Search

Christian Kakisingi,1 Claude Mwamba,1 Michel Kasongo Muteba,2 Eric Kasamba,3 Michel Kabamba,4 Aristophane Tanon,5 Hippolyte Situakibanza6 1Internal Medicine Department, University of Lubumbashi, Lubumbashi, Democratic Republic of the Congo; 2Division of Epidemiology and Biostatistics, University ofWitwatersrand, Johannesburg, Republic of South Africa; 3Department of Basics Sciences. University of Lubumbashi, Lubumbashi, Democratic Republic of the Congo; 4Public Health Department, University of Kamina, Kamina, Democratic Republic of the Congo; 5Specialities and Medicine Department, University of Félix Houphouët-Boigny of Abidjan Cocody, Abidjan, Ivory Coast; 6Internal Medicine Department, Tropical Diseases, Infectious and Parasitic Department, University of Kinshasa, Kinshasa, Democratic Republic of the CongoCorrespondence: Christian Kakisingi, Email chriskakis@yahoo.frBackground: Tuberculosis and diabetes mellitus are major public health challenges worldwide. The two scourges have bidirectional relationship with high morbidity and mortality.Objective: The present study was conducted to determine the prevalence of diabetes mellitus and identify related factors in patients with tuberculosis.Methods: A cross-sectional study was conducted in 11 tuberculosis screening and treatment centers in Lubumbashi (DRC) from September to December 2022. Adult patient with a positive smear for tuberculosis were systematically screened for diabetes mellitus. Demographic characteristics, history and symptomatology were the variables of interest. Data was entered using Microsoft Excel software. STATA 16 software was used for analysis.Results: A total of 255 tuberculosis patients were recruited and the prevalence of diabetes mellitus among these patients was 11.4% (ie, 29 out of 255). After uni and multivariate logistic regression, a BMI ≤ 18.5 Kg/m2, lack of employment, polyuria and intense thirst were the factors associated with diabetes mellitus in tuberculosis patients.Conclusion: The prevalence of diabetes mellitus in smear positive tuberculosis patients in the present study was higher than the one observed in Central Africa.Keywords: tuberculosis, diabetes mellitus, comorbidity, prevalence, associated factors

Christian Kakisingi,1 Michel Kabamba,2 Michel Muteba,3 Hermann Tamunbango,4 Aristophane Tanon,5 Hippolyte Situakibanza,6 Claude Mwamba1 1Internal Medicine Department, University of Lubumbashi, Lubumbashi, Democratic Republic of the Congo; 2Public Health Department, University of Kamina, Kamina, Democratic Republic of the Congo; 3Division of Epidemiology and Biostatistics, University of Witwatersrand, Johannesburg, Republic of South Africa; 4Epidemiology Department, Institut Supérieur de Techniques Médicales de Likasi, Likasi, Democratic Republic of the Congo; 5Specialities and Medicine Department, University of Félix Houphouët-Boigny of Abidjan Cocody, Abidjan, Ivory Coast; 6Internal Medicine Department, Tropical Diseases, Infectious and Parasitic Department, University of Kinshasa, Kinshasa, Democratic Republic of the CongoCorrespondence: Christian Kakisingi, Email chriskakis@yahoo.frBackground: Tuberculosis-Diabetes mellitus (TB-DM) co-morbidity is a growing scourge in the world. The new approaches and interventions for TB control implemented by the Tuberculosis National Control Program (TNCP) in DRC require the involvement of health care providers for their success.Objective: The objective of this study is to assess the knowledge of health care provider on different aspects of the management of TB-DM co-morbidity and to compare this knowledge according to the health care system, the type of providers and the number of years of experience.Methods: Cross-sectional and analytic study was conducted in 11 health care facilities in the Lubumbashi Health District, selected by reasoned choice, and an electronic questionnaire was administered to health care providers. These providers were interviewed on the different aspects of the management of the TB-DM comorbidity. The data were presented and compared in relation to knowledge about TB, DM, and TB-DM comorbidity.Results: A total of 113 providers were interviewed, predominantly males and physicians. Questions related to knowledge about DM were better answered. Doctors compared to paramedics; tertiary-level providers compared to secondary-level providers had better responses to the different questions. There is a statistically significant correlation between the level of knowledge of TB, DM and the type of health care provider, the number of years of experience.Conclusion: The present study shows that there are gaps in the knowledge of our health care providers and community members on the recommendations of the DRC TB guidelines (Programme AntiTuberculeux Intégré 5: PATI 5) in general and on the management of TB-DM. It is therefore important and necessary to put in place strategies to improve this level of knowledge, which will focus on the extension of the guidelines, the awareness and the training of the stakeholders involved in the control.Keywords: tuberculosis, diabetes mellitus, comorbidity, healthcare providers, knowledge

