Publications by authors named "B J Aronow"
Requests for medical and anaesthetic care that is 'vegan' or free of animal-derived components are becoming increasingly common in the cultural landscape. Such requests are often rooted in religious beliefs and practices. There are currently no requirements for the disclosure of animal-derived components in medical items.
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- The study aimed to analyze the physical therapy (PT) treatment provided to children with cerebral palsy after multi-level surgery, focusing on how it varies based on their ability to walk and the extent of surgery.
- Data were collected from outpatient PT records for 17 children, revealing that they had a high number of therapy visits and that the intensity and types of activities differed significantly between ambulatory and non-ambulatory children.
- The findings suggest that specific guidelines for PT treatments should be established based on a child's ambulatory status, as their PT needs varied greatly in the year following surgery.
Deep learning-based multimodal spatial transcriptomics analysis for cancer.
Adv Cancer Res
September 2024
The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications.
View Article and Find Full Text PDFKey Points: cells exist long term during kidney homeostasis and become activated upon injury, contributing to regeneration. cells and their progeny emerge during tubulogenesis and contribute to proximal tubule and inner medullary collecting duct development. cells expand and differentiate into a mature nephron lineage in response to AKI to repair the proximal tubule.
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- In the study of histopathology, researchers are exploring the classification of whole slide images (WSIs) to assess disease progression in gliomas, which are brain tumors divided into categories like astrocytomas, oligodendrogliomas, and glioblastomas.
- The focus is particularly on the IDH1 mutation, as it is associated with a better prognosis for patients with low-grade gliomas, making it a critical factor for glioma classification.
- The research employs ensemble learning techniques combining imaging data from WSIs and clinical information, achieving promising results with the best model yielding an AUC of 0.852, demonstrating that integrating diverse data sources enhances prediction accuracy for IDH1 mutations.