The Politics of Medicine: Power, Actors, and Ideas in the Making of Health

The practice of medicine has become the prescribing of medicine. Reflecting a construct of health defined by Rationalism, individualism, and biomedical science, medicines (pharmaceuticals) are politically constructed to be the first – and sometimes only prescribed – line of defense against illness and disease. Pharmaceuticals also represent a highly desirable, ‘recession-proof’ component of many Nation-states’ (states’) export strategies, helping advanced economies, in particular, to maintain favorable trade balances and economic growth amidst the headwinds of deindustrialization. Higher use and the overreliance on pharmaceuticals promote an outsized role for certain actors and ideas in the making of global health, referring to the systems of medical practice, the norms defining health subconsciously and consciously, the politico-economic relations and decisions that prioritize certain qualities and determinants of health, and interventions relating to health. Concentrations of power deepened under globalization, reinforcing and internationalizing specific, hegemonic ideas about health that reflect the ideas and interests of dominant actors. These dynamics further privilege certain actors and ideas in political and economic processes, which have the practical effect of predetermining outcomes. In this way, power sustains the global normative and politico-economic conditions that comprise modern health—power makes health. This dissertation employs pharmaceuticals as a proxy to examine power asymmetries and market-oriented norms relating to health. The research examines the formative ideas and structuring role of power on the political salience, interests, values, and choices of the leading actors in global health. Rather than an exclusive focus on health’s visible outcomes, the research distinguishes the influence of power asymmetries expressed through norm formation and spread. It finds that health is a core issue of the 21st century global political economy and equitable scholarly focus and practical solutioning must be applied to the ideas, contexts, content, and processes that make health

Clinical, ultrasound parameters and tumor marker-based mathematical models and scoring systems in pre-surgical diagnosis of adnexal tumors

The choice of management for patients with adnexal tumors requires careful pre-surgical assessment. In case of adnexal masses, the diagnostic difficulties arise from the heterogenic nature of the adnexal diseases, presence of multiple functional changes, and lack of early symptoms of malignancy. A reliable pre-surgical differentiation cannot be performed using clinical features, ultrasound examination, or tumor markers alone. New diagnostic techniques and novel markers are under investigations, however no single test can be used to conclusively differentiate between malignant and non-malignant adnexal masses. Mathematical models and scoring systems based on different clinical, ultrasonographic and laboratory parameters alone or together may facilitate the diagnosis. Selected mathematical models and scoring systems are presented in this article. Models using only ultrasound features include simple rules, regression models, Gynecologic Imaging Report and Data System, and various morphologic scores. Some logistic regression models are based on multiple clinical and ultrasound data. The OVA1 test is based on five tumor markers without using other data. The Risk of Malignancy Algorithm uses two tumor markers with one clinical parameter. i.e. the menopausal status. Some models used clinical, ultrasound and tumor marker data together. This group of models includes risk of malignancy indices, artificial neural networks, and the ADNEX model. Although some of these models have been compared in the literature, more prospective studies are needed to select the most effective model, to develop the existing models, or to create new more effective models of oncological assessment of the adnexal tumors

Towards the operational estimation of a radiological plume using data assimilation after a radiological accidental atmospheric release

International audienceIn the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real-time forecasting of the activity concentrations of radionuclides is required by the decision makers for the preparation of adequate countermeasures. The accuracy of the forecast plume is highly dependent on the source term estimation. On several academic test cases, including real data, inverse modelling and data assimilation techniques were proven to help in the assessment of the source term. In this paper, a semi-automatic method is proposed for the sequential reconstruction of the plume, by implementing a sequential data assimilation algorithm based on inverse modelling, with a care to develop realistic methods for operational risk agencies. The performance of the assimilation scheme has been assessed through the intercomparison between French and Finnish frameworks. Two dispersion models have been used: Polair3D and Silam developed in two different research centres. Different release locations, as well as different meteorological situations are tested. The existing and newly planned surveillance networks are used and realistically large multiplicative observational errors are assumed. The inverse modelling scheme accounts for strong error bias encountered with such errors. The efficiency of the data assimilation system is tested via statistical indicators. For France and Finland, the average performance of the data assimilation system is strong. However there are outlying situations where the inversion fails because of a too poor observability. In addition, in the case where the power plant responsible for the accidental release is not known, robust statistical tools are developed and tested to discriminate candidate release sites

