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AbstractBackground Early in the COVID-19 pandemic, GPs had to distinguish SARS-CoV-2 from other aetiologies in patients presenting with respiratory tract infection (RTI) symptoms on clinical grounds and adapt management accordingly.Objectives To test the diagnostic accuracy of GPs’ clinical diagnosis of a SARS-CoV-2 infection in a period when COVID-19 was a new disease. To describe GPs’ management of patients presenting with RTI for whom no confirmed diagnosis was available. To investigate associations between patient and clinical features with a SARS-CoV-2 infection.Methods In April 2020–March 2021, 876 patients (9 countries) were recruited when they contacted their GP with symptoms of an RTI of unknown aetiology. A swab was taken at baseline for later analysis. Aetiology (PCR), diagnostic accuracy of GPs’ clinical SARS-CoV-2 diagnosis, and patient management were explored. Factors related to SARS-CoV-2 infection were determined by logistic regression modelling.Results GPs suspected SARS-CoV-2 in 53% of patients whereas 27% of patients tested positive for SARS-CoV-2. True-positive patients (23%) were more intensively managed for follow-up, antiviral prescribing and advice than true-negatives (42%). False negatives (5%) were under-advised, particularly for social distancing and isolation. Older age (OR: 1.02 (1.01–1.03)), male sex (OR: 1.68 (1.16–2.41)), loss of taste/smell (OR: 5.8 (3.7–9)), fever (OR: 1.9 (1.3–2.8)), muscle aches (OR: 2.1 (1.5–3)), and a known risk factor for COVID-19 (travel, health care worker, contact with proven case; OR: 2.7 (1.8–4)) were predictive of SARS-CoV-2 infection. Absence of loss of taste/smell, fever, muscle aches and a known risk factor for COVID-19 correctly excluded SARS-CoV-2 in 92.3% of patients, whereas presence of 3, or 4 of these variables correctly classified SARS-CoV-2 in 57.7% and 87.1%.Conclusion Correct clinical diagnosis of SARS-CoV-2 infection, without POC-testing available, appeared to be complicated.

IntroductionBased on a large body of previous research suggesting that smell loss was a predictor of COVID-19, we investigated the ability of SCENTinel®, a newly validated rapid olfactory test that assesses odor detection, intensity, and identification, to predict SARS-CoV-2 infection in a community sample.MethodsBetween April 5, 2021, and July 5, 2022, 1,979 individuals took one SCENTinel® test, completed at least one physician-ordered SARS-CoV-2 PCR test, and endorsed a list of self-reported symptoms.ResultsAmong the of SCENTinel® subtests, the self-rated odor intensity score, especially when dichotomized using a previously established threshold, was the strongest predictor of SARS-CoV-2 infection. SCENTinel® had high specificity and negative predictive value, indicating that those who passed SCENTinel® likely did not have a SARS-CoV-2 infection. Predictability of the SCENTinel® performance was stronger when the SARS-CoV-2 Delta variant was dominant rather than when the SARS-CoV-2 Omicron variant was dominant. Additionally, SCENTinel® predicted SARS-CoV-2 positivity better than using a self-reported symptom checklist alone.DiscussionThese results indicate that SCENTinel® is a rapid assessment tool that can be used for population-level screening to monitor abrupt changes in olfactory function, and to evaluate spread of viral infections like SARS-CoV-2 that often have smell loss as a symptom.

BackgroundInformation on antibody responses following SARS-CoV-2 infection, including the magnitude and duration of responses, is limited. In this analysis, we aimed to identify clinical biomarkers that can predict long-term antibody responses following natural SARS-CoV-2 infection.MethodologyIn this prospective study, we enrolled 100 COVID-19 patients between November 2020 and February 2021 and followed them for 6 months. The association of clinical laboratory parameters on enrollment, including lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and D-dimer, with predicting the geometric mean (GM) concentration of SARS-CoV-2 receptor-binding domain (RBD)-specific IgG antibody at 3 and 6 months post-infection was assessed in multivariable linear regression models.ResultThe mean ± SD age of patients in the cohort was 46.8 ± 14 years, and 58.8% were male. Data from 68 patients at 3 months follow-up and 55 patients at 6 months follow-up were analyzed. Over 90% of patients were seropositive against RBD-specific IgG till 6 months post-infection. At 3 months, for any 10% increase in absolute lymphocyte count and NLR, there was a 6.28% (95% CI: 9.68, −2.77) decrease and 4.93% (95% CI: 2.43, 7.50) increase, respectively, in GM of IgG concentration, while any 10% increase for LDH, CRP, ferritin, and procalcitonin was associated with a 10.63, 2.87, 2.54, and 3.11% increase in the GM of IgG concentration, respectively. Any 10% increase in LDH, CRP, and ferritin was similarly associated with an 11.28, 2.48, and 3.0% increase in GM of IgG concentration at 6 months post-infection.ConclusionSeveral clinical biomarkers in the acute phase of SARS-CoV-2 infection are associated with enhanced IgG antibody response detected after 6 months of disease onset. The measurement of SARS-CoV-2 specific antibody responses requires improved techniques and is not feasible in all settings. Baseline clinical biomarkers can be a useful alternative as they can predict antibody response during the convalescence period. Individuals with an increased level of NLR, CRP, LDH, ferritin, and procalcitonin may benefit from the boosting effect of vaccines. Further analyses will determine whether biochemical parameters can predict RBD-specific IgG antibody responses at later time points and the association of neutralizing antibody responses.

