The Impact of Post Embryo Transfer SARS-CoV-2 Infection on Pregnancy in In Vitro Fertilization: A Prospective Cohort Study (preprint)

ImportanceLimited knowledge exists on the effects of SARS-CoV-2 infection after embryo transfer, despite an increasing number of studies exploring the impact of previous SARS-CoV-2 infection on IVF outcomes. ObjectiveThis prospective cohort study aimed to assess the influence of SARS-CoV-2 infection at various time stages after embryo transfer on pregnancy outcomes in patients undergoing conventional in vitro fertilization/intracytoplasmic sperm injection-embryo transfer (IVF/ICSI) treatment. DesignThe study was conducted at a single public IVF center in China. SettingThis was a population-based prospective cohort study. ParticipantsFemale patients aged 20 to 39 years, with a body mass index (BMI) between 18 and 30 kg/m2, undergoing IVF/ICSI treatment, were enrolled from September 2022 to December 2022, with follow-up until March 2023. ExposureThe pregnancy outcome of patients was compared between those SARS-CoV-2-infected after embryo transfer and those noninfected during the follow-up period. Main Outcomes and MeasuresThe pregnancy outcomes included biochemical pregnancy rate, implantation rate, clinical pregnancy rate, and early miscarriage rate. ResultsA total of 857 female patients undergoing IVF/ICSI treatment were included in the analysis. We observed the incidence of SARS-CoV-2 infection within 10 weeks after embryo transfer. The biochemical pregnancy rate and implantation rate were lower in the infected group than the uninfected group (58.1% vs 65.9%; 36.6% vs 44.0%, respectively), but no statistically significant. Although, the clinical pregnancy rate was significant lower in the infection group when compared with the uninfected group (49.1%vs 58.2%, p < 0.05), after adjustment for confounders, this increased risk was no longer significant between the two groups (adjusted OR, 0.736, 95% CI, 0.518-1.046). With continued follow-up, a slightly higher risk of early miscarriage in the infected group compared to the uninfected group (9.3% vs 8.8%), but it was not significant (adjusted OR, 0.907, 95% CI, 0.414-1.986). Conclusions and RelevanceThe studys findings suggested that SARS-CoV-2 infection within 10 weeks after embryo transfer may have not significantly affect pregnancy outcomes. This evidence allays concerns and provides valuable insights for assisted reproduction practices. Key pointsO_ST_ABSQuestionC_ST_ABSDid the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after embryo transfer affect pregnancy outcomes? FindingsIn this prospective cohort study involving 857 patients, we made a pioneering discovery that SARS-CoV-2 infection following embryo transfer did not exhibit adverse impact on the biochemical pregnancy rate, embryo implantation rate, clinical pregnancy rate, and early miscarriage rate. MeaningThe evidence from this study alleviates existing concerns and offers new insights into the actual risk of SARS-CoV-2 infection after embryo transfer in assisted reproduction.

Human exposure risk assessment for infectious diseases due to temperature and air pollution: an overview of reviews (preprint)

Air pollution and global temperature change are expected to affect infectious diseases. Yet to date overview of systematic reviews assessing the exposure risk of air pollutants and temperature on infectious diseases is unavailable. PubMed, Embase, the Cochrane Library, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature were searched. Systematic reviews and meta-analyses investigated the exposure risk of pollutants or temperature on infectious diseases were included. Two investigators screened literature, extracted data and performed the risk of bias assessments independently. A total of 23 articles met the inclusion criteria, which 3 (13%) were "low" quality and 20 (87%) were "critically low" quality. COVID-19 morbidity was associated with long-term exposure PM2.5 (RR = 1.056 per 1μg/m  3, 95% CI: 1.039-1.072) and NO2 (RR = 1.042 per 1 μg/m 3, 95% CI: 1.017-1.068). In addition, for each 1°C increase in temperature, the risk of dengue fever morbidity increased 13% (RR = 1.130 per 1°C, 95% CI: 1.120-1.150), infectious diarrhea morbidity increased 8% (RR =1.080 per 1°C, 95% CI: 1.050-1.200), and hand, foot and mouth disease (HFMD) morbidity increased 5% (RR = 1.050 per 1 °C, 95% CI: 1.020-1.080). In conclusion, PM2.5 and NO2 increased the risk of COVID-19 and temperatures were associated with dengue, infectious diarrhoea and HFMD morbidity. Moreover, the exposure risk of temperature on COVID-19 need to be further explored. 

