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Mathematical Modelling of Parasite Dynamics: A Stochastic Simulation-Based Approach and Parameter Estimation via Modified Sequential-Type Approximate Bayesian Computation.
The development of mathematical models for studying newly emerging and re-emerging infectious diseases has gained momentum due to global events. The gyrodactylid-fish system, like many host-parasite systems, serves as a valuable resource for ecological, evolutionary, and epidemiological investigations owing to its ease of experimental manipulation and long-term monitoring. Although this system has an existing individual-based model, it falls short in capturing information about species-specific microhabitat preferences and other biological details for different Gyrodactylus strains across diverse fish populations. This current study introduces a new individual-based stochastic simulation model that uses a hybrid τ -leaping algorithm to incorporate this essential data, enhancing our understanding of the complexity of the gyrodactylid-fish system. We compare the infection dynamics of three gyrodactylid strains across three host populations. A modified sequential-type approximate Bayesian computation (ABC) method, based on sequential Monte Carlo and sequential importance sampling, is developed. Additionally, we establish two penalised local-linear regression methods (based on L1 and L2 regularisations) for ABC post-processing analysis to fit our model using existing empirical data. With the support of experimental data and the fitted mathematical model, we address open biological questions for the first time and propose directions for future studies on the gyrodactylid-fish system. The adaptability of the mathematical model extends beyond the gyrodactylid-fish system to other host-parasite systems. Furthermore, the modified ABC methodologies provide efficient calibration for other multi-parameter models characterised by a large set of correlated or independent summary statistics.
Determinants of durable humoral and T cell immunity in myeloma patients following COVID-19 vaccination.
OBJECTIVE: To describe determinants of persisting humoral and cellular immune response to the second COVID-19 vaccination among patients with myeloma. METHODS: This is a prospective, observational study utilising the RUDYstudy.org platform. Participants reported their second and third COVID-19 vaccination dates. Myeloma patients had an Anti-S antibody level sample taken at least 21 days after their second vaccination and a repeat sample before their third vaccination. RESULTS: 60 patients provided samples at least 3 weeks (median 57.5 days) after their second vaccination and before their third vaccination (median 176.0 days after second vaccine dose). Low Anti-S antibody levels (<50 IU/mL) doubled during this interval (p = .023) and, in the 47 participants with T-spot data, there was a 25% increase negative T-spot tests (p = .008). Low anti-S antibody levels prior to the third vaccination were predicted by lower Anti-S antibody level and negative T-spot status after the second vaccine. Independent determinants of a negative T-spot included increasing age, previous COVID infection, high CD4 count and lower percentage change in Anti-S antibody levels. CONCLUSIONS: Negative T-spot results predict low Anti-S antibody levels (<50 IU/mL) following a second COVID-19 vaccination and a number of biomarkers predict T cell responses in myeloma patients.
Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study.
BACKGROUND: Recent advancements in rehabilitation sciences have progressively used computational techniques to improve diagnostic and treatment approaches. However, the analysis of high-dimensional, time-dependent data continues to pose a significant problem. Prior research has used clustering techniques on rehabilitation data to identify movement patterns and forecast recovery outcomes. Nonetheless, these initiatives have not yet used force or motion datasets obtained outside a clinical setting, thereby limiting the capacity for therapeutic decisions. Biomechanical data analysis has demonstrated considerable potential in bridging these gaps and improving clinical decision-making in rehabilitation settings. OBJECTIVE: This study presents a comprehensive clustering analysis of multidimensional movement datasets captured using a novel home exercise device, the "Slider". The aim is to identify clinically relevant movement patterns and provide answers to open research questions for the first time to inform personalized rehabilitation protocols, predict individual recovery trajectories, and assess the risks of potential postoperative complications. METHODS: High-dimensional, time-dependent, bilateral knee kinetic datasets were independently analyzed from 32 participants using four unsupervised clustering techniques: k-means, hierarchical clustering, partition around medoids, and CLARA (Clustering Large Applications). The data comprised force, laser-measured distance, and optical tracker coordinates from lower limb activities. The optimal clusters identified through the unsupervised clustering methods were further evaluated and compared using silhouette analysis to quantify their performance. Key determinants of cluster membership were assessed, including demographic factors (eg, gender, BMI, and age) and pain levels, by using a logistic regression model with analysis of covariance adjustment. RESULTS: Three distinct, time-varying movement patterns or clusters were identified for each knee. Hierarchical clustering performed best for the right knee datasets (with an average silhouette score of 0.637), while CLARA was the most effective for the left knee datasets (with an average silhouette score of 0.598). Key predictors of the movement cluster membership were discovered for both knees. BMI was the most influential determinant of cluster membership for the right knee, where higher BMI decreased the odds of cluster-2 membership (odds ratio [OR] 0.95, 95% CI 0.94-0.96; P
Modelling the Transmission Dynamics of Tuberculosis in the Ashanti Region of Ghana.
