Volume-5 Issue-6, October 2025 – International Journal of Advanced Medical Sciences and Technology (IJAMST)

F305105061025

IJAMST Journal — Thu, 23 Oct 2025 12:27:42 +0000

The International Journal of Advanced Medical Sciences and Technology (IJAMST) has ISSN 2582-7596 (online), an open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in February, April, June, August, October and December. The journal aims to publish high-quality peer–reviewed original articles in the area of Medical Science that covers Anatomy, Biochemistry, Pharmacology, Pathology, Forensic Medicine, Cardiology, Dermatology & Venereology, Hematology, Anesthesia, Obstetrics & Gynecology, Pediatrics, Medicine, Physiology, Anatomy and Surgery and Dentary & Dental Surgery, Microbiology, Community Medicine, Ophthalmology, Otorhinolaryngology, Internal Medicine, General Surgery, Paediatrics, Orthopedics, Psychiatry, Radiology, Pulmonary Medicine, Dermatology and Venereal diseases, Infectious Diseases, Anesthesia, Cardiology, Diabetes, Cancer Research, Serology, Endocrinology, Urology, Neurosurgery, Geriatric Medicine, Gastroenterology, Neurology, Nephrology, Dentistry and Medical Education, Health Improvement Strategies. #Anatomy #Physiology #Biochemistry #Pharmacology #Pathology #Forensic Medicine #Cardiology #Dermatology & Venereology #Hematology #Anesthesia #Radiology #Obstetrics & Gynecology #Pediatrics #Medicine #Physiology #Anatomy and Surgery and Dentary & Dental Surgery #Microbiology #Community Medicine #Ophthalmology #Otorhinolaryngology #Internal Medicine #General Surgery #Paediatrics #Orthopedics #Psychiatry #Radiology #Pulmonary Medicine #Dermatology and Venereal Diseases #Infectious Diseases #Anesthesia, Cardiology #Diabetes, Cancer Research #Serology, Endocrinology #Urology, Neurosurgery #Geriatric Medicine #Gastroenterology #Neurology#Nephrology #Dentistry and Medical Education #Health Improvement Strategies #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Evaluate the Effects of Gamma Radiation (16 Sv/h), Neutron Radiation (3 Sv/h), and a Mixed Radiation Field on the Structural and Biophysical Properties of Bones
Mohamed S. Nasr Eldin¹, Ahmed. A Emara², Hala M. Ahmed³

¹Dr. Mohamed S. Nasr Eldin, Department of Radiology and Medical Imaging, Faculty of Applied Health Sciences Technology, October 6 University, 6th of October City, Egypt.

²Dr. Ahmed. A Emara, Department of Radiology and Medical Imaging, Faculty of Applied Health Sciences Technology, October 6 University, 6th of October City, Egypt.

³Prof. Dr. Hala M. Ahmed, Department of Medical Biophysics: Biomedical Equipment, Faculty of Applied Health Sciences Technology, October 6 University, 6th of October City, Egypt.

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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Background: Ionizing radiation, including gamma and neutron radiation, can adversely affect bone structure, mineralization, and tissue integrity. While individual effects of gamma or neutron exposure have been studied, comparative analyses of their isolated and combined impacts on bone’s structural and biophysical properties remain limited. Objective: This study aimed to evaluate the effects of gamma radiation (16 μSv/h), neutron radiation (3 μSv/h), and combined exposure on bone mineral content, collagen synthesis, cytokine levels, biomechanical properties, and histopathological changes in rats. Materials and Methods: Eighty male albino rats were divided into four groups: control (no radiation), gamma-exposed, neutron-exposed, and combined gamma-neutron exposure. Bone calcium was measured using atomic absorption spectrophotometry, and collagen content was quantified via hydroxyproline-based colourimetric assays. Serum interleukin-1β (IL-1β) and tumour necrosis factor-alpha (TNF-α) levels were determined using ELISA. Biomechanical properties of tibia bones, including tensile strength, stiffness, and energy absorption, were assessed through stress-strain analysis and cyclic loading. Skin and mammary tissues were examined histologically using hematoxylin and eosin staining. Results: Radiation exposure reduced calcium and collagen content, with the most pronounced effects observed in neutron and combined radiation groups. TNF-α levels were significantly elevated in irradiated rats, while IL-1β showed a non-significant upward trend, indicating an inflammatory response. Biomechanical analysis revealed reduced bone strength and increased energy dissipation, suggesting microstructural damage. Histological examination confirmed inflammation, necrosis, and impaired regenerative capacity, particularly in the combined radiation group. Conclusion: Gamma and neutron radiation, both individually and in combination, hurt bone mineralisation, collagen synthesis, inflammatory cytokine balance, and biomechanical integrity. These findings underscore the susceptibility of skeletal tissue to ionizing radiation and highlight the importance of protective strategies in clinical, occupational, and spaceflight environments. Future research should explore interventions targeting oxidative stress and inflammation to mitigate radiation-induced musculoskeletal damage.

