"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."