Genetic and Antigenic Analysis of the First A/New Caledonia/20/99-like H1N1 Influenza Isolates Reported in the Americas

From February through May of 1999, 13 cases of Influenza A virus (FLUAV), type H1N1 were reported at a Department of Defense influenza surveillance sentinel site in Lima, Peru. Genetic and antigenic analysis by hemagglutination inhibition and direct nucleotide sequencing of the HA1 region of the hemagglutinin gene were performed on two isolates, A/Peru/1641/99 and A/Peru/1798/99. Both isolates were distinct from the Bayern/7/95-like viruses circulating in the Americas and closely related to a Beijing/262/95-like variant, A/New Caledonia/20/99. With the exception of travel-related cases, the detection of these isolates represents the first appearance of New Caledonia/20/99-like viruses in the Americas. Since the characterization of these Peru isolates, a number of New Caledonia/20/99-like viruses have been reported worldwide. For the 2000/01 and 2001/02 influenza seasons, the World Health Organization (WHO) has recommended the inclusion of A/New Caledonia/20/99 as the H1N1 vaccine component for both the southern and northern hemispheres

Design Thinking for Better Community in the City of Bridgeport

Founded in 1821, the city of Bridgeport is not only the largest city in Connecticut, but one of the most diverse communities as well, with over 20 countries represented in a city of over 150,000 citizens. The goal for this project was to focus on revitalization, waste management and bringing the waterfront scene back to life. As Design Management student, we explored this problem through the lens of design thinking. Using the design thinking process, while considering profitability, sustainability and social responsibility, we developed a series of proposals which activate the existing key resources in order to bring more attention which will benefit the city

Overexpression of adenosine A2A receptors in rats: effects on depression, locomotion, and anxiety

Adenosine A2A receptors (A2AR) are a sub-type of receptors enriched in basal ganglia, activated by the neuromodulator adenosine, which interact with dopamine D2 receptors. Although this reciprocal antagonistic interaction is well-established in motor function, the outcome in dopamine-related behaviors remains uncertain, in particular in depression and anxiety. We have demonstrated an upsurge of A2AR associated to aging and chronic stress. Furthermore, Alzheimer's disease patients present A2AR accumulation in cortical areas together with depressive signs. We now tested the impact of overexpressing A2AR in forebrain neurons on dopamine-related behavior, namely depression. Adult male rats overexpressing human A2AR under the control of CaMKII promoter [Tg(CaMKII-hA2AR)] and aged-matched wild-types (WT) of the same strain (Sprague-Dawley) were studied. The forced swimming test (FST), sucrose preference test (SPT), and the open-field test (OFT) were performed to evaluate behavioral despair, anhedonia, locomotion, and anxiety. Tg(CaMKII-hA2AR) animals spent more time floating and less time swimming in the FST and presented a decreased sucrose preference at 48 h in the SPT. They also covered higher distances in the OFT and spent more time in the central zone than the WT. The results indicate that Tg(CaMKII-hA2AR) rats exhibit depressive-like behavior, hyperlocomotion, and altered exploratory behavior. This A2AR overexpression may explain the depressive signs found in aging, chronic stress, and Alzheimer's disease

Molecular epidemiology of HIV type 1 infection in Portugal: high prevalence of non-B subtypes