Abstract Introduction In low-income countries such as the Democratic Republic of the Congo (DRC), the strategies implemented to combat tuberculosis (TB) are threatened by the emergence of non-communicable diseases (NCDs), such as diabetes mellitus (DM). Very little data on the implementation of services to manage TB-DM are generally available in these low-income countries. The aim of this study was therefore to assess the level of implementation of DM screening and treatment activities in TB unit clinics (TUCs) in Lubumbashi, DRC. Methods A cross-sectional study was conducted using the Service Availability and Readiness Assessment (SARA) questionnaire from June to July 2023. Fourteen tracer items, divided into 4 domains—i) guidelines and staff, ii) basic equipment, iii) diagnostic capacity, and iv) drugs and products—were assessed. The readiness indices were compared according to the managerial instance and the activity package organized in each of the selected TUCs. A Chi2 test with a significance level set at p = 0.05 was used for this comparison, and Cronbach's α coefficient was calculated to estimate the reliability or consistency of the questionnaire. Results Of the 35 TUCs visited, 19 (54.3%) were public health facilities, and 20 (57.1%) had a supplementary package of activities (SPA). The readiness of TUCs for providing DM diagnostic and treatment services was around 50%. A statistically significant difference was observed based on the managerial instance overseeing the TUC (p = 0.00) and the package of activities offered within these institutions (p = 0.00). Conclusion The current study has underscored the limited capability of TUCs in Lubumbashi to provide services for managing TB-DM comorbidity in DRC. It is imperative to implement strategies aimed at enhancing this capacity and taking into account the local context and influencing factors.

The development of high-resolution mapping models for forest attributes based on remote sensing data combined with machine or deep learning techniques has become a prominent topic in the field of forest observation and monitoring. This has resulted in the availability of multiple, sometimes conflicting, sources of information, but, at face value, it also makes it possible to learn about forest attribute uncertainty through the joint interpretation of multiple models. This article seeks to endorse the latter by utilizing the Bayesian model averaging approach to diagnose and interpret the differences between predictions from different models. The predictions in our case are forest canopy height estimations for metropolitan France arising from five different models. An independent reference dataset, containing four different definitions of forest height (dominant, mean, maximum, and Lorey's) was established based on around 5500 plots of the French National Forest Inventory (NFI), distributed across the entire area of interest. In this study, we evaluate models with respect to their probabilities of correctly predicting measurements or estimations obtained from NFI plots, highlighting the spatial variability in respective model probabilities across the study area. We observed significant variability in these probabilities depending on the forest height definition used, implying that the different models inadvertently predict different types of canopy height. We also present the respective inter-model and intra-model variance estimations, enabling us to grasp where the employed models have comparable contributions but contrasting predictions. We show that topography has an important impact on the models spread. Moreover, we observed that the forest stand vertical structure, the dominant tree species, and the type of forest ownership systematically emerge as statistically significant factors influencing the model divergences. Finally, we observed that the fitted higher-order mixtures, which enabled the presented analyses, do not necessarily reduce bias or prevent the saturation of the predicted heights observed in the individual models.