Chimeric mRNA based COVID-19 vaccine elicits potent neutralizing antibodies and protection against Omicron and Delta (preprint)

The emerging SARS-CoV-2 variants of concern (VOCs) exhibit enhanced transmission and immune escape, reducing the efficacy and effectiveness of the two FDA-approved mRNA vaccines currently in use. Here, we explored various strategies to develop mRNA vaccines that offer potentially safer and wider coverage of VOCs. The initial mouse vaccination results showed that the individual VOC mRNAs carrying furin cleavage mutation induced the generation of neutralizing antibody in a VOC-specific manner. Moreover, we discovered that the antibodies produced from mice immunized with Beta-Furin and Washington (WA)-Furin mRNAs cross-reacted with other VOCs. The broad spectrum of generated nAb was further confirmed when vaccinated mice were challenged with the respective live viruses. However, neither WA-Furin nor Beta-Furin mRNA elicited potent neutralizing activity against the omicron variant. Interestingly, in a mix-and-match booster experiment, omicron-Furin and WA-Furin mRNA elicited comparable protection against omicron. Finally, we tested the concept of bivalent vaccine by introducing the RBD of Delta strain into the intact S antigen of Omicron. The chimeric mRNA induces potent and broadly acting nAb against Omicron and Delta, which paves the way to develop vaccine candidate to target emerging variants in the future.

Computational Mapping of the Human-SARS-CoV-2 Protein-RNA Interactome (preprint)

It is well known that viruses make extensive use of the host cell's machinery, hijacking it for the purpose of viral replication and interfere with the activity of master regulatory proteins - including RNA binding proteins (RBPs). RBPs recognize and bind RNA molecules to control several steps of cellular RNA metabolism, such as splicing, transcript stability, translation and others, and recognize their targets by means of sequence or structure motifs. Host RBPs are critical factors for viral replication, especially for RNA viruses, and have been shown to influence viral RNA stability, replication and escape of host immune response. While current research efforts have been centered around identifying mechanisms of host cell-entry, the role of host RBPs in the context of SARS-CoV-2 replication remains poorly understood. Few experimental studies have started mapping the SARS-CoV-2 RNA-protein interactome in infected human cells, but they are limited in the resolution and exhaustivity of their output. On the other hand, computational approaches enable screening of large numbers of human RBPs for putative interactions with the viral RNA, and are thus crucial to prioritize candidates for further experimental investigation. Here, we investigate the role of RBPs in the context of SARS-CoV-2 by constructing a first single-nucleotide \textit{in silico} map of human RBP / viral RNA interactions by using deep learning models trained on RNA sequences. Our framework is based on Pysster and DeepRiPe, two deep learning method which use a convolutional neural network to learn sequence-structure preferences of a specific RBP. Models were trained using eCLIP and PAR-CLIP datasets for >150 RBP generated on human cell lines and applied cross-species to predict the propensity of each RBP to bind the SARS-CoV-2 genome. After extensive validation of predicted binding sites, we generate RBP binding profiles across different SARS-CoV-2 variants and 6 other betacoronaviruses. We address the questions of (1) conservation of binding between pathogenic betacoronaviruses, (2) differential binding across viral strains and (3) gain and loss of binding events in novel mutants which can be linked to disease severity and spread in the population. In addition, we explore the specific pathways hijacked by the virus, by integrating host factors linked to these virus-binding RBPs through protein-protein interaction networks or genome wide CRISPR screening. We believe that identifying viral RBP binding sites will give valuable insights into the mechanisms of host-virus interaction, thus giving us a deeper understanding of the life cycle of SARS-CoV-2 but also opening new avenues for the development of new therapeutics.

A novel strategy by integrating chemical profiling, molecular networking, chemical isolation, and activity evaluation to target isolation of potential anti-ACE2 candidates in Forsythiae Fructus.