Combining bioinformatics and in vitro cytology assays, a predictive method was established to quickly evaluate the protective effect of immunity acquired through SARS-CoV-2 infection against variants. Bioinformatics software was first used to predict the changes in the affinity of variant antigens to the CV30 monoclonal antibody by integrating bioinformatics and cytology assays. Then, the ability of the antibody to neutralize the variant antigen was further verified, and the ability of the CV30 to neutralize the new variant strain was predicted through pseudovirus neutralization experiments. The current study has demonstrated that when the Molecular Operating Environment (MOE) predicts |ΔBFE| ≤ 3.0003, it suggests that the CV30 monoclonal antibody exhibits some affinity toward the variant strain and can potentially neutralize it. However, if |ΔBFE| ≥ 4.1539, the CV30 monoclonal antibody does not display any affinity for the variant strain and cannot neutralize it. In contrast, if 3.0003 < |ΔBFE| < 4.1539, it is necessary to conduct a series of neutralization tests promptly with the CV30 monoclonal antibody and the variant pseudovirus to obtain results and supplement the existing method, which is faster than the typical procedures. This approach allows for a rapid assessment of the protective efficacy of natural immunity gained through SARS-CoV-2 infection against variants. [ABSTRACT FROM AUTHOR]

The prediction of a ligand potency to inhibit SARS-CoV-2 main protease (M-pro) would be a highly helpful addition to a virtual screening process. The most potent compounds might then be the focus of further efforts to experimentally validate their potency and improve them. A computational method to predict drug potency, which is based on three main steps, is defined: (1) defining the drug and protein in only one 3D structure; (2) applying graph autoencoder techniques with the aim of generating a latent vector; and (3) using a classical fitting model to the latent vector to predict the potency of the drug. Experiments in a database of 160 drug-M-pro pairs, from which the p I C 50 is known, show the ability of our method to predict their drug potency with high accuracy. Moreover, the time spent to compute the p I C 50 of the whole database is only some seconds, using a current personal computer. Thus, it can be concluded that a computational tool that predicts, with high reliability, the p I C 50 in a cheap and fast way is achieved. This tool, which can be used to prioritize which virtual screening hits, will be further examined in vitro. [ABSTRACT FROM AUTHOR]

Background: The recent COVID vaccinations have successfully reduced death and severity but did not stop the transmission of viruses by the emerging SARS-CoV-2 strain. There is a need for better and long-lasting dynamic vaccines for numerous prevailing strains and the evolving SARS-CoV-2 virus, necessitating the development of broad-spectrum strains being used to stop infection by reducing the spread rate and re-infection. The spike (S) glycoprotein is one of the proteins expressed commonly in the early phases of SARS-CoV-2 infection. It has been identified as the most immunogenic protein of SARS-CoV-2. Methods: In this study, advanced bioinformatics techniques have been exploited to design the novel multi-epitope vaccine using conserved S protein portions from widespread strains of SARS-CoV-2 to predict B cell and T cell epitopes. These epitopes were selected based on toxicity, antigenicity score and immunogenicity. Epitope combinations were used to construct the maximum potent multi-epitope construct with potential immunogenic features. EAAAK, AAY, and GPGPG were used as linkers to construct epitopes. Results: The developed vaccine has shown positive results. After the chimeric vaccine construct was cloned into the PET28a (+) vector for expression screening in Escherichia coli, the potential expression of the construct was identified. Conclusion: The construct vaccine performed well in computer-based immune response simulation and covered a variety of allelic populations. These computational results are more helpful for further analysis of our contract vaccine, which can finally help control and prevent SARS-CoV-2 infections worldwide.

