Publications by authors named "S K Sharan"

Introduction: Pediatric hip fractures comprised <1% of all pediatric fractures. These fractures are usually associated with high-energy trauma, such as motor vehicle accidents or fall from height. The Delbet classification of pediatric hip fractures is frequently used to describe these injuries and to relate the fracture type to the development of avascular necrosis.

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Background/aim: Surgery is the cornerstone of treatment for papillary thyroid cancer (PTC), yet some patients refuse surgery, which may impact their survival outcomes. Understanding factors associated with surgery refusal could inform interventions to improve acceptance of recommended care.

Patients And Methods: This retrospective cohort study evaluated the impact of surgery refusal on overall survival (OS) and identified associated demographic and clinical factors using data on patients with PTC from the National Cancer Database from 2004 to 2019.

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Purpose: To evaluate ocular treatment adherence and its impact on clinical outcomes in patients with cystinosis in southwestern Ontario, where the disease incidence is higher due to a founder effect in the Old Order Amish population.

Methods: This was a retrospective case series of patients with ocular cystinosis seen at Victoria Hospital and the Ivey Eye Institute at St Joseph's Health Care in London, Ontario, Canada, from 2008 to 2023. The authors investigated the demographics, ocular manifestations, and visual outcomes and characteristics in pediatric patients with ocular cystinosis.

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Introduction: The topoisomerase 1 (TOP1) inhibitor irinotecan is a standard-of-care agent for relapsed Ewing sarcoma (EWS), but its efficacy is limited by chemical instability, rapid clearance and reversibility, and dose-limiting toxicities, such as diarrhea. Indenoisoquinolines (IIQs) represent a new class of clinical TOP1 inhibitors designed to address these limitations.

Methods: In this study, we evaluated the preclinical efficacy of three IIQs (LMP400, LMP744, and LMP776) in relevant models of EWS.

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Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes. While neural networks have achieved competitive performance, the resulting policies are often over-parameterized black boxes that are difficult to interpret and deploy efficiently. More recent SRL frameworks have shown that high-level domain-specific programming logic can be designed to handle both policy learning and symbolic planning.

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