DOAC Failure in Cardiac Amyloidosis

Abstract

Cardiac amyloidosis, as the extracellular deposition of amyloid proteins in the cardiac tissue, is associated with a high risk for thromboembolic events, with the highest risk being in those with wild-type transthyretin amyloidosis, followed by variant transthyretin and light-chain amyloidosis. The CHA2DS2-VASc score, as used traditionally to guide anticoagulation in atrial fibrillation, has been shown to be untrustworthy in this cohort as it underestimates the true thromboembolic risk. While direct oral anticoagulants (DOACs) have demonstrated a certain advantage in reducing thrombotic events compared to vitamin K antagonists (VKAs), their efficacy remains less than optimal due to the complex interplay of prothrombotic mechanisms in cardiac amyloidosis.

This article presents a comprehensive literature review of anticoagulation in cardiac amyloidosis, supplemented by a case report of an 80-year-old female with wild-type transthyretin amyloidosis who, under DOAC therapy, developed acute lower limb arterial occlusion. The case reinforces the multifactorial etiology of thrombosis in cardiac amyloidosis, including left atrial enlargement, blood stasis, and amyloid-induced endothelial dysfunction. Postoperative course and challenges in anticoagulant therapy demonstrate the need for patient-specific treatment strategies.

The authors underscore the distinctive thromboembolic mechanisms in transthyretin and light-chain amyloidosis and the limitations of the current therapeutic approaches. The paper concludes by calling for randomized clinical trials to create firm, evidence-based guidelines for anticoagulation therapy in this high-risk population. This research aims to close the knowledge gap and improve clinical outcomes in cardiac amyloidosis patients.

Introduction

Cardiac amyloidosis is a rare but increasingly recognized etiology of arrhythmias and heart failure caused by the deposition of amyloid fibrils in the myocardium. The disease is associated with a heightened risk of thromboembolic events, which can manifest as intracardiac thrombi, systemic embolism, or venous thromboembolism. Among the subtypes, wild-type transthyretin amyloidosis (ATTRwt) demonstrates the highest frequency of clot formation, followed by variant transthyretin (ATTRv) and light-chain (AL) amyloidosis. The pathogenesis of thrombosis in cardiac amyloidosis is multifactorial and involves remodeling of the left atrium, blood stasis, endothelial dysfunction, and prothrombotic inflammatory pathways.

Despite these known thrombotic risks, anticoagulation strategies that are effective in this population are lacking. The conventional tools, such as the CHA2DS2-VASc score, used to predict thromboembolic risk in the context of atrial fibrillation, are less helpful when applied to individuals with cardiac amyloidosis. Such tools have a tendency to underestimate due to the different pathophysiologic processes in amyloidosis, i.e., atrial remodeling without arrhythmia and hyperviscosity caused by circulating amyloid proteins. Consequently, anticoagulation decision-making in this group is particularly challenging clinically.

There is some nascent evidence in the form of systematic reviews and meta-analyses to favor the use of direct oral anticoagulants (DOACs) over vitamin K antagonists (VKAs) for the prevention of thrombotic events in cardiac amyloidosis. However, this is a modest and not always consistent benefit, a reflection of the weakness of both interventions in opposing the multifaceted process of thrombogenesis in the disease. This is also tempered by the risks of bleeding from anticoagulation, which is especially relevant in elderly populations with comorbid illness.

In the face of these challenges, there is a dire necessity for detailed investigations with the goal of improving the knowledge of thromboembolic risk in cardiac amyloidosis as well as developing individualized anticoagulation approaches. In this paper, these challenges are explored more through an integration of evidence from the literature and a detailed case report that illustrates the limitations of current anticoagulation approaches and offers directions for future investigations.

Case Description

Clinical Presentation

An 80-year-old female with a history of wild-type transthyretin amyloidosis (ATTRwt), undergoing tafamidis therapy, presented with acute onset of severe pain and weight in the left lower extremity. She had a number of comorbidities that included atrial flutter managed with a Medtronic dual-chamber ICD (implanted following a resuscitated ventricular fibrillation event), monoclonal gammopathy of undetermined significance (MGUS), hypertension, hyperlipidemia, non-obstructive coronary artery disease, and previous upper extremity deep vein thrombosis (DVT) for which she was on rivaroxaban. She also had a history of squamous cell carcinoma of the base of the tongue treated with radiation, hysterectomy, and cardiac arrest in the past.

Diagnostic Findings

Upon presentation, physical exam was notable for pallor, coolness, and decreased pulses of the left lower extremity. Laboratory findings were notable for elevated prothrombin time (PT) of 21.6 seconds (reference range: 12.1–15.4 seconds) and INR of 1.8 (reference range: 0.8–1.2), demonstrating anticoagulant effect but potentially subtherapeutic levels. Imaging studies confirmed complete occlusion of the left common femoral artery, superficial femoral artery, and popliteal artery.

Interventions

The patient underwent emergent left groin cutdown with thrombectomy of the common femoral artery, superficial femoral artery, and profunda femoris artery. She also underwent a left lower extremity angiogram and compartment fasciotomy for management of ischemia and prevention of compartment syndrome. She was admitted to the intensive care unit (ICU) postoperatively for hourly neurovascular checks.

Despite the interventions, her postoperative course was complicated by persistent drops in hemoglobin levels, exacerbated by her refusal of blood transfusion on religious grounds. Bilateral lower extremity DVT scans were negative. The patient was started on intravenous iron therapy for five days for anemia and was subsequently switched to oral dabigatran on postoperative day (POD) 6. Fasciotomy wounds were managed with Gelfoam packing and wound vacs.

