The Johns Hopkins University

Boston Scientific Scimed, Inc.

Peter V Johnston

Chao-Wei Hwang

Gordon Tomaselli

Robert G Weiss

Gary Gerstenblith

Steven P Schulman

Jeffrey A Brinker

Zhiyong Xia

Virginia E Bogdan

Andrew D Bicek

George L Coles

Umang Anand

Soumyadipta Acharya

David A Kass

Jason J Benkoski

Nichaluk Leartprapun

Viachaslau Barodka

Qian Liu

Jackline Wanjala

Majd Alghatrif

B Westbrook Bernier

Trent Langston

Certain embodiments according to the present invention provide sleeve devices suitable for a wide range of therapeutic uses. In accordance with certain embodiments, the therapeutic sleeve device includes a nanofiber fabric assembly, which defines a plurality of pores, and at least one layer of cells embedded in the nanofiber fabric assembly.

Cell impregnated sleeve for paracrine and other factor production

A method for promoting healing of tissue by delivering a bioreactor into a subject is provided. The bioreactor is an enclosed housing with paracrine factor producing cells enclosed within the housing. The housing is impermeable to the paracrine factor producing cells, impermeable to immunological cells outside of the housing, and permeable to paracrine factors produced by the paracrine factor producing cells. The paracrine factors produced by the paracrine factor producing cells are released out of the housing to promote healing of the tissue.

Methods for promoting healing of tissue

The present application relates to systems and methods for non-invasively determining at least one of left ventricular end diastolic pressure (LVEDP) or pulmonary capillary wedge pressure (PCWP) in a subject's heart, comprising: receiving, by a computer, a plurality of signals from a plurality of non-invasive sensors that measure a plurality of physiological effects that are correlated with functioning of said subject's heart, said plurality of physiological effects including at least one signal correlated with left ventricular blood pressure and at least one signal correlated with timing of heartbeat cycles of said subject's heart; training a machine learning model on said computer using said plurality of signals for periods of time in which said plurality of signals were being generated during a heart failure event of said subject's heart; determining said LVEDP or PCWP using said machine learning model at a time subsequent to said training and subsequent to said heart failure event.

Device and method for non-invasive left ventricular end diastolic pressure (LVEDP) measurement

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Baltimore, MD, United States of America

Peter V Johnston