirus disease 2019 outbreak. Amniotic membranes transplantation and cauterization of neovascularization was performed, respectively. The graft failure resolved successfully after the procedure. To the best of our knowledge, amniotic membranes transplantation and cauterization of new vessels are the firstly reported in treating APCS graft failure. Amniotic membranes transplantation or cauterization of neovascularization appear to be a safe and costeffective method for treating graft failure.

Traditional Chinese Medicine in Treating Children With Coronavirus Disease 2019: A Scoping Review

Coronavirus disease 2019 (COVID-19) is currently widely spread across the world. Traditional Chinese Medicine (TCM) plays an important role in the overall treatment process. As a special group of population, the treatment outcome of children with COVID-19 has attracted much attention. Our study summarizes the current situation of TCM treatment of children with COVID-19. The results showed that TCM displayed a positive role in the treatment process, and that no significant adverse reactions were found. Our findings provide analytical evidence for the efficacy and safety of TCM participation in the treatment of COVID-19 in children.

Improving Vaccine Stance Detection by Combining Online and Offline Data (preprint)

Differing opinions about COVID-19 have led to various online discourses regarding vaccines. Due to the detrimental effects and the scale of the COVID-19 pandemic, detecting vaccine stance has become especially important and is attracting increasing attention. Communication during the pandemic is typically done via online and offline sources, which provide two complementary avenues for detecting vaccine stance. Therefore, this paper aims to (1) study the importance of integrating online and offline data to vaccine stance detection; and (2) identify the critical online and offline attributes that influence an individual's vaccine stance. We model vaccine hesitancy as a surrogate for identifying the importance of online and offline factors. With the aid of explainable AI and combinatorial analysis, we conclude that both online and offline factors help predict vaccine stance.

Bridging the Gap: Commonality and Differences between Online and Offline COVID-19 Data (preprint)

With the onset of the COVID-19 pandemic, news outlets and social media have become central tools for disseminating and consuming information. Because of their ease of access, users seek COVID-19-related information from online social media (i.e., online news) and news outlets (i.e., offline news). Online and offline news are often connected, sharing common topics while each has unique, different topics. A gap between these two news sources can lead to misinformation propagation. For instance, according to the Guardian, most COVID-19 misinformation comes from users on social media. Without fact-checking social media news, misinformation can lead to health threats. In this paper, we focus on the novel problem of bridging the gap between online and offline data by monitoring their common and distinct topics generated over time. We employ Twitter (online) and local news (offline) data for a time span of two years. Using online matrix factorization, we analyze and study online and offline COVID-19-related data differences and commonalities. We design experiments to show how online and offline data are linked together and what trends they follow.

Rejection of Acellular Porcine Corneal Stroma Transplantation During Coronavirus Disease 2019 Pandemic

: To report 2 successfully managed cases of graft rejection with acellular porcine corneal stroma (APCS) transplantation in patients with fungal corneal ulcer. Two patients were diagnosed with fungal corneal ulcer and received APCS transplantation. Graft rejection developed due to the lost follow-up during the period of coronavirus disease 2019 outbreak. Amniotic membranes transplantation and cauterization of neovascularization was performed, respectively. The graft failure resolved successfully after the procedure. To the best of our knowledge, amniotic membranes transplantation and cauterization of new vessels are the firstly reported in treating APCS graft failure. Amniotic membranes transplantation or cauterization of neovascularization appear to be a safe and costeffective method for treating graft failure.

Molecular Basis for Paradoxical Activities of Polymorphonuclear Neutrophils in Inflammation/Anti-Inflammation, Bactericide/Autoimmunity, Pro-Cancer/Anticancer, and Antiviral Infection/SARS-CoV-II-Induced Immunothrombotic Dysregulation.

Polymorphonuclear neutrophils (PMNs) are the most abundant white blood cells in the circulation. These cells act as the fast and powerful defenders against environmental pathogenic microbes to protect the body. In addition, these innate inflammatory cells can produce a number of cytokines/chemokines/growth factors for actively participating in the immune network and immune homeostasis. Many novel biological functions including mitogen-induced cell-mediated cytotoxicity (MICC) and antibody-dependent cell-mediated cytotoxicity (ADCC), exocytosis of microvesicles (ectosomes and exosomes), trogocytosis (plasma membrane exchange) and release of neutrophil extracellular traps (NETs) have been successively discovered. Furthermore, recent investigations unveiled that PMNs act as a double-edged sword to exhibit paradoxical activities on pro-inflammation/anti-inflammation, antibacteria/autoimmunity, pro-cancer/anticancer, antiviral infection/COVID-19-induced immunothrombotic dysregulation. The NETs released from PMNs are believed to play a pivotal role in these paradoxical activities, especially in the cytokine storm and immunothrombotic dysregulation in the recent SARS-CoV-2 pandemic. In this review, we would like to discuss in detail the molecular basis for these strange activities of PMNs.

