A Dataset for Personalized Low-Invasive Evaluation of Chronic Endometritis in Premenopausal Women

Objective:The primary purpose of this dataset is to provide a set of specific parameters associated with Chronic Endometritis (CE), derived from a non-selective population of reproductive-aged women who underwent endometrial biopsy.Data Description:The dataset comprises fully anonymized anthropometric and clinical data from 108 non-obese patients, as defined by the study's inclusion criteria. It includes a comprehensive set of clinical, anamnestic, laboratory, and instrumental parameters that are established or suspected risk factors for chronic endometritis. In addition to anamnestic data\u2014such as prolonged menstrual bleeding, spontaneous abortions, and prior surgeries (e.g., cesarean section)\u2014as well as pelvic ultrasound results documenting intrauterine pathology (e.g., endometrial polyps, uterine fibroids), the dataset includes key laboratory indicators. These encompass markers of systemic inflammation: levels of C-reactive protein, interleukin-1 (IL-1), tumor necrosis factor \u03b1 (TNF-\u03b1), adiponectin, among others.An important addition is the results of detailed hormonal profiling, which includes baseline levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol, progesterone, testosterone, thyroid-stimulating hormone (TSH), anti-M\u00fcllerian hormone (AMH), and prolactin. This allows for an assessment of endocrine status and its relationship with the inflammatory process. The gold standard for diagnosis verification is provided by immunohistochemical analysis of endometrial biopsies for the presence of plasma cells (CD138 marker), which was confirmed in a subset of the women.The dataset was collected from 108 participants and is provided in .csv format.Potential Applications:This comprehensive dataset can be utilized for an in-depth investigation of the pathogenesis of chronic endometritis (CE) within a non-selective population sample, as well as for the development of precise diagnostic and predictive models using various methods, including machine learning.

研究目标：本数据集的核心目的是提供一组与慢性子宫内膜炎（Chronic Endometritis, CE）相关的特定参数，数据来源于接受子宫内膜活检的育龄女性非选择性人群。
数据说明：本数据集包含108名符合研究纳入标准的非肥胖患者的完全匿名人体测量学与临床数据。其中涵盖了已明确或疑似为慢性子宫内膜炎危险因素的全套临床、病史、实验室及器械检查参数。除病史数据（如经期延长、自然流产及既往手术史，例如剖宫产史）、记录宫内病变（如子宫内膜息肉、子宫肌瘤）的盆腔超声结果外，数据集还包含关键实验室指标：包括系统性炎症标志物，如C反应蛋白、白细胞介素-1（IL-1）、肿瘤坏死因子α（TNF-α）、脂联素等。
此外，数据集还纳入了详细的激素谱检测结果，涵盖促卵泡生成素（FSH）、黄体生成素（LH）、雌二醇、孕酮、睾酮、促甲状腺激素（TSH）、抗缪勒管激素（AMH）及催乳素的基线水平，可用于评估内分泌状态及其与炎症进程的关联。本数据集的诊断验证金标准为子宫内膜活检组织的免疫组化分析（检测浆细胞CD138标志物表达），该验证结果已在部分受试者中完成。
本数据集共收录108名参与者的相关数据，以.csv格式提供。
潜在应用场景：本综合性数据集可用于在非选择性人群样本中深入探究慢性子宫内膜炎（CE）的发病机制，也可借助包括机器学习在内的多种方法，开发精准的诊断与预测模型。