Intelligent Tutoring Systems (ITS) are computer-based learning environments that aim to imitate to the greatest possible extent the behavior of a human tutor in their capacity as a pedagogical and subject expert. One of the major challenges of these systems is to know how to adapt the training both to changing requirements of all kinds and to student knowledge and reactions. The activities recommended by these systems mainly involve active student performance prediction that, nowadays, becomes problematic in the face of the expectations of the present world. In the associated literature, several approaches, using various attributes, have been proposed to solve the problem of performance prediction. However, these approaches have failed to take advantage of the synergistic effect of students' social and emotional factors as better prediction attributes. This paper proposes an approach to predict student performance called "SoEmo"-WMRMF that exploits not only cognitive abilities, but also group work relationships between students and the impact of their emotions. More precisely, this approach models five types of domain relations through a Weighted Multi-Relational Matrix Factorization (WMRMF) model. An evaluation carried out on a data sample extracted from a survey carried out in a general secondary school showed that the proposed approach gives better performance in terms of reduction of the Root Mean Squared Error (RMSE) compared to other models simulated in this paper.

Abstract Background In resource-limited settings where nevirapine-containing regimen is the preferred regimen in women, data on severe adverse events (SAEs) according to CD4 cell count are limited. We estimated the incidence of SAEs according to CD4 cell count and identify their risk factors in nevirapine-treated women. Methods All HIV-infected women who initiated nevirapine-containing regimen in the MTCT-Plus operational program in Abidjan, Côte d'Ivoire, were eligible for this study. Laboratory and clinical (rash) SAEs were classified as grade 3 and 4. Cox models were used to identify factors associated with the occurrence of SAEs. Results From August 2003 to October 2006, 290 women initiated a nevirapine-containing regimen at a median CD4 cell count of 186 cells/mm3 (IQR 124-266). During a median follow-up on treatment of 25 months, the incidence of all SAEs was 19.5/100 patient-years. The 24-month probability of occurrence of hepatotoxicity or rash was not different between women with a CD4 cell count >250 cells/mm3 and women with a CD4 cell count ≤250 cells/mm3 (8.3% vs. 9.9%, Log-rank test: p = 0.75). In a multivariate proportional hazard model, neither CD4 cell count >250 cells/mm3 at treatment initiation nor initiation NVP-based regimen initiated during pregnancy were associated with the occurrence of SAEs. Conclusion CD4 cell count >250 cells/mm3 was not associated with a higher risk of severe hepatotoxicity and/or rash, as well as initiation of ART during pregnancy. Pharmacovogilance data as well as meta-analysis on women receiving NVP in these settings are needed for better information about NVP toxicity.

Introduction: The prevalence of both type 2 diabetes mellitus (T2DM) and obesity is increasing among people living with HIV (PLHIV) in sub-Saharan Africa. We examined the perceptions and management of these two conditions among PLHIV and healthcare workers in Côte D'Ivoire. Method: From June to August 2022, we conducted semi-structured face-to-face interviews with PLHIV diagnosed with T2DM and/or obesity, as well as healthcare workers, in one of the major HIV clinics in Abidjan, Cote d'Ivoire. We explored topics such as experiences, perceptions and acceptability of the diagnosis and management of T2DM and obesity among PLHIV. Among healthcare workers, professional experience, professional relationships with patients diagnosed with T2DM/obesity, involvement in patient management and care provision were explored. Interviews were audio recorded and transcribed manually. Data were analysed using thematic analysis. Results: A total of 15 PLHIV and 5 healthcare workers participated in semi-structured in-depth interviews. Perceptions towards T2DM and obesity were largely influenced by cultural factors, PLHIV reported negative perceptions of T2DM and positive perceptions of obesity. Both patients and healthcare providers considered the management of these conditions as sub-optimal. Patient-reported barriers to care for these metabolic disorders were mainly socio-economic and environmental, while healthcare workers emphasized patients' denial of their illness and the limited range of treatment options available at the clinic. Conclusion: These results highlight the complexity surrounding the perceptions and management of T2DM and obesity among PLHIV in Abidjan, Côte d'Ivoire. In order to implement innovative and efficient intervention strategies to prevent and treat these metabolic conditions, cultural beliefs as well as socio-economic barriers must be addressed. [ABSTRACT FROM AUTHOR]

