Publications by authors named "M BENJELLOUN"

Objectives: This retrospective study aims to evaluate the efficacy of continuous positive airway pressure (CPAP) therapy in treating severe obstructive sleep apnea (OSA), based on polysomnographic parameters, and to highlight the importance of hospital-based titration in optimizing treatment and guiding choices of alternative ventilation modes.

Methods: Sixty-nine patients (n=69, 100%), predominantly female (n=49, 71%), were included in the study. Polysomnographic data were collected during hospital-based CPAP titration.

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Background: Despite the availability of effective asthma medications, the condition remains poorly controlled, making it one of the most common emergency room and physician visits each year. Appropriate asthma education programs are needed to maintain or improve optimal lung function in asthmatics. This study aimed to evaluate changes in lung function parameters in adult asthmatics before and after a structured educational intervention.

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This review focuses on the application of β-cyclodextrin (β-CD) polymer nanocomposites (NCs) in the heavy metals (HMs) removal from contaminated water sources. This manuscript's originality consists of an in-depth analysis of recent advances in using β-cyclodextrin nanocomposites (β-CD-NCs) to remove HMs from wastewater, highlighting literature gaps, innovations, and challenges in this field, suggesting perspectives on existing theories, and outlining implications for future research directions. Combining nanoparticles with the β-CD polymer yields stable, reusable β-CD-NCs that are effective and efficient in HM adsorption.

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This paper introduces a novel technology for the monolithic integration of GaN-based vertical and lateral devices. This approach is groundbreaking as it facilitates the drive of high-power GaN vertical switching devices through lateral GaN HEMTs with minimal losses and enhanced stability. A significant challenge in this technology is ensuring electrical isolation between the two types of devices.

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Article Synopsis
  • The text discusses the challenges of using standardized treatments for patients with the same disease, highlighting the need for personalized medicine to address individual variability in reactions to treatments.
  • It emphasizes the potential of AI and diagnostic tests to optimize treatment plans, improve prognosis accuracy, and automate monitoring to reduce errors commonly seen with traditional methods.
  • The analysis of existing studies on breast cancer treatment response indicates that various AI technologies, including machine learning and imaging techniques, show promise in predicting patient outcomes and suggests future research directions in AI-driven oncology.
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