Hsiang-Chun Dong

Premature ovarian insufficiency (POI) is a heterogeneous disorder, and cyclophosphamide (CTX)-induced ovarian injury represents an acquired POI-like condition rather than the full clinical spectrum of POI. In this study, CTX was used to establish a granulosa cell dysfunction model to evaluate whether primed avian mesenchymal stem cell-derived small extracellular vesicles (primed AMSC-sEVs) could restore granulosa cell homeostasis under human menopausal gonadotropin (hMG) stimulation, a clinically relevant gonadotropin context used in ovarian stimulation. Human granulosa cells were exposed to CTX and subsequently treated with hMG, naïve AMSC-sEVs, or primed AMSC-sEVs. Cell viability, mitochondrial membrane potential, AMH and FSHR expression, and hormone secretion were examined. CTX reduced cell viability, mitochondrial membrane potential, AMH and FSHR expression, and AMH/estradiol secretion, confirming the establishment of a POI-like granulosa cell dysfunction state. hMG partially restored selected functional markers but showed limited effects on cell viability and mitochondrial recovery. In contrast, primed AMSC-sEVs markedly restored granulosa cell viability, mitochondrial membrane potential, AMH and FSHR expression, and endocrine output, with stronger effects than naïve AMSC-sEVs. Co-treatment with hMG did not consistently enhance the restorative effects of primed AMSC-sEVs beyond those achieved by primed AMSC-sEVs alone. These findings indicate that primed AMSC-sEVs primarily act by restoring granulosa cell and mitochondrial homeostasis, thereby supporting the cellular competence required for gonadotropin responsiveness in an acquired POI-like condition.

Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial intelligence (AI) for detecting the depth of myometrial invasion using a deep learning technique on magnetic resonance images. We obtained 4896 contrast-enhanced T1-weighted images (T1w) and T2-weighted images (T2w) from 72 patients who were diagnosed with surgico-pathological stage I endometrial carcinoma. We used the images from 24 patients (33.3%) to train the AI. The images from the remaining 48 patients (66.7%) were used to evaluate the accuracy of the model. The AI then interpreted each of the cases and sorted them into stage IA or IB. Compared with the accuracy rate of radiologists’ diagnoses (77.8%), the accuracy rate of AI interpretation in contrast-enhanced T1w was higher (79.2%), whereas that in T2w was lower (70.8%). The diagnostic accuracy was not significantly different between radiologists and AI for both T1w and T2w. However, AI was more likely to provide incorrect interpretations in patients with coexisting benign leiomyomas or polypoid tumors. Currently, the ability of this AI technology to make an accurate diagnosis has limitations. However, in hospitals with limited resources, AI may be able to assist in reading magnetic resonance images. We believe that AI has the potential to assist radiologists or serve as a reasonable alternative for pre-operative evaluation of the myometrial invasion depth of stage I endometrial cancers.