Modelling longitudinal data on respiratory infections to inform health policy

Detecting the start of an outbreak, quantifying its burden, disentangling the contribution of different pathogens and evaluating the effectiveness of an intervention are research questions common to several infectious diseases. The answers to these questions provide the epidemiological understanding to prevent future outbreaks, by informing public health policies such as drug stockpiling, vaccination regimes or non-medical interventions. We investigate the use of statistical models to quantify burden of respiratory disease and evaluate effectiveness of public health interventions, while accounting for the challenges posed by surveillance data. The observational nature of the available information, affected by confounding, makes causal statements difficult. Improvements to routinely employed methodologies are proposed, employing phenomenological models to estimate a counterfactual, i.e. what what would have happened in the absence of a contributing factor or intervention. We apply these methods to different types of studies, to address specific gaps in the literature. S. pneumoniae is the leading cause of respiratory morbidity and mortality globally, especially in young children and in the elderly. To improve the understanding of factors triggering disease progression, we firstly analyse individual-level information about pneumococcal carriage and lower respiratory tract infection with a multi-state model, using data from a cohort study in Thailand. Secondly, we clarify the role of viral coinfection and meteorological conditions in invasive pneumococcal disease (IPD) incidence using English surveillance data. A novel multivariate linear regression model is proposed to estimate the influenza-specific contribution additional to the seasonal IPD burden across age groups. We then quantify the impact of the currently implemented vaccination policy, by estimating the counterfactual of IPD incidence in absence of vaccination. This allows disentangling serotype replacement from the vaccine effect, making use of a synthetic control approach. Finally, an empirical dynamical modelling strategy is employed to quantify the interaction between influenza and pneumococcus. Counterfactual analysis can also be employed to quantify the burden of novel respiratory pathogens. The last application of this approach is to estimate the excess mortality during the the COVID-19 pandemic in England

Neutralizing monoclonal antibodies to fight HIV-1 : on the threshold of success

Anti-human immunodeficiency virus type-1 (anti-HIV-1) neutralizing monoclonal antibodies are broadening the spectrum of pre- and post-exposure treatment against HIV-1. A better understanding of how these antibodies develop and interact with particular regions of the viral envelope protein is guiding a more rational structure-based immunogen design. The aim of this article is to review the most recent advances in the field, from the development of these particular antibodies during natural HIV-1 infection, to their role preventing infection, boosting endogenous immune responses and clearing both free viral particles and persistently infected cells.Inst.de VirologíaFil: Jaworski, Juan Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Virología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Vendrell, Alejandrina. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Chiavenna, Sebastián Matias. Ferrer Advanced Biotherapeutics, Ferrer Internacional; Españ

Estimating age-stratified influenza-associated invasive pneumococcal disease in England: A time-series model based on population surveillance data.

BACKGROUND: Measures of the contribution of influenza to Streptococcus pneumoniae infections, both in the seasonal and pandemic setting, are needed to predict the burden of secondary bacterial infections in future pandemics to inform stockpiling. The magnitude of the interaction between these two pathogens has been difficult to quantify because both infections are mainly clinically diagnosed based on signs and symptoms; a combined viral-bacterial testing is rarely performed in routine clinical practice; and surveillance data suffer from confounding problems common to all ecological studies. We proposed a novel multivariate model for age-stratified disease incidence, incorporating contact patterns and estimating disease transmission within and across groups. METHODS AND FINDINGS: We used surveillance data from England over the years 2009 to 2017. Influenza infections were identified through the virological testing of samples taken from patients diagnosed with influenza-like illness (ILI) within the sentinel scheme run by the Royal College of General Practitioners (RCGP). Invasive pneumococcal disease (IPD) cases were routinely reported to Public Health England (PHE) by all the microbiology laboratories included in the national surveillance system. IPD counts at week t, conditional on the previous time point t-1, were assumed to be negative binomially distributed. Influenza counts were linearly included in the model for the mean IPD counts along with an endemic component describing some seasonal background and an autoregressive component mimicking pneumococcal transmission. Using age-specific counts, Akaike information criterion (AIC)-based model selection suggested that the best fit was obtained when the endemic component was expressed as a function of observed temperature and rainfall. Pneumococcal transmission within the same age group was estimated to explain 33.0% (confidence interval [CI] 24.9%-39.9%) of new cases in the elderly, whereas 50.7% (CI 38.8%-63.2%) of incidence in adults aged 15-44 years was attributed to transmission from another age group. The contribution of influenza on IPD during the 2009 pandemic also appeared to vary greatly across subgroups, being highest in school-age children and adults (18.3%, CI 9.4%-28.2%, and 6.07%, CI 2.83%-9.76%, respectively). Other viral infections, such as respiratory syncytial virus (RSV) and rhinovirus, also seemed to have an impact on IPD: RSV contributed 1.87% (CI 0.89%-3.08%) to pneumococcal infections in the 65+ group, whereas 2.14% (CI 0.87%-3.57%) of cases in the group of 45- to 64-year-olds were attributed to rhinovirus. The validity of this modelling strategy relies on the assumption that viral surveillance adequately represents the true incidence of influenza in the population, whereas the small numbers of IPD cases observed in the younger age groups led to significant uncertainty around some parameter estimates. CONCLUSIONS: Our estimates suggested that a pandemic wave of influenza A/H1N1 with comparable severity to the 2009 pandemic could have a modest impact on school-age children and adults in terms of IPD and a small to negligible impact on infants and the elderly. The seasonal impact of other viruses such as RSV and rhinovirus was instead more important in the older population groups

