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A new report is being released today that unveils 50 female leaders in healthcare business in the UK, which includes Oxford's Professor Sumi Biswas.
Rapid antiretroviral therapy in primary HIV-1 infection enhances immune recovery.
Objective:We present findings from a large cohort of individuals treated during Primary HIV Infection (PHI) and examine the impact of time from HIV-1 acquisition to antiretroviral therapy (ART) initiation on clinical outcomes. We also examine the temporal changes in the demographics of individuals presenting with PHI to inform HIV-1 prevention strategies.Methods:Individuals who fulfilled the criteria of PHI and started ART within three months of confirmed HIV-1 diagnosis were enrolled between 2009 and 2020. Baseline demographics of those diagnosed between 2009-2015 (before preexposure prophylaxis (PrEP) and universal ART availability) and 2015-2020 (post-PrEP and universal ART availability) were compared. We examined the factors associated with immune recovery and time to viral suppression.Results:204 individuals enrolled, 144 from 2009-2015 and 90 from 2015-2020; median follow-up was 33 months. At PHI, the median age was 33 years; 4% were women, 39% were UK-born, and 84% were MSM. The proportion of UK-born individuals was 47% in 2009-2015, compared with 29% in 2015-2020. There was an association between earlier ART initiation after PHI diagnosis and increased immune recovery; each day that ART was delayed was associated with a lower likelihood of achieving a CD4>900 cells/mm3 [HR 0.99 (95%CI 0.98, 0.99), P = 0.02) and CD4/CD8>1.0 (HR 0.98 (95%CI 0.97, 0.99).Conclusion:Early initiation of ART at PHI diagnosis is associated with enhanced immune recovery, providing further evidence to support immediate ART in the context of PHI. Non-UK-born MSM accounts for an increasing proportion of those with primary infection; UK HIV-1 prevention strategies should better target this group.
Supramolecular Cylinders Target Bulge Structures in the 5' UTR of the RNA Genome of SARS-CoV-2 and Inhibit Viral Replication.
The untranslated regions (UTRs) of viral genomes contain a variety of conserved yet dynamic structures crucial for viral replication, providing drug targets for the development of broad spectrum anti-virals. We combine in vitro RNA analysis with molecular dynamics simulations to build the first 3D models of the structure and dynamics of key regions of the 5' UTR of the SARS-CoV-2 genome. Furthermore, we determine the binding of metallo-supramolecular helicates (cylinders) to this RNA structure. These nano-size agents are uniquely able to thread through RNA junctions and we identify their binding to a 3-base bulge and the central cross 4-way junction located in stem loop 5. Finally, we show these RNA-binding cylinders suppress SARS-CoV-2 replication, highlighting their potential as novel anti-viral agents.
Turning high-throughput structural biology into predictive inhibitor design.
A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high-throughput crystallography data into predictive models for ligand design. Here, we designed a simple machine learning approach that predicts protein-ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein-ligand complexes and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high-throughput crystallography campaign against the SARS-CoV-2 main protease (MPro), obtaining parallel measurements of over 200 protein-ligand complexes and their binding activities. This allows us to design one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a noncovalent and nonpeptidomimetic inhibitor with 120 nM antiviral efficacy. Crucially, our approach successfully extends ligands to unexplored regions of the binding pocket, executing large and fruitful moves in chemical space with simple chemistry.
An ACAT inhibitor suppresses SARS-CoV-2 replication and boosts antiviral T cell activity.
The severity of disease following infection with SARS-CoV-2 is determined by viral replication kinetics and host immunity, with early T cell responses and/or suppression of viraemia driving a favourable outcome. Recent studies uncovered a role for cholesterol metabolism in the SARS-CoV-2 life cycle and in T cell function. Here we show that blockade of the enzyme Acyl-CoA:cholesterol acyltransferase (ACAT) with Avasimibe inhibits SARS-CoV-2 pseudoparticle infection and disrupts the association of ACE2 and GM1 lipid rafts on the cell membrane, perturbing viral attachment. Imaging SARS-CoV-2 RNAs at the single cell level using a viral replicon model identifies the capacity of Avasimibe to limit the establishment of replication complexes required for RNA replication. Genetic studies to transiently silence or overexpress ACAT isoforms confirmed a role for ACAT in SARS-CoV-2 infection. Furthermore, Avasimibe boosts the expansion of functional SARS-CoV-2-specific T cells from the blood of patients sampled during the acute phase of infection. Thus, re-purposing of ACAT inhibitors provides a compelling therapeutic strategy for the treatment of COVID-19 to achieve both antiviral and immunomodulatory effects. Trial registration: NCT04318314.
