Xiao Jing Chen

US Patent:

6720329, Apr 13, 2004

Filed:

Mar 12, 2002

Appl. No.:

10/096804

Inventors:

Gregory R. Ott - Media PA
Xiao Tao Chen - Newark DE
Jingwu Duan - Newark DE
Matthew E. Voss - Lincoln University PA

Assignee:

Bristol-Myers Squibb Pharma - Princeton NJ

International Classification:

A61K 31443

US Classification:

514278, 514342, 514339, 514336, 546 15, 546152, 5462681, 5462764, 5462797, 5462817

Abstract:

The present application describes novel spiro-cyclic -amino acid derivatives of formula I: or pharmaceutically acceptable salt forms thereof, wherein ring B is a 3-13 membered carbocycle or heterocycle, ring C forms a 3-11 membered spiro-carbocycle or spiro-heterocycleon ring B, and the other variables are defined in the present specification, which are useful as as matrix metalloproteinases (MMP), TNF- converting enzyme (TACE), and/or aggrecanase inhibitors.

US Patent:

6962938, Nov 8, 2005

Filed:

Dec 18, 2003

Appl. No.:

10/741326

Inventors:

Gregory R. Ott - Media PA, US
Xiao Tao Chen - Furlong PA, US
Jingwu Duan - Yardley PA, US
Matthew E. Voss - Lincoln University PA, US

Assignee:

Bristol-Myers Squibb Pharma Company - Princeton NJ

International Classification:

A61K031/403
A61K031/38
C07D327/04
C07D207/04
C07D207/18

US Classification:

514409, 514438, 514462, 549 30, 549200, 549300, 549322, 549337, 549341, 548407

Abstract:

The present application describes novel spiro-cyclic β-amino acid derivatives of formula I: or pharmaceutically acceptable salt forms thereof, wherein ring B is a 3-13 membered carbocycle or heterocycle, ring C forms a 3-11 membered spiro-carbocycle or spiro-heterocycleon ring B, and the other variables are defined in the present specification, which are useful as as matrix metalloproteinases (MMP), TNF-α converting enzyme (TACE), and/or aggrecanase inhibitors.

US Patent:

20130196902, Aug 1, 2013

Filed:

Jan 31, 2013

Appl. No.:

13/755637

Inventors:

Dennis Yamashita - Wayne PA, US
Xiao Tao Chen - Furlong PA, US

Assignee:

Trevena, Inc. - King of Prussia PA

International Classification:

C07K 7/06

US Classification:

514 37, 530328, 514 217, 514 164

Abstract:

This application describes compounds acting as, for example, β-arrestin effectors and uses thereof, in, for example, the treatment of chronic and acute cardiovascular diseases.

US Patent:

20130303517, Nov 14, 2013

Filed:

Dec 19, 2011

Appl. No.:

13/996361

Inventors:

Christopher W. Boyce - Flemington NJ, US
Sylvia Joanna Degrado - Scotch Plains NJ, US
Xiao Chen - Edison NJ, US
Jun Qin - Edison NJ, US
Younong Yu - East Brunswick NJ, US
Kevin D. McCormick - Basking Ridge NJ, US
Anandan Palani - Bridgewater NJ, US
Dong Xiao - Warren NJ, US
Robert George Aslanian - Rockaway NJ, US
Jie Wu - Scotch Plains NJ, US
Ashwin Umesh Rao - Morganville NJ, US
Phieng Siliphaivanh - Newton MA, US
Joey L. Methot - Westwood MA, US
Hongjun Zhang - Newton MA, US
Elizabeth Helen Kelley - Lynnfield MA, US
William Colby Brown - Cleveland Heights OH, US
Qin Jiang - Latham NY, US
Jolicia Polivina Gauuan - Schenectady NY, US
Andrew J. Leyhane - Latham NY, US
Purakkattle Johny Biju - Piscataway NJ, US
Pawan K. Dhondi - Elizabeth NJ, US
Li Dong - Lawrenceville NJ, US
Salem Fevrier - Cranford NJ, US
Xianhai Huang - Warren NJ, US
Henry M. Vaccaro - South Plainfield NJ, US

International Classification:

C07D 241/42
C07D 401/12
C07D 403/06
C07D 471/04
A61K 31/4985
C07D 471/10
C07D 405/12
C07D 401/14
A61K 31/506
A61K 31/55
C07D 417/12
C07D 498/04
C07D 491/107
A61K 45/06
A61K 31/498

US Classification:

51421018, 544353, 514249, 544350, 544230, 51421021, 544295, 540523, 51421207

Abstract:

The invention provides certain quinoxalines and aza-quinoxalines of the Formula (I), and their pharmaceutically acceptable salts, wherein J, J, R, R, R, R, R, R, R, R, X, Y, b, n, and q are as defined herein. The invention also provides pharmaceutical compositions comprising such compounds, and methods of using the compounds for treating diseases or conditions associated with uncontrolled or inappropriate stimulation of CRTHfunction.

