Ethics statement and patient consent
This study was approved by the Research Ethics Committees of Graduate School of Medicine, Chiba University (M10505). The instructions for patients include the following: Research purpose, research methods, expected effects and risks, not being disadvantaged even if you do not consent, being able to withdraw consent at any time after consent, strict management of personal information, research results being reported in academic journals, research organization and funding sources. Participant gave written informed consent, according to CARE guidelines and in compliance with the Declaration of Helsinki principles.
The patient received three courses of RDV medication. During each course, the patient received an initial dose of 200mg IV, followed by four daily doses of 100mg IV (5 days in total).
SARS-CoV-2 RNA was detected using a real-time RT-PCR kit (Ampdirect 2019-nCoV detection kit; Shimadzu, Kyoto, Japan).
Reverse transcription was performed using a LunaScript RT SuperMix Kit (New England Biolabs, MA, USA) as the following cycling condition: 25C, 2min; 55C, 10min; 95C, 1min. Then, a 2kb tiling PCR was performed using a standard protocol with Tks Gflex DNA polymerase (Takara Bio, Shiga, Japan) and four primer pools (Supplementary Table2, synthesised by Integrated DNA Technologies, IA, USA) as the following cycling condition: 94C, 1min; 40 cycles at 98C, 10s, 60C, 15s, and 68C, 1min. After amplification, a library was prepared using the xGen DNA Library EZ UNI Kit (Integrated DNA Technologies, IA, USA) and sequenced using an iSeq100 instrument (Illumina, CA, USA). Sequencing data were pre-processed using fastp (trimming 1 base in 5- and 3-ends of reads)27 and mapped onto the SARS-CoV-2 genome (NC_045512) using a BWA aligner28. Trimming of primer sequences, variant calling, and consensus sequence generation were performed using iVar with default settings (variants with 3% frequencies were called)29. Used commands are shown in Supplementary information. WGS (Whole Genome Sequencing) data are available in the NCBI Sequence Read Archive (SRA), submission SUB13521440 under BioProject number PRJNA983865 (BioSample accession numbers SAMN35736960SAMN35736967, https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA983865).
SARS-CoV-2 lineage was assigned by both Pangolin (https://cov-lineages.org/index.html)30 and Nextclade (https://clades.nextstrain.org/)31 web applications. In addition, the phylogenetic analysis was performed using whole genome sequence data of current strains and consensus sequence of WHO designated VOC, Alpha, Beta, Gamma, Delta, and Omicron (BA.1, BA.2, BA.4, BA.5, BA.2.75, and XBB.1.5) variants. The multiple sequence alignment was performed by MAFFT32. The phylogenetic tree was inferred by IQ-TREE with 1000 bootstrap resampling33,34. The best-fit substitution model (TIM+F+I) was selected by ModelFinder plus option35. The inferred phylogenetic tree was visualised by iTOL (https://itol.embl.de/) web application36.
TMPRSS2-expressing VeroE6 cells (JCRB1819), a SARS-CoV-2-susceptive cell line11, were obtained from the JCRB (Japanese Collection of Research Bioresources Cell Bank, Tokyo, Japan), and cultured in Dulbeccos modified Eagle medium-high glucose (DMEM; Sigma-Aldrich, London, UK) supplemented with 5% (v/v) fetal bovine serum (FBS; Cytiva, Tokyo, Japan) and an antibiotic mixture containing penicillin G (100 units/ml), streptomycin (100g/ml), and amphotericin B (0.25g/ml) (Nacalai Tesque, Kyoto, Japan). Viral isolation was achieved by inoculating a portion of a saline extract of a nasopharyngeal swab from the patient (Day 22 and thereafter) onto a VeroE6/TMPRSS2 culture in a 6-well plate. The cultures were incubated at 37C/5% CO2 and monitored by daily microscopic observation.
Conspicuous CPE (usually cell rounding) spread throughout the well of the culture plate, usually 24 d.p.i. After low-speed centrifugation (800g, 6min), the culture supernatant was harvested and stored at 70C as isolated virus stock. For further experiments, a working virus solution was prepared by inoculating a portion of the stock onto fresh VeroE6/TMPRSS2 cells cultured in a 25-cm2 flask. The cells were cultured for 23 days, and the supernatants were harvested when full-blown CPE was observed and stored in the same manner as the original stock.
To examine the viral growth properties of respective isolates, we performed the following experiments in triplicate. First, VeroE6/TMPRSS2 cells were seeded in 6-well plates (4ml/well) and allowed to nearly reach confluence within 2 days. The medium was removed, and 1ml of the new medium was added. Then, 100l of virus solution adjusted to a multiplicity of infection (MOI) of 0.01 (~4000 TCID50 of each virus / (4105 cells per well)) was inoculated, and the plates were placed in a CO2 incubator for 1h, with occasional gentle shaking. This virus inoculum solution was removed and washed once with 2ml of the new medium, and 4ml of the fresh medium was added again. These were cultured further at 37C/5% CO2. Aliquots of the culture supernatants were harvested every 24h for 5 days, and their viral titres were quantified as described below.
To investigate the extent of RDV resistance in the various isolates, inhibitor assessment experiments were performed according to the method described by Stevens et al.12. First, VeroE6/TMPRSS2 cells were seeded in 24-well plates (1ml/well) and allowed to nearly reach confluence within 2 days. The medium was removed, and 500l of the new medium containing various concentrations of RDV (GS-5734; Aobious, MA, USA), ranging from 0.125M, was added. Then, 100l of the test specimen virus solution (CH-LT1 to CH-LT3m) was added into each well at a MOI of 0.001 (~100 TCID50 of each virus / (105 cells per well)), and the plates were placed in a CO2 incubator for 1h with occasional gentle shaking. This virus inoculum solution was removed and washed once with 500l of the RDV-containing medium, and 1ml of the medium containing various concentrations of RDV was added again. These were cultured further at 37C/5% CO2. Culture supernatants were harvested 72h after virus inoculation, and viral titres were quantified. Viral infectivity (%) was calculated from the viral titres (expressed as TCID50/ml values) in the harvested culture supernatants 72h after virus inoculation in the presence of various concentrations (ranging from 0.1 to 25M) of RDV divided by the titre without RDV (100). Dose-response analysis and calculation of median effect concentration (EC50) values were performed using the drc package (version 3.0-1) from the R statistical software (version 4.2.2)37.
We did not include CH-LT4 and later isolates in this assay because their growth curves were different from those of earlier isolates. We wanted to focus on the impact of mutation(s) that occurred in nsp12. Alternately, we separately performed the comparison experiments of later isolates with the original isolate CH-LT1 in the presence or absence of 5M RDV at 3 and 4 d.p.i. We used 6-well plates, and the MOI was adjusted to 0.01. Other conditions were the same as described above. All RDV resistance experiments were performed in triplicate and statistical analysis was treated.
VeroE6/TMPRSS2 cells were seeded in 96-well plates (100l/well) in a similar manner as described in the growth kinetics experiment, and allowed to nearly reach confluence within 2 days, with 100l of 10-fold serial dilutions of virus-containing culture supernatants added into each well. The presence of live virus in each well was determined based on the CPE at 4 d.p.i., and the TCID50 values were calculated using the BehrensKrber method. The viral titres are expressed as the TCID50/ml.
Antibody responses against the S and N proteins were analyzed using Anti-SARS CoV-2 S RUO and Elecsys Anti-SARS-CoV-2 RUO (Roche Diagnostics, Switzerland), respectively, on the Cobas 8000 e801 module (Roche Diagnostics). The former system allows for the quantitative detection of antibodies, predominantly IgG, that target the viral S protein receptor-binding domain. The measurement threshold is 0.4U/ml, and values of 0.8U/ml were considered positive. The latter system allows for the quantitative detection of antibodies targeting the viral N antigen, with values of 1.0 considered positive.
The three-dimensional structure of the SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp) harbouring V792I, M794I, and C799F mutations at Domain-II was constructed with MOE, version 2022.02 (CCG Inc, Montreal, Canada), based on the Brookhaven Protein Databank 6XEZ. Docking simulations of SARS-CoV-2 RdRp of V792I, M794I, and C799F mutants with RDV-TP (PubChem CID 56832906) were performed using the Amber99 force field in MOE.
All the software(s) used in our study are freely available through the following sites except Molecular Operating Environment (MOE).
fastp (version 0.23.2, https://github.com/OpenGene/fastp).
bwa (version: 0.7.17-r1188, https://github.com/lh3/bwa).
iVar (version 1.4, https://andersen-lab.github.io/ivar/html/index.html).
MAFFT (version 7, https://mafft.cbrc.jp/alignment/software/).
IQ-TREE (version 2.2.6, http://www.iqtree.org).
iTOL (version 6.8.1, https://itol.embl.de).
R (version 4.2.2, https://www.r-project.org).
drc package (version 3.0-1, https://cran.r-project.org/web/packages/drc/index.html).
Molecular Operating Environment (MOE), version 2016.08 (CCG Inc, Montreal, Canada) is commercially available (https://www.chemcomp.com/index.htm).
Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.
