ABSTRACT
Mechanosensitive ion channel protein 1 (Piezo1) is a large homotrimeric membrane protein. Piezo1 has various effects and plays an important and irreplaceable role in the maintenance of human life activities and homeostasis of the internal environment. In addition, recent studies have shown that Piezo1 plays a vital role in tumorigenesis, progression, malignancy and clinical prognosis. Piezo1 is involved in regulating the malignant behaviors of a variety of tumors, including cellular metabolic reprogramming, unlimited proliferation, inhibition of apoptosis, maintenance of stemness, angiogenesis, invasion and metastasis. Moreover, Piezo1 regulates tumor progression by affecting the recruitment, activation, and differentiation of multiple immune cells. Therefore, Piezo1 has excellent potential as an anti-tumor target. The article reviews the diverse physiological functions of Piezo1 in the human body and its major cellular pathways during disease development, and describes in detail the specific mechanisms by which Piezo1 affects the malignant behavior of tumors and its recent progress as a new target for tumor therapy, providing new perspectives for exploring more potential effects on physiological functions and its application in tumor therapy.
ABSTRACT
Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.
