Dear Editor: Vagal paragangliomas are uncommon tumors of the parapharyngeal space, approximately 200 cases having been reported. They arise from tissue of the extra adrenal sympathetic nervous system. They mostly manifest as a painless neck mass near the angle of the mandible usually between the skull base and the hyoid bone.

This article provides a comprehensive overview of the role of exercise physiology in enhancing athletic performance, with a specific focus on endurance sports. It begins by introducing the concept of exercise physiology and its significance in sports science, emphasizing the importance of endurance in athletic pursuits. The article then delves into the science of exercise physiology, defining key terms and explaining how the discipline studies the body's responses and adaptations to physical activity. It highlights crucial physiological factors influencing endurance, such as VO2 max and lactate threshold. The principles of endurance training are explored, detailing training methodologies like overload, specificity, and recovery, and their application in endurance training programs. The article also discusses the critical roles of nutrition, hydration, and psychological aspects, including mental toughness and coping strategies for fatigue. The impact of technological advancements, such as wearable fitness trackers and training software, is examined, demonstrating how these tools aid in monitoring and enhancing endurance training. Real-world case studies are presented to illustrate the practical application of exercise physiology principles, showcasing how athletes across various sports have benefited from tailored training programs. The article concludes with a summary of the key points and a reflective note on the future of endurance training, highlighting potential areas of research that could further advance our understanding and application of exercise physiology in sports.

Diabetes, a metabolic disorder, poses significant annual risks due to various factors, requiring effective management strategies to prevent life-threatening complications. Classified into Type 1, Type 2, and Gestational diabetes, its impact spans diverse demographics, with Type 2 diabetes being particularly concerning due to cellular insulin deficiencies. Early prediction is crucial for intervention and complication prevention. While machine learning and artificial intelligence show promise in predictive modeling for diabetes, challenges in interpreting models hinder widespread adoption among physicians and patients. The complexity of these models often raises doubts about their reliability and practical utility in clinical settings. Addressing interpretability challenges is crucial to fully harnessing predictive analytics in diabetes management, leading to improved patient outcomes and reduced healthcare burdens. Previous research has utilized various algorithms like Naïve Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and decision trees for patient classification. In this study using the Pima dataset, we applied a preprocessing technique that utilized the most important features identified by the Random Forest algorithm and we used an ensemble method combining the SVM algorithm and Naïve Bayes for the model. In the first section of the proposed method, we provided explanations regarding the dataset. In the second section, we elucidated all preprocessing steps applied to this dataset, and in the third section, we evaluated the model using the selected algorithm under investigation. The proposed model, after going through the various stages, was able to report an accuracy of 81.82%, a precision of 82.34%, an AUC of 88.19% and a Recall of 70.68%. Considering the review of similar studies, an improvement of 3.99% in accuracy demonstrates a significant advancement that highlights the benefits of traditional methods in disease prediction. These findings suggest the potential use of web-based applications to encourage both physicians and patients in diabetes prediction efforts.

This review evaluates the present condition of multimedia integration into English language classrooms in Malaysia, focusing particularly on its influence on students' oral communication abilities. Oral proficiency and engagement are the primary foci of this study, which seeks to assess how well teachers make use of multimedia to enhance students' speaking while learning English. The research technique comprehensively examines 50 pertinent papers released from 2000 to 2023. Data extraction, analysis, and synthesis are centered on how multimedia affects and is utilized in teaching English, particularly in enhancing speaking skills. The study highlights the positive effects of multimedia technology, such as spoken podcasts, voice recognition software, and platforms like Prezi, on students' oral communication skills, linguistic talents, and language competency. These products allow students to engage in self-directed study and customize their language learning to their individual requirements. However, the challenges of multimedia integration, including the need for suitable content and instructor involvement, are acknowledged. To overcome these problems, it seems necessary to implement new and culturally suitable teaching practices. This study emphasizes the importance of designing educational environments that effectively utilize multimedia for engaging and interactive language activities, with a specific focus on improving speaking skills. Integrating multimedia into English language schools in Malaysia could greatly improve language instruction. Instructors are provided with a variety of tools to meet their students' speaking skill needs. Further study is recommended on the appropriate use of multimedia to enhance speaking skills to promote language instruction and better equip students for a globally connected society. This study could offer valuable insights, analyses the practical and policy implications, and highlight prospective areas for future research in English language education, focusing on speaking skills in Malaysia.