Dyslipidemia is a disorder where abnormally lipid concentrations circulate in the bloodstream. The disorder is common in type 2 diabetics (T2D) and is linked with T2D comorbidities, particularly cardiovascular disease...Dyslipidemia is a disorder where abnormally lipid concentrations circulate in the bloodstream. The disorder is common in type 2 diabetics (T2D) and is linked with T2D comorbidities, particularly cardiovascular disease. Dyslipidemia in T2D is typically characterized by elevated plasma triglyceride and low high-density lipoprotein cholesterol (HDL-C) levels. There is a significant gap in the literature regarding dyslipidemia in rural parts of Africa, where lipid profiles may not be captured through routine surveillance. This study aimed to characterize the prevalence and demo-graphic profile of dyslipidemia in T2D in the rural community of Ganadougou, Mali. We performed a cross-sectional study of 104 subjects with T2D in Ganadougou between November 2021 and March 2022. Demographic and lipid profiles were collected through cross-sectional surveys and serological analyses. The overall prevalence of dyslipidemia in T2D patients was 87.5% (91/104), which did not differ by sex (P = .368). High low-density lipoprotein cholesterol (LDL-C) was the most common lipid abnormality (78.9%, [82/104]). Dyslipidemia was associated with age and hypertension status (P = .013 and.036, respectively). High total and high LDL-C parameters were significantly associated with hypertension (P = .029 and .006, respectively). In low-resource settings such as rural Mali, there is a critical need to improve infrastructure for routine dyslipidemia screening to guide its prevention and intervention approaches. The high rates of dyslipidemia observed in Gandadougou, consistent with concomitant increases in cardiovascular diseases in Africa suggest that lipid profile assessments should be incorporated into routine medical care for T2D patients in African rural settings.展开更多
Among all the plagues threatening cocoa cultivation in general, and particularly in West Africa, the swollen shoot viral disease is currently the most dangerous. The greatest challenge in the fight to eradicate this p...Among all the plagues threatening cocoa cultivation in general, and particularly in West Africa, the swollen shoot viral disease is currently the most dangerous. The greatest challenge in the fight to eradicate this pandemic remains its early detection. Traditional methods of swollen shoot detection are mostly based on visual observations, leading to late detection and/or diagnostic errors. The use of machine learning algorithms is now an alternative for effective plant disease detection. It is therefore crucial to provide efficient solutions to farmers’ cooperatives. In our study, we built a database of healthy and diseased cocoa leaves. We then explored the power of feature extractors based on convolutional neural networks such as VGG 19, Inception V3, DenseNet 201, and a custom CNN, combining their strengths with the XGBOOST classifier. The results of our experiments showed that this fusion of methods with XGBOOST yielded highly promising scores, outperforming the results of algorithms using the sigmoid function. These results were further consolidated by the use of evaluation metrics such as accuracy, mean squared error, F score, recall, and Matthews’s correlation coefficient. The proposed approach, combining state of the art feature extractors and the XGBOOST classifier, offers an efficient and reliable solution for the early detection of swollen shoot. Its implementation could significantly assist West African cocoa farmers in combating this devastating disease and preserving their crops.展开更多
Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detecti...Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.展开更多
Introduction: Spondylolisthesis is defined as a permanent anterior sliding of the vertebral body accompanied by the pedicles, the transverse processes and the posterior joints in relation to the underlying vertebra. T...Introduction: Spondylolisthesis is defined as a permanent anterior sliding of the vertebral body accompanied by the pedicles, the transverse processes and the posterior joints in relation to the underlying vertebra. The prevalence of spondylolisthesis varies between 4% and 9%, according to different authors in France, China and Japan. In Africa, some African authors find a spondylolisthesis rate of 9.93%. The objective of this study was to study the clinical and radiological aspects of spondylolisthesis at the Mother-Child Hospital of Bamako. Methods: Prospective and retrospective descriptive study carried out in the neurosurgery department of CH Mother-Child Luxembourg in Bamako from 2010 to 2018. Result: We collected 42 patients operated on for spondylolisthesis out of 145 patients, i.e., 29% of the department’s activities. The average age of our patients was 51 years old, with extremes of 20 and 75 years old. The sex ratio was 0.81 in favor of women. 61.9% of our patients performed work requiring physical strength. Neurogenic claudication was the predominant clinical sign with 88.1% of cases. CT was the most common way of exploration in 57.1% of cases and CT myelo in 47.6% of cases. Disc herniation was associated with spondylolisthesis in 23% of cases. Spondylolisthesis was grade 1 in 59.6% of cases and was responsible for mixed stenosis in 73.82% of cases. All our patients underwent laminectomy. The evolution at 6 months postoperative was favorable in 64.3% of cases. Conclusion: The diagnosis of spondylolisthesis is radiological and makes it possible to define the type of lesion according to its etiology and its severity, but also to highlight the conflicts with the nervous system which will have to be cured. Field and household work, pregnancy, carrying loads or children on the back could be incriminated.展开更多
