JPH0638065B2 - Densitogram correction method - Google Patents

Description

TECHNICAL FIELD The present invention relates to a method for correcting a densitogram in an electrophoretic device.

[Conventional technology]

Serum protein fractionation by electrophoresis is widely used as one item of primary screening because of the large number of pathological information obtained in a single analysis. Several years ago, an automated technique was established for serum protein fractionation by this electrophoresis method, and nowadays, automatic analysis using an electrophoresis device has become the mainstream. In this electrophoresis apparatus, a sample such as serum is applied to a support such as a cellulose acetate membrane by an applicator and allowed to migrate for a predetermined time in an electrophoresis tank,
After dyeing, decolorizing and drying in a dyeing tank, photometry is performed with a densitometer, and based on the sampling data, albumin (Alb), α ₁ -globulin (α ₁ ), α _2-
A / G ratio, which is the fraction% of each fraction of globulin (α ₂ ), β-globulin (β) and γ-globulin (γ), and the fraction% of Alb to total globulin of α ₁ , α ₂ , β, γ The absolute concentration value of each fraction is calculated and displayed along with the migration pattern (densitogram) on a predetermined report or on a display device such as a CRT.

[Problems to be solved by the invention]

However, in the past, in order to decipher the pathological condition information from the analysis results displayed in reports and CRTs, considerable experience and skill are required, and it is the current situation that it depends on the skill of the doctors and laboratory technologists involved in clinical practice.

On the other hand, recently, an attempt has been made to easily read the pathological condition information from the analysis result obtained by the electrophoresis apparatus, and for example, a flowchart of the pathological condition classification as shown in FIG. 21 has been proposed. In this classification of pathological conditions, the characteristic points of electrophoretic images of M protein (monoclonal protein), γ inhibition, β inhibition, and β-γ bridging must be read from the densitogram, but in the conventional densitogram, the highest concentration Since the peak of Alb with high concentration is always constant and each of the fractionated protein concentrations is expressed relatively, it is difficult to interpret the above feature points from such a densitogram. It is extremely difficult even by a skilled doctor or laboratory technician who has experience.

As a method for solving such a problem, it is considered that the application amount of serum is always constant and the measured concentration is recorded as it is, but it is extremely difficult to always apply a constant amount of a small amount of sample uniformly. In addition, when comparing the analysis result of the measurement sample with the analysis result of the normal sample as a reference and the data related to the normal fluctuation range, or when comparing the time-series data of the subject, the comparison target The data are: temperature and humidity of the analysis environment, fatigue of buffer solution (pH value,
Unless analyzed under the same analytical conditions with respect to ionic strength, buffer capacity), support separation capacity, etc., the degree of influence will vary depending on the item, but a slight change will occur in the migration image. That is, if the analysis conditions are different, even if the same sample is analyzed, its densitogram becomes as shown by the solid line and the broken line in FIG. 22, and there is a problem that an accurate comparison judgment cannot be made.

The present invention was made by paying attention to such conventional problems, and can analyze the pathological condition information by the electrophoretic image with high accuracy even if the application amount of the sample and the analysis conditions are different. It is an object of the present invention to provide a method for correcting a densitogram that can reduce the burden on doctors and laboratory technologists.

[Means and Actions for Solving Problems]

In order to achieve the above object, in the present invention, a standard densitogram related to a normal sample is obtained in advance, a normalized densitogram of a measurement sample is obtained, and the analysis conditions of this measurement sample are almost the same as the analysis conditions. Then, a normalized densitogram of a normal sample is obtained, the standard densitogram is compared with the normalized densitogram of the normal sample, and the normalized densitogram of the measured sample is corrected based on the comparison result. It is characterized by that.

〔Example〕

FIG. 1 is a schematic cross-sectional view showing the structure of a main part of an example of a densitometer in an electrophoretic device embodying the present invention.

Support 1 that has been dyed, decolorized and dried in a dyeing tank
Is conveyed by the feed roller 2 to the photometric unit 4 containing the decalin 3, where it is photometrically scanned by the photometric device 5 at a constant speed, for example, 8 mm / sec, and then ejected by the paper ejection roller 6.

The photometric device 5 is configured to receive the light from the light source 5a passing through the support 1 by the light receiving element 5b, and the photometric device 5 having the light source 5a and the light receiving element 5b is used as shown in FIG. The electrophoretic image 7 formed on the support 1 is photometrically scanned by moving the electrophoretic image 7 on the support 1 in the scanning direction b orthogonal to the transport direction a.

