Multivalent S2-subunit Vaccines Provide Broad Protection Against Clade 1 Sarbecoviruses (preprint)

The continuing emergence of immune evasive SARS-CoV-2 variants and the previous SARS-CoV-1 outbreak have accentuated the need for broadly protective sarbecovirus vaccines. Targeting the conserved S2-subunit of SARS-CoV-2 is a particularly promising approach to elicit broad protection. Here, expanding on our previous work with S2-based vaccines, we developed a nanoparticle vaccine displaying multiple copies of the SARS-CoV-1 S2 subunit. This vaccine alone, or as a cocktail with a SARS-CoV-2 S2 subunit vaccine, protected transgenic K18-hACE2 mice from challenges with Omicron subvariant XBB as well as several sarbecoviruses identified as having pandemic potential including the bat sarbecovirus WIV1, BANAL-236, and a pangolin sarbecovirus. Challenge studies in Fc-g receptor knockout mice revealed that antibody-based cellular effector mechanisms played a role in protection elicited by these vaccines. These results demonstrate that our S2-based vaccines provide broad protection against clade 1 sarbecoviruses and offer insight into the mechanistic basis for protection.

Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning (preprint)

Objective: Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a UMLS-colloquial symptom dictionary from COVID-19-related tweets as proof of concept. Methods: COVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The pipeline includes three modules: a named entity recognition module to detect symptoms in tweets; an entity normalization module to aggregate detected entities; and a mapping module that iteratively maps entities to Unified Medical Language System concepts. A random 500 entity sample were drawn from the final dictionary for accuracy validation. Additionally, we conducted a symptom frequency distribution analysis to compare our dictionary to a pre-defined lexicon from previous research. Results: We identified 498,480 unique symptom entity expressions from the tweets. Pre-processing reduces the number to 18,226. The final dictionary contains 38,175 unique expressions of symptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom distribution analysis found that our dictionary detects more symptoms and is effective at identifying psychiatric disorders like anxiety and depression, often missed by pre-defined lexicons. Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data. The final lexicon's high accuracy, validated by medical professionals, underscores the potential of this methodology to reliably interpret and categorize vast amounts of unstructured social media data into actionable medical insights across diverse linguistic and regional landscapes.

Exploring Social Media for Early Detection of Depression in COVID-19 Patients (preprint)

The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly,We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients

Risk assessment of global COVID-19 pandemic in October 2022

Objective: To assess the global epidemic of Coronavirus disease 2019(COVID-19) in October 2022 and the risk of importation.

Changes of peripheral blood T lymphocyte subsets in 15 patients with COVID-19 and its clinical significance

Objective: To analyze the change characteristics of peripheral blood leukocyte classification and T lymphocyte subsets in patients. with corona virus disease 2019(COVID-19).

Trend and co-occurrence network study of symptoms through social media: an example of COVID-19 (preprint)