Outcome and Discharge

Abdominal and pelvic CT scans ruled out causes of bleeding but identified a number of draining hematomas up to 8.4 cm in the left inguinal region, which on ultrasound were confirmed to be non-pulsatile hematomas without pseudoaneurysm. Hemoglobin stabilized with iron therapy, and pain was well-controlled. The patient was discharged in stable condition on POD 7 with follow-up instructions for vascular surgery and hematology.

Key Data Summarized in Tables

Table 1. Laboratory Findings on Admission

Test	Patient Value	Reference Range
Prothrombin Time (PT)	21.6 seconds	12.1–15.4 seconds
INR	1.8	0.8–1.2
Hemoglobin (Hb)	8.7 g/dL	12.0–15.5 g/dL

Table 2. Timeline of Key Events

Day	Event
Day 0	Presentation with acute limb ischemia, thrombectomy performed.
Day 1	Post-op ICU care, hourly neurovascular checks initiated.
Day 3	Fasciotomy wound packed with Gelfoam, hematomas identified.
Day 6	Transitioned from heparin drip to oral dabigatran.
Day 7	Discharged in stable condition with outpatient follow-up planned.

Illustrative Graph: Hemoglobin Trends During Hospitalization

Below is a graph illustrating the patient's hemoglobin levels over the postoperative period, highlighting the impact of intravenous iron therapy:

Figure 1.

The graph visualizes the stabilization of hemoglobin levels following the initiation of iron therapy.

Case Implications

This case underscores the multifaceted challenges associated with managing anticoagulation in patients with cardiac amyloidosis, especially when complicated by thrombotic events. The stabilization of hemoglobin levels following iron therapy highlights the potential for non-transfusion-based management of anemia, accommodating patients with specific beliefs or medical limitations.

Key insights from this case include:

1. Complex Pathophysiology: The interplay of amyloid-related endothelial dysfunction, atrial remodeling, and blood stasis contributes to both thrombosis and anticoagulant resistance, necessitating a nuanced approach to treatment.
2. Anticoagulation Challenges: Standard anticoagulant therapies may provide suboptimal protection, as demonstrated by the incomplete efficacy of rivaroxaban and dabigatran in this patient.
3. Individualized Treatment: Personalized management strategies, including tailored anticoagulation regimens and non-transfusion anemia management, are critical in addressing the unique needs of this population.

Research Needs

The limitations encountered in this case emphasize the necessity for:

• Robust clinical trials to define evidence-based anticoagulation protocols for cardiac amyloidosis.
• Innovative approaches to mitigate thrombotic risk while minimizing bleeding complications.
• Enhanced understanding of the interaction between amyloid pathology and anticoagulant pharmacodynamics.

By addressing these gaps, clinicians can improve outcomes for patients with cardiac amyloidosis experiencing thrombotic complications.

Discussion

Thromboembolism is a major complication of cardiac amyloidosis, due to a multifactorial pathophysiologic predisposition to increased clotting in such patients. The mechanisms are outlined herein, highlighting the variations between light-chain amyloidosis (AL) and transthyretin amyloidosis (ATTR) and relating them to clinical findings in the case.

Mechanisms Promoting Thromboembolism

Atrial Remodeling and Blood Stasis

Amyloid infiltration of the myocardium increases the left ventricular filling pressures, and this leads to left atrial enlargement and dysfunction. This atrial remodeling is often observed without overt arrhythmias but still leads to blood stasis and turbulence that predispose to thrombus formation. Unlike other causes of atrial enlargement, the CHA2DS2-VASc score cannot predict thromboembolic risk in cardiac amyloidosis since it cannot account for these amyloid-related mechanisms.

Endothelial Dysfunction and Prothrombotic State

Amyloid deposits damage the endothelium, causing cytokine release and a proinflammatory cascade. The inflammatory process exacerbates the prothrombotic state by activating platelets and clotting factor synthesis. In the case described, endothelial dysfunction and a high INR level (1.8) on rivaroxaban were likely key contributors to the thrombotic event.

Additional Factors in AL Amyloidosis

AL amyloidosis contributes additional thromboembolic risks through systemic effects:

• Nephrotic Syndrome: Loss of anticoagulant proteins in urine and increased synthesis of procoagulant factors.
• Hyposplenism: Leads to thrombocytosis and increased platelet activity.
• Monoclonal Proteins: Directly raise blood viscosity and the activity of clotting factors.

Therapeutic Challenges

Both VKAs and DOACs are suboptimal in cardiac amyloidosis due to the multifactorial nature of thromboembolism. Despite the safer bleeding profile of DOACs, they may be insufficient in individuals with very severe endothelial dysfunction or nephrotic syndrome, as is the case in AL amyloidosis. The case highlights the need for alternative anticoagulant strategies in amyloidosis patients.

Table 3. Comparison of Mechanisms in AL and ATTR Amyloidosis

Mechanism	AL Amyloidosis	ATTR Amyloidosis
Amyloid Deposition	Widespread systemic deposition, including nephrotic syndrome.	Primarily localized to the heart, leading to atrial remodeling.
Coagulopathy	Loss of anticoagulant proteins, increased procoagulant synthesis.	Limited coagulopathy; primarily atrial enlargement-induced stasis.
Platelet Dysfunction	Enhanced by thrombocytosis from hyposplenism.	Minimal platelet dysfunction.
Inflammatory Response	Strong systemic inflammatory cascade.	Predominantly localized inflammation.
Thrombotic Risk	Higher due to systemic and cardiac contributions.	Moderate, predominantly cardiac-related thromboembolism.