Study population and data
The first autochthonous case of COVID-19 in Italy was microbiologically diagnosed in the Lombardy Region on February 20, 2020. At the time, intensive testing, isolation of confirmed cases, and quarantine of case contacts were in place in the entire country [19]. Following the rapid increase of SARS-CoV-2 laboratory-confirmed infections, local and national health authorities imposed increasingly strict physical distancing measures, with a quarantine imposed on all individuals residing in 10 municipalities in the Lombardy Region and one in the Veneto Region on February 23, 2020 [2]. A regional lockdown in Lombardy and a national lockdown were imposed respectively on March 8 and March 10, 2020 [20]. Applied measures included the suspension of teaching activities and restrictions on individuals movements across different regions and culminated in the closure of all non-essential retail and shops and a stay at home order applied throughout the entire Italian territory.
Since January 2020, data on PCR-confirmed SARS-CoV-2 infections have been collected in the 19 Italian Regions and the two Autonomous Provinces and reported to National Integrated Surveillance System [19]. A central database of all infections confirmed in Italy was formally established the February 27,2020 and managed by the Italian National Institute of Health. For any confirmed infection, information was collected on the date of diagnosis, municipality of residence, and clinical severity; the date of symptom onset was also recorded for symptomatic cases. The initial line list of laboratory-confirmed cases was retrospectively consolidated, through information gathered with standardized interviews to ascertained infections and PCR testing of their close contacts.
Our analysis is based on the consolidated dataset of all ascertained cases with symptom onset between January 26 and March 7, 2020, corresponding to the 6 epidemiological weeks preceding the first regional lockdown imposed in Lombardy on March 8, 2020. We focus our analysis on this period to reduce the potential biases led by the introduction of strict restrictions to the population. Data used to perform the presented analysis were extracted in February 2021.
By adapting a method previously developed to estimate sources and sinks of malaria parasites in Madagascar [21], we investigate the likely source locations of infection of each symptomatic case retrospectively identified by public health authorities in Italy with symptom onset in the 6weeks between January 26 and March 7. For each case residing in municipality i with symptom onset on day t, we describe the risk that the case was infected T days previously because of contacts with people residing in the municipality j as:
$${{text{L}}}_{{text{i}},{text{j}}}left(t,Tright)={C}_{i,j}mathcal{G}left(Tright)frac{{Y}_{j}left(t-Tright)}{{N}_{j}}$$
where ({C}_{i,j}) represents the number of individuals daily traveling from (i) to (j), (mathcal{G}left(Tright)) is the probability distribution of the SARS-CoV-2 generation time (assumed to be equal to the distribution of the serial interval estimated in [2]), ({Y}_{j}left(t-Tright)) is the number of infected individuals residing in j who developed symptoms at time (t-T), and ({N}_{j}) is the total number of individuals residing in j.
The amount of travels across the different municipalities of Italy (({C}_{i,j})) is modeled by means of a radiation model [22], which is based on data on the size of the population residing in each municipality, the distance between their centroids, and the proportion of daily commuters recorded by Italian National Institute of Statistics in 2019 (Figure S1) [23].
We estimate the probability that a case residing in municipality i with symptom onset on dayt, was infected by a case residing in municipality j as:
$${{text{p}}}_{{text{i}},{text{j}}}left(tright)=frac{{sum }_{T=1}^{infty }{L}_{i,j}left(t,Tright)}{{sum }_{j=1}^{M}{sum }_{T=1}^{infty }{L}_{i,j}left(t,Tright)}$$
where M is the total number of municipalities in Italy in 2020 (namely, 7926).
