A specimen introduction method of the present disclosure includes: a preparation step of, by using a switching member, connecting a first feeder to a first inlet-and-outlet, connecting a second inlet-and-outlet to a discharge container, and connecting a third inlet-and-outlet to a first inflow port, and further connecting a second feeder to a second inflow port; a first step of filling a path from the second feeder to the switching member through the second inflow port, a specimen treatment device, and the first inflow port with a second liquid; a second step of filling a path from the first feeder to the switching member with a first liquid; and a third step of subsequently introducing the first liquid into the first inflow port from the first feeder through the switching member, and introducing the second liquid into the second inflow port from the second feeder.

The present invention relates to a multisystem for simultaneously performing a biochemical examination and a blood test or an immunity test, the multisystem including a multi-disc in which a biochemical examination unit, a blood testing unit and an immunity test unit are divided and formed in a fan shape based on a rotation center. There is an effect that it is possible to perform all of a biochemical examination for measuring concentration values of biochemical components of serum to diagnose vital functions, a blood testing for separating blood samples to measure and test the number and deformation of red blood cells or white blood cells, and an immunity test for measuring an antigen-antibody reaction of serum to diagnose and test cancer or viruses in one device at the same time by using the multi-disc according to the present invention.

According to an aspect of the invention, a specimen information detection apparatus includes, an image capture device configured to capture an image of a specimen container that contains a specimen, an illumination device configured to irradiate the specimen container with light sideways with respect to an image capturing direction when the image is captured, and an information processing portion configured to detect a state in the specimen container through image processing based on image information of the specimen container acquired by capturing the image of the specimen container.

An analyzer having an inner chassis surrounded by a housing includes sample and dilution probes, a mixing housing including first and second mixing chambers, a flow cytometer including a flow cell, and sample and sheath pumps configured to perform first and second pluralities of tasks, respectively. The first plurality of tasks includes: aspirating sample into the sample probe, dispensing sample from the sample probe into the first and second mixing chambers, delivering first sample-dilution fluid mixture to the flow cell, and delivering second sample-dilution fluid mixture to the flow cell. The second plurality of tasks includes: dispensing sheath to the flow cell in cooperation with the delivery of the first sample-dilution fluid mixture to the flow cell, and dispensing sheath to the flow cell in cooperation with the delivery of the second sample-dilution fluid mixture to the flow cell.

An automatic analyzer for analyzing a medical probe includes an analysis cell for the probe, a piezo element, and an analysis device. The piezo element can be operated by applying a voltage and a frequency, and in the process an acoustic wave field is generated. A probe located in the analysis cell is located in the acoustic wave field when the piezo element is being operated.

The types of cells that cannot be determined by use of a conventional scattergram are determined. The problem is solved by a cell analysis method for analyzing cells contained in a biological sample, by using a deep learning algorithm having a neural network structure, the cell analysis method including: causing the cells to flow in a flow path; obtaining a signal strength of a signal regarding each of the individual cells passing through the flow path, and inputting, into the deep learning algorithm, numerical data corresponding to the obtained signal strength regarding each of the individual cells; and on the basis of a result outputted from the deep learning algorithm, determining, for each cell, a type of the cell for which the signal strength has been obtained.

The disclosure provides devices, device systems, and methods for analyzing cells (e.g., blood cells) or particles in a sample. In some embodiments, the disclosure provides various devices and device systems including: a light source; a collecting lens; and one, two, or more detectors. In other embodiments, the devices and device systems include a flow cell or a cartridge device with a flow cell. In further embodiments, the disclosure provides various methods including the steps of: using a light source to emit an irradiation light; using the irradiation light to illuminate a sample flow; using a collecting lens to collect both scattered light and fluorescent light from the sample flow; and using one, two, or more detectors to detect the collected scattered light and fluorescent light. Optionally, these methods include using a flow cell to form a sample flow.

The present disclosure presents systems, apparatuses, and methods of holographic imaging. In this regard, a method comprises transmitting light and illuminating a semi-transparent sample object; and forming, at a hologram plane, an interference pattern of a real image of the sample object from a scattered object beam and an unscattered reference beam from the transmitted light. To do so, the scattered object beam and the unscattered reference beam are in-line with one another, and a distance between the hologram plane to the sample object is set at a distance that substantially weakens a virtual image of the sample object formed from the scattered object beam and the unscattered reference beam. Accordingly, the method further comprises recording the interference pattern of a hologram formed from the scattered object beam and the unscattered reference beam at a detector; and reconstructing a 3D optical field of the hologram without phase retrieval.

The present disclosure discloses an antibody combination for substituting a side scatter signal in mass cytometry hematologic tumor immunophenotyping, including a Lactoferrin antibody and a Lysozyme antibody. The present disclosure also discloses a gating method for mass cytometry hematologic tumor immunophenotyping. The present disclosure also discloses a kit for mass cytometry hematologic tumor immunophenotyping. According to the present disclosure, the Lactoferrin antibody and the Lysozyme antibody are used for the first time, are combined with a CD45 antibody for two-stage gating strategy, and are combined with a mass cytometer to substitute traditional flow cytometry CD45/SSC to distinguish mature granulocytes, monocytes, nucleated red blood cells, lymphocytes, primitive and juvenile cells, and abnormal cell subsets in bone marrow. Combined with the multi-parameter high-throughput characteristics of the mass cytometry, the present disclosure can improve the depth of the current hematologic tumor immunophenotyping.

Diagnostic and screening technologies, therapy recommendations, and computer systems based on red blood cell membrane permeability characteristics are provided herein.