Browsing by Author "Patel, Neil"

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A Comprehensive Review on Neuroendocrine Neoplasms
(MDPI, 2023-08-05) Sultana, Qamar; Kar, Jill; Verma, Amogh; Sanghvi, Shreya; Kaka, Nirja; Patel, Neil; Sethi, Yashendra; Chopra, Hitesh; Kamal, Mohammad Amjad; Greig, Nigel H.
Neuroendocrine neoplasms (NENs) are a group of heterogeneous tumors with neuroen- docrine differentiation that can arise from any organ. They account for 2% of all malignancies in the United States. A significant proportion of NEN patients experience endocrine imbalances consequent to increased amine or peptide hormone secretion, impacting their quality of life and prognosis. Over the last decade, pathologic categorization, diagnostic techniques and therapeutic choices for NENs—both well-differentiated neuroendocrine tumors (NETs) and poorly differen- tiated neuroendocrine carcinomas (NECs)—have appreciably evolved. Diagnosis of NEN mostly follows a suspicion from clinical features or incidental imaging findings. Hormonal or non-hormonal biomarkers (like serum serotonin, urine 5-HIAA, gastrin and VIP) and histology of a suspected NEN is, therefore, critical for both confirmation of the diagnosis and classification as an NET or NEC. Therapy for NENs has progressed recently based on a better molecular understanding, including the involvement of mTOR, VEGF and peptide receptor radionuclide therapy (PRRT), which add to the growing evidence supporting the possibility of treatment beyond complete resection. As the incidence of NENs is on the rise in the United States and several other countries, physicians are more likely to see these cases, and their better understanding may support earlier diagnosis and tailoring treatment to the patient. We have compiled clinically significant evidence for NENs, including rele- vant changes to clinical practice that have greatly updated our diagnostic and therapeutic approach for NEN patients.
Acute Hepatitis of Unknown Origin in Pediatric Age Group
(MDPI Publications, 2022-12-20) Patel, Neil; Sethi, Yashendra; Kaka, Nirja; Kaiwan, Oroshay; Gupta, Ishita; Shaheen, Rahma Sameh; Sapoor, Shady; Chopra, Hitesh; Popoviciu, Mihaela Simona; Emran, Talha Bin; Cavalu, Simona
Acute hepatitis has always been a public health concern, but the recent clustering of cases in various parts of the world has drawn some special attention. The sudden rise in cases has mainly been among the pediatric population of around 35 countries around the world, including developed countries such as the United States, the United Kingdom, and European countries. The outbreaks have had a devastating impact, with around 10% of the affected patients developing liver failure. The clinical presentation of patients resembles any other case of acute hepatitis, with the major symptoms being: jaundice (68.8%), vomiting (57.6%), and gastrointestinal symptoms such as abdominal pain (36.1%) and nausea (25.7%). Interestingly, the cases have tested negative for hepatotropic viruses Hep A, B, C, and E, thus giving rise to the terms Hepatitis of Unknown Origin or non-HepA–E hepatitis. Many causes have been attributed to the disease, with major evidence seen for adenovirus and SARS-CoV-2. International agencies have stressed on establishing diagnostic and management protocols to limit these outbreaks. As the understanding has evolved over time, diagnostic and management faculties have found more shape. The current review was designed to comprehensively compile all existing data and whittle it down to evidence-based conclusions to help clinicians.
Artificial Intelligence in Pediatric Cardiology: a Scoping Review
(Scopus, 22-11-29) Sethi, Yashendra; Patel, Neil; Kaka, Nirja; Desai, Ami; Kaiwan, Oroshay; Sheth, Mili; Sharma, Rupal; Huang, Helen; Chopra, Hitesh; Khandaker, Mayeen Uddin; Lashin, Maha M A; Hamd, Zuhal Y; Emran, Talha Bin
The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computed tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.
Cardiac Amyloidosis: Evolving Pathogenesis, Multimodal Diagnostics, and Principles of Treatment
(Creative Commons Attribution 4.0 International License., 2023-09-03) Medarametla, Gnana Deepthi; Kahlon, Ripudaman Singh; Mahitha, Lampimukhi; Shariff, Sanobar; Vakkalagadda, Naga Praneeth; Chopra, Hitesh; Kamal, Mohammad Amjad; Patel, Neil; Sethi, Yashendra; Kaka, Nirja
Amyloidosis is a protein deposition disorder in which insoluble fibril structures accumulate in the bodily tissues damaging the organ function. Cardiac amyloidosis is a severe but under-reported medical condition characterized by the accumulation of amyloid in the extracellular area of the myocardium, which results in thickening and stiffening of ventricular walls. Cardiac amyloidosis has recently gained much attention with its slowly surging incidence. With this study, we seek to comprehensively compile the pathophysiology and clinical picture of cardiac amyloidosis subtypes, extending a clinically oriented, up-to-date clinical approach to diagnosis and therapy. Cardiac amyloidosis can be caused by rare genetic mutations which may be inherited or acquired. The growing incidence can be attributed to advancements in imaging methods and other diagnostic modalities. Most occurrences of cardiac amyloidosis result from two forms of precursor protein: transthyretin [TTR] amyloid and immunoglobulin-derived light-chain amyloid. Prompt identification of cardiac amyloidosis can facilitate the implementation of evolving therapeutic interventions to enhance the outcomes. The modalities for the management of CA have evolved significantly in the last ten years. Apart from therapies for modifying disease and heart failure, a myriad of novel therapeutic approaches that target specific aspects of the disease, including gene therapies, are being researched. These aim at impeding its progression and improving clinical outcomes.
Environmental Correlates of Infectious Diseases in South-East Asia: A Perspective on the Missed Link
(Elsevier, 2023-03-30) Sethi, Yashendra; Kaka, Nirja; Patel, Neil; Roy, Debabrata; Chopra, Hitesh; Emran, Talha Bin
Highlights • The environmental correlates of infectious diseases have been widely ignored. • Changing climatic circumstances affect species' range and density. • The likelihood of humans coming in contact with infected vectors is directly influenced by the environment. • The diseases endemic to a particular region are prone to change with changing environment. • “One health” needs more attention, especially in regions more prone to environmental variations.