Since the first news about the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appeared, a large amount of data on the variability of the virus genome have accumulated. Most of the mutations entrenched in the viral genome are aimed at improving the mechanisms of host cell penetration, altering the degree of binding to protein receptors, evading the immune system, and suppressing the antiviral immune response. Knowledge of the functional role of mutations will allow improving diagnostic methods, treatment, and vaccine prophylaxis regimens, as well as predicting the further spread and evolution of the virus. In this study, we analyzed a number of SARS-CoV-2 virus mutations in the context of viral epitope affinity for most common in Russia HLA class I and class II alleles (according to Allele Frequency Net Database). This study examined clade-forming mutations of viral clades that are classified as variants of concern according to the WHO classification. We found that some mutations reduce the number of predicted epitopes, the number of HLA alleles that bind them, or the number of both epitopes and HLA alleles simultaneously. Mutations of the viral clade B.1.1.7 (S:Y144del, S:H69-V70del, and S:A570D), mutations of the viral clade B.1.617.2 (S:T19R, S:G12D, S:F157del, and S:R158del), and mutation N:R203K related to all clades except B.1.617.2 have pronounced effects on reducing epitope affinity for HLA alleles, with some of them affecting on epitopes of all the studied strong and weak binders lengths in both HLA class I and class II.

Combining bioinformatics and in vitro cytology assays, a predictive method was established to quickly evaluate the protective effect of immunity acquired through SARS-CoV-2 infection against variants. Bioinformatics software was first used to predict the changes in the affinity of variant antigens to the CV30 monoclonal antibody by integrating bioinformatics and cytology assays. Then, the ability of the antibody to neutralize the variant antigen was further verified, and the ability of the CV30 to neutralize the new variant strain was predicted through pseudovirus neutralization experiments. The current study has demonstrated that when the Molecular Operating Environment (MOE) predicts |ΔBFE| ≤ 3.0003, it suggests that the CV30 monoclonal antibody exhibits some affinity toward the variant strain and can potentially neutralize it. However, if |ΔBFE| ≥ 4.1539, the CV30 monoclonal antibody does not display any affinity for the variant strain and cannot neutralize it. In contrast, if 3.0003 < |ΔBFE| < 4.1539, it is necessary to conduct a series of neutralization tests promptly with the CV30 monoclonal antibody and the variant pseudovirus to obtain results and supplement the existing method, which is faster than the typical procedures. This approach allows for a rapid assessment of the protective efficacy of natural immunity gained through SARS-CoV-2 infection against variants.

Abstract Background Evidence on vaccine-specific protection over time, in particular against the Delta variant, and protection afforded by a homologous third dose is urgently needed. Methods We used a previously published model and neutralization data for five vaccines—mRNA-1273, BNT162b2, NVX-CoV2373, V01, and CoronaVac— to evaluate long-term neutralizing antibody dynamics and predict time-varying efficacy against the Delta variant by specific vaccine, age group, and clinical severity. Results We found that homologous third-dose vaccination produces higher neutralization titers compared with titers observed following primary-series vaccination for all vaccines studied. We estimate the efficacy of mRNA-1273 and BNT162b2 against Delta variant infection to be 63.5% (95% CI: 51.4–67.3%) and 78.4% (95% CI: 72.2–83.5%), respectively, 14–30 days after the second dose, and that efficacy decreases to 36.0% (95% CI: 24.1–58.0%) and 38.5% (95% CI: 28.7–49.1%) 6–8 months later. Fourteen to 30 days after administration of homologous third doses, efficacy against the Delta variant would be 97.0% (95% CI: 96.4–98.5%) and 97.2% (95.7–98.1%). All five vaccines are predicted to provide good protection against severe illness from the Delta variant after both primary and homologous third dose vaccination. Conclusions Timely administration of third doses of SARS-CoV-2-prototype-based vaccines can provide protection against the Delta variant, with better performance from mRNA vaccines than from protein and inactivated vaccines. Irrespective of vaccine technology, a homologous third dose for all types of vaccines included in the study will effectively prevent symptomatic and severe COVID-19 caused by the Delta variant. Long-term monitoring and surveillance of antibody dynamics and vaccine protection, as well as further validation of neutralizing antibody levels or other markers that can serve as correlates of protection against SARS-CoV-2 and its variants, are needed to inform COVID-19 pandemic responses.

SARS-CoV-2 breakthrough infections have been reported because of the reduced efficacy of vaccines against the emerging variants globally. However, an accurate model to predict SARS-CoV-2 breakthrough infection is still lacking. In this retrospective study, 6,189 vaccinated individuals, consisting of SARS-CoV-2 test-positive cases (n = 219) and test-negative controls (n = 5970) during the outbreak of the Delta variant in September 2021 in Xiamen and Putian cities, Fujian province of China, were included. The vaccinated individuals were randomly split into a training (70%) cohort and a validation (30%) cohort. In the training cohort, a visualized nomogram was built based on the stepwise multivariate logistic regression. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.819 (95% CI, 0.780–0.858) and 0.838 (95% CI, 0.778–0.897). The calibration curves for the probability of SARS-CoV-2 breakthrough infection showed optimal agreement between prediction by nomogram and actual observation. Decision curves indicated that nomogram conferred high clinical net benefit. In conclusion, a nomogram model for predicting SARS-CoV-2 breakthrough infection based on the real-world setting was successfully constructed, which will be helpful in the management of SARS-CoV-2 breakthrough infection.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective.MethodsWe developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors.ResultFrom April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination.ConclusionAs found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.

