An analytical device includes a substrate, which is provided with a plurality of cells, and a pressing member that presses an analysis chip; an item-information reading unit that reads item information of the analysis chip; a stain detection mechanism that includes a processor stores, in a memory, stain-related information on each of the cells, the stain-related information including stain information related to presence or absence of a stain on each of the cells and previous item information that is item information of a loaded analysis chip, the stain information and the previous item information being associated with each other; and the processor that executes, before loading of a to-be-loaded analysis chip to be loaded in a to-be-used cell, carryover suppression processing for suppressing carryover, in which a stain due to previous measurement affects current measurement, based on stain-related information and current item information.

Systems and methods for passively redirecting liquid contaminants from the active region of riding surfaces of track systems for liquid handler systems, including surface energy gradient, channels, and roughness gradients. The described techniques redirect liquids in a passive manner, without requiring any additional electromechanical components or active control systems that would draw additional power and require additional layers of control architecture to manage.

A device for examining microscope specimens includes a microscope, wherein the microscope specimens include an object to be examined by the microscope and a specimen carrier holding the object, and wherein the device is configured to calculate a digital identification code of the microscope specimen by fingerprinting the microscope specimen using at least one optical marker in at least one digital image of at least a part of the object.

A method of training a model of a diagnostic apparatus includes providing one or more first tube assemblies of a first type and one or more second tube assemblies of a second type in a diagnostic apparatus; capturing one or more first images of at least a portion of each of the one or more first tube assemblies and the second tube assemblies using the imaging device. Training the model includes identifying tube assemblies of the first type and tube assemblies of the second type based on the one or more first images and the one or more second images. Tubes assemblies of the first type are grouped into a first group and tube assemblies of the second type are grouped into a second group. Other methods and apparatus are disclosed.

A microscopy method for microscope specimens, wherein the microscope specimens each include an object to be examined by a microscope and a specimen carrier holding the object, the method including calculating a digital identification code for at least one microscope specimen of the microscope specimens. The digital identification code is calculated using optical markers in at least one digital image of at least a part of the object of each of the at least one microscope specimens. The method further includes using at least one of the optical markers as a position reference.

Systems and methods for using smart sample containers to manage complex sample evaluation workflows, are disclosed. An example method for using a smart sample container configured to manage a sample evaluation workflow according to the present invention comprises, obtaining a sample evaluation workflow for the one or more samples, receiving interactions with external devices, and based on the sample evaluation workflow, and causing the external devices to perform actions to advance the sample evaluation workflow. The smart sample container may further modify the sample evaluation workflow based on results of actions performed by the sample evaluation workflow and/or store information relating to the results of such actions. In this way, the smart sample containers are able to dynamically drive the evaluation of a sample through its sample evaluation workflow.