Robust COVID-19 Detection in CT Images with CLIP (preprint)

In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources, the paucity of well-annotated datasets, and a significant amount of unlabeled data. In this work, we introduce the first lightweight detector designed to overcome these obstacles, leveraging a frozen CLIP image encoder and a trainable multilayer perception (MLP). Enhanced with Conditional Value at Risk (CVaR) for robustness and a loss landscape flattening strategy for improved generalization, our model is tailored for high efficacy in COVID-19 detection. Furthermore, we integrate a teacher-student framework to capitalize on the vast amounts of unlabeled data, enabling our model to achieve superior performance despite the inherent data limitations. Experimental results on the COV19-CT-DB dataset demonstrate the effectiveness of our approach, surpassing baseline by up to 10.6% in `macro' F1 score in supervised learning. The code is available at https://github.com/Purdue-M2/COVID-19_Detection_M2_PURDUE.

Oxalic acid blocked the binding of spike protein from SARS-CoV-2 Delta (B.1.617.2) and Omicron (B.1.1.529) variants to human angiotensin-converting enzymes 2.

An epidemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading worldwide. Moreover, the emergence of SARS-CoV-2 variants of concern, such as Delta and Omicron, has seriously challenged the application of current therapeutics including vaccination and drugs. Relying on interaction of spike protein with receptor angiotensin-converting enzymes 2 (ACE2), SARS-CoV-2 successfully invades to the host cells, which indicates a strategy that identification of small-molecular compounds to block the entry is of great significance for COVID-19 prevention. Our study evaluated the potential efficacy of natural compound oxalic acid (OA) as an inhibitory agent against SARS-CoV-2 invasion, particular on the interaction of the receptor binding domain (RBD) of Delta and Omicron variants to ACE2. By employing a competitive binding assay in vitro, OA significantly blocked the binding of RBDs from Delta B.1.617.2 and Omicron B.1.1.529 to ACE2, but has no effect on the wide-type SARS-CoV-2 strain. Furthermore, OA inhibited the entries of Delta and Omicron pseudovirus into ACE2 high expressing-HEK293T cells. By surface plasmon resonance (SPR) assay, the direct bindings of OA to RBD and ACE2 were analyzed and OA had both affinities with RBDs of B.1.617.2 and B.1.1.529 and with ACE2. Molecular docking predicted the binding sites on the RBD-ACE2 complex and it showed similar binding abilities to both complex of variant Delta or Omicron RBD and ACE2. In conclusion, we provided a promising novel small-molecule compound OA as an antiviral candidate by blocking the cellular entries of SARS-CoV-2 variants.

Luteolin inhibits spike protein of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) binding to angiotensin-converting enzyme 2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that poses a serious threat to global public health. In an essential step during infection, SARS-CoV-2 uses the receptor-binding domain (RBD) of the spike (S) protein to engage with angiotensin-converting enzyme 2 (ACE2) in host cells. Chinese herbal medicines and their active components exhibit antiviral activity, with luteolin being a flavonoid that can significantly inhibit SARS-CoV infection. However, whether it can block the interaction between the S-protein RBD of SARS-CoV-2 and ACE2 has not yet been elucidated. Here, we investigated the effects of luteolin on the binding of the S-protein RBD to ACE2. By employing a competitive binding assay in vitro, we found that luteolin significantly blocked the binding of S-protein RBD to ACE2 with IC50 values of 0.61 mM, which was confirmed by the neutralized infection with SARS-CoV-2 pseudovirus in vivo. A surface plasmon resonance-based competition assay revealed that luteolin significantly affects the binding of the S-protein RBD to the ACE2 receptor. Molecular docking was performed to predict the binding sites of luteolin to the S-protein RBD-ACE2 complex. The active binding sites were defined based on published literature, and we found that luteolin might interfere with the mixture at residues including LYS353, ASP30, and TYR83 in the cellular ACE2 receptor and GLY496, GLN498, TYR505, LEU455, GLN493, and GLU484 in the S-protein RBD. These residues may together form attractive charges and destroy the stable interaction of S-protein RBD-ACE2. Luteolin also inhibits SARS-CoV-2 spike protein-induced platelet spreading, thereby inhibiting the binding of the spike protein to ACE2. Our results are the first to provide evidence that luteolin is an anti-SARS-CoV-2 agent associated with interference between viral S-protein RBD-ACE2 interactions.

