Patent Application
20090248318
This application claims priority under 35 U.S.C. §119 to Japanese Patent Application Nos. JP2008-088825 and JP2008-088826 both filed Mar. 28, 2008, the entire contents of which are hereby incorporated by references.
The present invention relates to a sample analyzer, a sample analyzing method, and computer program product for analyzing a sample such as blood, urine and the like.
Particle analyzers for classifying particles in a sample such as blood and urine and the like into a plurality of types of particles are known.
For example, U.S. Patent Publication No. 2007/0111197 discloses a blood analyzer which pre-stores, in a memory, analysis conditions corresponding to animal types, and changes the setting to the correct animal type and reanalyzes a sample using analysis conditions corresponding to the correct animal type after the setting has been changed, when the sample has been analyzed using an incorrect analysis condition. Specifically, this blood analyzer changes the setting range of the fraction level used for fractionating the particles in a particle distribution diagram in accordance with the type of animal.
U.S. Pat. No. 6,391,263 also discloses a blood analyzer for analyzing predetermined components in a sample by changing the degree of detection sensitivity for detecting the components in the sample according to the animal type.
Japanese Patent Publication No. 2002-277381 also discloses a particle analyzer which executes a first processing for discriminating leukocytes and bacteria from other particles in a sample using a first threshold value and a second processing for discriminating only leukocytes in the sample from other particles which may include bacteria using a second threshold value that is larger than the first threshold value, while a flow of a single measurement sample is being formed within a flow cell, when it has been determined that there is a high concentration of bacteria in the sample based on a result of the first processing. Samples which contain a high concentration of bacteria can therefore be accurately measured.
The blood analyzer disclosed in U.S. Patent Publication No. 2007/0111197 fixedly allocates a predetermined analysis condition to a predetermined animal type. However, the allocated analysis condition is not necessarily the optimum condition for the analysis of the target sample when analyzing samples of various types. A problem therefore arises in that analysis precision may be inadequate when the allocated analysis condition is not a suitable condition.
The blood analyzer disclosed in U.S. Pat. No. 6,391,263 requires a sample to be re-aspirated and re-measured to correct the measurement value when there is a low reliability of the analysis result based on detection data obtained at the set detection sensitivity. A problem arises in that the user must re-obtain and measure the sample which requires remeasurement, thus complicating the operation. Furthermore, the disclosure of the particle analyzer of Japanese Patent Publication No. 2002-277381 does not suggest art for changing the condition for detecting particles in a sample, although there is mention of art for accurately measuring a sample which contains a high concentration of bacteria.
A first aspect of the present invention is a sample analyzer, comprising: a sample preparing section for preparing a measurement sample from a sample and a reagent; a detector for detecting a predetermined component contained in one measurement sample prepared by the sample preparing section; and a data processing section being configured to perform operations comprising: (a) generating a plurality of analysis data for analyzing the predetermined component based on a detection result by the detector; (b) selecting one analysis data from the plurality of analysis data; (c) analyzing the predetermined component based on at least the one analysis data selected in the operation (b); and (d) outputting an analysis result obtained in the operation (c).
A second aspect of the present invention is a sample analyzer, comprising: a sample preparing section for preparing a measurement sample from a sample and a reagent; a detector for detecting a predetermined component contained in the measurement sample prepared by the sample preparing section; detection control means for controlling the detector so as to detect the predetermined component contained in one measurement sample under each detection condition of a plurality of detection conditions; and analyzing means for analyzing the predetermined component based on at least one of a plurality of detection results obtained by the detector under each detection condition.
A third aspect of the present invention is a sample analyzing method comprising steps of: (a) detecting a predetermined component from one measurement sample prepared from a sample and a reagent; (b) generating a plurality of analysis data for analyzing the predetermined component based on a detection result obtained in the step (a); (c) selecting one analysis data from the plurality of analysis data; (d) analyzing the predetermined component based on the one analysis data selected in the step (c); and (e) outputting an analysis result obtained in the step (d).
