Methods and apparatuses relating to measuring sample parameters and cell parameters (e.g., cell size, cell shape) are provided herein. The present disclosure provides additional methods, systems and techniques for improving osmotic gradient generating systems for vise in technologies to accurately determine red blood cell volume and the osmolality at which cells achieve a maximum volume.

A microfluidic diagnostic chip may comprise a main fluid channel comprising a main pump, a secondary fluid channel branching off from the main fluid channel, and a secondary pump within the secondary fluid channel wherein the secondary pump is to pull a particle of analyte of a first size from a fluid passing through the main channel, the fluid comprising particles of analyte of the first size and of a number of larger sizes. A method of analyzing an analyte on a microfluidic chip may comprise pumping, with a main microfluidic pump, a fluid comprising an analyte particle through a main microfluidic channel fluidly coupled to a fluid slot and sorting the analyte particle within the fluid through a secondary microfluidic channel by pulling the analyte particle into the secondary microfluidic channel with a secondary microfluidic pump.

A microfluidic device that reads a colloidal mixture and separates the colloids based upon size and shape. and in the case of polymer colloids such as DNA, it reads patterns of markers attached to DNA. The combination of different separated fractions and DNA markers (it mapping) constitutes the physical code.

A process for optically sorting a plurality of particles includes: providing a particle receiver; producing particles; receiving the particles by the particle receiver; receiving a light by the particle receiver; producing a standing wave optical interference pattern in an optical interference site of the particle receiver from the light; subjecting the particles to an optical gradient force from the standing wave optical interference pattern; deflecting the particles into a plurality of deflected paths to form the sorted particles from the particles; and propagating the sorted particles from the optical interference site through the deflected paths to optically sort the particles

The device (100) comprises a cavity (101) and at least two microporous membranes (102), wherein the microporous membranes (102) are arranged in series in the cavity (101) and divide the cavity (101) into a plurality of chambers (1011); each of the microporous membranes (102) is provided with micropores (103), and two adjacent chambers (1011) are in communication via the micropores (103); and each of the chambers (1011) is provided with an electrode (1012).

The present disclosure provides improved systems and methods for automated cell identification/classification. More particularly, the present disclosure provides advantageous systems and methods for automated cell identification/classification using shearing interferometry with a digital holographic microscope. The present disclosure provides for a compact, low-cost, and field-portable 3D printed system for automatic cell identification/classification using a common path shearing interferometry with digital holographic microscopy. This system has demonstrated good results for sickle cell disease identification with human blood cells. The present disclosure provides that a robust, low cost cell identification/classification system based on shearing interferometry can be used for accurate cell identification. For example, by combining both the static features of the cell along with information on the cell motility, classification can be performed to determine the type of cell present in addition to the state of the cell (e.g., diseased vs. healthy).

A microfluidic device comprising a channel within a substrate and a condenser or a hydrodynamic focusing chamber along the channel, configured to focus a fluid containing particles of a plurality of sizes. A negative angle deterministic lateral displacement (DLD) array is configured to receive the focused fluid and separate the particles in the focused fluid into three sizes ranges. The negative angle DLD array comprises a plurality of rows of pillars, wherein the rows of pillars are positioned to repeat a pattern every N rows with a shift of M columns, N and M are relatively coprime, and N is greater than 1.

A method for measuring the concentration of a micro/nano particle, including: allowing the to-be-measured micro/nano particle to bind with one or more kinds of marker to form a new particle, the new particle having a change in at least one of particle size, charge state, and particle morphology compared with the to-be-measured micro/nano particle or the marker; measuring the particle size, charge state, or particle morphology of the new particle and the to-be-measured micro/nano particle or the marker, and counting the new particle and the to-be-measured micro/nano particle or the marker respectively to obtain their respective count results, and, on the basis of the count results, calculating the concentration of the to-be-measured micro/nano particle bound with the marker. The method of the present application has the advantages of high measurement accuracy, low measurement limit, and stability of chemical reagents.

The magnetic chip detector system can have a first conductor member and a second conductor member both exposed to a liquid flow path and separated from one another by gap, each one of the conductor members having a magnetic field oriented into the liquid flow path, at least a first one of the magnetic fields being actively modifiable; an electrical energy source configured to induce a current circulation across the gap; and a meter configured to measure a response of the gap to the induced current circulation.

The present invention extends to methods, systems, and computer program products for estimating oocyte quality. A machine learning algorithm accesses oocyte training data for a mammalian species (e.g., humans) and trains a neural network to estimate oocyte quality for the mammalian species based on the oocyte training data. The neural network accesses a microscopic image of an oocyte and identifies oocyte features of the oocyte. Based on the identified oocyte features, the neural network estimates oocyte quality, including: (a) predicting a probability of a corresponding embryo maintaining sufficient developmental competence until a specified time after fertilization and (b) predicting another probability of the corresponding embryo reaching a specific embryonic stage after fertilization. An oocyte is selected, from among a plurality of human oocytes including the human oocyte, for a potential recipient based at least in part on the oocyte quality, including based on the probability and the other probability.