Similarly, the probability that a case residing in municipality i and developing symptoms during the period (uppi) was infected by a case from municipality j is computed as:
$${{text{p}}}_{{text{i}},{text{j}}}left(uppi right)=frac{{sum }_{tinuppi }{p}_{i,j}left(tright){{text{Y}}}_{{text{i}}}left({text{t}}right)}{{sum }_{tinuppi }{{text{Y}}}_{{text{i}}}left({text{t}}right)}.$$
Finally, we estimate the probability that individuals developing symptoms during the period (uppi) were infected within a distance D from their residence as:
$${p}_{D}left(uppi right)=frac{{sum }_{i}{sum }_{j:{d}_{i,j} where possible sources j run over all municipalities with a distance from i (namely, ({d}_{i,j})) lower than D. The contribution of each municipality j in the number of infection episodes occurring at time (t) in all the other municipalities of Italy is quantified as ({sum }_{ine j}{p}_{i,j}left(tright){Y}_{i}left(tright)/{sum }_{{text{j}}=1}^{{text{M}}}{sum }_{ine j}{p}_{i,j}left(tright){Y}_{i}left(tright)). We estimate the number of epidemic foci occurred in Italy up to March 7, 2020. To this aim, we identify for each week (w) those municipalities characterized by a non-negligible number of ascertained symptomatic cases (({sum }_{tin w}{{text{Y}}}_{{text{i}}}left({text{t}}right)>10)) and incidence (({sum }_{tin w}{{text{Y}}}_{{text{i}}}left({text{t}}right)/{{text{N}}}_{{text{i}}}>0.001)), and by the majority of transmission episodes estimated as occurring between individuals residing in the municipality (({p}_{i,i}left(wright)>0.5)). In the probabilistic approach, we assume that the mobility fluxes among municipalities can be modeled through a radiation model. Although the radiation model has been effectively employed to describe the spatial spread of infectious diseases in high-income countries [22, 24], following the approach already used in Gatto et al. [13], we show that the flows of individuals obtained through the radiation model are in good agreement with mobility data across the 12 provinces of the Lombardy region, based on 2016 census data adjusted with the population projections for 2020 [25] (see Figures S2 and S3). Furthermore, we use a dynamic metapopulation transmission model based on a susceptible-infectious-recovered (SIR) schema to test if the radiation model is reasonably able to capture the observed spatial spread of COVID-19 in Italy and the overall temporal increase of COVID-19 patients across regions from February 1 up to March 7, 2020. To compare model simulations with data, we assume that 3% of all infections were ascertained by public health authorities, either in real time or retrospectively through contact tracing operations and epidemiological investigations [26]. In the dynamic model, infected individuals residing in the municipality j are assumed to exert a time dependent force of infection ({lambda }_{i,j}left(tright)) on individuals residing in municipality (i) defined as ({lambda }_{i,j}left(tright)=beta {C}_{i,j}{I}_{j}left(tright)/{{text{N}}}_{j}), where (beta) is the SARS-CoV-2 transmission rate, ({C}_{i,j}) is the amount of individuals daily traveling from (i) to (j) as obtained by using the radiation model, ({I}_{j}(t)) and ({N}_{j}) are, respectively, the overall number of infectious individuals and the population size in municipality (j). Based on the simulation results, we compute the probability that an individual residing in municipality i and infected at day t was infected by a case from municipality j as ({{text{p}}}_{{text{i}},{text{j}}}left(tright)={uplambda }_{i,j}left(tright)/{sum }_{j=1}^{M}{lambda }_{i,j}left(tright)), with M representing the overall number of municipalities of Italy in 2020; ({{text{p}}}_{{text{i}},{text{j}}}left(uppi right)) is computed as in the probabilistic approach, but using the overall number of infections estimated by the dynamic model instead of the symptomatic cases ascertained in the data. Given the large uncertainty surrounding the ability of the public health system in identifying (either in real time or retrospectively) cases that occurred in the early pandemic phase, we repeat the analysis and estimate the risk of SARS-CoV-2 transmission at different distances by assuming also a 10% ascertainment ratio. The SIR model is parametrized to reproduce at the national level an epidemic curve associated with an exponential growth rate (r) corresponding to a basic reproduction number ({R}_{0}=2.8), representing the transmissibility potential of SARS-CoV-2, estimated for the Lombardy Region between February 12 and March 9, 2020 [2, 20]. The average duration of the infectivity period is assumed to be equal to the mean serial interval (G) [2]. The ({R}_{0}) associated with the simulated epidemic curve is computed by considering the growth rate (r) associated with the number of new cases simulated by the model at the national level and using the standard equation ({R}_{0}=1+rG). The model is initialized on February 1 (at ({t}_{0}=0)) with a number of infected individuals ({I}_{0}) that is consistent with the ascertainment ratio in Italy during the early pandemic phase (3% by March 8, 2020 [26]; 10% was considered for sensitivity analysis), and the consolidated number of ascertained cases developing symptoms before strict restrictions were imposed on the general population (namely, 517 individuals on February 23, 2020). The dynamic model considered in this work is deterministic. However, initial infections are distributed over the national territory by random sampling from a multinomial distribution with probabilities proportional to the cumulative number of symptomatic cases retrospectively identified in Italy across the different municipalities as of February 15, 2020. To explore the uncertainty characterizing the initial spatial dispersal of SARS-CoV-2 infections, model simulations are repeated 100 times by randomly sampling the municipalities of residence of infectious individuals at the start of simulations. Results are presented both in terms of model mean estimates and 95% Prediction Intervals (PI) associated with different initial conditions, and in terms of model estimates associated with initial conditions minimizing the root mean square error between the time series of cases retrospectively identified at the regional level and those estimated by simulating the dynamic SIR model.
Key differences in how the nasal cells of young and elderly people respond to the SARS-CoV-2 virus may explain why children typically experience milder symptoms of COVID-19, say researchers.
They say that understanding how the type of nasal cells people have change with age and how this affects the ability to combat SARS-CoV-2 infection could be crucial in developing effective antiviral treatments, especially for older people who are at higher risk of severeCOVID-19.
