Hydroxyapatite (HA) has been earmarked as suitable for implantation within the human of its chemical makeup to human bone. In this paper, HA powders were synthesized via the precipitation method where phosphoric acid (H3PO4) was titrated into calcium hydroxide solution [Ca(OH)2]. Two parameters such as temperature and stirring rate were identified as factors that influenced the amount and purity of HA powder. Phase identification of the synthesized powder was done using X-Ray Diffraction (XRD). The results show that HA phase can be synthesized from this titration process of Ca(OH)2 and H3PO4 with yield amount of HA powder around 45 - 61 grams but with less than hundred percent purity. In order to study the effect of heat treatment to HA crystals structure, HA powder was calcined at 850 degrees C for 2 hours. It's found that the degree of crystallinity increases after calcination because of lattice expansion when the materials were heated at higher temperature
Sjogren's syndrome B antibody ; Powders ; Durapatite ; Precipitation ; hydroxyl group

Objectives: This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection. Methods: Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies. Results: The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks. Conclusions The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.

Introduction: The use of assessment tools to measure postnatal mental illness is essential in healthcare settings. However, variations in the types of tools and their reliability in a particular population lead to under-recognition of mental health status in postnatal mothers. The aim of this review is to evaluate the most recent 10 year of research on the validity and reliability of postnatal mental illness assessment tools. Methods: A literature search of studies from online databases PubMed, Scopus, and Science Direct was conducted. Results: A total of 59 studies were selected for this review. Several studies utilised multiple assessment tools, and a total of 96 assessment tools were identified and classified into six domains: postnatal blues, postnatal stress, postnatal anxiety, postnatal depression, postnatal psychosis, and postnatal psychological disorder. In this review, EPDS was the most common tool used to identify postnatal depression and anxiety while DASS 21 was the most common tool used to identify postnatal psychological disorder. There is a wide range in preponderance of evidence for the reliability of each assessment tool and there were inconsistencies in assessing the validity of the assessment tools. Conclusion This review provides information regarding some of the main assessment tools currently available to measure postnatal mental illnesses. There were no standardised tools that were used in a particular setting. The results may differ in different population because there are differences in not only languages and dialects, but also cultural and racial backgrounds, which greatly influences their perception and interpretation of postnatal mental illness.
Mental Disorders