Inline classification of a biological specimen including mammalian cells can include generating an alternating current (AC) electrical stimulus to an electrode structure. The electrode structure can be electrically coupled with a flow cell. A response, elicited by the electrical stimulus, can be received when a model specimen class traverses the flow cell. Using the received response, a corresponding impedance parameter value can be determined, the value indicative of a specified biophysical characteristic corresponding to the model specimen class. The first impedance parameter can be translated to a value corresponding to the specified biophysical characteristic.

In one aspect, a method to detect white blood cells and/or white blood cell subtypes from non-invasive capillary videos is featured. The method includes acquiring a first plurality of images of a region of interest including one or more capillaries of a predetermined area of a human subject from non-invasive capillary videos captured with an optical device, processing the first plurality of images to determine one or more optical absorption gaps located in said capillary, and annotating the first plurality of images with an indication of any optical absorption gap detected in the first plurality of images. The method also includes acquiring a second plurality of images of the same region of interest of the same capillary with an advanced optical device capable of resolving cellular structure of white blood cells and white blood cell subtypes and spatiotemporally annotating the second plurality of images with an indication of any white blood cell detected and/or a subtype of any white blood cell detected in the second plurality of images. The method also includes inputting the first plurality of images and annotated information from the first plurality of images and annotated information from the spatiotemporally annotated second plurality of images into a machine learning subsystem configured to determine a presence of white blood cells and/or the subtype of any white blood cells present in the one or more optical absorption gaps in the first plurality of images.

Aspects of the present disclosure include methods for detecting events in a flow cytometer. Also provided are methods of detecting cells in a flow cytometer. Other aspects of the present disclosure include methods for determining a level of contamination in a flow cell. Computer-readable media and systems, e.g., for practicing the methods summarized above, are also provided.

Provided herein is a method for determining the volume or hemoglobin content of an individual red blood cell in a sample containing a population of red blood cells. The method may be performed on a hematology analyzer. Also provided are a hematology analyzer for performing the method and a computer-readable medium containing programming for performing the method.

Aspects of the present disclosure include methods for detecting events in a flow cytometer. Also provided are methods of detecting cells in a flow cytometer. Other aspects of the present disclosure include methods for determining a level of contamination in a flow cell. Computer-readable media and systems, e.g., for practicing the methods summarized above, are also provided.

Continuous monitoring of blood cultures using pH- (or CO.sub.2) based detection platforms is the current clinical gold standard. Despite the ubiquity of these systems in state-of-the-art clinical microbiology laboratories, they offer slow times-to-result (TTR) because microorganism detection typically requires >10.sup.9 colony forming units (CFU) to be present whereas only 1-1000 CFU are typically present in septic patient blood samples. These TTRs are further lengthened for samples collected from spoke sites in consolidated hub-and-spoke laboratory models, an increasingly common model for integrated hospital networks and reference laboratories, because sample transport time, typically >4 hours, is lost. Here we introduce new methods that allow microorganisms to be detected at <10.sup.5 CFU and that enable sample incubation during courier transport from spoke collection sites to the central laboratory hub.

The invention relates to a device for examining particles in a liquid to be examined, comprising a flow passage through which the liquid to be examined is moved. The flow passage has at least one inlet through which at least one sheath fluid flows into the flow passage such that the at least one sheath fluid forms at least one sheath flow in the flow passage. The device further comprises a wave generating device for piezoacoustically generating sound waves which propagate through the flow passage transversely to the flow direction of the liquid to be examined and form wave nodes on a monitoring plane such that particles to be examined of the liquid to be examined are moved onto the monitoring plane and accumulate thereon on the basis of the pressure effect of the sound waves in the transverse direction.

The present disclosure provides systems and methods for sorting a cell. The system may comprise a flow channel configured to transport a cell through the channel. The system may comprise an imaging device configured to capture an image of the cell from a plurality of different angles as the cell is transported through the flow channel. The system may comprise a processor configured to analyze the image using a deep learning algorithm to enable sorting of the cell.

The invention is a method for estimating a representative volume of particles of interest (10 i) immersed in a sample, the sample extending in at least one plane, referred to as the sample plane (P 10), the sample comprising a sphering agent, capable of modifying the shape of the particles, the method comprising the following steps: a) illuminating the sample by means of a light source (11), the light source emitting an incident light wave (12) propagating towards the sample (10) along a propagation axis (Z); b) acquiring, by means of an image sensor (16), an image (I 0) of the sample (10), formed in a detection plane (P 0), the sample being arranged between the light source (11) and the image sensor (16), each image being representative of a light wave (14) referred to as an exposure light wave, to which the image sensor (16) is exposed under the effect of illumination; c) using the image of the sample (I 0), acquired during step b), and a holographic propagation operator, to calculate a complex expression (A (x, y, z)) of the exposure light wave (14) in different positions relative to the detection plane; the method comprising a step of estimating the representative volume (AA) of the particles of interest (10 i) depending on the complex expressions calculated during step c).

Systems and methods are provided for sample processing. A device may be provided, capable of receiving the sample, and performing one or more of a sample preparation, sample assay, and detection step. The device may be capable of performing multiple assays. The device may comprise one or more modules that may be capable of performing one or more of a sample preparation, sample assay, and detection step. The device may be capable of performing the steps using a small volume of sample.