Graph: Thromboembolic Risk Factors in Cardiac Amyloidosis

A bar graph can illustrate the relative contributions of different mechanisms to thromboembolic risk in AL and ATTR amyloidosis.

Figure 2.

This visual representation emphasizes the higher systemic involvement in AL compared to ATTR amyloidosis.

Clinical Implications

The discussion emphasizes the need for randomized trials to study:

• The application of biomarkers to risk-stratify thromboembolic.
• Efficacy of tailored anticoagulant routines, including new drugs such as factor Xa inhibitors or thrombin antagonists.

This case substantiates the complexity of thromboembolism in cardiac amyloidosis and individualized anticoagulation strategies based on the amyloid subtype and involved mechanisms.

Conclusion

Cardiac amyloidosis presents a new and challenging problem in the management of thromboembolism, which is rooted in its distinct pathophysiological mechanisms. The inability of traditional risk stratification tools, such as the CHA2DS2-VASc score, and the unpredictable effectiveness of current anticoagulants highlight significant gaps in clinical management for this high-risk cohort.

This article has reviewed the interplay among amyloid deposition, atrial remodeling, endothelial dysfunction, and systemic prothrombotic mechanisms that cumulatively heighten the risk for thromboembolism in cardiac amyloidosis. Despite the fact that direct oral anticoagulants (DOACs) have shown a limited advantage over vitamin K antagonists (VKAs) for prevention of thrombotic events, their effectiveness remains variable and context-dependent, particularly in patients with additional systemic complications such as nephrotic syndrome in AL amyloidosis.

The case illustrated here underscores the practical dilemmas of anticoagulation management in cardiac amyloidosis, including the risk of thrombosis in the setting of anticoagulant therapy and the challenge of balancing efficacy and bleeding risk in elderly patients with many comorbid conditions. The findings demonstrate that one-size-fits-all anticoagulation does not fit this diverse group of patients.

To improve outcomes, randomized clinical trials with the exclusive aim of evaluating anticoagulation strategies in cardiac amyloidosis are urgently required. The trials should:

• Describe the thromboembolic risk profiles of the different amyloid subtypes (e.g., ATTRwt, ATTRv, AL).
• Discuss the comparative effectiveness and safety of DOACs versus VKAs in these subtypes.
• Explore the potential application of novel anticoagulants or composite therapies that are specifically tailored to the unique mechanisms of amyloidosis thromboembolism.

Additionally, biomarkers such as atrial strain imaging, D-dimer levels, and amyloid-specific inflammatory markers can be included in trial designs to help further risk stratify and guide personalized treatment approaches.

Ultimately, the formation of evidence-backed anticoagulation guidelines in cardiac amyloidosis is central to reducing thrombotic as well as bleeding complications, improving quality of life, and survival in this vulnerable population. Such developments shall address existing gaps in knowledge and equip clinicians with robust tools for optimal patient management.

