Sperm identification and selection is an essential task when processing human testicular samples for in vitro fertilization. Locating and identifying sperm cell(s) in human testicular biopsy samples is labor intensive and time consuming. We developed a new computer-aided sperm analysis (CASA) system, which utilizes deep learning for near human-level performance on testicular sperm extraction (TESE), trained on a custom dataset. The system automates the identification of sperm in testicular biopsy samples. A dataset of 702 de-identified images from testicular biopsy samples of 30 patients was collected. Each image was normalized and passed through glare filters and diffraction correction. The data were split 80%, 10%, and 10% into training, validation, and test sets, respectively. Then, a deep object detection network, composed of a feature extraction network and object detection network, was trained on this dataset. The model was benchmarked against embryologists' performance on the detection task. Our deep learning CASA system achieved a mean average precision (mAP) of 0.741, with an average recall (AR) of 0.376 on our dataset. Our proposed method can work in real time; its speed is effectively limited only by the imaging speed of the microscope. Our results indicate that deep learning-based technologies can improve the efficiency of finding sperm in testicular biopsy samples.
Current preimplantation genetic testing (PGT) enables the selection of embryos based on fetal aneuploidy or the presence a small number of preselected disease-associated variants. Here we present a new approach that takes advantage of the improved genome coverage and uniformity of primary template-directed amplification (PTA) to call most early embryo genetic variants accurately and reproducibly from a preimplantation biopsy. With this approach, we identified clonal and mosaic chromosomal aneuploidy, de novo mitochondrial variants, and variants predicted to cause mendelian and non-mendelian diseases. In addition, we utilized the genome-wide information to compute polygenic risk scores for common diseases. Although numerous computational, interpretive, and ethical challenges, this approach establishes the technical feasibility of screening for and preventing numerous debilitating inherited diseases.
INTRODUCTION:The burden of liver disease is substantial and increasing; the impact of comorbid chronic diseases on the clinical course of patients with compensated and decompensated cirrhosis is not well-defined. The aim of this study was to examine the individual and additive impact of comorbid chronic diseases on mortality in patients with cirrhosis.METHODS:In this population-based study, we used Cox proportional hazards modeling with time-dependent covariates to assess the impact of comorbid chronic diseases (diabetes mellitus, chronic kidney disease, and cardiovascular disease [CVD]) on mortality in patients with cirrhosis in a large, diverse Metroplex.RESULTS:There were 35,361 patients with cirrhosis (mean age 59.5 years, 41.8% females, 29.7% non-White, and 17.5% Hispanic ethnicity). Overall, the presence of chronic comorbidities was 1 disease (28.9%), 2 diseases (17.5%), and 3 diseases (12.6%) with a majority having CVD (45%). Adjusted risk of mortality progressively increased with an increase in chronic diseases from 1 (hazard ratio [HR] 2.5, 95% confidence interval [CI] 2.23–2.8) to 2 (HR 3.27.95% CI 2.9–3.69) to 3 (HR 4.52, 95% CI 3.99–5.12) diseases. Survival of patients with compensated cirrhosis and 3 chronic diseases was similar to subsets of decompensated cirrhosis (67.7% as compared with decompensated cirrhosis with 1–3 conditions, 61.9%–63.9%).DISCUSSION:In patients with cirrhosis, a focus on comorbid chronic disease(s) as potential management targets may help avoid premature mortality, regardless of etiology. Multidisciplinary care early in the clinical course of cirrhosis is needed in addition to the current focus on management of complications of portal hypertension.
Human embryonic stem cells (hESCs) are derived from the inner cell mass (ICM) of blastocyst staged embryos. Spare blastocyst staged embryos were obtained by in vitro fertilization (IVF) and donated for research purposes. hESCs carrying specific mutations can be used as a powerful cell system in modeling human genetic disorders. We obtained preimplantation genetic diagnosed (PGD) blastocyst staged embryos with genetic mutations that cause human disorders and derived hESCs from these embryos. We applied laser assisted micromanipulation to isolate the inner cell mass from the blastocysts and plated the ICM onto the mouse embryonic fibroblast cells. Two hESC lines with lesions in FOXP3 and NF1 were established. Both lines maintain a typical undifferentiated hESCs phenotype and present a normal karyotype. The two lines express a panel of pluripotency markers and have the potential to differentiate to the three germ layers in vitro and in vivo. The hESC lines with lesions in FOXP3 and NF1 are available for the scientific community and may serve as an important resource for research into these disease states.
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