The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%.

Early and fast detection of COVID-19 patients help limit the transmission and wide spread of the virus in the community and will have impact on mortality by reducing the incidence of infection among vulnerable people. Therefore, community-based screening is critical. We aimed to identify clinical signs and symptoms and epidemiological features that could help discriminate confirmed cases of COVID-19 from SARS-CoV-2 negative patients. We found that age (aOR:1.02, 95%CI:1.02–1.03, p < 0.001), symptoms onset between 3 and 14 days (aOR:1.35, 95%CI:1.09)1.68, p = 0.006), fever or history of fever (aOR:1.75, 95%CI:1.42–2.14, p < 0.001), cough (aOR:1.68, 95%CI:1.31–2.04), sore throat (aOR:0.65, 95%CI:0.49–0.85, p = 0.002), ageusia (aOR:2.24, 95%CI:1.42–3.54, p = 0.001), anosmia (aOR:6.04, 95%CI:4.19–8.69, p < 0.001), chest pain (aOR:0.63, 95%CI:0.47–0.85, p = 0.003), myalgia and/or arthralgia (aOR:1.64, 95%CI:1.31–2.04, p < 0.001), household cluster (aOR:1.49, 95%CI:1.17–1.91, p = 0.001) and evidence of confirmed cases in the neighbourhood (aOR:1.92, 95%CI:1.56–2.37, p < 0.001) could help discriminate COVID-19 patients from SARS-CoV-2 negative. A screening score derived from multivariate logistic regression was developed to assess the probability of COVID-19 in patients. We suggest that a patient with a score ≥14 should undergo SARS-CoV-2 PCR testing. A patient with a score ≥30 should be considered at high risk of COVID-19 and should undergo testing but also needs prompt isolation and contact tracing.

The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%. [ABSTRACT FROM AUTHOR]

Aim: To find whether an emergent airborne infection is more likely to spread among healthcare workers (HCW) based on data of SARS-CoV-2 and whether the number of new cases of such airborne viral disease can be predicted using a method traditionally used in weather forecasting called Autoregressive Fractionally Integrated Moving Average (ARFIMA). Methods: We analyzed SARS-CoV-2 spread among HCWs based on outpatient nasopharyngeal swabs for real-time polymerase chain reaction (RT-PCR) tests and compared it to non-HCW in the first and the second wave of the pandemic. We also generated an ARFIMA model based on weekly case numbers from February 2020 to April 2021 and tested it on data from May to July 2021. Results: Our analysis of 8998 tests in the 15 months period showed a rapid rise in positive RT-PCR tests among HCWs during the first wave of pandemic. In the second wave, however, positive patients were more commonly non-HCWs. The ARFIMA model showed a long-memory pattern for SARS-CoV-2 (seven months) and predicted future new cases with an average error of ± 1.9 cases per week. Conclusion: Our data indicate that the virus rapidly spread among HCWs during the first wave of the pandemic. Review of published literature showed that this was the case in multiple other areas as well. We therefore suggest strict policies early in the emergence of a new infection to protect HCWs and prevent spreading to the general public. The ARFIMA model can be a valuable forecasting tool to predict the number of new cases in advance and assist in efficient planning. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 breakthrough infections have been reported because of the reduced efficacy of vaccines against the emerging variants globally. However, an accurate model to predict SARS-CoV-2 breakthrough infection is still lacking. In this retrospective study, 6,189 vaccinated individuals, consisting of SARS-CoV-2 test-positive cases (n = 219) and test-negative controls (n = 5970) during the outbreak of the Delta variant in September 2021 in Xiamen and Putian cities, Fujian province of China, were included. The vaccinated individuals were randomly split into a training (70%) cohort and a validation (30%) cohort. In the training cohort, a visualized nomogram was built based on the stepwise multivariate logistic regression. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.819 (95% CI, 0.780–0.858) and 0.838 (95% CI, 0.778–0.897). The calibration curves for the probability of SARS-CoV-2 breakthrough infection showed optimal agreement between prediction by nomogram and actual observation. Decision curves indicated that nomogram conferred high clinical net benefit. In conclusion, a nomogram model for predicting SARS-CoV-2 breakthrough infection based on the real-world setting was successfully constructed, which will be helpful in the management of SARS-CoV-2 breakthrough infection. [ABSTRACT FROM AUTHOR]

