Abstract: A medical device system including a first container and a second container, the first container including a first container outer body defining a first interior space connected to a first opening of the first container; and a first container-side valve configured to be activated, upon connection of the second container to the first container, from a normally closed position into the first interior space to an activated position, and the second container including a second container outer body defining a second container interior space and a stem fluidly connected to the second container interior space, wherein, upon connection of the second container to the first container, the first container-side valve is configured to be brought into contact with the stem and the stem is configured to activate the first container-side valve from the normally closed position to the activated position.

Abstract: A method for selecting a final model for detecting cells of interest in image datasets includes dividing a curated image dataset into a training set, a validation set, and a testing set where each image in the curated image dataset has been labeled as positive or negative for a cell of interest. The method trains each model of an ensemble of neural networks using the training and validation sets. Next, each model of the ensemble is tested using the testing set and the predictions of the ensemble are combined. The combined prediction is compared to the label and the method determines whether the combined prediction satisfies a pre-determined level of detection (LOD). If so, the method outputs the ensemble as a final ensemble. If not, the method modifies a hyperparameter of at least one of the models of the ensemble until the combined prediction satisfies the pre-determined LOD.

Abstract: Provided herein are methods of obtaining induced pluripotent stem cells from cells of a hematopoietic lineage.

Abstract: A method for selecting a final model for detecting cells of interest in image datasets includes dividing a curated image dataset into a training set, a validation set, and a testing set where each image in the curated image dataset has been labeled as positive or negative for a cell of interest. The method trains each model of an ensemble of neural networks using the training and validation sets. Next, each model of the ensemble is tested using the testing set and the predictions of the ensemble are combined. The combined prediction is compared to the label and the method determines whether the combined prediction satisfies a pre-determined level of detection (LOD). If so, the method outputs the ensemble as a final ensemble. If not, the method modifies a hyperparameter of at least one of the models of the ensemble until the combined prediction satisfies the pre-determined LOD.

Abstract: Provided herein are genetically engineered mammalian (e.g., human) cells that express one or more transgenes at a sustained expression level. Also provided are methods of making and using the cells.

Abstract: In some embodiments, a delivery device includes a device actuator and a cannula portion through which a therapeutic substance is ejected. The cannula portion includes an outer shaft, a needle that is configured to move through the outer shaft, and a plunger that is configured to move through the needle, forming a positive displacement arrangement.

Abstract: In some embodiments, a delivery device includes a device actuator and a cannula portion through which a therapeutic substance is ejected. The cannula portion includes an outer shaft, a needle that is configured to move through the outer shaft, and a plunger that is configured to move through the needle, forming a positive displacement arrangement.

Abstract: Provided herein are methods of obtaining induced pluripotent stem cells from cells of a hematopoietic lineage.

Abstract: Provided herein are enriched populations of ventricular compact cardiomyocytes and enriched populations of mature ventricular or atrial cardiomyocytes, as well as methods of generating the enriched cell populations and methods of using the enriched cell populations in regenerative cardiac cell therapies.

Abstract: A method for selecting a final model for detecting cells of interest in image datasets includes dividing a curated image dataset into a training set, a validation set, and a testing set where each image in the curated image dataset has been labeled as positive or negative for a cell of interest. The method trains each model of an ensemble of neural networks using the training and validation sets. Next, each model of the ensemble is tested using the testing set and the predictions of the ensemble are combined. The combined prediction is compared to the label and the method determines whether the combined prediction satisfies a pre-determined level of detection (LOD). If so, the method outputs the ensemble as a final ensemble. If not, the method modifies a hyperparameter of at least one of the models of the ensemble until the combined prediction satisfies the pre-determined LOD.

Abstract: In some embodiments, a delivery device includes a device actuator and a cannula portion through which a therapeutic substance is ejected. The cannula portion includes an outer shaft, a needle that is configured to move through the outer shaft, and a plunger that is configured to move through the needle, forming a positive displacement arrangement.