Publications by authors named "Mathilde Oranger"

Article Synopsis
  • The study aimed to develop a prediction model to identify mild COVID-19 patients at risk of worsening, utilizing clinical, biological, and chest CT data.
  • It involved training and validating the model across multiple hospitals, incorporating factors like age, gender, and lymphocyte counts alongside CT scan analysis.
  • Results indicated that combining CT scan quantification and radiomics with clinical parameters significantly improved prediction accuracy for identifying patients likely to develop more severe COVID-19.*
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Rationale: Lung fibroblast senescence is involved in the pathophysiology of chronic obstructive pulmonary disease (COPD). However, the mechanisms underlining this phenomenon are still poorly understood. Secreted phospholipases (sPLA2, a subclass of phospholipases) are secreted by senescent cells and can in turn induce senescence.

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Non-invasive ventilation (NIV) is commonly used at home for patient with nocturnal hypoventilation caused by a chronic respiratory failure. Monitoring NIV is required to optimize the ventilator settings when the lung condition changes over time, and to detect common problems such as unintentional leaks, upper airway obstructions, and patient-ventilator asynchronies. This review describes the accuracy and limitations of the data recorded by the ventilator.

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Objective: The coronavirus disease pandemic (COVID-19) increased the risk of shortage in intensive care devices, including fittings with intentional leaks. 3D-printing has been used worldwide to produce missing devices. Here we provide key elements towards better quality control of 3D-printed ventilation fittings in a context of sanitary crisis.

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