HLA-E–restricted SARS-CoV-2–specific T cells from convalescent COVID-19 patients suppress virus replication despite HLA class Ia down-regulation
Pathogen-specific CD8 + T cell responses restricted by the nonpolymorphic nonclassical class Ib molecule human leukocyte antigen E (HLA-E) are rarely reported in viral infections. The natural HLA-E ligand is a signal peptide derived from classical class Ia HLA molecules that interact with the NKG2/CD94 receptors to regulate natural killer cell functions, but pathogen-derived peptides can also be presented by HLA-E. Here, we describe five peptides from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that elicited HLA-E–restricted CD8 + T cell responses in convalescent patients with coronavirus disease 2019. These T cell responses were identified in the blood at frequencies similar to those reported for classical HLA-Ia–restricted anti–SARS-CoV-2 CD8 + T cells. HLA-E peptide–specific CD8 + T cell clones, which expressed diverse T cell receptors, suppressed SARS-CoV-2 replication in Calu-3 human lung epithelial cells. SARS-CoV-2 infection markedly down-regulated classical HLA class I expression in Calu-3 cells and primary reconstituted human airway epithelial cells, whereas HLA-E expression was not affected, enabling T cell recognition. Thus, HLA-E–restricted T cells could contribute to the control of SARS-CoV-2 infection alongside classical T cells.
A landscape analysis of the key global stakeholders working on interventions around preterm birth that improve neonatal mortality and morbidity.
Background Over a decade after the landmark ‘Born too Soon’ report, preterm birth remains a leading cause of under-five mortality. Addressing its global burden is key to meeting United Nations Sustainable Development Goal 3; to end preventable deaths of newborns and children by 2030. We conducted a landscape analysis to explore the types of organisations addressing preterm birth, highlight the scope of interventions and initiatives, and identify gaps and opportunities for shared learning. Methods We combined google searches with citation searching, and opinion of experts in child health, to identify the major global stakeholders working to improve outcomes of preterm birth, with evidence of activity since 2012. We conducted a thematic analysis and narrative synthesis of key stakeholder websites to categorise their functions and priorities, and the types of interventions they were implementing. Results A total of 38 key organisations and 28 interventions were derived from the searches. Organisations were thematically grouped into knowledge sharing (n = 15), knowledge production (n = 12), funders (n = 6), legislation and advocacy (n = 15), implementer (n = 14) and network organisations (n = 11). Interventions covered a wide scope of functions including education (n = 11), research (n = 10), resources (n = 7), legislation (n = 2), and health systems (n = 2) interventions. The majority of global stakeholders were funded from and headquartered within high-income settings. Discussion There is scope for significant learning across global stakeholders, in particular to support carers in low-resource settings. Further opportunities for impact include a need for community-based initiatives and whole systems approach that address the long-term needs of preterm babies and their families, particularly in low- and middle-income countries (LMIC) settings. Greater knowledge production and funding from LMICs is needed to create contextually relevant resources and address implementation challenges.
SARS-CoV-2, influenza A/B and respiratory syncytial virus positivity and association with influenza-like illness and self-reported symptoms, over the 2022/23 winter season in the UK: a longitudinal surveillance cohort.