Estimation of errors in the inverse modeling of accidental release of atmospheric pollutant: Application to the reconstruction of the cesium-137 and iodine-131 source terms from the Fukushima Daiichi power plant

International audienceA major difficulty when inverting the source term of an atmospheric tracer dispersion problem is the estimation of the prior errors: those of the atmospheric transport model, those ascribed to the representativity of the measurements, those that are instrumental, and those attached to the prior knowledge on the variables one seeks to retrieve. In the case of an accidental release of pollutant, the reconstructed source is sensitive to these assumptions. This sensitivity makes the quality of the retrieval dependent on the methods used to model and estimate the prior errors of the inverse modeling scheme. We propose to use an estimation method for the errors' amplitude based on the maximum likelihood principle. Under semi-Gaussian assumptions, it takes into account, without approximation, the positivity assumption on the source. We apply the method to the estimation of the Fukushima Daiichi source term using activity concentrations in the air. The results are compared to an L-curve estimation technique and to Desroziers's scheme. The total reconstructed activities significantly depend on the chosen method. Because of the poor observability of the Fukushima Daiichi emissions, these methods provide lower bounds for cesium-137 and iodine-131 reconstructed activities. These lower bound estimates, 1.2 × 1016 Bq for cesium-137, with an estimated standard deviation range of 15%-20%, and 1.9 − 3.8 × 1017 Bq for iodine-131, with an estimated standard deviation range of 5%-10%, are of the same order of magnitude as those provided by the Japanese Nuclear and Industrial Safety Agency and about 5 to 10 times less than the Chernobyl atmospheric releases

Tyrosol and Hydroxytyrosol: Their Role in Cardioprotection

Introduction: Recent research are focused on natural compounds for preventing cardiovascular diseases, with emphasis on tyrosol and hydroxytyrosol in olive oil. Cardiovascular diseases are linked to risk factors, and adopting a Mediterranean diet rich in these compounds is recognized for reducing risks.  Understanding these compounds' actions may inform new strategies for preventing and treating cardiovascular diseases.  Aim: The aim of this paper is a systematic review of articles and research regarding the Role of tyrosol and hydroxytyrosol in Cardioprotection  Review methods: An systematic review of scientific literature was conducted using the following keywords: Tyrosol, hydroxytyrosol, cardioprotection, cardiovascular diseases, olive oil cardioprotective role. Thirty-four articles published until 2023 were searched and analyzed.  Abbreviated description of the state of knowledge: Tyrosol and hydroxytyrosol, prominent in olive oil, are studied for their potential cardioprotective properties. Linked to a Mediterranean diet, these compounds show promise in reducing cardiovascular disease risk. They counter oxidative stress, improve lipid profiles, and modulate inflammatory processes. Clinical studies suggest their positive impact, with tyrosol also explored as a potential anticoagulant. Ongoing research aims to uncover optimal doses and mechanisms, highlighting their significance in cardiovascular health.  Conclusions: Tyrosol and hydroxytyrosol, found in olive oil, show promise in cardioprotection by combating oxidative stress, improving lipid profiles, and modulating inflammation. Clinical studies suggest their positive impact on cardiovascular health. Tyrosol has potential as a anticoagulant and exhibits antioxidant effects. These compounds present a compelling avenue for future therapeutic interventions, with emphasis on understanding mechanisms and optimizing supplementation.&nbsp

