Abstract: The present invention provides methods for diagnosing lung cancer in a subject comprising (a) generating circulating tumor cell (CTC) data from a blood sample obtained from the subject based on a direct analysis comprising immunofluorescent staining and morphological characteristics of nucleated cells in the sample, wherein CTCs are identified in context of surrounding nucleated cells based on a combination of the immunofluorescent staining and morphological characteristics; (b) obtaining clinical data for the subject; (c) combining the CTC data with the clinical data to diagnose lung cancer in the subject.

Abstract: The invention provides seminal computational approaches utilizing data from non-rare cells to detect rare cells, such as circulating tumor cells (CTCs). The invention is applicable at two distinct stages of CTC detection; the first being to make decisions about data collection parameters and the second being to make decisions during data reduction and analysis. Additionally, the invention utilizes both one and multi-dimensional parameterized data in a decision making process.

Abstract: Methods, systems, and apparatus for a method that predicts an individual survival survival time of a patient. The method includes obtaining clinical data associated with health factors of the patient. The method includes obtaining liquid biopsy data associated with one or more attributes of diseased cells within the patient. The method includes predicting or determining a survival time of the patient using a deep learning model based on the clinical data and the liquid biopsy data. The method includes providing or outputting the survival time.

Abstract: The disclosure provides methods for detecting circular endothelial cells (CECs) in a non-enriched blood sample. The present disclosure is based, in part, on the unexpected discovery that CECs can be detected in non-enriched blood samples. The present disclosure is further based, in part, on the discovery that CECs can be detected in non-enriched blood samples by combining the detection of one or more immunofluorescent markers in the nucleated cells of a non-enriched blood sample with an assessment of the morphology of the nucleated cells. The present disclosure is further based, in part, on the discovery that CECs can be detected in non-enriched blood samples by comparing the immunofluorescent marker staining and morphological characteristics of CECs with the immunofluorescent marker staining and morphological characteristics of WBCs. The methods disclosed herein serve to classify human subject in myocardial infarction (MI) patients or healthy controls.

Abstract: The present invention provides methods for diagnosing lung cancer in a subject comprising (a) generating circulating tumor cell (CTC) data from a blood sample obtained from the subject based on a direct analysis comprising immunofluorescent staining and morphological characteristics of nucleated cells in the sample, wherein CTCs are identified in context of surrounding nucleated cells based on a combination of the immunofluorescent staining and morphological characteristics; (b) obtaining clinical data for the subject; (c) combining the CTC data with the clinical data to diagnose lung cancer in the subject.

Abstract: Methods, systems, and apparatus for a method that predicts an individual survival survival time of a patient. The method includes obtaining clinical data associated with health factors of the patient. The method includes obtaining liquid biopsy data associated with one or more attributes of diseased cells within the patient. The method includes predicting or determining a survival time of the patient using a deep learning model based on the clinical data and the liquid biopsy data. The method includes providing or outputting the survival time.

Abstract: The invention provides seminal computational approaches utilizing data from non-rare cells to detect rare cells, such as circulating tumor cells (CTCs). The invention is applicable at two distinct stages of CTC detection; the first being to make decisions about data collection parameters and the second being to make decisions during data reduction and analysis. Additionally, the invention utilizes both one and multi-dimensional parameterized data in a decision making process.

Abstract: The invention provides seminal computational approaches utilizing data from non-rare cells to detect rare cells, such as circulating tumor cells (CTCs). The invention is applicable at two distinct stages of CTC detection; the first being to make decisions about data collection parameters and the second being to make decisions during data reduction and analysis. Additionally, the invention utilizes both one and multi-dimensional parameterized data in a decision making process.

Abstract: The invention provides seminal computational approaches utilizing data from non-rare cells to detect rare cells, such as circulating tumor cells (CTCs). The invention is applicable at two distinct stages of CTC detection; the first being to make decisions about data collection parameters and the second being to make decisions during data reduction and analysis. Additionally, the invention utilizes both one and multi-dimensional parameterized data in a decision making process.

Abstract: The invention provides seminal computational approaches utilizing data from non-rare cells to detect rare cells, such as circulating tumor cells (CTCs). The invention is applicable at two distinct stages of CTC detection; the first being to make decisions about data collection parameters and the second being to make decisions during data reduction and analysis. Additionally, the invention utilizes both one and multi-dimensional parameterized data in a decision making process.

Abstract: The disclosure provides methods for detecting circular endothelial cells (CECs) in a non-enriched blood sample. The present disclosure is based, in part, on the unexpected discovery that CECs can be detected in non-enriched blood samples. The present disclosure is further based, in part, on the discovery that CECs can be detected in non-enriched blood samples by combining the detection of one or more immunofluorescent markers in the nucleated cells of a non-enriched blood sample with an assessment of the morphology of the nucleated cells. The present disclosure is further based, in part, on the discovery that CECs can be detected in non-enriched blood samples by comparing the immunofluorescent marker staining and morphological characteristics of CECs with the immunofluorescent marker staining and morphological characteristics of WBCs. The methods disclosed herein serve to classify human subject in myocardial infarction (MI) patients or healthy controls.

Abstract: The present invention provides methods for diagnosing lung cancer in a subject comprising (a) generating circulating tumor cell (CTC) data from a blood sample obtained from the subject based on a direct analysis comprising immunofluorescent staining and morphological characteristics of nucleated cells in the sample, wherein CTCs are identified in context of surrounding nucleated cells based on a combination of the immunofluorescent staining and morphological characteristics; (b) obtaining clinical data for the subject; (c) combining the CTC data with the clinical data to diagnose lung cancer in the subject.

Abstract: Apparatus, systems and methods are provided for the identification of various objects, particularly circulating tumor cells. In one aspect the system includes, but is not limited to, a scanning system, an image storage system, and an analysis system. The analysis system preferably identifies desired objects, such as complete cells, based on various criteria, which may include cell nuclear area or volume, CD-45 negative status, and cytokeratine positive status. Preferably included is a slide for containing the cells during the imaging step, the well including a planar bottom surface, a border at the periphery of the well defining sides for the well, the border being adjacent the bottom surface of the well and providing a fluidic seal there between. The invention herein provides for a single imaging well, providing for substantially a monolayer of objects, e.g., cells.

Abstract: The invention provides seminal computational approaches utilizing data from non-rare cells to detect rare cells, such as circulating tumor cells (CTCs). The invention is applicable at two distinct stages of CTC detection; the first being to make decisions about data collection parameters and the second being to make decisions during data reduction and analysis. Additionally, the invention utilizes both one and multi-dimensional parameterized data in a decision making process.

Abstract: The invention relates to the three-dimensional structure of a crystal of an EphB4 receptor complexed with a ligand. The three-dimensional structure of a Receptor-Ligand Complex is disclosed. The receptor-ligand crystal structure, wherein the ligand is an inhibitor molecule, is useful for providing structural information that may be integrated into drug screening and drug design processes. Thus, the invention also relates to methods for utilizing the crystal structure of the Receptor-Ligand Complex for identifying, designing, selecting, or testing inhibitors of the EphB4 receptor protein, such inhibitors being useful as therapeutics for the treatment or modulation of i) diseases; ii) disease symptoms; or iii) the effect of other physiological events mediated by the receptor.