A study published inNature Microbiologyfocused on the early effects of SARS-CoV-2 infection on the cells first targeted by the viruses, called human nasal epithelial cells (NECs).
The study was funded by UK Research and Innovation, the National Institute for Health and Care Research, Great Ormond Street Hospital Biomedical Research Centre, Wellcome, and the Chan Zuckerberg Foundation.
By carrying out SARS-CoV-2infections of epithelial cellsin vitroand studying the responses with single cell sequencing, we get a much more detailed understanding of the viral infection kinetics and see big differences in the innate immune response between cell types, said co-senior author, Dr Kerstin Meyer from the Wellcome Sanger Institute.
Children infected with SARS-CoV-2 rarely progress to respiratory failure, but the risk of mortality in infected people over the age of 85 remains high, despite vaccination and improved treatment options.
Researchers say their findings underscore the importance of considering age as a critical factor in both research and treatment of infectious diseases.
Participants were recruited for the study from five large hospital sites in London.Cells were donated by healthy participants including children under the age of 11, adults ages 30 to 50, and, for the first time, adults over 70.
The cells were then cultured using specialized techniques, allowing them to regrow into the different types of cells originally found in the nose. Using single-cell RNA sequencing to identify the unique genetic networks and functions of thousands of individual cells, researchers identified 24 distinct epithelial cell types. Cultures from each age group were then either mock-infected or infected with SARS-CoV-2.
After three days the NECs of children responded quickly to SARS-CoV-2 by increasing interferon, the bodys first line of antiviral defense, restricting viral replication. However, this early antiviral effect became less pronounced with age.
In the study, NECs from elderly individuals not only produced more infectious virus particles but also experienced increased cell shedding and damage.
Researchers say the strong antiviral response in the NECs of children could explain why younger people typically experience milder symptoms. In contrast, the increased damage and higher viral replication found in NECs from elderly individuals could be linked to the greater severity of the disease observed in older adults.
These findings provide insights into age-relatedCOVID-19pathogenesis and demonstrate how impaired repair processes enhance SARS-CoV-2 infection in older individuals, the authors wrote.
Co-senior author Dr. Marko Nikolic, of University College Londons Division of Medicine, said, It is fascinating that when we take away immune cells from nasal samples, and are only left with nasal epithelial cells grown in a dish, we are still able to identify age-specific differences in our bodys response to the SARS-CoV-2 between the young and elderly to explain why children are generally protected from severeCOVID-19.
Researchers now hope to investigate the long-term implications of the cellular changes and test therapeutic interventions using their cell culture model.
They suggest that future research should consider how aging impacts the bodys response to other viral infections.
In a recent study published in the journal JAMA Network Open, researchers aimed to quantitatively assess the long-term smell and taste-associated outcomes of coronavirus disease 2019 (COVID-19) using validated psychophysical tests to circumvent the inaccuracies that could occur with self-reported taste dysfunction.
Study:Long-Term Taste and Smell Outcomes After COVID-19. Image Credit:megaflopp/Shutterstock.com
The COVID-19 pandemic has been successfully contained after the development of various vaccines and worldwide efforts to vaccinate large parts of the population. However, post-COVID-19 condition (PCC), commonly referred to as long coronavirus disease (long COVID), is an ongoing public health concern.
Statistics show that between 10 and 35 million adults in the United States (U.S.) are affected by PCC. Long COVID or PCC consists of a wide range of symptoms, with the common ones being fatigue, post-exertional malaise, and dyspnea.
However, the condition affects various organ systems, with severe cases involving cardiovascular and renal complications and neurological symptoms such as brain fog or inability to concentrate.
A common symptom of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections was the loss of taste and smell.
However, there is a lack of studies that have assessed the chemosensory dysfunction in PCC patients over follow-up intervals of a year or more after the SARS-CoV-2 infection.
Furthermore, since self-reports of loss of taste or smell can be inaccurate, there is a need to assess the occurrence of taste dysfunction in PCC quantitatively.
In the present study, the researchers aimed to assess the long-term SARS-CoV-2-associated outcomes on the sense of taste and smell using nationwide data and a validated quantitative taste test. They also compared the results from this taste test with a reliable and commonly used olfactory test.
The study evaluated the prevalence of smell and taste dysfunction a year after the initial SARS-CoV-2 infection. It examined whether COVID-19 resulted in changes in the perceptions of sour, sweet, salty, bitter, and umami flavors in individuals experiencing smell and taste dysfunction a year after the initial SARS-CoV-2 infection.
They also assessed whether these findings were correlated with sex or age and investigated the association between the test scores and the SARS-CoV-2 variant responsible for the initial infection.