FIG. 3 is a block diagram showing a configuration of a main part of an example of a data processing device that processes an output of the photometric device 5. This data processing device includes a logarithmic amplifier 12, an A / D converter 13, a CPU 14, a memory.
It is equipped with 15, keyboard 16, CRT 17, floppy disk 18 and printer 19. The photoelectric conversion output obtained from the light receiving element 5b by photometric scanning is logarithmically amplified by the logarithmic amplifier 12 and converted into absorbance, and then the sampling rate is determined by the analysis conditions such as the migration time in the A / D converter 13 (for example, 12 msec). It is sampled in synchronization with the clock corresponding to, converted into a digital signal, and stored in the memory 15 under the control of the CPU 14.

In this embodiment, for each support, a normal sample serving as a reference is applied to at least one of the supports and allowed to migrate together with the measurement sample to be analyzed, and the sampling data stored in the memory 15 of these migration images is moved respectively. A smoothing process, which is an averaging process, is performed, and a normalization process is performed after an auto-zero process that removes a base floating amount due to the amount of light. In addition, memory 15
Alternatively, in the floppy disk 18, a normalized densitogram (hereinafter referred to as a reference densitogram) regarding a normal specimen to be used and reference data related to a normal electrophoretic image are stored in advance. Here, the reference densitogram for a normal sample is a pooled serum prepared from normal human serum as a normal sample or a commercially available control serum, which is analyzed multiple times under predetermined analysis conditions, and the normalized data are obtained. Store after averaging. Further, as reference data related to a normal electrophoretic image, based on the analysis results of a large number of normal human sera under predetermined analysis conditions, data on the concentration (span) at each point and the normal fluctuation range at that point, Data regarding various waveform feature points is determined and stored.

Next, obtain the ratio _RA of the data at each point of the normalized densitogram of the normal sample that was run along with the measurement sample and the data of each corresponding point of the previously stored reference densitogram, and obtain the data at each point. _A correction process is performed to correct the normalized densitogram of the measurement sample based on the ratio _RA .
Then, based on the corrected densitogram of the measured sample, arithmetic processing for obtaining each fraction%, A / G ratio, etc. is performed, and increase / decrease of each fraction, M protein, γ suppression, β-γ bridging, An analysis process that automatically analyzes the presence or absence of characteristic points such as reading based on comparison with pre-stored reference data, displays the results in the prescribed columns of CRT17 and report 20, and at the same time The corrected densitograms of the above are also displayed in the prescribed columns of CRT17 and Report 20, respectively, in the same manner, overlapping with the migration patterns associated with the densitograms of normal samples.

Hereinafter, the above-described normalization processing, correction processing, analysis processing, and duplicate display example of densitogram will be described in more detail.

FIG. 4 shows a flowchart of an example of the normalization process. In this example, the number of data points related to the specified migration length in normalization is 350, and the stable Alb peak position as a prominent peak in the migration image is shown at 100 data points at 200 data points in the migration image. Match the peak position of β whose appearance position is stable. Ie Alb
Using the respective peak positions of β and β as reference points, these reference points are made to correspond to 100 data points and 200 data points of 350 data positions in the normalization, respectively.

The number of sampling data in the scanning range of the photometric device 5 is more than 350.

In this normalization process, first, in step I, the memory 15 is set for each electrophoretic image of the normal sample and the measurement sample.
A related to migration length from sampling data stored in
Detect the reference points that are the peak positions of lb and β.

The method of detecting this reference point will be described below.

First reference point detection method From the sampling data of the electrophoretic image, data that exceeds a predetermined threshold value such that both end points become the end points of the electrophoretic image as shown in FIG. 5 is extracted, and both end points of the extracted data are extracted. The presence or absence of peaks in a predetermined range l ₁ and l ₂ from
The reference point is detected as the peak position of Alb and the peak position of β.

Second reference point detection method Sampling data of electrophoretic images are sequentially integrated from both end points,
Positions at which those values become a predetermined value or a predetermined ratio with respect to the total integrated value are set as end points of the electrophoretic image, and the peaks in a predetermined range are set from the end points in the same manner as in the first method. Then, the reference points are detected by using those peak positions as the peak positions of Alb and β. For example, if the cumulative value from the positive polarity side of the migration polarity to the Alb direction is set to 2% of the total cumulative value and the cumulative value from the negative electrode side to the γ direction is set to 1% of the total cumulative value, it will be almost the same as the migration length visually obtained. To do.

Third reference point detection method First, for a normal sample, the peak position of Alb is detected by the above first or second method, and then the third peak position in the negative polarity direction of the migration polarity from Alb is the peak position of β. To detect each reference point. After that, the peak position of Alb was similarly detected for the measurement sample, and then the Alb peak of the normal sample previously obtained from this Alb peak position was detected.
A peak is detected in the vicinity of a data point number equal to the data point number associated with the migration length between the peak position and the β peak position, and each reference point is detected using the peak position as the β peak position. When detecting the peak position of Alb from the electrophoretic sampling data,
Is stable as a prominent peak, so a relatively high threshold value may be set, and the maximum data density value exceeding this may be detected as the peak position of Alb. As described in −22622, the maximum value D _M is detected from all the sampled data, and the peak of 1/16 or more of the maximum value is detected from the positive electrode side of the migration polarity.
May be detected as the peak position P _M. Where 1/1
6 to remove the peaks of prealbumin, D _M is
It is empirically determined according to various pathological conditions so that P _M can be detected as the peak position of Alb even if it is not Alb but other components, and it is not particularly fixed.

