A Systematic Review of Cervical Cancer Mobile Applications and a Future Directions for Developers.

Objective: The objective of this study is to evaluate the quality of mobile health (mHealth) applications that promote cervical cancer awareness and provide screening assistance, with an emphasis on apps available on the Google Play Store and iOS.

Methods: From December 2023 to February 2024, we assessed mobile applications focused on cervical cancer screening that are available on Google Play and Apple iTunes. The "Cervical Cancer," "Mobile Application," "Pap Test," "Cervical Cancer Guide," "Human Papillomavirus," plus "Cervical Screening are the keywords used to search the applications.

Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques.

Objectives: The aim of the study was to analyze the data of diabetic patients regarding warning signs of hypoglycemia to predict it at an early stage using various novel machine learning (ML) algorithms. Individual interviews with diabetic patients were conducted over 6 months to acquire information regarding their experience with hypoglycemic episodes.

Design: This information included warning signs of hypoglycemia, such as incoherent speech, exhaustion, weakness, and other clinically relevant cases of low blood sugar.

Effect of antibiotics on physical and physiological development of children under 5-A scoping review.

The scoping review aimed to investigate and compile the effects of antibiotics on children under the age of five's physiological development. A PubMed, CINAHL, and Medline online database search was conducted, and related studies were included in the databases to carry out a more detailed search of the available literature utilizing keywords like "Antibiotics in children's"; "Children under 5"; and "Physiological Development, Physical Development," as well as Boolean operators to generate papers pertinent which were correlating with the objective of the study. It is imperative to demonstrate that a comprehensive, wide-ranging, and exhaustive search was carried out.

Public health nurse perspectives on predicting nonattendance for cervical cancer screening through classification, ensemble, and deep learning models.

Objectives: Women's attendance to cervical cancer screening (CCS) is a major concern for healthcare providers in community. This study aims to use the various algorithms that can accurately predict the most barriers of women for nonattendance to CS.

Design: The real-time data were collected from women presented at OPD of primary health centers (PHCs).

Prediction and Diagnosis of Breast Cancer Using Machine and Modern Deep Learning Models.

Unlabelled: 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.

The Effect of Multimodal Interventions Regarding Early Cervical Cancer Diagnosis on the Women's Knowledge, Attitude and Participation in Cervical Screening Program.

Objective: The objective of the present study was to assess the effect of multimodal interventions on women's knowledge, attitude, and behavior towards the participation in the cervical screening test.

Methods: A quasi-experimental design is executed with a multi-stage sampling of 300 women residing in rural India.  Various multimodal interventions, including a documentary film, face-to-face meetings, written booklets, reminder letters, SMS, and telephone calls, are used to motivate the women for cervical cancer screening.

Fresh Umbilical Cord Blood-A Source of Multipotent Stem Cells, Collection, Banking, Cryopreservation, and Ethical Concerns.

Umbilical cord (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.

Prediction and Detection of Cervical Malignancy Using Machine Learning Models.

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.