Abstract: A method for enhancing gating performance of a cell sorter to prepare an enriched specimen for optical tomography cell analysis includes introducing a specimen into a FACS to generate 2D event data; generating a first scatterplot of the 2D data; identifying target objects; constructing a boundary within the first scatterplot to produce a first gate; counting target objects within the first gate; comparing the number of target objects within the first gate to a first predetermined value and adjusting the first gate as necessary. A boundary around a set of target objects is constructed in a second scatterplot to produce a subset second gate and target objects within the second gate are counted and the count compared to a second predetermined value. When a boundary around target objects meets specifications the first and second gates are stored in memory and used to enrich patient specimens.

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 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 for enhancing gating performance of a cell sorter to prepare an enriched specimen for optical tomography cell analysis includes introducing a specimen into a FACS to generate 2D event data; generating a first scatterplot of the 2D data; identifying target objects; constructing a boundary within the first scatterplot to produce a first gate; counting target objects within the first gate; comparing the number of target objects within the first gate to a first predetermined value and adjusting the first gate as necessary. A boundary around a set of target objects is constructed in a second scatterplot to produce a subset second gate and target objects within the second gate are counted and the count compared to a second predetermined value. When a boundary around target objects meets specifications the first and second gates are stored in memory and used to enrich patient specimens.

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 for enhancing gating performance of a cell sorter to prepare an enriched specimen for optical tomography cell analysis includes introducing a specimen into a FACS to generate 2D event data; generating a first scatterplot of the 2D data; identifying target objects; constructing a boundary within the first scatterplot to produce a first gate; counting target objects within the first gate; comparing the number of target objects within the first gate to a first predetermined value and adjusting the first gate as necessary. A boundary around a set of target objects is constructed in a second scatterplot to produce a subset second gate and target objects within the second gate are counted and the count compared to a second predetermined value. When a boundary around target objects meets specifications the first and second gates are stored in memory and used to enrich patient specimens.

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 for enhancing gating performance of a cell sorter to prepare an enriched specimen for optical tomography cell analysis includes introducing a specimen into a FACS to generate 2D event data; generating a first scatterplot of the 2D data; identifying target objects; constructing a boundary within the first scatterplot to produce a first gate; counting target objects within the first gate; comparing the number of target objects within the first gate to a first predetermined value and adjusting the first gate as necessary. A boundary around a set of target objects is constructed in a second scatterplot to produce a subset second gate and target objects within the second gate are counted and the count compared to a second predetermined value. When a boundary around target objects meets specifications the first and second gates are stored in memory and used to enrich patient specimens.