Polycystic ovary syndrome (PCOS) is a common gynecological endocrine disorder characterized by abnormalities in the inflammation-like ovulatory process. Elevated thrombospondin-1 (THBS1) is associated with these disruptions, but the impact of Leu-Ser-Lys-Leu-NH₂ (LSKL), a THBS1 inhibitor, on PCOS-related abnormalities remains unclear. This study aims to explore the roles of THBS1 and LSKL in PCOS pathogenesis. PCOS were induced by administration of dehydroepiandrosterone (DHEA) to rats and granulosa cells (GCs) for 21 days. Thereafter, LSKL was administered to rats. Molecular docking was employed to assess the binding affinity between LSKL and THBS1. Cell viability, apoptosis, and reactive oxygen species (ROS) levels in GCs were evaluated using the Cell Counting Kit-8 (CCK8) assay and flow cytometry. In rats, parameters including body weight, estrous cycle, ovarian histopathology, serum hormone levels (luteinizing hormone, follicle-stimulating hormone, testosterone, estradiol), ovarian oxidative stress, and the expression of PI3K/AKT signaling and apoptosis-related proteins were analyzed. In vitro, molecular docking confirmed a strong binding affinity between LSKL and THBS1. LSKL alleviated DHEA-induced damage, reduced ROS accumulation, and inhibited apoptosis in dysfunctional GCs, while enhancing PI3K/AKT pathway activation. In vivo, LSKL mitigated ovarian injury and oxidative stress, restored estrous cycle regularity, normalized serum hormone levels, improved ovarian morphology (including reduced cystic dilation and follicular dysplasia), and suppressed ovarian apoptosis, all while boosting PI3K/AKT activity. These results position THBS1 as a potential therapeutic target for PCOS and demonstrate that LSKL mitigates ovarian damage, oxidative stress, and apoptosis in PCOS by inhibiting THBS1 and activating the PI3K/AKT pathway.

The umbilical coiling index (UCI), which quantifies the degree of vascular coiling in the umbilical cord, is a crucial indicator for assessing fetal intrauterine development and predicting perinatal outcomes. However, the existing methods for measuring the UCI primarily rely on manual assessment, which suffers from low efficiency and susceptibility to inter-observer variability. In response to the challenges in measuring the umbilical coiling index during obstetric ultrasound, we propose UCINet, a multi-task neural network engineered explicitly for this purpose. UCINet demonstrates enhanced operational efficiency and significantly improved accuracy in detection, catering to the nuanced requirements of obstetric imaging. Firstly, this paper proposes a Frequency–Spatial Domain Downsampling Module (FSDM) to extract features in both the frequency and spatial domains, thereby reducing the loss of umbilical cord features and enhancing their representational capacity. The proposed Multi-Receptive Field Feature Perception Module (MRPM) employs receptive fields of varying sizes across different stages of the feature maps, enhancing the richness of feature representation. This approach allows the model to capture a more diverse set of spatial information, contributing to improved overall performance in feature extraction. A Multi-Scale Feature Aggregation Module (MSAM) comprehensively leverages multi-scale features via a dynamic fusion mechanism, optimizing the integration of disparate feature scales for enhanced performance. In addition, the UCI dataset, which consisted of 2018 annotated ultrasound images, was constructed, each labeled with the number of vascular coils and keypoints at both ends of the umbilical cord. Compared with state-of-the-art methods, UCINet achieves consistent improvements across two tasks. In object detection, UCINet outperforms Deformable DETR-R50 with an improvement of 1.2% points in mAP@50. In keypoint localization, it further exceeds YOLOv11 with a 3.0% gain in mAP@50, highlighting its effectiveness in both detection accuracy and fine-grained keypoint prediction.