Gene/Protein Disease Symptom Drug Enzyme Compound
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A low dose of Cyproterone acetate (CPA; 1 mg/kg body weight/day for 70 days) was administered to adult male rhesus monkeys to assess its effects on testicular and epididymal structure and function in a nonhuman primate species. CPA caused extensive degenerative changes in morphology of seminiferous, efferent duct, and epididymal epithelia, including decrease in diameter of seminiferous and epididymal tubules and their lumen, height of epididymal epithelium, and an increase in intertubular connective tissue. The protein profile of spermatozoa showed alterations during their epididymal transit in control and CPA-treated monkeys. In CPA-treated animals, 19 polypeptides were acquired and nine were eliminated during epididymal transit in contrast to acquisition of 12 and loss of 14 polypeptides in control animals. Treatment with CPA also resulted in the appearance of 14 new polypeptides in epididymal cytosol and luminal fluid, probably of lysosomal origin. The protein pattern of caput and cauda epididymal tubule cytosol, maintained in organ culture and exposed to 100 microM CPA for 3 days, showed absence of eight polypeptides. These results indicate that even at the low dose used in this study, CPA has caused spermatogenic arrest, degenerative changes in the epididymal structure, and alterations in epididymal and sperm protein profile. Suppression of serum testosterone levels indicates the need for androgen supplementation if CPA is to be used for male contraception.
Anat Rec 1992 Sep
PMID:Effect of cyproterone acetate on structure and function of rhesus monkey reproductive organs. 141 98

SPECT imaging has been identified as an effective medical modality for diagnosis, treatment, evaluation and prevention of a range of serious diseases and medical conditions. Bone SPECT scan has the potential to provide more accurate assessment of disease stage and severity. Segmenting hotspot in bone SPECT images plays a crucial role to calculate metrics like tumor uptake and metabolic tumor burden. Deep learning techniques especially the convolutional neural networks have been widely exploited for reliable segmentation of hotspots or lesions, organs and tissues in the traditional structural medical images (i.e., CT and MRI) due to their ability of automatically learning the features from images in an optimal way. In order to segment hotspots in bone SPECT images for automatic assessment of metastasis, in this work, we develop several deep learning based segmentation models. Specifically, each original whole-body bone SPECT image is processed to extract the thorax area, followed by image mirror, translation and rotation operations, which augments the original dataset. We then build segmentation models based on two commonly-used famous deep networks including U-Net and Mask R-CNN by fine-tuning their structures. Experimental evaluation conducted on a group of real-world bone SEPCT images reveals that the built segmentation models are workable on identifying and segmenting hotspots of metastasis in bone SEPCT images, achieving a value of 0.9920, 0.7721, 0.6788 and 0.6103 for PA (accuracy), CPA (precision), Rec (recall) and IoU, respectively. Finally, we conclude that the deep learning technology have the huge potential to identify and segment hotspots in bone SPECT images.
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PMID:Deep learning based automatic segmentation of metastasis hotspots in thorax bone SPECT images. 3327 Jul 46