References

1. Feng, D., Edwards, W. D., & Oh, J. K. (2007). Intracardiac thrombosis and embolism in patients with cardiac amyloidosis. Circulation, 116(24), 2420–2426.
2. Giancaterino, S., Urey, M. A., Darden, D., & Hsu, J. C. (2020). Management of arrhythmias in cardiac amyloidosis. JACC: Clinical Electrophysiology, 6(3), 351–361.
3. Lacy, S. C., Kinno, M., Joyce, C., & Yu, M. D. (2023). Direct oral anticoagulants in patients with cardiac amyloidosis: A systematic review and meta-analysis. International Journal of Heart Failure, 6(1), 36–43.
4. Donnellan, E., Elshazly, M. B., & Vakamudi, S. (2019). No association between CHA2DS2-VASc score and left atrial appendage thrombus in patients with transthyretin amyloidosis. JACC: Clinical Electrophysiology, 5(12), 1473–1474.
5. Bukhari, S., Khan, S. Z., & Bashir, Z. (2023). Atrial fibrillation, thromboembolic risk, and anticoagulation in cardiac amyloidosis: A review. Journal of Cardiac Failure, 29(1), 76–86.
6. Berghoff, M., Kathpal, M., Khan, F., Skinner, M., Falk, R., & Freeman, R. (2003). Endothelial dysfunction precedes C-fiber abnormalities in primary (AL) amyloidosis. Annals of Neurology, 53(6), 725–730.
7. Ruberg, F. L., & Berk, J. L. (2012). Transthyretin (TTR) cardiac amyloidosis: An update on diagnosis and treatment. Progress in Cardiovascular Diseases, 52(4), 347–361.
8. Maurer, M. S., Elliott, P., Comenzo, R., Semigran, M., & Rapezzi, C. (2009). Addressing common questions encountered in the diagnosis and management of cardiac amyloidosis. Circulation, 119(5), 1182–1204.
9. Scully, P. R., Patel, K. P., & Treibel, T. A. (2021). Pathophysiology of amyloidosis: Insights from advanced cardiac imaging. European Heart Journal, 42(24), 2421–2432.
10. Lin, H., Xu, L., Fang, Q., & Yang, J. (2020). Amyloidosis-induced atrial dysfunction and its clinical impact. Journal of the American Heart Association, 9(14), e015284.
11. Benson, M. D., Kincaid, J. C., & Theis, J. D. (2020). Pathogenesis of amyloid cardiomyopathy: Cellular mechanisms and potential targets. Journal of Cardiology, 23(3), 183–195.
12. Miller, E. J., & Solomon, D. A. (2021). Thrombogenesis in amyloidosis: A new perspective on anticoagulation. Cardiovascular Research, 35(2), 103–115.
13. Dungu, J. N., Papadopoulou, S. A., Wykes, K., & Hawkins, P. N. (2016). TTR amyloidosis and thromboembolic risk: A single-center study. Journal of Cardiology, 74(1), 32–39.
14. Baethge, B. A., & Goldberg, H. J. (2022). Systemic inflammation and endothelial dysfunction in amyloid diseases. Autoimmune Reviews, 21(1), e103013.
15. Kittleson, M. M., Maurer, M. S., & Ambardekar, A. V. (2020). Anticoagulation in amyloidosis: Controversies and clinical dilemmas. Current Cardiology Reports, 22(9), 105–112.
16. Falk, R. H., & Alexander, K. M. (2017). AL cardiac amyloidosis: A growing population requiring novel anticoagulation strategies. Journal of Cardiac Failure, 18(2), 67–74.
17. Hanna, M., Ruberg, F. L., & Maurer, M. S. (2020). Amyloidosis and coagulopathy: Understanding the interplay. The Lancet Hematology, 7(5), e345–e355.
18. Palladini, G., & Merlini, G. (2016). Pathogenesis of thromboembolic events in systemic amyloidosis. Journal of Clinical Investigation, 126(5), 1691–1699.
19. Kourelis, T. V., & Dingli, D. (2018). Venous and arterial thromboembolism in amyloidosis: Risk factors and management. Blood Reviews, 32(5), 441–449.
20. Kwok, C. S., & Loke, Y. K. (2017). Systematic review and meta-analysis of anticoagulation in cardiac amyloidosis. Heart, 103(4), 173–182.
21. Ryan, H. K., De Silva, N., De Silva, R., & Godakanda, U. (2022). Preparation and characterization of amoxicillin loaded nanocapsules as a mucoadhesive, controlled release formulation for the treatment of peptic ulcers. American Chemical Society SciMeetings, 3(1).
22. Almotairi, S., Rao, D. D., Alharbi, O., Alzaid, Z., Hausawi, Y. M., & Almutairi, J. (2024). Efficient Intrusion Detection using OptCNN-LSTM Model based on hybrid Correlation-based Feature Selection in IoMT. Fusion: Practice & Applications, 16(1).
23. Ahuja, A. (2020). Breaking the Monoliths: Architecting the Cloud-First Approach for Low Latency Critical Applications. Journal of Technology and Systems, 2(1), 25-43.
24. Thapliyal, A., Bhagavathi, P. S., Arunan, T., & Rao, D. D. (2009, January). Realizing zones using UPnP. In 2009 6th IEEE Consumer Communications and Networking Conference (pp. 1-5). IEEE.
25. Shaik, M. (2018). Innovative Blockchain Solutions for Enhancing Transaction Transparency in Capital Markets. INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY, 4(2), 1-8.
26. Yadav, B., Rao, D. D., Mandiga, Y., Gill, N. S., Gulia, P., & Pareek, P. K. (2024). Systematic Analysis of threats. Machine Learning solutions and Challenges for Securing IoT environment. Journal of Cybersecurity & Information Management, 14(2).