A Newly Identified Spike Protein Targeted Linear B-Cell Epitope Based Dissolvable Microneedle Array Successfully Eliciting Neutralizing Activities against SARS-CoV-2 Wild-Type Strain in Mice.

Vaccination is a cost-effective medical intervention. Inactivated whole virusor large protein fragments-based severe acute respiratory syndrome coronavirus (SARS-CoV-2) vaccines have high unnecessary antigenic load to induce allergenicity and/orreactogenicity, which can be avoided by peptide vaccines of short peptide fragments that may induce highly targeted immune response. However, epitope identification and peptide delivery remain the major obstacles in developing peptide vaccines. Here, a multi-source data integrated linear B-cell epitope screening strategy is presented and a linear B-cell epitope enriched hotspot region is identified in Spike protein, from which a monomeric peptide vaccine (Epitope25) is developed and applied to subcutaneously immunize wildtype BALB/c mice. Indirect ELISA assay reveals specific and dose-dependent binding between Epitope25 and serum IgG antibodies from immunized mice. The neutralizing activity of sera from vaccinated mice is validated by pseudo and live SARS-CoV-2 wild-type strain neutralization assays. Then a dissolvable microneedle array (DMNA) is developed to pain-freely deliver Epitope25. Compared with intramuscular injection, DMNA and subcutaneous injection elicit neutralizing activities against SARS-CoV-2 wild-type strain as demonstrated by live SARS-CoV-2 virus neutralization assay. No obvious damages are found in major organs of immunized mice. This study may lay the foundation for developing linear B-cell epitope-based vaccines against SARS-CoV-2.

Tracing the Origin of Genotype II African Swine Fever Virus in China by Genomic Epidemiology Analysis

The pandemic spread of African swine fever (ASF) has caused serious effects on the global pig industry. Virus genome sequencing and genomic epidemiology analysis play an important role in tracking the outbreaks of the disease and tracing the transmission of the virus. Here we obtained the full-length genome sequence of African swine fever virus (ASFV) in the first outbreak of ASF in China on August 3rd, 2018 and compared it with other published genotype II ASFV genomes including 9 genomes collected in China from September 2018 to October 2020. Phylogenetic analysis on genomic sequences revealed that genotype II ASFV has evolved into different genetic clusters with temporal and spatial correlation since being introduced into Europe and then Asia. There was a strong support for the monophyletic grouping of all the ASFV genome sequences from China and other Asian countries, which shared a common ancestor with those from the Central or Eastern Europe. An evolutionary rate of 1.312 × 10−5 nucleotide substitutions per site per year was estimated for genotype II ASFV genomes. Eight single nucleotide variations which located in MGF110-1L, MGF110-7L, MGF360-10L, MGF505-5R, MGF505-9R, K145R, NP419L, and I267L were identified as anchor mutations that defined genetic clusters of genotype II ASFV in Europe and Asia. This study expanded our knowledge of the molecular epidemiology of ASFV and provided valuable information for effective control of the disease.

Epidemiological investigation on a cluster epidemic of COVID-19 in a school in Longchuan County, Yunnan Province

Objective: To investigate a cluster epidemic of coronavirus disease 2019 (COVID-19) infections in a school in Longchuan County, Yunnan Province, and further guide the prevention and control of COVID-19 in the border area. Methods: According to the Protocol on Prevention and Control of Novel Coronavirus Pneumonia (8th Edition), an epidemiological investigation was performed on all COVID-19 cases to collect the information on demographics, onset, diagnosis and treatment, prognosis, and epidemiological history. Close contacts were also tracked to determine the transmission chains. Results: In this cluster epidemic, a total of 37 COVID-19 cases were identified, including 32 females and 5 males aged from 13 to 25 years, who were 35 students and 2 teachers. The student cases were found in four classes of two grades. Furthermore, gene sequencing showed that all cases had been infected with delta variants, belonging to the same transmission chain that was not related to the previous epidemics in Dehong Prefecture. In additionally, a total of 2 127 close contacts were found. After 21 days of centralized quarantine for medical observation, all close contacts tested negative for SARS-CoV-2. In the COVID-19 cases, only one case remained positive for SARS-CoV-2, while the other 36 cases were successfully treated and became negative. Conclusion: This school cluster is caused by the border villagers who contacted the water polluted with SARS-CoV-2. It warrants more strict management of students from border villages and their belongings to prevent similar epidemics in school settings.

