Capacity building of Multi-Stakeholder Innovation Platform (PIMA) actors in members in community governance, crop calendar construction, and implementation of nature-based solutions

Conversion of dry season pasture to potato and vegetable fields in inland valleys is resulting in biodiversity loss, water resources degradation, pastoral marginalization, and increasing conflicts between herders and farmers. The AICCRA project established multi-stakeholders platforms (MSP) at three inland valleys (Finkolo Ganadougou, Blendio, and Loutana) in Mali and strengthened MSP members' capacity in community governance, crop calendar construction, and implementation of nature-based solutions, resulting in enhanced connections between the stakeholders actors, but also better access to input, information and water governance of inland valleys resources

A python module to normalize microarray data by the quantile adjustment method

International audienceMicroarray technology is widely used for gene expression research targeting the development of new drug treatments. In the case of a two-color microarray, the process starts with labeling DNA samples with fluorescent markers (cyanine 635 or Cy5 and cyanine 532 or Cy3), then mixing and hybridizing them on a chemically treated glass printed with probes, or fragments of genes. The level of hybridization between a strand of labeled DNA and a probe present on the array is measured by scanning the fluorescence of spots in order to quantify the expression based on the quality and number of pixels for each spot. The intensity data generated from these scans are subject to errors due to differences in fluorescence efficiency between Cy5 and Cy3, as well as variation in human handling and quality of the sample. Consequently, data have to be normalized to correct for variations which are not related to the biological phenomena under investigation. Among many existing normalization procedures, we have implemented the quantile adjustment method using the python computer language, and produced a module which can be run via an HTML dynamic form. This module is composed of different functions for data files reading, intensity and ratio computations and visualization. The current version of the HTML form allows the user to visualize the data before and after normalization. It also gives the option to subtract background noise before normalizing the data. The output results of this module are in agreement with the results of other normalization tools. Published by Elsevier B.V

A python module to normalize microarray data by the quantile adjustment method

Microarray technology is widely used for gene expression research targeting the development of new drug treatments. In the case of a two-color microarray, the process starts with labeling DNA samples with fluorescent markers (cyanine 635 or Cy5 and cyanine 532 or Cy3), then mixing and hybridizing them on a chemically treated glass printed with probes, or fragments of genes. The level of hybridization between a strand of labeled DNA and a probe present on the array is measured by scanning the fluorescence of spots in order to quantify the expression based on the quality and number of pixels for each spot. The intensity data generated from these scans are subject to errors due to differences in fluorescence efficiency between Cy5 and Cy3, as well as variation in human handling and quality of the sample. Consequently, data have to be normalized to correct for variations which are not related to the biological phenomena under investigation. Among many existing normalization procedures, we have implemented the quantile adjustment method using the python computer language, and produced a module which can be run via an HTML dynamic form. This module is composed of different functions for data files reading, intensity and ratio computations and visualization. The current version of the HTML form allows the user to visualize the data before and after normalization. It also gives the option to subtract background noise before normalizing the data. The output results of this module are in agreement with the results of other normalization tools

High SARS-CoV-2 Seroprevalence among Healthcare Workers in Bamako, Mali

In Mali, a country in West Africa, cumulative confirmed COVID-19 cases and deaths among healthcare workers (HCWs) remain enigmatically low, despite a series of waves, circulation of SARS-CoV-2 variants, the country’s weak healthcare system, and a general lack of adherence to public health mitigation measures. The goal of the study was to determine whether exposure is important by assessing the seroprevalence of anti-SARS-CoV-2 IgG antibodies in HCWs. The study was conducted between November 2020 and June 2021. HCWs in the major hospitals where COVID-19 cases were being cared for in the capital city, Bamako, Mali, were recruited. During the study period, vaccinations were not yet available. The ELISA of the IgG against the spike protein was optimized and quantitatively measured. A total of 240 HCWs were enrolled in the study, of which seropositivity was observed in 147 cases (61.8%). A continuous increase in the seropositivity was observed, over time, during the study period, from 50% at the beginning to 70% at the end of the study. HCWs who provided direct care to COVID-19 patients and were potentially highly exposed did not have the highest seropositivity rate. Vulnerable HCWs with comorbidities such as obesity, diabetes, and asthma had even higher seropositivity rates at 77.8%, 75.0%, and 66.7%, respectively. Overall, HCWs had high SARS-CoV-2 seroprevalence, likely reflecting a “herd” immunity level, which could be protective at some degrees. These data suggest that the low number of cases and deaths among HCWs in Mali is not due to a lack of occupational exposure to the virus but rather related to other factors that need to be investigated

Characteristics of HIV-2 and HIV-1/HIV-2 Dually Seropositive Adults in West Africa Presenting for Care and Antiretroviral Therapy: The IeDEA-West Africa HIV-2 Cohort Study.

HIV-2 is endemic in West Africa. There is a lack of evidence-based guidelines on the diagnosis, management and antiretroviral therapy (ART) for HIV-2 or HIV-1/HIV-2 dual infections. Because of these issues, we designed a West African collaborative cohort for HIV-2 infection within the framework of the International epidemiological Databases to Evaluate AIDS (IeDEA).We collected data on all HIV-2 and HIV-1/HIV-2 dually seropositive patients (both ARV-naive and starting ART) and followed-up in clinical centres in the IeDEA-WA network including a total of 13 clinics in five countries: Benin, Burkina-Faso Côte d'Ivoire, Mali, and Senegal, in the West Africa region.Data was merged for 1,754 patients (56% female), including 1,021 HIV-2 infected patients (551 on ART) and 733 dually seropositive for both HIV-1 and HIV 2 (463 on ART). At ART initiation, the median age of HIV-2 patients was 45.3 years, IQR: (38.3-51.7) and 42.4 years, IQR (37.0-47.3) for dually seropositive patients (p = 0.048). Overall, 16.7% of HIV-2 patients on ART had an advanced clinical stage (WHO IV or CDC-C). The median CD4 count at the ART initiation is 166 cells/mm(3), IQR (83-247) among HIV-2 infected patients and 146 cells/mm(3), IQR (55-249) among dually seropositive patients. Overall, in ART-treated patients, the CD4 count increased 126 cells/mm(3) after 24 months on ART for HIV-2 patients and 169 cells/mm(3) for dually seropositive patients. Of 551 HIV-2 patients on ART, 5.8% died and 10.2% were lost to follow-up during the median time on ART of 2.4 years, IQR (0.7-4.3).This large multi-country study of HIV-2 and HIV-1/HIV-2 dual infection in West Africa suggests that routine clinical care is less than optimal and that management and treatment of HIV-2 could be further informed by ongoing studies and randomized clinical trials in this population