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To use an existing database from a large cohort study with follow-up as long as 5.5 years to assess the extended prognosis of patients who survived their hospitalizations for severe acute respiratory failure (ARF). Secondary analysis of an inception cohort of 1,722 patients with ARF requiring mechanical ventilation from five major medical centers who were entered into the prospective Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment. The 1,075 patients (62.4%) who survived hospitalization had systematic follow-up of vital status for a median time of 662 days (interquartile range, 327 to 1,049 days; range, 2 to 2,014 days). Interviews performed a median of 5 months after hospital discharge assessed functional capacity and quality of life (QOL). The main outcome measure was survival after hospital discharge. Secondary measures were functional status and QOL. Cox proportional hazard regression identified factors influencing posthospital survival. The median survival time after hospital discharge for ARF was > 5.3 years. The posthospital survival time was shorter for those with older age, male gender, several preexisting comorbid conditions, worse prehospital functional status, greater acute physiologic derangement, and a do-not-resuscitate order while in the hospital, and for those discharged to a location other than home. Five months after hospital discharge, 48% of survivors needed help with at least one activity of daily living, and 27% rated their QOL as poor or fair. However, most of these impairments were present before respiratory failure occurred. Extended survival is common among patients with ARF who require mechanical ventilation and who survive hospitalization. Among these patients, only a small fraction of the impairment in activity and QOL can be considered to be a sequela of the respiratory failure or its therapy. These findings are relevant to the care decisions for such critically ill patients.

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders 1 . However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications 2 . Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging. RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled images, is trained on 1.6 million unlabelled images by self-supervised learning and then adapted to disease detection tasks with explicit labels.

Pulmonary arterial hypertension is a severe and progressive disease characterized by a pulmonary vascular remodeling process with expansion of collateral endothelial cells and total vessel occlusion. Endothelial cells are believed to be at the forefront of the disease process. Vascular endothelial growth factor (VEGF) and its tyrosine kinase receptor, VEGF receptor-2 (VEGFR-2), play a central role in angiogenesis, endothelial cell protection, but also in the destabilization of endothelial barrier function. Therefore, we investigated the consequences of altered VEGF signaling in an experimental model, and looked for translational correlates of this observation in patients. We performed an endothelial cell-specific conditional deletion of the kinase insert domain protein receptor (kdr) gene, coding for VEGFR-2, in C57/BL6 mice (Kdr∆end) and held them in an environmental chamber with 10% FiO2 or under normoxia for 6 weeks. Kdr knockout led to a mild PH phenotype under normoxia that worsened under hypoxia. Kdr∆end mice exhibited a significant increase in pulmonary arterial wall thickness, muscularization, and VEGFR-3+ endothelial cells obliterating the pulmonary artery vessel lumen. We observed the same proliferative vasculopathy in our rodent model as seen in patients receiving anti-angiogenic therapy. Serum VEGF-a levels were elevated both in the experimental model and in humans receiving bevacizumab. Interrupted VEGF signaling leads to a pulmonary proliferative arteriopathy in rodents after direct ablative gene manipulation of Kdr. Histologically, similar vascular lesions can be observed in patients receiving anti-VEGF treatment. Our findings illustrate the importance of VEGF signaling for maintenance of pulmonary vascular patency.

Die Echinokokkose ist in Deutschland eine seltene Erkrankung. Dennoch sollte sie bei raumfordernden Prozessen der Leber und Lunge in die differenzialdiagnostischen Erwägungen einbezogen werden, da eine auf einer Fehldiagnose beruhende falsche Behandlung den Verlauf deutlich verschlechtern kann. Seit Einführung der Meldepflicht im Jahr 2001 liegen deutschlandweit nun erstmals Daten für alle Echinokokkoseformen vor.