Characterization and evaluation of the performance of starch and cellulose as excipients for direct compression technique

Purpose: To investigate the influence of two often-used excipients (starch and microcrystalline cellulose) on the physical properties of powder blends and tablets that contain mannitol as diluent.Methods: Powder and powder mixtures of three commonly used excipients (starch, mannitol and microcrystalline cellulose) were thoroughly examined using the angle of repose for flowability, particle size analyzer to determine the diameter of the particles, scanning electron microscopy (SEM) for morphological assessment, and x-ray diffraction to determine crystalline/amorphous characteristics. Tablets were prepared by direct compression technique and were evaluated for mechanical strength and disintegration behavior as part of quality control test.Results: The results showed that increase in MCC concentration of the mixture leads to significantly enhanced flowability (p < 0.05) when compared to starch. The angle of repose for mannitol/MCC powder mixture with 70 % w/w MCC was approximately 29°, indicating good flow properties of thepowder mix. Moreover, starch tablets containing MCC exhibited better mechanical strength and longer disintegration time, while, at 1:1 ratio of MCC and mannitol, tablet disintegration was faster (33.0 ± 5.2s)Conclusion: MCC (at 30 %w/w in the blend) produces optimal flow of the powder blend and superior mechanical strength, Keywords: Tablet disintegration, Flowability, Starch, Hardness, Mechanical strengt

A CLIPS-Based Expert System for Brain Tumor Diagnosis

Brain tumors pose significant challenges in modern healthcare, with accurate and timely diagnosis crucial for determining appropriate treatment strategies. Artificial intelligence has made significant advancements in recent years. Rule-based expert systems (if-then rule-based systems) have emerged as a promising approach for clinical decision-making in brain tumor diagnosis. In this paper, we present "A CLIPS-Based Expert System for Brain Tumor Diagnosis," which leverages a set of 14 if-then rules to diagnose brain tumors with three possible outcomes: 1) Confirm the diagnosis of a brain tumor, 2) Consider the possibility of a brain tumor that has metastasized, and 3) Consider the possibility of a brain tumor. Our expert system offers a user-friendly interface, enabling users to select symptoms and receive a diagnosis based on the provided information. This paper discusses the expert system's development, implementation, and evaluation, highlighting its potential to facilitate brain tumor diagnosis and decision-making in clinical settings. Additionally, we provide a literature review that contextualizes our expert system within the broader landscape of rule-based expert systems for brain tumor diagnosis, examining their effectiveness, limitations, and challenges

Harnessing Artificial Intelligence to Enhance Medical Image Analysis

Abstract: The integration of Artificial Intelligence (AI) into medical imaging marks a transformative advancement in healthcare, significantly enhancing diagnostic accuracy, efficiency, and patient outcomes. This paper delves into the application of AI technologies in medical image analysis, with a particular focus on techniques such as convolutional neural networks (CNNs) and deep learning models. We examine how these technologies are employed across various imaging modalities, including X-rays, MRIs, and CT scans, to improve disease detection, image segmentation, and diagnostic support. Furthermore, the paper discusses the challenges associated with AI-driven medical imaging, such as data quality, model interpretability, and ethical considerations. By analyzing recent advancements and real-world case studies, this paper offers insights into the current landscape of AI in medical imaging and explores its potential future directions. The findings underscore the ongoing evolution of AI technologies and their pivotal role in advancing medical diagnostics and treatment strategies

Evaluation of immunomodulatory effects of lamotrigine in BALB/c mice

Modulation of the immune system has recently been shown to be involved in the pharmacological effects of old antiepileptic drugs and in the pathogenesis of epilepsy. Therefore, the most recent guidelines for immunotoxicological evaluation of drugs were consulted to investigate the immunomodulatory effects of lamotrigine, a newer antiepileptic drug, in BALB/c mice. These included the in vivo effects of lamotrigine on delayed-type hypersensitivity (DTH) response to sheep red blood cell (SRBC) antigens, hemagglutination titer assays and hematological changes. In vitro effects of lamotrigine on ConA-induced splenocyte proliferation and cytokine secretion were assessed. The results showed that lamotrigine treatment significantly increased the DTH response to SRBC in the mouse model of this study. This was accompanied by a significant increase in relative monocyte and neutrophil counts and in spleen cellularity. Lamotrigine significantly inhibited ConA-induced splenocyte proliferation in vitro and it significantly inhibited IL-2 and TNF-α secretion in ConA-stimulated splenocytes. In conclusion, the results demonstrated significant immunomodulatory effects of lamotrigine in BALB/c mice. These data could expand the understanding of lamotrigine-induced adverse reactions and its role in modulating the immune system in epilepsy