A calibration apparatus comprises estimation circuitry configured to estimate, based on a calibration factor, an estimated number of cells of a first type in a dyed biological sample containing an unknown number of cells. Determination circuitry determines the actual number of cells of the first type in the dyed biological sample. Processing circuitry adjusts the calibration factor. The estimation circuitry is configured with the processing circuitry to estimate the estimated number of the cells of the first type in the dyed biological sample one or more times, based on a different value of the calibration factor for each of the one or more times, until the estimated number of the cells of the first type approaches the actual number of cells of the first type.

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.

Provided are a sample image analyzer and a corresponding method. The sample image analyzer includes: an object stage for supporting a sample carrier; an imaging device for capturing an image of an object in a sample on the sample carrier; a driving device for driving the object stage and the imaging device to move relative to each other; and a control device configured to control the driving device to deliver the sample carrier to a position below the imaging device, control the driving device to drive the object stage and the imaging device to move horizontally relative to each other, and to move vertically relative to each other, control the imaging device to capture, at least during the relative vertical movement, images of the object at different horizontal positions and at different vertical positions, and fuse the images of the object to obtain a target image of the object.

A particle analysis method and apparatus, including a spectrometry-based analysis of a fluid sample (1), comprises the steps of creating a sample light beam S and a probe light beam P with a light source device (10) and periodically varying a relative phase between the sample and probe light beams S, P with a phase modulator device (20), irradiating the fluid sample (1) with the sample light beam S, detecting the sample and probe light beams S, P with a detector device (40), and providing a spectral response of the at least one particle (3), wherein the light source device (10) comprises at least one broadband source, which has an emission spectrum covering a mid-infrared MIR frequency range, and the phase modulator device (20) varies the relative phase with a scanning period equal to or below the irradiation period of irradiating the at least one particle (3, 4).

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.

To provide a technology of efficiently and effectively sorting microparticles to be sorted from a sample solution. The present technology provides a control device being a device that controls a processing condition when sorting microparticles from a sample liquid flowing through a flow path, the control device provided with a control unit that controls a sorting processing condition on the basis of a content of microparticles to be sorted in the sample liquid. In the control device according to the present technology, the control unit may control the sorting processing condition on the basis of a surviving rate and/or an activation rate of biological particles to be sorted with respect to the sorting processing condition.

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.

The present disclosure provides methods, systems, and kits for detecting molecules in a sample with a pre-equilibrium digital immunoassay. The methods and systems provide means for quantifying molecules in a biological sample of minimal volume in short amounts of time.

Photodetector clamps are provided. Clamps of interest include one or more flexure arms for applying an immobilizing force to one or more photodetectors positioned within a light detection module, and are configured to be positioned on top of a detector block. In embodiments, the bottom of the one or more flexure arms include an opening for contacting the photodetector(s). Light detection modules, systems and methods employing the subject clamps are also provided.

The disclosure features systems and methods for measuring and diagnosing target constituents bound to labeling particles in a sample. The systems include a radiation source, a sample holder, a detector configured to obtain one or more diffraction patterns of the sample each including information corresponding to optical properties of sample constituents, and an electronic processor configured to, for each of the one or more diffraction patterns: (a) analyze the diffraction pattern to obtain amplitude information and phase information corresponding to the sample constituents; (b) identify one or more particle-bound target sample constituents based on at least one of the amplitude information and the phase information; and (c) determine an amount of at least one of the particle-bound target sample constituents in the sample based on at least one of the amplitude information and the phase information.