A fourth aspect of the present invention is a computer program product for enabling a computer to control a sample analyzer including: a sample preparing section for preparing a measurement sample from a sample and a reagent; and a detector for detecting a predetermined component contained in the measurement sample, comprising: a computer readable medium, and software instructions, on the computer readable medium, for enabling the computer to perform predetermined operations comprising: (a) generating a plurality of analysis data for analyzing the predetermined component based on a detection result obtained by the detector; (b) selecting one analysis data from the plurality of analysis data; (c) analyzing the predetermined component based on the one analysis data selected in the operation (b); and (d) outputting an analysis result obtained in the operation (c).
A fifth aspect of the present invention is a sample analyzing method comprising steps of: (a) preparing a measurement sample from a sample and a reagent; (b) detecting, under each detection condition of a plurality of detection conditions, a predetermined component contained in one measurement sample prepared in the step (a); and (c) analyzing the predetermined component based on at least one of a plurality of detection results obtained under each detection condition.
A blood analyzer for analyzing blood is specifically described hereinafter as an example of the sample analyzer of the present embodiment based on the drawings. The analysis process thus is a blood cell classification process, and the analysis data are generated as classification data.
The measuring device 1 and the operation and display device 2 are connected via a communication line which is not shown in the drawing. The operation and display device 2 controls the operation of the measuring device 1, processes the measurement data output from the measuring device 1, and obtains analysis results through data communication with the measuring device 1. The measuring device 1 and the operation and display device 2 may also be connected over a network, or may be configured as a single integrated device so as to send and receive data by interprocess communication and the like.
The measuring device 1 detects characteristics information of the leukocytes, reticulocytes, and platelets in the blood using flow cytometry, and transmits the detection data as measurement data to the operation and display device 2. Flow cytometry is a measurement method which forms a sample flow that includes a measurement sample, detects light such as forward scattered light, side scattered light, and side fluorescent light that is emitted by the particles (blood cells) in the measurement sample when the measurement sample is irradiated by laser light to detect the particles (blood cells) in the measurement sample.
The control board 9 is provided with a controller 91 which has a control processor and a memory for the operation of the control processor, twelve-bit A/D converter 92 for converting the signals output from the analog processing section 6 to digital signals, And an operation section 93 for storing the digital signals output from the A/D converter 92 and executing a process for selecting data to be output top the controller 91. The controller 91 is connected to the display and operation section 7 through a bus 94a and an interface 95b, connected to the device mechanism 4 through the bus 94a and an interface 95a, and connected to the operation and display device 2 through a bus 94b and an interface 95c. The operation section 93 outputs the operation results to the controller 91 through an interface 95d and the bus 94a. The controller 91 also transmits the operation results (measurement data) to the operation and display device 2.
The device mechanism 4 is provided with a sample preparing section 41 for preparing a measurement sample from blood and reagent. The sample preparing section 41 prepares leukocyte measurement samples, reticulocyte measurement samples, and platelet measurement samples.
The sampling valve 41b is configured to be capable of determining the amount of blood within the collection tube 41a aspirated by an aspirating pipette which is not shown in the drawing. The reaction chamber 41c is connected to the sampling valve 41b, and is configured to be capable of mixing a predetermined reagent and staining solution with the fixed amount of blood determined by the sampling valve 41b. The reaction chamber 41c is also connected to the detecting section 5, and is configured so that a measurement sample prepared by mixing the predetermined reagent and staining solution in the reaction chamber 41c inflows to the detecting section 5.
The sample preparing section 41 can thus prepare a measurement sample in which the leukocytes are stained and the erythrocytes are hemolyzed as the leukocyte measurement sample. The sample preparing section 41 can also prepare a measurement sample in which the reticulocytes are stained as a reticulocyte measurement sample, and prepare a measurement sample in which the platelets are stained as a platelet measurement sample. The prepared measurement sample is supplied together with a sheath fluid to a sheath flow cell of the detecting section 5 which will be described later.
The light-emitting part 501 is provided to irradiate light on a sample flow which contains a measurement sample passing through the interior of the sheath flow cell 503. The irradiating lens unit 502 is provided to render the light emitted from the light-emitting part 501 into parallel rays. The PD 506 is provided to receive the forward scattered light emitted from the sheath flow cell 503 Note that information relating to the size of the particles (blood cells) in the measurement sample can be obtained from the forward scattered light emitted from the sheath flow cell 503.
