Objective: Human papillomavirus and other predicting factors are responsible causing cervical cancer, and early prediction and diagnosis is the solution for preventing this condition. The objective is to find out and analyze the predictors of cervical cancer and to study the issues of unbalanced datasets using various Machine Learning (ML) algorithm-based models. Methods: A multi-stage sampling strategy was used to recruit 501 samples for the study. The educational intervention was the video-assisted counseling which is consisted of two educational methods: a documentary film and face-to- face interaction with women followed by reminders. Following the collection of baseline data from these subjects, they were encouraged to undergo Pap smear screening. Women having abnormal Pap tests were sent for biopsy. Machine learning classification methods such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Multi-layer Perceptron (MLP) and Naive Bayes(NB) were used to evaluate the unbalanced input and target datasets. Result: Merely 398 women out of 501 showed an interest to participate in the study, but only 298 stated a willingness for cervical screening. Atypical malignant cells were discovered on the cervix of 26 women who had abnormal pap tests. These women had guided for further tests, such as a cervical biopsy, and seven women had been diagnosed with cervical cancer. LR in models 1, 2, and 4 showed 88% to 94% sensitivity with 84% to 89% accuracy, respectively for cervical cancer prediction, whereas DT in models 3, 5, and 6 algorithms exhibited 83% to 84% sensitivity with 84% to 88% accuracy, respectively. The NB and LR algorithms produced the highest area under the ROC curve for testing dataset, but all models performed similarly for training data. Conclusion: In current study , Logistic Regression and Decision Tree algorithms were identified as the best-performed ML algorithm classifiers to detect the significant predictors.

Umbilical cord blood (UCB) is a rich source of hematopoietic cells that can be used to replace bone marrow components. Many blood disorders and systemic illnesses are increasingly being treated with stem cells as regenerative medical therapy. Presently, collected blood has been stored in either public or private banks for allogenic or autologous transplantation. Using a specific keyword, we used the English language to search for relevant articles in SCOPUS and PubMed databases over time frame. According to our review, Asian countries are increasingly using UCB preservation for future use as regenerative medicine, and existing studies indicate that this trend will continue. This recent literature review explains the methodology of UCB collection, banking, and cryopreservation for future clinical use. Between 2010 and 2022, 10,054 UCB stem cell samples were effectively cryopreserved. Furthermore, we have discussed using Mesenchymal Stem Cells (MSCs) as transplant medicine, and its clinical applications. It is essential for healthcare personnel, particularly those working in labor rooms, to comprehend the protocols for collecting, transporting, and storing UCB. This review aims to provide a glimpse of the details about the UCB collection and banking processes, its benefits, and the use of UCB-derived stem cells in clinical practice, as well as the ethical concerns associated with UCB, all of which are important for healthcare professionals, particularly those working in maternity wards; namely, the obstetrician, neonatologist, and anyone involved in perinatal care. This article also highlights the practical and ethical concerns associated with private UCB banks, and the existence of public banks. UCB may continue to grow to assist healthcare teams worldwide in treating various metabolic, hematological, and immunodeficiency disorders.