BACKGROUND: Syndromic surveillance often relies on patients presenting to healthcare. Community cohorts, although more challenging to recruit, could provide additional population-wide insights, particularly with SARS-CoV-2 co-circulating with other respiratory viruses. METHODS: We estimated the positivity and incidence of SARS-CoV-2, influenza A/B, and RSV, and trends in self-reported symptoms including influenza-like illness (ILI), over the 2022/23 winter season in a broadly representative UK community cohort (COVID-19 Infection Survey), using negative-binomial generalised additive models. We estimated associations between test positivity and each of the symptoms and influenza vaccination, using adjusted logistic and multinomial models. RESULTS: Swabs taken at 32,937/1,352,979 (2.4%) assessments tested positive for SARS-CoV-2, 181/14,939 (1.2%) for RSV and 130/14,939 (0.9%) for influenza A/B, varying by age over time. Positivity and incidence peaks were earliest for RSV, then influenza A/B, then SARS-CoV-2, and were highest for RSV in the youngest and for SARS-CoV-2 in the oldest age groups. Many test positives did not report key symptoms: middle-aged participants were generally more symptomatic than older or younger participants, but still, only ~ 25% reported ILI-WHO and ~ 60% ILI-ECDC. Most symptomatic participants did not test positive for any of the three viruses. Influenza A/B-positivity was lower in participants reporting influenza vaccination in the current and previous seasons (odds ratio = 0.55 (95% CI 0.32, 0.95)) versus neither season. CONCLUSIONS: Symptom profiles varied little by aetiology, making distinguishing SARS-CoV-2, influenza and RSV using symptoms challenging. Most symptoms were not explained by these viruses, indicating the importance of other pathogens in syndromic surveillance. Influenza vaccination was associated with lower rates of community influenza test positivity.
The binding and mechanism of a positive allosteric modulator of Kv3 channels.
Small-molecule modulators of diverse voltage-gated K+ (Kv) channels may help treat a wide range of neurological disorders. However, developing effective modulators requires understanding of their mechanism of action. We apply an orthogonal approach to elucidate the mechanism of action of an imidazolidinedione derivative (AUT5), a highly selective positive allosteric modulator of Kv3.1 and Kv3.2 channels. AUT5 modulation involves positive cooperativity and preferential stabilization of the open state. The cryo-EM structure of the Kv3.1/AUT5 complex at a resolution of 2.5 Å reveals four equivalent AUT5 binding sites at the extracellular inter-subunit interface between the voltage-sensing and pore domains of the channel's tetrameric assembly. Furthermore, we show that the unique extracellular turret regions of Kv3.1 and Kv3.2 essentially govern the selective positive modulation by AUT5. High-resolution apo and bound structures of Kv3.1 demonstrate how AUT5 binding promotes turret rearrangements and interactions with the voltage-sensing domain to favor the open conformation.
Global fertility in 204 countries and territories, 1950-2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021.
BACKGROUND: Accurate assessments of current and future fertility-including overall trends and changing population age structures across countries and regions-are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios. METHODS: To estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10-54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression-Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values-a metric assessing gain in forecasting accuracy-by comparing predicted versus observed ASFRs from the past 15 years (2007-21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline. FINDINGS: During the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63-5·06) to 2·23 (2·09-2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137-147), declining to 129 million (121-138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1-canonically considered replacement-level fertility-in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7-29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59-2·08) in 2050 and 1·59 (1·25-1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6-43·1) in 2050 and 54·3% (47·1-59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions-decreasing, for example, in south Asia from 24·8% (23·7-25·8) in 2021 to 16·7% (14·3-19·1) in 2050 and 7·1% (4·4-10·1) in 2100-but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40-1·92) in 2050 and 1·62 (1·35-1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction. INTERPRETATION: Fertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world. FUNDING: Bill & Melinda Gates Foundation.
Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine-learning approaches.
BackgroundPyrazinamide is one of four first-line antibiotics used to treat tuberculosis; however, antibiotic susceptibility testing for pyrazinamide is challenging. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, encoding an enzyme that converts pyrazinamide into its active form.MethodsWe curated a dataset of 664 non-redundant, missense amino acid mutations in PncA with associated high-confidence phenotypes from published studies and then trained three different machine-learning models to predict pyrazinamide resistance. All models had access to a range of protein structural-, chemical- and sequence-based features.ResultsThe best model, a gradient-boosted decision tree, achieved a sensitivity of 80.2% and a specificity of 76.9% on the hold-out test dataset. The clinical performance of the models was then estimated by predicting the binary pyrazinamide resistance phenotype of 4027 samples harbouring 367 unique missense mutations in pncA derived from 24 231 clinical isolates.ConclusionsThis work demonstrates how machine learning can enhance the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs.
