Mitchell Le (petsword65)

Currently, there are no publicly available interactive software applications that can perform statistical analyses and visualization of data from CGM studies. With the rapidly increasing popularity of CGM studies, such an application is becoming necessary for anyone who works with these large CGM datasets, in particular for those with little background in programming or statistics. CGMStatsAnalyser is a publicly available, user-friendly, web-based application, which can be used to interactively visualize, summarize, and statistically analyze voluminous and complex CGM datasets together with the subject characteristics with ease. Portable retinal cameras and deep learning (DL) algorithms are novel tools adopted by diabetic retinopathy (DR) screening programs. Our objective is to evaluate the diagnostic accuracy of a DL algorithm and the performance of portable handheld retinal cameras in the detection of DR in a large and heterogenous type 2 diabetes population in a real-world, high burden setting. Participants underwent fundus photographs of both eyes with a portable retinal camera (Phelcom Eyer). Classification of DR was performed by human reading and a DL algorithm (PhelcomNet), consisting of a convolutional neural network trained on a dataset of fundus images captured exclusively with the portable device; both methods were compared. We calculated the area under the curve (AUC), sensitivity, and specificity for more than mild DR. A total of 824 individuals with type 2 diabetes were enrolled at Itabuna Diabetes Campaign, a subset of 679 (82.4%) of whom could be fully assessed. The algorithm sensitivity/specificity was 97.8 % (95% CI 96.7-98.9)/61.4 % (95% CI 57.7-65.1); AUC was 0·89. NMU chemical chemical structure All false negative cases were classified as moderate non-proliferative diabetic retinopathy (NPDR) by human grading. The DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs. The DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs.Human skin contains keratinocytes in the epidermis. Such cells share their ectodermal origin with the central nervous system (CNS). Recent studies have demonstrated that terminally differentiated somatic cells can adopt a pluripotent state, or can directly convert its phenotype to neurons, after ectopic expression of transcription factors. In this article we tested the hypothesis that human keratinocytes can adopt neural fates after culturing them in suspension with a neural medium. Initially, keratinocytes expressed Keratins and Vimentin. After neural induction, transcriptional upregulation of NESTIN, SOX2, VIMENTIN, SOX1, and MUSASHI1 was observed, concomitant with significant increases in NESTIN detected by immunostaining. However, in vitro differentiation did not yield the expression of neuronal or astrocytic markers. We tested the differentiation potential of control and neural-induced keratinocytes by grafting them in the developing CNS of rats, through ultrasound-guided injection. For this purpose, keratinocytes were transduced with lentivirus that contained the coding sequence of green fluorescent protein. Cell sorting was employed to select cells with high fluorescence. Unexpectedly, 4 days after grafting these cells in the ventricles, both control and neural-induced cells expressed green fluorescent protein together with the neuronal proteins