The dichroic mirror 508 is provided to separate the side scattered light and the side fluorescent light emitted from the sheath flow cell 503. Specifically, the dichroic mirror 508 is provided to direct the side scattered light emitted from the sheath flow cell 503 to the PD 512, and to direct the side fluorescent light emitted from the sheath flow cell 503 to the APD 511. The PD 512 is also provided to receive the side scattered light. Internal information relating to the size and the like of the nucleus of the particles (blood cells) within the measurement sample can be obtained from the side scattered light emitted from the sheath flow cell 503.
The APD 511 is also provided to receive the side fluorescent light. When light irradiates a fluorescent substance such as a stained blood cell, light is emitted which has a longer wavelength that that of the irradiating light. The intensity of the fluorescence increases as the degree of staining increases. Therefore, characteristic information related to the degree of staining of the blood cell can be obtained by measuring the intensity of the side fluorescent light emitted from the sheath flow cell 503. It is therefore possible to perform other measurements in addition to classifying leukocytes by the difference in the side fluorescent light intensity. PD 506, PD 512, and APD 511 convert the optical signals of the respectively received light to electrical signals, and the converted electrical signals are then amplified by amplifiers 61, 62, and 63 and the amplified signals are transmitted to the control board 9.
In the first embodiment, the light-emitting part 501 emits light with an output of 3.4 mW during the leukocyte classification measurement (hereinafter referred to as “DIFF measurement”). The light-emitting part 501 also emits light with an output of 6 mW during the reticulocyte measurement (hereinafter referred to as “RET measurement”). The light-emitting part also emits light at an output of 10 mW during platelet measurement (hereinafter referred to as “PLT measurement”).
The memory device 23 is configured by an internal fixed type memory device (hard disk) or the like. The memory device 23 is also provided with a patient information memory device 232 which stores information relating to patients and including the age information of the patient (subject) associated with identification information which can be obtained by reading a barcode label.
The communication interface 27 is connected to the internal bus 28, and is capable of sending and receiving data when connected to the measuring device 1 through a communication line. That is, the communication interface 27 sends information instructing the start of a measurement and the like to the measuring device 1, and receives measurement data.
The input device 24 is a data input medium such as a keyboard and mouse or the like. The display device 25 is a display device such as a CRT monitor, LCD or the like, and graphically displays the analysis results. The output device 26 is a printing device such as a laser printer, inkjet printer or the like.
In the measuring device 1 and operation and display device 2 of the sample analyzer having the structure described above, a scattergram such as that shown in
In the sample analyzer of the first embodiment, a lymphocyte distribution region 101 in which lymphocytes are assumed to be distributed, a monocyte distribution region 102 in which monocytes are assumed to be distributed, an eosinphil distribution region 103 in which eosinophils are assumed to be distributed, a neutrophil distribution region 104 in which neutrophils are assumed to be distributed, and a basophil distribution region in which basophils are assumed to be distributed are predetermined based on previous statistical values of adult blood, as shown in
The present inventors acknowledge that the blood cells contained in child blood have lower stainability than blood cell contained in adult blood. It is therefore clear that the sampling values will be distributed somewhat lower in each region of the original distributions shown in
As shown in
When the distribution trend from the scattergram is entirely shifted below the assumed region as described above, the measurement data can be determined to be data from child blood, and it can be understood that the region 111 in which the sampling values cluster must be shifted in the direction of the arrow 112 to improve the accuracy of the classification process. A means is disclosed below for shifting the measurement data of child blood in order to realize a classification process which has better accuracy using the same blood cell classification method as when classifying leukocytes based on adult blood, even when the measurement data are data from child blood.