The participants largely represented the relative populations of 48 U.S. states and were recruited without specifying COVID-19 as a criterion in the recruitment advertisements. Healthy individuals were included in the study if they had no other disorder, such as a neurodegenerative disease or head trauma condition that could impact the ability to smell and taste.
Medical histories of the participants consisting of COVID-19 diagnosis dates and methods used to diagnose, such as antibody and polymerase chain reaction (PCR) tests, were obtained.
The taste function test used was the Waterless Empirical Taste Test, where 53 non-liquid test items were provided on taste strips, with no rinsing required between trials.
These items belonged to five categories, namely, sweet, salty, bitter, sour, and brothy. The 40-item smell function test consisted of four booklets containing scratch and sniff cards for various odorants, with four alternatives provided for each odorant to be identified through a multiple-choice question.
The results showed that the chemosensory dysfunction associated with taste did not last a year after the initial SARS-CoV-2 infection. However, a third of the individuals exposed to COVID-19 complained of loss of smell for over a year after the initial exposure to the virus, providing a possible explanation for the loss of taste reported by many PCC patients.
Furthermore, the effect size of the patients reporting a continued loss of smell was insignificant, indicating a progressive improvement in all chemosensory functions.
The study also found that the degree of loss of smell was different for each variant of SARS-CoV-2, and the relative loss of smell remained consistent for close to a year after the initial infection.
Similar to the results from other studies, the researchers reported that the Omicron variant caused loss of smell less frequently than the different variants. The Alpha variant was reported to have the highest prevalence of loss of smell, followed by the original strain and the Delta variant.
While declines in test measures associated with age were observed, age was independent of the chemosensory outcomes of SARS-CoV-2 infections.
For the overall taste test, as well as for categories such as citric acid, sucrose, sodium chloride, and caffeine, the test scores were higher for females than for males.
Overall, the findings reported that taste-associated chemosensory dysfunction does not persist for more than a year after a SARS-CoV-2 infection. Still, approximately a third of the COVID-19 patients report continued loss of smell for over a year after recovering from the initial infection.
The earlier SARS-CoV-2 variants, including the original strain and the Alpha and Delta variants, had a higher prevalence of loss of smell and taste symptoms than the Omicron variant.
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The idea that SARS-CoV-2, the virus behind COVID-19, may be able to persist in the body long after the initial symptoms have faded has captivated scientists, especially those researching long COVID. A recent study has added another piece to this puzzle by demonstrating the persistence of viral proteins in blood plasma samples for up to 14 months after the initial infection.
The research team obtained samples of frozen plasma from 171 adults who had been recruited for a study back in 2020. The vast majority were people whod been infected early in the pandemic before vaccines against COVID-19 were a thing. Their samples were compared with plasma from 250 people collected pre-2020, in the halcyon days before COVID-19 entered our lives (remember those?).
The detection platform was set up to look for signals from three SARS-CoV-2 antigens: the S1 surface protein, the nucleocapsid protein, and the spike protein.
In total, 660 specimens from the pandemic group were tested, covering timepoints of 3-6 months, 6-10 months, and 10-14 months after their original COVID-19 infections. Of the individuals within the group, 25 percent had one or more detectable antigens in at least one of their samples. The most frequently detected was the spike protein, followed by S1 and nucleocapsid, which had similar frequencies to each other.
Patients who had been hospitalized when they originally had COVID-19 were almost twice as likely to have antigens present. Among those who did not receive hospital treatment, the people who self-reported worse health were also more likely to have positive antigen detection, suggesting a correlation with the severity of the acute phase of COVID.
Linking their results with those from another study, which found replication-competent virus particles i.e. virus that could still grow and infect cells in the blood of a woman who had recently died from COVID, the authors write that their findings suggest that SARS-CoV-2 might seed distal sites through the bloodstream and establish protected reservoirs in some sites.
Alternatively, they suggest, it could be that those with more severe infections got a heftier dose of virus in the first place, meaning there was more of it around to potentially evade the immune system for longer.
"The thing that I find so compelling about the data in this study is that there is a pretty striking relationship between how sick people were during their acute COVID infection and how likely they were to have evidence of antigen persistence," first author Dr Michael Peluso told Psychology Today.
"To a clinician like me, that is very convincing, because intuitively, it makes sense that people who perhaps have a higher burden of virus upfront would be more likely to have a virus that sticks around."
In an appendix to their work, the authors detail several limitations of the study. Since the majority of the patients were infected before we had vaccines and antiviral treatmentsfor the virus, its unclear whether these same results would be seen in people who caught COVID later on. Its also possible some of the participants got reinfected with COVID without knowing, meaning that some of the antigen signals could be from later infections.
However, the question of whether persistent SARS-CoV-2 may be related to either long COVID or complications later down the line remains an important one.
