As the leading cause of neonatal morbidity and mortality, preterm birth is recognized as a major public health concern around the world. The purpose of this review is to analyze the connection between infections and premature birth. Spontaneous preterm birth is commonly associated with intrauterine infection/inflammation. The overproduction of prostaglandins caused by the inflammation associated with an infection could lead to uterine contractions, contributing to preterm delivery. Many pathogens, particularly Chlamydia trachomatis, Neisseria gonorrhoeae, Trichomonas vaginalis, Gardnerella vaginalis, Ureaplasma urealyticum, Mycoplasma hominis, Actinomyces, Candida spp., and Streptococcus spp. have been related with premature delivery, chorioamnionitis, and sepsis of the neonate. Further research regarding the prevention of preterm delivery is required in order to develop effective preventive methods with the aim of reducing neonatal morbidity.

Objective To evaluate the performance of diagnostic prediction models for ovarian malignancy in all patients with an ovarian mass managed surgically or conservatively. Design Multicentre cohort study. Setting 36 oncology referral centres (tertiary centres with a specific gynaecological oncology unit) or other types of centre. Participants Consecutive adult patients presenting with an adnexal mass between January 2012 and March 2015 and managed by surgery or follow-up. Main outcome measures Overall and centre specific discrimination, calibration, and clinical utility of six prediction models for ovarian malignancy (risk of malignancy index (RMI), logistic regression model 2 (LR2), simple rules, simple rules risk model (SRRisk), assessment of different neoplasias in the adnexa (ADNEX) with or without CA125). ADNEX allows the risk of malignancy to be subdivided into risks of a borderline, stage I primary, stage II-IV primary, or secondary metastatic malignancy. The outcome was based on histology if patients underwent surgery, or on results of clinical and ultrasound follow-up at 12 (±2) months. Multiple imputation was used when outcome based on follow-up was uncertain. Results The primary analysis included 17 centres that met strict quality criteria for surgical and follow-up data (5717 of all 8519 patients). 812 patients (14%) had a mass that was already in follow-up at study recruitment, therefore 4905 patients were included in the statistical analysis. The outcome was benign in 3441 (70%) patients and malignant in 978 (20%). Uncertain outcomes (486, 10%) were most often explained by limited follow-up information. The overall area under the receiver operating characteristic curve was highest for ADNEX with CA125 (0.94, 95% confidence interval 0.92 to 0.96), ADNEX without CA125 (0.94, 0.91 to 0.95) and SRRisk (0.94, 0.91 to 0.95), and lowest for RMI (0.89, 0.85 to 0.92). Calibration varied among centres for all models, however the ADNEX models and SRRisk were the best calibrated. Calibration of the estimated risks for the tumour subtypes was good for ADNEX irrespective of whether or not CA125 was included as a predictor. Overall clinical utility (net benefit) was highest for the ADNEX models and SRRisk, and lowest for RMI. For patients who received at least one follow-up scan (n=1958), overall area under the receiver operating characteristic curve ranged from 0.76 (95% confidence interval 0.66 to 0.84) for RMI to 0.89 (0.81 to 0.94) for ADNEX with CA125. Conclusions Our study found the ADNEX models and SRRisk are the best models to distinguish between benign and malignant masses in all patients presenting with an adnexal mass, including those managed conservatively. Trial registration ClinicalTrials.gov NCT01698632.