Mathematical models can aid in elucidating the spread of infectious disease dynamics within a given population over time. In an attempt to model tuberculosis (TB) dynamics among high-burden districts in the Ashanti Region of Ghana, the SEIR epidemic model with demography was employed within both deterministic and stochastic settings for comparison purposes. The deterministic model showed success in modelling TB infection in the region to the transmission dynamics of the stochastic SEIR model over time. It predicted tuberculosis dying out in ten of twelve high-burden districts in the Ashanti Region, but an outbreak in Obuasi municipal and Amansie West district. The effect of introducing treatment at the incubation stage of TB transmission was also investigated, and it was discovered that treatment introduced at the exposed stage decreased the spread of TB. Branching process approximation was used to derive explicit forms of relevant epidemiological quantities of the deterministic SEIR model for stability analysis of equilibrium points. Numerical simulations were performed to validate the overall infection rate, basic reproductive number, herd immunity threshold, and Malthusian parameter based on bootstrapping, jackknife, and Latin Hypercube sampling schemes. It was recommended that the Ghana Health Service should find a good mechanism to detect TB in the early stages of infection in the region. Public health attention must also be given to districts with a potentially higher risk of experiencing endemic TB even though the estimates of the overall epidemic thresholds from our SEIR model suggested that the Ashanti Region as a whole had herd immunity against TB infection.
Markov Chain Modeling of HIV, Tuberculosis, and Hepatitis B Transmission in Ghana.
Several mathematical and standard epidemiological models have been proposed in studying infectious disease dynamics. These models help to understand the spread of disease infections. However, most of these models are not able to estimate other relevant disease metrics such as probability of first infection and recovery as well as the expected time to infection and recovery for both susceptible and infected individuals. That is, most of the standard epidemiological models used in estimating transition probabilities (TPs) are not able to generalize the transition estimates of disease outcomes at discrete time steps for future predictions. This paper seeks to address the aforementioned problems through a discrete-time Markov chain model. Secondary datasets from cohort studies were collected on HIV, tuberculosis (TB), and hepatitis B (HB) cases from a regional hospital in Ghana. The Markov chain model revealed that hepatitis B was more infectious over time than tuberculosis and HIV even though the probability of first infection of these diseases was relatively low within the study population. However, individuals infected with HIV had comparatively lower life expectancies than those infected with tuberculosis and hepatitis B. Discrete-time Markov chain technique is recommended as viable for modeling disease dynamics in Ghana.
Statistical Modeling of HIV, Tuberculosis, and Hepatitis B Transmission in Ghana.
