Intervention to severe lower trachea obstruction supported by extracorporeal membrane oxygenation in a human immunodeficiency virus patient: A case report and literature review.

Here we reported a case, male, 33 years old, diagnosed with human immunodeficiency virus (HIV) infection 5 months ago, but he didn't take antiretroviral drugs regularly. He was admitted to intensive care unit emergently due to hypoxemia, hypercapnia, and hypotension. CT showed severe lower trachea obstruction caused by soft tissue. After rapid bedside assessment, the patient was considered to need endotracheal operation, but he couldn't tolerate intubation and mechanical ventilation. Extracorporeal membrane oxygenation (ECMO) was used. Hemodynamics improved significantly along with rehydration and low-dose vasoactive drugs. Subsequently, the patient underwent rigid bronchoscopy, airway tumor resection and Y-type silicone stent implantation. Postoperatively protective endotracheal intubation and mechanical ventilation was followed. ECMO was weaned off after the operation, and endotracheal cannula was removed 6 h later. The pathological examination of excisional tissue showed lung squamous cell carcinoma. Finally, the patient was discharged safely and went to local hospital for further treatment. From this case, we conclude that ECMO could play a key role for those who need endotracheal surgery while cannot endure conventional intubation and mechanical ventilation.

Comparison of segmentation algorithms for fluorescence microscopy images of cells.

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability.