Volume 13 | Issue 3 |

 Abstract

Aim: To compare the efficacy of topical tacrolimus ointment with that of triamcinolone Acetonide ointment in patients with symptomatic oral lichen planus. Methods and Material: In this prospective randomized comparative study, 30 consecutive patients with oral lesions consistent clinically and histologically with OLP were recruited. The patients were divided into 2 groups to receive topical triamcinolone acetonide 0.1% or tacrolimus 0.1% ointment and treated for 6 weeks. The clinical effect of treatment in the patients were graded after 6 weeks by the treating physician using an ordinal score and recorded as worse, unchanged, improved or healed. Statistical analysis was done between no improvement and improvement-Healing group with Fisher's Exact Test. Results and conclusion: The results of this study allow the conclusion that treatment with topical tacrolimus 0.1% ointment four times daily induced a better initial therapeutic response than triamcinolone acetonide 0.1% ointment in patients with symptomatic OLP.

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 Keywords

Oral Lichen Planus, Tacrolimus, Triamcinilone Acetonide

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

রাধাকৃষ্ণণের দর্শনে নারীর ভূমিকা: একটি অনুসন্ধান

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

রাধাকৃষ্ণণের দর্শনে নারীর ভূমিকা: একটি অনুসন্ধান

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Learnify, redefines online education with AI-driven adaptive learning, gamification, and multilingual support, making learning personalized, engaging, and accessible. Unlike traditional platforms, it dynamically adjusts content, offers real-time challenges, and enhances retention through AI-powered insights. Learnify's interactive approach boosts motivation, ensures inclusivity, and sets a new standard for digital learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI-Powered Personalization, Gamified Intelligence, Multilingual Immersion, Interactive Knowledge Ecosystem, Real-Time Adaptive Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Batteries are essential components in modern devices, especially in electric vehicles (EVs), where their performance and longevity are critical. The functioning of an EV is intricately linked to the health of its battery, which demands continuous monitoring and effective management. Therefore, the development of an efficient Battery Management System (BMS) is vital not only for ensuring safe vehicle operation but also for optimizing battery life and enhancing overall driving safety. This article explores the challenges and opportunities in implementing BMS technologies within electric vehicles, offering an in-depth review of current literature on the subject. The aim is to highlight the latest innovations in EV battery management systems and identify promising avenues for future research, ultimately contributing to the advancement of EV technology. By providing a comprehensive understanding of existing approaches, this review aims to inform the ongoing evolution of BMS solutions and their integration into next-generation electric vehicles.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Battery, Battery management System (BMS), Battery protection, Safety measures, Battery performance

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Blockchain technology is transforming the healthcare sector by offering safe, decentralized, and transparent data management solutions. The growing dependence on digital medical records, AI-based diagnostics, and interoperability among healthcare providers has heightened the necessity for stringent data security and privacy safeguards.[1] Blockchain guarantees immutable storage of medical data, mitigating the danger of data breaches, unauthorized alterations, and fraudulent actions. This article explores the disruptive impact of blockchain on disease detection, highlighting its integration with artificial intelligence (AI) and machine learning (ML) for early and precise diagnosis. Blockchain improves data integrity by utilizing decentralized ledgers, guarantees real-time access to verified medical records, and enables secure exchange across hospitals, research institutes, and patients. We investigate the uses of blockchain in early disease detection, namely in diagnosing severe health diseases such as cardiovascular disease, diabetes, renal disorders, and hepatic malignancies. AI-driven medical imaging and predictive models, integrated with blockchain technology, facilitate a more dependable, efficient, and privacy-conscious healthcare system. Additionally, we examine the potential obstacles, emerging trends, and future research avenues regarding the implementation of blockchain in the healthcare sector, facilitating a more secure, transparent, and AI-enhanced medical environment.

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 Keywords

Blockchain in Healthcare, Medical Data Security, Disease Detection with AI, Decentralized Healthcare Systems, Blockchain for Medical Imaging, Liver Tumor Detection, AI-Blockchain Integration, Electronic Health Records (EHRs), Smart Contracts in Healthcare, Secure Medical Data Sharing, Interoperability in Healthcare, Tamper-Proof Medical Records, Decentralized AI for Diagnosis, Machine Learning in Medical Imaging, Privacy-Preserving Healthcare Systems

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cervical spondylosis is a general term for age related wear and tear affecting the spinal disks in your neck. As the disks dehydrate and shrink, signs of osteoarthritis develop including bony projections along the edges of bones [bone spurs]. Cervical spondylosis is very common and worsens with the age. More than 85% of people older than age of 50 years are affected by cervical sopndylosis. Young generation is also facing the problem of cervical spondylosis due to over use of computers and mobiles, and other electronic gadgets. For most people, cervical spondylosis causes no symptoms, when symptoms do occur, non-surgical treatments often are effective. In present case study, a diagnosed case of cervical spondylosis, chief complaints were pain and stiffness over the neck since 8 months and pain over the neck was radiating towards the right arm. In treatment externally manyabasti and patyra pottali swedan was given and during this procedure, ayurvedic medicines also given. There is a complete relief in the parameters like neck and stiffness whereas the parameters like pain in arm and vertigo has also shown significant improvement.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Ayurveda, Manyastambha, Manyabasti, Cervical spondylosis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Web API , Open API Documentation, Natural language processing ,Natural languag

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rise of Data Analytics, alongside Machine Learning (ML) and Deep Learning (DL) in big data is rendering profound differences in the fields of healthcare applications, including predictive analytics, computer analysis of medical images, drug discovery, personalized medicine and EHR analysis. This new technology creates new research opportunities and improves decision-making and patient outcomes. But capturing this massive and unstructured information -- from medical devices and sensors -- presents a huge challenge. Using bibliometric and network analyses, this study investigates research trends in the adoption of ML and DL in healthcare and offers insights for academics, researchers, and healthcare professionals to direct future states of progress.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Bibliometric, predictive analytics, patient outcomes

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Healthcare AI faces challenges like limited, low-quality data and privacy regulations (HIPAA, GDPR), making data collection and sharing difficult. Synthetic data offers a solution by creating realistic datasets that preserve statistical properties while protecting patient privacy. It helps reduce biases, enables federated learning, enhances rare disease datasets, and improves AI models for diagnosis and public health simulations. Techniques like GANs generate medical imaging and clinical records, but challenges remain in maintaining data integrity and preventing privacy breaches. Despite these challenges, synthetic data continues to drive innovation in healthcare AI, with the FDA approving over 70 AI-based models using this approach.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Synthetic Data, Patient Data Privacy, (GANs), Data Augmentation, Synthetic Patient Profiles, Medical Imaging GANs, Rare Disease Data Simulation, Medical AI Models

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study explores the development of an Adaptive Operating System Framework utilizing Large Language Models (LLMs) for real-time resource management, user personalization, and system security. The study compares traditional operating system models with an adaptive AI-driven approach using predictive and self-optimizing mechanisms. Two models, the Classical Rule-Based OS Management and AI-Driven Adaptive OS Framework, are evaluated for their efficiency in dynamic resource allocation, security enhancements, and user experience improvements. A dataset of system performance metrics, user interaction logs, and security threats from 2019 to 2024 is analyzed using machine learning and statistical modeling.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Adaptive OS, LLM, Resource Management, AI Security, User Personalization

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Creative Commons Attribution 4.0 and The Open Definition