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- Bouakaze, Caroline, et al.
(författare)
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Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex) : Unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters
- 2020
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Ingår i: Forensic Science International. - : Elsevier BV. - 1872-4973 .- 1878-0326. ; 48
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Tidskriftsartikel (refereegranskat)abstract
- We developed a new mutationally well-balanced 32 Y-STR multiplex (CombYplex) together with a machine learning (ML) program PredYMaLe to assess the impact of STR mutability on haplogourp prediction, while respecting forensic community criteria (high DC/HD). We designed CombYplex around two sub-panels M1 and M2 characterized by average and high-mutation STR panels. Using these two sub-panels, we tested how our program PredYmale reacts to mutability when considering basal branches and, moving down, terminal branches. We tested first the discrimination capacity of CombYplex on 996 human samples using various forensic and statistical parameters and showed that its resolution is sufficient to separate haplogroup classes. In parallel, PredYMaLe was designed and used to test whether a ML approach can predict haplogroup classes from Y-STR profiles. Applied to our kit, SVM and Random Forest classifiers perform very well (average 97 %), better than Neural Network (average 91 %) and Bayesian methods (< 90 %). We observe heterogeneity in haplogroup assignation accuracy among classes, with most haplogroups having high prediction scores (99-100 %) and two (E1b1b and G) having lower scores (67 %). The small sample sizes of these classes explain the high tendency to misclassify the Y-profiles of these haplogroups; results were measurably improved as soon as more training data were added. We provide evidence that our ML approach is a robust method to accurately predict haplogroups when it is combined with a sufficient number of markers, well-balanced mutation rate Y-STR panels, and large ML training sets. Further research on confounding factors (such as CNV-STR or gene conversion) and ideal STR panels in regard to the branches analysed can be developed to help classifiers further optimize prediction scores.
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| 2. |
- Chevreul, Karine, et al.
(författare)
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Social/economic costs and health-related quality of life in patients with cystic fibrosis in Europe
- 2016
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Ingår i: European Journal of Health Economics. - : Springer Science and Business Media LLC. - 1618-7598 .- 1618-7601. ; 17, s. 7-18
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Our goal was to provide data on the economic burden and health-related quality of life (HRQOL) of patients with cystic fibrosis (CF) and their caregivers in Europe. Methods: A cross-sectional study was carried out on adults and children with CF in eight European countries. Patients completed an anonymous questionnaire regarding their socio-demographic characteristics, use of healthcare services and presence of a caregiver. Costs were calculated with a bottom-up approach using unit costs from each participating country, and HRQOL was assessed using EQ-5D. The principal caregiver also answered a questionnaire on their characteristics, HRQOL and burden. Results: A total of 905 patients with CF was included (399 adults and 506 children). The total average annual cost per patient varied from €21,144 in Bulgaria to €53,256 in Germany. Adults had higher direct healthcare costs than children, but children had much higher informal care costs (P
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| 3. |
- Luquiens, Amandine, et al.
(författare)
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Pictograms to aid laypeople in identifying the addictiveness of gambling products (PictoGRRed study)
- 2022
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- The structural addictive characteristics of gambling products are important targets for prevention, but can be unintuitive to laypeople. In the PictoGRRed (Pictograms for Gambling Risk Reduction) study, we aimed to develop pictograms that illustrate the main addictive characteristics of gambling products and to assess their impact on identifying the addictiveness of gambling products by laypeople. We conducted a three-step study: (1) use of a Delphi consensus method among 56 experts from 13 countries to reach a consensus on the 10 structural addictive characteristics of gambling products to be illustrated by pictograms and their associated definitions, (2) development of 10 pictograms and their definitions, and (3) study in the general population to assess the impact of exposure to the pictograms and their definitions (n = 900). French-speaking experts from the panel assessed the addictiveness of gambling products (n = 25), in which the mean of expert’s ratings was considered as the true value. Participants were randomly provided with the pictograms and their definitions, or with a standard slogan, or with neither (control group). We considered the control group as representing the baseline ability of laypeople to assess the addictiveness of gambling products. Each group and the French-speaking experts rated the addictiveness of 14 gambling products. The judgment criterion was the intraclass coefficients (ICCs) between the mean ratings of each group and the experts, reflecting the level of agreement between each group and the experts. Exposure to the pictograms and their definition doubled the ability of laypeople to assess the addictiveness of gambling products compared with that of the group that read a slogan or the control group (ICC = 0.28 vs. 0.14 (Slogan) and 0.14 (Control)). Laypeople have limited awareness of the addictive characteristics of gambling products. The pictograms developed herein represent an innovative tool for universally empowering prevention and for selective prevention.
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