A method for measuring concentrations of blood cell components is provided. The method comprises: obtaining a blood sample from a subject, the blood sample comprising at least one of red blood cells (RBCs), white blood cells (WBCs), and platelets (PLTs); mixing the blood sample with a non-lysing aqueous solution to form a sample mixture comprising a predetermined tonicity; passing the sample mixture through a flow cell; emitting light towards the flow cell; measuring at least one of an amount of light absorbed by the RBCs to obtain an RBC absorption value, an amount of light scattered by WBCs to obtain a WBC scatter value, and an amount of light scattered by PLTs to obtain a PLT scatter value; and determining a concentration of at least one of the RBCs, WBCs, and PLTs present in the sample mixture.

Described herein are apparatuses for analyzing an optical signal decay. In some embodiments, an apparatus includes: a source of a beam of pulsed optical energy; a sample holder configured to expose a sample to the beam; a detector comprising a number of spectral detection channels configured to convert the optical signals into respective electrical signals; and a signal processing module configured to perform a method. In some embodiments, the method includes: receiving the electrical signals from the detector; mathematically combining individual decay curves in the electrical signals into a decay supercurve, the supercurve comprising a number of components, each component having a time constant and a relative contribution to the supercurve; and numerically fitting a model to the supercurve.

A method for evaluating a biological material for unassociated virus-like particles virus size having a particular epitope uses a fluorescent antibody stain specific for binding with the epitope and a fluid sample with the virus-size particles and fluorescent antibody stain is subjected to flow cytometry with identification of fluorescent emission detection events indicative of passage through a flow cell of a flow cytometer of unassociated labeled particles of virus size including such a virus-like particle and fluorescent antibody stain.

Introduced here is an approach to improving the automatic identification of hematological diseases using computer-implemented models that are trained to rapidly distinguish between different collections of immunophenotypes that represent different disease types or disease states. Understanding the different patterns of immunophenotype collections contained in a given sample may permit a proposed diagnosis for a given hematological disease to be produced for the corresponding patient. For example, the proposed diagnoses may be output by a classification model based on the distribution of immunophenotypes across the given sample.

There is provided a cell analysis apparatus that comprises image capture circuitry for capturing a brightfield image of a cell using brightfield imaging. The cell has been dyed by a functional dye that indicates, during fluorescence imaging and during brightfield imaging, whether the cell has a given characteristic. A model derived by machine learning is stored and used in combination with the brightfield image to determine whether the cell has the given characteristic. There is also provided a method for creating a cell categorisation model, comprising applying a functional dye to one or more samples comprising a plurality of cells. The functional dye indicates during fluorescence imaging and during brightfield imaging whether each of the cells has a given characteristic. A brightfield image and a corresponding fluorescence image for each of the plurality of cells to which the dye has been applied are captured and a machine learning process is used to generate a model that predicts whether a cell has the given characteristic from a brightfield image. The model is generated by using the brightfield image and the corresponding fluorescence image of each of the plurality of cells as training data.

A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.

Provided is a new method for more efficiently generating emulsion particles each containing one fine particle.

The present technology provides a method of collecting fine particles, in which in a fine particle sorting mechanism having a channel structure including a main channel through which the fine particles flow, a collection channel into which particles to be collected are collected from among the fine particles, a connection channel that connects the main channel and the collection channel, and a liquid supply channel connected to the connection channel so as to supply a liquid, the method includes: a flow step of causing a first liquid containing the fine particles to flow through the main channel; a determination step of determining whether or not the fine particles flowing through the main channel are the particles to be collected; and a collection step of collecting the particles to be collected into the collection channel, and, in the collection step, the particles to be collected are collected into a second liquid that is immiscible with the first liquid in the collection channel while being contained in the first liquid.

Techniques for label-free determination of a value of at least one blood property are presented. The techniques may utilize a device that includes an optical objective including at least one lens, at least a first light source situated so as to provide light to a body part at a location that is off-center from a central axis of the objective, at least a first electronic detector situated to receive light gathered by the optical objective and generate image data, at least one electronic processor communicatively coupled to the first electronic detector, the at least one electronic processor configured to determine the value of the at least one blood property based at least in part on the image data, and an output interface communicatively coupled to the at least one electronic processor and configured to provide the value of the at least one blood property.

High-throughput methods and systems for using morphological and/or autofluorescence signatures of cells to characterize unknown cell/tissue types within a forensic sample are provided. Machine learning algorithms are used to correlate morphological and/or autofluorescence signatures to characteristics such as cell type.

The present disclosure discloses a method for determining optimal preservation temperature of the anaerobic ammonia oxidation biofilm in wastewater treatment, and belongs to the technical field of environmental engineering. The method of the present disclosure characterizes the ratio of living cells, early apoptotic cells, late apoptotic cells and dead cells in the anaerobic ammonia oxidation biofilm by flow cytometry, and the optimum storage temperature can be measured within a few hours. The method of the present disclosure performs correlation analysis on the characteristic indexes of the anaerobic ammonia oxidation biofilm activity recovery process to verify the reliability of the data. By using the method of the present disclosure, the step of recovering the biofilm activity can be omitted, the removal rates of ammonia nitrogen and total nitrogen were over 90% and 85%, respectively.