Keywords: Gamma Radiation; Neutron Radiation; Bone Biomechanics; Collagen; Calcium; Cytokines; Histopathology.
Scope of the Article: Cancer Research

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F305005061025

IJAMST Journal — Thu, 23 Oct 2025 12:18:08 +0000

Formulating Reliable Deep Acquisition Hypotheses for Medical Diagnosis Utilizing Explainable AI
Abhay Bhatia¹, Rajeev Kumar², Golnoosh Manteghi³

¹Dr. Abhay Bhatia, Post Doctoral Researcher, Department of Computer Science & Engineering, Kuala Lumpur University of Science & Technology (KLUST), Jalan Ikram-Uniten, Kajang, Selangor, Malaysia.

²Prof. (Dr.) Rajeev Kumar, Professor, Department of Computer Science & Engineering, Moradabad Institute of Technology, Moradabad (Uttar Pradesh), India.

³Dr. Golnoosh Manteghi, Faculty of Architecture and Built Environment, Kuala Lumpur University of Science & Technology (KLUST), Jalan Ikram-Uniten, Kajang, Selangor, Malaysia.

Abstract: The rise of deep learning has revolutionized various fields, including healthcare. Deep learning models excel at analyzing analyzable medical collections, such as surgical representations and EHRs, offering immense potential for improved medical diagnosis and decision-making. However, a significant barrier to their widespread adoption in clinical practice lies in their inherent “black-box” nature. This deficiency of transparence hinders reliance and raises concerns about accountability in critical medical decisions. This paper explores the concept of Explainable AI (XAI) for medical diagnosis, focusing on building trustworthy deep learning models for clinical decision support. We begin by highlighting the advantages of deep learning in medical diagnosis, emphasizing its ability to identify subtle patterns in data that may elude human experts. We then delve into the limitations of traditional deep learning models, explaining the challenges associated with their opacity and the impact on physician trust. Model-specific methods, on the other hand, leverage the inherent characteristics of specific deep learning architectures to provide insights into their decision-making processes. We then explore the integration of XAI with clinical workflows. This section emphasizes the importance of tailoring explanations to the needs of physicians, ensuring the information is clear, actionable, and aligns with established medical knowledge. We discuss strategies for visualizing explanations in a user-friendly format that facilitates physician understanding and promotes informed clinical decision-making. Furthermore, the paper addresses the ethical considerations surrounding XAI in healthcare. We explore issues like fairness, bias, and potential misuse of explanations. Mitigating bias in deep learning models and ensuring explanations are not misinterpreted become crucial aspects of building trustworthy systems. Finally, the paper concludes by outlining the future directions of XAI for medical diagnosis. We discuss the ongoing research efforts to develop more robust and user-centric XAI methods specifically suited for the complexities of medical data and decision-making. By fostering collaboration among AI researchers, medical professionals, and ethicists, we can develop trustworthy deep learning models that empower physicians and ultimately lead to enhanced patient care.

Keywords: XAI, Healthcare, Deep Learning, HER, Medical Diagnostics.
Scope of the Article: Health Improvement Strategies

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Volume-5 Issue-6, October 2025 – International Journal of Advanced Medical Sciences and Technology (IJAMST)

F305105061025

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F305005061025

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