The CPU 21 of the operation display device 2 determines whether or not a measurement start instruction has been received (step S903); when the CPU 21 determines that the measurement start instruction has been received (step S903: YES), the CPU 21 transmits the instruction information specifying the start of measurement to the measuring device 1 (step S904). The controller 91 of the measuring device 1 determines whether or not instruction information specifying to start a measurement has been received (step S916); when the controller 91 has determined that a instruction information specifying to start a measurement has been received (step S916: YES), the controller 91 has the barcode reader (not shown in the drawing) read the barcode label (not shown in the drawing) adhered to the container which contains the blood to obtain the blood identification information (sample ID) (step S917). When the controller 91 has determined that instruction information specifying to start a measurement has not been received (step S916: NO), the controller 91 skips steps 917 through S921.
The controller 91 transmits the obtained identification information (sample ID) to the operation display device 2 (step S918), and the CPU 21 of the operation display device 2 determines whether or not the identification information (sample ID) has been received (step S905). When the CPU 21 determines that the identification information (sample ID) has not been received (step S905: NO), the CPU 21 enters a reception standby state. When the CPU 21 determines that the identification information (sample ID) has been received (step S905: YES), the CPU 21 obtains the patient information by querying the patient information memory section 232 of the memory device 23 (step S906), and transmits the patient information to the measuring device 1 (step S907).
The controller 91 of the measuring device 1 then determines whether or not the patient information has been received (step S919); when the controller 91 determines that the patient information has not been received (step S919: NO), the controller 91 enters a reception standby state. When the controller 91 determines that the patient information has been received (step S919: YES), the controller 91 controls the sample preparing section 41 so as to prepare a measurement sample, and thereafter starts the measurement of the measurement sample (step S920). Specifically, the DIFF measurement is executed, and the electrical signals corresponding to the intensity of the received side scattered light and side fluorescent light are transmitted to the control board 9 via the detecting section 5 and the analog processing section 6. The A/D converter 92 of the control board 9 converts the obtained analog signals to 12-bit digital signals, and the operation section 93 subjects the digital signals output from the A/D converter 92 to predetermined processing, and transmits the processed signals to the controller 91. The controller 91 transmits the received 12-bit integer sequence information as measurement data to the operation display device 2 (step S921).
The CPU 21 of the operation display device 2 determines whether or not the measurement data have been received (step S908); when the CPU 21 determines that the measurement data have been received (step S908: YES), the CPU 21 executes an analysis process based on the received measurement data (step S909). The CPU 21 skips steps S904 through S909 when the CPU 21 has determined that a measurement start instruction has not been received (step S903: NO), and the CPU 21 enters a reception standby state when the CPU 21 has determined that the measurement data have not been received (step S908: NO).
The CPU 21 determines whether or not n is greater than a predetermined number (step S1003); when the CPU 21 has determined that n is less than the predetermined number (step S1003: NO), the CPU 21 increments n by 1 (step S1004), changes the compression ratio of the measurement data (step S1005), and returns the process to step S1002. Then, the above process is repeated. When the CPU 21 has determined that n is greater than the predetermined number (step S1003: YES), the CPU 21 executes the classification process using the respective first through nth classification data (step S1006), and stores the respective classification results in the memory device 23 (step S1007).
Specifically, when the CPU 21 generates the classification data, the 12-bit integer sequence information obtained from the measurement device 1 is compressed by a predetermined compression ratio. For example, the information may be compressed to 8-bit integer sequence information, compressed to 10-bit integer sequence information, or an optional compression ratio may be selected.
In the first embodiment, measurement data are pre-obtained as integer sequence information which has a number of bits (12-bits) that is greater than the number of bits (8-bits) used as classification data, and a plurality of classification data at various compression ratios are generated by compressing the pre-obtained measurement data by an optional compression ratio. A proportion which maintains the continuity of the integer sequence values can be increased thereby. For example, since the 12-bit integer sequence information is multiplied 1.2/16 times when generating the second classification data for child blood compared to multiplying the 12-bit integer sequence information 1/16 times when generating the classification data for adult blood, the range of the measurement data which are the same integer values when multiplied 1.2/16 times is broadened, thus making errors difficult to occur.
More specifically, consider the case when the number of each element (X1, X2) (where X1, X2=0, 1, 2 . . . ) is designated F (X1, X2) in two-dimensional distribution data DN which has, for example, N(N (N being a natural number) individual elements, and the two-dimensional distribution data Dn are compressed to two-dimensional distribution data Dm which has M(M (M being a natural number) individual elements. Furthermore, M<N obtains.