Emotion Self-Regulation in Neurotic Students: A Pilot Mindfulness-Based Intervention to Assess Its Effectiveness through Brain Signals and Behavioral Data.

Neuroticism has recently received increased attention in the psychology field due to the finding of high implications of neuroticism on an individual's life and broader public health. This study aims to investigate the effect of a brief 6-week breathing-based mindfulness intervention (BMI) on undergraduate neurotic students' emotion regulation. We acquired data of their psychological states, physiological changes, and electroencephalogram (EEG), before and after BMI, in resting states and tasks. Through behavioral analysis, we found the students' anxiety and stress levels significantly reduced after BMI, with p-values of 0.013 and 0.027, respectively. Furthermore, a significant difference between students in emotion regulation strategy, that is, suppression, was also shown. The EEG analysis demonstrated significant differences between students before and after MI in resting states and tasks. Fp1 and O2 channels were identified as the most significant channels in evaluating the effect of BMI. The potential of these channels for classifying (single-channel-based) before and after BMI conditions during eyes-opened and eyes-closed baseline trials were displayed by a good performance in terms of accuracy (~77%), sensitivity (76-80%), specificity (73-77%), and area-under-the-curve (AUC) (0.66-0.8) obtained by k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. Mindfulness can thus improve the self-regulation of the emotional state of neurotic students based on the psychometric and electrophysiological analyses conducted in this study.

Molecular Basis for Paradoxical Activities of Polymorphonuclear Neutrophils in Inflammation/Anti-Inflammation, Bactericide/Autoimmunity, Pro-Cancer/Anticancer, and Antiviral Infection/SARS-CoV-II-Induced Immunothrombotic Dysregulation

Polymorphonuclear neutrophils (PMNs) are the most abundant white blood cells in the circulation. These cells act as the fast and powerful defenders against environmental pathogenic microbes to protect the body. In addition, these innate inflammatory cells can produce a number of cytokines/chemokines/growth factors for actively participating in the immune network and immune homeostasis. Many novel biological functions including mitogen-induced cell-mediated cytotoxicity (MICC) and antibody-dependent cell-mediated cytotoxicity (ADCC), exocytosis of microvesicles (ectosomes and exosomes), trogocytosis (plasma membrane exchange) and release of neutrophil extracellular traps (NETs) have been successively discovered. Furthermore, recent investigations unveiled that PMNs act as a double-edged sword to exhibit paradoxical activities on pro-inflammation/anti-inflammation, antibacteria/autoimmunity, pro-cancer/anticancer, antiviral infection/COVID-19-induced immunothrombotic dysregulation. The NETs released from PMNs are believed to play a pivotal role in these paradoxical activities, especially in the cytokine storm and immunothrombotic dysregulation in the recent SARS-CoV-2 pandemic. In this review, we would like to discuss in detail the molecular basis for these strange activities of PMNs.

Comparing different machine learning techniques for predicting COVID-19 severity.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is still ongoing spreading globally, machine learning techniques were used in disease diagnosis and to predict treatment outcomes, which showed favorable performance. The present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest (RF), support vector machine (SVM), and logistic regression (LR). Feature importance to COVID-19 severity were further identified. METHODS: A retrospective design was adopted in the JinYinTan Hospital from January 26 to March 28, 2020, eighty-six demographic, clinical, and laboratory features were selected with LassoCV method, Spearman's rank correlation, experts' opinions, and literature evaluation. RF, SVM, and LR were performed to predict severe COVID-19, the performance of the models was compared by the area under curve (AUC). Additionally, feature importance to COVID-19 severity were analyzed by the best performance model. RESULTS: A total of 287 patients were enrolled with 36.6% severe cases and 63.4% non-severe cases. The median age was 60.0 years (interquartile range: 49.0-68.0 years). Three models were established using 23 features including 1 clinical, 1 chest computed tomography (CT) and 21 laboratory features. Among three models, RF yielded better overall performance with the highest AUC of 0.970 than SVM of 0.948 and LR of 0.928, RF also achieved a favorable sensitivity of 96.7%, specificity of 69.5%, and accuracy of 84.5%. SVM had sensitivity of 93.9%, specificity of 79.0%, and accuracy of 88.5%. LR also achieved a favorable sensitivity of 92.3%, specificity of 72.3%, and accuracy of 85.2%. Additionally, chest-CT had highest importance to illness severity, and the following features were neutrophil to lymphocyte ratio, lactate dehydrogenase, and D-dimer, respectively. CONCLUSIONS: Our results indicated that RF could be a useful predictive tool to identify patients with severe COVID-19, which may facilitate effective care and further optimize resources.