文摘Dyslipidemia is a disorder where abnormally lipid concentrations circulate in the bloodstream. The disorder is common in type 2 diabetics (T2D) and is linked with T2D comorbidities, particularly cardiovascular disease. Dyslipidemia in T2D is typically characterized by elevated plasma triglyceride and low high-density lipoprotein cholesterol (HDL-C) levels. There is a significant gap in the literature regarding dyslipidemia in rural parts of Africa, where lipid profiles may not be captured through routine surveillance. This study aimed to characterize the prevalence and demo-graphic profile of dyslipidemia in T2D in the rural community of Ganadougou, Mali. We performed a cross-sectional study of 104 subjects with T2D in Ganadougou between November 2021 and March 2022. Demographic and lipid profiles were collected through cross-sectional surveys and serological analyses. The overall prevalence of dyslipidemia in T2D patients was 87.5% (91/104), which did not differ by sex (P = .368). High low-density lipoprotein cholesterol (LDL-C) was the most common lipid abnormality (78.9%, [82/104]). Dyslipidemia was associated with age and hypertension status (P = .013 and.036, respectively). High total and high LDL-C parameters were significantly associated with hypertension (P = .029 and .006, respectively). In low-resource settings such as rural Mali, there is a critical need to improve infrastructure for routine dyslipidemia screening to guide its prevention and intervention approaches. The high rates of dyslipidemia observed in Gandadougou, consistent with concomitant increases in cardiovascular diseases in Africa suggest that lipid profile assessments should be incorporated into routine medical care for T2D patients in African rural settings.
文摘Among all the plagues threatening cocoa cultivation in general, and particularly in West Africa, the swollen shoot viral disease is currently the most dangerous. The greatest challenge in the fight to eradicate this pandemic remains its early detection. Traditional methods of swollen shoot detection are mostly based on visual observations, leading to late detection and/or diagnostic errors. The use of machine learning algorithms is now an alternative for effective plant disease detection. It is therefore crucial to provide efficient solutions to farmers’ cooperatives. In our study, we built a database of healthy and diseased cocoa leaves. We then explored the power of feature extractors based on convolutional neural networks such as VGG 19, Inception V3, DenseNet 201, and a custom CNN, combining their strengths with the XGBOOST classifier. The results of our experiments showed that this fusion of methods with XGBOOST yielded highly promising scores, outperforming the results of algorithms using the sigmoid function. These results were further consolidated by the use of evaluation metrics such as accuracy, mean squared error, F score, recall, and Matthews’s correlation coefficient. The proposed approach, combining state of the art feature extractors and the XGBOOST classifier, offers an efficient and reliable solution for the early detection of swollen shoot. Its implementation could significantly assist West African cocoa farmers in combating this devastating disease and preserving their crops.
文摘Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.
文摘Introduction: Spondylolisthesis is defined as a permanent anterior sliding of the vertebral body accompanied by the pedicles, the transverse processes and the posterior joints in relation to the underlying vertebra. The prevalence of spondylolisthesis varies between 4% and 9%, according to different authors in France, China and Japan. In Africa, some African authors find a spondylolisthesis rate of 9.93%. The objective of this study was to study the clinical and radiological aspects of spondylolisthesis at the Mother-Child Hospital of Bamako. Methods: Prospective and retrospective descriptive study carried out in the neurosurgery department of CH Mother-Child Luxembourg in Bamako from 2010 to 2018. Result: We collected 42 patients operated on for spondylolisthesis out of 145 patients, i.e., 29% of the department’s activities. The average age of our patients was 51 years old, with extremes of 20 and 75 years old. The sex ratio was 0.81 in favor of women. 61.9% of our patients performed work requiring physical strength. Neurogenic claudication was the predominant clinical sign with 88.1% of cases. CT was the most common way of exploration in 57.1% of cases and CT myelo in 47.6% of cases. Disc herniation was associated with spondylolisthesis in 23% of cases. Spondylolisthesis was grade 1 in 59.6% of cases and was responsible for mixed stenosis in 73.82% of cases. All our patients underwent laminectomy. The evolution at 6 months postoperative was favorable in 64.3% of cases. Conclusion: The diagnosis of spondylolisthesis is radiological and makes it possible to define the type of lesion according to its etiology and its severity, but also to highlight the conflicts with the nervous system which will have to be cured. Field and household work, pregnancy, carrying loads or children on the back could be incriminated.
