The United States of America, as Represented by the Secretary, Department of Health and Human Services

Other

Georgia Tech Research Corporation

Emory University

Children's Healthcare of Atlanta, Inc.

Nathan A Hotaling

Kapil Bharti

Nicholas J Schaub

Carl G Simon

Wilbur A Lam

Erika Amy Tyburski

Morgan Byrd

Siobhan O'Connor

Michael L McKinnon

A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired noninvasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.

Using machine learning and/or neural networks to validate stem cells and their derivatives (2-D cells and 3-D tissues) for use in cell therapy and tissue engineered products

A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.

Diagnostic kits and methods configured to rapidly and non-invasively determine physiologic levels of hemoglobin. A diagnostic kit may include a chamber pre-filled with an indicator, the indicator solution including a tetramethylbenzidine (TMB) solution, the indicator being configured to change color; a collection device configured to collect a test sample from a subject. The kit may also include a hemoglobin physiologic level identifier legend, the legend indicating 1) at least one color of the indicator and 2) a physiologic level and/or range of the hemoglobin and/or disease state associated with the color.

Kits and methods for determining physiologic level(s) and/or range(s) of hemoglobin and/or disease state

Growing community of inventors

Washington, DC, United States of America

Nathan A Hotaling