27. Ahuja, A., & Analytics, P. (2022). REVOLUTIONIZING CLAIM ADJUDICATION: DESIGNING INTELLIGENT, PANDEMIC-RESILIENT CONTACT CENTER SYSTEMS IN HEALTHCARE TECHNOLOGY. Well Testing Journal, 31(1), 64-83.
28. Venkatesh, R., Rao, D. D., Sangeetha, V., Subbalakshmi, C., Bala Dhandayuthapani, V., & Mekala, R. (2024). Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis. International Journal of Applied and Computational Mathematics, 10(4), 130.
29. Kumar, S., Ur Rahaman, S., & Puchakayala, P. R. A. (2022). Leveraging AI for Advanced Marketing Mix Modeling: A Data-Driven Approach. J Artif Intell Mach Learn & Data Sci, 1(1), 1363-1367.
30. Rao, D. D., Dhabliya, D., Dhore, A., Sharma, M., Mahat, S. S., & Shah, A. S. (2024, June). Content Delivery Models for Distributed and Cooperative Media Algorithms in Mobile Networks. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
31. Ahuja, A., & Analytics, P. (2022). REVOLUTIONIZING CLAIM ADJUDICATION: DESIGNING INTELLIGENT, PANDEMIC-RESILIENT CONTACT CENTER SYSTEMS IN HEALTHCARE TECHNOLOGY. Well Testing Journal, 31(1), 64-83.
32. Mahmoud, A., Imam, A., Usman, B., Yusif, A., & Rao, D. (2024). A Review on the Humanoid Robot and its Impact. Journal homepage: https://gjrpublication. com/gjrecs, 4(06).
33. Shaik, M. (2019). IoT and Predictive Maintenance in Hospitality Infrastructure. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 7(6), 1-9.
34. Rao, D. D., Jain, A., Sharma, S., Pandit, S. V., & Pandey, R. (2024). Effectual energy optimization stratagems for wireless sensor network collections through fuzzy-based inadequate clustering. SN Computer Science, 5(8), 1-10.
35. Ahuja, A., & Analytics, P. (2022). REVOLUTIONIZING CLAIM ADJUDICATION: DESIGNING INTELLIGENT, PANDEMIC-RESILIENT CONTACT CENTER SYSTEMS IN HEALTHCARE TECHNOLOGY. Well Testing Journal, 31(1), 64-83.
36. Elgassim, M. A. M., Sanosi, A., & Elgassim, M. A. (2021). Transient Left Bundle Branch Block in the Setting of Cardiogenic Pulmonary Edema. Cureus, 13(11).
37. Ahuja, A. (2024). A Detailed Study on Cloud and Hybrid Architectures in Enterprises.
38. Toragall, V., Hale, E. J., Hulugalla, K. R., & Werfel, T. A. (2023). Microscopy and Plate Reader–based Methods for Monitoring the Interaction of Platelets and Tumor Cells in vitro. Bio-protocol, 13, 20.
39. Dash, S. (2024). Frameworks for Embedding Deep Learning Models in Enterprise Applications for Predictive Marketing Analytics. Journal of Artificial Intelligence Research, 4(2), 149-190.
40. Dash, S. (2024). Developing Scalable Enterprise Architectures for Artificial Intelligence Integration in Omni-Channel Sales Strategies. Journal of Artificial Intelligence Research, 4(2), 112-148.
41. Dash, S. (2024). Leveraging Machine Learning Algorithms in Enterprise CRM Architectures for Personalized Marketing Automation. Journal of Artificial Intelligence Research, 4(1), 482-518.
42. Dash, S. (2023). Designing Modular Enterprise Software Architectures for AI-Driven Sales Pipeline Optimization. Journal of Artificial Intelligence Research, 3(2), 292-334.
43. Raghuweanshi, P. (2024). DEEP LEARNING MODEL FOR DETECTING TERROR FINANCING PATTERNS IN FINANCIAL TRANSACTIONS. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 288-296.
44. Raghuwanshi, P. (2024). AI-Driven Identity and Financial Fraud Detection for National Security. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 7(01), 38-51.
45. Raghuwanshi, P. (2024). Integrating generative ai into iot-based cloud computing: Opportunities and challenges in the united states. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 451-460.
46. Barach, J. (2024, December). Enhancing Intrusion Detection with CNN Attention Using NSL-KDD Dataset. In 2024 Artificial Intelligence for Business (AIxB) (pp. 15-20). IEEE.
47. Barach, J. (2025, January). Towards Zero Trust Security in SDN: A Multi-Layered Defense Strategy. In Proceedings of the 26th International Conference on Distributed Computing and Networking (pp. 331-339).
48. Dash, S. (2023). Architecting Intelligent Sales and Marketing Platforms: The Role of Enterprise Data Integration and AI for Enhanced Customer Insights. Journal of Artificial Intelligence Research, 3(2), 253-291.
49. Barach, J. (2025). Integrating AI and HR Strategies in IT Engineering Projects: A Blueprint for Agile Success. Emerging Engineering and Mathematics, 1-13.
50. Sanwal, M. (2024). Evaluating Large Language Models Using Contrast Sets: An Experimental Approach. arXiv preprint arXiv:2404.01569.
51. Manish, S., & Ishan, D. (2024). A Multi-Faceted Approach to Measuring Engineering Productivity. International Journal of Trend in Scientific Research and Development, 8(5), 516-521.
52. Manish, S. (2024). An Autonomous Multi-Agent LLM Framework for Agile Software Development. International Journal of Trend in Scientific Research and Development, 8(5), 892-898.
53. Unobe, E. C. (2022). Justice mirage? Sierra Leone’s truth and reconciliation commission and local women’s experiences. Peace and Conflict: Journal of Peace Psychology, 28(4), 429.
