Publications by authors named "S Arif Kazmi"

Background: Angiosarcoma is a rapidly proliferating vascular tumor that originates in endothelial cells of vessels. Rarely, it can be associated with consumptive coagulopathy due to disseminated intravascular coagulation eventually leading to thrombocytopenia and microangiopathic hemolytic anemia. This specific manifestation is termed Kasabach-Merritt syndrome.

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The long non-coding RNA Growth Arrest-Specific 5 (GAS5) is pivotal in modulating key signaling pathways by functioning as a molecular sponge for microRNAs (miRNAs). GAS5 is notably recognized for its antitumor properties, primarily through its ability to sequester oncogenic miRNAs, thereby influencing critical pathways such as p53, Wnt/β-catenin, and PI3K/Akt, all of which are integral to cell proliferation, apoptosis, and metastasis. The disruption of GAS5-miRNA interactions has been implicated in various malignancies, reinforcing its potential as both a biomarker and a therapeutic target.

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Background: Appendicitis is a common surgical emergency in the pediatric population, affecting over 70,000 children per year in the United States alone. While historically practitioners predominately used computed tomography (CT) as the main diagnostic imaging modality, multiple professional societies have released guidelines recommending an ultrasound (US) first strategy when using imaging to confirm suspected appendicitis in pediatric populations. To date, no studies have quantified the change in imaging trends for pediatric appendicitis across the spectrum of healthcare facilities in the United States utilizing the Nationwide Emergency Department Sample (NEDS).

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Background: The impact of smoking on ulcerative colitis (UC) among Asian populations is controversial, and interactions between genetic factors and smoking are not well studied. We evaluated the association between smoking and UC and assessed interactions between smoking and family history.

Methods: Using the National Health Insurance database, we identified 5.

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The emergence of 6G networks promises ultra-high data rates and unprecedented connectivity. However, the effective utilization of the millimeter-wave (mmWave) as a critical enabler of foreseen potential in 6G, poses significant challenges due to its unique propagation characteristics and security concerns. Deep learning (DL)/machine learning (ML) based approaches emerged as potential solutions; however, DL/ML contains centralization and data privacy issues.

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