Significance of wastewater surveillance in detecting the prevalence of SARS-CoV-2 variants and other respiratory viruses in the community - A multi-site evaluation.

Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome in wastewater has proven to be useful for tracking the trends of virus prevalence within the community. The surveillance also provides precise and early detection of any new and circulating variants, which aids in response to viral outbreaks. Site-specific monitoring of SARS-CoV-2 variants provides valuable information on the prevalence of new or emerging variants in the community. We sequenced the genomic RNA of viruses present in the wastewater samples and analyzed for the prevalence of SARS-CoV-2 variants as well as other respiratory viruses for a period of one year to account for seasonal variations. The samples were collected from the Reno-Sparks metropolitan area on a weekly basis between November 2021 to November 2022. Samples were analyzed to detect the levels of SARS-CoV-2 genomic copies and variants identification. This study confirmed that wastewater monitoring of SARS-CoV-2 variants can be used for community surveillance and early detection of circulating variants and supports wastewater-based epidemiology (WBE) as a complement to clinical respiratory virus testing as a healthcare response effort. Our study showed the persistence of the SARS-CoV-2 virus throughout the year compared to a seasonal presence of other respiratory viruses, implicating SARS-CoV-2's broad genetic diversity and strength to persist and infect susceptible hosts. Through secondary analysis, we further identified antimicrobial resistance (AMR) genes in the same wastewater samples and found WBE to be a feasible tool for community AMR detection and monitoring.

Mosaic Patterned Surfaces toward Generating Hardly‐Volatile Capsular Droplet Arrays for High‐Precision Droplet‐Based Storage and Detection

Mosaic Patterned SurfacesIn article number 2206274, Yanjun Hu, Lin Li, and co‐workers report a mosaic patterned surface‐based chip that acquires mutually independent and hardly‐volatile capsular droplet arrays. The concept shows high compatibility and practicability, paving the way for the new microfluidic chips used in COVID‐19 diagnosis and other high‐precision detection.

An Agenda-Setting Account for Psychological Typhoon Eye Effect on Responses to the Outbreak of COVID-19 in Wuhan

During the outbreak of COVID-19 in Wuhan in 2020, we conducted a nationwide survey of 8170 respondents from 31 provinces/municipalities in China via Sojump to examine the relationship between the distance to respondents' city of residence from Wuhan and their safety concerns and risk perception of the epidemic that occurred in Wuhan City. We found that (1) the farther (psychologically or physically) people were from Wuhan, the more concerned they were with the safety of the epidemic risk in Wuhan, which we dubbed the psychological typhoon eye (PTE) effect on responses to the outbreak of COVID-19;(2) agenda setting can provide a principled account for such effect: the risk information proportion mediated the PTE effect. The theoretical and managerial implications for the PTE effect and public opinion disposal were discussed, and agenda setting was identified to be responsible for the preventable overestimated risk perception.

Unraveling the mechanism of action of cepharanthine for the treatment of novel coronavirus pneumonia (COVID-19) from the perspectives of systematic pharmacology

Graphical abstract Natural products play an irreplaceable role in the treatment of SARS-CoV-2 infection. Nevertheless, the underlying molecular mechanisms involved remain elusive. To better understand their potential therapeutic effects, more validation studies are needed to explore underlying mechanisms systematically. This study aims to explore the potential targets of action and signaling pathways of cepharanthine for the treatment of COVID-19. This study revealed that a total of 173 potential targets of action for Cepharanthine and 86 intersectional targets for Cepharanthine against COVID-19 were screened and collected. Gene Ontology enrichment analysis suggested that inflammatory, immune cell and enzyme activities were the critical terms for cepharanthine against COVID-19. Pathway enrichment analysis showed that five pathways associated with COVID-19 were the main signaling pathways for the treatment of COVID-19 via cepharanthine. Molecular docking and molecular dynamics simulations suggested that 6 core targets were regarded as potential targets for cepharanthine against COVID-19. In brief, the study demonstrates that cepharanthine may play an important role in the treatment of SARS-CoV-2 infection through its harmonious activity against SARS-CoV-2 pathways and multiple related targets. This article provides valuable insights required to respond effectively to concerns of western medical community.

