Notes

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significant correlation (r = 0.32, p ≤ 0.01) with the MSSD of passive suicidal ideation, but not with active suicidal ideation or the MSSD total score. CONCLUSION Overall the findings are in line with our assumptions and the IPTS and underline that trait impulsivity is related to suicidal behavior and the fluctuation of suicidal ideation, but not to suicidal ideation itself. Thus, trait impulsivity seems to act as a distal risk factor via capability for suicide and it seems to play a role for the dynamics of suicidal ideation. The results have to be investigated in larger samples, with a higher risk of suicide and in prospective studies. Moreover, the role of the fluctuation of suicidal ideation for the prediction of suicide risk should be investigated in future studies. BACKGROUND Medication adherence is especially challenging in a chronic condition such as Relapsing Multiple Sclerosis (RMS). Medication adherence among persons with MS (PwMS) is usually assessed via a single measure, mostly electronic pharmacy records. OBJECTIVES Assess medication adherence in multiple modes across time among PwMS; examine consistency across time and associations between measures. METHODS PwMS (N = 194) were surveyed prospectively at three time points (baseline, 6 and 12 months later) and their health records and medication claims were retrospectively obtained. Adherence score was based on medication possession ratio (MPR) and two patient-reported outcome (PRO) measures. Electronic monitoring devices assessing medication adherence were also initiated. check details RESULTS MPR of each nonadherent PwMS, once compared to medical records containing prescription changes, was found as underestimating adherence. MPR was between the two PROs in identifying nonadherence and associations between the measures and across time was moderate (Kappa ranged 0.37-0.42). The use of electronic monitoring devices was not adopted by patients. A score indicated adherence as 66% and 64.9% at Time1 and Time 2, respectively, with 21.1% of PwMS nonadherent at both time points. Adherence did not vary significantly by DMT type. CONCLUSIONS Being a dynamic behavior, medication adherence should be repeatedly monitored by using multiple modalities and focused on in clinician-patient encounters, especially in chronic diseases such as MS, which requires long-term treatments. Applying PROs in monitoring medication adherence would facilitate implementation of Participatory Medicine and patient-centered strategies in MS care. BACKGROUND One of the main challenges in multiple sclerosis (MS) is to predict disease progression based on patient characteristics and therapeutic strategies. We therefore performed a systematic review to critically appraise the composite tools available for this purpose. METHODS We performed electronic database searches in MEDLINE, EMBASE, Web of Science and the Cochrane Library. We included studies in English or French that developed and/or validated a predictive model for MS patients. Two reviewers independently screened articles by title and abstract. Three teams of two reviewers assessed the full text of each relevant study. RESULTS Database searches yielded 6,035 studies after deduplication. Among the 42 screened full texts, 15 articles satisfied the eligibility criteria. Of these, six articles examined the development of predictive tools, six articles aimed to validate existing tools and three articles proposed both development and validation. We identified numerous methodological pitfalls, especially the lack of adequate validations in terms of discrimination and calibration. Only two scoring systems were externally validated several times the Rio and the modified Rio scores. Nevertheless, their accuracies were highly variable, ranging from 65% to 91%. CONCLUSIONS Overall, there is a lack of validated predictive tools in MS, and further external validation of the existing ones are required. Demonstration of the clinical usefulness is also needed prior to being transferred into clinical practice. Finally, our study illustrates that the MS literature needs to integrate good standards in developing and validating predictive models. The automatic identification (location, segmentation, and classification) by UAV- based optical imaging of spills of transparent floating Hazardous and Noxious Substances (HNS) benefits the on-site response to spill incidents, but it is also challenging. With a focus on the on-site optical imaging of HNS, this study explores the potential of single spectral imaging for HNS identification using the Faster R-CNN architecture. Images at 365 nm (narrow UV band), blue channel images (visible broadband of ∼400-600 nm), and RGB images of typical HNS (benzene, xylene, and palm oil) in different scenarios were studied with and without Faster R-CNN. Faster R-CNN was applied to locate and classify the HNS spills. The segmentation using Faster R-CNN-based methods and the original masking methods, including Otsu, Max entropy, and the local fuzzy thresholding method (LFTM), were investigated to explore the optimal wavelength and corresponding image processing method for the optical imaging of HNS. We also compared the classification and segmentation results of this study with our previously published studies on multispectral and whole spectral images. The results demonstrated that single spectral UV imaging at 365 nm combined with Faster R-CNN has great potential for the automatic identification of transparent HNS floating on the surface of the water. RGB images and images using Faster R-CNN in the blue channel are capable of HNS segmentation. Despite food technology advancements, food safety policies and alert systems, foodborne diseases are still a relevant concern for consumers and public health authorities, with great impacts on the economy and the society. Evaluating the cost of foodborne diseases may support the design and the implementation of policy interventions. This paper proposes a simple method for cost identification of foodborne diseases, accessible to researchers and practitioners who are not specialist in economics. The method is based on the assumption that epidemiological and economic models can be integrated to understand how the burden of disease determines costs in a wider socio-economic perspective. Systems thinking and interdisciplinary approach are the pivotal conceptual tools of the method. Systems thinking allows for the understanding of the complex relationships working among the elementary units of a system (e.g. wildlife, bred animals, consumers, environment, agro-food industry) in the occurrence of a health problem such foodborne diseases.
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