By any one of the above detection methods, the stable Alb peak position as a prominent peak position for each of the normal sample and the measured sample, and the stable appearance position β
And the peak position of are detected as reference points.

Next, the Alb peak position and the β peak position detected in Step II have 100 data points and 20 data points on the X-axis having 350 data points related to a given migration length.
0 The X-axis is normalized so that the positions of the data points match. For example, if the peak position of Alb is 120 points and the peak position of β is 230 points, those data positions are X points.
Match the 100 and 200 data points on the axis, and the other sampling data have a migration length of 110 at the reference point.
Corresponding to the number of data points (230-120), on the X-axis
Since it corresponds to 100 data points (200-100), shift to the data points on the X axis according to the ratio.

Here, if there is no sampling data corresponding to the data point on the X axis, interpolation processing or extrapolation, that is, processing for aligning with both end data is performed.

This completes the normalization of the X-axis regarding the migration length.

Next, the value of the sampling data of 350 points on the X axis normalized in step III is set to the number of data between the reference points so that the integrated value becomes almost equal to the integrated value between the corresponding original data positions. The Y-axis is normalized based on the ratio with the number of data (100 in this example) while these reference points are located on the X-axis. For example, in the above example, the Alb peak position is 120 data points and the β peak position is 230 data points, and the number of 110 data points between them is normalized to 100 data points. The value of the sampling data at the point is multiplied by 110/100 to normalize the Y axis.

The above processing is performed by reading the sampling data stored in the memory 15 under the control of the CPU 14, and the processed data is stored in the floppy disk 18.

Next, in step IV, the concentration is normalized so that the integrated value in each fraction and the absolute amount of degree are associated with each other. When normalizing this concentration, the total protein value or Alb concentration value is measured in advance with a biochemical analyzer or the like for the measurement sample, and the value is connected via the keyboard 16 or from or to the biochemical analyzer. It is entered online or offline via the computer system for testing and stored in the floppy disk 18, and for normal samples, the total protein value or concentration value such as Alb is entered via the keyboard 16 and the floppy disk 18 is entered. Remember. Similarly, the reference integrated value for the unit concentration (1 g / dl) of the input absolute amount is also stored. For example, Alb
When the concentration value of is input, the reference integrated value is set to 15,000 (in A / D conversion value) for Alb of 1 g / dl. Assuming that the Alb concentration value is input as 4 g / dl, first calculate the integrated value of the Alb fraction of the normalized data, and then calculate the reference value corresponding to the integrated value and the input Alb concentration value. Calculate the ratio with the integrated value. For example, if the normalized integrated value of the Alb fraction is 80,000 (in A / D conversion value), the input Alb concentration value is 4g / dl and the reference integrated value is 4 (g / d) x 15,000. = 60,000, so 8
The ratio of 0,000 to 60,000 is Becomes Then, the data value at each point is multiplied by this ratio to complete the normalization of the density. Incidentally, in this density normalization processing, as described in Japanese Patent Application No. 60-36606 proposed by the present applicant, it is also possible to simultaneously correct the difference in dyeability of each fraction. Further, when the total protein value is input, the same processing can be performed by obtaining the ratio of the reference integrated value corresponding to the input value and the integrated value of all the fractions.

Normalize the normal sample and the measured sample as described above, and save the data on the floppy disk 18
To store. The reference densitogram of a normal sample is also subjected to the same normalization process and stored in advance, and the reference data is also determined from the similarly normalized data and stored.

Next, the correction process of the normalized densitogram of the measurement sample will be described.

In this correction process, as shown in the flowchart of FIG. 6, first, in step I, the data di of each point of the normalized densitogram of the normal sample that has been electrophoresed together with the measurement sample and the reference densitogram of the standard densitogram stored in advance. Calculate the ratio _RA = doi / di with the data doi at each corresponding point (i
= 1 to 350). Next, in step II, the data at each point of the normalized densitogram of the measurement sample is multiplied by the ratio _RA = doi / di at the corresponding point obtained previously, and the correction process is completed.

The densitogram of the measurement sample obtained by this correction process is the basis for estimating the result when the analysis is performed under the same conditions as the predetermined analysis conditions when the previously stored reference densitogram is obtained. Therefore, the fluctuation of the migration image due to the difference in the analysis conditions is effectively corrected.

Next, the densitogram of the corrected measurement sample obtained as described above and the analysis process for automatically analyzing the presence or absence of various feature points of the measurement sample based on the comparison with the previously stored reference data will be described. To do.