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2, the causative agent of coronavirus disease (COVID-19)) has caused relatively high mortality rates in humans throughout the world since its first detection in late December 2019, leading to the most devastating pandemic of the current century. Consequently, SARS-CoV-2 therapeutic interventions have received high priority from public health authorities. Despite increased COVID-19 infections, a vaccine or therapy to cover all the population is not yet available. Herein, immunoinformatics and custommune tools were used to identify B and T-cells epitopes from the available SARS-CoV-2 sequences spike (S) protein. In the in silico predictions, six B cell epitopes QTGKIADYNYK, TEIYQASTPCNGVEG, LQSYGFQPT, IRGDEVRQIAPGQTGKIADYNYKLPD, FSQILPDPSKPSKRS and PFAMQMAYRFNG were cross-reacted with MHC-I and MHC-II T-cells binding epitopes and selected for vaccination in experimental animals for evaluation as candidate vaccine(s) due to their high antigenic matching and conserved score. The selected six peptides were used individually or in combinations to immunize female Balb/c mice. The immunized mice raised reactive antibodies against SARS-CoV-2 in two different short peptides located in receptor binding domain and S2 region. In combination groups, an additive effect was demonstrated in-comparison with single peptide immunized mice. This study provides novel epitope-based peptide vaccine candidates against SARS-CoV-2.

Background: COVID-19, caused by the novel SARS-CoV-2, is considered the most threatening respiratory infection in the world, with over 40 million people infected and over 0.934 million related deaths reported worldwide. It is speculated that epidemiological and clinical features of COVID-19 may differ across countries or continents. Genomic comparison of 48,635 SARS-CoV-2 genomes has shown that the average number of mutations per sample was 7.23, and most SARS-CoV-2 strains belong to one of 3 clades characterized by geographic and genomic specificity: Europe, Asia, and North America. Objective: The aim of this study was to compare the genomes of SARS-CoV-2 strains isolated from Italy, Sweden, and Congo, that is, 3 different countries in the same meridian (longitude) but with different climate conditions, and from Brazil (as an outgroup country), to analyze similarities or differences in patterns of possible evolutionary pressure signatures in their genomes. Methods: We obtained data from the Global Initiative on Sharing All Influenza Data repository by sampling all genomes available on that date. Using HyPhy, we achieved the recombination analysis by genetic algorithm recombination detection method, trimming, removal of the stop codons, and phylogenetic tree and mixed effects model of evolution analyses. We also performed secondary structure prediction analysis for both sequences (mutated and wild-type) and "disorder" and "transmembrane" analyses of the protein. We analyzed both protein structures with an ab initio approach to predict their ontologies and 3D structures. Results: Evolutionary analysis revealed that codon 9628 is under episodic selective pressure for all SARS-CoV-2 strains isolated from the 4 countries, suggesting it is a key site for virus evolution. Codon 9628 encodes the P0DTD3 (Y14_SARS2) uncharacterized protein 14. Further investigation showed that the codon mutation was responsible for helical modification in the secondary structure. The codon was positioned in the more ordered region of the gene (41-59) and near to the area acting as the transmembrane (54-67), suggesting its involvement in the attachment phase of the virus. The predicted protein structures of both wild-type and mutated P0DTD3 confirmed the importance of the codon to define the protein structure. Moreover, ontological analysis of the protein emphasized that the mutation enhances the binding probability. Conclusions: Our results suggest that RNA secondary structure may be affected and, consequently, the protein product changes T (threonine) to G (glycine) in position 50 of the protein. This position is located close to the predicted transmembrane region. Mutation analysis revealed that the change from G (glycine) to D (aspartic acid) may confer a new function to the protein--binding activity, which in turn may be responsible for attaching the virus to human eukaryotic cells. These findings can help design in vitro experiments and possibly facilitate a vaccine design and successful antiviral strategies. [ABSTRACT FROM AUTHOR]

Currently, the pandemic caused by a novel coronavirus, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the most serious issues worldwide. SARS-CoV-2 was first observed in Wuhan, China, on December 31, 2019; this disease has been rapidly spreading worldwide. Iran was the first Middle East country to report a coronavirus death, it has been severely affected. Therefore, it is crucial to forecast the pandemic spread in Iran. This study aims to develop a prediction model for the daily total confirmed cases, total confirmed new cases, total deaths, total new deaths, growth rate in confirmed cases, and growth rate in deaths. The model utilizes SARS-CoV-2 daily data, which are mainly collected from the official website of the European Centre for Disease Prevention and Control from February 20 to May 04, 2020 and other appropriated references. Autoregressive integrated moving average (ARIMA) is employed to forecast the trend of the pandemic spread. The ARIMA model predicts that Iran can easily exhibit an increase in the daily total confirmed cases and the total deaths, while the daily total confirmed new cases, total new deaths, and growth rate in confirmed cases/deaths becomes stable in the near future. This study predicts that Iran can control the SARS-CoV-2 disease in the near future. The ARIMA model can rapidly aid in forecasting patients and rendering a better preparedness plan in Iran.

