https://theamericanjournals.com/index.php/tajmspr/issue/feed The American Journal of Medical Sciences and Pharmaceutical Research 2025-01-14T03:35:26+00:00 The USA Journals editor@theamericanjournals.com Open Journal Systems <p>E-ISSN <strong>2689-1026</strong></p> <p>DOI Prefix <strong>10.37547/tajmspr</strong></p> <p>Started Year <strong>2019</strong></p> <p>Frequency <strong>Monthly</strong></p> <p>Language <strong>English</strong></p> <p>APC <strong>$250</strong></p> https://theamericanjournals.com/index.php/tajmspr/article/view/5815 Surgical approach to idiopathic scoliosis: a systematic review with meta-analysis 2025-01-14T03:35:26+00:00 Bianca Gabriella de Oliveira bianca@theamericanjournals.com Lucas da Silva Lucena lucas@theamericanjournals.com Tiago Alves da Silva tiago@theamericanjournals.com Fred Schinaider Cerqueira fred@theamericanjournals.com Marcella Rodrigues Costa Simões marcella@theamericanjournals.com <p>Objectives: to analyze surgical approaches for the treatment of idiopathic scoliosis and the prognosis achieved. Methodology: A systematic review with meta-analysis was carried out using the electronic databases PubMed/MEDLINE and Cochrane Library. Results: The sample consisted of 217 patients with a mean age of 15 years diagnosed with idiopathic scoliosis. In all the studies included, the pathological curvature was reduced by more than 49%. Conclusion: The pathological curvature was reduced in all surgical interventions, and the short- and long-term post-operative results were satisfactory.</p> 2025-01-12T00:00:00+00:00 Copyright (c) 2025 Bianca Gabriella de Oliveira, Lucas da Silva Lucena, Tiago Alves da Silva, Fred Schinaider Cerqueira, Marcella Rodrigues Costa Simões https://theamericanjournals.com/index.php/tajmspr/article/view/5809 Comparative Analysis of Machine Learning Models for Automated Skin Cancer Detection: Advancements in Diagnostic Accuracy and AI Integration 2025-01-11T16:42:26+00:00 An Thi Phuong Nguyen nguyen@theamericanjournals.com Rasel Mahmud Jewel jewel@theamericanjournals.com Arjina Akter akter@theamericanjournals.com <p>Skin cancer detection remains a critical challenge in dermatology, with early diagnosis significantly improving patient outcomes. This study presents a comparative analysis of machine learning models for automated skin cancer detection, highlighting the superior performance of Convolutional Neural Networks (CNNs). The CNN model achieved the highest accuracy (92.5%), sensitivity (91.8%), and specificity (93.1%) compared to other algorithms such as Support Vector Machines (SVMs) and Random Forests. The use of advanced preprocessing techniques and diverse datasets ensured the model's robustness and generalizability. While the findings demonstrate the potential of deep learning in dermatological diagnostics, limitations such as model interpretability and dataset diversity were identified. This research underscores the transformative role of AI in improving diagnostic accuracy, enabling early detection, and addressing healthcare disparities, particularly in resource-constrained settings. Future work aims to enhance model explainability and expand its applicability across diverse populations.</p> 2025-01-10T00:00:00+00:00 Copyright (c) 2025 An Thi Phuong Nguyen, Rasel Mahmud Jewel, Arjina Akter https://theamericanjournals.com/index.php/tajmspr/article/view/5791 THE ANTIMICROBIAL ACTION OF PETIVERIA ALLIACEA STEM EXTRACT: INVESTIGATING MECHANISMS AND EFFICACY 2025-01-01T07:30:34+00:00 Astria Santoso astria@theamericanjournals.com <p>Petiveria alliacea, a plant traditionally used in folk medicine, has shown significant potential for antimicrobial activity. This study investigates the antimicrobial properties and mechanisms of action of the stem extract of Petiveria alliacea against a range of pathogenic microorganisms. The extract was tested against both Gram-positive and Gram-negative bacteria, as well as fungi, using standard microbiological assays. The results demonstrated strong antimicrobial efficacy, with notable inhibition zones against several bacterial and fungal strains. Additionally, the study explores the mechanisms underlying these effects, including the disruption of microbial cell walls, membrane integrity, and enzyme inhibition. These findings suggest that Petiveria alliacea stem extract could serve as a promising natural antimicrobial agent, contributing to the development of alternative therapies for combating infections.</p> 2025-01-01T00:00:00+00:00 Copyright (c) 2025 Astria Santoso https://theamericanjournals.com/index.php/tajmspr/article/view/5810 Predicting the Effectiveness of Laser Therapy in Periodontal Diseases Using Machine Learning Models 2025-01-11T16:47:33+00:00 Han Thi Ngoc Phan phan@theamericanjournals.com Arjina Akter akter@theamericanjournals.com <p>This study evaluates the effectiveness of machine learning models in predicting the outcomes of laser therapy for periodontal diseases. Various algorithms, including Neural Networks, Gradient Boosting, Random Forest, and Support Vector Machine, were applied to a dataset containing clinical variables such as pocket depth and gingival inflammation. The Neural Network model achieved the highest predictive accuracy with an AUC-ROC score of 0.91, followed by Gradient Boosting at 0.90. These models outperformed traditional techniques, demonstrating that machine learning can accurately predict treatment success. The findings suggest that machine learning can aid clinicians in personalizing laser therapy, optimizing treatment, and improving patient outcomes. Further research with diverse datasets is recommended to refine these models.</p> 2025-01-10T00:00:00+00:00 Copyright (c) 2025 Han Thi Ngoc Phan, Arjina Akter https://theamericanjournals.com/index.php/tajmspr/article/view/5805 Evaluation of radiographers knowledge about radiation safety and cancer risks of ionizing radiation exposure 2025-01-08T10:41:55+00:00 Walid Mahmoud Khalilia walid@theamericanjournals.com <p>Ionizing Radiation (IR) crucial to both therapeutic and diagnostic methods. However, it has hazardous exposure effects on patients and workers in radiation environment personnel. This study aimed to assess the level of knowledge among radiographers working in the private and public hospitals in Palestine about radiation safety and cancer risks of radiation exposure. Online questionnaires were distributed to 74 radiographers at seven private and public hospitals in Palestine. Four demographic characteristics and 17 several options questions about radiation protection were included in the survey. This study revealed that the mean of correct scores was (7.20) out of 17 enquiries from Palestinian radiographers on radiation safety. The current investigation revealed a inadequate knowledge of radiation protection and safety. (40.5%) of the radiographers admitted seldom received any training about radiation protection. While only (2.7%) of them reported that they regularly attended such training. and (27.0%) they never attended it. The knowledge score according to work experience, hospital type, and gender did not have statistical significance. In terms of academic level showed significant differences (P&lt; 0.05), postgraduates’ level of knowledge score was (10.2±2.13), higher than undergraduates (6.88±1.51). The results show that the radiographers involved in this study lacked sufficient understanding on radiation protection and safety. Therefore, the most crucial topic is the administrations of the foundations using radiation have to exercise prudence by supplying staff or the essential infrastructure in the form of equipment and training.</p> 2025-01-08T00:00:00+00:00 Copyright (c) 2025 Walid Mahmoud Khalilia