Yu Huang

US Patent:

20220367001, Nov 17, 2022

Filed:

May 25, 2022

Appl. No.:

17/824098

Inventors:

- Tarrytown NY, US
Yu Huang - Ossining NY, US

International Classification:

G16B 5/20
G06F 17/11
G01N 33/68

Abstract:

Methods of predicting an in vivo serum concentration of an antibody with a post-translational modification of interest after administration of the antibody are provided, as are methods for predicting a subject's exposure to post-translational variants of the antibody. The methods include predicting a percentage of the antibody with the post-translational modification of interest using an in vivo rate constant determined for the post-translational modification, and multiplying the predicted percentage of the antibody with the post-translational modification of interest by the in vivo concentration of the antibody to determine the concentration of the antibody with the post-translational modification of interest.

US Patent:

20230089727, Mar 23, 2023

Filed:

Sep 16, 2022

Appl. No.:

17/946757

Inventors:

- Tarrytown NY, US
Yu Huang - Ossining NY, US
Xiaojing Zheng - Gaithersburg MD, US
Ning Li - New Canaan CT, US

International Classification:

G01N 33/68
G01N 30/72
G01N 30/88
G01N 30/86

Abstract:

The present invention generally pertains to methods of characterizing at least one protein of interest in a biological sample. In particular, the present invention pertains to the use of automated iterative tandem mass spectrometry (AIMS) to identify, quantify and characterize at least one protein of interest and/or biomarker from a biological sample such as plasma.

US Patent:

20210340267, Nov 4, 2021

Filed:

Mar 5, 2021

Appl. No.:

17/193586

Inventors:

- TARRYTOWN NY, US
Drew Dudgeon - Union NJ, US
Yu Huang - Ossining NY, US
Rosalynn Molden - Fox Island WA, US
William Olson - Yorktown Heights NY, US
Matthew Sleeman - Yorktown Heights NY, US
Dimitris Skokos - New York NY, US
Bei Wang - Hastings-on-Hudson NY, US

International Classification:

C07K 16/28
A61P 35/00
A61K 45/06
A61K 39/395

Abstract:

Provided herein are antibodies, and antigen-binding fragments thereof that specifically bind glucocorticoid-induced tumor necrosis factor receptor (GITR), compositions comprising the antibodies or antigen-binding fragments thereof, and methods of using the same, including, e.g., methods of treatment using the same.

US Patent:

20190237157, Aug 1, 2019

Filed:

Jan 31, 2019

Appl. No.:

16/264044

Inventors:

- Tarrytown NY, US
Yu Huang - Ossining NY, US

International Classification:

G16B 5/20
G06F 17/11

Abstract:

Methods of predicting an in vivo serum concentration of an antibody with a post-translational modification of interest after administration of the antibody are provided, as are methods for predicting a subject's exposure to post-translational variants of the antibody. The methods include predicting a percentage of the antibody with the post-translational modification of interest using an in vivo rate constant determined for the post-translational modification, and multiplying the predicted percentage of the antibody with the post-translational modification of interest by the in vivo concentration of the antibody to determine the concentration of the antibody with the post-translational modification of interest.

US Patent:

20160228702, Aug 11, 2016

Filed:

Sep 30, 2014

Appl. No.:

15/025760

Inventors:

- New York NY, US
Yu Huang - New York NY, US
Lucas C. Parra - New York NY, US

International Classification:

A61N 1/36
G06F 19/00
A61N 1/04

Abstract:

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for developing transcranial electrical stimulation protocols are disclosed. In one aspect, a method includes obtaining an image of target tissue, assigning to the image tissue electrical conductance values, arranging a plurality of stimulation electrodes around the target tissue, obtaining, from the locations of electrodes and tissue electrical conductances, a forward model of the response of the tissue to applied currents for each electrode and computing an expected tissue response for an arbitrarily sized and shaped electrode defining an electrode area by combining the forward model for each electrode of an array of electrodes covering the defined electrode area.