Coronaviruses, including the recent pandemic strain SARS-Cov-2, use a multifunctional 2′-O-methyltransferase (2′-O-MTase) to restrict the host defense mechanism and to methylate RNA. The nonstructural protein 16 2′-O-MTase (nsp16) becomes active when nonstructural protein 10 (nsp10) and nsp16 interact. Novel peptide drugs have shown promise in the treatment of numerous diseases and new research has established that nsp10 derived peptides can disrupt viral methyltransferase activity via interaction of nsp16. This study had the goal of optimizing new analogous nsp10 peptides that have the ability to bind nsp16 with equal to or higher affinity than those naturally occurring. The following research demonstrates that in silico molecular simulations can shed light on peptide structures and predict the potential of new peptides to interrupt methyltransferase activity via the nsp10/nsp16 interface. The simulations suggest that misalignments at residues F68, H80, I81, D94, and Y96 or rotation at H80 abrogate MTase function. We develop a new set of peptides based on conserved regions of the nsp10 protein in the Coronaviridae species and test these to known MTase variant values. This results in the prediction that the H80R variant is a solid new candidate for potential new testing. We envision that this new lead is the beginning of a reputable foundation of a new computational method that combats coronaviruses and that is beneficial for new peptide drug development.

In SARS-CoV-2-infected patients, obesity, hypertension, and diabetes are dangerous factors that may result in death. Priority in detection and specific therapies for these patients are necessary. We wanted to investigate the impact of obesity and metabolic syndrome (MS) on the clinical course of COVID-19 and whether prognostic biomarkers described are useful to predict the evolution of COVID-19 in patients with obesity or MS. This prospective cohort study included 303 patients hospitalized for COVID-19. Participants were first classified according to the presence of obesity; then, they were classified according to the presence of MS. Clinical, radiologic, and analytical parameters were collected. We reported that patients with obesity presented moderate COVID-19 symptoms and pneumonia, bilateral pulmonary infiltrates, and needed tocilizumab more frequently. Meanwhile, patients with MS presented severe pneumonia and respiratory failure more frequently, they have a higher mortality rate, and they also showed higher creatinine and troponin levels. The main findings of this study are that IL-6 is a potential predictor of COVID-19 severity in patients with obesity, while troponin and LDH can be used as predictive biomarkers of COVID-19 severity in MS patients. Therefore, treatment for COVID-19 in patients with obesity or MS should probably be intensified and personalized.

Background: The global COVID-19 pandemic has resulted in over seven million deaths, and IFI can further complicate the clinical course of COVID-19. Coinfection of COVID-19 and IFI (secondary IFI) pose significant threats not only to healthcare systems but also to patient lives. After the control measures for COVID-19 were lifted in China, we observed a substantial number of ICU patients developing COVID-19-associated IFI. This creates an urgent need for predictive assessment of COVID-19 patients in the ICU environment for early detection of suspected fungal infection cases. Methods: This study is a single-center, retrospective research endeavor. We conducted a case-control study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive patients. The cases consisted of patients who developed any secondary IFI during their ICU stay at Jilin University China-Japan Union Hospital in Changchun, Jilin Province, China, from December 1st, 2022, to August 31st, 2023. The control group consisted of SARS-CoV-2 positive patients without secondary IFI. Descriptive and comparative analyses were performed, and a logistic regression prediction model for secondary IFI in COVID-19 patients was established. Additionally, we observed an increased incidence of COVID-19-associated pulmonary aspergillosis (CAPA) during this pandemic. Therefore, we conducted a univariate subgroup analysis on top of IFI, using non-CAPA patients as the control subgroup. Results: From multivariate analysis, the prediction model identified 6 factors that are significantly associated with IFI, including the use of broad-spectrum antibiotics for more than 2 weeks (aOR=4.14, 95% CI 2.03-8.67), fever (aOR=2.3, 95%CI 1.16-4.55), elevated log IL-6 levels (aOR=1.22, 95% CI 1.04-1.43) and prone position ventilation (aOR=2.38, 95%CI 1.15-4.97) as independent risk factors for COVID-19 secondary IFI. High BMI (BMI = 28 kg/m²) (aOR=0.85, 95% CI 0.75-0.94) and the use of COVID-19 immunoglobulin (aOR=0.45, 95% CI 0.2-0.97) were identified as independent protective factors against COVID-19 secondary IFI. The Receiver Operating Curve (ROC) area under the curve (AUC) of this model was 0.81, indicating good classification. Conclusion: We recommend paying special attention for the occurrence of secondary IFI in COVID-19 patients with low BMI (BMI < 28 kg/m²), elevated log IL-6 levels and fever. Additionally, during the treatment of COVID-19 patients, we emphasize the importance of minimizing the duration of broad-spectrum antibiotic use and highlight the potential of immunoglobulin application in reducing the incidence of IFI. [ABSTRACT FROM AUTHOR]

