Machine learning analysis for classifying agglutination of fluid samples. A method includes scanning a unique scannable code printed on a test card, wherein the test card comprises a negative control fluid sample, a positive control fluid sample, and a test fluid sample. The method includes capturing an image of the test card and providing the image of the test card to a machine learning algorithm configured to assess agglutination of the test fluid sample based on the image. The method includes receiving from the machine learning algorithm one or more of a qualitative analysis or a quantitative analysis of the agglutination of the test fluid sample.

A system includes a processor receiving spectrometer data representative of a scanned sample and generated by a spectrometer and a cloud server including a server processor. The server processor receives the spectrometer data generated by the spectrometer from the processor, analyzes the spectrometer data, identifies, based on a machine learning application, one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample and provides to the processor data representative of a graphical display, which includes an indication of whether or not the scanned sample includes the one or more unique characteristics of the spectrometer data.

There is disclosed a system for monitoring a state of fluid present within a fluid source comprising an apparatus configured to be mounted with respect to the fluid source, and having an inlet in fluid communication with the fluid source; a testing system mounted within the apparatus and configured to obtain a sample of fluid from the fluid source via the inlet and to perform a test of the sample of fluid; an image capture device for capturing an image of the tested sample of fluid; and a controller for controlling the operation of the apparatus so as to coordinate the collection of the sample, the testing of the sample, capture of the image, disposal of the sample and transmission of the image to a remote control centre for analysis and action.

Disclosed are a sample analysis apparatus and method which include: obtaining a first light signal by irradiating a first test sample from blood and a first-channel reagent to differentiate neutrophils, eosinophils, lymphocytes, and monocytes; obtaining a second light signal from a second test sample from the blood and a second-channel reagent; counting the nucleated red blood cells and lymphocytes based on the second light signal; determining accuracy of the lymphocyte result from the first test sample; and if inaccurate, correcting the lymphocyte result based on the lymphocyte result of the second test sample.

A cell analysis apparatus including: a cell image acquisition unit that acquires an image of cells in a culture vessel in which cells are cultured; a smoothing processing unit that smooths luminance values in the image acquired by the cell image acquisition unit; a minimum-value detecting unit that detects minimum values of the luminance values smoothed by the smoothing processing unit; a smallest-minimum-value extracting unit that extracts smallest minimum values which are the smallest in regions according to the sizes of the cells, from the minimum values detected by the minimum-value detecting unit; a counting unit that counts the number of smallest minimum values extracted by the smallest-minimum-value extracting unit; and a cell-count calculating unit that calculates the number of cells in the culture vessel on the basis of the number of smallest minimum values counted by the counting unit.

A system usable to adapt an optical device to calculate a condition value. The system utilizes data from an optical device about a field of vision to calculate a condition value such as temperature for a target within the field of vision. The system makes use of an adapter connected to the optical device for transmitting adapter output data and a converter that accesses the adapter output data to calculate the condition value. The adapter components can weigh less than 3 ounces, and encompass a volume of less than 4 cubic inches, making it suitable for deployment on a drone, or remotely operated vehicle.

The present disclosure describes a computer implemented method, a system, and a computer program product of purifying a sample solution via real-time multi-angle light scattering. In an embodiment, the method, system, and computer program product include receiving from a MALS instrument baseline scattering intensity values of a pure buffer, receiving from the MALS instrument scattering intensity values of a sample solution, and characterizing at least one component of the sample solution, resulting in a time series of values of a dimension, D, of the at least one component and a time series of values of excess Rayleigh ratio, R0, of the at least one component, and determining that the values of the dimension, D, fall within a dimension, D, value range and that the values of excess Rayleigh ratio, R0, fall within an excess Rayleigh ratio value range, and transmitting a collect sample solution command to collect the sample solution.

In some examples, a medical system includes a medical device. The medical device may include a housing configured to be implanted in a target site of a patient, a light emitter configured to emit a signal configured to cause a fluorescent marker to emit a fluoresced signal into the target site, and a light detector that may be configured to detect the fluoresced signal. The medical system may include processing circuitry configured to determine a characteristic of the fluorescent marker based on the emitted signal and the fluoresced signal. The characteristic of the fluorescent marker may be indicative of a presence of a compound in the patient, and the processing circuitry may be configured to track the presence of the compound of the patient based on the characteristic of the fluorescent marker.

Protecting information by partitioning data across multiple storage resources. A method includes receiving an image of a test card and an identity of a patient associated with the test card. The method includes associating the image of the test card with the identity of the patient as a data pair on a first storage resource. The method includes providing the image of the test card to a second storage resource by way of a network, wherein the first storage resource is independent of the second storage resource. The method includes receiving a result for the test card and associating the result for the test card with the identity of the patient based on the data pair stored on the first storage resource.

Test cards for agglutination assays, wherein the test cards are configured for computer-implemented image analysis. A test card includes a negative control test region for receiving a negative control fluid sample, a positive control test region for receiving a positive control fluid sample, and a test sample region for receiving a test fluid sample. The test card includes one or more unique scannable codes comprising data for instructing a processor to capture an image of the test card that is suitable for computer-implemented image analysis.