Publications by authors named "A Jacq"
Introduction: Coronavirus disease 2019 (COVID-19) poses an important risk of morbidity and of mortality, in patients after solid organ transplantation. Recommendations have been issued by various transplantation societies at the national and European level to manage the immunosuppressive (IS) regimen upon admission to intensive care unit (ICU).
Method: The aim of this study was to evaluate the adequacy of IS regimen minimization strategy in kidney transplant recipients hospitalized in an ICU for severe COVID-19, in relation to the issued recommendations.
Background: Interstitial inflammation and peritubular capillaritis are observed in many diseases on native and transplant kidney biopsies. A precise and automated evaluation of these histological criteria could help stratify patients' kidney prognoses and facilitate therapeutic management.
Methods: We used a convolutional neural network to evaluate those criteria on kidney biopsies.
Article Synopsis
- The Pacific oyster Crassostrea gigas faces a deadly condition known as Pacific Oyster Mortality Syndrome (POMS), triggered by the herpesvirus OsHV-1 µVar, leading to an opportunistic bacterial infection.
- Researchers combined metabarcoding and metatranscriptomic techniques to investigate POMS, discovering a consistent progression of disease and identifying a core group of bacteria that, along with the virus, contribute to the syndrome.
- The identified bacteria exhibit low competition for nutrients, which might enhance their ability to colonize the oyster's tissues and maintain the POMS pathobiota despite varying environmental challenges.
Deep learning automation of MEST-C classification in IgA nephropathy.
Nephrol Dial Transplant
June 2023
Article Synopsis
- The study focuses on using a neural network to enhance the MEST-C classification method for diagnosing immunoglobulin A nephropathy (IgAN), which currently has variability in results between different pathologists.
- A dataset of biopsies was divided into training, testing, and application groups to train the neural network and evaluate its accuracy compared to human assessments.
- Results showed that the neural network could correctly classify over 73% of biopsy pixels and had substantial agreement with pathologists for most scores, highlighting its potential for reliable, automated analysis in clinical settings.