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Cosmetic skin lightening: Contextualizing biomedical and ethical issues
Clinics in Dermatology · 13 April 2024
James Bradley M. Parente, BA, Genevieve S. Silva, BS, Jeromy W. Gotschall, BA, Alana L. Ferreira, BA, Jane M. Grant-Kels MD
The skin-lightening (SL) industry has a global reach and is projected to continue to grow over the coming decade. Although SL treatments may be safely prescribed for the treatment of some dermatologic conditions, many over-the-counter SL products contain ingredients that can cause harm to the skin and other organ systems. Given a lack of transparent information to patients and the historical colorist foundation that contextualizes a component of the cosmetic SL industry, dermatologists need to navigate biomedical and ethical concerns when explaining SL products to patients. This commentary briefly outlines the medical ethical issues surrounding this topic and describes avenues by which dermatologists may provide informed patient care that best supports beneficence, justice, autonomy, and nonmaleficence.
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Radiological Society of North America · Jun 21, 2023
Abhinav Suri , Sisi Tang, Daniel Kargilis, Elena Taratuta, Bruce J. Kneeland, Grace Choi, Alisha Agarwal, Nancy Anabaraonye, Winnie Xu, James B. Parente, Ashley Terry, Anita Kalluri, Katie Song, Chamith S. Rajapakse
Abstract: The proposed neural network automatically and accurately measured the Cobb angle on radiographs with various imaging conditions (surgical hardware obstructing the spine, inconsistent region imaged) in patients with scoliosis.
Scoliosis is a disease estimated to affect more than 8% of adults in the United States. It is diagnosed with use of radiography by means of manual measurement of the angle between maximally tilted vertebrae on a radiograph (ie, the Cobb angle). However, these measurements are time-consuming, limiting their use in scoliosis surgical planning and postoperative monitoring. In this retrospective study, a pipeline (using the SpineTK architecture) was developed that was trained, validated, and tested on 1310 anterior-posterior images obtained with a low-dose stereoradiographic scanning system and radiographs obtained in patients with suspected scoliosis to automatically measure Cobb angles. The images were obtained at six centers (2005–2020). The algorithm measured Cobb angles on hold-out internal (n = 460) and external (n = 161) test sets with less than 2° error (intraclass correlation coefficient, 0.96) compared with ground truth measurements by two experienced radiologists. Measurements, produced in less than 0.5 second, did not differ significantly (P = .05 cutoff) from ground truth measurements, regardless of the presence or absence of surgical hardware (P = .80), age (P = .58), sex (P = .83), body mass index (P = .63), scoliosis severity (P = .44), or image type (low-dose stereoradiographic image vs radiograph; P = .51) in the patient. These findings suggest that the algorithm is highly robust across different clinical characteristics. Given its automated, rapid, and accurate measurements, this network may be used for monitoring scoliosis progression in patients.
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International Journal of Molecular Sciences · Oct 27, 2022
Michelle Dai, Winnie Xu, Helene Chesnais, Nancy Anabaraonye, James Bradley Parente, Shampa Chatterjee, Chamith S. Rajapakse
Abstract: A major pathophysiological cause of cardiovascular disease is vascular plaque calcification. Fluorine 18–Sodium Fluoride (18F-NaF) PET/CT can be used as a sensitive imaging modality for detection of vascular calcification. The aim of this study was to find a non-invasive, cost-efficient, and readily available metric for predicting vascular calcification severity. This retrospective study was performed on 36 participants who underwent 18F-NaF fused PET/CT scans. The mean standard uptake values (SUVs) were calculated from manually sectioned axial sections over the aortic arch and thoracic aorta. Correlation analyses were performed between SUVs and calculated atherogenic indices (AIs). Castelli’s Risk Index I (r = 0.63, p < 0.0001), Castelli’s Risk Index II (r = 0.64, p < 0.0001), Atherogenic Coefficient (r = 0.63, p < 0.0001), Atherogenic Index of Plasma (r = 0.51, p = 0.00152), and standalone high-density lipoprotein (HDL) cholesterol (r = −0.53, p = 0.000786) were associated with aortic calcification. AIs show strong association with aortic arch and thoracic aorta calcifications. AIs are better predictors of vascular calcification compared to standalone lipid metrics, with the exception of HDL cholesterol. Clinical application of AIs provides a holistic metric beneficial for enhancing screening and treatment protocols.
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Artificial Intelligence in Radiology Across Four Modalities
Penn's Center for Undergraduate & Research Fellowships · Sept 5, 2020
James Bradley Parente, Alisha Agarwal, Anjali Gupta, Abigail Manion, Lauren Rodio, Samantha Turner, Chamith S. Rajapakse
A culmination of multiple projects that my intern team and I worked on in Dr. Chamith Rajapakse's Radiology & Orthopedic Surgery Laboratory.
