Outcomes of out-of-hospital cardiac arrest in Ireland 2012-2020: Protocol for an observational study [version 2; peer review: 1 approved, 3 approved with reservations]

Background Out-of-hospital cardiac arrest (OHCA) is a leading cause of preventable mortality that now affects almost 3,000 people each year in Ireland. Survival is low at 6–7%, compared to a European average of 8%. The Irish Out-of-Hospital Cardiac Registry (OHCAR) prospectively gathers data on all OHCA in Ireland where emergency medical services attempted resuscitation. The Irish health system has undergone several developments that are relevant to OHCA care in the period 2012–2020. OHCAR data provides a means of exploring temporal trends in OHCA incidence, care, and outcomes over time. It also provides a means of exploring whether system developments were associated with a change in key outcomes. This research aims to summarise key trends in available OHCAR data from the period 2012 – 2020, to explore and model predictors of bystander CPR, bystander defibrillation, and survival, and to explore the hypothesis that significant system level temporal developments were associated with improvements in these outcomes. Methods The following protocol sets out the relevant background and research approach for an observational study that will address the above aims. Key trends in available OHCAR data (2012 – 2020) will be described and evaluated using descriptive summaries and graphical displays. Multivariable logistic regression will be used to model predictors of ‘bystander CPR’, ‘bystander defibrillation’ and ‘survival to hospital discharge’ and to explore the effects (if any) of system level developments in 2015/2016 and the COVID-19 pandemic (2020) on these outcomes. Discussion The findings of this research will be used to understand temporal trends in the care processes and outcomes for OHCA in Ireland over the period 2012-2020. The results can further be used to optimise future health system developments for OHCA in both Ireland and internationally

Integrated microfluidic device for in-line monitoring of glyphosate assisted by Surface Enhanced Raman Spectroscopy

Glyphosate is one of the most widely used pesticides as it is a non-selective systemic herbicide that is quickly degrade in soil. Glyphosate disrupts the biochemical shikimate pathway, which produces aromatic amino acids that are essential for plant growth and development. It acts as a competitive inhibitor of the 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase, an enzyme absent from mammals. Therefore, glyphosate has been presented as (a) non-toxic for humans and (b) non-persistent. However, these premises have been recently questioned since traces of glyphosate and its main metabolite (aminomethylphosphonic acid, AMPA) have been detected in surface water, groundwater, soil as well as in cotton products. Quantifying and monitoring trace amounts of glyphosate and pesticide residues in the environment typically relies on analytical methods such as Gas Chromatography (GC) or Liquid Chromatography (LC) coupled with mass spectrometry (MS). However, these types of methods are expensive and often not suitable for in situ field analysis. Surface Enhanced Raman Spectroscopy (SERS) coupled with microfluidic could be an alternative method, which allows a fast and direct quantification on the field with miniaturised instrumentation. Indeed, SERS combines the advantage of Raman spectroscopy and is able to detect traces due to the signal enhancement resulting from the adsorption of the analyte on the rough nanoparticle surface. Here we focus on the development of an inline analytical method for water monitoring assisted by SERS. The inline detection of glyphosate is performed in a microfluidic setup, constructed with high-purity PFA coils (1/16" o.d., 0.01" i.d.), divided in three compartments (Figure 1) : (a) in situ synthesis of silver nanoparticles (Ag NPs), (b) mixing of the analyte with the Ag NPs and (c) the detection zone. Experimental parameters such as the type of micromixers for the synthesis of Ag NPs and the mixing between glyphosate and Ag NPs, the concentration of reagents for the synthesis of Ag NPs, the residence time or the flow applied in the setup were investigated and optimized through D-optimal (26 runs) and I-optimal design of experiments. In particular, we will discuss how the experimental parameters influence the quantitative detection of SERS intensity.Développement d'une approche intégrée en microfluidique pour la détection SERS en ligne de pesticide

In-line monitoring of glyphosate in a microfluidic setup by SERS, development and optimization by DoE

Quantifying and monitoring trace amounts of glyphosate and pesticide residues in the environment typically relies on analytical methods such as Gas Chromatography (GC) or Liquid Chromatography (LC) coupled with mass spectrometry (MS) [4]. However, these types of methods are expensive and often less suitable for in situ field analysis. Surface Enhanced Raman Spectroscopy (SERS) coupled with microfluidic could be an alternative method, which allows a fast and direct quantification on the field [5] with miniaturised instrumentation. Indeed, SERS combines the advantage of Raman spectroscopy and is able to detect traces due to the signal enhancement resulting from the adsorption of the analyte on the rough nanoparticle surface [6]

