Post-acute Sequelae of COVID-19 (PASC), also known as Long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19 infection. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts. However, current knowledge regarding PASC risk factors is limited. Using a sample of 55,257 participants from the National COVID Cohort Collaborative, as part of the NIH Long COVID Computational Challenge, we sought to predict individual risk of PASC diagnosis from a curated set of clinically informed covariates. We predicted individual PASC status, given covariate information, using Super Learner (an ensemble machine learning algorithm also known as stacking) to learn the optimal, AUC-maximizing combination of gradient boosting and random forest algorithms. We were able to predict individual PASC diagnoses accurately (AUC 0.947). Temporally, we found that baseline characteristics were most predictive of future PASC diagnosis, compared with characteristics immediately before, during, or after COVID-19 infection. This finding supports the hypothesis that clinicians may be able to accurately assess the risk of PASC in patients prior to acute COVID diagnosis, which could improve early interventions and preventive care. We found that medical utilization, demographics and anthropometry, and respiratory factors were most predictive of PASC diagnosis. This highlights the importance of respiratory characteristics in PASC risk assessment. The methods outlined here provide an open-source, applied example of using Super Learner to predict PASC status using electronic health record data, which can be replicated across a variety of settings.
During the previous years, particularly at the beginning of the COVID- 19 pandemic, the potential role of metabo-nutritional comorbidities in the severity and lethality of SARS-CoV2 infection has been widely dis- cussed, often describing ambiguous outcomes. Here we investigate the prevalence of metabo-nutritional comorbidities among COVID-19 patients in Mexico. Using a retrospective observational study design, data was collected from official databases of COVID-19 patients admitted to pub- lic and private hospitals in Mexico City. Our study found a discordant prevalence of metabo-nutritional comorbidities among COVID-19 patients, particularly obesity, hypertension, and diabetes. Discordance consists in geographic location-dependent over and under-representation phenomena, that is the prevalence of such comorbidities in COVID-19 patients was significantly over or under the reported value for the general population in each location. These findings highlight the importance of screening for metabo-nutritional comorbidities in COVID-19 patients and suggest the need for tailored interventions for this population. The study also provides insights into the complex relationships between COVID-19 and metabo-nutritional comorbidities, which may inform future research and clinical practice.
There are many studies that require researchers to extract specific information from the published literature, such as details about sequence records or about a randomized control trial. While manual extraction is cost efficient for small studies, larger studies such as systematic reviews are much more costly and time-consuming. To avoid exhaustive manual searches and extraction, and their related cost and effort, natural language processing (NLP) methods can be tailored for the more subtle extraction and decision tasks that typically only humans have performed. The need for such studies that use the published literature as a data source became even more evident as the COVID-19 pandemic raged through the world and millions of sequenced samples were deposited in public repositories such as GISAID and GenBank, promising large genomic epidemiology studies, but more often than not lacked many important details that prevented large-scale studies. Thus, granular geographic location or the most basic patient-relevant data such as demographic information, or clinical outcomes were not noted in the sequence record. However, some of these data was indeed published, but in the text, tables, or supplementary material of a corresponding published article. We present here methods to identify relevant journal articles that report having produced and made available in GenBank or GISAID, new SARS-CoV-2 sequences, as those that initially produced and made available the sequences are the most likely articles to include the high-level details about the patients from whom the sequences were obtained. Human annotators validated the approach, creating a gold standard set for training and validation of a machine learning classifier. Identifying these articles is a crucial step to enable future automated informatics pipelines that will apply Machine Learning and Natural Language Processing to identify patient characteristics such as co-morbidities, outcomes, age, gender, and race, enriching SARS-CoV-2 sequence databases with actionable information for defining large genomic epidemiology studies. Thus, enriched patient metadata can enable secondary data analysis, at scale, to uncover associations between the viral genome (including variants of concern and their sublineages), transmission risk, and health outcomes. However, for such enrichment to happen, the right papers need to be found and very detailed data needs to be extracted from them. Further, finding the very specific articles needed for inclusion is a task that also facilitates scoping and systematic reviews, greatly reducing the time needed for full-text analysis and extraction.