One of the most important features of electrophoretic images is the above-mentioned M protein. This M protein may be fractionated at any position in the serum protein image, but most of them appear in the β fraction to the γ fraction, and because they are monoclonal, they have an extremely narrow band width. There is. Further, there are benign and malignant M proteins, and in many cases benign M protein appears in a form in which M protein is superimposed on the fractionation pattern, and malignant M protein appears in the fractionation pattern. Often accompanied by suppression of other proteins.

Due to these characteristics of M protein, M protein can be detected by detecting the presence or absence of a peak on the densitogram between β-γ fractions. That is, in the densitogram without the appearance of M protein, as shown in FIG. 7A, between β-γ fractions, there is a gentle valley between β-γ and a gentle peak of the γ fraction. In other words, looking at the change in the curvature between the β-γ fractions, the pattern becomes convex upward, concave downward, convex upward, and concave downward. On the other hand, when a benign M protein appears, another peak exists between β-γ fractions, as shown in FIG. 7B, for example, and the change in curvature becomes complicated. Also, if there is the appearance of malignant M protein,
For example, as shown in FIG. 7C, the pattern change between β-γ fractions is the same as that in the normal case of FIG. 7A,
The appearance peak becomes sharper than that in the normal pattern, and a clear reduced portion in which the γ fraction is suppressed appears before or after the M protein peak.

Therefore, when simply detecting the M protein peak, when detecting the M protein peak in the required migration length portion of the densitogram, for example, between β-γ fractions, 200 data points (β It is only necessary to detect the presence or absence of a convex portion between 300 data points from the peak position), but in order to determine whether it is benign or malignant, the γ fraction is suppressed near the peak position of M protein. Whether or not
It needs to be detected by comparison with reference data representing the normal fluctuation range in the corresponding part.

Below, β- referring to the flowchart shown in FIG.
An example of the M protein detection process between the γ fractions will be described.

First, in step I, the degree (convex value) of the convex portion of the pattern between the predetermined data points corresponding to the β-γ fractions of the normalized and corrected densitogram is calculated.
The method of calculating this convex value will be described below.

First convex value calculation method Number of data k on each side of data point i
The detection width is set to 2k, and as shown in FIG.
How much the densitogram protrudes from the straight line connecting the data value D _{ik of the} point and the data value D i + k of the point _{i + k} is the area S of the part surrounded by the straight line and the densitogram.
Evaluate with. In this case, for example, the area S when the trapezoid is approximated is It is represented by. The area S can be obtained by various quadrature methods such as Simpson other than the trapezoidal approximation.

Here, the value of k is preferably 3 to 6, and if it is smaller than this, a minute change can be read, but fine noise is also picked up, and if it is large, the value of S itself becomes large. , The effect of smoothing makes it stronger against noise, but fine changes are lost. The value of k is the same in the second to fourth calculation methods described below.

Looking at the change in the value of S thus obtained, not only M protein having an independent peak but also a trace amount of M protein having no clear peak as shown in FIG. 10 can be detected. .

Second convex value calculation method A function S / 2k is set with respect to S obtained by the first calculation method. Since the value of S / 2k represents the average height of the convex portion in the detection width 2k, the dependency on the detection width 2k is lower than that of S (S is 2k for the apex of a triangle, for example). Corresponding to the degree of convexity on the densitogram strongly appears.

Third convex value calculation method A function S / (2k) ² is set for S obtained by the first calculation method. The value of this S / (2k) ² is the detection width 2k and the average height.
The ratio with S / 2k represents the degree of convexity per unit detection width,
If the shapes of the protrusions are similar, the values will be the same regardless of the detection width 2k. Therefore, the detected value 2k in this case determines the degree of smoothing.

Fourth convex value calculation method The convex value is calculated by the second derivative. In this case, since the functional expression of the densitogram is unknown, a function approximation is performed from the sequential data value of the detection width 2k by the method of least squares, etc., and the second derivative value of the obtained approximate functional expression is used as a convex value. To do. Below, y = ax ² when the detection width is 5, 7 and 9 data
The approximated second derivative F ″ _(i) when the least-squares approximation is performed on the parabola generally represented by + bx + c is shown.

5 data (2k = 4) F ″ _(i) = (2D _i-2 −D _i-1 −2D _i −D _{i + 1} + 2D _{i + 2} ) / 7 7 data (2k = 6) F ″ _(I) = (5D _i-3 −3D _i-1 −4D _i −3D _{i + 1} + 5D _{i + 3} ) / 42 For 9 data (2k = 8) F ″ _(i) = (28D _i-4 ＋ 7D _i-3 -8D _i-2 -17D _i-1 -20D _i -17D _{i + 1} -8D _{i + 2} ＋ 7D _{i + 3} ＋ 28D
_{i + 4} ) / 462 These values are essentially independent of the detection width 2k, so 2k will determine the degree of smoothing as in the third convex value calculation method.