• Fast detection of cases is essential to limit the transmission of COVID-19. • Combination of signs and epidemiological features could help detect cases. • A clinical screening score could be helpful to assess the probability of COVID-19. • A patient with high screening score has higher probability of COVID-19. Early and fast detection of COVID-19 patients help limit the transmission and wide spread of the virus in the community and will have impact on mortality by reducing the incidence of infection among vulnerable people. Therefore, community-based screening is critical. We aimed to identify clinical signs and symptoms and epidemiological features that could help discriminate confirmed cases of COVID-19 from SARS-CoV-2 negative patients. We found that age (aOR:1.02, 95%CI:1.02–1.03, p < 0.001), symptoms onset between 3 and 14 days (aOR:1.35, 95%CI:1.09)1.68, p = 0.006), fever or history of fever (aOR:1.75, 95%CI:1.42–2.14, p < 0.001), cough (aOR:1.68, 95%CI:1.31–2.04), sore throat (aOR:0.65, 95%CI:0.49–0.85, p = 0.002), ageusia (aOR:2.24, 95%CI:1.42–3.54, p = 0.001), anosmia (aOR:6.04, 95%CI:4.19–8.69, p < 0.001), chest pain (aOR:0.63, 95%CI:0.47–0.85, p = 0.003), myalgia and/or arthralgia (aOR:1.64, 95%CI:1.31–2.04, p < 0.001), household cluster (aOR:1.49, 95%CI:1.17–1.91, p = 0.001) and evidence of confirmed cases in the neighbourhood (aOR:1.92, 95%CI:1.56–2.37, p < 0.001) could help discriminate COVID-19 patients from SARS-CoV-2 negative. A screening score derived from multivariate logistic regression was developed to assess the probability of COVID-19 in patients. We suggest that a patient with a score ≥14 should undergo SARS-CoV-2 PCR testing. A patient with a score ≥30 should be considered at high risk of COVID-19 and should undergo testing but also needs prompt isolation and contact tracing. [ABSTRACT FROM AUTHOR]

Background: Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model. Methods: A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4–8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Results: A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort. Conclusions: We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID. [ABSTRACT FROM AUTHOR]

Background: With the variability in emerging data, guidance on the isolation duration for patients with coronavirus disease 2019 (COVID-19) due to the Omicron variant is controversial. This study aimed to determine the predictors of prolonged viral RNA shedding in patients with non-severe COVID-19 and construct a nomogram to predict patients at risk of 14-day PCR conversion failure. Methods: Adult patients with non-severe COVID-19 were enrolled from three hospitals of eastern China in Spring 2022. Viral shedding time (VST) was defined as either the day of the first positive test or the day of symptom onset, whichever was earlier, to the date of the first of two consecutively negative PCR tests. Patients from one hospital (Cohort I, n = 2033) were randomly grouped into training and internal validation sets. Predictors of 14-day PCR conversion failure were identified and a nomogram was developed by multivariable logistic regression using the training dataset. Two hospitals (Cohort II, n = 1596) were used as an external validation set to measure the performance of this nomogram. Results: Of the 2033 patients from Cohort I, the median VST was 13.0 (interquartile range: 10.0‒16.0) days; 716 (35.2%) lasted > 14 days. In the training set, increased age [per 10 years, odds ratio (OR) = 1.29, 95% confidence interval (CI): 1.15‒1.45, P < 0.001] and high Charlson comorbidity index (OR = 1.25, 95% CI: 1.08‒1.46, P = 0.004) were independent risk factors for VST > 14 days, whereas full or boosted vaccination (OR = 0.63, 95% CI: 0.42‒0.95, P = 0.028) and antiviral therapy (OR = 0.56, 95% CI: 0.31‒0.96, P = 0.040) were protective factors. These predictors were used to develop a nomogram to predict VST > 14 days, with an area under the ROC curve (AUC) of 0.73 in the training set (AUC, 0.74 in internal validation set; 0.76 in external validation set). Conclusions: Older age, increasing comorbidities, incomplete vaccinations, and lack of antiviral therapy are risk factors for persistent infection with Omicron variant for > 14 days. A nomogram based on these predictors could be used as a prediction tool to guide treatment and isolation strategies. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID‐19) is an infectious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Although the mortality rate of the disease has been relatively under control as of 2022, more than 15 million confirmed COVID‐19 cases have been detected in Turkey to date, causing more than 100,000 deaths. The clinical manifestation of the disease varies widely, ranging from asymptomatic to acute respiratory distress syndrome causing death. The immune response mechanisms have an important impact on the fine adjustment between healing and enhanced tissue damage. This study aims to investigate the relationship between the variants of the interleukin 1 receptor antagonist (IL1RN), interleukin 17A (IL17A), and interleukin 17F (IL17F) genes and COVID‐19 severity. The study population comprised 202 confirmed COVID‐19 cases divided into three groups according to severity. The IL1RN variable number of a tandem repeat (VNTR) polymorphism was genotyped by polymerase chain reaction (PCR), and IL17A rs2275913, IL17F rs763780 and rs2397084 polymorphisms were genotyped by the PCR‐based restriction fragment length polymorphism method. Statistical analysis revealed a significant association between IL17A rs2275913 variant and COVID‐19 severity. The AA genotype and the A allele of IL17A rs2275913 were found significant in the severe group. Additionally, we found a significant relationship between haplotype frequency distributions and severity of COVID‐19 for the IL17F rs763780/rs2397084 (p = 0.044) and a combination of IL17F rs763780/rs2397084/ IL17A rs2275913 (p = 0.04). The CG and CGA haplotype frequencies were significantly higher in the severe group. IL17A rs2275913, IL17F rs763780 and rs2397084 variants appear to have important effects on the immune response in COVID‐19. In conclusion, variants of IL17A rs2275913, IL17F rs763780 and rs2397084 may be the predictive markers for the clinical course and potential immunomodulatory treatment options in COVID‐19, a disease that has placed a significant burden on our country. [ABSTRACT FROM AUTHOR]