Each element (X1, X2) in the two-dimensional distribution data Dn which has N(N individual elements corresponds to the elements (U1, U2) (where U1, U2=0, 1, 2, . . . , M) shown in equation (1) in the distribution data Dm. In equation (1), Int(x) is a function which represents the integer part of the argument x. This is equivalent, for example, to the process of compressing 12-bit measurement data to 8-bits.
(U1, U2)=(Int(X1(M/N), Int(X2(M/N) (1)
Next, when the two-dimensional distribution data DL which has L(L individual elements in a partial region within the two-dimensional distribution data Dm are converted to two-dimensional distribution data which has M(M individual elements (L<M<N), the elements (X1, X2) (where X1, X2=0, 1, 2, . . . , N(L/M) in the distribution data Dn corresponds to the elements (V1, V2) (where V1, V2=0, 1, 2, . . . , M) in the distribution data Dml, as shown in equation (2). This is equivalent to a process which shifts up the 8-bit data.
(V1, V2)=(Int(X1(M2/(N(L), Int(X2(M2/(N(L) (2)
That is, the number of elements of the distribution data Dml can be calculated and converted to smooth distribution data by initially converting (expanding) the two-dimensional distribution data DL which have an L(L element partial region to two-dimensional distribution data which have N(N elements, and then converting to two-dimensional distribution data which have M(M elements.
Returning to
The sequence of the classification data selection process shown in step S1008 of
The CPU 21 of the operation processing device 2 determines whether or not a subject is a child based on the age information included in the patient information received from the measuring device 1 (step S1011). “Child” in this case may mean a newborn, infant, or toddler. The user of the sample analyzer of the first embodiment of the present invention may optionally set the sample analyzer, for example, so that a subject admitted to a pediatric department or obstetrics and gynecology department is designated as a “child,” or a child who is a preschooler may be designated as a “child,” rather than a subject below a predetermined age. The manufacturer who fabricates the sample analyzer may also set the range of the “child.” When the CPU 21 has determined that the subject is a child (step S1101: YES), the CPU 21 selects a second classification information obtained by a second compression ratio (step S1102), and the process returns to step S1009.
When the CPU 21 has determined that the subject is not a child (step S1101: NO), the CPU 21 counts the particles contained in a region in which there are overlapping collection region of sampling values of the lymphocytes, monocytes, eosinophils, neutrophils, basophils and the like, for example region A in
When the CPU 21 has determined that the particle number (N1) of the overlap region in the first classification data is less than the particle number (N2) of the overlap region in the second classification data (step S1104: YES), the CPU 21 then determines whether or not the particle number (N1) of the overlap region in the first classification data is less than the particle number (N3) of the overlap region in the third classification data (step S1105).
When the CPU 21 has determined that the particle number (N1) of the overlap region in the first classification data is less than the particle number (N3) of the overlap region in the third classification data (step S1105: YES), the CPU 21 selects the first classification data (step S1106), and the process returns to step S1009.
When the CPU 21 has determined that the particle number (N1) of the overlap region in the first classification data is greater than the particle number (N2) of the overlap region in the second classification data (step S1104: NO), or when the CPU 21 has determined that the particle number (N 1) of the overlap region in the first classification data is greater than the particle number (N3) of the overlap region in the third classification data (step S1105: NO), then the CPU 21 determines whether or not the particle number (N2) of the overlap region in the second classification data is less than the particle number (N3) of the overlap region in the third classification data (step S1107).
When the CPU 21 has determined that the particle number (N2) of the overlap region in the second classification data is less than the particle number (N3) of the overlap region in the third classification data (step S1107: YES), the CPU 21 selects the second classification data (step S1102), and the process returns to step S1009. When the CPU 21 has determined that the particle number (N2) of the overlap region in the second classification data is greater than the particle number (N3) of the overlap region in the third classification data (step S1107: NO), the CPU 21 selects the third classification data (step S1108), and the process returns to step S1009.