In this study, we have investigated the diversity of current HIV-1 strains circulating in the metropolitan area of Lisbon, Portugal. A total of 217 HIV-1-positive blood samples, collected between October 1998 and December 2000, was genetically characterized in the gp120 C2V3C3 region (n = 205) or part of the gp41 N-terminal segment (n = 12) by heteroduplex mobility assay (HMA) and/or DNA sequencing. The HMA subtyping efficiency (number of samples unambiguously subtyped by HMA divided by the total number of samples subtyped) was 65.9% (143 of 217), with indeterminate migration patterns of subtype A and G strains contributing significantly to this value. On the overall, subtype B was the most prevalent (50.2%), followed by subtypes G (21.7%), A (17.5%), and F (5.5%), whereas subtypes C, D, H, and J accounted altogether for 5.1% of the infections. Non-B subtypes were responsible for 77.4 and 33.1% of the infections among African immigrants and Portuguese subjects, respectively. Angolan individuals (n = 25) were the only ones infected with all the HIV-1 subtypes documented, probably reflecting a high degree of viral genetic diversification in their country of origin. Phylogenetic analysis showed a predominance of IbNG-like viruses among subtype A sequences and two new major subclusters within subtype G (G(P) and G(P)'). The majority of the Portuguese G sequences described formed a well-defined subcluster (G(P)), supported by bootstrap values >90%, phylogenetically distant from clade G sequences in databases. gag (p24/p7) sequence analysis of these variants confirmed the maintenance of the subtype G subclusters. The multiple subclustering observed for the major clades A, B, D, and G, as well as the variety of subtypes found, indicate a high diversity of HIV-1 variants circulating in Portugal and suggest a need for continuous epidemiologic surveillance

Influenza A (H3N2) Outbreak, Nepal

Worldwide emergence of variant viruses has prompted a change in the 2005–2006 H3N2 influenza A vaccine strain

Identifying Influenza Viruses with Resequencing Microarrays

Resequencing microarrays rapidly identify influenza viruses

Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study

BACKGROUND: As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. METHODS: In this prospective, observational study, we did modelling using longitudinal, self-reported data from users of the COVID Symptom Study app in England between March 24, and Sept 29, 2020. Beginning on April 28, in England, the Department of Health and Social Care allocated RT-PCR tests for COVID-19 to app users who logged themselves as healthy at least once in 9 days and then reported any symptom. We calculated incidence of COVID-19 using the invited swab (RT-PCR) tests reported in the app, and we estimated prevalence using a symptom-based method (using logistic regression) and a method based on both symptoms and swab test results. We used incidence rates to estimate the effective reproduction number, R(t), modelling the system as a Poisson process and using Markov Chain Monte-Carlo. We used three datasets to validate our models: the Office for National Statistics (ONS) Community Infection Survey, the Real-time Assessment of Community Transmission (REACT-1) study, and UK Government testing data. We used geographically granular estimates to highlight regions with rapidly increasing case numbers, or hotspots. FINDINGS: From March 24 to Sept 29, 2020, a total of 2 873 726 users living in England signed up to use the app, of whom 2 842 732 (98·9%) provided valid age information and daily assessments. These users provided a total of 120 192 306 daily reports of their symptoms, and recorded the results of 169 682 invited swab tests. On a national level, our estimates of incidence and prevalence showed a similar sensitivity to changes to those reported in the ONS and REACT-1 studies. On Sept 28, 2020, we estimated an incidence of 15 841 (95% CI 14 023-17 885) daily cases, a prevalence of 0·53% (0·45-0·60), and R(t) of 1·17 (1·15-1·19) in England. On a geographically granular level, on Sept 28, 2020, we detected 15 (75%) of the 20 regions with highest incidence according to government test data. INTERPRETATION: Our method could help to detect rapid case increases in regions where government testing provision is lower. Self-reported data from mobile applications can provide an agile resource to inform policy makers during a quickly moving pandemic, serving as a complementary resource to more traditional instruments for disease surveillance. FUNDING: Zoe Global, UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Alzheimer's Society, Chronic Disease Research Foundation

SARS-CoV-2 infection following booster vaccination: Illness and symptom profile in a prospective, observational community-based case-control study

