RESUMO
Introduction: Of the 45.4 million children under five affected by acute malnutrition in the world, the majority (31.8 million) are affected by moderate acute malnutrition (MAM). Its treatment is particularly complex in emergency settings such as the Diffa region in Niger. This study aims to evaluate the effectiveness and coverage of a simplified treatment protocol with Community Health Workers (CHWs) as treatment providers. Methods: This study is a non-randomized controlled trial. The control group (n = 181) received the standard protocol currently used in country, delivered by nursing staff only in health centres and health posts, while the intervention group (n = 483) received the simplified protocol which included nursing at health centres and CHWs at health post as treatment providers. Results: The recovery rate was higher in the simplified protocol group (99.6% vs. 79.56%, p < 0.001) recording lower time to recover and higher anthropometric gain. Treatment coverage in the intervention group increased from 28.8% to 84.9% and reduced in the control group (25.3% to 13.6%). No differences were found in the recovery rate of children treated by CHWs and nursing staff. Conclusion: The outcomes using the simplified protocol exceeded humanitarian requirements and demonstrated improvements compared to the standard protocol showing that the simplified protocol could be safely provided by CHWs in an emergency context. Further research in other contexts is needed to scale up this intervention.
RESUMO
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.