Objectives: From a public health perspective, it is important to clarify the associations between mask usage and the associated reasons in situations when mask usage is promoted or mitigated. Therefore, I clarified the changes in mask usage and the associated reasons before and after the downgrading of the legal status of COVID-19 in Japan, and analyzed the bi-directional associations between the two. Design: Longitudinal study. Methods: Online surveys were conducted in two waves, between April 18-19, 2023 and June 6-15, 2023, among people aged 20-69 years living in Japan. A total of 291 participants completed both the surveys. The associations between mask usage and beliefs about the reasons for mask usage were analyzed using a cross-lagged panel model. Results: Mask usage decreased slightly, but significantly, from the first to the second wave (P < 0.001, Cohen9s d = -0.23). Of the eight beliefs regarding mask usage, slight but significant decreases were observed in terms of relief and information effects (P = 0.046, Cohen9s d = -0.12; P = 0.018, Cohen9s d = -0.14). There was a significant association between socio-psychological reasons other than infection risk avoidance (such as norm and relief) during the first wave and mask usage during the second wave [standard estimates:0.25 (95% confidence interval (CI):0.06-0.44)]. Contrarily, mask usage during the first wave was significantly associated with the reasons for infection risk avoidance during the second wave [standard estimates:0.13 (0.03-0.24)]. Conclusions: The impact of downgrading the legal status of COVID-19 in Japan on mask usage and the associated reasons were found to be limited. In terms of promoting or mitigating mask usage, the significance of risk communication based on socio-psychological reasons other than infection risk avoidance, such as norms and relief, was highlighted.
Introduction: Peer review is paramount to the scholarly article paradigm, helping to ensure the integrity and credibility of research. The Lancet played a crucial role in disseminating key information on the COVID-19 pandemic, publishing early clinical descriptions, risk factors for death, and effectiveness of measures like physical distancing and masks. Notably, The Lancet was the world9s most cited journal for COVID-19 research, emphasising its significant impact on disseminating critical findings during the pandemic. Methods: Geographic data for The Lancet9s peer reviewers in 2019 (pre-pandemic) and 2020 (pandemic) were analysed at the country level, ranking reviewer countries. A test of proportions compared reviewer numbers between the years. Results: In 2020, China emerged as one of the top ten reviewer countries for the first time, with a significant increase from 1% (25 of 1843) in 2019 to 3% (54 of 1850), p=0.001. Italy also entered the top five reviewer countries, rising from 4% (67) to 5% (90), p=0.065. Reviewers from Africa 43 (2%) and South America 31 (2%) represented their continents in 2020. The top ten reviewer nations for The Lancet in 2020 largely mirrored the top ten countries in global COVID-19 research output. Conclusion: During the COVID-19 pandemic9s acute phase in 2020, The Lancet, the world9s most cited journal for COVID-19 research, featured peer reviewers who were largely representative of global COVID-19 research output. Notably, reviewers from China, the first country affected by COVID-19, increased significantly. However, underrepresentation of some continents persisted. To foster global idea exchange and enhance pandemic preparedness, research capacity worldwide must expand, broadening the reviewer pool; a vital step given uncertainties in future pandemic geographic origin.
The sudden outbreak of the COVID-19 pandemic presented governments, policy makers and health services with an unprecedented challenge of taking real-time decisions that could keep the disease under control with non-pharmaceutical interventions, while at the same time limit as much as possible severe consequences of a very strict lockdown. Mathematical modelling has proved to be a crucial element for informing those decisions. Here we report on the rapid development and application of the Swansea Model, a mathematical model of disease spread in real time, to inform policy decisions during the COVID-19 pandemic in Wales.
Post-acute health care costs following SARS-CoV-2 infection are not known. Beginning 56 days following SARS-CoV-2 polymerase chain reaction (PCR) testing, we compared person-specific total and component health care costs across their distribution for the following year (test-positive versus test-negative, matched people, January 1, 2020-March 31, 2021). For 531,182 individuals, mean person-specific total health care costs were $513.83 (95% CI $387.37-$638.40) higher for test-positive females and $459.10 (95% CI $304.60-$615.32) higher for test-positive males, or >10% increase in mean per-capita costs, driven by hospitalization, long-term care, and complex continuing care costs. At the 99th percentile of each subgroup, person-specific health care costs were $12,533.00 (95% CI $9,008.50-$16,473.00) higher for test-positive females and $14,604.00 (95% CI $9,565.50-$19,506.50) for test-positive males, driven by hospitalization, specialist (males), and homecare costs (females). Cancer costs were lower. Six-month and 1-year costs differences were similar. These findings can inform planning for post-acute SARS-CoV-2 health care costs.