After the convex value is obtained by any one of the above calculation methods, it is then determined in step II whether the convex value is larger than a predetermined threshold SL _1, and when the convex value is SL ₁ or less, the M protein peak is determined. If it is determined that there is none and SL ₁ is exceeded, then step III
In order to detect whether or not the convex portion is an M protein peak in, the half-value width (data number) of the convex portion is within a predetermined range SL ₂
It determines whether the to SL _3. Here, the full width at half maximum is SL ₂
When it is out of the range of ~ SL ₃ , it is judged that there is no M protein peak, and when it is in the range, it is considered that there is an M protein peak, and then the convex value is determined from the predetermined threshold value SL ₄ (> SL ₁ ) in step IV. Judge whether it is large or not. In this determination process, when the convex value is SL ₄ or less, it is determined that the M protein peak is suspected,
If there is a clear M protein peak above SL ₄ ,
Next, in step V, the peak data position of the convex portion is detected in order to detect the presence or absence of γ suppression.

In the above processing, the densitogram of the measurement sample was adjusted to a total protein value of 7 g / dl
After normalizing according to the flowchart in the figure, correct it,
When the convex value was calculated by the second calculation method and various experiments were examined, M protein having an almost independent peak was detected at an S / 2k value of 30 or more at k = 3 to 6, and it was clear at 10 to 30. A trace amount of M protein peak having no significant peak could be detected. Also, by setting the range of the half-value width of the convex part to 10 to 20 data items, the main peak (half-value width of 20 or more) and β of the γ fraction are set.
-β _1C peak appearing near the valley between γ fractions, fibrinogen peak (for plasma analysis) (half-value width of 10 or less)
And M protein peak could be separated reliably.

In FIG. 8, when the position of the peak data of the convex portion as the M protein peak is detected, it is extracted from the data of the measurement sample and the reference data representing the normal fluctuation range before and after the peak data point detected in step VI. It is judged whether there is a point below the normal fluctuation range by comparing with the data at the corresponding data point, and if there is a point below the normal fluctuation range, it is judged that there is γ suppression and malignant M protein (myeloma) is present. Then
If not present, it is determined that there is a benign M protein. Here, the normal fluctuation range is set to, for example, about ± 25% of the measured value obtained by normalizing and correcting the normal sample.

Next, an example of β-γ bridging detection processing will be described.

β-γ bridging means that the valley between β-γ is the γ fraction (I _g
This is a phenomenon in which the G, I _g M, I _g A) is buried due to a polyclonal increase, and the β-γ separation becomes unclear. When this phenomenon is extreme, as shown in FIG. 11, β-γ is smoothly connected, and there is no fractional point between β-γ. In conventional autospan densitoblam, β-γ bridging was found to be among the β-γ bridging found on the densitogram, and β-γ bridging decreased even if the γ fraction was normal. The same pattern was shown, and the distinction could not be made without checking the fractional concentration (g / dl).

In this embodiment, as shown in the flow chart of FIG. 12, first, a portion exceeding the normal fluctuation range is detected between the β-γ peaks of the densitogram of the densitogram subjected to the normalization and correction processing of the measurement sample. In this detection process, first, in step I, the γ peak position is detected from the densitogram of the normal sample. Next, in step II, the data of the part corresponding to between the β peak and the γ peak of the densitogram of the normal sample is extracted from the normalized densitogram of the measured sample, and the extracted data exceeds the normal fluctuation range. A comparative judgment is made as to whether or not there is a part. The extraction of the data between β-γ peaks of the measurement sample is performed using a standard densitogram, or a predetermined β from the densitogram of the measurement sample without using such a normal sample densitogram or a standard densitogram. The data of the portion corresponding to the peak and the γ peak is extracted, and the extracted data is compared with the reference data representing the normal variation range in the corresponding portion to detect the portion exceeding the normal variation range.

In this detection process, when a portion exceeding the normal fluctuation range is not detected, it is determined that there is no β-γ bridging, and when a portion exceeding the normal fluctuation range is detected, next, step III
In order to determine whether or not the exceeding portion is β-γ bridging, it is determined whether or not the width (number of data) of the exceeding portion is equal to or larger than the reference width. That is, since β-γ bridging shows a polyclonal increase, the range over the normal variation range is wide. On the other hand, the width of β _1C and fibrinogen appearing near the valley between β-γ fractions is narrower than that of β-γ bridging even if the change exceeds the normal fluctuation range. Therefore, it is possible to detect β-γ bridging and B _1C by detecting whether the width exceeding the normal fluctuation range is equal to or larger than the reference width, for example, 60% or more of the number of data between β-γ peaks.
And fibrinogen can be reliably separated. Here, when the width exceeding the normal fluctuation range is less than or equal to the reference width, it is determined that there is no β-γ bridging. In order to distinguish whether it is due to β-γ bridging or due to β-γ bridging,
The presence or absence of the M protein peak is detected by performing steps I and II or steps I to IV of the M protein detection process shown in the figure.
When the M protein peak was detected, it was determined that the increase was due to the M protein, and it was judged that there was no β-γ bridging, and when the M protein peak was not detected, the increase was a polyclonal increase and there was β-γ bridging. To determine.