Most mortality studies usually attribute death to single disease, while various other diseases could also act in the same individual or a population at large. Few works have been done by considering HIV, Tuberculosis (TB), and Hepatitis B (HB) as jointly acting in a population in spite of their high rate of infections in Ghana. This study applied competing risk methods on these three diseases by assuming they were the major risks in the study population. Among all opportunistic infections that could also act within HIV-infected individuals, TB has been asserted to be the most predominant. Other studies have also shown cases of HIV and Hepatitis B coinfections. The validity of these comorbidity assertions was statistically determined by exploring the conditional dependencies existing among HIV, TB, and HB through Bayesian networks or directed graphical model. Through Classification tree, sex and age group of individuals were found as significant demographic predictors that influence the prevalence of HIV and TB. Females were more likely to contract HIV, whereas males were prone to contracting TB.
Spatial and temporal parasite dynamics: microhabitat preferences and infection progression of two co-infecting gyrodactylids.
BACKGROUND: Mathematical modelling of host-parasite systems has seen tremendous developments and broad applications in theoretical and applied ecology. The current study focuses on the infection dynamics of a gyrodactylid-fish system. Previous experimental studies have explored the infrapopulation dynamics of co-infecting ectoparasites, Gyrodactylus turnbulli and G. bullatarudis, on their fish host, Poecilia reticulata, but questions remain about parasite microhabitat preferences, host survival and parasite virulence over time. Here, we use more advanced statistics and a sophisticated mathematical model to investigate these questions based on empirical data to add to our understanding of this gyrodactylid-fish system. METHODS: A rank-based multivariate Kruskal-Wallis test coupled with its post-hoc tests and graphical summaries were used to investigate the spatial and temporal parasite distribution of different gyrodactylid strains across different host populations. By adapting a multi-state Markov model that extends the standard survival models, we improved previous estimates of survival probabilities. Finally, we quantified parasite virulence of three different strains as a function of host mortality and recovery across different fish stocks and sexes. RESULTS: We confirmed that the captive-bred G. turnbulli and wild G. bullatarudis strains preferred the caudal and rostral regions respectively across different fish stocks; however, the wild G. turnbulli strain changed microhabitat preference over time, indicating microhabitat preference of gyrodactylids is host and time dependent. The average time of host infection before recovery or death was between 6 and 14 days. For this gyrodactylid-fish system, a longer period of host infection led to a higher chance of host recovery. Parasite-related mortalities are host, sex and time dependent, whereas fish size is confirmed to be the key determinant of host recovery. CONCLUSION: From existing empirical data, we provided new insights into the gyrodactylid-fish system. This study could inform the modelling of other host-parasite interactions where the entire infection history of the host is of interest by adapting multi-state Markov models. Such models are under-utilised in parasitological studies and could be expanded to estimate relevant epidemiological traits concerning parasite virulence and host survival.
Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana
The major challenge in managing blood products lies in the uncertainty of blood demand and supply, with a trade-off between shortage and wastage, especially in most developing countries. Thus, reliable demand predictions can be imperative in planning voluntary blood donation campaigns and improving blood availability within Ghana hospitals. However, most historical datasets on blood demand in Ghana are predominantly contaminated with missing values and outliers due to improper database management systems. Consequently, time-series prediction can be challenging since data cleaning can affect models’ predictive power. Also, machine learning (ML) models’ predictive power for backcasting past years’ lost data is understudied compared to their forecasting abilities. This study thus aims to compare K-Nearest Neighbour regression (KNN), Generalised Regression Neural Network (GRNN), Neural Network Auto-regressive (NNAR), Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM) and Long Short-Term Memory (LSTM) models via a rolling-origin strategy, for forecasting and backcasting a blood demand data with missing values and outliers from a government hospital in Ghana. KNN performed well in forecasting blood demand (12.55% error); whereas, ELM achieved the highest backcasting power (19.36% error). Future studies can also employ ML algorithms as a good alternative for backcasting past values of time-series data that are time-reversible.
Diagnostic ultrasound to inform the surgical approach to cesarean delivery in patients at high risk for placenta accreta spectrum disorders.
