Search results for: Abdellatif Elghali

Authors: Dehni Abdellatif, Lounis Mourad

Abstract:

The new approach of applied spatial geostatistics in materials sciences, agriculture accuracy, agricultural statistics, permitted an apprehension of managing and monitoring the water and groundwater qualities in a relationship with salt-affected soil. The anterior experiences concerning data acquisition, spatial-preparation studies on optical and multispectral data has facilitated the integration of correction models of electrical conductivity related with soils temperature (horizons of soils). For tomography apprehension, this physical parameter has been extracted from calibration of the thermal band (LANDSAT ETM+6) with a radiometric correction. Our study area is Oran region (Northern West of Algeria). Different spectral indices are determined such as salinity and sodicity index, the Combined Spectral Reflectance Index (CSRI), Normalized Difference Vegetation Index (NDVI), emissivity, Albedo, and Sodium Adsorption Ratio (SAR). The approach of geostatistical modeling of electrical conductivity (salinity), appears to be a useful decision support system for estimating corrected electrical resistivity related to the temperature of surface soils, according to the conversion models by substitution, the reference temperature at 25°C (where hydrochemical data are collected with this constraint). The Brightness temperatures extracted from satellite reflectance (LANDSAT ETM+) are used in consistency models to estimate electrical resistivity. The confusions that arise from the effects of salt stress and water stress removed followed by seasonal application of the geostatistical analysis in Geographic Information System (GIS) techniques investigation and monitoring the variation of the electrical conductivity in the alluvial aquifer of Es-Sénia for the salt-affected soil.

Keywords: geostatistical modelling, landsat, brightness temperature, conductivity

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Authors: Mayada Mazher, Ahmed Moustafa, Ahmed Abdellatif

Abstract:

Background: Autophagy is a eukaryotic, highly conserved catabolic process implicated in many pathophysiologies such as wound healing. Autophagy-associated genes serve as a scaffolding platform for signal transduction of macrophage polarization during the inflammatory phase of wound healing and tissue repair process. In the current study, we report a model for the interplay between autophagy-associated genes and macrophages polarization associated genes. Methods: In silico analysis was performed on 249 autophagy-related genes retrieved from the public autophagy database and gene expression data retrieved from Gene Expression Omnibus (GEO); GSE81922 and GSE69607 microarray data macrophages polarization 199 DEGS. An integrated protein-protein interaction network was constructed for autophagy and macrophage gene sets. The gene sets were then used for GO terms pathway enrichment analysis. Common transcription factors for autophagy and macrophages' polarization were identified. Finally, microRNAs enriched in both autophagy and macrophages were predicated. Results: In silico prediction of common transcription factors in DEGs macrophages and autophagy gene sets revealed a new role for the transcription factors, HOMEZ, GABPA, ELK1 and REL, that commonly regulate macrophages associated genes: IL6,IL1M, IL1B, NOS1, SOC3 and autophagy-related genes: Atg12, Rictor, Rb1cc1, Gaparab1, Atg16l1. Conclusions: Autophagy and macrophages' polarization are interdependent cellular processes, and both autophagy-related proteins and macrophages' polarization related proteins coordinate in tissue remodelling via transcription factors and microRNAs regulatory network. The current work highlights a potential new role for transcription factors HOMEZ, GABPA, ELK1 and REL in wound healing.