Incidentally, as in the flow chart shown in FIG. 21,
First, when the peak of M protein is detected and β-γ bridging is detected in the absence of M protein peak,
Step IV can be omitted in FIG.

By the above processing, β-as a polyclonal increase
γ bridging can be detected with high accuracy.

Next, reading detection processing will be described by taking Alb fractionation as an example.

The Alb fraction is generally composed of a single protein, the pattern is almost symmetrical with respect to the peak position, the mobility is stable, and the concentration is high, so it is the most prominent fraction in the electrophoresis image. However, during administration of high jaundice serum, high lipid serum, antibiotics, etc., bilirubin, free fatty acids, and drugs bind to Alb, causing a change in mobility, causing migration to the positive polarity side as shown in Fig. 13. There are many cases in which the Alb fraction becomes asymmetric due to the reading of the.

In this example, the leading of the Alb fraction to the positive electrode side is detected according to the flowchart shown in FIG. First, in step I, it is detected from the densitogram obtained by normalizing and correcting the measurement sample whether or not there is a portion beyond the normal fluctuation range on the positive electrode side from the Alb peak position (100th data point). If it is judged that there is no reading, and if there is, the increase in Al in the next step II.
In order to distinguish between an overall increase in b and an increase due to reading, a discriminant value representing the degree of symmetry of the Alb fraction is calculated. The method of calculating the discriminant value will be described below.

First Discriminant Value Calculation Method As shown in FIG. 15A, the integrated value ΣI from the densitogram of the measurement sample to the Alb peak from the positive electrode side, and the integrated value ΣII from the Alb peak to the fractional points of Alb to α _1. And are respectively calculated, and the ratio ΣI / ΣII is set as a discriminant value.

Second discriminant value calculation method As shown in FIG. 15B, the center of gravity G of the Alb fraction is obtained, and the deviation (ip−ig) between the position ig of the center of gravity and the Alb peak position ip.
Is the discriminant value.

Third Discriminant Value Calculation Method A predetermined ratio to the peak concentration of Alb or a desired value is set as a threshold SL as shown in FIG.
The range in which the individual data of the b fraction exceeds the threshold SL is detected, and the width L ₁ between the end position of each of the positive electrode side and the negative electrode side and the Alb peak position ip in the detected range L ₁ and
The ratio L ₁ / L ₂ of L ₂ and discrimination value.

If the discriminant value is obtained by any one of the above calculation methods,
Next, in step III, the semi-differential value in the measurement sample is
It is determined whether the value exceeds the value related to the normal range of the discriminant value obtained by a similar calculation method based on the electrophoretic image of a normal sample, and when it does not exceed, it is determined that there is no reading,
When it exceeds, it is judged that there is a reading on the Alb positive electrode side.

The determination results regarding M protein, β-γ bridging, and Alb reading analyzed as described above are calculated based on the data obtained by normalizing and correcting the measurement sample, each fraction%, A / G ratio, each It is displayed on the CRT 17 together with the fractional density value, and is also recorded in a predetermined column of the report 20 on the printer 19.

Next, an example of overlapping display of the normalized and corrected densitogram of the measurement sample and the migration pattern associated with the normal sample densitogram will be described.

FIG. 16A shows a reference densitogram I for a normal sample in a broken line and a densitogram II of a measured sample in a solid line, and FIG. 16B displays both densitograms I and II in a solid line and a densitogram of the measured sample. The line of togram II is thicker or darker than the reference densitogram I.
In addition, it is also possible to display them in different colors.
In addition, by using the reference data that represents the normal fluctuation range, the portion of the densitogram of the measurement sample that exceeds the normal fluctuation range can be displayed with different line types, colors, boldness, and darkness,
As shown in FIG. 17, hatching of the same color or different colors may be applied. Further, instead of displaying the reference densitogram, or at the same time as displaying the reference densitogram, reference data representing the normal fluctuation range is used to represent the pattern Imax and the lower limit value representing the upper limit value of the normal fluctuation range as shown in FIG. The pattern Imin may be displayed, and the densitogram II of the measurement sample may be displayed overlapping with these patterns. Further, in this case, the normal fluctuation region defined by the patterns Imax and Imin and the other regions may be color-coded and distinguished by hatching or the like. Furthermore, in addition to the normal variation area, a caution area and a dangerous area can be set as reference data, and these areas can be displayed in different colors, such as by hatching.

