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Case Report
Anecdotal Report: Resolution of Purported mRNA
Vaccine-Induced Arrhythmia Following Novavax Administration
Russell Hill*
,
Jill Panitch
Issue:
Volume 14, Issue 4, August 2026
Pages:
53-60
Received:
8 June 2026
Accepted:
22 June 2026
Published:
11 July 2026
Abstract: This case report and clinical overview examines rare but persistent post-vaccination cardiac complications associated with mRNA COVID-19 vaccine platforms, which exhibit a higher incidence rate ratio of post-vaccination arrhythmia compared to traditional or vector-based designs. Currently, there is a distinct clinical gap regarding the management of long-term, vaccine-associated arrhythmias that remain refractory over multiple years. We present the case of a 77-year-old male with no prior cardiac history who developed persistent ventricular bigeminy and frequent premature ventricular contractions (PVCs) following a primary mRNA vaccination series (Moderna). The arrhythmia continuously recurred through subsequent mRNA booster doses (Moderna and Pfizer) over a three-year period. Extensive clinical evaluations, including echocardiography and myocardial perfusion stress testing, ruled out structural heart disease, ischemia, or metabolic abnormalities. Because the overall arrhythmia burden was measured at a low 6%, below the standard 15% clinical treatment threshold, no antiarrhythmic medications or invasive interventions were initiated. However, following a self-initiated platform switch to a single dose of the protein-based recombinant adjuvanted vaccine (Novavax), the patient’s multi-year arrhythmia resolved completely within days, and normal sinus rhythm has been continuously documented for over a year. Putative mechanisms for mRNA-induced myocardial electrical instability include prolonged intracellular processing of encoded spike proteins within host cardiomyocytes, lipid nanoparticle-mediated systemic inflammation, and autoimmune molecular mimicry—cascades that are entirely avoided by recombinant protein vaccines. Ultimately, this case highlights a profound temporal association between long-term ventricular arrhythmia and mRNA vaccines, followed by rapid, sustained resolution upon switching platforms. Clinicians should consider enhanced, long-term arrhythmia monitoring for select patients presenting with post-mRNA palpitations, and therapeutic platform-switching to non-mRNA configurations warrants immediate further investigation as a safe, highly viable mitigation strategy for persistent vaccine-associated cardiac symptoms.
Abstract: This case report and clinical overview examines rare but persistent post-vaccination cardiac complications associated with mRNA COVID-19 vaccine platforms, which exhibit a higher incidence rate ratio of post-vaccination arrhythmia compared to traditional or vector-based designs. Currently, there is a distinct clinical gap regarding the management o...
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Research Article
Advances in Rapid Nucleic Acid Diagnostics for Disease Prevention and Control
Jinhui Sun,
Baosheng Sun,
Xiuli Ruan,
Yinzhen Sheng*
Issue:
Volume 14, Issue 4, August 2026
Pages:
61-75
Received:
23 April 2026
Accepted:
25 June 2026
Published:
6 August 2026
Abstract: Animal diseases present a major challenge to sustainable livestock production and global public health security. Rapid nucleic acid diagnostics, characterized by high specificity, sensitivity, and speed, have emerged as indispensable tools for early disease detection and precise control. This review provides a comprehensive overview of the recent advancements in rapid nucleic acid diagnostic technologies for animal diseases. We systematically categorize and evaluate current platforms, focusing on isothermal amplification techniques, CRISPR-based assays, and microfluidic micro-systems. Particular attention is given to how modern iterations have revolutionized the diagnostic process, pushing sensitivity toward the single-molecule realm and reducing turnaround times from hours to minutes. Furthermore, this paper analyzes the critical roles of these rapid-response systems in pandemic surveillance, vaccine efficacy assessment, and cross-border biosecurity risk management. Finally, we address current technical bottlenecks, such as sample preparation complexity and field-deployability constraints. We conclude that the integration of functional nanomaterials and artificial intelligence (AI) will be the driving force behind the next generation of intelligent, sample-in-answer-out, and field-deployable diagnostic tools, ultimately shaping the future of smart veterinary medicine.
Abstract: Animal diseases present a major challenge to sustainable livestock production and global public health security. Rapid nucleic acid diagnostics, characterized by high specificity, sensitivity, and speed, have emerged as indispensable tools for early disease detection and precise control. This review provides a comprehensive overview of the recent a...
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Research Article
Development of a Machine Learning Model for the Management of Asthma
Issue:
Volume 14, Issue 4, August 2026
Pages:
76-89
Received:
12 July 2026
Accepted:
24 July 2026
Published:
17 August 2026
DOI:
10.11648/j.ajbls.20261404.13
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Views:
Abstract: Asthma management requires continuous monitoring of both physiological conditions and environmental triggers to prevent exacerbations and improve patient outcomes. Conventional inhalers, however, lack real-time monitoring and predictive capabilities, limiting their effectiveness in proactive healthcare. This study presents the design and implementation of a Smart Asthma Inhaler system that integrates sensor-based data acquisition, cloud computing, and machine learning (ML) for real-time monitoring and predictive risk assessment. The proposed system captures key parameters, including oxygen saturation (SpO2), air quality index (AQI), temperature, humidity, and inhaler usage frequency. A Multiple Linear Regression model was employed to analyze the variables and generate predicted asthma risk scores, which were further categorized into risk levels for actionable feedback. Experimental evaluation was conducted using a pilot dataset collected from 20 asthma patients under varying environmental conditions. The system achieved an inhaler detection accuracy of 97.5%, with average data synchronization and prediction times of 2.3 and 2.7 seconds, respectively. The predictive model demonstrated strong performance with a coefficient of determination (R2) of approximately 0.986, indicating high predictive accuracy. Non-functional evaluation further revealed high usability (4.5/5), scalability (handling up to 20 concurrent users), and reliability (94% uptime). The results demonstrate that the proposed system is efficient, accurate, and suitable for real-time asthma monitoring and prediction. The potential of integrating Internet of Things (IoT) and machine learning to enhance proactive healthcare and improve asthma management was highlighted.
Abstract: Asthma management requires continuous monitoring of both physiological conditions and environmental triggers to prevent exacerbations and improve patient outcomes. Conventional inhalers, however, lack real-time monitoring and predictive capabilities, limiting their effectiveness in proactive healthcare. This study presents the design and implementa...
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