Note that the method for selecting classification data is not specifically limited, inasmuch as, for example, the CPU 21 may make the selection based on the position of appearance, and the degree of overlap of the lymphocyte, monocyte, basophil, neutrophil, and eosinophil collection regions or the like. Specifically, the selection may be made by (1) selecting via the magnitude of the number of particles contained in the region in which there are overlapping collection regions, (2) selecting via the magnitude of the distance between the representative value of the collection region and the representative value of each presumed region, (3) selecting via the relative position of the collection region and each presumed region, and (4) selecting via the magnitude of the surface area of the collection region and the surface area of each presumed region, or combinations of these methods.
Returning to
The reclassification instruction is issued by using the mouse to select either of the secondary display regions 212 and 213 which display classification results based on classification data which the user wants reclassified. For example, when the secondary display region 212 is selected, the displayed contents of the secondary display region 212 and the primary display region 211 are switched, and the counting process is executed.
Returning to
The controller 91 of the measuring device 1 determines whether or not shutdown instruction information has been received (step S922); when the controller 91 has determined that shutdown instruction information has not been received (step S922: NO), the controller 91 returns the process to step S916 and the previously described process is repeated. When the controller 91 has determined that shutdown instruction information has been received (step S922: YES), the controller 91 executes shutdown (step S923) and the process ends.
The first embodiment is capable of executing the counting process using optimum classification data and even in the case of differences such as different animal species, different ages and different sex, by pre-generating a plurality of classification data having mutually different compression ratios and selecting optimum classification data according to the sample. The sample analysis precision can therefore be improved.
The sample analyzer of a second embodiment of the present invention is described in detail below based on the drawings. Structures of the sample analyzer of the second embodiment of the present invention which are identical to the first embodiment are designated by like reference numbers and detailed description thereof is omitted. The second embodiment differs from the first embodiment in that a plurality of classification data having mutually different detection conditions when detection characteristic information are generated at the same compression ratios without pre-generating a plurality of classification data having mutually different compression ratios.
The CPU 21 of the operation display device 2 determines whether or not a measurement start instruction has been received (step S1303); when the CPU 21 has determined that a measurement start instruction has not been received (step S1303: NO), the CPU 21 skips the subsequent steps S1304 through S1309. When the CPU 21 has determined that a measurement start instruction has been received (step S903: YES), the CPU 21 transmits instruction information specifying to start a measurement to the measuring device 1 (step S1304). The controller 91 of the measuring device 1 determines whether or not instruction information specifying to start a measurement has been received (step S1316); when the controller 91 has determined that a instruction information specifying to start a measurement has been received (step S916: YES), the controller 91 has the barcode reader (not shown in the drawing) read the barcode label (not shown in the drawing) adhered to the container which contains the blood to obtain the blood identification information (sample ID) (step S1317). When the controller 91 has determined that instruction information specifying to start a measurement has not been received (step S1316: NO), the controller 91 skips steps 1317 through S1321.
The controller 91 transmits the obtained identification information (sample ID) to the operation display device 2 (step S1318), and the CPU 21 of the operation display device 2 determines whether or not the identification information (sample ID) has been received (step S1305). When the CPU 21 determines that the identification information (sample ID) has not been received (step S1305: NO), the CPU 21 enters a reception standby state. When the CPU 21 determines that the identification information (sample ID) has been received (step S1305: YES), the CPU 21 obtains the patient information by querying the patient information memory section 232 of the memory device 23 (step S1306), and transmits the patient information to the measuring device 1 (step S1307).
The controller 91 of the measuring device 1 then determines whether or not the patient information has been received (step S1319); when the controller 91 determines that the patient information has not been received (step S1319: NO), the controller 91 enters a reception standby state. When the controller 91 determines that the patient information has been received (step S1319: YES), the controller 91 controls the sample preparing section 41 so as to prepare a measurement sample, and thereafter starts the measurement of the measurement sample (step S1320). Specifically, the DIFF measurement is executed, and the electrical signals corresponding to the intensity of the received side scattered light and side fluorescent light are transmitted to the control board 9 via the detecting section 5 and the analog processing section 6. The A/D converter 92 of the control board 9 converts the obtained analog signals to 12-bit digital signals, and the operation section 93 subjects the digital signals output from the A/D converter 92 to predetermined processing, and transmits the signals to the controller 91. The controller 91 transmits the received 12-bit integer sequence information as measurement data to the operation display device 2 (step S1321).