The effect of Huashibaidu formula on the blood oxygen saturation status of severe COVID-19: A retrospective cohort study.

BACKGROUND: Huashibaidu Formula (HSBD) for the COVID-19 treatment has been supported by the China's Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia. However, it is not clear whether HSBD can improve blood oxygen saturation and when it should be used with conventional therapies. PURPOSE: To access the effect of HSBD combined with conventional treatment on blood oxygen saturation of COVID-19 patients. METHODS: A single-center retrospective cohort study was conducted to collect the confirmed severe COVID-19 patients' information, treated by the National Traditional Chinese Medicine Medical Team at the Jinyintan hospital between January 24 and March 31, 2020. According to whether HSBD was used during hospitalization, participants were separated into the conventional treatment group and the HSBD group (HSBD and conventional treatment). The primary observation indicators included the time for relieving blood oxygen saturation and the improvement ratio of blood oxygen saturation in each group. RESULTS: Of 111 patients with severe COVID-19, 53.2% (59/111) received HSBD, and 46.8% (52/111) only received conventional treatment, respectively. No statistically significant difference was found in image, clinical symptoms, and past medical history between the two groups (p > 0.05). Notably, the median time for relieving blood oxygen saturation in the conventional treatment group was 11 days (IQR, 8-14.25), while that in the HSBD group was only 6 days (IQR, 3.25-10.75), which was significantly shortened by 4.09 days (95%CI, 2.07-6.13; p= 0.0001), compared with the conventional treatment group. After repeated measurement design analysis, the main effect within times (p< 0.001) and the main effect were significantly different under the oxygen saturation dimension between two groups (p= 0.004). However, time and group interaction were observed no significant difference (p= 0.094). After 14 days of treatment, the improvement ratio of the HSBD group over the conventional treatment group was 1.20 (95%CI, 0.89-1.61). CONCLUSION: For severe COVID-19 patients, the HSBD has a tendency to shorten the time for relieving blood oxygen saturation. After taking a course of HSBD, the effect can be more obvious.

Factors Defining the Development of Severe Illness in Patients with COVID-19: A Retrospective Study.

OBJECTIVE: Early triage of patients with coronavirus disease 2019 (COVID-19) is pivotal in managing the disease. However, studies on the clinical risk score system of the risk factors for the development of severe disease are limited. Hence, we conducted a clinical risk score system for severe illness, which might optimize appropriate treatment strategies. METHODS: We conducted a retrospective, single-center study at the JinYinTan Hospital from January 24, 2020 to March 31, 2020. We evaluated the demographic, clinical, and laboratory data and performed a 10-fold cross-validation to split the data into a training set and validation set. We then screened the prognostic factors for severe illness using the least absolute shrinkage and selection operator (LASSO) and logistic regression, and finally conducted a risk score to estimate the probability of severe illness in the training set. Data from the validation set were used to validate the score. RESULTS: A total of 295 patients were included. From 49 potential risk factors, 3 variables were measured as the risk score: neutrophil to lymphocyte ratio ( OR, 1.27; 95% CI, 1.15-1.39), albumin ( OR, 0.76; 95% CI, 0.70-0.83), and chest computed tomography abnormalities ( OR, 2.01; 95% CI, 1.41-2.86) and the AUC of the validation cohort was 0.822 (95% CI, 0.7667-0.8776). CONCLUSION: This report may help define the potential of developing severe illness in patients with COVID-19 at an early stage, which might be related to the neutrophil to lymphocyte ratio, albumin, and chest computed tomography abnormalities.