Genomic Data: Building Blocks for Life or Abstract Art?
The genes found in the genetic code (genome) are sometimes called the “building blocks for life” but knowing how they impact human health can be more complicated than it sounds. This article aims to show how difficult it can be to understand how our genes can affect our health, and why it is not always easy to work out a patient’s result from genetic tests. We follow the story of Ben, whose muscles have been getting weaker for a few years. To find out why, Ben has had his genetic code sequenced, and we will walk you through a process by which his results can be analyzed. Through this activity, we will show you that analyzing patients’ genome tests is a bit like interpreting abstract art, in which different people might see and value different things.
Medication adherence framework: A population‐based pharmacokinetic approach and its application in antimalarial treatment assessments
AbstractWe reported here on the development of a pharmacometric framework to assess patient adherence, by using two population‐based approaches – the percentile and the Bayesian method. Three different dosing strategies were investigated in patients prescribed a total of three doses; (1) non‐observed therapy, (2) directly observed administration of the first dose, and (3) directly observed administration of the first two doses. The percentile approach used population‐based simulations to derive optimal concentration percentile cutoff values from the distribution of simulated drug concentrations at a specific time. This was done for each adherence scenario and compared to full adherence. The Bayesian approach calculated the posterior probability of each adherence scenario at a given drug concentration. The predictive performance (i.e., Youden index, receiver operating characteristic [ROC] curve) of both approaches were highly influenced by sample collection time (early was better) and interindividual variability (smaller was better). The complexity of the structural model and the half‐life had a minimal impact on the predictive performance of these methods. The impact of the assay limitation (LOQ) on the predictive performance was relatively small if the fraction of LOQ data was less than 20%. Overall, the percentile method performed similar or better for adherence predictions compared to the Bayesian approach, with the latter showing slightly better results when investigating the adherence to the last dose only. The percentile approach showed acceptable adherence predictions (area under ROC curve > 0.74) when sampling the antimalarial drugs piperaquine at day 7 postdose and lumefantrine at day 3 postdose (i.e., 12 h after the last dose). This could be a highly useful approach when evaluating programmatic implementations of preventive and curative antimalarial treatment programs in endemic areas.
Defining the hidden burden of disease in rural communities in Bangladesh, Cambodia and Thailand: a cross-sectional household health survey protocol
IntroductionIn low-income and middle-income countries in Southeast Asia, the burden of diseases among rural population remains poorly understood, posing a challenge for effective healthcare prioritisation and resource allocation. Addressing this knowledge gap, the South and Southeast Asia Community-based Trials Network (SEACTN) will undertake a survey that aims to determine the prevalence of a wide range of non-communicable and communicable diseases, as one of the key initiatives of its first project—the Rural Febrile Illness project (RFI). This survey, alongside other RFI studies that explore fever aetiology, leading causes of mortality, and establishing village and health facility maps and profiles, will provide an updated epidemiological background of the rural areas where the network is operational.Methods and analysisDuring 2022–2023, a cross-sectional household survey will be conducted across three SEACTN sites in Bangladesh, Cambodia and Thailand. Using a two-stage cluster-sampling approach, we will employ a probability-proportional-to-size sample method for village, and a simple random sample for household, selection, enrolling all members from the selected households. Approximately 1500 participants will be enrolled per country. Participants will undergo questionnaire interview, physical examination and haemoglobin point-of-care testing. Blood samples will be collected and sent to central laboratories to test for chronic and acute infections, and biomarkers associated with cardiovascular disease, and diabetes. Prevalences will be presented as an overall estimate by country, and stratified and compared across sites and participants’ sociodemographic characteristics. Associations between disease status, risk factors and other characteristics will be explored.Ethics and disseminationThis study protocol has been approved by the Oxford Tropical Research Ethics Committee, National Research Ethics Committee of Bangladesh Medical Research Council, the Cambodian National Ethics Committee for Health Research, the Chiang Rai Provincial Public Health Research Ethical Committee. The results will be disseminated via the local health authorities and partners, peer-reviewed journals and conference presentations.Trial registration numberNCT05389540.