Publications by authors named "B Boeddi"

The advent of automated insulin delivery (AID) systems in 2020 marked a disruptive event in managing type 1 diabetes, benefiting children and adults alike. By 2024, advances in real-world data and research motivated an update to the French consensus on AID systems to expand accessibility, refine guidelines, and optimize patient follow-up. AID systems have consistently improved glycemic control by reducing HbA1c, increasing time-in-range (TIR), and minimizing hypoglycemia, with significant benefits even for specific populations such as individuals with poor glycemic control, brittle diabetes, children, very young children, pregnant women, those with insulin resistance or gastroparesis, or after bariatric surgery.

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Background/purpose: Proton radiation-therapy (PrT) may provide clinical benefit for lung cancer compared to photon radiation-therapy (PhT), however is more costly. Literature reporting costs for PrT, PhT, and comparisons thereof, particularly from a societal perspective, are scarce. This study aims to provide an adaptable framework to estimate PrT/PhT costs, demonstrated through application to lung cancer, from societal and healthcare perspectives.

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Maize is an important food source standing first in production around the world. It has become an essential raw material in most of the food processing industries and is extensively cultivated for its post-harvest by-products. So, it is necessary to develop genotypes adapted to varied climate and soil conditions favoring its productivity.

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Determination of antibiotic susceptibility is a crucial aspect of antimicrobial therapy. The objective of this study was to determine the specific susceptibility of Staphylococcus aureus (SA) to the antibiotic doripenem. A total of 180 clinical isolates from VRSA patients were tested against doripenem using minimum inhibitory concentration (MIC) and disk diffusion tests.

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The accurate evaluation of embryonic quality which is a key aspect in Assisted Reproductive Technology (ART) and it is crucial to ensure the success of in vitro fertilization (IVF), especially at critical developmental phases like day 3 and day 5. To increase the success rate of implantation, morphological feature-based scoring techniques are currently the mainstay of embryo evaluation. Addressing the need for enhanced accuracy and latency, our work uses advanced algorithms based on deep learning to assess microscopic images of embryos at these critical junctures.

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