The authors have withdrawn this manuscript owing to the paper being rewritten with a stronger focus on COVID-19 upon request from UK Biobank and to comply more clearly with the primary care data usage agreement. An updated version will be re-uploaded as soon as possible. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding authors.
Novel computational and drug design strategies for inhibition of monkeypox virus and Babesia microti: molecular docking, molecular dynamic simulation and drug design approach by natural compounds - CONCLUSION: These advanced computational strategies reported that 11 lead compounds, including dieckol and amentoflavone, exhibited high potency, excellent drug-like properties, and no toxicity. These compounds demonstrated strong binding affinities to the target enzymes, especially dieckol, which displayed superior stability during molecular dynamics simulations. The MM/PBSA method confirmed the favorable binding energies of amentoflavone and dieckol. However, further in vitro and in vivo…
Reflections on access to care for heavy menstrual bleeding: Past, present, and in times of the COVID-19 pandemic - The symptom of heavy menstrual bleeding (HMB) affects at least a quarter of reproductive-age menstruators. However, given the variance in diagnosing the underlying causes, barriers, and inequity in access to care for HMB, and therefore reporting of HMB, this figure is likely to be a gross underestimate. HMB can have a detrimental impact on quality of life. From the limited reports available it is estimated that around 50%-80% of people with HMB do not seek care for this debilitating symptom, and…
Inhibition by components of Glycyrrhiza uralensis of 3CLpro and HCoV-OC43 proliferation - Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). 3CLpro is a key enzyme in coronavirus proliferation and a treatment target for COVID-19. In vitro and in silico, compounds 1-3 from Glycyrrhiza uralensis had inhibitory activity and binding affinity for 3CLpro. These compounds decreased HCoV-OC43 cytotoxicity in RD cells. Moreover, they inhibited viral growth by reducing the amounts of the necessary proteins (M, N,…
An engineered recombinant protein containing three structural domains in SARS-CoV-2 S2 protein has potential to act as a pan-human coronavirus entry inhibitor or vaccine antigen - The threat to global health caused by three highly pathogenic human coronaviruses (HCoV), SARS-CoV-2, MERS-CoV and SARS-CoV, calls for the development of pan-HCoV therapeutics and vaccines. This study reports the design and engineering of a recombinant protein designated HR1LS. It contains 3 linked molecules, each consisting of three structural domains, including a heptad repeat 1 (HR1), a central helix (CH), and a stem helix (SH) region, in the S2 subunit of SARS-CoV-2 spike (S) protein. It was…
Structural-Based Virtual Screening of FDA-Approved Drugs Repository for NSP16 Inhibitors, Essential for SARS-COV-2 Invasion Into Host Cells: Elucidation From MM/PBSA Calculation - NSP16 is one of the structural proteins of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) necessary for its entrance to the host cells. It exhibits 2’O-methyl-transferase (2’O-MTase) activity of NSP16 using methyl group from S-adenosyl methionine (SAM) by methylating the 5-end of virally encoded mRNAs and shields viral RNA, and also controls its replication as well as infection. In the present study, we used in silico approaches of drug repurposing to target and inhibit the SAM…
Invalidation of geraniin as a potential inhibitor against SARS-CoV-2 main protease - Recently, geraniin has been identified as a potent antiviral agent targeting SARS-CoV-2 main protease (Mpro). Considering the potential of geraniin in COVID-19 treatment, a stringent validation for its Mpro inhibition is necessary. Herein, we rigorously evaluated the in vitro inhibitory effect of geraniin on Mpro using the fluorescence resonance energy transfer (FRET), fluorescence polarization (FP), and dimerization-dependent red fluorescent protein (ddRFP) assays. Our data indicate that…
Crystal structures of main protease (Mpro) mutants of SARS-CoV-2 variants bound to PF-07304814 - There is an urgent need to develop effective antiviral drugs to prevent the viral infection caused by constantly circulating SARS-CoV-2 as well as its variants. The main protease (M^(pro)) of SARS-CoV-2 is a salient enzyme that plays a vital role in viral replication and serves as a fascinating therapeutic target. PF-07304814 is a covalent inhibitor targeting SARS-CoV-2 M^(pro) with favorable inhibition potency and drug-like properties, thus making it a promising drug candidate for the treatment…
