Abstract:
A method for holographic characterization of a particle contained in a sample, based on an image, or hologram, of the sample obtained by an image sensor when the sample is illuminated by a light source. The hologram is the subject of a holographic reconstruction, to obtain a reference complex image, representative of the light wave transmitted by the sample in a reconstruction plane. A holographic propagation operator is applied to the reference complex image, to obtain a plurality of secondary complex images, from which a profile is determined describing the change in an optical feature of the light wave transmuted by the sample along the axis of propagation of the light wave.
Abstract:
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).
Abstract:
The present application relates to: (i) a sampler device for taking a sample of biological fluid, which comprises a capillary component, and a base rigidly connected to said capillary component and provided with a first connector capable of being reversibly attached in a leaktight manner to a second connector of a dispensing device; (ii) a dispensing device which also comprises means for transferring diluting fluid which open into said second connector. The application also relates to a biological analysis apparatus implementing the sampler and dispensing devices and to a method for sampling and dispensing a biological fluid.
Abstract:
A medical analysis device with cellular impedance signal processing comprises a memory (4) arranged to receive pulse data sets, each pulse data set comprising impedance value data that are associated each time with a time marker, these data together representing a curve of cellular impedance values that are measured as a cell passes through a polarised opening. This device further comprises a classifier (6) comprising a convolutional neural network receiving the pulse data sets as input and is provided with at least one convolutional layer, which convolutional layer has a depth greater than or equal to 3, and at least two fully connected layers, in addition to an output layer rendering a cell classification from which a pulse data set is derived.
Abstract:
A device for medical analyses with cellular impedance signal processing comprises a memory (4) arranged to receive pulse data sets, each pulse data set comprising impedance value data that are associated each time with a time marker, these data together representing a curve of cellular impedance values that are measured as a cell passes through a polarized opening, a computer (6) arranged to process a pulse data set by determining a rotation value indicating whether the cell from which this pulse data set has been taken has undergone a rotation during its passage through the polarized opening, and a classifier (8) arranged to retrieve from the computer (6) a given pulse data set, and to use the resulting rotation value to classify the given pulse data set in a rotation pulse data set group (10) or a rotationless pulse data set group (12).