Elizabeth Anne Sweeney

US Patent:

20220323357, Oct 13, 2022

Filed:

Apr 5, 2022

Appl. No.:

17/714010

Inventors:

- Washington DC, US
Elizabeth Sweeney - Washington DC, US
Preethi Bala Balakrishnan - Washington DC, US
Jacob Medina - Washington DC, US

International Classification:

A61K 9/14
A61K 9/51
A61K 9/28
A61N 5/067
A61P 35/00

Abstract:

Embodiments of the instant disclosure relate to novel methods and compositions for treating tumors resistant to immune checkpoint inhibitors. In certain embodiments, compositions herein can have at least one nanoparticle formed of Prussian blue materials and, optionally, one or more CD137 agonists. In other embodiments, methods of treating tumors herein can include administering an effective amount of at least photothermal therapy agent in combination with at least one CD137 agonist separately or in a combination therapy/combination composition.

US Patent:

20200163899, May 28, 2020

Filed:

Mar 8, 2017

Appl. No.:

16/083416

Inventors:

- Washington DC, US
Raymond W. SZE - Washington DC, US
Anthony D. SANDLER - Washington DC, US
Catherine M. BOLLARD - Washington DC, US
Elizabeth E. SWEENEY - Washington DC, US
Juliana CANO-MEJIA - Washington DC, US
Rachel BURGA - Washington DC, US
Mathieu F. DUMONT - Washington DC, US

Assignee:

Children's National Medical Center - Washington DC

International Classification:

A61K 9/51
A61K 35/17
A61K 9/127
A61K 39/395
A61K 38/19
A61K 38/21
A61K 38/20
A61K 47/69
A61K 41/00
A61K 45/06
A61K 9/50
A61K 33/26
A61P 35/00
A61N 5/06

Abstract:

Combination treatment with Prussian blue nanoparticles and at least one immunotherapeutic treatment. Stable, functionalized Prussian blue nanoparticles, including those with enhanced stability under alkaline conditions, and methods of cancer, neoplasm, and tumor treatment using them, including photothermal treatment and combination immunotherapeutic treatments.

US Patent:

20150045651, Feb 12, 2015

Filed:

Mar 21, 2013

Appl. No.:

14/385509

Inventors:

- Baltimore MD, US
- Rockville MD, US
- Bethesda MD, US
Daniel S. Reich - Washington DC, US
Navid Shiee - North Bethesda MD, US
Russell T. Shinohara - Philadelphia PA, US
Elizabeth M. Sweeney - Baltimore MD, US

International Classification:

A61B 5/00
G01R 33/36
G06T 7/00
G01R 33/385

US Classification:

600410, 382131

Abstract:

The present invention, referred to as Oasis is Automated Statistical Inference for Segmentation (OASIS), is a fully automated and robust statistical method for cross-sectional MS lesion segmentation. Using intensity information from multiple modalities of MRI, a logistic regression model assigns voxel-level probabilities of lesion presence. The OASIS model produces interpretable results in the form of regression coefficients that can be applied to imaging studies quickly and easily. OASIS uses intensity-normalized brain MRI volumes, enabling the model to be robust to changes in scanner and acquisition sequence. OASIS also adjusts for intensity inhomogeneities that preprocessing bias field correction procedures do not remove, using BLUR volumes. This allows for more accurate segmentation of brain areas that are highly distorted by inhomogeneities, such as the cerebellum. One of the most practical properties of OASIS is that the method is fully transparent, easy to implement, and simple to modify for new data sets.