Direct blue 53, a biological dye, inhibits SARS-CoV-2 infection by blocking ACE2 and spike interaction in vitro and in vivo - COVID-19 is a global health problem caused by SARS-CoV-2, which has led to over 600 million infections and 6 million deaths. Developing novel antiviral drugs is of pivotal importance to slow down the epidemic swiftly. In this study, we identified five azo compounds as effective antiviral drugs to SARS-CoV-2, and mechanism study revealed their targets for impeding viral particles’ ability to bind to host receptors. Direct Blue 53, which displayed the strongest inhibitory impact, inhibited five…
Chicoric Acid Presented NLRP3-Mediated Pyroptosis through Mitochondrial Damage by PDPK1 Ubiquitination in an Acute Lung Injury Model - Chicoric acid (CA), a functional food ingredient, is a caffeic acid derivative that is mainly found in lettuce, pulsatilla, and other natural plants. However, the anti-inflammatory effects of CA in acute lung injury (ALI) remain poorly understood. This study was conducted to investigate potential drug usage of CA for ALI and the underlying molecular mechanisms of inflammation. C57BL/6 mice were given injections of liposaccharide (LPS) to establish the in vivo model. Meanwhile, BMDM cells were…
Therapeutic effects of tea polyphenol-loaded nanoparticles coated with platelet membranes on LPS-induced lung injury - Patients with ALI (acute lung injury)/ARDS (acute respiratory distress syndrome) are often septic and with poor prognosis, which leads to a high mortality rate of 25-40%. Despite the advances in medicine, there are no effective pharmacological therapies for ALI/ARDS due to the short systemic circulation and poor specificity in the lungs. To address this problem, we prepared TP-loaded nanoparticles (TP-NPs) through the emulsification-and-evaporation method, and then the platelet membrane vesicles…
Combination of Chinese herbal medicine and conventional western medicine for coronavirus disease 2019: a systematic review and meta-analysis - CONCLUSIONS: Potentially, CHM listed in this study, as an adjunctive therapy, combining with CWM is an effective and safe therapy mode for COVID-19. However, more high-quality RCTs are needed to draw more accurate conclusions.
SARS-CoV-2 main protease targeting potent fluorescent inhibitors: Repurposing thioxanthones - The coronavirus disease, COVID-19, is the major focus of the whole world due to insufficient treatment options. It has spread all around the world and is responsible for the death of numerous human beings. The future consequences for the disease survivors are still unknown. Hence, all contributions to understand the disease and effectively inhibit the effects of the disease have great importance. In this study, different thioxanthone based molecules, which are known to be fluorescent compounds,…
Identification of a small chemical as a lysosomal calcium mobilizer and characterization of its ability to inhibit autophagy and viral infection - We previously identified GAPDH as one of the cyclic adenosine diphosphoribose (cADPR)’s binding proteins and found that GAPDH participates in cADPR-mediated Ca^(2+) release from ER via ryanodine receptors (RyRs). Here we aimed to chemically synthesize and pharmacologically characterize novel cADPR analogues. Based on the simulated cADPR-GAPDH complex structure, we performed the structure-based drug screening, identified several small chemicals with high docking scores to cADPR’s binding pocket…
Discovery and evaluation of active compounds from Xuanfei Baidu formula against COVID-19 via SARS-CoV-2 Mpro - CONCLUSION: Acteoside is regarded as a representative active natural compound in XFBD to inhibit replication of SARS-CoV-2, which provides the antiviral evidence and some insights into the identification of SARS-CoV-2 M^(pro) natural inhibitors.
Neurological side effects and drug interactions of antiviral compounds against SARS-CoV-2 - CONCLUSION: Neurological side effects and drug interactions must be considered for antiviral compounds against SARS-CoV-2. Further studies are required to better evaluate their efficacy and adverse events in patients with concomitant neurological diseases. Moreover, evidence from real-world studies will complement the current knowledge.