Introduction: In low-income countries such as the Democratic Republic of the Congo (DRC), the strategies implemented to combat tuberculosis (TB) are threatened by the emergence of non-communicable diseases (NCDs), such as diabetes mellitus (DM). Very little data on the implementation of services to manage TB-DM are generally available in these low-income countries. The aim of this study was therefore to assess the level of implementation of DM screening and treatment activities in TB unit clinics (TUCs) in Lubumbashi, DRC. Methods: A cross-sectional study was conducted using the Service Availability and Readiness Assessment (SARA) questionnaire from June to July 2023. Fourteen tracer items, divided into 4 domains—i) guidelines and staff, ii) basic equipment, iii) diagnostic capacity, and iv) drugs and products—were assessed. The readiness indices were compared according to the managerial instance and the activity package organized in each of the selected TUCs. A Chi2 test with a significance level set at p = 0.05 was used for this comparison, and Cronbach's α coefficient was calculated to estimate the reliability or consistency of the questionnaire. Results: Of the 35 TUCs visited, 19 (54.3%) were public health facilities, and 20 (57.1%) had a supplementary package of activities (SPA). The readiness of TUCs for providing DM diagnostic and treatment services was around 50%. A statistically significant difference was observed based on the managerial instance overseeing the TUC (p = 0.00) and the package of activities offered within these institutions (p = 0.00). Conclusion: The current study has underscored the limited capability of TUCs in Lubumbashi to provide services for managing TB-DM comorbidity in DRC. It is imperative to implement strategies aimed at enhancing this capacity and taking into account the local context and influencing factors. [ABSTRACT FROM AUTHOR]

The development of high-resolution mapping models for forest attributes based on remote sensing data combined with machine or deep learning techniques has become a prominent topic in the field of forest observation and monitoring. This has resulted in the availability of multiple, sometimes conflicting, sources of information, but, at face value, it also makes it possible to learn about forest attribute uncertainty through the joint interpretation of multiple models. This article seeks to endorse the latter by utilizing the Bayesian model averaging approach to diagnose and interpret the differences between predictions from different models. The predictions in our case are forest canopy height estimations for metropolitan France arising from five different models. An independent reference dataset, containing four different definitions of forest height (dominant, mean, maximum, and Lorey's) was established based on around 5500 plots of the French National Forest Inventory (NFI), distributed across the entire area of interest. In this study, we evaluate models with respect to their probabilities of correctly predicting measurements or estimations obtained from NFI plots, highlighting the spatial variability in respective model probabilities across the study area. We observed significant variability in these probabilities depending on the forest height definition used, implying that the different models inadvertently predict different types of canopy height. We also present the respective inter-model and intra-model variance estimations, enabling us to grasp where the employed models have comparable contributions but contrasting predictions. We show that topography has an important impact on the models spread. Moreover, we observed that the forest stand vertical structure, the dominant tree species, and the type of forest ownership systematically emerge as statistically significant factors influencing the model divergences. Finally, we observed that the fitted higher-order mixtures, which enabled the presented analyses, do not necessarily reduce bias or prevent the saturation of the predicted heights observed in the individual models. [ABSTRACT FROM AUTHOR]

