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Background: This study presents a comprehensive study on applying the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Q1A R2 guideline for conducting stability tests on new drug substances and products. Materials and Methods: This study focused on the stability of multivitamin syrup under specific storage conditions, following ICH guidelines. The syrup was subjected to various tests, including pH measurement, weight per mL determination, microbial limit testing and assay analysis. Along with OOS, OOT and root cause analysis. Results: Showed that the syrup maintained its yellow color, met pH specifications and had the appropriate weight per mL. Microbial limit tests revealed no presence of harmful bacteria and assay analysis confirmed the purity and quantity of the components in the syrup. A survey on tablet preparations also demonstrated stability and quality. Conclusion: Overall, the stability studies indicated that the multivitamin syrup met all specifications and was considered stable. These findings contribute to ensuring the safety and effectiveness of the product throughout its shelf life. [ABSTRACT FROM AUTHOR]

Today we have a tremendous measure of information accessible in each area, with the appearance of innovation accessible, giving answers for some problems is conceivable. In this venture we will give answers for the issues connected with medical care information and the executives utilising Machine Learning and Blockchain. Extricating just the vital data from the information is conceivable with the utilisation of Machine Learning. This is finished utilising prepared calculations. When this information is put away, the following issue is Data sharing and its dependability. This is where Blockchain comes into picture. The agreement in Blockchain innovation ensures that information is authentic and exchanges are secure. Blockchain innovation might possibly improve medical services by setting patients at the focal point of the medical care framework and expanding the protection and interoperability of wellbeing information. This paper centres fundamentally around taking care of medical services information the board issues by utilising Blockchain innovation and including a few key elements utilising Machine Learning. Motivation: The main motive of this project is to bring the characteristics of Machine learning and Blockchain together. Machine learning plays a crucial role in detecting deadly diseases. On the other hand, blockchain technology can revolutionise medical database interoperability and restrict unauthorised access to records. This would ensure that the sensitive patient data is tightly secured. Summary of the outcomes: This project aims to build a secure, ML driven healthcare management system which would ensure that the diseases are accurately predicted and categorised in the early stage. Further, it ensures that the trained model filters the data and discards all the personal details of the patient and protects it from data leaks and breaches. It powers the system with blockchain to secure the transactions between patients and the authorised doctor and give patients the flexibility to choose which doctor should view his health record and who should not. [ABSTRACT FROM AUTHOR]

Predictive analytics for healthcare using machine learning is a challenging task. Development of a fast and accurate predictive analytics tool can lead to a lot of advancement in healthcare. Early detection of any disease leads to early medication. Our project is determined to solve one such real world problem i.e., early detection of chronic kidney disease, with a good accuracy and at a faster rate compared to the existing manual process. The aim of our project is to see how Machine learning algorithms such as the Support vector machine (SVM), Logistic Regression and K-Nearest Neighbor (KNN) classifiers help us to detect CKD, compare their accuracies and to use the best model among them to build a web application. [ABSTRACT FROM AUTHOR]