ABSTRACT
This study aims to investigate the association between anemia and early recurrence in endometrial cancer patients. We retrospectively analyzed the data of 473 endometrial cancer patients treated at our hospital from January 2015 to December 2020. Patients were divided into two groups based on their hemoglobin (Hb) level: anemia group (Hb < 12 g/dL) and non-anemia group (Hb ≥12 g/dL). Early recurrence was defined as recurrence within 2 years of diagnosis. Univariate and multivariate logistic regression analyses were used to identify the predictors of early recurrence. The prevalence of anemia was 38.26% (181/473). The incidence of early recurrence was 12.89% (61/473) in the anemia group and 9.24% (38/412) in the non-anemia group (p = 0.004). Univariate analysis showed that anemia was a significant predictor of early recurrence (odds ratio (OR) = 2.27, 95% confidence interval (CI): 1.35-3.80, p = 0.003). Multivariate analysis confirmed that anemia was an independent predictor of early recurrence (OR = 2.11, 95% CI: 1.21-3.84, p = 0.01). Anemia is an independent predictor of early recurrence in endometrial cancer patients. Patients with endometrial cancer should be screened for anemia and treated if present. Additionally, patients with anemia should be closely monitored for early signs of recurrence and treated aggressively.
ABSTRACT
During the COVID-19 pandemic, significant shifts occurred in reproductive health, especially among teenagers and young adult women in Romania. This study, conducted from 2020 to 2022, aimed to longitudinally assess contraceptive awareness and its correlation with mental well-being in this demographic. A cohort of 210 participants aged 15-25, with a history of wanted or unwanted pregnancy, was studied. The research involved collaborations with Romanian educational institutions and strict adherence to ethical standards. Participants' data on contraceptive knowledge and practices were analyzed, considering factors like substance use and prior sexual education. Mental well-being was evaluated using the SF-36, WHOQOL-BREF, GAD-7, and PHQ-9 scales. The study revealed a positive correlation between increased contraceptive knowledge and improved mental health scores. In 2022, 68% of participants displayed proficient contraceptive awareness, up from 52% in 2020. Those with good contraceptive knowledge had an average SF-36 score of 72, indicating a better quality of life, compared to a score of 58 among those with limited knowledge. Furthermore, there was a notable decrease in GAD-7 and PHQ-9 scores among individuals with better contraceptive awareness, suggesting reduced anxiety and depression levels. The SF-36 survey results showed significant improvements across the years: the physical score increased from 52.1 (±6.3) in 2020 to 56.5 (±6.8) in 2022, the mental score from 51.4 (±7.2) to 55.0 (±6.9), and the total score from 53.6 (±7.9) to 57.5 (±8.0). WHOQOL-BREF results showed a substantial increase in the social domain score from 53.6 (±18.2) in 2020 to 63.0 (±20.5) in 2022. GAD-7 scores declined from 7.9 (±2.6) in 2020 to 6.5 (±3.3) in 2022, indicating a decrease in anxiety symptoms. PHQ-9 scores, measuring depression, also showed a downward trend, from 4.8 (±2.2) in 2020 to 3.9 (±2.8) in 2022. These findings highlight the intertwined nature of contraceptive awareness and mental well-being. The improvements in contraceptive awareness positively impacted mental health outcomes, emphasizing the need for targeted educational interventions in this demographic, particularly during global crises like the pandemic.
ABSTRACT
The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures currently in use, and evaluate the clinical relevance of these diagnostic imaging methods. A systematic search of PubMed, Web of Science, Cochrane, and Scopus databases was conducted in February 2023, encompassing the literature published up until December 2022. The review included nine studies, comprising five case-control studies, three retrospective cohort studies, and one prospective cohort study. Various ML architectures were analyzed, including artificial neural network (ANN), entropy degradation method (EDM), probabilistic neural network (PNN), support vector machine (SVM), partially observable Markov decision process (POMDP), and random forest neural network (RFNN). The ML architectures demonstrated promising results in detecting and classifying lung cancer across different lesion types. The sensitivity of the ML algorithms ranged from 0.81 to 0.99, while the specificity varied from 0.46 to 1.00. The accuracy of the ML algorithms ranged from 77.8% to 100%. The AI architectures were successful in differentiating between malignant and benign lesions and detecting small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This systematic review highlights the potential of ML AI architectures in the detection and classification of lung cancer, with varying levels of diagnostic accuracy. Further studies are needed to optimize and validate these AI algorithms, as well as to determine their clinical relevance and applicability in routine practice.
ABSTRACT
Postpartum depression is a major mental health disorder that can negatively affect both mother and baby. In addition, the COVID-19 pandemic associated with extreme measures of the lockdown had profound effects on humanity, increasing the rates of anxiety and depression, especially among women in the postpartum period. The aim of this study was threefold: to determine the prevalence of postpartum depression, to compare the prevalence of postpartum depression at two different times during the COVID-19 pandemic, and to assess a possible association between the timing of childbirth in a given period of the pandemic and the risk of postpartum depression. A cross-sectional study involving 154 women who were interviewed immediately postpartum, using the EPDS scale, was conducted at the Timisoara Municipal Hospital, Romania at two different periods during the COVID-19 pandemic (March−April 2020 during the first wave and August−September 2021 during the fourth wave). The overall prevalence of postpartum depression (EPDS score > 13) was 18.8%, with a statistically significantly higher rate among participants surveyed during the fourth wave of the COVID-19 pandemic in Romania; the COVID-19 pandemic represents an impact on women's mental health in the postpartum period, increasing the risk of developing postpartum depression.