The controller 91 starts supplying the measurement target measurement sample to the sheath flow cell503 (step S1404), and starts storing the characteristic information detected at the nth detection sensitivity in an internal memory (step S1405). The controller 91 determines whether or not 10 seconds have elapsed since the start of storing characteristic information at the nth detection sensitivity (step S1406); when the controller 91 has determined that 10 seconds have not elapsed (step S1406: NO), the controller 91 enters a time-elapse standby state; when the controller 91 has determined that 10 seconds have elapsed (step S1406: YES), the controller 91 stops storing in the internal memory the characteristic information detected at the nth detection sensitivity (step S1407), and determines whether or not the counter n has exceeded a predetermined number (step S1408). When the controller 91 has determined that n does not exceed the predetermined number (step S1408: NO), the controller 91 increments the counter n by 1 (step S1409), the process returns to step S1403 and the above process is repeated. When the controller 91 has determined that n does exceed the predetermined number (step S1408: YES), the controller 91 returns the process to step SI 321 of
Returning to
The CPU 21 determines whether or not n is greater than a predetermined number (step S1503); when the CPU 21 has determined that n is less than the predetermined number (step S1503: NO), the CPU 21 increments n by 1 (step S1504), and returns the process to step S1502 using the same compression ratio. When the CPU 21 has determined that n is greater than the predetermined number (step S1503: YES), the CPU 21 executes the classification process using the respective first through nth classification data (step S1505), and stores the respective classification results in the memory device 23 (step S1506).
Specifically, when the CPU 21 generates the classification data, the 12-bit integer sequence information obtained from the measurement device 1 is compressed by a predetermined compression ratio. For example, the information may be compressed to 8-bit integer sequence information, compressed to 10-bit integer sequence information, or an optional compression ratio may be selected.
The CPU 21 selects one classification data from the plurality of classification data stored in the storage device 23 (step S1507), reads the selected classification data from the storage device 23, counts the numbers of blood cells such as lymphocytes, monocytes, eosinophils, neutrophils, and basophils and the like (step S1508), and stores the count results in the storage device 23 (step 1409). The CPU 21 also generates a scattergram such as that shown in
Note that the sequence of the selection classification data process in step S1507 is identical to that of
Returning to
The screen displaying the classification results displayed on the display device 25 of the operation display device 2 of the second embodiment of the present invention is identical to that of
The CPU 21 determines whether or not a shutdown instruction has been received (step S1313); when the CPU 21 determines that a shutdown instruction has not been received (step S1313: NO), the CPU 21 returns the process to step SI 303 and the process described above is repeated. When the CPU 21 has determined that a shutdown instruction has been received (step S1313: YES), the CPU 21 transmits shutdown instruction information to the measuring device 1 (step S1314).
The controller 91 of the measuring device 1 determines whether or not shutdown instruction information has been received (step S1322); when the controller 91 has determined that shutdown instruction information has not been received (step S1322: NO), the controller 91 returns the process to step S1316 and the previously described process is repeated. When the controller 91 has determined that shutdown instruction information has been received (step S1322: YES), the controller 91 executes shutdown (step S1323) and the process ends.
The second embodiment is capable of executing the counting process using optimum classification data and thus improves the accuracy of sample analysis even in the case of differences such as different animal species, different ages and different sex, by pre-generating a plurality of classification data having mutually different detection conditions and selecting optimum classification data according to the sample.
According to the second embodiment, components contained in a single measurement sample can be detected under a plurality of detection conditions. Analysis can therefore be performed based on detection results obtained under optimum detection conditions, thereby improving analysis precision without requiring the same sample to be re-measured, and without the user performing complex operations even when reanalysis must be performed under changed detection conditions.