[O]ur data provide strong evidence that SARS-CoV-2, in some form or location, persists for up to 14 months following acute SARS-CoV-2 infection, the authors conclude. This persistence is influenced by the events of acute infection. These findings motivate an urgent research agenda regarding the clinical manifestations of SARS-CoV-2 persistence, specifically whether it is causally related to either post-acute chronic symptoms [...] or discrete incident complications.
The study is published in The Lancet Infectious Diseases.
NEW DELHI: UK Pharmaceutical company AstraZeneca has admitted that its Covid-19 vaccine has the potential to cause to a rare side effect called Thrombosis with Thrombocytopenia Syndrome (TTS), as reported by The Telegraph. AstraZeneca which collaborated with the University of Oxford to create the vaccine is currently dealing with a lawsuit that claims their vaccine has caused deaths and severe harm to those who received it.
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A University of California at Los Angeles (UCLA)study late last week in JAMA Network Open finds that, despite less access to healthcare, undocumented Latino workers who visited the emergency department (ED) received COVID-19 vaccines at the same rate as US citizens.
The researchers interviewed a sample of adult non-Latino patients, legal Latino residents or citizens, and undocumented Latino patients at two California healthcare centers from September 2021 to March 2022.
The median age of the 306 participants was 51 years, 48% were women, 68% were Latino, 14% were White, 11% were Black, and 7% were of other race. Of undocumented Latinos, 25% were uninsured, and 30% usually visited the ED for healthcare.
Among all participants, 87% said they had received one or more doses of COVID-19 vaccine, and 13% reported declining the vaccine. Concern about potential adverse effects of the vaccine was the most common reason (37%) for not getting vaccinated.
Undocumented Latino workers were much more likely to report a previous COVID-19 infection than non-Latinos and legal Latino residents.
Relative to undocumented Latinos, non-Latino patients were much less likely to believe that undocumented workers could receive the COVID-19 vaccine in the United States (odds ratio [OR], 0.09). Thirteen percent of interviewees said they knew undocumented people who didn't get vaccinated because they worried about deportation. Of those who had declined the vaccine, 22% said they were interested in receiving a dose in the ED.
Undocumented Latino workers were much more likely to report a previous COVID-19 infection than non-Latinos (OR, 3.42) and legal Latino residents (OR, 2.73).
"We would have expected Latinx patients to have lower rates of vaccination, considering higher rates of infection, hospitalizations, and death," lead author Jesus Torres, MD, MPH, said in a UCLAnews release. Torres noted that EDs are one of the main healthcare access points for undocumented workers, who make up about 3% of the US population but are not often included in research.
From a public health perspective, he added, it's important to identify disadvantaged groups for research, policy work, resource allocation, and targeted vaccine campaigns.
This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:
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There was a marked increase in negativity about vaccines on Twitter after COVID-19 vaccines became available, according to a presentation at the ESCMID Global Congress (formerly ECCMID) in Barcelona, Spain (2730 April).
The analysis also found that spikes in the number of negative tweets coincided with announcements from governments and health care authorities about vaccination.
It's time to take a new approach to addressing negative messaging about vaccines, including avoiding the use of the term "anti-vaxxers," say the researchers.
"Vaccines are one of humanity's greatest achievements," explains lead researcher Dr. Guillermo Rodriguez-Nava, of Stanford University School of Medicine, Stanford, U.S..
"They have the potential to eradicate dangerous diseases such as smallpox, prevent deaths from diseases with 100% mortality rates, like rabies, and prevent cancers such as those caused by HPV.
"Moreover, vaccines can prevent complications from diseases for which we have limited treatment options, such as influenza and COVID-19, but there has been growing opposition to their use in recent years.
"The damage caused by negative voices is already apparent, with clusters of measles re-emerging in countries where it was previously considered eradicated.
"This situation harms children who cannot make decisions for themselves regarding vaccines, as well as immunocompromised patients who are unable to get vaccinated."
Dr. Rodriguez-Nava and colleagues analyzed the impact of the introduction of COVID-19 vaccines on the sentiment of vaccine-related posts on Twitter.
Open-source software (the Snscrape library in Python) was used to download tweets with the hashtag "vaccine" from 1 January 2018 to 31 December 2022.
Cutting-edge AI methods were then used to perform sentiment analysis and classify as the tweets having either positive or negative sentiment. Finally, modeling techniques were used to create a "counterfactual scenario." This showed what the pattern of tweets would have looked like if COVID vaccines hadn't been introduced in December 2020.