US Patent:

20210272335, Sep 2, 2021

Filed:

Mar 2, 2020

Appl. No.:

16/805903

Inventors:

- Erlangen, DE
Xiao Chen - Princeton NJ, US
Mariappan S. Nadar - Plainsboro NJ, US
Boris Mailhe - Plainsboro NJ, US
Simon Arberet - Princeton NJ, US

International Classification:

G06T 11/00
G06T 7/00
G06T 15/08

Abstract:

For k-space trajectory infidelity correction, a model is machine trained to correct k-space measurements in k-space. K-space trajectory infidelity correction uses deep learning. Trajectory infidelity is corrected from a k-space point of view. Since the image artifacts arise from k-space acquisition distortion, a machine learning model is trained to correct in k-space, either changing values of k-space measurements or estimating the trajectory shifts in k-space.

US Patent:

20210150783, May 20, 2021

Filed:

Nov 19, 2019

Appl. No.:

16/688170

Inventors:

- Erlangen, DE
Boris Mailhe - Plainsboro NJ, US
Xiao Chen - Princeton NJ, US
Mariappan S. Nadar - Plainsboro NJ, US

International Classification:

G06T 11/00
G06N 3/08
G06N 3/04
G16H 30/40
G06T 7/00

Abstract:

For magnetic resonance imaging reconstruction, using a cost function independent of the ground truth and many samples of k-space measurements, machine learning is used to train a model with unsupervised learning. Due to use of the cost function with the many samples in training, ground truth is not needed. The training results in weights or values for learnable variables, which weights or values are fixed for later application. The machine-learned model is applied to k-space measurements from different patients to output magnetic resonance reconstructions for the different patients. The weights and/or values used are the same for different patients.

US Patent:

20200065969, Feb 27, 2020

Filed:

Jul 9, 2019

Appl. No.:

16/506123

Inventors:

- Erlangen, DE
Xiao Chen - Princeton NJ, US
Mariappan S. Nadar - Plainsboro NJ, US
Boris Mailhe - Plainsboro NJ, US

International Classification:

G06T 7/11
G06T 7/00
G06N 3/08
G16H 50/50
G06N 3/04
G06N 20/00

Abstract:

Various approaches provide improved segmentation from raw data. Training samples are generated by medical imaging simulation from digital phantoms. These training samples provide raw measurements, which are used to learn to segment. The segmentation task is the focus, so image reconstruction loss is not used. Instead, an attention network is used to focus the training and trained network on segmentation. Recurrent segmentation from the raw measurements is used to refine the segmented output. These approaches may be used alone or in combination, providing for segmentation from raw measurements with less influence of noise or artifacts resulting from a focus on reconstruction.

US Patent:

20200049785, Feb 13, 2020

Filed:

Oct 17, 2018

Appl. No.:

16/162559

Inventors:

- Erlangen, DE
Xiao Chen - Princeton NJ, US
Silvia Bettina Arroyo Camejo - Nuremberg, DE
Benjamin L. Odry - West New York NJ, US
Mariappan S. Nadar - Plainsboro NJ, US

International Classification:

G01R 33/565
G06T 11/00
G06N 3/08
G06F 15/18
G01R 33/48

Abstract:

For determination of motion artifact in MR imaging, motion of the patient in three dimensions is used with a measurement k-space line order based on one or more actual imaging sequences to generate training data. The MR scan of the ground truth three-dimensional (3D) representation subjected to 3D motion is simulated using the realistic line order. The difference between the resulting reconstructed 3D representation and the ground truth 3D representation is used in machine-based deep learning to train a network to predict motion artifact or level given an input 3D representation from a scan of a patient. The architecture of the network may be defined to deal with anisotropic data from the MR scan.