BACKGROUND: Uterine-sparing surgery has become an option for patients with placenta accreta spectrum disorders. The decision to perform a cesarean hysterectomy vs uterine-sparing surgery is made intraoperatively. This study was undertaken to assess the value of ultrasound markers in predicting the need for hysterectomy. OBJECTIVE: This study aimed to describe ultrasound markers associated with the need for cesarean hysterectomy among patients at risk of placenta accreta spectrum. STUDY DESIGN: This was an analysis of prospectively collected data of high-risk placenta accreta spectrum cases between September 2023 and August 2024. Ultrasound examination was performed by an expert focusing on the diagnosis of placenta accreta spectrum. All patients were counseled regarding the management options available at our center, namely uterine-sparing surgery and hysterectomy. All patients opted for uterine-sparing surgery if safe and technically feasible. The final choice of surgical management approach was solely based on the intraoperative topography, which describes the size and location of the abnormally adherent placenta. The primary outcome was the need for hysterectomy despite a preoperative plan for uterine-sparing surgery. RESULTS: A total of 123 participants were enrolled: 93 placenta accreta spectrum cases and 30 non-placenta accreta scar dehiscence cases. Uterine-sparing surgery was successful in 74 of 93 (79.6%) placenta accreta spectrum cases and in 100% of non-placenta accreta scar dehiscence cases. LASSO (least absolute shrinkage and selection operator) penalized regression revealed intracervical hypervascularity >50%, urinary bladder wall distortion, and parametrial hypervascularity as the most influential predictors for hysterectomy. This best-fitted model achieved an accuracy of 94% (95% confidence interval, 81.3%-99.3%) after model cross-validation. The combination of intracervical hypervascularity >50% and bladder wall distortion had the highest predictive probability for hysterectomy, with a value of 0.87 (95% confidence interval, 0.81-0.93), a sensitivity of 96.0% (95% confidence interval, 89.0%-99.0%), and a specificity of 92.0% (95% confidence interval, 62.0-100.0). CONCLUSION: Comprehensive preoperative ultrasound can reasonably predict the appropriate surgical approach to placenta accreta spectrum. This can be achieved by assessing intracervical hypervascularity and urinary bladder wall distortion using a combination of transabdominal, transvaginal, and color Doppler ultrasound techniques because these signs have a strong correlation with the need for hysterectomy in a cohort for which the intended treatment was uterine-sparing surgery.
Modelling catastrophic extinction in stochastic birth-death process: Analytical insights, estimation, and efficient simulation
A comprehensive analytical and computational framework is developed for the linear birth-death process (LBDP) with catastrophic extinction (BDC process), a continuous-time Markov model that incorporates sudden extinction events into the classical LBDP. Despite its conceptual simplicity, the underlying BDC process poses substantial challenges in deriving exact transition probabilities and performing reliable parameter estimation, particularly under discrete-time observations. While previous work established foundational properties using spectral methods and probability generating functions (PGFs), explicit analytical expressions for transition probabilities and theoretical moments have remained unavailable, limiting practical applications in extinction-prone systems. This limitation is addressed by reparameterising the PGF through functional restructuring, yielding exact closed-form expressions for the transition probability function and the theoretical moments of the discretely observed BDC process, with results validated through comprehensive numerical experiments for the first time. Three parameter estimation approaches tailored to the BDC process are introduced and evaluated: maximum likelihood estimation (MLE), generalised method of moments (GMM), and an embedded Galton-Watson (GW) approach, with trade-offs between computational efficiency and estimation accuracy examined across diverse simulation scenarios. To improve scalability, a Monte Carlo simulation framework based on a hybrid tau-leaping algorithm is formulated, specifically adapted to extinction-driven dynamics, offering a computationally efficient alternative to the exact stochastic simulation algorithm (SSA). The proposed methodologies offer a tractable and scalable foundation for incorporating the BDC process into applied stochastic models, particularly in ecological, epidemiological, and biological systems where populations are susceptible to sudden collapse due to catastrophic events such as host mortality or immune response.
