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. MeSH words used for the search. MeSH is an is an effective tool for indexing and classifying literature on biology and health. MeSH terms are affixed to articles to enable precise and effective literature searches, guaranteeing that scholars, medical professionals, and other users can locate pertinent data within the extensive PubMed database. MeSH provides researchers with a standardized and structured method of indexing topics in the field of medicine and related disciplines, which aids in the identification and organization of pertinent articles during scoping reviews. PRISMA checklist was followed while doing the data collection and data extraction. The findings revealed that antibiotics hurt the physical and physiological development of children under 5. The study findings show that after exposure to antibiotics children get obese, it also affects the gut microbiota. Antibiotics also have an impact on the language and behaviors of children under 5. It also shows that children are more prone to get different medical disorders. These results highlight how crucial it is to make well-informed decisions about the use of antibiotics in pediatric care. To sum up, giving antibiotics to kids younger than five can have a big impact on how their bodies develop. This study also provides and implements guidelines that consider the possible long-term effects on the development of children under the age of five when prescribing antibiotics. Encourage healthcare professionals, parents, and other caregivers to learn about the proper use of antibiotics for young children as well as the possible risks of overusing or not using antibiotics at all. Promote funding and research for alternative approaches, such as targeted vaccines or probiotics, to treat and prevent infections in young children.

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). About 1046 women's data regarding attendance and nonattendance to CCS were included. In this study, we have used three models, classification, ensemble, and deep learning models, to compare the specific accuracy and AU-ROC for predicting non-attenders for CC. RESULTS: The current model employs 22 predictors, with soft voting in ensemble models showing slightly higher specificity (96%) and sensitivity (93%) than weighted averaging. Bagging excels with the highest accuracy (98.49%), specificity (97.3%), and ideal sensitivity (100%) with an AUC of 0.99. Classification models reveal Naive Bayes with higher specificity (97%) but lower sensitivity (91%) than Logistic Regression. Random Forest and Neural Network achieve the highest accuracy (98.49%), with an AUC of 0.98. In deep learning, LSTM has an accuracy of 95.68%, higher specificity (97.60%), and lower sensitivity (93.42%) compared to other models. MLP and NN showed the highest AUC values of 0.99. CONCLUSION: Employing ensemble and deep learning models proved most effective in predicting barriers to nonattendance in cervical screening.

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

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.

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. RESULTS: Following the interventions, 99% of the participants became aware of cervical cancer and increased their participation in screening from 3.0 % (Pretest) to 79.9% (Posttest). Three reminders have been sent to the participants, throughout the intervention period which has led to a considerable rise in the participants' willingness to participate in screening, hiking from 58% to 79.9%. The Pap smear test results have shown that: among 288 women, 21 have Typical Malignant cells on their cervix, and two women have been diagnosed with cervical cancer (Stage 1a and Stage 1b). CONCLUSION: The findings of the study indicate that utilizing diverse interventions in health education alters women's behavior, enhances the compliance of cervical cancer screening, and ultimately helps to prevent life-threatening risks.

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

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.

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. 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.