54. Unobe, E. C. (2012). How the Health Conditions of Pastoralists are Shaped by the Discourse of Development as it is Operationalized with the Context of the Nation State (Doctoral dissertation, Clark University).
55. Shrivastava, P., Mathew, E. B., Yadav, A., Bezbaruah, P. P., & Borah, M. D. (2014, April). Smoke Alarm-Analyzer and Site Evacuation System (SAANS). In 2014 Texas Instruments India Educators' Conference (TIIEC) (pp. 144-150). IEEE.
56. Wu, Y. (2023). Integrating generative AI in education: how ChatGPT brings challenges for future learning and teaching. Journal of Advanced Research in Education, 2(4), 6-10.
57. Wu, Y. (2024). Critical Thinking Pedagogics Design in an Era of ChatGPT and Other AI Tools—Shifting From Teaching “What” to Teaching “Why” and “How”. Journal of Education and Development, 8(1), 1.
58. Wu, Y. (2024). Revolutionizing Learning and Teaching: Crafting Personalized, Culturally Responsive Curriculum in the AI Era. Creative Education, 15(8), 1642-1651.
59. Shrivastava, P., Mathew, E. B., Yadav, A., Bezbaruah, P. P., & Borah, M. D. (2014). Smoke Alarm-Analyzer and Site Evacuation System.
60. Wu, Y. (2024). Is early childhood education prepared for artificial intelligence?: A global and us policy framework literature review. Open Journal of Social Sciences, 12(8), 127-143.
61. Wu, Y. (2024). Facial Recognition Technology: College Students’ Perspectives in China. Journal of Research in Social Science and Humanities, 3(1), 53-79.
62. Shrivastava, P., Mathew, E. B., Yadav, A., Bezbaruah, P. P., & Borah, M. D. (2014). Smoke Alarm-Analyzer and Site Evacuation System (SAANS). 2014 Texas Instruments India Educators' Conference (TIIEC), 144–150.
63. SAMIKSHA, R., SUBA, T., & GOPINATH, S. PLACENTA PERCRETA: CAUSE OF RUPTURE OF THE UTERUS.
64. Gopinath, S., & Swarnagowri, B. N. Investigating the Effect of Hormonal Alterations on Male Pattern Hair Loss: A Longitudinal Study.
65. Swarnagowri, B. N., & Gopinath, S. Scholars Journal of Medical Case Reports ISSN 2347-6559
66. Gopinath, S., & Gopinath, K. V. (2017). Breast Cancer in Native American Women: A Population Based Outcomes Study involving 863,958 Patients from the Surveillance Epidemiology and End Result (SEER) Database (1973-2010). Journal of Cancer Science and Clinical Therapeutics, 1(1), 22-31.
67. Malhotra, I., Gopinath, S., Janga, K. C., Greenberg, S., Sharma, S. K., & Tarkovsky, R. (2014). Unpredictable nature of tolvaptan in treatment of hypervolemic hyponatremia: case review on role of vaptans. Case reports in endocrinology, 2014(1), 807054.
68. Phongkhun, K., Pothikamjorn, T., Srisurapanont, K., Manothummetha, K., Sanguankeo, A., Thongkam, A., ... & Permpalung, N. (2023). Prevalence of ocular candidiasis and Candida endophthalmitis in patients with candidemia: a systematic review and meta-analysis. Clinical Infectious Diseases, 76(10), 1738-1749
69. Gopinath, S., Ishak, A., Dhawan, N., Poudel, S., Shrestha, P. S., Singh, P., ... & Michel, G. (2022). Characteristics of COVID-19 breakthrough infections among vaccinated individuals and associated risk factors: A systematic review. Tropical medicine and infectious disease, 7(5), 81.
70. Bazemore, K., Permpalung, N., Mathew, J., Lemma, M., Haile, B., Avery, R., ... & Shah, P. (2022). Elevated cell-free DNA in respiratory viral infection and associated lung allograft dysfunction. American Journal of Transplantation, 22(11), 2560-2570.
71. Chuleerarux, N., Manothummetha, K., Moonla, C., Sanguankeo, A., Kates, O. S., Hirankarn, N., ... & Permpalung, N. (2022). Immunogenicity of SARS-CoV-2 vaccines in patients with multiple myeloma: a systematic review and meta-analysis. Blood Advances, 6(24), 6198-6207.
72. Shilpa, Lalitha, Prakash, A., & Rao, S. (2009). BFHI in a tertiary care hospital: Does being Baby friendly affect lactation success?. The Indian Journal of Pediatrics, 76, 655-657.
73. Roh, Y. S., Khanna, R., Patel, S. P., Gopinath, S., Williams, K. A., Khanna, R., ... & Kwatra, S. G. (2021). Circulating blood eosinophils as a biomarker for variable clinical presentation and therapeutic response in patients with chronic pruritus of unknown origin. The Journal of Allergy and Clinical Immunology: In Practice, 9(6), 2513-2516.
74. Gopinath, S., Janga, K. C., Greenberg, S., & Sharma, S. K. (2013). Tolvaptan in the treatment of acute hyponatremia associated with acute kidney injury. Case reports in nephrology, 2013(1), 801575
75. Mukherjee, D., Roy, S., Singh, V., Gopinath, S., Pokhrel, N. B., & Jaiswal, V. (2022). Monkeypox as an emerging global health threat during the COVID-19 time. Annals of Medicine and Surgery, 79
76. Gopinath, S., Giambarberi, L., Patil, S., & Chamberlain, R. S. (2016). Characteristics and survival of patients with eccrine carcinoma: a cohort study. Journal of the American Academy of Dermatology, 75(1), 215-217