Uteroplacental detachment on transvaginal ultrasound is associated with substantial antepartum hemorrhage requiring early delivery in high-risk placenta accreta spectrum cases
Introduction Accurately predicting progression from mild to substantial antepartum hemorrhage requiring early delivery in patients with a high risk for placenta accreta spectrum remains a clinical challenge, limiting effective monitoring and counseling. This study aims to identify ultrasound features associated with substantial antepartum hemorrhage necessitating early cesarean delivery using a comprehensive imaging protocol. Materials and methods This was secondary analysis of a prospectively collected data of high-risk placenta accreta spectrum patients between September 2023 and August 2024 in Indonesia. A comprehensive ultrasound protocol combining transabdominal and transvaginal approaches was used to assess various ultrasound signs. Antepartum hemorrhage was classified as >/=500 mL (substantial antepartum hemorrhage) or <500 mL (mild antepartum hemorrhage). Results The study included 123 participants with low-lying placenta or placenta previa, comprising 93 (75.6%) cases of placenta accreta spectrum and 30 (24.4%) cases of nonaccreta uterine scar dehiscence. Antepartum hemorrhage occurred in 50 patients (40.6%). Of these, 19 (38%) cases with mild antepartum hemorrhage progressed to substantial hemorrhage requiring an early cesarean delivery with a median gestational age of 33 (31–35) weeks, while 31 (62%) cases proceeded with an elective cesarean delivery at a median gestational age of 36 (34–37) weeks. Within this group, uteroplacental detachment observed on transvaginal ultrasound was the only significant factor independently associated with progression to substantial hemorrhage (adjusted odds ratio, 22.4; 95% confidence interval, 2.04–246.3; P =.011), demonstrating a sensitivity of 71.0% (95% confidence interval, 55.0%–84.7%) and specificity of 88.0% (95% confidence interval, 47.0%–100%). Further time-to-event analysis using Cox regression revealed a hazard ratio of 4.03 (95% confidence interval, 1.56–10.43; P =.0041), indicating a significantly shorter interval to early cesarean delivery in affected cases. Conclusion Uteroplacental detachment identified on transvaginal ultrasound in women with placenta previa and mild antepartum hemorrhage is associated with the progression to substantial antepartum hemorrhage requiring early cesarean delivery irrespective of the presence of placenta accreta spectrum. This sign may serve as a useful marker to guide individual delivery timing, helping clinicians mitigate the risks of hemorrhage against the risks of prematurity.
Obesity differs from diabetes mellitus in antibody and T-cell responses post-COVID-19 recovery.
OBJECTIVE: Obesity and type 2 diabetes (DM) are risk factors for severe coronavirus disease 2019 (COVID-19) outcomes, which disproportionately affect South Asian populations. This study aims to investigate the humoral and cellular immune responses to SARS-CoV-2 in adult COVID-19 survivors with overweight/obesity (Ov/Ob, BMI ≥ 23 kg/m2) and DM in Bangladesh. METHODS: In this cross-sectional study, SARS-CoV-2-specific antibody and T-cell responses were investigated in 63 healthy and 75 PCR-confirmed COVID-19 recovered individuals in Bangladesh, during the pre-vaccination first wave of the COVID-19 pandemic in 2020. RESULTS: In COVID-19 survivors, SARS-CoV-2 infection induced robust antibody and T-cell responses, which correlated with disease severity. After adjusting for age, sex, DM status, disease severity, and time since onset of symptoms, Ov/Ob was associated with decreased neutralizing antibody titers, and increased SARS-CoV-2 spike-specific IFN-γ response along with increased proliferation and IL-2 production by CD8 + T cells. In contrast, DM was not associated with SARS-CoV-2-specific antibody and T-cell responses after adjustment for obesity and other confounders. CONCLUSION: Ov/Ob is associated with lower neutralizing antibody levels and higher T-cell responses to SARS-CoV-2 post-COVID-19 recovery, while antibody or T-cell responses remain unaltered in DM.
