Disclosed is a disease differentiation support method for supporting disease differentiation, the disease differentiation support method including: obtaining a first parameter obtained by analyzing an image including a cell contained in a sample collected from a subject; obtaining a second parameter regarding a number of cells contained in the sample; and generating, by using a computer algorithm, differentiation support information for supporting disease differentiation, on the basis of the first parameter and the second parameter.

A counting method includes aggregating particles in a sample by action of first-direction dielectrophoretic force, dispersing the aggregated particles by action of second-direction dielectrophoretic force, which is different from the first-direction dielectrophoretic force, capturing a dispersion image including the dispersed particles, and determining the number of particles on the basis of the dispersion image.

A fine particle measuring device performing fine particle measurement includes a particle probe, a pipe connected to the particle probe, and a particle counter connected to the pipe. A cylindrical pipe is disposed on an outer periphery of the pipe and an air flow path is provided between the pipe and the cylindrical pipe.

Disclosed herein are platforms, systems, and methods including a cell culture system that includes a cell culture container comprising a cell culture, the cell culture receiving input cells, a cell imaging subsystem configured to acquire images of the cell culture, a computing subsystem configured to perform a cell culture process on the cell culture according to the images acquired by the cell imaging subsystem, and a cell editing subsystem configured to edit the cell culture to produce output cell products according to the cell culture process.

A method for detecting biomarkers with shortened test time and maximized precision. A sample from the body fluid is made to flow over a sensor surface coated with a capture antibody to allow binding of a biomarker in the sample to the capture body. An optical method detects and counts the individual binding events along the sensor surface with single molecule resolution, and difference in the binding events along the sensor surface is detected in real time and analyzed to determine the biomarker concentration.

Disclosed is a method for assisting prediction of onset of cerebral infarction, based on the number of red blood cells contained in a blood sample collected from a subject, comprising the steps of: calculating an exponent value for the prediction from a first measured value indicating red blood cell count measured by electrical resistance measurement method and a second measured value indicating red blood cell count measured by optical measurement method,
comparing the exponent value with a reference range, and
suggesting that the subject develops cerebral infarction when the exponent value is outside the reference range.

A method for measuring the concentration of a micro/nano particle, including: allowing the to-be-measured micro/nano particle to bind with one or more kinds of marker to form a new particle, the new particle having a change in at least one of particle size, charge state, and particle morphology compared with the to-be-measured micro/nano particle or the marker; measuring the particle size, charge state, or particle morphology of the new particle and the to-be-measured micro/nano particle or the marker, and counting the new particle and the to-be-measured micro/nano particle or the marker respectively to obtain their respective count results, and, on the basis of the count results, calculating the concentration of the to-be-measured micro/nano particle bound with the marker. The method of the present application has the advantages of high measurement accuracy, low measurement limit, and stability of chemical reagents.

A particle sizing method which allows for counting and sizing of particles within a colloidal suspension flowing through a single-particle optical sizing sensor SPOS apparatus using pulse height detection and utilizing non-parallel and non-uniform illumination within the sensing region of the flow cell. The method involves utilizing a deconvolution process which requires the SPOS apparatus to be characterized during a calibration phase. Once the SPOS apparatus has been characterized, the process of deconvolution after a data collection run, recursively eliminates the expected statistical contribution to the pulse height distribution PHD histogram in all the lower channels from the highest channel height detected, and repeating this for all remaining channels in the PHD, removing the contributions from largest to smallest sizes.

A particle detection device includes a detection tube, a light emitter, a light receiver, and a processing unit. The detection tube is for a detection solution to pass through. The light emitter generates a detection light and emits the detection light to the detection solution. The light receiver receives the detection light scattered from the detection solution. The processing unit generates a received light intensity value according to the detection signal generated by the light receiver, and determines whether the received light intensity value is greater than a first threshold value: if greater, generating a detection result of particles; otherwise, generating a detection result of no particles. Then it provides a basis for semiconductor manufacturing companies to evaluate whether the detection solution can be used in a high-precision manufacturing processes, thereby optimizing the manufacturing process and improving the yield rate of the high-precision manufacturing process.

In a disclosed example, a computer-implemented method includes storing image data that includes an input image of a blood sample within a blood monitoring device. The method also includes generating, by a machine learning model, a segmentation mask that assigns pixels in the input image to one of a plurality of classes, which correlate to respective known biophysical properties of blood cells. The method also includes extracting cell images from the input image based on the segmentation mask, in which each extracted cell image includes a respective cluster of the pixels assigned to a respective one of the plurality of classes.