Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

Abstract: A method of reducing mortality in a human patient with pulmonary inflammation due to coronavirus or other pathogen, the method including administering an oral dose of a prostacyclin analog drug to the patient within a therapeutic window. The prostacyclin analog drug includes oral iloprost or iloprost betadex clathrate.

Abstract: A method of treating a malignancy in a human subject by analyzing pseudo-projection images of cells obtained from a sputum specimen obtained from a subject employs a biological specimen classifier that identifies cells from the sputum specimen as normal or abnormal. If abnormal cells are detected, then the abnormal cells are further classified as dysplastic or cancerous. If the cells are classified as dysplastic, then an immunomodulating agent is administered to the subject over a predetermined time period designed to achieve a therapeutic dosage.

Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

Abstract: A method to develop one or more morphometric classifiers to identify a mismatch repair deficiency (MMRD). The method provides a non-invasive method of characterizing MMRD that is responsive to a tumor in its early stages of development and irrespective of the tumor size. The method allows targeting cancer therapy to the specific characteristics of the cancer that the patient may have, allowing more efficient cancer management with far fewer side effects.

Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

Abstract: A method to develop one or more morphometric classifiers to identify a tumor mutation burden (TMB). The method provides a non-invasive method of characterizing TMB that is responsive to a tumor in its early stages of development and irrespective of the tumor size. The method allows targeting cancer therapy to the specific characteristics of the cancer that the patient may have, allowing more efficient cancer management with far fewer side effects.

Abstract: A method of treating a malignancy in a human subject by analyzing pseudo-projection images of cells obtained from a sputum specimen obtained from a subject employs a biological specimen classifier that identifies cells from the sputum specimen as normal or abnormal. If abnormal cells are detected, then the abnormal cells are further classified as dysplastic or cancerous. If the cells are classified as dysplastic, then an immunomodulating agent is administered to the subject over a predetermined time period designed to achieve a therapeutic dosage.

Abstract: A method for 3D imaging of cells in an optical tomography system includes moving a biological object relatively to a microscope objective to present varying angles of view. The biological object is illuminated with radiation having a spectral bandwidth limited to wavelengths between 150 nm and 390 nm. Radiation transmitted through the biological object and the microscope objective is sensed with a camera from a plurality of differing view angles. A plurality of pseudoprojections of the biological object from the sensed radiation is formed and the plurality of pseudoprojections is reconstructed to form a 3D image of the cell.

Abstract: A method for 3D imaging of cells in an optical tomography system includes moving a biological object relatively to a microscope objective to present varying angles of view. The biological object is illuminated with radiation having a spectral bandwidth limited to wavelengths between 150 nm and 390 nm. Radiation transmitted through the biological object and the microscope objective is sensed with a camera from a plurality of differing view angles. A plurality of pseudoprojections of the biological object from the sensed radiation is formed and the plurality of pseudoprojections is reconstructed to form a 3D image of the cell.

Abstract: A method for 3D imaging of cells in an optical tomography system includes moving a biological object relatively to a microscope objective to present varying angles of view. The biological object is illuminated with radiation having a spectral bandwidth limited to wavelengths between 150 nm and 390 nm. Radiation transmitted through the biological object and the microscope objective is sensed with a camera from a plurality of differing view angles. A plurality of pseudoprojections of the biological object from the sensed radiation is formed and the plurality of pseudoprojections is reconstructed to form a 3D image of the cell.

Abstract: An optical tomography system for imaging an object of interest including a light source for illuminating the object of interest with a plurality of radiation beams. The object of interest is held within an object containing tube such that it is illuminated by the plurality of radiation beams to produce emerging radiation from the object containing tube, a detector array is located to receive the emerging radiation and produce imaging data used by a mechanism for tracking the object of interest.

Abstract: An optical tomography system for imaging an object of interest including a light source for illuminating the object of interest with a plurality of radiation beams. The object of interest is held within an object containing tube such that it is illuminated by the plurality of radiation beams to produce emerging radiation from the object containing tube, a detector array is located to receive the emerging radiation and produce imaging data used by a mechanism for tracking the object of interest.

Abstract: A scanning method for scanning samples of biological cells using optical tomography includes preparing, acquiring, reconstructing and viewing three-dimensional images of cell samples. Concentration and enrichment of the cell sample follows. The cell sample is stained. Cells are isolated from the cell sample and purified. A cell/solvent mixture is injected into a gel by centrifugation. A cell/gel mixture is injected into a capillary tube until a cell appears centered in a field of view using a stopped-flow method. An optical imaging system, such as a fixed or variable motion optical tomography system acquires a projection image. The sample is rotated about a tube axis to generate additional projections. Once image acquisition is completed, the acquired image projections are corrected for errors. A computer or other equivalent processor is used to compute filtered backprojection information for 3D reconstruction.

Abstract: A system for optical imaging of a thick specimen that permits rapid acquisition of data necessary for tomographic reconstruction of the three-dimensional (3D) image. One method involves the scanning of the focal plane of an imaging system and integrating the range of focal planes onto a detector. The focal plane of an optical imaging system is scanned along the axis perpendicular to said plane through the thickness of a specimen during a single detector exposure. Secondly, methods for reducing light scatter when using illumination point sources are presented. Both approaches yield shadowgrams. This process is repeated from multiple perspectives, either in series using a single illumination/detection subsystem, or in parallel using several illumination/detection subsystems. A set of pseudo-projections is generated, which are input to a three dimensional tomographic image reconstruction algorithm.

Abstract: A method for 3D imaging of cells in an optical tomography system includes moving a biological object relatively to a microscope objective to present varying angles of view. The biological object is illuminated with radiation having a spectral bandwidth limited to wavelengths between 150 nm and 390 nm. Radiation transmitted through the biological object and the microscope objective is sensed with a camera from a plurality of differing view angles. A plurality of pseudoprojections of the biological object from the sensed radiation is formed and the plurality of pseudoprojections is reconstructed to form a 3D image of the cell.