The Chinese government relaxed the Zero-COVID policy on Dec 15, 2022, and reopened the border on Jan 8, 2023. Therefore, COVID prevention in China is facing new challenges. Though there are plenty of prior studies on COVID, none is regarding the predictions on daily confirmed cases, and medical resources needs after China reopens its borders. To fill this gap, this study innovates a combination of the Erdos Renyl network, modified computational model SEIRS , and python code instead of only mathematical formulas or computer simulations in the previous studies. The research background in this study is Shanghai, a representative city in China. Therefore, the results in this study also demonstrate the situation in other regions of China. According to the population distribution and migration characteristics, we divided Shanghai into six epidemic research areas. We built a COVID spread model of the Erodos Renyl network. And then, we use python code to simulate COVID spread based on modified SEIRS model. The results demonstrate that the second and third waves will occur in July–September and Oct-Dec, respectively. At the peak of the epidemic in 2023, the daily confirmed cases will be 340,000, and the cumulative death will be about 31,500. Moreover, 74,000 hospital beds and 3,700 Intensive Care Unit (ICU) beds will be occupied in Shanghai. Therefore, Shanghai faces a shortage of medical resources. In this simulation, daily confirmed cases predictions significantly rely on transmission, migration, and waning immunity rate. The study builds a mixed-effect model to verify further the three parameters' effect on the new confirmed cases. The results demonstrate that migration and waning immunity rates are two significant parameters in COVID spread and daily confirmed cases. This study offers theoretical evidence for the government to prevent COVID after China opened its borders. [ABSTRACT FROM AUTHOR]

The ongoing evolution and mutation of SARS-CoV2 pose a significant challenge to the development of effective medication, as genetic changes can render previously developed drugs ineffective. To address this issue, researchers are exploring various strategies to predict and assess the emergence of novel SARS-CoV2 strains through phylogenetic analysis and mutation prediction. In recent years, deep learning approaches have been applied to studying viruses, including SARS-CoV2, to improve our understanding of virus evolution, structure, categorization, and prediction. In this study, a novel deep learning approach is proposed to predict and assess SARS-CoV2 protein sequences. Specifically, Long Short-Term Memory (LSTM) is utilized to predict protein sequences from aligned input sequences, with a bioinformatics tool used to detect mutations. The deep learning model proposed in this study exhibits high accuracy in predicting several key SARS-CoV2 protein sequences, including spike, replicase, putative, ORF1a, and nucleocapsid. The study uses genome sequencing data from the National Center for Biotechnology Information (NCBI) and demonstrates a 98% accuracy in predicting genomic sequences, with minimal changes observed in protein sequences. This study represents a significant improvement over previous research, which has focused only on predicting mutations in viral RNA sequences using datasets from other viruses. [ABSTRACT FROM AUTHOR]

Background: The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems. The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19. We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data. Methods: Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties, we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29, 2020 to June 30, 2022. We included covariates such as COVID-19 vaccine coverage in the county, comorbidities, demographic variables, and holiday gatherings. Findings: Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission. Models that included wastewater had higher predictive power than models that included clinical cases only, increasing the accuracy of the model by 15%. Predicted hospital admissions correlated highly with observed admissions (r = 0.77) with an average difference of 0.013 hospitalizations per 100,000 (95% CI = [0.002, 0.025]). Interpretation: Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone. The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges. [ABSTRACT FROM AUTHOR]

Background: The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems. The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19. We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data. Methods: Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties, we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29, 2020 to June 30, 2022. We included covariates such as COVID-19 vaccine coverage in the county, comorbidities, demographic variables, and holiday gatherings. Findings: Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission. Models that included wastewater had higher predictive power than models that included clinical cases only, increasing the accuracy of the model by 15%. Predicted hospital admissions correlated highly with observed admissions (r = 0.77) with an average difference of 0.013 hospitalizations per 100,000 (95% CI = [0.002, 0.025]) Interpretation: Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone. The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.