Gestational diabetes mellitus (GDM) occurs in approximately 9–25% of pregnancies and, if left undiagnosed or inadequately controlled, can lead to adverse outcomes for both the mother and the fetus, short and long term. GDM is characterized by glucose intolerance with onset or first recognition during pregnancy and is a multifactorial condition with a pathophysiology that remains incompletely understood. It is strongly associated with a chronic low-grade inflammatory state that contributes to insulin resistance, a hallmark of GDM pathogenesis. Among the fundamental pro-inflammatory cytokines implicated in this process, TNF-α and IL-6 play central roles. TNF-α is a cytokine primarily secreted by activated macrophages, as well as by adipocytes in the context of obesity. Many studies have shown that its levels are elevated in pregnant women with GDM compared to normoglycemic pregnant individuals. IL-6 is another pro-inflammatory cytokine secreted by immune cells, adipose tissue, and the placenta. It is found in higher concentrations in the maternal circulation during pregnancies complicated by GDM. Both TNF-α and IL-6 act synergistically to perpetuate a pro-inflammatory intrauterine environment. Their combined effects exacerbate insulin resistance and may impair pancreatic β-cell compensation during pregnancy, facilitating the onset of GDM in genetically or metabolically susceptible individuals. Recent research has identified various maternal serum biomarkers, such as TNF-α and IL-6, that may hold promise for the early detection of GDM. The aim of our study is to evaluate whether TNF-α and IL-6 can be used as diagnostic tools for the early diagnosis of GDM, allowing for timely intervention and reducing the risk of associated maternal and fetal complications.

Background/Objectives: This study aims to create a strong binary classifier and evaluate the performance of pre-trained convolutional neural networks (CNNs) to effectively distinguish between benign and malignant ovarian tumors from still ultrasound images. Methods: The dataset consisted of 3510 ultrasound images from 585 women with ovarian tumors, 390 benign and 195 malignant, that were classified by experts and verified by histopathology. A 20% to80% split for training and validation was applied within a k-fold cross-validation framework, ensuring comprehensive utilization of the dataset. The final classifier was an aggregate of three pre-trained CNNs (VGG16, ResNet50, and InceptionNet), with experimentation focusing on the aggregation weights and decision threshold probability for the classification of each mass. Results: The aggregate model outperformed all individual models, achieving an average sensitivity of 96.5% and specificity of 88.1% compared to the subjective assessment’s (SA) 95.9% sensitivity and 93.9% specificity. All the above results were calculated at a decision threshold probability of 0.2. Notably, misclassifications made by the model were similar to those made by SA. Conclusions: CNNs and AI-assisted image analysis can enhance the diagnosis and aid ultrasonographers with less experience by minimizing errors. Further research is needed to fine-tune CNNs and validate their performance in diverse clinical settings, potentially leading to even higher sensitivity and overall accuracy.

In-hospital patients who are in the gynecologic oncology setting often suffer from malnutrition, which is one of the primary problems, the rate of which reportedly ranges from 28% to 70%. Malnutrition is a significant risk factor for immunosuppression, negatively impacting immune response and postoperative recovery capacity. At the time of the surgeries, due to their wide scope and aggressive treatments such as chemotherapy and radiotherapy, the situation becomes more serious. Those micronutrients taking part in immunonutrition, namely, arginine, omega-3 fatty acids, nucleotides, and antioxidants, have the potential to prevent inflammation, protect against infections, and promote healing after the surgery. Research has shown that immunonutrition can lower the risk of postoperative infection, promote the normal healing of wounds, and reduce the hospital stays of patients, as well as support malnutrition status during chemotherapy. This review is based on a literature search conducted in Medline, Scopus, Clinicaltrials.gov, Cochrane CENTRAL, and Google Scholar, with the last search date being November 2024. Some studies. found that perioperative immunonutrition decreases wound infections and affects some immune indexes in gynecologic oncology patients positively. However, factors such as non-compliant patients, high costs, and non-standard formulations can deter its wider use. Patient adherence drops postoperatively mainly due to nausea and decreased appetite, whereas the cost of enriched formulations acts as an economic barrier. Postoperative compliance drops from ~78% prior to surgery to ~28% due to nausea, anorexia, and chemotherapy. Additionally, cost remains a constraining factor since special formulas are 2–4 times that of normal nutrition. While immunonutrition reduces hospital stay (by ~2–3 days) and infection rate (by 25–40%), access is hindered by prohibitive initial costs and lack of insurance coverage. Approaches such as subsidized schemes, enhanced palatability, and cost–benefit analyses are required to increase adoption. In addition, the lack of standardized protocols makes the clinical community hesitant to adopt this approach. Immunonutrition is, despite these problems, still hoped to be the new adjunct to gynecologic oncology patients. In future studies, it is imperative to pay attention to the best formulations that produce the best outcomes and evaluate and implement guidelines that are based on evidence. Together, with these improvements, immunonutrition could very well be an integral part of perioperative care thus completing the process by which patients in intense treatments are benefited not only via treatment but also via quality of life.

