Publications by authors named "A C Amini"

Background: This study addresses the lack of research on Medicaid expansion's impact on melanoma staging, treatment utilization, and outcomes by evaluating its effects under the Affordable Care Act (ACA), particularly focusing on staging at diagnosis, treatment use, and 3-year mortality outcomes. The objective is to determine whether Medicaid expansion led to earlier melanoma diagnosis and improved survival rates among non-elderly adults (ages 40-64) by analyzing data from the National Cancer Database (NCDB).

Methods: A total of 12,667 patients, aged 40-64, diagnosed with melanoma from 2010 to 2020 were identified using the NCDB.

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Machine learning sequence-function models for proteins could enable significant advances in protein engineering, especially when paired with state-of-the-art methods to select new sequences for property optimization and/or model improvement. Such methods (Bayesian optimization and active learning) require calibrated estimations of model uncertainty. While studies have benchmarked a variety of deep learning uncertainty quantification (UQ) methods on standard and molecular machine-learning datasets, it is not clear if these results extend to protein datasets.

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Tunnel field-effect transistors (TFETs) are gaining interest for low-power applications, but challenges like poor drive current, delayed saturation, and ambipolarity can hinder their performance. This work proposes a dopingless heterojunction TFET (DL-HTDET) utilizing advanced materials, all based on phosphorus, to address these issues. Our approach involves a comprehensive and accurate analysis of the DL-HTDET's behavior.

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Purpose: There are currently limited data regarding patient perceptions and fears related to radiation therapy (RT). This study sought to identify and quantify patient concerns regarding RT and to determine the potential value of assessing these expectations prior to initial consultation.

Methods And Materials: Patients with no prior history of RT were invited to complete an investigator-developed anonymous electronic survey prior to consultation.

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