Introduction: The knowledge about forest growth, influenced by factors such as tree species, tree age, and environmental conditions, is a key for future forest preservation. Height and age data can be combined to describe forest growth and used to infer known environmental effects. Methods: In this study, we built 14 height growth curves for stands composed of monospecific or mixed species using ground measurements and satellite data. We built a random forest height model from tree species, age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Using feature elimination and SHapley Additive exPlanations (SHAP) analysis, we identified six key features explaining the forest growth and investigated how they affect the height. Results: The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the main predictors of tree height (49% and 10%, respectively). The disturbed patch area, revealing the regeneration method, impacts post-disturbance growth at 19%. The soil pH, altitude, and climatic water budget in summer impact tree height differently depending on the age and tree species. Discussion: Methods integrating satellite and field data show promise for analyzing future forest evolution. [ABSTRACT FROM AUTHOR]

Abstract Disruption of iron metabolism is closely related to metabolic diseases. Iron deficiency is frequently associated with obesity and hepatic steatosis. However, the effects of iron supplementation on obesity and energy metabolism remain unclear. Here we show that a high-fat diet supplemented with iron reduces body weight gain and hepatic lipid accumulation in mice. Iron supplementation was found to reduce mitochondrial morphological abnormalities and upregulate gene transcription involved in mitochondrial function and beta oxidation in the liver and skeletal muscle. In both these tissues, iron supplementation increased the expression of genes involved in heme or iron–sulfur (Fe–S) cluster synthesis. Heme and Fe–S cluster, which are iron prosthetic groups contained in electron transport chain complex subunits, are essential for mitochondrial respiration. The findings of this study demonstrated that iron regulates mitochondrial signaling pathways—gene transcription of mitochondrial component molecules synthesis and their energy metabolism. Overall, the study elucidates the molecular basis underlying the relationship between iron supplementation and obesity and hepatic steatosis progression, and the role of iron as a signaling molecule.

1 | INTRODUCTION In 2016, the World Health Organization recommended that all HIV-infected patients be treated with antiretroviral therapy (ART) irrespective of CD4-cell counts and clinical stage [1]. The introduction [...], Introduction: Sex differences have already been reported in sub-Saharan Africa for attrition and immunological response after antiretroviral therapy (ART) initiation, but follow-up was usually limited to the first two to three years after ART initiation. We evaluated sex differences on the same outcomes in the 10 years following ART initiation in West African adults. Methods: We used cohort data of patients included in the IeDEA West Africa collaboration, who initiated ART between 2002 and 2014. We modelled no-follow-up and 10-year attrition risks, and immunological response by sex using logistic regression analysis, survival analysis with random effect and linear mixed models respectively. Results: A total of 71,283 patients (65.8% women) contributed to 310,007 person-years of follow-up in 16 clinics in eight West African countries. The cumulative attrition incidence at 10-year after ART initiation reached 75% and 68% for men and women respectively. Being male was associated with an increased risk of no follow-up after starting ART (5.1% vs. 4.0%, adjusted Odds Ratio: 1.25 [95% CI: 1.15 to 1.35]) and of 10-year attrition throughout the 10-year period following ART initiation: adjusted Hazard Ratios were 1.22 [95% CI: 1.17 to 1.27], 1.08 [95% CI: 1.04 to 1.12] and 1.04 [95% CI: 1.01 to 1.08] during year 1, years 2 to 4 and 5 to 10 respectively. A better immunological response was achieved by women than men: monthly CD4 gain was 30.2 and 28.3 cells/mL in the first four months and 2.6 and 1.9 cells/[micro]L thereafter. Ultimately, women reached the average threshold of 500 CD4 cells/[micro]L in their sixth year of follow-up, whereas men failed to reach it even at the end of the 10-year follow-up period. The proportion of patients reaching the threshold was much higher in women than in men after 10 years since ART initiation (65% vs. 44%). Conclusions: In West Africa, attrition is unacceptably high in both sexes. Men are more vulnerable than women on both attrition and immunological response to ART in the 10 years following ART initiation. Innovative tracing strategies that are sex-adapted are needed for patients in care to monitor attrition, detect early high-risk groups so that they can stay in care with a durably controlled infection. Keywords: HIV; antiretroviral therapy; sex; attrition; Immunological response; West Africa