Central precocious puberty (CPP) is a condition where the hypothalamus-pituitary-gonadal axis is activated earlier than normal, leading to premature development of secondary sexual characteristics before eight years of age in girls and nine years of age in boys. The purpose of this study was to critically and systematically evaluate the literature regarding CPP rise during the COVID-19 pandemic. We searched PubMed and Google Scholar for relevant articles using the following MeSH terms: "COVID-19, "precocious puberty," "early puberty," "pediatric endocrinology," and "pandemic effects." We included studies calculating the risk of CPP before and during the COVID-19 pandemic. We excluded studies looking at patients with an identifiable cause for CPP or with peripheral precocious puberty. The primary outcome was the prevalence of central precocious puberty during the pandemic compared to the pre-pandemic period. We analyzed data regarding anthropometric, biochemical, and pelvic ultrasound data between the two groups. Overall, 16 studies with 2.175 subjects were included, of which 1.818 were diagnosed with CPP. There was a rise in the number of new diagnoses of CPP during the COVID-19 pandemic (985 subjects) compared with the pre-pandemic period (833 subjects). The mean age of diagnosis in the first group was 7.42 years versus 7.54 years in the second group. Notably, CPP during the pandemic was associated with a higher body mass index (BMI) compared with the group of the pre-pandemic period (17.50 versus 17.08). The pandemic and lockdowns led to changes in lifestyle habits, social isolation, sleep disturbance, excess screen time, and increased stress levels. We hypothesize that these alterations influenced the increase in CPP frequency.

Background: The importance of integrating enhanced recovery after surgery protocols in gynecologic oncology has been proven in numerous studies. However, the actual adherence to protocol among institutions remains inconsistent in clinical practice, particularly among those without prior structured implementation. This pragmatic multicenter study provides a preliminary report from the ongoing ERGO (Enhanced Recovery in Gynecologic Oncology) cohort study (ClinicalTrials.gov: NCT06655506) and aims to evaluate adherence to enhanced recovery protocols during the early phases of its adoption as well as identify factors that determine low uptake. Methods: Overall, 300 consecutive patients undergoing gynecologic oncology surgery across five institutions were included in the present study. Adherence to preoperative, intraoperative, and postoperative enhanced recovery elements was documented using standardized forms. Optimal adherence was predetermined as fulfillment of more than 70% of the enhanced recovery components included in the pathway. Multinomial analysis was used to identify predictors of adherence. Results: Overall, 70.3% of patients achieved optimal adherence; however, rates varied across centers (26.9–84.4%), reflecting the limited institutional familiarity with enhanced recovery pathways in most participating centers. The actual volume of cases handled was an important determinant of adherence, with high-volume units consistently demonstrating substantially higher compliance compared with lower-volume hospitals. Routine preoperative items demonstrated high uptake, whereas several intraoperative and early postoperative components showed low and heterogeneous implementation, which might be the result of anesthesiology-driven practices. Higher surgical complexity and poorer performance status independently predicted reduced adherence. Visual mapping confirmed that complex procedures resulted in lower adherence. Conclusions: The significant variability in enhanced recovery protocol adherence that was observed in our study indicates the need to institute structured workflows that help increase team familiarization, particularly in high-complexity cases and centers new to these elements.