Urinary incontinence in women: prevalence rates, risk factors and impact on quality of life.

OBJECTIVES: To determine the prevalence, risk factors of urinary incontinence (UI) and to assess its impact on the quality of women's life. METHODS: This cross-sectional study was performed 1050 female participants aged between 20-80 years. A questionnaire form, including the socio-demographic characteristics and risk factors and the "International Consultation on Incontinence Questionnaire-Short Form" were used for the data collection. RESULTS: The mean age of women was 48.80±11.53 years. The prevalence of UI was 44.6%. The distribution of the types of UI was 31% stress incontinence, 47.4% urge, and 33.1% mixed type. Although 95.5% of the women reported a negative impact on the quality of life, admission to a health center was only 63.9%, and 64.7% of the women had not received any medical help. The statistical analysis revealed that menopause, constipation, hypertension, diabetes, family history and parity are associated with UI as risk factors. CONCLUSION: We suggest that in the early diagnosis and treatment of urinary incontinence (UI), mental, educational and psychosocial support should be given to patients together with medical therapy.

Transformer encoder with multiscale deep learning for pain classification using physiological signals.

Pain, a pervasive global health concern, affects a large segment of population worldwide. Accurate pain assessment remains a challenge due to the limitations of conventional self-report scales, which often yield inconsistent results and are susceptible to bias. Recognizing this gap, our study introduces PainAttnNet, a novel deep-learning model designed for precise pain intensity classification using physiological signals. We investigate whether PainAttnNet would outperform existing models in capturing temporal dependencies. The model integrates multiscale convolutional networks, squeeze-and-excitation residual networks, and a transformer encoder block. This integration is pivotal for extracting robust features across multiple time windows, emphasizing feature interdependencies, and enhancing temporal dependency analysis. Evaluation of PainAttnNet on the BioVid heat pain dataset confirm the model's superior performance over the existing models. The results establish PainAttnNet as a promising tool for automating and refining pain assessments. Our research not only introduces a novel computational approach but also sets the stage for more individualized and accurate pain assessment and management in the future.

Analysis of pain research literature through keyword Co-occurrence networks.

Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics.

Sequential use of Foley catheter with misoprostol for second trimester pregnancy termination in women with and without caesarean scars: a prospective cohort study.

PURPOSE: The aim of this study is to evaluate the effectiveness and safety of misoprostol and Foley catheter in second trimester termination in women with and without caesarean section (CS) scars. MATERIALS AND METHODS: Women with an indication for pregnancy termination between 14 and 22 completed weeks of gestation were included to the study. Enrolled women were allocated into three groups: (1) women with no history of CS, (2) women with one CS and (3) women with ≥2 CS. Study consisted 337 patients (233 group 1, 88 group 2 and 16 group 3). Misoprostol and Foley catheter were used sequentially. The primary outcome was the induction to abortion interval. Secondary outcomes were the successful vaginal abortion rate, the percentage of abortions in 24 h and the rates of surgical removal of the placenta, Foley catheter use and major maternal complications (transfusions, thromboembolic events, uterine rupture and death). RESULTS: Demographic characteristics were comparable. All study outcomes were statistically similar among groups. There was no major maternal complication among all patients. CONCLUSIONS: Sequential use of misoprostol and Foley catheter is safe and effective in second trimester pregnancy termination for patients with and without CS scars.