ImportanceCOVID-19 is a multi-organ disease with broad-spectrum manifestations. Clinical data-driven research can be difficult because many patients do not receive prompt diagnoses, treatment, and follow-up studies. Social medias accessibility, promptness, and rich information provide an opportunity for large-scale and long-term analyses, enabling a comprehensive symptom investigation to complement clinical studies. ObjectivePresent an efficient workflow to identify and study the characteristics and co-occurrences of COVID-19 symptoms using social media. Design, Setting, and ParticipantsThis retrospective cohort study analyzed 471,553,966 COVID-19-related tweets from February 1, 2020, to April 30, 2022. A comprehensive lexicon of symptoms was used to filter tweets through rule-based methods. 948,478 tweets with self-reported symptoms from 689,551 Twitter users were identified for analysis. Main Outcomes and MeasuresThe overall trends of COVID-19 symptoms reported on Twitter were analyzed (separately by the Delta strain and the Omicron strain) using weekly new numbers, overall frequency, and temporal distribution of reported symptoms. A co-occurrence network was developed to investigate relationships between symptoms and affected organ systems. ResultsThe weekly quantity of self-reported symptoms has a high consistency (0.8528, P<0.0001) and one-week leading trend (0. 8802, P<0.0001) with new infections in four countries. We grouped 201 common symptoms (mentioned [≥] 10 times) into 10 affected systems. The frequency of symptoms showed dynamic changes as the pandemic progressed, from typical respiratory symptoms in the early stage to more musculoskeletal and nervous symptoms at later stages. When comparing symptoms reported during the Delta strain versus the Omicron variant, significant changes were observed, with dropped odd ratios of coma (95%CI 0.55-0.49, P<0.01) and anosmia (95%CI, 0.6-0.56), and more pain in the throat (95%CI, 1.86-1.96) and concentration problems (95%CI, 1.58-1.70). The co-occurrence network characterizes relationships among symptoms and affected systems, both intra-systemic, such as cough and sneezing (respiratory), and inter-systemic, such as alopecia (integumentary) and impotence (reproductive). Conclusions and RelevanceWe found dynamic COVID-19 symptom evolution through self-reporting on social media and identified 201 symptoms from 10 affected systems. This demonstrates that social medias prevalence trends and co-occurrence networks can efficiently identify and study public health problems, such as common symptoms during pandemics. Key pointsO_ST_ABSQuestionsC_ST_ABSWhat are the epidemic characteristics and relationships of COVID-19 symptoms that have been extensively reported on social media? FindingsThis retrospective cohort study of 948,478 related tweets (February 2020 to April 2022) from 689,551 users identified 201 self-reported COVID-19 symptoms from 10 affected systems, mitigating the potential missing information in hospital-based epidemiologic studies due to many patients not being timely diagnosed and treated. Coma, anosmia, taste sense altered, and dyspnea were less common in participants infected during Omicron prevalence than in Delta. Symptoms that affect the same system have high co-occurrence. Frequent co-occurrences occurred between symptoms and systems corresponding to specific disease progressions, such as palpitations and dyspnea, alopecia and impotence. MeaningTrend and network analysis in social media can mine dynamic epidemic characteristics and relationships between symptoms in emergent pandemics.

METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets (preprint)

The COVID-19 pandemic continues to bring up various topics discussed or debated on social media. In order to explore the impact of pandemics on people's lives, it is crucial to understand the public's concerns and attitudes towards pandemic-related entities (e.g., drugs, vaccines) on social media. However, models trained on existing named entity recognition (NER) or targeted sentiment analysis (TSA) datasets have limited ability to understand COVID-19-related social media texts because these datasets are not designed or annotated from a medical perspective. This paper releases METS-CoV, a dataset containing medical entities and targeted sentiments from COVID-19-related tweets. METS-CoV contains 10,000 tweets with 7 types of entities, including 4 medical entity types (Disease, Drug, Symptom, and Vaccine) and 3 general entity types (Person, Location, and Organization). To further investigate tweet users' attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9,101 entities (in 5,278 tweets). To the best of our knowledge, METS-CoV is the first dataset to collect medical entities and corresponding sentiments of COVID-19-related tweets. We benchmark the performance of classical machine learning models and state-of-the-art deep learning models on NER and TSA tasks with extensive experiments. Results show that the dataset has vast room for improvement for both NER and TSA tasks. METS-CoV is an important resource for developing better medical social media tools and facilitating computational social science research, especially in epidemiology. Our data, annotation guidelines, benchmark models, and source code are publicly available (https://github.com/YLab-Open/METS-CoV) to ensure reproducibility.

Safety and immunogenicity of a broad-spectrum mosaic vaccine as a booster dose against SARS-CoV-2 Omicron and other circulating variants (preprint)

