Cerebral Palsy risk factors associated with pregnancy and delivery

Objective: To identify and analyze the risk factors associated with pregnancy and delivery that contribute to the development of cerebral palsy in children. Material and Methods: To better understand what factors lead to cerebral palsy in children, a cross-sectional study was conducted at the CRP pediatric hospital in Savar, Dhaka. The study's sample size of 56 was reached by a convenience sample of mothers of children with cerebral palsy. In-person interviews were conducted utilizing a survey instrument translated into Bengali or the native tongue and then pilot tested. Excel and SPSS were used for statistical analysis. Informed consent and confidentiality were ensured under ethical guidelines. Results: The data shows past socioeconomic variables. Population age distribution: 37.50% 29–35. 88.1% were Muslim. 59% rural residential areas. 35.70% SSC education.  85.7% had two or more children, 45.6% under three. 55% were males, 45% female. 41% of pregnancies had issues, and 24.9% of women underwent abortions. Normal births were 51.80% and cesarean sections 48.20%. Post-birth statistics included crying immediately (34%), yellowish eyes (50%), fever with seizures (61%), head injuries (48%), and birth hypoxia (38%). 35.70% were born at home, 30.40% in hospitals (17.90% public, 16.10% private). Figure 3 showed hypertension (20%), diabetes (16%), and anemia (23%). Age, religion, education, number of children, past abortions, delivery method, and birthplace were correlated. Diabetes, hypertension, anemia, and birthplace were unrelated. Conclusion: Awareness of cerebral palsy is poor despite its prevalence. In developed country physiotherapy is considered as an important treatment for cerebral palsy children. Quantitative research was used to survey pediatric patients and identify risk variables in this study. Factors shared by many were old age, illiteracy, and origins in rural areas. Risk recognition and mitigation must be prioritized

An advanced data fabric architecture leveraging homomorphic encryption and federated learning

Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method's effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning