1.Pathological observation of brain arteries and spontaneous aneurysms in hypertensive rats.
Dong ZHANG ; Jizong ZHAO ; Yilin SUN ; Shuo WANG ; Wa Hou TAI ; Douglas D COCHRANE ; Jingsheng LI
Chinese Medical Journal 2003;116(3):424-427
OBJECTIVETo investigate the role of hypertension in the pathogenesis of cerebral aneurysms in rats.
METHODSTwenty spontaneous hypertensive rats (SHR) and 10 Wistar-Kyoto rats (WKY) were included in this observational study. Animals were fed with normal diet and drinking water. No experimental modifications were undertaken in either group. They were sacrificed at one year of age, the bifurcations of the circle of Willis were dissected and longitudinal serial sections were prepared for light microscopic and transmission electron microscopic study.
RESULTSIn the SHR group, 2 of the 20 rats formed an aneurysm respectively at the bifurcations of the basilar artery. As revealed by electron microscopy, injury at the bifurcation of the artery first occurred on the steeper side of the intimal pad. Furthermore, loss of endothelial cells, small depressions on the intima, disruptive internal elastic lamina and lymphocytes or red blood cells infiltration were noted at the steeper side of the intimal pad. No significant changes were observed in WKY group.
CONCLUSIONSCerebral aneurysms can form spontaneously in SHR without ligation of the common carotid artery and without a diet containing beta-aminoproprionitrile. Long-standing systemic arterial hypertension is one of the etiological factors that contributes to aneurysm formation in SHR rats.
Animals ; Cerebral Arteries ; pathology ; Hypertension ; complications ; Intracranial Aneurysm ; etiology ; pathology ; Male ; Rats ; Rats, Inbred SHR ; Rats, Inbred WKY
2.Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos
Ling SUN ; Jiahui LI ; Simiao ZENG ; Qiangxiang LUO ; Hanpei MIAO ; Yunhao LIANG ; Linling CHENG ; Zhuo SUN ; Hou Wa TAI ; Yibing HAN ; Yun YIN ; Keliang WU ; Kang ZHANG
Chinese Medical Journal 2024;137(16):1939-1949
Background::In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. Methods::We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes.Results::These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709–0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. Conclusions::Our study underscores the AI model’s ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.