BACKGROUNDThe rising breakthrough infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, especially Omicron and its sub-lineages, have raised an urgent need to develop broad-spectrum vaccines against coronavirus disease 2019 (COVID-19). We have developed a mosaic-type recombinant vaccine candidate, named NVSI-06-09, having immune potentials against a broad range of SARS-CoV-2 variants. METHODSAn ongoing randomized, double-blind, controlled phase 2 trial was conducted to evaluate the safety and immunogenicity of NVSI-06-09 as a booster dose in subjects aged 18 years and older from the United Arab Emirates (UAE), who had completed two or three doses of BBIBP-CorV vaccinations at least 6 months prior to the enrollment. The participants were randomly assigned with 1:1 to receive a booster dose of NVSI-06-09 or BBIBP-CorV. The primary outcomes were immunogenicity and safety against SARS-CoV-2 Omicron variant, and the exploratory outcome was cross-immunogenicity against other circulating strains. RESULTSA total of 516 participants received booster vaccination. Interim results showed a similar safety profile between NVSI-06-09 and BBIBP-CorV booster groups, with low incidence of adverse reactions of grade 1 or 2. For immunogenicity, by day 14 after the booster vaccination, the fold rises in neutralizing antibody geometric mean titers (GMTs) from baseline level elicited by NVSI-06-09 were remarkably higher than those by BBIBP-CorV against the prototype strain (19.67 vs 4.47-fold), Omicron BA.1.1 (42.35 vs 3.78-fold), BA.2 (25.09 vs 2.91-fold), BA.4 (22.42 vs 2.69-fold), and BA.5 variants (27.06 vs 4.73-fold). Similarly, the neutralizing GMTs boosted by NVSI-06-09 against Beta and Delta variants were also 6.60-fold and 7.17-fold higher than those boosted by BBIBP-CorV. CONCLUSIONSA booster dose of NVSI-06-09 was well-tolerated and elicited broad-spectrum neutralizing responses against SARS-CoV-2 prototype strain and immune-evasive variants, including Omicron and its sub-lineages. The immunogenicity of NVSI-06-09 as a booster vaccine was superior to that of BBIBP-CorV. (Funded by LIBP and BIBP of Sinopharm; ClinicalTrials.gov number, NCT05293548).

Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications (preprint)

Understanding public discourse on emergency use of unproven therapeutics is crucial for monitoring safe use and combating misinformation. We developed a natural language processing-based pipeline to comprehend public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter over time. This retrospective study included 609,189 US-based tweets from January 29, 2020, to November 30, 2021, about four drugs that garnered significant public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatments for eligible patients. Time-trend analysis was employed to understand popularity trends and related events. Content and demographic analyses were conducted to explore potential rationales behind people's stances on each drug. Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin discussions were highly politicized, related to conspiracy theories, hearsay, and celebrity influences. The distribution of stances between the two major US political parties was significantly different (P < .001); Republicans were more likely to support Hydroxychloroquine (55%) and Ivermectin (30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (7%) more than the general population, while the general population was more likely to support Ivermectin (14%). Our study found that social media users have varying perceptions and stances on off-label versus FDA-authorized drug use at different stages of COVID-19. This indicates that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation to promote safe drug use.

The Alleviation of LPS-Induced Murine Acute Lung Injury by GSH-Mediated PEGylated Artesunate Prodrugs

Acute lung injury (ALI) or its aggravated stage acute respiratory distress syndrome (ARDS) is a common severe clinical syndrome in intensive care unit, may lead to a life-threatening form of respiratory failure, resulting in high mortality up to 30–40% in most studies. Nanotechnology-mediated anti-inflammatory therapy is an emerging novel strategy for the treatment of ALI, has been demonstrated with unique advantages in solving the dilemma of ALI drug therapy. Artesunate (ART), a derivative of artemisinin, has been reported to have anti-inflammatory effects. Therefore, in the present study, we designed and synthesized PEGylated ART prodrugs and assessed whether ART prodrugs could attenuate lipopolysaccharide (LPS) induced ALI in vitro and in vivo. All treatment groups were conditioned with ART prodrugs 1 h before challenge with LPS. Significant increased inflammatory cytokines production and decreased GSH levels were observed in the LPS stimulated mouse macrophage cell line RAW264.7. Lung histopathological changes, lung W/D ratio, MPO activity and total neutrophil counts were increased in the LPS-induced murine model of ALI via nasal administration. However, these results can be reversed to some extent by treatment of ART prodrugs. The effectiveness of mPEG2k-SS-ART in inhibition of ALI induced by LPS was confirmed. In conclusion, our results demonstrated that the ART prodrugs could attenuate LPS-induced ALI effectively, and mPEG2k-SS-ART may serve as a novel strategy for treatment of inflammation induced lung injury.

Birds carry a diversity of significant viruses in their virome (preprint)

Birds can carry and transmit viruses to humans and other animals. Thus, understanding the viral community hosted by birds could help us predict future outbreaks of human disease. A recent metagenomics study took a broad look at the viruses found in the gut of wild and captive birds. The dataset included samples from over 3,000 birds that represented over 87 species and 10 different phylogenetic orders and the researchers characterized genomes from numerous viral families including astroviruses, coronaviruses, parvoviruses, and adenoviruses. Examining trends, they found that wild birds had higher viral diversity than captive birds. There was also evidence of potential cross-species transmission between wild birds and domestic poultry. Further analysis of the viral genomic sequences revealed differences in virus distribution patterns between wild and captive birds. Different phylogenetic orders of birds and geographic sites also had distinct distribution patterns. Interestingly, there were no significant differences in virus distribution patterns between migratory and resident birds. While further studies are needed to explore the diversity and potential pathogenicity of these viruses in more detail, this study expanded our understanding of viral diversity in birds.