According to the second embodiment, detection conditions are continuously changed while a single measurement sample passes through a sheath flow cell, and components contained in a single measurement sample are detected under each of the continuously set detection conditions. The measurement can therefore be completed in a short time since there is no need to supply the single measurement sample a plurality of times to the flow cell. Furthermore, degradation of the measurement sample due to supplying a single measurement sample to the flow cell multiple times, as well as reduction of analysis precision are prevented.
In the first and second embodiments, it is unnecessary the user of the analyzer herself to select the optimum classification data from among a plurality of classification data since a single classification data is automatically selected from a plurality of classification data. The operational burden on the user is therefore reduced.
Note that although the second embodiment has been described in terms of multiple setting of the detection sensitivity of the PD 506 and512, and APD 511 which are photoreceptors for receiving the light emitted from the light-emitter 501 as the plurality of detection conditions, the detection conditions are not limited to detection sensitivity. For example, there may be multiple setting of the amplification factor of the amplifiers 61, 62, and 63 which amplify the electric signals resulting from the photoelectric conversion of the received light signals. Moreover, the intensity of the light emitted by the light-emitter 501 may also have multiple settings.
Although the controller 91 of the measuring device 1 executes controls to change the detection sensitivity of the detecting section 5 in the second embodiment described above, the CPU 21 of the operation display device 2 may also execute controls for changing the detection sensitivity of the detecting section 5. The CPU 21 of the operation display device 2 may also execute the control for changing the amplification factor when changing the detection condition of the detecting section 5 by changing the amplification factor of the amplifiers 61, 62, and 63, and similarly, the CPU 21 of the operation display device 2 may also execute control to change the emission intensity when changing the detection condition of the detecting section 5 by changing the intensity of the light emitted from the light-emitter 501. The CPU 21 of the operation display device 2 may also execute controls and the like relating to starting and stopping the storage to the internal memory of characteristic information detected by the detecting section 5, as well as controls relating to starting and stopping the supply of the measurement sample, and controls for preparing the measurement sample in the measurement sample preparing section 41.
Although a blood analyzer which analyzes blood cells contained in blood that is used as a sample is described by way of example in the above first and second embodiments, the present invention is not limited to this example inasmuch as the same effect may be expected when the present invention is applied to a sample analyzer which analyzes samples which contain biological particles such as cells in urine. Although the analysis results are displayed by the display device 25 of the operation display device 2 in the first and second embodiments described above, the present invention is not specifically limited to this example inasmuch as the results may also be displayed on a display device of another computer connected to a network.
Although classification data are generated by obtaining 12-bit integer sequence information as measurement data from the measuring device 1 and 0compressing the 12-bit integer sequence information to 8-bit integer sequence information in the first and second embodiments, the present invention is not limited to this example inasmuch as, for example, 16-bit integer sequence information may be obtained from the measuring device 1 to generate 10-bit classification data. The measurement data and classification data need not be integer sequence information. Although the plurality of classification data represents the side scattered light intensity and side fluorescent light intensity as a plurality of common indicators in the first and second embodiments, the side scattered light intensity alone or the side fluorescent light intensity alone may represent a single indicator, a single classification data may be represented by the side scattered light intensity and the side fluorescent light intensity, other classification data may be represented by the forward scattered light intensity and side fluorescent light intensity and the like to represent a mutually different plurality of indicators.
The first and second embodiments are also applicable when analyzing blood which contains, for example, megakaryocytes, since the blood cell classification process is conducted based on a plurality of generated classification data. Since a megakaryocyte is cell which has a large nucleus, the megakaryocyte is characteristically easily stainable. Therefore, blood containing megakaryocytes can be measured by flow cytometry and a two-dimensional scattergram can be prepared which has side fluorescent light as a single parameter, and there may be cases when megakaryocytes cannot be classified readily from the cells in the blood since the megakaryocytes collect in the upper level position of the scattergram. In this case, for example, a third classification data can be generated which has integer sequence information which is more compressed than the first classification information to be used as classification data when classifying the megakaryocytes. When a scattergram such as that shown in
Note that the present invention is not limited to the above embodiments and may be variously modified and transposed insofar as such modification is within the scope of the meaning of the present invention.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2008-088825 | Mar 2008 | JP | national |
| 2008-088826 | Mar 2008 | JP | national |