Design of experiments and design space approaches in the pharmaceutical bioprocess optimization

peer reviewedThe optimization of pharmaceutical bioprocesses suffers from several challenges like complexity, upscaling costs, regulatory approval, leading to the risk of delivering substandard drugs to patients. Bioprocess is very complex and requires the evaluation of multiple components that need to be monitored and controlled in order to attain the desired state when the process ends. Statistical design of experiments (DoE) is a powerful tool for optimizing bioprocesses because it plays a critical role in the quality by design strategy as it is useful in exploring the experimental domain and providing statistics of interest that enable scientists to understand the impact of critical process parameters on the critical quality attributes. This review summarizes selected publications in which DoE methodology was used to optimize bioprocess. The main objective of the critical review was to clearly demonstrate potential benefits of using the DoE and design space methodologies in bioprocess optimization.Icon

Optimisation of a surface enhanced Raman scattering method using design of experiments and Bayesian design space modelling

Surface enhanced Raman scattering (SERS) is an alternative technique based on Raman spectroscopy, which has been increasingly applied to pharmaceutical analytical chemistry in the last decade. It consists in enhancing the Raman effect by performing analyses using metallic surfaces, such as silver and gold colloids, on which the target molecules are adsorbed to be detected. It has been observed that in this way, an enhancement factor of 103-106 times can be obtained and the lack of sensibility related to conventional Raman scattering overcome [1]. Nowadays, design of experiment (DoE) is widely employed for modelling phenomena in analytical method development and optimisation, especially in the context of separation techniques. It is a structured approach that allows correlating key responses to controllable variables. Ideally, a certain number of factors may affect the critical method attributes (CMAs) of an analytical process in a negative or positive way. These factors are named critical method parameters (CMPs). DoE is employed, as a chemometric tool, to individuate CMPs and then, deeply study how they affect the process under study. To do so, CMAs are linked to CMPs by a regression model built by means of multivariate linear or partial least squares regression. Generally, the designs can be classified in two categories: screening and optimization designs. The formers are generally implemented when a high number of parameters are supposed to influence the analytical process and no much prior information is available. They result in useful tools to study the effects of both continuous and discontinuous factors. Instead, the optimisation designs are principally used to study wisely selected continuous factors [2]. The design space (DS) is defined as a multidimensional area in which the specifications given to the CMAs are met with a defined level of probability. Obviously, the larger the DS is, the more robust the method is. Its computation is achieved by several approaches, such as Monte-Carlo simulations, Bayesian methods as well as bootstrapping techniques [3]. The aim of this project was to combine and apply two potent chemometric tools such as DoE and Bayesian DS to SERS method development and optimisation. [1] Cailletaud, J., De Bleye, C., Dumont, E., Sacré, P.-Y., Netchacovitch, L., Gut, Y., Boiret, M., Ginot, Y.-M., Hubert, P., Ziemons, E., Critical review of surface-enhanced Raman spectroscopy applications in the pharmaceutical field. J. Pharm. Biomed. Anal. 147, 458-472, 2018. [2] Sahu, P.K., Ramisetti, N.R., Cecchi, T., Swain, S., Patro, C.S., Panda, J., An overview of experimental design in HPLC method development and validation, J. Pharm. Biomed. Anal. 147, 590-611, 2018. [3] Deidda, R., Orlandini, S., Hubert, P., Hubert, C., Risk-based approach for method development in pharmaceutical quality control context: A critical review. J. Pharm. Biomed. Anal. 161, 110-121, 2018

Development and validation of an integrated microfluidic device with an in-line Surface Enhanced Raman Spectroscopy (SERS) detection of glyphosate in drinking water

peer reviewedGlyphosate, also known as N-(phosphonomethyl)glycine, is one of the most widely used herbicides in the world. However, the controversy surrounding the toxicity of glyphosate and its main breakdown product, aminomethylphosphonic acid (AMPA), remains a serious public concern. Therefore, there is a clear need to develop a rapid, sensitive and automated alternative method for the quantification of glyphosate and AMPA. In this context, surface enhanced Raman spectroscopy (SERS) coupled with a microfluidic system for the determination of glyphosate in tap water was developed, optimized and validated. The design of the microfluidic configuration for this application was built constructed to integrate the synthesis of the SERS substrate through to the detection of the analyte. To optimize the microfluidic setup, a design of experiments approach was used to maximize the SERS signal of glyphosate. Subsequently, an approach based on the European guideline document SANTE/11312/2021 was used to validate the method in the range of 78–480 μg/L using the normalized band intensities. The limit of detection and quantification obtained for glyphosate were 40 and 78 μg/L, respectively. Recoveries were in the range 76–117%, while repeatability and intra-day repro- ducibility were ≤17%. Finally, the method was also tested for the determination of AMPA in tap water matrix and for the simultaneous detection of AMPA and glyphosate