Potential treatment of COVID-19 with traditional chinese medicine: What herbs can help win the battle with SARS-CoV-2?

Traditional Chinese medicine (TCM) has been successfully applied worldwide in the treatment of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the pharmacological mechanisms underlying this success remain unclear. Hence, the aim of this review is to combine pharmacological assays based on the theory of TCM in order to elucidate the potential signaling pathways, targets, active compounds, and formulas of herbs that are involved in the TCM treatment of COVID-19, which exhibits combatting viral infections, immune regulation, and amelioration of lung injury and fibrosis. Extensive reports on target screening are elucidated using virtual prediction via docking analysis or network pharmacology based on existing data. The results of these reports indicate that an intricate regulatory mechanism is involved in the pathogenesis of COVID-19. Therefore, more pharmacological research on the natural herbs used in TCM should be conducted in order to determine the association between TCM and COVID-19 and account for the observed therapeutic effects of TCM against COVID-19.

An Agenda-Setting Account for Psychological Typhoon Eye Effect on Responses to the Outbreak of COVID-19 in Wuhan.

During the outbreak of COVID-19 in Wuhan in 2020, we conducted a nationwide survey of 8170 respondents from 31 provinces/municipalities in China via Sojump to examine the relationship between the distance to respondents' city of residence from Wuhan and their safety concerns and risk perception of the epidemic that occurred in Wuhan City. We found that (1) the farther (psychologically or physically) people were from Wuhan, the more concerned they were with the safety of the epidemic risk in Wuhan, which we dubbed the psychological typhoon eye (PTE) effect on responses to the outbreak of COVID-19; (2) agenda setting can provide a principled account for such effect: the risk information proportion mediated the PTE effect. The theoretical and managerial implications for the PTE effect and public opinion disposal were discussed, and agenda setting was identified to be responsible for the preventable overestimated risk perception.

Nirmatrelvir-Ritonavir (Paxlovid) for Mild Coronavirus Disease 2019 (COVID-19) in Pregnancy and Lactation.

Nirmatrelvir-ritonavir (Paxlovid) is recommended to reduce the risk of hospitalization from coronavirus disease 2019 (COVID-19) in pregnancy. Data on use in pregnancy, including prescribing patterns and patient experience (adverse effects, incidence of rebound), are limited. We performed a cross-sectional study in which we surveyed a cohort of vaccinated pregnant or lactating individuals with breakthrough COVID-19. Of 35 pregnant respondents, 51.4% were prescribed and 34.3% took nirmatrelvir-ritonavir; of these, 91.7% experienced dysgeusia and 50.0% had rebound (50.0% positive test result, 33.3% return of symptoms). Three of five lactating respondents were prescribed and two took nirmatrelvir-ritonavir. There were no significant adverse outcomes. Unknown risk was the most common reason for declining nirmatrelvir-ritonavir. More research is needed to establish the safety of nirmatrelvir-ritonavir in pregnancy and lactation, to improve public health messaging, and to increase uptake of this treatment.

Human mobility increased with vaccine coverage and attenuated the protection of COVID-19 vaccination: A longitudinal study of 107 countries.