Background &Objective: Carcinoma of the breast is one of the major issues causing death in women, especially in developing countries. Timely prediction, detection, diagnosis, and efficient therapies have become critical to reducing death rates. Increased use of artificial intelligence, machine, and deep learning techniques create more accurate and trustworthy models for predicting and detecting breast cancer. This study aims to examine the effectiveness of several machine and modern deep learning models for prediction and diagnosis of breast cancer. Methods: This research compares traditional machine learning classification methods to innovative techniques that use deep learning models. Established usual classification models such as k-Nearest Neighbors (kNN), Gradient Boosting, Support Vector Machine (SVM), Neural Network, CN2 rule inducer, Naive Bayes, Stochastic Gradient Descent (SGD), and Tree, and deep learning models such as Neural Decision Forest and Multilayer Perceptron used. The investigation, which was carried out using the Orange and Python tools, evaluates their diagnostic effectiveness in breast cancer detection. The evaluation uses UCI’s publicly accessible Wisconsin Diagnostic Data Set, enabling transparency and accessibility in the study approach. Result: The mean radius ranges from 6.981 to 28.110, while the mean texture runs from 9.71 to 39.28 in malignant and benign cases. Gradient boosting and CN2 rule inducer classifiers outperform SVM in accuracy and sensitivity, whereas SVM has the lowest accuracy and sensitivity at 88%. The CN2 rule inducer classifier achieves the greatest ROC curve score for benign and malignant breast cancer datasets, with an AUC score of 0.98%. MLP displays distinguish positive and negative classes, with a higher AUC-ROC of 0.9959. with accuracy of 96.49%, precision of 96.57%, recall of 96.49%, and an F1-Score of 96.50%. Conclusion: Among the most commonly used classifier models, CN2 rule and GB performed better than other models. However, MLP from deep learning produced the greatest overall performance.

Objective: This review aims to examine the effect of PARP inhibitors on PFS, OS, and adverse events in women with advanced ovarian cancer (OC). Methods: The PRISMA 2020 guidelines are followed while conducting this comprehensive review. Data from 17 randomized control trails (RCT) published between 2014 and June 2024 were included. These trials compared PARPi maintenance therapy to placebo women with newly diagnosed and recurrent advanced OC. The specific keywords were used to search relevant studies in databases including PubMed, SCOPUS, Cochrane library, and WoS. The main outcomes were the Progression free survival (PFS), overall survival (OS), or adverse events (AEs). The combined hazard ratios (HRs) and risk ratios (RRs) were determined, together with 95% confidence intervals (CIs). Each of the analyses were conducted using a model with random effects. Results: Despite high heterogeneity, the meta-analysis found that poly (ADP-ribose) polymerase inhibitors (PARPi) maintenance therapy ominously improved PFS compared to placebo, with a combined HR of 1.33 (95% CI: 1.10-1.61) in newly diagnosed cases and 0.88 (95% CI: 0.59-1.30) in relapsed cases. However, the OS improvement was not significantly substantial, with a collective HR of 1.06 (95% CI: 0.99–1.13). AEs are considerably higher in the PARPi groups, notably hematologic toxicities including anaemia, thrombocytopenia, and neutropenia. However, these adverse effects may be controlled with dosage modifications, and therapy was discontinued only in few cases. Conclusion: PARPi are an effective therapy in both newly discovered and relapsed. Although there is a modest rise in the frequency of severe adverse reactions, they are usually handled well.

Concept mapping (CM) is a pedagogical approach that allows nursing students to improve their critical thinking (CT) abilities, which is paramount for providing quality patient care that underpins their ability to make sound clinical decisions and judgments by visually understanding the relationships and interconnections between various concepts. The purpose of the present review was to assess the outcome of CM on the CT skills. We searched Scopus, Web of Science, Science Direct, PubMed, and other sources to evaluate the effect of CM on CT among nursing students. The review included articles published between 2012 and 2023. Two authors individually extracted the data, identified the bias risk, and analyzed the data using the PRISMA-ScR framework. Database searches found 985 articles, 11 of which matched the inclusion criteria. 63.64% employed quasi-experimental approaches. 45.45% employed concept mapping instead of integration, whereas 9.09% used sophisticated technology. Second-year BSc Nursing students accounted for 54.55%. In 45.45% of the studies, sample sizes varied between 51 and 100. The studies’ overall post-test CT scores were higher in groups which received CM as compared to other conventional pedagogical approaches. Most studies demonstrated minimal bias risk and P values greater than 0.05. The findings of the present study considered CM as an effective teaching method to enhance CT skills and also suggest the implementation of CM. It is recommended that nursing educators, administrators, and faculty evaluate curriculum to integrate CM into courses focused at promoting CT abilities in nursing students to improve the quality of patient care.