Safety and immunogenicity of the ChAdOx1 nCoV-19 (AZD1222) vaccine against SARS-CoV-2 in HIV infection: a single-arm substudy of a phase 2/3 clinical trial.
BACKGROUND: Data on vaccine immunogenicity against SARS-CoV-2 are needed for the 40 million people globally living with HIV who might have less functional immunity and more associated comorbidities than the general population. We aimed to explore safety and immunogenicity of the ChAdOx1 nCoV-19 (AZD1222) vaccine in people with HIV. METHODS: In this single-arm open-label vaccination substudy within the protocol of the larger phase 2/3 trial COV002, adults aged 18-55 years with HIV were enrolled at two HIV clinics in London, UK. Eligible participants were required to be on antiretroviral therapy (ART), with undetectable plasma HIV viral loads (<50 copies per mL), and CD4 counts of more than 350 cells per μL. A prime-boost regimen of ChAdOx1 nCoV-19, with two doses was given 4-6 weeks apart. The primary outcomes for this substudy were safety and reactogenicity of the vaccine, as determined by serious adverse events and solicited local and systemic reactions. Humoral responses were measured by anti-spike IgG ELISA and antibody-mediated live virus neutralisation. Cell-mediated immune responses were measured by ex-vivo IFN-γ enzyme-linked immunospot assay (ELISpot) and T-cell proliferation. All outcomes were compared with an HIV-uninfected group from the main COV002 study within the same age group and dosing strategy and are reported until day 56 after prime vaccination. Outcomes were analysed in all participants who received both doses and with available samples. The COV002 study is registered with ClinicalTrials.gov, NCT04400838, and is ongoing. FINDINGS: Between Nov 5 and Nov 24, 2020, 54 participants with HIV (all male, median age 42·5 years [IQR 37·2-49·8]) were enrolled and received two doses of ChAdOx1 nCoV-19. Median CD4 count at enrolment was 694·0 cells per μL (IQR 573·5-859·5). No serious adverse events occurred. Local and systemic reactions occurring during the first 7 days after prime vaccination included pain at the injection site (26 [49%] of 53 participants with available data), fatigue (25 [47%]), headache (25 [47%]), malaise (18 [34%]), chills (12 [23%]), muscle ache (19 [36%]), joint pain (five [9%]), and nausea (four [8%]), the frequencies of which were similar to the HIV-negative participants. Anti-spike IgG responses by ELISA peaked at day 42 (median 1440 ELISA units [EUs; IQR 704-2728]; n=50) and were sustained until day 56 (median 941 EUs [531-1445]; n=49). We found no correlation between the magnitude of the anti-spike IgG response at day 56 and CD4 cell count (p=0·93) or age (p=0·48). ELISpot and T-cell proliferative responses peaked at day 14 and 28 after prime dose and were sustained to day 56. Compared with participants without HIV, we found no difference in magnitude or persistence of SARS-CoV-2 spike-specific humoral or cellular responses (p>0·05 for all analyses). INTERPRETATION: In this study of people with HIV, ChAdOx1 nCoV-19 was safe and immunogenic, supporting vaccination for those well controlled on ART. FUNDING: UK Research and Innovation, National Institutes for Health Research (NIHR), Coalition for Epidemic Preparedness Innovations, NIHR Oxford Biomedical Research Centre, Thames Valley and South Midland's NIHR Clinical Research Network, and AstraZeneca.
Immunogenicity of standard and extended dosing intervals of BNT162b2 mRNA vaccine.