Background: The World Health Organization has raised concerns that vaccinated people may reduce physical and social distancing more than necessary. With imperfect vaccine protection and the lifting of mobility restrictions, understanding how human mobility responded to vaccination and its potential consequence is critical. We estimated vaccination-induced mobility (VM) and examined whether it attenuates the effect of COVID-19 vaccination on controlling case growth. Methods: We collected a longitudinal data set of 107 countries between 15 February 2020 and 6 February 2022 from Google COVID-19 Community Mobility Reports, the Oxford COVID-19 Government Response Tracker, Our World in Data, and World Development Indicators. We measured mobility in four categories of location: retail and recreational places, transit stations, grocery stores and pharmacies, and workplaces. We applied panel data models to address unobserved country characteristics and used Gelbach decomposition to evaluate the extent to which VM has offset vaccination effectiveness. Results: Across locations, a 10-percentage-point (pp) increase in vaccine coverage was associated with a 1.4-4.3 pp increase in mobility (P < 0.001). VM was greater in lower-income countries (up to 7.9 pps; 95% confidence interval (CI) = 5.3 to 10.5, P < 0.001) and in earlier stages of vaccine rollouts (up to 19.2 pps; 95% CI = 15.1 to 23.2%, P < 0.001). VM decreased the effectiveness of vaccines in controlling case growth by 33.4% in retail and recreation places (P < 0.001), 26.4% in transit stations (P < 0.001), and 15.4% in grocery stores and pharmacies (P = 0.002). Conclusions: VM provides support for the Peltzman effect; it attenuates but does not completely counter vaccine effectiveness. Our study findings suggest strategies for mitigating the unintended consequences of VM, including reducing short-term mobility responses after vaccination, prioritizing mobility in grocery-type places and workplaces, and accelerating rollouts at earlier stages of vaccination, especially in lower-income countries.

Specialty preferences of studying-abroad medical students from low- and middle-income countries.

BACKGROUND: This study explored the specialty preferences of China-educated international medical students (IMSs), who are mainly from low- and middle-income countries (LMICs) and constitute a potential medical workforce both for their home countries and foreign countries, and the influence of migration intentions on their specialty preferences. METHODS: A cross-sectional, questionnaire-based survey was conducted at 5 universities in China. The questionnaire link was distributed electronically among the IMSs at the 5 universities via emails. The questionnaire enquired IMSs' demographic information, migration intentions and their specialty preferences. The Chi-square test was applied to determine the influence of the respondent's gender, intention to practise in the home country and intention to practise in a high-income country on their specialty choices. The Chi-square test was also applied to determine the influence of the respondent's gender, year of study and country of origin on their preferences for generalist-orientated or non-generalist orientated specialties. RESULTS: Altogether, 452 IMSs returned their responses, yielding a response rate of 64.1%. Approximately half of the IMSs planned to not return to their home country. The most selected specialty was general surgery and the least selected specialty was physical medicine and rehabilitation. No significant differences were evident in most specialty preferences between those who intended to return home and those who intended to stay abroad. Among the IMSs having intentions of returning to their home country, male students tended to choose a generalist-orientated specialty, while female students tended to choose a non-generalist-orientated specialty. CONCLUSION: China-educated IMSs could play important roles in the primary care services as well as other shortage specialties both for their home countries or foreign countries. Therefore, it is recommended that governments in these countries plan migration and recruitment policies that cater for these studying-abroad medical students from LMICs, especially in this challenging time during the COVID-19 pandemic.

Unraveling the mechanism of action of cepharanthine for the treatment of novel coronavirus pneumonia (COVID-19) from the perspectives of systematic pharmacology.

Natural products play an irreplaceable role in the treatment of SARS-CoV-2 infection. Nevertheless, the underlying molecular mechanisms involved remain elusive. To better understand their potential therapeutic effects, more validation studies are needed to explore underlying mechanisms systematically. This study aims to explore the potential targets of action and signaling pathways of cepharanthine for the treatment of COVID-19. This study revealed that a total of 173 potential targets of action for Cepharanthine and 86 intersectional targets for Cepharanthine against COVID-19 were screened and collected. Gene Ontology enrichment analysis suggested that inflammatory, immune cell and enzyme activities were the critical terms for cepharanthine against COVID-19. Pathway enrichment analysis showed that five pathways associated with COVID-19 were the main signaling pathways for the treatment of COVID-19 via cepharanthine. Molecular docking and molecular dynamics simulations suggested that 6 core targets were regarded as potential targets for cepharanthine against COVID-19. In brief, the study demonstrates that cepharanthine may play an important role in the treatment of SARS-CoV-2 infection through its harmonious activity against SARS-CoV-2 pathways and multiple related targets. This article provides valuable insights required to respond effectively to concerns of western medical community.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification.

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Identifying informative tweets during a pandemic via a topic-aware neural language model.

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.