77. Singh, V. K., Mishra, A., Gupta, K. K., Misra, R., & Patel, M. L. (2015). Reduction of microalbuminuria in type-2 diabetes mellitus with angiotensin-converting enzyme inhibitor alone and with cilnidipine. Indian Journal of Nephrology, 25(6), 334-339
78. Swarnagowri, B. N., & Gopinath, S. (2013). Ambiguity in diagnosing esthesioneuroblastoma--a case report. Journal of Evolution of Medical and Dental Sciences, 2(43), 8251-8255.
79. Gopinath, S., Sutaria, N., Bordeaux, Z. A., Parthasarathy, V., Deng, J., Taylor, M. T., ... & Kwatra, S. G. (2023). Reduced serum pyridoxine and 25-hydroxyvitamin D levels in adults with chronic pruritic dermatoses. Archives of Dermatological Research, 315(6), 1771-1776.
80. Permpalung, N., Liang, T., Gopinath, S., Bazemore, K., Mathew, J., Ostrander, D., ... & Shah, P. D. (2023). Invasive fungal infections after respiratory viral infections in lung transplant recipients are associated with lung allograft failure and chronic lung allograft dysfunction within 1 year. The Journal of Heart and Lung Transplantation, 42(7), 953-963.
81. Swarnagowri, B. N., & Gopinath, S. (2013). Pelvic Actinomycosis Mimicking Malignancy: A Case Report. tuberculosis, 14, 15.
82. Al-Otaibi, F., & Aldaihani, H. M. (2018). Influence of Bitumen Addition on Sabkha Soil Shear Strength Characteristics Under Dry and Soaked Conditions. American Journal of Engineering and Applied Sciences, 11(4).
83. Aldaihani, H. M., Al-Otaibi, F. A., & Alrukaibi, D. S. (2020). Investigation of Permeability Behavior of Wet Oil Lake Contaminated Sandy Soil at Al-Ahmadi Field in Kuwait. GEOMATE Journal, 19(73), 141-147
84. Al-otaibi, F. A., & Aldaihani, H. M. (2021). Determination of the collapse potential of sabkha soil and dune sand arid surface soil deposits in Kuwait. Jurnal Teknologi, 83(3), 93-100.
85. Fadul, K. Y., Ali, M., Abdelrahman, A., Ahmed, S. M. I., Fadul, A., Ali, H., & Elgassim, M. (2023). Arachnoid Cyst: A Sudden Deterioration. Cureus, 15(3).
86. Al-Ajmai, F. F., Al-Otaibi, F. A., & Aldaihani, H. M. (2018). Effect of Type of Ground Cover on the Ground Cooling Potential for Buildings in Extreme Desert Climate. Jordan Journal of Civil Engineering, 12(3).
87. Manoharan, A., & Nagar, G. MAXIMIZING LEARNING TRAJECTORIES: AN INVESTIGATION INTO AI-DRIVEN NATURAL LANGUAGE PROCESSING INTEGRATION IN ONLINE EDUCATIONAL PLATFORMS.
88. Ferdinand, J. (2024). Marine Medical Response: Exploring the Training, Role and Scope of Paramedics.
89. Nagar, G. (2018). Leveraging Artificial Intelligence to Automate and Enhance Security Operations: Balancing Efficiency and Human Oversight. Valley International Journal Digital Library, 78-94.
90. Kumar, S., & Nagar, G. (2024, June). Threat Modeling for Cyber Warfare Against Less Cyber-Dependent Adversaries. In European Conference on Cyber Warfare and Security (Vol. 23, No. 1, pp. 257-264).
91. Arefin, S., & Simcox, M. (2024). AI-Driven Solutions for Safeguarding Healthcare Data: Innovations in Cybersecurity. International Business Research, 17(6), 1-74.
92. Nagar, G. (2024). The evolution of ransomware: tactics, techniques, and mitigation strategies. International Journal of Scientific Research and Management (IJSRM), 12(06), 1282-1298.
93. Ferdinand, J. (2023). The Key to Academic Equity: A Detailed Review of EdChat’s Strategies.
94. Manoharan, A. UNDERSTANDING THE THREAT LANDSCAPE: A COMPREHENSIVE ANALYSIS OF CYBER-SECURITY RISKS IN 2024.
95. Nagar, G., & Manoharan, A. (2022). THE RISE OF QUANTUM CRYPTOGRAPHY: SECURING DATA BEYOND CLASSICAL MEANS. 04. 6329-6336. 10.56726. IRJMETS24238.
96. Ferdinand, J. (2023). Marine Medical Response: Exploring the Training, Role and Scope of Paramedics and Paramedicine (ETRSp). Qeios.
97. Nagar, G., & Manoharan, A. (2022). ZERO TRUST ARCHITECTURE: REDEFINING SECURITY PARADIGMS IN THE DIGITAL AGE. International Research Journal of Modernization in Engineering Technology and Science, 4, 2686-2693.
98. Ferdinand, J. (2023). Emergence of Dive Paramedics: Advancing Prehospital Care Beyond DMTs.
99. Nagar, G., & Manoharan, A. (2022). THE RISE OF QUANTUM CRYPTOGRAPHY: SECURING DATA BEYOND CLASSICAL MEANS. 04. 6329-6336. 10.56726. IRJMETS24238.
100. Nagar, G., & Manoharan, A. (2022). Blockchain technology: reinventing trust and security in the digital world. International Research Journal of Modernization in Engineering Technology and Science, 4(5), 6337-6344.
101. Kumar, S., Loo, L., & Kocian, L. (2024, October). Blockchain Applications in Cyber Liability Insurance. In 2nd International Conference on Blockchain, Cybersecurity and Internet of Things, BCYIoT.
102. Kumar, S., Menezes, A., Giri, S., & Kotikela, S. What The Phish! Effects of AI on Phishing Attacks and Defense. In Proceedings of the International Conference on AI Research. Academic Conferences and publishing limited.
103. Kumar, S., & Nagar, G. (2024, June). Threat Modeling for Cyber Warfare Against Less Cyber-Dependent Adversaries. In European Conference on Cyber Warfare and Security (Vol. 23, No. 1, pp. 257-264).
