Abstract
Background
Post-coronovirus disease (COVID) symptoms can persist several months after severe acute respiratory syndrome coronavirus 2 infection. Little is known, however, about the ...prevalence of post-COVID condition following infections from Omicron variants and how this varies according to vaccination status. This study evaluates the prevalence of symptoms and functional impairment 12 weeks after an infection by Omicron variants (BA.1 and BA.2) compared with negative controls tested during the same period.
Methods
Outpatient individuals who tested positive or negative for COVID-19 infection between December 2021 and February 2022 at the Geneva University Hospitals were followed 12 weeks after their test date.
Results
Overall, 11.7% of Omicron cases had symptoms 12 weeks after the infection compared with 10.4% of individuals who tested negative during the same period (P < .001), and symptoms were much less common in vaccinated versus nonvaccinated individuals with Omicron infection (9.7% vs 18.1%; P < .001). There were no significant differences in functional impairment at 12 weeks between Omicron cases and negative controls, even after adjusting for multiple potential confounders.
Conclusions
The differential prevalence of post-COVID symptoms and functional impairment attributed to Omicron BA.1 and BA.2 infection is low when compared with negative controls. Vaccination is associated with lower prevalence of post-COVID symptoms.
In this prospective survey-based study, the prevalence of post-COVID symptoms at 12 weeks was only slightly higher in outpatients with Omicron infection versus PCR-negative controls (11.7% vs 10.4%). Vaccination was associated with a lower prevalence of post-COVID symptoms.
•We sequenced 11.8 % of all SARS-CoV-2 cases in Switzerland from Dec 2020 to Mar 2021.•We estimate a transmission fitness advantage of 43–52 % for the B.1.1.7 variant.•We estimate the reproductive ...number of B.1.1.7.
In December 2020, the United Kingdom (UK) reported a SARS-CoV-2 Variant of Concern (VoC) which is now named B.1.1.7. Based on initial data from the UK and later data from other countries, this variant was estimated to have a transmission fitness advantage of around 40–80 % (Volz et al., 2021; Leung et al., 2021; Davies et al., 2021).
This study aims to estimate the transmission fitness advantage and the effective reproductive number of B.1.1.7 through time based on data from Switzerland.
We generated whole genome sequences from 11.8 % of all confirmed SARS-CoV-2 cases in Switzerland between 14 December 2020 and 11 March 2021. Based on these data, we determine the daily frequency of the B.1.1.7 variant and quantify the variant’s transmission fitness advantage on a national and a regional scale.
We estimate B.1.1.7 had a transmission fitness advantage of 43–52 % compared to the other variants circulating in Switzerland during the study period. Further, we estimate B.1.1.7 had a reproductive number above 1 from 01 January 2021 until the end of the study period, compared to below 1 for the other variants. Specifically, we estimate the reproductive number for B.1.1.7 was 1.24 1.07–1.41 from 01 January until 17 January 2021 and 1.18 1.06–1.30 from 18 January until 01 March 2021 based on the whole genome sequencing data. From 10 March to 16 March 2021, once B.1.1.7 was dominant, we estimate the reproductive number was 1.14 1.00–1.26 based on all confirmed cases. For reference, Switzerland applied more non-pharmaceutical interventions to combat SARS-CoV-2 on 18 January 2021 and lifted some measures again on 01 March 2021.
The observed increase in B.1.1.7 frequency in Switzerland during the study period is as expected based on observations in the UK. In absolute numbers, B.1.1.7 increased exponentially with an estimated doubling time of around 2–3.5 weeks. To monitor the ongoing spread of B.1.1.7, our plots are available online.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In vitro static digestion models, with constant pH and volume, are commonly used to evaluate supplementary enzyme activity. The research objective was to apply a dynamic stomach model, Dynamic ...Gastric Simulating Model (DGSM), to evaluate the efficacy of supplementary enzymes in assisting food digestion. DGSM incorporates compressive forces to disintegrate food, mimics continuous gastric emptying, and simulates gastric secretion that generated pH profiles similar to human stomach. Static and dynamic models were used to evaluate supplementary proteases (SPs) efficacy. Additionally, acid-stable supplementary fungal enzyme blends were tested on food matrix containing oil, starch, and tuna using DGSM. A prescribed pancreatic enzyme drug, CREON, was used as a comparison. In static model, porcine pepsin (PP) had higher proteolytic activities generating significantly higher average Tyrosine Equivalent (TE) concentrations at pH 1.3 and 3. DS Proteases generated comparable TE concentrations to PP at pH 5. Under DGSM, addition of DS Proteases and Peptidases generated significantly greater TE and Primary Amino Nitrogen (PAN) than PP alone. Main findings show enhanced food digestion upon adding supplementary enzymes and blends. Furthermore, DGSM showed great potential as being an alternative tool used to study enzyme performances inside the human stomach as affected by dynamic physiological conditions.
•Supplementary Digestive Enzymes aid food breakdown in gastric digestion simulation.•Dynamic Gastric Simulation Model is an alternative method to study food digestion.•Product formation trends differ between static and dynamic gastric simulation.•Digesta volume, digesta pH, and enzymes types influence enzymes efficacies in human.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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