Estimating model evidence using data assimilation

We review the field of data assimilation (DA) from a Bayesian perspective and show that, in addition to its by now common application to state estimation, DA may be used for model selection. An important special case of the latter is the discrimination between a factual model–which corresponds, to the best of the modeller's knowledge, to the situation in the actual world in which a sequence of events has occurred–and a counterfactual model, in which a particular forcing or process might be absent or just quantitatively different from the actual world. Three different ensemble‐DA methods are reviewed for this purpose: the ensemble Kalman filter (EnKF), the ensemble four‐dimensional variational smoother (En‐4D‐Var), and the iterative ensemble Kalman smoother (IEnKS). An original contextual formulation of model evidence (CME) is introduced. It is shown how to apply these three methods to compute CME, using the approximated time‐dependent probability distribution functions (pdfs) each of them provide in the process of state estimation. The theoretical formulae so derived are applied to two simplified nonlinear and chaotic models: (i) the Lorenz three‐variable convection model (L63), and (ii) the Lorenz 40‐variable midlatitude atmospheric dynamics model (L95). The numerical results of these three DA‐based methods and those of an integration based on importance sampling are compared. It is found that better CME estimates are obtained by using DA, and the IEnKS method appears to be best among the DA methods. Differences among the performance of the three DA‐based methods are discussed as a function of model properties. Finally, the methodology is implemented for parameter estimation and for event attribution

Towards the operational application of inverse modelling for the source identification and plume forecast of an accidental release of radionuclides

International audienceIn the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real-time forecasting of the activity concentrations of radionuclides is required by the decision makers for the preparation of adequate countermeasures. Yet, the accuracy of the forecast plume is highly dependent on the source term estimation. Inverse modelling and data assimilation techniques should help in that respect. In this presentation, a semi-automatic method is proposed for the sequential reconstruction of the plume, by implementing a sequential data assimilation algorithm based on inverse modelling, with a care to develop realistic methods for operational risk agencies. The performance of the assimilation scheme has been assessed through the intercomparison between French and Finnish frameworks. Three dispersion models have been used: Polair3D, with or without plume-in-grid, both developed at CEREA, and SILAM, developed at FMI. Different release locations, as well as different meteorological situations are tested. The existing and newly planned surveillance networks are used and realistically large observational errors are assumed. Statistical indicators to evaluate the efficiency of the method are presented and the results are discussed. In addition, in the case where the power plant responsible for the accidental release is not known, robust statistical tools aredeveloped and tested to discriminate candidate release sites

Estimation of the caesium-137 source term from the Fukushima Daiichi nuclear power plant using a consistent joint assimilation of air concentration and deposition observations

International audienceInverse modelling techniques can be used to estimate the amount of radionuclides and the temporal profile of the source term released in the atmosphere during the accident of the Fukushima Daiichi nuclear power plant in March 2011. In Winiarek et al. (2012b), the lower bounds of the caesium-137 and iodine-131 source terms were estimated with such techniques, using activity concentration measurements. The importance of an objective assessment of prior errors (the observation errors and the background errors) was emphasised for a reliable inversion. In such critical context where the meteorological conditions can make the source term partly unobservable and where only a few observations are available, such prior estimation techniques are mandatory, the retrieved source term being very sensitive to this estimation. We propose to extend the use of these techniques to the estimation of prior errors when assimilating observations from several data sets. The aim is to compute an estimate of the caesium-137 source term jointly using all available data about this radionuclide, such as activity concentrations in the air, but also daily fallout measurements and total cumulated fallout measurements. It is crucial to properly and simultaneously estimate the background errors and the prior errors relative to each data set. A proper estimation of prior errors is also a necessary condition to reliably estimate the a posteriori uncertainty of the estimated source term. Using such techniques, we retrieve a total released quantity of caesium-137 in the interval 11.6 − 19.3 PBq with an estimated standard deviation range of 15 − 20% depending on the method and the data sets. The "blind" time intervals of the source term have also been strongly mitigated compared to the first estimations with only activity concentration data

Causes of hoarseness - systematic review

Introduction and purpose of the work: Hoarseness is a symptom of a disease characterized by a hoarse, tense or hoarse voice resulting from disturbance of the vibration of the vocal folds. It is a common symptom of the disease in patients during medical consultations in primary health care, and the causes of its occurrence can be varied. State of knowledge (brief description): Treating hoarseness depends on what causes it. For this reason, during the diagnosis, various causes that may be the cause of hoarseness should be taken into account, such as infections, structural changes caused by the use of the voice organ, psychogenic causes, autoimmune diseases, systemic diseases or neoplastic diseases. Summary: In-depth diagnosis and an interdisciplinary approach are necessary to identify the cause of chronic hoarseness and initiate effective treatment