Framework, principles and recommendations for utilising participatory methodologies in the co-creation and evaluation of public health interventions

Background: Due to the chronic disease burden on society, there is a need for preventive public health interventions to stimulate society towards a healthier lifestyle. To deal with the complex variability between individual lifestyles and settings, collaborating with end-users to develop interventions tailored to their unique circumstances has been suggested as a potential way to improve effectiveness and adherence. Co-creation of public health interventions using participatory methodologies has shown promise but lacks a framework to make this process systematic. The aim of this paper was to identify and set key principles and recommendations for systematically applying participatory methodologies to co-create and evaluate public health interventions. Methods: These principles and recommendations were derived using an iterative reflection process, combining key learning from published literature in addition to critical reflection on three case studies conducted by research groups in three European institutions, all of whom have expertise in co-creating public health interventions using different participatory methodologies. Results: Key principles and recommendations for using participatory methodologies in public health intervention co-creation are presented for the stages of: Planning (framing the aim of the study and identifying the appropriate sampling strategy); Conducting (defining the procedure, in addition to manifesting ownership); Evaluating (the process and the effectiveness) and Reporting (providing guidelines to report the findings). Three scaling models are proposed to demonstrate how to scale locally developed interventions to a population level. Conclusions: These recommendations aim to facilitate public health intervention co-creation and evaluation utilising participatory methodologies by ensuring the process is systematic and reproducible

Clinical oxidative stress during leprosy multidrug therapy:impact of dapsone oxidation

This study aims to assess the oxidative stress in leprosy patients under multidrug therapy (MDT; dapsone, clofazimine and rifampicin), evaluating the nitric oxide (NO) concentration, catalase (CAT) and superoxide dismutase (SOD) activities, glutathione (GSH) levels, total antioxidant capacity, lipid peroxidation, and methemoglobin formation. For this, we analyzed 23 leprosy patients and 20 healthy individuals from the Amazon region, Brazil, aged between 20 and 45 years. Blood sampling enabled the evaluation of leprosy patients prior to starting multidrug therapy (called MDT 0) and until the third month of multidrug therapy (MDT 3). With regard to dapsone (DDS) plasma levels, we showed that there was no statistical difference in drug plasma levels between multibacillary (0.518±0.029 μg/mL) and paucibacillary (0.662±0.123 μg/mL) patients. The methemoglobin levels and numbers of Heinz bodies were significantly enhanced after the third MDTsupervised dose, but this treatment did not significantly change the lipid peroxidation and NO levels in these leprosy patients. In addition, CAT activity was significantly reduced in MDT-treated leprosy patients, while GSH content was increased in these patients. However, SOD and Trolox equivalent antioxidant capacity levels were similar in patients with and without treatment. These data suggest that MDT can reduce the activity of some antioxidant enzyme and influence ROS accumulation, which may induce hematological changes, such as methemoglobinemia in patients with leprosy. We also explored some redox mechanisms associated with DDS and its main oxidative metabolite DDS-NHOH and we explored the possible binding of DDS to the active site of CYP2C19 with the aid of molecular modeling software

Accelerated discovery of two crystal structure types in a complex inorganic phase field

The discovery of new materials is hampered by the lack of efficient approaches to the exploration of both the large number of possible elemental compositions for such materials, and of the candidate structures at each composition1. For example, the discovery of inorganic extended solid structures has relied on knowledge of crystal chemistry coupled with time-consuming materials synthesis with systematically varied elemental ratios2,3. Computational methods have been developed to guide synthesis by predicting structures at specific compositions4,5,6 and predicting compositions for known crystal structures7,8, with notable successes9,10. However, the challenge of finding qualitatively new, experimentally realizable compounds, with crystal structures where the unit cell and the atom positions within it differ from known structures, remains for compositionally complex systems. Many valuable properties arise from substitution into known crystal structures, but materials discovery using this approach alone risks both missing best-in-class performance and attempting design with incomplete knowledge8,11. Here we report the experimental discovery of two structure types by computational identification of the region of a complex inorganic phase field that contains them. This is achieved by computing probe structures that capture the chemical and structural diversity of the system and whose energies can be ranked against combinations of currently known materials. Subsequent experimental exploration of the lowest-energy regions of the computed phase diagram affords two materials with previously unreported crystal structures featuring unusual structural motifs. This approach will accelerate the systematic discovery of new materials in complex compositional spaces by efficiently guiding synthesis and enhancing the predictive power of the computational tools through expansion of the knowledge base underpinning them

Multichannel Online Blind Speech Dereverberation with Marginalization of Static Observation Parameters in a Rao-Blackwellized Particle Filter

Room reverberation leads to reduced intelligibility of audio signals and spectral coloration of audio signals. Enhancement of acoustic signals is thus crucial for high-quality audio and scene analysis applications. Multiple sensors can be used to exploit statistical evidence from multiple observations of the same event to improve enhancement. Whilst traditional beamforming techniques suffer from interfering reverberant reflections with the beam path, other approaches to dereverberation often require at least partial knowledge of the room impulse response which is not available in practice, or rely on inverse filtering of a channel estimate to obtain a clean speech estimate, resulting in difficulties with non-minimum phase acoustic impulse responses. This paper proposes a multi-sensor approach to blind dereverberation in which both the source signal and acoustic channel are directly estimated from the distorted observations using their optimal estimators. The remaining model parameters are sampled from hypothesis distributions using a particle filter, thus facilitating real-time dereverberation. This approach was previously successfully applied to single-sensor blind dereverberation. In this paper, the single-channel approach is extended to multiple sensors. Performance improvements due to the use of multiple sensors are demonstrated on synthetic and baseband speech examples