Abstract Background Depression is one of the most common psychiatric disorders in people living with HIV (PLHIV). Depression has a negative impact on both mental and physical health and is mainly associated with suboptimal HIV treatment outcomes. To encourage successful aging and the achievement of the 3 × 90 objectives in older PLHIV, the psychological domain must not be neglected. In this context and as data are scarce in West Africa, this study aimed to evaluate the prevalence and the factors associated with severe depressive symptoms in older PLHIV living in this region of the world. Methods Data from PLHIV aged ≥50 years and on ART since ≥6 months were collected in three clinics (two in Côte d’Ivoire, one in Senegal) participating in the West Africa International epidemiological Databases to Evaluate AIDS (IeDEA) collaboration. The severity of depressive symptoms was measured using the Center for Epidemiological Studies Depression scale (CES-D), and associated factors were identified using logistic regressions. Results The median age of the 334 PLHIV included in the study was 56.7 (53.5–61.1), 57.8% were female, and 87.1% had an undetectable viral load. The prevalence of severe depressive symptoms was 17.9% [95% Confidence Interval (95% CI): 13.8–22.0]. PLHIV with severe depressive symptoms were more likely to be unemployed (adjusted Odd Ratio (aOR) = 2.8; 95% CI: 1.4–5.7), and to be current or former tobacco smokers (aOR = 2.6; 95% CI: 1.3–5.4) but were less likely to be overweight or obese (aOR = 0.4; 95% CI: 0.2–0.8). Conclusions The prevalence of severe depressive symptoms is high among older PLHIV living in West Africa. Unemployed PLHIV and tobacco smokers should be seen as vulnerable and in need of additional support. Further studies are needed to describe in more details the reality of the aging experience for PLHIV living in SSA. The integration of screening and management of depression in the standard of care of PLHIV is crucial.

The development of high-resolution mapping models for forest attributes based on remote sensing data combined with machine or deep learning techniques, has become a prominent topic in the field of forest observation and monitoring. This has resulted in an extensive availability of multiple sources of information, which can either lead to a potential confusion, or to the possibility to learn both about models and about forest attributes through the joint interpretation of multiple models. This article seeks to endorse the latter, by relying on the Bayesian model averaging (BMA) approach, which can be used to diagnose and interpret differences among predictions of different models. The predictions in our case are the forest canopy height estimations for the metropolitan France coming from five different models (Lang et al., 2023; Liu et al., 2023; Morin et al., 2022; Potapov et al., 2021; Schwartz et al., 2024). An independent reference dataset, containing four different definitions of the forest height (dominant, mean, maximum and Lorey's), comes from the French National Forest Inventory (NFI) providing some 5 500 plots used in the study, distributed across the entire area of interest. In this contribution, we line up the evoked models with respect to their probabilities to be the ones generating measurements/estimations at the NFI plots. Stratifying the probabilities based on French sylvo-ecological regions reveals spatial variation in the respective model probabilities across the area of interest. Furthermore, we observe significant variability in these probabilities depending on the forest height definition used. This leads us to infer that the different models inadvertently exhibit dominant predictions for different types of canopy height. We also present the respective inter-model and intra-model variance estimations, allowing us to come to understand where the employed models have comparable weights but contrasted predictions. We show that the mountainous terrain has an important impact on the models spread. Moreover, we observe that the forest stand vertical structure, the dominant tree species and the type of forest ownership systematically appear to be statistically significant factors influencing the models divergence. Finally, we demonstrate that the derived mixture models exhibit higher R 2 scores and lower RMSE values compared to individual models, although they may not necessarily exhibit lower biases. [ABSTRACT FROM AUTHOR]