Extension of the interval between vaccine doses for the BNT162b2 mRNA vaccine was introduced in the United Kingdom to accelerate population coverage with a single dose. At this time, trial data were lacking, and we addressed this in a study of United Kingdom healthcare workers. The first vaccine dose induced protection from infection from the circulating alpha (B.1.1.7) variant over several weeks. In a substudy of 589 individuals, we show that this single dose induces severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) neutralizing antibody (NAb) responses and a sustained B and T cell response to the spike protein. NAb levels were higher after the extended dosing interval (6-14 weeks) compared with the conventional 3- to 4-week regimen, accompanied by enrichment of CD4+ T cells expressing interleukin-2 (IL-2). Prior SARS-CoV-2 infection amplified and accelerated the response. These data on dynamic cellular and humoral responses indicate that extension of the dosing interval is an effective immunogenic protocol.
Association between convalescent plasma treatment and mortality in COVID-19: a collaborative systematic review and meta-analysis of randomized clinical trials.
BACKGROUND: Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX ). METHODS: In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung-Knapp-Sidik-Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. RESULTS: A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% of the weight in the meta-analysis. CONCLUSIONS: Convalescent plasma treatment of patients with COVID-19 did not reduce all-cause mortality. These results provide strong evidence that convalescent plasma treatment for patients with COVID-19 should not be used outside of randomized trials. Evidence synthesis from collaborations among trial investigators can inform both evidence generation and evidence application in patient care.
Impact of obesity and diabetes on immune responses to SARS-CoV-2 in Bangladeshi adults
Overweight/obesity (Ov/Ob) and diabetes mellitus (DM) are known as independent risk factors for severe COVID-19 outcomes, which disproportionately affect the South Asian population. South Asians with a body mass index (BMI) of 27 kg/m2 have the same risk of COVID-19 mortality as white ethnicities at a BMI of 40 kg/m2. This thesis investigates the effects of Ov/Ob and DM on humoral and cellular immune responses to SARS-CoV-2 in 198 individuals during the first pandemic wave prior to the global rollout of vaccines in Bangladesh. The study cohorts include healthy controls (n=63), individuals with severe acute COVID-19 (n=60), and those who have recovered from COVID-19 (n=75). A variety of immunoassays were performed, including the Meso Scale Discovery (MSD) immunoassay, B cell FluoroSpot, Focus Reduction Neutralisation assay for antibody responses, and IFN-γ ELISpot, intracellular cytokine staining, and proliferation assays for T cell responses. Acute patients with Ov/Ob (BMI ≥ 23 kg/m2) demonstrated higher levels of IgG responses to the SARS-CoV-2 spike compared to those without Ov/Ob. Following COVID-19 recovery, individuals with Ov/Ob displayed reduced neutralising antibody capacity against SARS-CoV-2 despite comparable IgG responses, alongside increased anti-SARS-CoV-2 IFN-γ responses, CD8+ T cell proliferation, and IL-2 production compared to those without Ov/Ob. DM was not associated with antibody and T cell responses in acute infection and recovery. Single-cell transcriptomic analysis of resting peripheral blood mononuclear cells from 11 COVID-19 survivors with and without DM revealed distinctive transcriptomic profiles marked by significant upregulation of immune activation pathways and metabolic reprogramming in DM across major immune cell types. T cells in DM showed upregulation in Th1 and Th17 differentiation pathways and oxidative phosphorylation. NK cell-mediated cytotoxicity as well as antigen processing and presentation by monocytes were upregulated in DM. B cells showed a metabolic shift towards glycolysis and a Th2 immune response under DM conditions. Collectively, the work presented in this thesis advances our understanding of the adaptive immune response to SARS-CoV-2 in individuals with metabolic diseases. This biological insight paves the way for future studies to improve the management of not only COVID-19 but also future pandemics, ensuring better outcomes for patients burdened with comorbidities such as Ov/Ob and DM.
T-cell and antibody responses to first BNT162b2 vaccine dose in previously infected and SARS-CoV-2-naive UK health-care workers: a multicentre prospective cohort study.