Keywords: autophagy related proteins, integrated network analysis, macrophages polarization M1 and M2, tissue remodelling

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Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdellatif, Rabah Siham

Abstract:

In the field of veterinary medicine, there is a growing application of artificial intelligence (AI) for diagnosing bovine mastitis, a prevalent inflammatory disease in dairy cattle. AI technologies, such as automated milking systems, have streamlined the assessment of key metrics crucial for managing cow health during milking and identifying prevalent diseases, including mastitis. These automated milking systems empower farmers to implement automatic mastitis detection by analyzing indicators like milk yield, electrical conductivity, fat, protein, lactose, blood content in the milk, and milk flow rate. Furthermore, reports highlight the integration of somatic cell count (SCC), thermal infrared thermography, and diverse systems utilizing statistical models and machine learning techniques, including artificial neural networks, to enhance the overall efficiency and accuracy of mastitis detection. According to a review of 15 publications, machine learning technology can predict the risk and detect mastitis in cattle with an accuracy ranging from 87.62% to 98.10% and sensitivity and specificity ranging from 84.62% to 99.4% and 81.25% to 98.8%, respectively. Additionally, machine learning algorithms and microarray meta-analysis are utilized to identify mastitis genes in dairy cattle, providing insights into the underlying functional modules of mastitis disease. Moreover, AI applications can assist in developing predictive models that anticipate the likelihood of mastitis outbreaks based on factors such as environmental conditions, herd management practices, and animal health history. This proactive approach supports farmers in implementing preventive measures and optimizing herd health. By harnessing the power of artificial intelligence, the diagnosis of bovine mastitis can be significantly improved, enabling more effective management strategies and ultimately enhancing the health and productivity of dairy cattle. The integration of artificial intelligence presents valuable opportunities for the precise and early detection of mastitis, providing substantial benefits to the dairy industry.

Keywords: artificial insemination, automatic milking system, cattle, machine learning, mastitis

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Authors: Alaa Abdellatif, Gabrièle Breda

Abstract:

Background. Advanced therapy medicinal products (ATMPs) are innovative therapies that mainly target orphan diseases and high unmet medical needs. ATMP includes gene therapy medicinal products (GTMP), somatic cell therapy medicinal products (CTMP), and tissue-engineered therapies (TEP). Since legislation opened the way in 2007, 25 ATMPs have been approved in the EU, which is about the same amount as the U.S. Food and Drug Administration. However, not all of the ATMPs that have been approved have successfully reached the market and retained their approval. Objectives. We aim to understand all the factors limiting the market access to very promising therapies in a systemic approach, to be able to overcome these problems, in the future, with scientific, regulatory and commercial innovations. Further to recent reviews that focus either on specific countries, products, or dimensions, we will address all the challenges faced by ATMP development today. Methodology. We used mixed methods and a multi-level approach for data collection. First, we performed an updated academic literature review on ATMP development and their scientific and market access challenges (papers published between 2018 and April 2023). Second, we analyzed industry feedback from cell and gene therapy webinars and white papers published by providers and pharmaceutical industries. Finally, we established a comparative analysis of the regulatory guidelines published by EMA and the FDA for ATMP approval. Results: The main challenges in bringing these therapies to market are the high development costs. Developing ATMPs is expensive due to the need for specialized manufacturing processes. Furthermore, the regulatory pathways for ATMPs are often complex and can vary between countries, making it challenging to obtain approval and ensure compliance with different regulations. As a result of the high costs associated with ATMPs, challenges in obtaining reimbursement from healthcare payers lead to limited patient access to these treatments. ATMPs are often developed for orphan diseases, which means that the patient population is limited for clinical trials which can make it challenging to demonstrate their safety and efficacy. In addition, the complex manufacturing processes required for ATMPs can make it challenging to scale up production to meet demand, which can limit their availability and increase costs. Finally, ATMPs face safety and efficacy challenges: dangerous adverse events of these therapies like toxicity related to the use of viral vectors or cell therapy, starting material and donor-related aspects. Conclusion. As a result of our mixed method analysis, we found that ATMPs face a number of challenges in their development, regulatory approval, and commercialization and that addressing these challenges requires collaboration between industry, regulators, healthcare providers, and patient groups. This first analysis will help us to address, for each challenge, proper and innovative solution(s) in order to increase the number of ATMPs approved and reach the patients

Keywords: advanced therapy medicinal products (ATMPs), product development, market access, innovation

Procedia PDF Downloads 77