BACKGROUND: Booster COVID-19 vaccines have shown efficacy in clinical trials and effectiveness in real-world data against symptomatic and severe illness. However, some people still become infected with SARS-CoV-2 following a third (booster) vaccination. This study describes the characteristics of SARS-CoV-2 illness following a third vaccination and assesses the risk of progression to symptomatic disease in SARS-CoV-2 infected individuals with time since vaccination. METHODS: This prospective, community-based, case-control study used data from UK-based, adult (≥18 years) users of the COVID Symptom Study mobile application, self-reporting a first positive COVID-19 test between June 1, 2021 and April 1, 2022. To describe the characteristics of SARS-CoV-2 illness following a third vaccination, we selected cases and controls who had received a third and second dose of monovalent vaccination against COVID-19, respectively, and reported a first positive SARS-CoV-2 test at least 7 days after most recent vaccination. Cases and controls were matched (1:1) based on age, sex, BMI, time between first vaccination and infection, and week of testing. We used logistic regression models (adjusted for age, sex, BMI, level of social deprivation and frailty) to analyse associations of disease severity, overall disease duration, and individual symptoms with booster vaccination status. To assess for potential waning of vaccine effectiveness, we compared disease severity, duration, and symptom profiles of individuals testing positive within 3 months of most recent vaccination (reference group) to profiles of individuals infected between 3 and 4, 4–5, and 5–6 months, for both third and second dose. All analyses were stratified by time period, based on the predominant SARS-CoV-2 variant at time of infection (Delta: June 1, 2021–27 Nov, 2021; Omicron: 20 Dec, 2021-Apr 1, 2022). FINDINGS: During the study period, 50,162 (Delta period) and 162,041 (Omicron) participants reported a positive SARS-CoV-2 test. During the Delta period, infection following three vaccination doses was associated with lower odds of long COVID (symptoms≥ 4 weeks) (OR=0.83, CI[0.50–1.36], p < 0.0001), hospitalisation (OR=0.55, CI[0.39–0.75], p < 0.0001) and severe symptoms (OR=0.36, CI[0.27–0.49], p < 0.0001), and higher odds of asymptomatic infection (OR=3.45, CI[2.86–4.16], p < 0.0001), compared to infection following only two vaccination doses. During the Omicron period, infection following three vaccination doses was associated with lower odds of severe symptoms (OR=0.48, CI[0.42–0.55], p < 0.0001). During the Delta period, infected individuals were less likely to report almost all individual symptoms after a third vaccination. During the Omicron period, individuals were less likely to report most symptoms after a third vaccination, except for upper respiratory symptoms e.g. sneezing (OR=1.40, CI[1.18–1.35], p < 0.0001), runny nose (OR=1.26, CI[1.18–1.35], p < 0.0001), sore throat (OR=1.17, CI[1.10–1.25], p < 0.0001), and hoarse voice (OR=1.13, CI[1.06–1.21], p < 0.0001), which were more likely to be reported. There was evidence of reduced vaccine effectiveness during both Delta and Omicron periods in those infected more than 3 months after their most recent vaccination, with increased reporting of severe symptoms, long duration illness, and most individual symptoms. INTERPRETATION: This study suggests that a third dose of monovalent vaccine may reduce symptoms, severity and duration of SARS-CoV-2 infection following vaccination. For Omicron variants, the third vaccination appears to reduce overall symptom burden but may increase upper respiratory symptoms, potentially due to immunological priming. There is evidence of waning vaccine effectiveness against progression to symptomatic and severe disease and long COVID after three months. Our findings support ongoing booster vaccination promotion amongst individuals at high risk from COVID-19, to reduce severe symptoms and duration of illness, and health system burden. Disseminating knowledge on expected symptoms following booster vaccination may encourage vaccine uptake

Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study

Background Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not suitable for the early detection of infection. We aimed to estimate the probability of an individual being infected with SARS-CoV-2 on the basis of early self-reported symptoms to enable timely self-isolation and urgent testing. Methods In this large-scale, prospective, epidemiological surveillance study, we used prospective, observational, longitudinal, self-reported data from participants in the UK on 19 symptoms over 3 days after symptoms onset and COVID-19 PCR test results extracted from the COVID-19 Symptom Study mobile phone app. We divided the study population into a training set (those who reported symptoms between April 29, 2020, and Oct 15, 2020) and a test set (those who reported symptoms between Oct 16, 2020, and Nov 30, 2020), and used three models to analyse the selfreported symptoms: the UK’s National Health Service (NHS) algorithm, logistic regression, and the hierarchical Gaussian process model we designed to account for several important variables (eg, specific COVID-19 symptoms, comorbidities, and clinical information). Model performance to predict COVID-19 positivity was compared in terms of sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the test set. For the hierarchical Gaussian process model, we also evaluated the relevance of symptoms in the early detection of COVID-19 in population subgroups stratified according to occupation, sex, age, and body-mass index. Findings The training set comprised 182 991 participants and the test set comprised 15 049 participants. When trained on 3 days of self-reported symptoms, the hierarchical Gaussian process model had a higher prediction AUC (0·80 [95% CI 0·80–0·81]) than did the logistic regression model (0·74 [0·74–0·75]) and the NHS algorithm (0·67 [0·67–0·67]). AUCs for all models increased with the number of days of self-reported symptoms, but were still high for the hierarchical Gaussian process model at day 1 (0·73 [95% CI 0·73–0·74]) and day 2 (0·79 [0·78–0·79]). At day 3, the hierarchical Gaussian process model also had a significantly higher sensitivity, but a non-statistically lower specificity, than did the two other models. The hierarchical Gaussian process model also identified different sets of relevant features to detect COVID-19 between younger and older subgroups, and between health-care workers and non-health-care workers. When used during different pandemic periods, the model was robust to changes in populations. Interpretation Early detection of SARS-CoV-2 infection is feasible with our model. Such early detection is crucial to contain the spread of COVID-19 and efficiently allocate medical resources. Funding ZOE, the UK Government Department of Health and Social Care, the Wellcome Trust, the UK Engineering and Physical Sciences Research Council, the UK National Institute for Health Research, the UK Medical Research Council, the British Heart Foundation, the Alzheimer’s Society, the Chronic Disease Research Foundation, and the Massachusetts Consortium on Pathogen Readiness

Disentangling post-vaccination symptoms from early COVID-19

Background: Identifying and testing individuals likely to have SARS-CoV-2 is critical for infection control, including post-vaccination. Vaccination is a major public health strategy to reduce SARS-CoV-2 infection globally. Some individuals experience systemic symptoms post-vaccination, which overlap with COVID-19 symptoms. This study compared early post-vaccination symptoms in individuals who subsequently tested positive or negative for SARS-CoV-2, using data from the COVID Symptom Study (CSS) app. Methods: We conducted a prospective observational study in 1,072,313 UK CSS participants who were asymptomatic when vaccinated with Pfizer-BioNTech mRNA vaccine (BNT162b2) or Oxford-AstraZeneca adenovirus-vectored vaccine (ChAdOx1 nCoV-19) between 8 December 2020 and 17 May 2021, who subsequently reported symptoms within seven days (N=362,770) (other than local symptoms at injection site) and were tested for SARS-CoV-2 (N=14,842), aiming to differentiate vaccination side-effects per se from superimposed SARS-CoV-2 infection. The post-vaccination symptoms and SARS-CoV-2 test results were contemporaneously logged by participants. Demographic and clinical information (including comorbidities) were recorded. Symptom profiles in individuals testing positive were compared with a 1:1 matched population testing negative, including using machine learning and multiple models considering UK testing criteria. Findings: Differentiating post-vaccination side-effects alone from early COVID-19 was challenging, with a sensitivity in identification of individuals testing positive of 0.6 at best. Most of these individuals did not have fever, persistent cough, or anosmia/dysosmia, requisite symptoms for accessing UK testing; and many only had systemic symptoms commonly seen post-vaccination in individuals negative for SARS-CoV-2 (headache, myalgia, and fatigue). Interpretation: Post-vaccination symptoms per se cannot be differentiated from COVID-19 with clinical robustness, either using symptom profiles or machine-derived models. Individuals presenting with systemic symptoms post-vaccination should be tested for SARS-CoV-2 or quarantining, to prevent community spread. Funding: UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Chronic Disease Research Foundation, Zoe Limited