BACKGROUND: Previous infection with SARS-CoV-2 affects the immune response to the first dose of the SARS-CoV-2 vaccine. We aimed to compare SARS-CoV-2-specific T-cell and antibody responses in health-care workers with and without previous SARS-CoV-2 infection following a single dose of the BNT162b2 (tozinameran; Pfizer-BioNTech) mRNA vaccine. METHODS: We sampled health-care workers enrolled in the PITCH study across four hospital sites in the UK (Oxford, Liverpool, Newcastle, and Sheffield). All health-care workers aged 18 years or older consenting to participate in this prospective cohort study were included, with no exclusion criteria applied. Blood samples were collected where possible before vaccination and 28 (±7) days following one or two doses (given 3-4 weeks apart) of the BNT162b2 vaccine. Previous infection was determined by a documented SARS-CoV-2-positive RT-PCR result or the presence of positive anti-SARS-CoV-2 nucleocapsid antibodies. We measured spike-specific IgG antibodies and quantified T-cell responses by interferon-γ enzyme-linked immunospot assay in all participants where samples were available at the time of analysis, comparing SARS-CoV-2-naive individuals to those with previous infection. FINDINGS: Between Dec 9, 2020, and Feb 9, 2021, 119 SARS-CoV-2-naive and 145 previously infected health-care workers received one dose, and 25 SARS-CoV-2-naive health-care workers received two doses, of the BNT162b2 vaccine. In previously infected health-care workers, the median time from previous infection to vaccination was 268 days (IQR 232-285). At 28 days (IQR 27-33) after a single dose, the spike-specific T-cell response measured in fresh peripheral blood mononuclear cells (PBMCs) was higher in previously infected (n=76) than in infection-naive (n=45) health-care workers (median 284 [IQR 150-461] vs 55 [IQR 24-132] spot-forming units [SFUs] per 106 PBMCs; p<0·0001). With cryopreserved PBMCs, the T-cell response in previously infected individuals (n=52) after one vaccine dose was equivalent to that of infection-naive individuals (n=19) after receiving two vaccine doses (median 152 [IQR 119-275] vs 162 [104-258] SFUs/106 PBMCs; p=1·00). Anti-spike IgG antibody responses following a single dose in 142 previously infected health-care workers (median 270 373 [IQR 203 461-535 188] antibody units [AU] per mL) were higher than in 111 infection-naive health-care workers following one dose (35 001 [17 099-55 341] AU/mL; p<0·0001) and higher than in 25 infection-naive individuals given two doses (180 904 [108 221-242 467] AU/mL; p<0·0001). INTERPRETATION: A single dose of the BNT162b2 vaccine is likely to provide greater protection against SARS-CoV-2 infection in individuals with previous SARS-CoV-2 infection, than in SARS-CoV-2-naive individuals, including against variants of concern. Future studies should determine the additional benefit of a second dose on the magnitude and durability of immune responses in individuals vaccinated following infection, alongside evaluation of the impact of extending the interval between vaccine doses. FUNDING: UK Department of Health and Social Care, and UK Coronavirus Immunology Consortium.
Two doses of SARS-CoV-2 vaccination induce robust immune responses to emerging SARS-CoV-2 variants of concern.
The extent to which immune responses to natural infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and immunization with vaccines protect against variants of concern (VOC) is of increasing importance. Accordingly, here we analyse antibodies and T cells of a recently vaccinated, UK cohort, alongside those recovering from natural infection in early 2020. We show that neutralization of the VOC compared to a reference isolate of the original circulating lineage, B, is reduced: more profoundly against B.1.351 than for B.1.1.7, and in responses to infection or a single dose of vaccine than to a second dose of vaccine. Importantly, high magnitude T cell responses are generated after two vaccine doses, with the majority of the T cell response directed against epitopes that are conserved between the prototype isolate B and the VOC. Vaccination is required to generate high potency immune responses to protect against these and other emergent variants.