RESUMO
Makorin-2 (Mkrn2) is an evolutionarily conserved gene whose biological functions are not fully known. Although recent studies have shed insights on the potential causes of male infertility, its underlining mechanisms still remain to be elucidated. We developed a Mrkn2 knockout mice model to study this gene and found that deletion of Mkrn2 in mice led to male infertility. Interestingly, the expression level of signal transducer and activator of the transcription (STAT)1 was significantly decreased in MKRN2 knockout testis and MEF cells. Co-IP assay showed an interaction between MKRN2 and STAT1. Moreover, our results further indicated that MKRN2 regulated the expression level of SIX4 and tenascin C (TNC) via the EBF transcription factor 2 (EBF2) in mice. The results of our study will provide insights into a new mechanism of male infertility.
Assuntos
Infertilidade Masculina , Ribonucleoproteínas , Animais , Humanos , Masculino , Camundongos , Fatores de Transcrição Hélice-Alça-Hélice Básicos/metabolismo , Proteínas de Homeodomínio/metabolismo , Infertilidade Masculina/genética , Ribonucleoproteínas/genética , Ribonucleoproteínas/metabolismo , Fator de Transcrição STAT1/metabolismo , Tenascina/metabolismo , Transativadores/metabolismoRESUMO
Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame.