The response to the pandemic requires access to accurate information and public understanding and adherence to preventive measures. This online cross-sectional study of adult Poles (n = 1337) assessed the frequency of COVID-19 preventive behaviors, fears related to the COVID-19 pandemic, and beliefs in COVID-19-related conspiracy theories during the early phase of the COVID-19 pandemic when the nationwide lockdown was imposed (April 2020). As shown, 22% of surveyed admitted not to wash their hands frequently, while 12% did not use disinfectants. These two behaviors were also less frequent in individuals with medical education. The highest levels of pandemic-related fears were associated with health loss in relatives, pandemic-induced economic crisis, and government using a pandemic to control citizens by the state. A significant share of surveyed individuals believed that the pandemic was intentional action to weaken non-Chinese economies (32%) or was deliberately induced for profits from selling vaccines (27%). Men, individuals with no children, and subjects with lower education were significantly less likely to adhere to sanitary measures (handwashing, disinfection, avoiding face touching, changes in greeting etiquette, face-covering when coughing or sneezing), and were less concerned over self and relatives’ health. At the same time, men were less prone than women to the conspiracy theories related to the COVID-19 pandemic. The results indicate that adherence to sanitary measures during the pandemic can be a challenge also in developed countries, while misinformation campaigns (also concerning vaccines) have already affected the general public during the early phase of the epidemiological outbreak. The study provides observations that may be useful in the management of the public response to future epidemics.

Cancer stem cells (CSCs) may contribute to an increased risk of recurrence in ovarian cancer (OC). Further research is needed to identify associations between CSC markers and OC patients’ clinical outcomes with greater certainty. If they prove to be correct, in the future, the CSC markers can be used to help predict survival and indicate new therapeutic targets. This study aimed to determine the CSC markers at mRNA and protein levels and their association with clinical presentation, outcome, and risk of recurrence in HGSOC (High-Grade Serous Ovarian Cancer). TCGA (The Cancer Genome Atlas) database with 558 ovarian cancer tumor samples was used for the evaluation of 13 CSC markers (ALDH1A1, CD44, EPCAM, KIT, LGR5, NES, NOTCH3, POU5F1, PROM1, PTTG1, ROR1, SOX9, and THY1). Data on mRNA and protein levels assessed by microarray and mass spectrometry were retrieved from TCGA. Models to predict chemotherapy response and survival were built using multiple variables, including epidemiological data, expression levels, and machine learning methodology. ALDH1A1 and LGR5 mRNA expressions indicated a higher platinum sensitivity (p = 3.50 × 10⁻³; p = 0.01, respectively). POU5F1 mRNA expression marked platinum-resistant tumors (p = 9.43 × 10⁻³). CD44 and EPCAM mRNA expression correlated with longer overall survival (OS) (p = 0.043; p = 0.039, respectively). THY1 mRNA and protein levels were associated with worse OS (p = 0.019; p = 0.015, respectively). Disease-free survival (DFS) was positively affected by EPCAM (p = 0.004), LGR5 (p = 0.018), and CD44 (p = 0.012). In the multivariate model based on CSC marker expression, the high-risk group had 9.1 months longer median overall survival than the low-risk group (p < 0.001). ALDH1A1, CD44, EPCAM, LGR5, POU5F1, and THY1 levels in OC may be used as prognostic factors for the primary outcome and help predict the treatment response.

Catamenial pneumothorax (CP) is an uncommon disease related to thoracic endometriosis or diaphragmatic perforation. It is defined as an occurrence of spontaneous pneumothorax around the time of menstruation in the population of women of reproductive age with no other lung diseases. Catamenial...

