Leucémie aiguë myéloblastique et translocation (8;16) (p11;p13), premier cas marocain d’une entité clinico- biologique distinct

La cytogénétique constitue un outil indispensable pour le diagnostic et le pronostic de la leucémie aigue myéloïde (LAM). La t(8 ;16)(p11 ;p13) est rare au cours de cette pathologie. Nous décrivons le cas d'une patiente de 22 ans, admise pour un syndrome d'insuffisance médullaire complet associé à une altération de l'état général. L'examen clinique initial montrait un purpura ecchymotique diffus et des adénopathies latérocérvicales centimétriques bilatérales. L'hémogramme avait montré une anémie à 7,6g /dl normochrome normocytaire, des globules blancs à 87,8×109/L, 15% de polynucléaires neutrophiles , 60% de blastes, 24% de lymphocytes, 1% de Monocytes et 65×109/L de plaquettes. Le myélogramme avait objectivé une LAM1. Sur l'immunophenotypage les marqueurs positifs étaient le CD33 (99%), le CD15 (73%), le CD38 (95%) et l'HLA-DR (88%), les marqueurs monocytoïdes CD14 et CD64 étaient positifs, le CD34, les marqueurs lymphopïdes, la MPO (26%) et le CD13 (2%) étaient négatifs. Le caryotype avait montré: t(8,16)(p11 , p13) add16 (20/20). L'inversion du chromosome 16 recherchée par FISH était négative. Le traitement avait consisté en 2 cures d'induction et 2 cures de consolidation selon le protocole national de traitement des LAM (Cytarabine, daunorubicine, etoposide), la rémission complète avait été obtenue en fin d'induction I, maintenue 9 mois suivie d'une rechute; Vu l'absence de possibilité d'une allogreffe, un traitement palliatif a été instauré, la malade est décédée de sa maladie un mois après la rechute. Notre cas se présente comme les cas décrits dans la littérature avec des données clinico- biologiques particulières.Pan African Medical Journal 2015; 2

Preprocessing Methods for Ambulatory HRV Analysis Based on HRV Distribution, Variability and Characteristics (DVC)

Thanks to wearable devices joint with AI algorithms, it is possible to record and analyse physiological parameters such as heart rate variability (HRV) in ambulatory environments. The main downside to such setups is the bad quality of recorded data due to movement, noises, and data losses. These errors may considerably alter HRV analysis and should therefore be addressed beforehand, especially if used for medical diagnosis. One widely used method to handle such problems is interpolation, but this approach does not preserve the time dependence of the signal. In this study, we propose a new method for HRV processing including filtering and iterative data imputation using a Gaussian distribution. The particularity of the method is that many physiological aspects are taken into consideration, such as HRV distribution, RR variability, and normal boundaries, as well as time series characteristics. We study the effect of this method on classification using a random forest classifier (RF) and compare it to other data imputation methods including linear, shape-preserving piecewise cubic Hermite (pchip), and spline interpolation in a case study on stress. Features from reconstructed HRV signals of 67 healthy subjects using all four methods were analysed and separately classified by a random forest algorithm to detect stress against relaxation. The proposed method reached a stable F1 score of 61% even with a high percentage of missing data, whereas other interpolation methods reached approximately 54% F1 score for a low percentage of missing data, and the performance drops to about 44% when the percentage is increased. This suggests that our method gives better results for stress classification, especially on signals with a high percentage of missing data

Preprocessing Methods for Ambulatory HRV Analysis Based on HRV Distribution, Variability and Characteristics (DVC)

Thanks to wearable devices joint with AI algorithms, it is possible to record and analyse physiological parameters such as heart rate variability (HRV) in ambulatory environments. The main downside to such setups is the bad quality of recorded data due to movement, noises, and data losses. These errors may considerably alter HRV analysis and should therefore be addressed beforehand, especially if used for medical diagnosis. One widely used method to handle such problems is interpolation, but this approach does not preserve the time dependence of the signal. In this study, we propose a new method for HRV processing including filtering and iterative data imputation using a Gaussian distribution. The particularity of the method is that many physiological aspects are taken into consideration, such as HRV distribution, RR variability, and normal boundaries, as well as time series characteristics. We study the effect of this method on classification using a random forest classifier (RF) and compare it to other data imputation methods including linear, shape-preserving piecewise cubic Hermite (pchip), and spline interpolation in a case study on stress. Features from reconstructed HRV signals of 67 healthy subjects using all four methods were analysed and separately classified by a random forest algorithm to detect stress against relaxation. The proposed method reached a stable F1 score of 61% even with a high percentage of missing data, whereas other interpolation methods reached approximately 54% F1 score for a low percentage of missing data, and the performance drops to about 44% when the percentage is increased. This suggests that our method gives better results for stress classification, especially on signals with a high percentage of missing data

Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models

Stress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring, identification and prevention of stress are of the utmost importance. While most stress monitoring is carried out through self-reporting, there are now several studies on stress detection from physiological signals using Artificial Intelligence algorithms. However, the generalizability of these models is only rarely discussed. The main goal of this work is to provide a monitoring proof-of-concept tool exploring the generalization capabilities of Heart Rate Variability-based machine learning models. To this end, two Machine Learning models are used, Logistic Regression and Random Forest to analyze and classify stress in two datasets differing in terms of protocol, stressors and recording devices. First, the models are evaluated using leave-one-subject-out cross-validation with train and test samples from the same dataset. Next, a cross-dataset validation of the models is performed, that is, leave-one-subject-out models trained on a Multi-modal Dataset for Real-time, Continuous Stress Detection from Physiological Signals dataset and validated using the University of Waterloo stress dataset. While both logistic regression and random forest models achieve good classification results in the independent dataset analysis, the random forest model demonstrates better generalization capabilities with a stable F1 score of 61%. This indicates that the random forest can be used to generalize HRV-based stress detection models, which can lead to better analyses in the mental health and medical research field through training and integrating different models