Background: Endometriosis is a common nonmalignant gynecological disorder that affects 10–15% women of reproductive age and causes several symptoms that result in decreased quality of life and a huge social burden. In recent decades, extracellular vesicles (EVs) have gained attention as a potential therapeutic tool; however, the therapeutic effects of EVs against endometriosis have not been reported. Accordingly, in this study, we investigated the feasibility of nanovesicles (NVs) derived from M1 macrophages (M1NVs) in treating endometriosis. Methods: M1NVs were prepared by serial extrusion. Co-culture assays were performed to investigate changes in tube formation and migration/invasion of eutopic endometrial stroma cells (ESCs) obtained from patients with endometriosis (EM-ESCs). A mouse model of endometriosis was established, and mice were treated with phosphate-buffered saline, M0NVs, or M1NVs to evaluate the efficacy and safety of M1NV for treating endometriosis. Results: M1NVs directly or indirectly inhibited the migration and invasion of EM-ESCs and reduced tube formation. In the mouse model, M1NVs suppressed the development of endometriosis through reprogramming of M2 macrophages, without causing damage to the organs. Conclusions: M1NVs inhibit the development of endometriosis directly, or through repolarizing macrophages from M2 to M1 phenotype. Hence, administration of M1NVs may represent a novel method for the treatment of endometriosis.

Accurate diagnosis of white blood cells from cytopathological images is a crucial step in evaluating leukaemia. In recent years, image classification methods based on fully convolutional networks have drawn extensive attention and achieved competitive performance in medical image classification. In this paper, we propose a white blood cell classification network called ResNeXt-CC for cytopathological images. First, we transform cytopathological images from the RGB color space to the HSV color space so as to precisely extract the texture features, color changes and other details of white blood cells. Second, since cell classification primarily relies on distinguishing local characteristics, we design a cross-layer deep-feature fusion module to enhance our ability to extract discriminative information. Third, the efficient attention mechanism based on the ECANet module is used to promote the feature extraction capability of cell details. Finally, we combine the modified softmax loss function and the central loss function to train the network, thereby effectively addressing the problem of class imbalance and improving the network performance. The experimental results on the C-NMC 2019 dataset show that our proposed method manifests obvious advantages over the existing classification methods, including ResNet-50, Inception-V3, Densenet121, VGG16, Cross ViT, Token-to-Token ViT, Deep ViT, and simple ViT about 5.5–20.43% accuracy, 3.6–23.56% F1-score, 3.5–25.71% AUROC and 8.1–36.98% specificity, respectively.

The main symptoms of adenomyosis may adversely affect physical and mental health and quality of life (QOL). However, studies are few on this topic. This study evaluated the effect of adenomyosis on anxiety, depression, and QOL. Participants with adenomyosis (n = 90) or leiomyoma (n = 59) completed questionnaires, including the visual analog scale (VAS) for pain, Hospital Anxiety and Depression Scale (HADS), and Short Form (SF)-36. HADS anxiety and depression positive rates, physical (PCS) and mental (MCS) component summary scores, and eight subscale scores of SF-36 were compared between the two groups. Among patients with adenomyosis, the incidence of anxiety symptoms was 28.9% (control group, 10.2%; OR = 3.589, 95% CI: 1.375–9.367), with 10% of patients showing moderate-to-severe symptoms. The incidence of depressive symptoms was 14.4% (control group, 3.4%; OR = 4.812, 95% CI: 1.044–22.168). The case group had significantly lower PCS and MCS scores than the control group. In patients with adenomyosis, being employed (OR = 6.393, 95% CI: 1.153–35.440) and perianal pain (OR = 25.419, 95% CI: 2.504–258.024) were risk factors for anxiety, and perianal pain (OR = 15.208, 95% CI: 3.050–75.836) was a risk factor for depression. Compared with leiomyoma, adenomyosis is associated with a higher risk of anxiety and depression, with a poorer QOL.