An infusion apparatus includes a housing and a chamber configured to be connected to the housing. The apparatus further includes a weight sensor coupled to a load connector connected to the housing and an optical sensor disposed in the housing. The weight sensor is configured to generate a first signal based on a measured weight of the fluid container attached to the housing in a weight-bearing configuration. The optical sensor is configured to generate a second signal based on detecting drops of the fluid traversing the chamber. The apparatus also includes a flow control mechanism to control a flow rate of the fluid into an outlet channel. The apparatus includes one or more processing devices configured to perform operations including transmitting a control signal to the flow control mechanism to adjust the flow rate.

The present disclosure provides apparatuses, systems, and methods for performing particle analysis through flow cytometry at comparatively high event rates and for gathering high resolution images of particles.

Methods and systems for operating a flow cytometer can include forward scatter values, side scatter values, and fluorescence intensity values for events of an unstained sample and associating the fluorescence intensity values with forward scatter-side scatter side scatter plot regions. Methods and systems for operating a flow cytometer can also include measuring forward scatter values, side scatter values, and fluorescence intensity values for events of a stained sample, determining forward scatter-side scatter plot locations for the events of the stained sample, and for each event of the stained sample, subtracting the fluorescence intensity value associated with the forward scatter-side scatter plot region that contains the forward scatter-side scatter plot location of the stained sample event from the measured fluorescence intensity value of the stained sample event at that forward scatter-side scatter plot location.

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.

An observation apparatus includes a light source unit, an irradiation optical system, an imaging optical system, a modulation unit, an imaging unit, an analysis unit, beam splitters and, and mirrors. The analysis unit obtains a real part of a function χ(t)=log [1+U.sub.obj(t)/U.sub.ref(t)], defined by time series data U.sub.obj(t) of a complex amplitude image of object light on an imaging plane and time series data U.sub.ref(t) of a complex amplitude image of reference light on the imaging plane, based on time series data I(t) of an intensity image of interference light on the imaging plane and time series data I.sub.ref(t) of an intensity image of the reference light on the imaging plane. Further, the analysis unit obtains an imaginary part of χ(t) from the real part of χ(t) using the Kramers-Kronig relations, and further obtains U.sub.obj(t).

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.

In one aspect, the present teachings provide a system for performing cytometry that can be operated in three operational modes. In one operational mode, a fluorescence image of a sample is obtained by exciting one or more fluorophore(s) present in the sample by an excitation beam formed as a superposition of a top-hat-shaped beam with a plurality of beams that are radiofrequency shifted relative to one another. In another operational mode, a sample can be illuminated successively over a time interval by a laser beam at a plurality of excitation frequencies in a scanning fashion. The fluorescence emission from the sample can be detected and analyzed, e.g., to generate a fluorescence image of the sample. In yet another operational mode, the system can be operated to illuminate a plurality of locations of a sample concurrently by a single excitation frequency, which can be generated, e.g., by shifting the central frequency of a laser beam by a radiofrequency. For example, a horizontal extent of the sample can be illuminated by a laser beam at a single excitation frequency. The detected fluorescence radiation can be used to analyze the fluorescence content of the sample, e.g., a cell/particle.

An analysis device includes a controller configured to count a pulse derived from a particles as a plural particles when a light reception level signal includes the pulse having a first extreme value point, a second extreme value point, and a third extreme value point, and the pulse fulfils a condition in which the third extreme value point is present between the first extreme value point and the second extreme value point in a pulse width direction of the pulse, the third extreme value point is present between the first extreme value point and a threshold in a pulse amplitude direction, the first extreme value point and the second extreme value point are each an extreme value point of a waveform projecting in a common direction, and the third extreme value point is an extreme value point of a waveform in a direction opposite to the common direction.

Techniques in connection with the use of a multi-pixel polarization filter in the light-microscopic examination of a sample object are described. In this way e.g. a particle analysis can be carried out, e.g. in particular for determining the technical cleanness of a surface of the sample object.

Embodiments of the present technology include a method for testing a blood sample for sepsis. The method may include receiving a blood sample from an individual. The method may also include executing an instruction to analyze the blood sample for sepsis. In addition, the method may include measuring values of a set of characteristics in the blood sample. The set of characteristics being determined prior to measuring the values. The method may further include analyzing the values of the set of characteristics to produce a representation of a suspicion of sepsis. In addition, the method may include displaying the representation. Embodiments also include systems for testing blood sample for sepsis.