Abstract: Methods of designing a hybrid clinical trial including an external control arm (ECA) study located in at least one site, to support a randomized clinical trial (RCT) study for a treatment of a condition are disclosed. A Mahalanobis distance value is calculated based on a point comprising a set of values corresponding to a first plurality of feature variables corresponding to at least one ECA candidate; and a distribution of points comprising a set of values corresponding to the first plurality of feature variables of each of a plurality of RCT participants that have received the treatment. ECA candidates may be excluded as ECA participants if they are deemed outliers based on the Mahalanobis distance value. Recruitment is dynamically adjusted into at least one ECA participant site database by comparing sets of feature variables in at least one ECA participant site database to corresponding sets of feature variables in the RCT participant database.

Abstract: A deep learning pipeline can be configured to use medical image data to generate predictions of therapeutic responses to a new treatment in members of a cohort of interest of treatment candidates. A plurality of respective deep learning networks may be trained using respective medical image datasets having respective degrees of relevance to the cohort of interest. Learned parameters of one deep learning network may be transferred in succession to another deep learning network after training the one deep learning network with a one of the respective medical image datasets and before training the other deep learning network with another medical image dataset of the respective medical image datasets.

Abstract: Disclosed herein are methods for training and deploying a predictive model for generating a prediction, e.g., patient eligibility for a CAR-T therapy. Datasets, such as open healthcare claims datasets, may be missing data. Missing data may hamper the ability to generate sufficient information needed for training a predictive model. Methods include leveraging comprehensive datasets, such as closed claims datasets, to create training examples for input into a machine learning algorithm. In various embodiments, the comprehensive dataset is modified to simulate the data missingness in the target dataset; then, the modified dataset is paired with the ground truth label derived from the comprehensive dataset to create training examples. In various embodiments, a comprehensive dataset is paired with a target dataset to create training examples. After training a predictive model on such examples, the model can be deployed to make predictions in the target dataset even in light of missing data.

Abstract: A method for identifying a compound which modulates the activity of acyl-coA: diacylglycerol acyltransferase comprises the steps of contacting a stable isotope labeled fatty acid with cells in either presence or absence of the compound, extracting the cells with isopropyl alcohol, and determining the level of a stable isotope labeled triglyceride in the presence or absence of the compound; wherein a change in the level of the stable isotope labeled triglyceride indicates that the compound modulates the DGAT activity.

Abstract: A method for evaluating the effect of a compound on vasomotor response in vivo comprises the steps of administering said compound to a rabbit and measuring the diameter of the vessel lumen of a central ear artery of said rabbit in comparison with the baseline diameter of the vessel lumen of said central ear artery of said rabbit, said baseline diameter being measured prior to the administration of said compound.

Abstract: A method of screening a compound which modulates dipeptidyl peptidase I (DPPI) activities comprises the steps of adding a peptide substrate of DPPI to a reaction mixture which comprises DPPI and a compound, wherein the peptide substrate of DPPI has at least 3 amino acids and binds to a binding site of DPPI in addition to the S1-S2 site; and measuring the molecular weight of the substrate, wherein a change in the molecular weight of the substrate is indicative of the presence of DPPI activity.