A total of 567,915 tweets were extracted and analyzed. Of these, 458,045 classified as negative and 109,870 as positive by the machine learning algorithm. Tweets that were negative in sentiment were predominant both before and after vaccines became available
Negative tweets included, "The EU Commission should immediately terminate contracts for new doses of fake #vaccines against #COVID19 and demand the return of the 2.5 billion euros paid so far. Everyone who lied that #vaccines prevent the spread of the virus must be held accountable."
Positive tweets included one that marked a baby receiving some of its childhood vaccinations and read: "Two month shots! #vaccines are always a reason to celebrate in our house. #VaccinesWork."
After COVID vaccines were introduced, there was a marked in increase the number of tweets about vaccines, with 10,201 more vaccine-related tweets per month, on average, than would be expected if vaccination hadn't started.
There was also a marked increase in negativity. There were 310,508 tweets (approx. 12,420 a month on average) with negative sentiment after December 11, 2020. This is 27% more than the 244,635 (9,785 a month) that would be expected if COVID vaccination hadn't started.
The proportion of positive tweets fell from 20.3% to 18.8% after the introduction of COVID vaccines and the percentage of negative tweets rose from 79.6% to 81.1%.
Spikes in negative activity coincided with announcements about vaccination. For example, the highest number of negative tweets was in April 2021, the month the White House announced that all people aged 16 and older would be eligible for the COVID-19 vaccine.
The lowest number of negative tweets after the introduction of COVID-19 vaccines was in April 2022, the month Elon Musk acquired Twitter. While it isn't known why this was, it may have been part of a seasonal pattern (the number of negative tweets tended to be highest in the winter). It's also possible that Twitter users were focusing on the changes to the platform that came with the new ownership, says Dr. Rodriguez-Nava.
The researchers conclude, "Negative sentiments toward vaccines were already prominent on social media prior to the arrival of COVID-19 vaccines. The introduction of these vaccines significantly increased the negative sentiments on X, formerly Twitter, regarding vaccines."
Dr. Rodriguez-Nava says, "Social media has the power to exponentially amplify health messages, both beneficial and harmful, and is an arena in which political figures, actors, singers, personalities and other 'influencers' outnumber health care voices.
"Unfortunately, in some countries, negative sentiments toward vaccines are not only health-related but also religious and political.
"This is a complex issue, with no easy solution, but we do need to change our approach because it is clearly not working.
"This begins with avoiding derogatory terms such as 'anti-vaxxers,' and perhaps even 'misinformation,' and approaching these individuals in a more respectful and understanding manner.
"Additionally, health care leaders should dedicate more effort to collaborating with social media influencers, religious leaders and lawmakers, who may be more trusted by their communities than health care professionals and more effective in amplifying a positive message.
"Social media companies also have a role to play. However, this is also a complex issue because each company may have different values and attitudes to free speech and countries may have different laws for free speech."
Provided by European Society of Clinical Microbiology and Infectious Diseases
Black Americans living in Tuskegee, Alabama, close to the location of the Tuskegee Syphilis Study, were slower to get their COVID-19 vaccines compared to white neighbors, according to a study conducted by University of Georgia researchers.
The study explored the long-term effects of the Tuskegee Study on on the behavior of Black populations regarding getting the COVID-19 vaccine.
The Tuskegee Syphilis Study began in 1932 in Macon County, Alabama. Researchers aimed to study the full progression of syphilis, which did not have a cure at the beginning of the study. About 600 Black men, 399 with latent syphilis and 201 healthy men, were recruited for the study, according to the History Channel. The men believed they were receiving free treatment for bad blood, a term used in the area referring to a variety of illnesses.
In 1947, penicillin became the recommended treatment for syphilis. However, researchers chose not to treat the men and gave them placebos like aspirin and mineral supplements. Researchers watched as men died, went blind or insane. The story was made public by an AP reporter in 1972, sparking public outrage and causing the study to be shut down.
Black Americans experienced more exposure, illness and deaths during the COVID-19 pandemic in comparison with other groups. However, Black Americans, especially those who live within 750 miles of Tuskegee, were less likely to get vaccinated in comparison with white individuals residing in the same counties, the UGA study found.
To confirm living close to Tuskegee was uniquely contributing to the vaccine resistance, the UGA researchers ran a number of analyses that acted as statistical placebo effects.
The research suggests Black populations who have memory of the Tuskegee Syphilis Study lack trust in government entities, like the Centers for Disease Control and Prevention and the National Institutes of Health, which were central to vaccine initiative and promotion.
Xiaolong Chris Hou, a graduate student in the UGA College of Public Health, added its important for policymakers and public health leaders who are seeking to close gaps in health disparities to take historical contexts into consideration.
Zhuo Adam Chen, an associate professor of health policy and management in the College of Public Health, said interventions to the community must be tailored and work through individuals, like faith and civic leaders, to build trust and buy-in.