Kinect V2 Performance Assessment in Daily-Life Gestures: Cohort Study on Healthy Subjects for a Reference Database for Automated Instrumental Evaluations on Neurological Patients

Background. The increase of sanitary costs related to poststroke rehabilitation requires new sustainable and cost-effective strategies for promoting autonomous and dehospitalized motor training. In the Riprendo@Home and Future Home for Future Communities research projects, the promising approach of introducing low-cost technologies that promote home rehabilitation is exploited. In order to provide reliable evaluation of patients, a reference database of healthy people’s performances is required and should consider variability related to healthy people performances. Methods. 78 healthy subjects performed several repetitions of daily-life gestures, the reaching movement (RM) and hand-to-mouth (HtMM) movement with both the dominant and nondominant upper limbs. Movements were recorded with a Kinect V2. A synthetic biomechanical protocol based on kinematical, dynamical, and motor control parameters was used to assess motor performance of the healthy people. The investigation was conducted by clustering participants depending on their limb dominancy (right/left), gender (male/female), and age (young/middle/senior) as sources of variability. Results. Results showed that limb dominancy has minor relevance in affecting RM and HtMM; gender has relevance in affecting the HtMM; age has major effect in affecting RM and HtMM. Conclusions. An investigation of healthy subjects’ upper limb performances during daily-life gestures was performed with the Kinect V2 sensor. Findings will be the basis for a database of normative data for neurological patients’ motor evaluation

Robotic Assistance for Upper Limbs May Induce Slight Changes in Motor Modules Compared With Free Movements in Stroke Survivors: A Cluster-Based Muscle Synergy Analysis

Background: The efficacy of robot-assisted rehabilitation as a technique for achieving motor recovery is still being debated. The effects of robotic assistance are generally measured using standard clinical assessments. Few studies have investigated the value of human-centered instrumental analysis, taking the modular organization of the human neuromotor system into account in assessing how stroke survivors interact with robotic set-ups. In this paper, muscle synergy analysis was coupled with clustering procedures to elucidate the effect of human-robot interaction on the spatial and temporal features, and directional tuning of motor modules during robot-assisted movements.Methods: Twenty-two stroke survivors completed a session comprising a series of hand-to-mouth movements with and without robotic assistance. Patients were assessed instrumentally, recording kinematic, and electromyographic data to extract spatial muscle synergies and their temporal components. Patients' spatial synergies were grouped by means of a cluster analysis, matched pairwise across conditions (free and robot-assisted movement), and compared in terms of their spatial and temporal features, and directional tuning, to examine how robotic assistance altered their motor modules.Results: Motor synergies were successfully extracted for all 22 patients in both conditions. Seven clusters (spatial synergies) could describe the original datasets, in both free and robot-assisted movements. Interacting with the robot slightly altered the spatial synergies' features (to a variable extent), as well as their temporal components and directional tuning.Conclusions: Slight differences were identified in the characteristics of spatial synergies, temporal components and directional tuning of the motor modules of stroke survivors engaging in free and robot-assisted movements. Such effects are worth investigating in the framework of a modular description of the neuromusculoskeletal system to shed more light on human-robot interaction, and the effects of robotic assistance and rehabilitation

Personal risk or societal benefit? Investigating adults’ support for COVID-19 childhood vaccination

Parental hesitancy poses a serious threat to the success of the COVID-19 childhood vaccination campaign. We investigate whether adults' opinions on childhood vaccination can be influenced via two survey experiments in Italy (n = 3,633 participants) and the UK (n = 3,314 participants). Respondents were randomly assigned to: a “risk treatment” that highlighted the potential risks of COVID-19 to a child, a “herd immunity treatment” that emphasized the community benefits of pediatric vaccination, or a control message. Participants’ probability of supporting COVID-19 childhood vaccination was then assessed on a 0–100 scale. We find that the “risk treatment” reduced the proportion of Italian parents strongly against vaccination by up to 29.6 %, while increasing the proportion of neutral parents by up to 45.0 %. The “herd immunity treatment”, instead, was only effective among non-parents, resulting in a lower proportion of individuals against pediatric vaccination and a higher proportion of individuals in favor (both shifted by around 20 %)

Complement as a biological tool to control tumor growth

Deposits of complement components have been documented in several human tumors suggesting a potential involvement of the complement system in tumor immune surveillance. In vitro and in vivo studies have revealed a double role played by this system in tumor progression. Complement activation in the cancer microenvironment has been shown to promote cancer growth through the release of the chemotactic peptide C5a recruiting myeloid suppressor cells. There is also evidence that tumor progression can be controlled by complement activated on the surface of cancer cells through one of the three pathways of complement activation. The aim of this review is to discuss the protective role of complement in cancer with special focus on the beneficial effect of complement-fixing antibodies that are efficient activators of the classical pathway and contribute to inhibit tumor expansion as a result of MAC-mediated cancer cell killing and complement-mediated inflammatory process. Cancer cells are heterogeneous in their susceptibility to complement-induced killing that generally depends on stable and relatively high expression of the antigen and the ability of therapeutic antibodies to activate complement. A new generation of monoclonal antibodies are being developed with structural modification leading to hexamer formation and enhanced complement activation. An important progress in cancer immunotherapy has been made with the generation of bispecific antibodies targeting tumor antigens and able to neutralize complement regulators overexpressed on cancer cells. A great effort is being devoted to implementing combined therapy of traditional approaches based on surgery, chemotherapy and radiotherapy and complement-fixing therapeutic antibodies. An effective control of tumor growth by complement is likely to be obtained on residual cancer cells following conventional therapy to reduce the tumor mass, prevent recurrences and avoid disabilities