Background/Objectives: Prognostic assessment in endometrial cancer (EC) is based on clinical and pathological features such as histological type, FIGO stage, tumor grade, LVSI, P53 status, and hormone receptor expression. Recent molecular research has distinguished four EC subtypes, with MMR status (pMMR vs. dMMR) providing clinically relevant stratification due to its predictive value for immunotherapy. The present study aims to compare dMMR and pMMR tumors in terms of the prevalence of adverse histopathological prognostic factors. Methods: This retrospective study included 179 patients with endometrioid endometrial carcinoma (EEC) treated at the authors’ institution (1 January 2023–31 August 2025). Patients were classified by MMR status (pMMR vs. dMMR) based on immunohistochemistry, and clinicopathological variables, including FIGO stage, myometrial invasion depth, tumor grade, LVSI, ER/PR expression, and P53 status, were analyzed. Normality was assessed using the Shapiro–Wilk test. Categorical variables were tested with chi-square or Fisher’s exact tests, reporting odds ratios with 95% CI, while continuous variables were compared using the Mann–Whitney test and presented as median (IQR) with the Hodges–Lehmann difference and 95% CI. Multivariable logistic regression with Wald tests was performed. Results: dMMR tumors accounted for 29.05% of all cases. Patients in the dMMR group were significantly more likely to present with FIGO stage III/IV disease (p = 0.036) and to exhibit LVSI (p = 0.008). No differences were observed between the groups with respect to tumor grade, estrogen receptor positivity, progesterone receptor positivity, or the prevalence of deep myometrial invasion. The most frequent pattern of protein loss in the dMMR population was concurrent loss of MLH1 and PMS2. Conclusions: In the studied population, dMMR tumors more frequently exhibited adverse prognostic features of EC, such as advanced stage of disease and lymphovascular space invasion. This suggests the potential for effective immunotherapy in this patient group.

Background: Endometrial cancer is among the most prevalent gynecological malignancies, with increasing mortality primarily due to initially advanced disease with lymph node metastasis or tumor recurrence. Current risk stratification models show limited accuracy, highlighting the need for more accurate biomarkers. This study aimed to identify metabolic compounds that can serve as predictors of recurrence risk and lymph node status in endometrial cancer. Methods: Targeted metabolomic profiling of preoperative serum samples from 123 patients with endometrial cancer, stratified into high- or low-risk and lymph node-positive or -negative groups, was conducted using the AbsoluteIDQ p180 Kit and high-performance liquid chromatography–mass spectrometry. Results: Analysis revealed significant differences in metabolites related to lipid and amino acid metabolism between groups. High-risk and lymph node-positive patients presented significantly lower concentrations of phosphatidylcholines, lysophosphatidylcholines, medium-chain acylcarnitines, and specific amino acids such as alanine, histidine, and tryptophan compared to low-risk and lymph node-negative patients. Receiver operating characteristic curve analyses highlighted the diagnostic potential of these metabolites, particularly alanine and taurine, in distinguishing patient groups. Conclusions: The findings indicate complex metabolic reprogramming associated with aggressive endometrial cancer phenotypes, involving enhanced lipid utilization and amino acid metabolism alterations, potentially supporting tumor proliferation and metastatic progression. Thus, targeted metabolomic serum profiling might be a powerful tool for improving risk assessment, enabling more personalized therapeutic approaches and management strategies in endometrial cancer.

Modern management of ovarian cancer (OC) relies on molecular diagnostics, with genetic testing playing a central role in therapeutic decisions. High-grade serous ovarian cancer (HGSOC) is frequently associated with mutations in the BRCA1 and BRCA2 genes, as well as other alterations within the homologous recombination repair (HRR) pathway. The identification of pathogenic variants is critical for selecting patients eligible for treatment with poly (ADP-ribose) polymerase inhibitors (PARPi), which significantly improve progression-free survival, especially in individuals with BRCA mutations and homologous recombination deficiency (HRD). Current guidelines recommend BRCA testing at diagnosis for all patients with HGSOC, followed by HRD testing. Various techniques are used to assess genetic alterations and HRD status. Commercial tests assess mutations in genes in HRR pathways, genomic instability, or HRR functional status to quantify HRD. Despite the availability of these assays, challenges remain regarding test standardization, predictive accuracy, and cost-effectiveness. Moreover, emerging research highlights the potential for artificial intelligence (AI) to enhance molecular profiling, utilizing whole-slide imaging (WSI) and deep learning to predict homologous recombination deficiency (HRD) and other tumor characteristics. The integration of molecular subtypes, as defined by The Cancer Genome Atlas (TCGA), into routine clinical practice holds promise for tailoring therapy beyond BRCA or homologous recombination deficiency (HRD) status. As the field advances, comprehensive genetic testing combined with AI-driven analytics may become the cornerstone of precision oncology in ovarian cancer.