Contribution à la dépollution des eaux usées de textile par électrocoagulation et par adsorption sur des composés à base de fer et d’aluminium

Les ressources hydriques au monde sont rares et la demande en eau connaît une croissance continue en liaison avec l’évolution démographique et les activités consommatrices en eau, notamment les industries de textiles se voient dans l’obligation de recycler les eaux résiduaires et en particulier celles colorées. Dans ce travail, nous nous sommes intéressés à l’étude de l’élimination des matières organiques et colorantes de deux rejets provenant des industries de textile, un de teinture du tissu de polyester à pH acide et l’autre de teinture du tissu de coton à pH basique. Ces rejets ont été traités de deux manières. La première est par électrocoagulation en utilisant des plaques de fer et/ou d’aluminium. La deuxième est par adsorption sur des composés synthétiques à base de fer et d’aluminium préparés par électrocoagulation. Dans le cas du traitement par le procédé d’électrocoagulation, nous avons constaté que le rendement d’élimination en demande chimique en oxygène (DCO) du rejet de polyester atteint un rendement de DCO de 60% pour un temps de 7 min de réaction. Pour le rejet de coton, le rendement d’élimination, par les plaques de fer/aluminium et l’élimination des matières colorantes, atteint une valeur de 45% en terme de DCO, et ceci en utilisant des plaques d’aluminium seul et de fer/aluminium pour un temps de 12 et 15 min respectivement. Dans le cas du traitement par ajout des coagulants synthétiques préparés au laboratoire, nous pouvons observer que le meilleur rendement d’élimination en DCO du rejet de polyester est obtenu pour une valeur de 48%, pour la faible granulométrie avec une concentration de 5 g/l du composé à base de fer /aluminium. Le rendement d’élimination en DCO du rejet de coton augmente jusqu'à une valeur de 60% avec une concentration de 5 g/l de coagulants appliqués à base d’aluminium seul. Les résultats de la dépollution de ces rejets, ont montré que le rendement d'élimination des matières organiques et colorantes par le procédé d'électrocoagulation est important, et la durée de traitement est courte, mais l'inconvénient de ce procédé c'est la saleté des plaques après chaque utilisation et la fabrication d'une grande quantité des boues par rapport à l’adsorption sur des composés à base de fer /aluminium où on utilise des poudres peu solubles et stables avec un bon rendement d’élimination et faibles quantités de fer et d'aluminium dans le surnageant traité.Keywords: Colorant textile, électrocoagulation, adsorption, dépollution, fer, aluminiu

Artificial intelligence extension of the OSCAR-IB criteria

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines

Potent antileukemic activity of curaxin CBL0137 against MLL-rearranged leukemia

© 2019 UICC Around 10% of acute leukemias harbor a rearrangement of the MLL/KMT2A gene, and the presence of this translocation results in a highly aggressive, therapy-resistant leukemia subtype with survival rates below 50%. There is a high unmet need to identify safer and more potent therapies for MLL-rearranged (MLL-r) leukemia that can be combined with established chemotherapeutics to decrease treatment-related toxicities. The curaxin, CBL0137, has demonstrated nongenotoxic anticancer and chemopotentiating effects in a number of preclinical cancer models and is currently in adult Phase I clinical trials for solid tumors and hematological malignancies. The aim of our study was to investigate whether CBL0137 has potential as a therapeutic and chemopotentiating compound in MLL-r leukemia through a comprehensive analysis of its efficacy in preclinical models of the disease. CBL0137 decreased the viability of a panel of MLL-r leukemia cell lines (n = 12) and xenograft cells derived from patients with MLL-r acute lymphoblastic leukemia (ALL, n = 3) in vitro with submicromolar IC50s. The small molecule drug was well-tolerated in vivo and significantly reduced leukemia burden in a subcutaneous MV4;11 MLL-r acute myeloid leukemia model and in patient-derived xenograft models of MLL-r ALL (n = 5). The in vivo efficacy of standard of care drugs used in remission induction for pediatric ALL was also potentiated by CBL0137. CBL0137 exerted its anticancer effect by trapping Facilitator of Chromatin Transcription (FACT) into chromatin, activating the p53 pathway and inducing an Interferon response. Our findings support further preclinical evaluation of CBL0137 as a new approach for the treatment of MLL-r leukemia

Artificial intelligence extension of the OSCAR-IB criteria

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines

Consumption and physico‐chemical characteristics of smoked and smoked‐dried fish commonly produced in South Benin and contribution to recommended nutrient intakes

The work aims to assess the consumption and the physico-chemical characteristics of smoked fish and smoked-dried fish commonly produced and consumed in the South Benin. The consumption data were obtained from a survey involving 250 consumers, conducted in selected production localities of the Southern part of Benin, where 36 samples of smoked fish and smoked-dried fish were collected. The highest protein contents (dry matter) were recorded in Cypselurus cyanopterus (85.1 ± 2.3%) and Sphyraena barracuda (84.5 ± 4.2%), while the highest lipid contents were recorded in Scomber scombrus (39.0 ± 9.2%) and Ethmalosa fimbriata (22.1 ± 6.3%). Smoked and smoked-dried fish produced in South Benin contained 0.1%-12.5% (of total fatty acids) eicosapentaenoic acid and 0.1%-33.2% docosahexaenoic acid, which are the most abundant omega 3 polyunsaturated acids in these fish products. The median consumption of smoked fish (60.2 g/day) and smoked-dried fish (18.2 g/day) contributed for 112% (281.1 mg) and 72% (180.4 mg), respectively, to the adult daily recommended intake of sum of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (250 mg/day). The daily protein intake related to the consumption of smoked fish and smoked-dried fish corresponded to 36% and 24%, respectively, of the recommended intake suggested by European Food Safety Authority (0.8 g/kg.bw/day)