BACKGROUND: Traditional Chinese medicine (TCM) is regarded as a large database containing hundreds to thousands of chemical constituents that can be further developed as clinical drugs, such as artemisinin in Artemisia annua. However, effectively exploring novel candidates is still a challenge faced by researchers. PURPOSE: In this work, an integrated strategy combining chemical profiling, molecular networking, chemical isolation, and activity evaluation (CMCA strategy) was proposed and applied to systematically characterize and screen novel candidates, and Forsythiae fructus (FF) was used as an example. STUDY DESIGN: It contained four parts. First, the chemical compounds in FF were detected by ultra-high-performance liquid chromatography-mass spectrometry (UPLC/Q-TOF MS) with data-dependent acquisition, and further, the targeted compounds were screened out based on an in-house database. In the meantime, the representative MS/MS fragmentation behaviors of different chemical structure types were summarized. Second, homologous constituents were grouped and organized based on feature-guided molecular networking, and the nontargeted components with homologous mass fragmentation behaviors were characterized. Third, the novel compounds were isolated and unambiguously identified by nuclear magnetic resonance (NMR). Finally, the anti-angiotensin-converting enzyme 2 (ACE2) activities of isolated chemical constituents were further evaluated by in vitro experiments. RESULTS: A total of 278 compounds were profiled in FF, including 151 targeted compounds and 127 nontargeted compounds. Among them, 16 were unambitiously identified by comparison with reference standards. Moreover, 25 were classified into potential novel compounds. Two novel compounds were unambiguously identified by using conventional chromatographic methods, and they were named phillyrigeninside D (peak 254) and forsythenside O (peak 155). Furthermore, the ACE2 activity of the compounds in FF was evaluated by modern pharmacological methods, and among them, suspensaside A was confirmed to present obvious anti-ACE2 activity. CONCLUSION: Our work provides meaningful information for revealing potential FF candidates for the treatment of COVID-19, along with new insight for exploring novel candidates from complex systems.

Pathogenic antibodies induced by spike proteins of COVID-19 and SARS-CoV viruses (preprint)

This study, using a virus-free mouse model, explores the pathogenic roles of certain antibodies specific to the spike proteins of highly pathogenic coronaviruses such as the COVID-19 and the SARS-CoV viruses. Our data showed that these pathogenic antibodies, through a mechanism of Antibody Dependent Auto-Attack (ADAA), target and bind to host vulnerable cells or tissues such as damaged lung epithelium cells, initiate a self-attack immune response, and lead to serious conditions including ARDS, cytokine release, and death. Moreover, the pathogenic antibodies also induced inflammation and hemorrhage of the kidneys, brain, and heart. Furthermore, the pathogenic antibodies can bind to unmatured fetal tissues and cause abortions, postpartum labors, still births, and neonatal deaths of pregnant mice. Novel clinical interventions, through disrupting the host-binding of these pathogenic antibodies, can be developed to fight the COVID-19 pandemic. In addition, the new concept of ADAA explored by this study may be applicable to other infectious diseases, such as the highly pathogenic influenza infections. It should be noted that the majority of anti-spike antibodies are non-pathogenic, as only 2 of 7 monoclonal antibodies tested showed significant pathogenic effects.

Who Are the `Silent Spreaders'?: Contact Tracing in Spatio-Temporal Memory Models (preprint)

The COVID-19 epidemic has swept the world for over a year. However, a large number of infectious asymptomatic COVID-19 cases (\textit{ACC}s) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (\textit{STEM-COVID}) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (\textit{ART}), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.

Impacts of Covid-19 mode shift on road traffic (preprint)

This article is driven by the following question: as the communities reopen after the COVID-19 pandemic, will changing transportation mode share lead to worse traffic than before? This question could be critical especially if many people rush to single occupancy vehicles. To this end, we estimate how congestion will increases as the number of cars increase on the road, and identify the most sensitive cites to drop in transit usage. Travel time and mode share data from the American Community Survey of the US Census Bureau, for metro areas across the US. A BPR model is used to relate average travel times to the estimated number of commuters traveling by car. We then evaluate increased vehicle volumes on the road if different portions of transit and car pool users switch to single-occupancy vehicles, and report the resulting travel time from the BPR model. The scenarios predict that cities with large transit ridership are at risk for extreme traffic unless transit systems can resume safe, high throughput operations quickly.