A method of forming a nanopore in a lipid bilayer is disclosed. A nanopore forming solution is deposited over a lipid bilayer. The nanopore forming solution has a concentration level and a corresponding activity level of pore molecules such that nanopores are substantially not formed un-stimulated in the lipid bilayer. Formation of a nanopore in the lipid bilayer is initiated by applying an agitation stimulus level to the lipid bilayer. In some embodiments, the concentration level and the corresponding activity level of pore molecules are at levels such that less than 30 percent of a plurality of available lipid bilayers have nanopores formed un-stimulated therein.

A method of forming a nanopore in a lipid bilayer is disclosed. A nanopore forming solution is deposited over a lipid bilayer. The nanopore forming solution has a concentration level and a corresponding activity level of pore molecules such that nanopores are substantially not formed un-stimulated in the lipid bilayer. Formation of a nanopore in the lipid bilayer is initiated by applying an agitation stimulus level to the lipid bilayer. In some embodiments, the concentration level and the corresponding activity level of pore molecules are at levels such that less than 30 percent of a plurality of available lipid bilayers have nanopores formed un-stimulated therein.

A multi-parameter automatic blood cell counting device includes a basic information generating part, an immune cell subgroup detection part, an immunodynamic change analyzing part, a storage part, and display part. The basic information generating part acquires examination information about a blood count of blood components and a white blood cell image and CD classification analysis information about monoclonal antibodies that bind to lymphocyte surface antigens. The immune cell subgroup detection part detects information about immune cell subgroups required for analyzing an immune status of a subject based on the examination information and the CD classification analysis information. The immunodynamic change analyzing part identifies information about the change in immunodynamics of the subject based on the information detected by the immune cell subgroup detection part and the examination information and CD classification analysis information stored in the storage part, and displays an image of the identified information on the display part.

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).

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).

Described herein are cartridges and devices for operating said cartridges for analyzing a biological sample, such as a blood or saliva sample. Also described herein is an impedance sensor for analyzing a biological sample. Further described herein are methods of determining a cell count or detecting an analyte in a biological sample, which can include transporting the biological sample through a sensor comprising a channel or pore; applying an electrical current or voltage to the channel or pore; detecting an impedance within the channel or pore; and determining a cell count or detecting the analyte based on the detected impedance. Also described herein is an electrowetting electrode array that is configured to transport aqueous solutions using low voltage, such as about 50 volts or less. Further described herein are methods of transporting an aqueous liquid using electrowetting electrodes.

A sample analyzer prepares a measurement sample from a blood sample or a body fluid sample which differs from the blood sample; measures the prepared measurement sample; obtains characteristic information representing characteristics of the components in the measurement sample; sets either a blood measurement mode for measuring the blood sample, or a body fluid measurement mode for measuring the body fluid sample as an operating mode; and measures the measurement sample prepared from the blood sample by executing operations in the blood measurement mode when the blood measurement mode has been set, and measuring the measurement sample prepared from the body fluid sample by executing operations in the body fluid measurement mode that differs from the operations in the blood measurement mode when the body fluid measurement mode has been set, is disclosed. A computer program product is also disclosed.

A sample analyzer prepares a measurement sample from a blood sample or a body fluid sample which differs from the blood sample; measures the prepared measurement sample; obtains characteristic information representing characteristics of the components in the measurement sample; sets either a blood measurement mode for measuring the blood sample, or a body fluid measurement mode for measuring the body fluid sample as an operating mode; and measures the measurement sample prepared from the blood sample by executing operations in the blood measurement mode when the blood measurement mode has been set, and measuring the measurement sample prepared from the body fluid sample by executing operations in the body fluid measurement mode that differs from the operations in the blood measurement mode when the body fluid measurement mode has been set, is disclosed. A computer program product is also disclosed.

Systems and methods for providing clinical decision support information including one or more clinical acuity recommendations to a clinician is provided. The systems and methods can include obtaining one or more parameters associated with a blood sample obtained from an individual, the one or more parameters can include a monocyte distribution width (MDW) value. The systems and methods can also include comparing the MDW value with one or more predetermined criteria; and providing a clinical acuity recommendation at least partly in response to the comparing the MDW value with the one or more predetermined criteria.

An apparatus using a feature value extracted from a pulse waveform representing a transient change in ion current flowing between electrodes when a particle passes through a pore, as teacher data and data subject to analysis for machine learning. The apparatus includes a machine-learning program, a searcher, a host attribute table, and a feature value table, a host attribute table is searched using first host attribute information as a search key to extract a first host ID and a second host ID associated with the first host attribute information, a feature value table is searched using a first host ID as a search key to extract a first teacher feature value group obtained from first known particles of a first type, a feature value table is searched using a second host ID as a search key to extract a second teacher feature value group obtained from second known particles of the first type, learning is performed using the first teacher feature value group and the second teacher feature value group as teacher data and first particle type information representing the first type as a teacher label to calculate machine learning optimization parameters, and the machine learning optimization parameters with an input value that is a feature value group subject to analysis obtained from an unknown particle with a first host attribute are used to discriminate whether or not the unknown particle is of the first type.