In the above display example, the pattern based on the reference densitogram and reference data is displayed together with the densitogram of the measurement sample.
I tried to display it on CRT17 and report 20, but the report
In FIG. 20, as shown in FIGS. 19A to 19C, a pattern relating to the reference densitogram is printed and recorded in advance in the recording field 21 of the densitogram, and the densitogram of the measurement sample is recorded in duplicate with this pattern. You may do it. In addition, FIG. 19A shows a pattern corresponding to the reference densitogram.
III is a broken line, and FIG. 19B is a solid line. Further, FIG. 19C shows the upper limit value pattern IIImax and the lower limit value pattern IIImin representing the normal fluctuation range, but a reference pattern is recorded in the middle of the normal fluctuation range or the normal fluctuation range region. The areas other than that may be distinguished by color coding, hatching, or the like, or the caution area and the dangerous area may be distinguished by color coding, hatching, etc. in addition to the normal variation area and recorded.

According to this example, the normal axis and the normal axis of the measurement sample are normalized with respect to the X-axis, the Y-axis and the concentration value of the input single fraction or the concentration based on the total protein. Since the densitogram is similarly corrected based on the normalized normal sample densitogram and the reference densitogram stored in advance for the normal sample, even if there is a difference in the application amount of the sample or the analysis conditions, It has the same effect as that when an electrophoretic image is created under the same analysis conditions with the coating amount being exactly constant. Therefore, a densitogram corresponding to the change in concentration of each fraction under a given analysis condition, that is, a span proportional to the obtained densitogram, is obtained for each fraction%, A / G
It is possible to accurately determine the ratio, etc., and improve the reading accuracy when automatically reading the characteristic points etc. from the densitogram, and it is possible to automatically obtain a lot of accurate pathological condition information at the same standard and at fine fractional positions. You can get it. Therefore, it is possible to reduce the burden on doctors and laboratory technicians,
Since a lot of information useful for diagnosis is obtained, the accuracy of diagnosis can be guaranteed. Also, when comparing samples or time-series data of the same subject, the position of each data point can be absolutely used as a reference under the same analysis conditions, so it is easy and accurate. it can.
Furthermore, by overlappingly displaying the densitogram of the measurement sample together with the pattern related to the reference densitogram, it is possible to easily compare the increase and decrease of each concentration value of the measurement sample and the change of the mobility, and to visually judge the pathological condition information. Easy to do,
By displaying the normal fluctuation range, it is possible to easily visually determine where the increase or decrease has occurred.

It should be noted that the present invention is not limited to the above-described embodiments, and many changes and modifications can be made. For example, in the above-described example, a normal sample was run on each support and the ratio R of each data point to its reference densitogram was used.
_A was determined and applied to the measurement sample applied to the support, but a normal sample was electrophoresed once or several times a day at predetermined time intervals or every predetermined number of supports. Ratio of reference point densitogram to data point R
_A may be determined and shared with multiple supports until the ratio _RA is updated. In addition, when changing the lot of normal samples, a new normal sample may be analyzed in the same manner to obtain the reference densitogram, or a new normal sample and an old normal sample may be analyzed simultaneously multiple times and normalized. For each new densitogram, the ratio of the data at each data point is calculated from the two densitograms, and the ratio is multiplied to the data at each corresponding data point of the old reference densitogram for the new normal sample. It is also possible to obtain the standard densitogram of. Furthermore, in order to compare data between samples and time-series data of the same subject, when storing data for a long period of time, a densitogram normalized and corrected for the measured sample, and refer to the densitogram. The data ratio at each point of the data, the data difference at each point of the densitogram of the measurement sample and the reference data, the ratio of this difference and the standard deviation indicating the normal variation range at the corresponding point of the reference data, or The ratio _RA for correction of the densitogram and the measured sample required to reproduce the densitogram of the normalized and corrected processed sample, as well as the reference densitogram, the normalized densitogram of the normal sample analyzed and the normality of the measured sample. Save the converted densitogram.