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Hospitals Vanishing from Rural America
Wharton Global Research & Consulting Group · Feb 24, 2020
A discussion of the decline of rural hospitals in the United States.
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Suri A, Ng G, Jones B, Anabaraonye N, Beyrer P, Choi G, Bastin N, Leichner T, Kim S, Jankelovits A, Chesnais H, Chen A, Agarwal A, Parente JB, Golden J, Turner S, Rizaldi A, Tu E, Rodio L, Missaghian N, Hasan E, Rajapakse CS. Automated Workflow for Vertebral Deformity Measurements on CT and MRI Scans Using Artificial Intelligence. ASBMR, 2020, Seattle, WA.
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Tang S, Anabaraonye N, Terry A, Agarwal A, Opperman A, Kalluri A, Parente J, Shan X, Rajapakse CS. Detection of Prevalent and Prediction of Incident Osteoporotic Fractures using Deep Learning of DXA Images. Scientific Oral/Poster Presentation Accepted for Orthopaedic Research Society Annual Meeting, 2021. Long Beach, CA.
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Tang S, Anabaraonye N, Terry A, Agarwal A, Opperman A, Kalluri A, Parente J, Shan X, Rajapakse CS. Detection of Prevalent and Prediction of Incident Osteoporotic Fractures using Deep Learning of DXA Images. Scientific Oral/Poster Presentation Accepted for International Society for Clinical Densitometry 27th Annual Meeting, March 3-6th, 2021. Held Virtually.
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Suri A, Tang S, Choi G, Anabaraonye N, Agarwal A, Xu W, Parente J, Terry A, Kalluri A, Song K, Taratuta E, Rajapakse CS. Rapid, Hardware-resilient Automated Cobb Angle Measurement From X-rays using Artificial Intelligence. Abstract submitted to Orthopaedic Research Society 2022 Annual Meeting, Tampa, Florida.
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Suri A, Tang S, Choi G, Anabaraonye N, Agarwal A, Xu W, Parente J, Terry A, Kalluri A, Song K, Taratuta E, Rajapakse CS. Automation of Scoliosis Detection and Cobb Angle Measurement from Radiographs Using Artificial Intelligence. Abstract submitted to American Roentgen Ray Society 2022 Annual Meeting, New Orleans, Louisiana.
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Suri A, Tang S, Choi G, Anabaraonye N, Agarwal A, Xu W, Parente J, Terry A, Kalluri A, Song K, Taratuta E, Rajapakse CS. Utilization of Landmark Detection Networks for Musculoskeletal Measurement Tasks. Educational Exhibit submitted to American Roentgen Ray Society 2022 Annual Meeting, New Orleans, Louisiana.
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Suri A, Tang S, Taratuta S, Kneeland BJ, Choi G, Agarwal A, Anabaraonye N, Xu Winnie, Parente JB, Terry A, Kalluri A, Song K, and Rajapakse CS. Conquering the Cobb Angle: Automated Hardware-Invariant Scoliosis Diagnosis in Radiographs using Deep Learning. American Society of Bone and Mineral Research Annual Meeting. 2022, Austin, TX.
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Xu W, Kargilis D, Kim SM, Gelston D, Reddy S, Gupta D, Turner S, Lyu T, Gan E, Song K, Rodio L, Manion A, Ma C, Missaghian N, Cai J, Jayaratne R, Parente J, Feng A, Balasubramanian V, Guha S, Nwogwugwu U, Wragan J, Gupta A, Chen Y, Terry A, Le A, Wang S, Rajapakse CS. Deep Learning Automatic 3D Segmentation of Mandible from Cone Beam CT for Preoperative Planning American Association of Oral and Maxillofacial Surgeons (AAOMS) Annual Meeting. 2022, New Orleans, LA.
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Kargilis D, Xu W, Kim SM, Gelston D, Reddy S, Gupta D, Turner S, Lyu T, Gan E, Song K, Rodio L, Manion A, Ma C, Missaghian N, Cai J, Jayaratne R, Parente J, Feng A, Balasubramanian V, Guha S, Nwogwugwu U, Wragan J, Gupta A, Chen Y, Terry A, Le A, Wang S, Rajapakse CS. Thin Bone Segmentation of the Mandible from Cone Beam CT for Maxillofacial Surgical Planning. Conference on Machine Intelligence in Medical Imaging (CMIMI). 2022, Baltimore, MD.
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The Continuing Discourse on How to Discuss & Solve U.S. Nursing Burnout: A Historical Perspective
This is a my capstone independent research project, which I was required to complete for my major. I gathered primary & secondary sources on the topic, as well as interviewed Dr. Julie Sochalski, PhD, RN, FAAN - former Chief Nurse of the United States of America under the Obama Administration - to write a 20-page historical paper on the general themes and time periods of burnout in the U.S. nursing profession from 1974 to 2023.