ABSTRACT
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.
ABSTRACT
There is no national referral criteria for burns in China till now, which brings inconvenience and confusion. Based on the oversea experiences and the actual situation in China, many famous experts on burns discussed and developed this Chinese burn referral criteria (2018 version). We hope these referral criteria will be helpful in clinical practice in burn field and can be improved continuously during application.
Subject(s)
Humans , Burn Units , Reference Standards , Burns , Therapeutics , China , Practice Guidelines as Topic , Reference Standards , Referral and Consultation , Reference StandardsABSTRACT
Epoxyeicosatrienoic acids [EETs], which are cytochrome P450 epoxygenase metabolites of arachidonic acid, have anti-inflammatory effects, modulate smooth muscle proliferation, and inhibit smooth muscle migration. This study was designed to determine whether exogenous EETs have any effect on the cell proliferation and apoptosis of carcinoma cell as well as the possible signaling pathways of EETs in this regulation. The effects of EETs on the proliferation and anti-apoptosis of human carcinoma cells were measured by MTT assay and flowcytometric analysis, and the regulation of PPARy, epithelial growth factor receptor [EGFR], extracellular signal-regulated kinase [ERK], phosphatidylinositol 3 [PI3]-Kinase/AKT pathways was investigated by reverse transcriptase polymerase chain reaction [RT-PCR] and western blot analysis. Results of this study suggested that 14, 15-EET may activate the expression of PPARy in Tea-8113 cells. 14,15-EET may stimulate cell proliferation, and increase the percentage of cells during S-G2-M phase in Tea-8113 cells significantly. The levels of EGFR, ERK, and PI3 kinase/AKT proteins were significantly induced by treatment of 14, 15-EET and 14,15-EET/ AUDA, but no significant changes were observed by addition of GW9662. These findings suggest that exogenous 14,15-EET has potent inhibitory effect on proliferation, and could induce apoptosis in Tea-8113 cell, and these changes are related to the expression of PPARganna, the activation of EGFR, ERK, and PI3 kinase/AKT proteins
ABSTRACT
Sewage solids are usually characterized by non-specific parameters such as suspended solids. The suspended solids has been shown to be an inadequate index for advanced water treatment processes. In this study, the sewages solids particle size distribution was used to provide more detailed information on sewage characteristic. It is hoped that, by introducing particle size distribution, the mechanism of sewage solids sedimentations can be better understood. The particle size distribution of the domestic sewage was measured by the Malvern laser scattering technique to link to its settlement efficiency. Experimental results show that 77% of particle volume was removed during the 90 min settling, of which 71.2% of particle volume was removed in the initial 30 min. The submicron particles were found to be removed by co-settling with large particles. The fractal dimensions of sewage solids could also be derived from the laser scattering measurement. The fractal dimension could also provide useful information on the shape and density of sewage solids. A mathematical model considering the particle sizes, shapes and density changes was then constructed to simulate the settlement of raw sewage particles. Comparison of the modeling results based on discrete and flocculant settling theory shows that settlement can be better predicted by considering the fractal nature of particles. The particle size distribution and fractal dimension data measured by Malvern laser scattering technique have been shown to be valuable data for in-depth understanding of the mechanisms of sewage solids sedimentation