Further, in FIG. 14, by detecting a portion exceeding the normal variation range on the negative electrode side from the Alb peak position and performing the same processing, or while detecting a portion below the normal variation range on the positive electrode side from the Alb peak position, It is also possible to automatically determine the appearance of the reading of the Alb fraction on the negative electrode side by detecting that the discriminant value indicating the symmetry falls below the normal range. Further, the presence / absence of this reading can be determined not only in the Alb fraction but also in other fractions. The calculation method of the discriminant value of symmetry in this case is, in addition to the above-described first to third discriminant value calculation methods, at the fractional peak position ip and the central position of both fractional points as shown in FIG. The discrepancy with the center position ic of a certain fraction is used as a discriminant value, and as shown in FIG.
The difference between the center position ic of the
As shown in, the data D at the data points distant from the center position ic of the fraction or the peak position ip by k points on both sides
_The discriminant value can be the difference between _{i (ck)} and D _{i (c + k)} , or the difference between D _{i (pk)} and D _{i (p + k)} , the integrated value of the square of the difference, or its correlation. . In addition, the above-mentioned M protein between β-γ fractions, the suppression of the γ fraction associated with its appearance, and the method for determining the detection of bridging are performed by increasing or decreasing each individual protein in other Alb, α _a , and α ₂ fractions. The same can be applied to the determination of appearance of minor peaks. Furthermore, the normal fluctuation range can be set according to the data position, instead of being set uniformly within ± 25% with respect to the data of the normal electrophoretic image, and the upper limit value and the lower limit value are asymmetrical. It can also be set. Further, although the smoothing process of the sampling data from the densitometer is performed before the normalization process in the above-described embodiment, it may be performed after the normalization process. Furthermore, the same effect can be obtained when the normalization processing is performed only on the X axis or only the X axis and the Y axis. Further, although the density normalization processing is performed last in the above-described embodiment, this processing can be performed before the X-axis normalization processing. Furthermore, the reference point in the migration image is the Alb peak position,
Not limited to the β peak position, the peak position of another fraction, the end point of the electrophoretic image, or the like can be used. Further, a one-dimensional array sensor or a two-dimensional array sensor can be used as the light receiving element that constitutes the measuring device. In addition, by comparing the normalized and corrected processed sample data with the reference data related to the normal fluctuation range, it is possible to automatically determine the increase / decrease of the components in the measured sample, and to determine the presence or absence of specific protrusions or depressions. To automatically analyze the presence or absence of a predetermined component in the measured sample from the detection of M protein or the like, or to detect the reading etc. by comparing the data in the predetermined range with the data in the corresponding range related to the normal variation range. It is also possible to automatically analyze the presence or absence of a predetermined component in the measurement sample.

〔The invention's effect〕

As described above, according to the present invention, even if there is a difference in the application amount of the sample or the analysis conditions, a densitogram equivalent to that analyzed under the constant application amount and the constant analysis conditions can be obtained. It is possible to perform visual analysis or automatic analysis of information with high accuracy, thereby reducing the burden on doctors and laboratory technologists.

[Brief description of drawings]

FIG. 1 is a schematic cross-sectional view showing the configuration of the main part of an example of a densitometer in an electrophoretic device for carrying out the invention, FIG. 2 is a view showing the scanning direction of an electrophoretic image, and FIG. 3 is a data processing device. FIG. 4 is a block diagram showing a configuration of a main part of an example, FIG. 4 is a flowchart showing an example of normalization processing, FIG. 5 is a view for explaining an example of reference point detection, and FIG. 6 is an example of correction processing. 7A to 7C are flowcharts showing densitogram patterns between β-γ fractions, FIG. 8 is a flowchart showing an example of M protein detection processing, and FIG. 9 is a convex value shown in FIG. 10 is a diagram for explaining an example of the calculation method of FIG. 10, FIG. 10 is a diagram showing an example of M protein on a densitogram that can be detected by the calculation method, and FIG. 11 is a diagram showing an example of β-γ bridging Fig. 12 shows an example of β-γ bridging detection processing. 13 is a flowchart showing an example of Alb reading, FIG. 14 is a flowchart showing an example of detection processing of Alb reading, and FIGS. 15A to 15C are three of the symmetry discriminant values shown in FIG. Figures for explaining one calculation example, Figures 16A, B, 17 and 18 show examples of duplicate display, and Figures 19A to C are three examples of the densitogram record section of the report. 20A to 20C are diagrams for explaining the other three calculation examples of the symmetry discriminant value, FIG. 21 is a flowchart of conventionally proposed pathological condition classification, and FIG. 22 is a difference in analysis conditions. It is a figure which shows an example of the change of the densitogram by. 1 ... Supporting body, 2 ... Feed roller, 3 ... Decalin, 4 ... Photometric unit, 5 ... Photometric device, 5a ... Light source, 5b ... Light receiving element, 6 ... Paper ejection roller, 7 ... Electrophoretic image, 12 …… Logarithmic amplifier 13 …… A / D converter, 14 …… CPU 15 …… Memory, 16 …… Keyboard 17 …… CRT, 18 …… Floppy disk 19 …… Printer, 20 …… Report 21 …… Densitogram recording field

Claims (1)

[Claims] 1. A standard densitogram related to a normal sample is obtained in advance, a normalized densitogram of the measurement sample is obtained, and a normal sample is normalized under almost the same analysis conditions as the analysis conditions of the measurement sample. Obtained densitogram, comparing the standard densitogram and the normalized densitogram of the normal sample, the densitogram characterized by correcting the normalized densitogram of the measured sample based on the comparison result. Togram correction method.