Risk assessment of global coronavirus disease 2019 pandemic in November 2021

Objective: To assess the global epidemic of Coronavirus disease 2019(COVID-19) in November 2021 and the risk of importation.

Content analysis of COVID-19 rumors

Objectives: This study provided a basis for the public to evaluate numerous sources of COVID-19 information to mitigate the harm caused by the repeated dissemination and sharing of misinformation. Methods: Content analysis was used to examine the spread of COVID-19 rumors on the Internet. The content characteristics and expressions of 113 COVID-19 rumors collected from the "Taiwan Fact Checking Center" website were used as samples for analysis. Results: The most common type of COVID-19 rumor was "aggressive," and "a particular behavior" and "specific type of food or appliance" were the most common objects of the rumors. The dates of occurrence were not clearly noted, but the contents of more than half of the rumors were accurately described. The main purposes were primarily "attention/warning" and "sharing new knowledge." These rumors mostly originated from the Internet, and the rumors were corroborated primarily by "photos/icons/videos" and "experts." Conclusions: The content analysis results of the COVID-19 rumors could enhance the public's basic awareness and ability to identify false COVID-19 rumors and to encourage social media users to be more vigilant against the spread of disinformation.

Tracking the impact of COVID-19 and lockdown policy on public mental health using social media (preprint)

The COVID-19 pandemic and its corresponding preventive and control measures have increased the mental burden on the public. Social media serve as important platforms to timely track public mental status. In this study, we conducted social-media-based analyses on temporal, geographical and occupational distributions of public mental health status during the pandemic, and how the public reacted to the lock-down policy from the perspective of mental health. We extracted 2,973,319 mental health-related tweets of 1,778,140 users from February 1, 2020 to September 30, 2021. We found that, compared to the general public, healthcare workers had higher concerns on three types of mental health problems (depression, insomnia, addiction) (P<0.001) and focused more on clinical topics while the public worried more about daily life issues. The lockdown policy in New York was correlated with a proportional decrease of mental health-related tweets, while Florida had an opposite correlation (both P<0.05). Our findings indicated that the mental burden brought by the pandemic varied across occupations and locations and changed over time.

Decreased COVID-related adaptive behavior and increased negative affect: A multivariate latent growth curve model.

Data from a longitudinal questionnaire investigation of three time waves were used to investigate affective and behavioral changes and their covariant relationship among Chinese general population during the COVID-19 pandemic from March to May 2020. 145 participants aging from 15 to 63 completed three waves of survey. Latent growth curve analyses found that negative affect gradually increased as the pandemic continued. A faster increase in negative affect was related to a greater decrease in adaptive behavior and faster increase in non-adaptive behavior. A higher initial level of negative affect was related to a slower increase in non-adaptive behavior.

An AAV-ie based Vaccine effectively protects against SARS-CoV-2 and Circulating Variants (preprint)

Prophylactic vaccines against SARS-CoV-2 have been extensively developed globally to overcome the COVID-19 pandemic. However, recently emerging SARS-CoV-2 variants B.1.1.7 and B.1.351 limit the vaccine protection effects and successfully escape antibody cocktail treatment. Herein, based on our previously engineered adeno-associated viral (AAV) vector, AAV-ie, and systematic immunogen screening, we developed an AAV-ie-S1 vaccine with thermostability, high efficiency, safety, and single-dose vaccination advantage. Importantly, the AAV-ie-S1 immune sera efficiently neutralize B.1.1.7 and B.1.351, indicating a potential to circumvent the spreading of SARS-CoV-2.