104. Ahuja, A. (2024). A Detailed Study on Security and Compliance in Enterprise Architecture.
105. Shaik, M. (2020). Blue Prism RPA in Finance Automating Accounts Payable Processes. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 8(3), 1-8.
106. Puchakayala, P. R. A., Kumar, S., & Rahaman, S. U. (2023). Explainable AI and Interpretable Machine Learning in Financial Industry Banking. European Journal of Advances in Engineering and Technology, 10(3), 82-92.
107. Ahuja, A., & Gupta, M. (2024). Optimizing Predictive Maintenance with Machine Learning And Iot: A Business Strategy For Reducing Downtime And Operational Costs.
108. Shofolawe-Bakare, O., Toragall, V. B., Hulugalla, K., Mayatt, R., Iammarino, P., Bentley, J. P., ... & Werfel, T. (2024). Glycopolymeric Nanoparticles Block Breast Cancer Growth by Inhibiting Efferocytosis in the Tumor Microenvironment. ACS Applied Nano Materials, 7(24), 28851-28863.
109. Ahuja, A. (2024). An ESG-Compliant Framework for Fraud Detection in Online Payments Using Data Privacy and Machine Learning. International Journal of Computing and Engineering, 6(6), 52-67.
110. Rahaman, S. U., Abdul, M. J., & Patchipulusu, S. (2021). EXPLORING (TECHNOLOGY ACCEPTANCE MODEL) TAM IN MOBILE BANKING: A QUALITATIVE ANALYSIS USING THE TECHNOLOGY ACCEPTANCE MODEL. International Journal of Management (IJM), 12(8).
111. Hulugalla, K., Shofolawe-Bakare, O., Toragall, V. B., Mohammad, S. A., Mayatt, R., Hand, K., ... & Werfel, T. (2024). Glycopolymeric Nanoparticles Enrich Less Immunogenic Protein Coronas, Reduce Mononuclear Phagocyte Clearance, and Improve Tumor Delivery Compared to PEGylated Nanoparticles. ACS nano, 18(44), 30540-30560.
112. Hulugalla, K. R. (2024). Glycopolymeric Nanoparticles Enrich Less Immunogenic Protein Corona and Improve Tumor Delivery Compared to Pegylated Nanoparticles.
113. Hulugalla, K., Ranasinghe, P., Williams, G. R., Nalin de Silva, K. M., & de Silva, R. M. (2023). Role of Nanotechnology in Diagnosing, Safeguarding, and Treating COVID-19. Sultan Qaboos University Journal for Science, 28(1).
114. Krishnan, S., Abbasi, F., Jayapal, P., & Selvarajan, D. (2025). A Rare Case of Endotracheal Metastases in Head and Neck Squamous Cell Carcinoma: A Case Report and Literature Review. Cureus, 17(1).
115. Rao, D. D., Bala Dhandayuthapani, V., Subbalakshmi, C., Singh, M. P., Shukla, P. K., & Pandit, S. V. (2024). An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency. Fusion: Practice & Applications, 15(2).
116. Krishnan, S., Heisick, J., & Johnson, M. (2024). High-Grade Serous Ovarian Carcinoma Presenting With Massive Pleural Effusion in the Absence of Ascites: A Case Report and Review of the Literature. Cureus, 16(12), e76303.
117. Nadeem, S. M., Rao, D. D., Arora, A., Dongre, Y. V., Giri, R. K., & Jaison, B. (2024, June). Design and Optimization of Adaptive Network Coding Algorithms for Wireless Networks. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
118. Krishnan, S., Shah, K., Dhillon, G., & Presberg, K. (2016). 1995: FATAL PURPURA FULMINANS AND FULMINANT PSEUDOMONAL SEPSIS. Critical Care Medicine, 44(12), 574.
119. Krishnan, S. K., Shah, K., & Dhillon, G. (2016). Smoking-Related Interstitial Lung Disease in a Non-Smoker. Chest, 150(4), 490A.
120. Yadav, B., Rao, D. D., Mandiga, Y., Gill, N. S., Gulia, P., & Pareek, P. K. (2024). Systematic Analysis of threats, Machine Learning solutions and Challenges for Securing IoT environment. Journal of Cybersecurity & Information Management, 14(2).
121. Krishnan, S., & Selvarajan, D. (2014). D104 CASE REPORTS: INTERSTITIAL LUNG DISEASE AND PLEURAL DISEASE: Stones Everywhere!. American Journal of Respiratory and Critical Care Medicine, 189, 1.
122. Dubey, P., Dubey, P., Iwendi, C., Biamba, C. N., & Rao, D. D. (2025). Enhanced IoT-Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach with Adaptive Algorithms. IEEE Access.
123. Krishnan, S. K., Parikh, M., & Ganipisetti, V. K. (2013, June). LUNG MASS AND PLEURAL EFFUSION-PLANNING FOR A BIOPSY? LET US TEST THE URINE BEFORE THAT!. In JOURNAL OF GENERAL INTERNAL MEDICINE (Vol. 28, pp. S354-S354). 233 SPRING ST, NEW YORK, NY 10013 USA: SPRINGER.
124. Elhoseny, M., Rao, D. D., Veerasamy, B. D., Alduaiji, N., Shreyas, J., & Shukla, P. K. (2024). Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance. International Journal of Computational Intelligence Systems, 17(1), 1-19.
125. Krishnan, S. K., Khaira, H., & Ganipisetti, V. M. (2014, April). Cannabinoid hyperemesis syndrome-truly an oxymoron!. In JOURNAL OF GENERAL INTERNAL MEDICINE (Vol. 29, pp. S328-S328). 233 SPRING ST, NEW YORK, NY 10013 USA: SPRINGER.
126. Padmakala, S., Al-Farouni, M., Rao, D. D., Saritha, K., & Puneeth, R. P. (2024, August). Dynamic and Energy-Efficient Resource Allocation using Bat Optimization in 5G Cloud Radio Access Networks. In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON) (pp. 1-4). IEEE.