Ovarian lesions are common and often incidentally detected. A critical shortage of expert ultrasound examiners has raised concerns of unnecessary interventions and delayed cancer diagnoses. Deep learning has shown promising results in the detection of ovarian cancer in ultrasound images; however, external validation is lacking. In this international multicenter retrospective study, we developed and validated transformer-based neural network models using a comprehensive dataset of 17,119 ultrasound images from 3,652 patients across 20 centers in eight countries. Using a leave-one-center-out cross-validation scheme, for each center in turn, we trained a model using data from the remaining centers. The models demonstrated robust performance across centers, ultrasound systems, histological diagnoses and patient age groups, significantly outperforming both expert and non-expert examiners on all evaluated metrics, namely F1 score, sensitivity, specificity, accuracy, Cohen’s kappa, Matthew’s correlation coefficient, diagnostic odds ratio and Youden’s J statistic. Furthermore, in a retrospective triage simulation, artificial intelligence (AI)-driven diagnostic support reduced referrals to experts by 63% while significantly surpassing the diagnostic performance of the current practice. These results show that transformer-based models exhibit strong generalization and above human expert-level diagnostic accuracy, with the potential to alleviate the shortage of expert ultrasound examiners and improve patient outcomes.

Background/Objectives: Inflammation, infections, and oxidative stress (OS) all have an impact on male infertility, which is a complicated, multifaceted illness. OS affects motility and fertilization capability. It accomplishes this through damaging sperm DNA, oxidizing proteins, and triggering lipid peroxidation. These effects occur due to an imbalance between reactive oxygen species (ROS) and antioxidant defenses. Methods: This review aims to evaluate the impact of oxidative stress and inflammation on male infertility by assessing recent literature. Results: Pro-inflammatory cytokines, like TNF-α and IL-6, interfere with spermatogenesis and promote oxidative damage. Additionally, infections caused by pathogens like Escherichia coli and Chlamydia trachomatis alter the reproductive microenvironment, leading to sperm dysfunction and inflammation. Conclusions: Early detection and targeted treatment are essential due to the intricate interactions among these elements. Microbiota-modulating techniques, antimicrobial therapies, anti-inflammatory drugs, and antioxidants are therapeutic approaches that may help reduce oxidative damage and enhance male fertility.

Chronic inflammation is increasingly recognized as a critical factor in female reproductive health; influencing natural conception and the outcomes of assisted reproductive technologies such as in vitro fertilization (IVF). An essential component of innate immunity, the NLR family pyrin domain-containing 3 (NLRP3) inflammasome is one of the major mediators of inflammatory responses, and its activation is closely linked to oxidative stress. This interaction contributes to a decline in oocyte quality, reduced fertilization potential, and impaired embryo development. In the ovarian milieu, oxidative stress and NLRP3 inflammasome activation interact intricately, and their combined effects on oocyte competence and reproductive outcomes are significant. The aims of this review are to examine these molecular mechanisms and to explore therapeutic strategies targeting oxidative stress and NLRP3 inflammasome activity, with the goal of enhancing female fertility and improving clinical outcomes in reproductive health.

Male factors may be present in up to 50–70% of infertile couples and the prevalence of male infertility accounts for 20–30% of infertility cases. Understanding the mechanisms and causes behind male infertility remains a challenge, but new diagnostic tools such as DNA fragmentation might aid in cases where the routine semen analysis is insufficient. DNA fragmentation, which refers to damages or breaks of the genetic material of the spermatozoa, is considered one of the main causes of male infertility due to impaired functional capability of sperm. The aim of the present narrative review is to investigate and enlighten the potential correlation between DNA fragmentation and male infertility parameters such as the seminal profile and the reproductive outcomes. Comprehensive research in PubMed/Medline and Scopus databases was conducted and 28 studies were included in the present review. Fourteen studies provided data regarding the impact of DNA fragmentation and seminal parameters and showed a correlation of significantly lower sperm count, lower concentration, motility, and abnormal morphology with an increased DNA fragmentation index (DFI). Similarly, 15 studies provided data regarding the impact of DFI on reproductive outcomes. Two studies showed higher aneuploidy rates with higher DFI values, and seven studies showed significantly lower pregnancy rates and live birth rates with higher DFI values. Ultimately, the studies included in this review highlight, collectively, the importance of measuring sperm DFI in the assessment of male infertility. Further studies are needed to explore the effectiveness of interventions aiming to reduce DFI levels.