Breastfeeding in Neonates During the Coronavirus Disease 2019 Pandemic in China: A Cross-Sectional Survey (preprint)

ObjectiveTo elucidate the current situation of breastfeeding in neonates in China and to investigate whether SARS-CoV-2 is transmitted through the mother’s milk.DesignA nationwide cross-sectional surveySettingThree hundred and forty-four member hospitals of the Chinese Neonatologist Association network from 31 provinces in China.SampleNine hundred and fourteen neonatologistsMain outcome measuresThese included (1) breastfeeding practices in the obstetrics ward; (2) breastfeeding implementation for neonates admitted to neonatal intensive care unit (NICU); (3) presence of SARS-CoV-2 in the breast milk of COVID-19 positive mothers based on the real-time reverse transcriptase-polymerase chain reaction (RT-PCT) test results.ResultsBreastfeeding was undermined during the COVID-19  pandemic. Of the 344 hospitals, 153 (44.48%) centers received breast milk from milk banks to feed babies in NICU. Eight (2.33%) Level III centers performed SARS-CoV-2 PCR tests on breast milk from 15 mothers with COVID-19 and found no SARS-CoV-2 RNA presence in breast milk. Moreover, none of the mothers engaged in breastfeeding. Further, only 52 (5.69%) neonatologists supported breastfeeding in mothers with COVID-19.ConclusionsBased on the available evidence, the benefits of breastfeeding for both infants and mothers outweigh the potential risk of SARS-CoV-2 transmission through breast milk. Amidst the COVID-19 pandemic, medical staff should encourage breastfeeding, in keeping with normal infant feeding guidelines, and provide skilled support to all mothers who choose to breastfeed.

Distribution of ABO Blood Groups and Their Association With Low Risk of Severe COVID-19 Infection in Patients (preprint)

Background: To date, only few studies have focused on the correlation between ABO blood groups and COVID-19 infection risk, especially gender differences in infection risk. Our study aimed to describe the ABO blood group distribution and its association with risk of severe COVID-19 infection for effective identification of the susceptible population. Method：From January 21 to February 20, 2020, we compared the ABO blood group distribution and gender distribution and performed a correlation analysis in severe, non-severe, and non-COVID-19 patients. Results The results showed that the laboratory indices were significantly different between blood type O and non-blood-type-O COVID-19 patients. This indicated that patients of the type O blood group had a relatively lower risk of severe COVID-19 infection than patients of other blood types; in particular, females with blood type O had a lower risk of severe COVID-19 infection than males. Conclusion: Herein, we report a potentially simple prediction decision system to minimize the risk of severe COVID-19 infection based on blood type. Special attention should be paid to patients with blood types other than type O to minimize their risk of severe COVID-19 infection.

The challenges of the coming mass vaccination and exit strategy in prevention and control of COVID-19, a modelling study (preprint)

With success in the development of COVID-19 vaccines, it is urgent and challenging to analyse how the coming large-scale vaccination in the population and the growing public desire of relaxation of non-pharmaceutical interventions (NPIs) interact to impact the prevention and control of the COVID-19 pandemic. Using mathematical models, we focus on two aspects: 1) how the vaccination program should be designed to balance the dynamic exit of NPIs; 2) how much the vaccination coverage is needed to avoid a second wave of the epidemics when the NPIs exit in stages. We address this issue globally, and take six countries--China, Brazil, Indonesia, Russia, UK, and US—in our case study. We showed that a dynamic vaccination program in three stages can be an effective approach to balance the dynamic exit of the NPIs in terms of mitigating the epidemics. The vaccination rates and the accumulative vaccination coverage in these countries are estimated by fitting the model to the real data. We observed that the required effective vaccination coverages are greatly different to balance the dynamic exit of NPIs in these countries, providing a quantitative criterion for the requirement of an integrative package of NPIs. We predicted the epidemics under different vaccination rates for these countries, and showed that the vaccination can significantly decrease the peak value of a future wave. Furthermore, we found that a lower vaccination coverage can result in a subsequent wave once the NPIs exit. Therefore, there is a critical (minimum) vaccination coverage, depending on effectiveness of NPIs to avoid a subsequent wave. We estimated the critical vaccination coverages for China, Brazil, and Indonesia under different scenarios. In conclusion, we quantitatively showed that the dynamic vaccination program can be the effective approach to supplement or even eventually replace NPIs in mitigating the epidemics and avoiding future waves, and we suggest that country level-based exit strategies of the NPIs should be considered, according to the possible quarantine rate and testing ability, and the accessibility, affordability and efficiency of the vaccines.